CN116823126A

CN116823126A - Article information processing method, apparatus, electronic device, and computer readable medium

Info

Publication number: CN116823126A
Application number: CN202210271745.6A
Authority: CN
Inventors: 潘宇通; 刘柏彤
Original assignee: Beijing Wodong Tianjun Information Technology Co Ltd
Current assignee: Beijing Wodong Tianjun Information Technology Co Ltd
Priority date: 2022-03-18
Filing date: 2022-03-18
Publication date: 2023-09-29

Abstract

Embodiments of the present disclosure disclose article information processing methods, apparatuses, electronic devices, and computer-readable media. One embodiment of the method comprises the following steps: for each item information in the item information set, the following processing steps are performed: determining whether an accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information, wherein the optimal dual condition is a condition constructed according to the accumulated inventory amount; in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition, adding the item information to a preset item sample information set; and determining the article sample information set as a target article sample information set in response to the target number being greater than or equal to a preset number, wherein the target number is the number of article sample information included in the article sample information set. This embodiment is related to an intelligent supply chain that reduces the waste of computing resources.

Description

Article information processing method, apparatus, electronic device, and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and in particular, to an article information processing method, an apparatus, an electronic device, and a computer readable medium.

Background

Currently, supply chain scenarios typically include a number of links such as bin net planning, item selection, item replenishment, warehouse allocation, and track distribution. In order for the days of turnover and spot rate of the warehouse to meet the requirements of the supply chain, the days of turnover and spot rate of the warehouse are typically determined in the following manner: sample information (circulation information) of more articles is required to be selected and used for determining the turnover days and spot rate of the warehouse.

However, the following technical problems generally exist in the above manner: as more and more sample information is acquired, more computing resources and more time are consumed.

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose article information processing methods, apparatuses, electronic devices, and computer-readable media to solve the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide an article information processing method, the method including: for each item information in the item information set, the following processing steps are performed: determining whether an accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information, wherein the optimal dual condition is a condition constructed according to the accumulated inventory amount; in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition, adding the item information to a preset item sample information set; and determining the article sample information set as a target article sample information set in response to the target number being greater than or equal to a preset number, wherein the target number is the number of article sample information included in the article sample information set.

Optionally, before the adding the item information to the preset item sample information set, the processing step further includes: and setting the value of an adjustment variable corresponding to the item information as a first adjustment variable value, wherein the adjustment variable represents whether the item information is selected as item sample information, and the first adjustment variable value represents that the item information is selected as item sample information.

Optionally, the method further comprises: in response to the target number being smaller than the preset number, the optimal dual-activity condition is updated according to the first adjustment variable value, the updated optimal dual-activity condition is taken as the optimal dual-activity condition, and the item information group from which the item information is deleted is taken as the item information group, and the processing step is executed again.

Optionally, the method further comprises: setting a value of an adjustment variable corresponding to the item information to a second adjustment variable value in response to determining that the accumulated inventory quantity does not satisfy the optimal dual-purpose condition; and updating the optimal dual-activity condition according to the second adjustment variable value, taking the updated optimal dual-activity condition as the optimal dual-activity condition, taking the article information group from which the article information is deleted as the article information group, and executing the processing steps again.

Optionally, before the performing the following processing steps for each item information in the item information group, the method further includes: acquiring an item circulation information set of each item in an item group in a preset time period to obtain an item circulation information set, wherein the item circulation information in the item circulation information set comprises a unit stock quantity and a unit circulation quantity; for each item circulation information group in the item circulation information group, generating an accumulated stock quantity and an accumulated circulation quantity according to each unit stock quantity and unit circulation quantity included in the item circulation information group; generating turnover days according to the generated accumulated stock quantity and accumulated circulation quantity; generating a spot rate group and a total spot rate corresponding to the article group according to the article circulation information group set, wherein the articles in the article group correspond to the spot rates in the spot rate group; and combining the accumulated stock quantity, the accumulated flow quantity and the spot rate corresponding to each article in the article group to generate article information, so as to obtain an article information group.

Optionally, the generating a spot rate group and a total spot rate corresponding to the article group according to the article circulation information group set includes: for each item circulation information group in the item circulation information group set, the following determination steps are executed: determining the number of each unit stock quantity included in the item circulation information group as a single item circulation day; determining a unit stock quantity greater than 0 in the unit stock quantities as a stock quantity to obtain a stock quantity group; determining the number of inventory levels included in the inventory level group as the number of days of inventory; and determining the ratio of the number of the stock days to the number of the single-item circulation days as the stock-in-stock rate.

Optionally, the generating a spot rate group and a total spot rate corresponding to the article group according to the article circulation information group set further includes: determining the sum of the determined individual item circulation days as a total number of days; determining the sum of the determined individual days of goods as the total days of goods; the ratio of the total number of days in stock to the total number of days is determined as the total stock-in rate.

Optionally, the optimal dual-matching condition corresponding to the item information is constructed by: constructing a turnover number constraint parameter according to the turnover number, the accumulated inventory quantity, the accumulated circulation quantity and a preset turnover number constraint coefficient included in the article information; constructing spot rate constraint parameters according to the total spot rate, the spot rate included in the article information and a preset spot rate constraint coefficient; and constructing an optimal dual condition according to the initial constraint parameter, the turnover number constraint parameter and the spot rate constraint parameter.

Optionally, the optimal dual condition includes constraint parameters; and updating the optimal dual-connectivity condition according to the first adjustment variable value, including: generating updated constraint parameters according to the constraint parameters, the step length parameters and the first adjustment variable values; and updating the optimal dual-coupling condition according to the updating constraint parameters.

Optionally, the method further comprises: clustering the target article sample information set to obtain a target article sample information set; for each target item sample information set in the target item sample information set, performing the following verification steps: acquiring an article inventory information group of each target article in a target article group in a simulation time period to obtain an article inventory information group set, wherein the target articles in the target article group correspond to target article sample information in the target article sample information group; generating a simulated total turnover number and a simulated total spot rate corresponding to the target object group according to the object inventory information group set; determining whether the absolute value of the difference between the number of target turnover days and the simulated total turnover number is smaller than or equal to a preset number of days error, and determining whether the absolute value of the difference between the target spot rate and the simulated total spot rate is smaller than or equal to a preset spot rate error, wherein the target turnover number is the total turnover number corresponding to the target article sample information group, and the target spot rate is the total spot rate corresponding to the target article sample information group; and storing the target object sample information set to a preset warehouse terminal in response to the absolute value of the difference of the days being smaller than or equal to the preset day error and the absolute value of the difference of the stock-in-stock rate being smaller than or equal to the preset stock-in-stock error.

Optionally, the method further comprises: and controlling the associated vehicle to schedule the articles according to each target article sample information group stored in the warehouse terminal.

In a second aspect, some embodiments of the present disclosure provide an article information processing apparatus, the apparatus comprising: an information processing unit configured to perform, for each item information in the item information group, the following processing steps: determining whether an accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information, wherein the optimal dual condition is a condition constructed according to the accumulated inventory amount; in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition, adding the item information to a preset item sample information set; and a determining unit configured to determine the article sample information set as a target article sample information set in response to a target number being equal to or greater than a preset number, wherein the target number is a number of article sample information included in the article sample information set.

Optionally, before the adding the item information to the preset item sample information set, the information processing unit is further configured to: and setting the value of an adjustment variable corresponding to the item information as a first adjustment variable value, wherein the adjustment variable represents whether the item information is selected as item sample information, and the first adjustment variable value represents that the item information is selected as item sample information.

Optionally, the apparatus further comprises: a first condition updating unit configured to update the optimal duality condition according to the first adjustment variable value in response to the target number being smaller than the preset number, take the updated optimal duality condition as the optimal duality condition, and take an item information group from which the item information is deleted as an item information group, and execute the processing step again.

Optionally, the apparatus further comprises: a setting unit configured to set a value of an adjustment variable corresponding to the item information to a second adjustment variable value in response to determining that the accumulated inventory amount does not satisfy the optimal dual-purpose condition; and a second condition updating unit configured to update the optimal duality condition based on the second adjustment variable value, take the updated optimal duality condition as the optimal duality condition, and take the item information group from which the item information is deleted as the item information group, and execute the processing step again.

Optionally, before the information processing unit, the apparatus further comprises: the device comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is configured to acquire an article circulation information set of each article in an article set in a preset time period to obtain an article circulation information set, and the article circulation information in the article circulation information set comprises a unit inventory and a unit circulation; a first generation unit configured to generate, for each of the item circulation information groups in the item circulation information group, an accumulated inventory amount and an accumulated circulation amount from respective unit inventory amounts and unit circulation amounts included in the item circulation information group; a second generation unit configured to generate a turnover number of days from the generated respective accumulated stock amounts and accumulated circulation amounts; a third generation unit configured to generate a spot rate group and a total spot rate corresponding to the article group according to the article circulation information group set, wherein the articles in the article group correspond to the spot rates in the spot rate group; and the combining unit is configured to combine the accumulated stock quantity, the accumulated flow quantity and the spot rate corresponding to each article in the article group so as to generate article information and obtain an article information group.

Optionally, the third generating unit is further configured to: for each item circulation information group in the item circulation information group set, the following determination steps are executed: determining the number of each unit stock quantity included in the item circulation information group as a single item circulation day; determining a unit stock quantity greater than 0 in the unit stock quantities as a stock quantity to obtain a stock quantity group; determining the number of inventory levels included in the inventory level group as the number of days of inventory; and determining the ratio of the number of the stock days to the number of the single-item circulation days as the stock-in-stock rate.

Optionally, the third generating unit is further configured to: determining the sum of the determined individual item circulation days as a total number of days; determining the sum of the determined individual days of goods as the total days of goods; the ratio of the total number of days in stock to the total number of days is determined as the total stock-in rate.

Optionally, the optimal dual condition includes constraint parameters.

Optionally, the first condition updating unit is further configured to: generating updated constraint parameters according to the constraint parameters, the step length parameters and the first adjustment variable values; and updating the optimal dual-coupling condition according to the updating constraint parameters.

Optionally, the apparatus further comprises: the clustering unit is configured to perform clustering processing on the target article sample information set to obtain a target article sample information set; a verification unit configured to perform, for each target item sample information set in the set of target item sample information sets, the following verification steps: acquiring an article inventory information group of each target article in a target article group in a simulation time period to obtain an article inventory information group set, wherein the target articles in the target article group correspond to target article sample information in the target article sample information group; generating a simulated total turnover number and a simulated total spot rate corresponding to the target object group according to the object inventory information group set; determining whether the absolute value of the difference between the number of target turnover days and the simulated total turnover number is smaller than or equal to a preset number of days error, and determining whether the absolute value of the difference between the target spot rate and the simulated total spot rate is smaller than or equal to a preset spot rate error, wherein the target turnover number is the total turnover number corresponding to the target article sample information group, and the target spot rate is the total spot rate corresponding to the target article sample information group; and storing the target object sample information set to a preset warehouse terminal in response to the absolute value of the difference of the days being smaller than or equal to the preset day error and the absolute value of the difference of the stock-in-stock rate being smaller than or equal to the preset stock-in-stock error.

Optionally, the apparatus further comprises: and the control unit is configured to control the associated vehicle to schedule the articles according to each target article sample information group stored in the warehouse terminal.

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

In a fourth aspect, some embodiments of the present 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.

The above embodiments of the present disclosure have the following advantageous effects: by the article information processing method of some embodiments of the present disclosure, the waste of computing resources is reduced, and the time for acquiring article sample information is reduced. Specifically, the reason why more computing resources and more time are consumed is that: as more and more sample information is acquired, more computing resources and more time are consumed. Based on this, the item information processing method of some embodiments of the present disclosure first performs the following processing steps for each item information in the item information group: determining whether the accumulated inventory included in the item information satisfies an optimal dual condition corresponding to the item information. Then, in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition, adding the item information to a preset item sample information set. Therefore, the article sample information can be screened out through the optimal dual condition, and the time for acquiring the article sample information is reduced. And the efficiency of acquiring the article sample information is improved because the article sample information is screened through the set optimal dual condition. And finally, determining the article sample information set as a target article sample information set in response to the target number being greater than or equal to a preset number. Here, the number of article sample information is limited by the optimal dual condition and the preset number, so that the long-time occupation of computing resources is avoided. Thus, the waste of computing resources is reduced.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a schematic illustration of one application scenario of an item information processing method of some embodiments of the present disclosure;

FIG. 2 is a flow chart of some embodiments of an item information processing method according to the present disclosure;

FIG. 3 is a flow chart of other embodiments of article information processing methods according to the present disclosure;

FIG. 4 is a flow chart of yet other embodiments of article information processing methods according to the present disclosure;

FIG. 5 is a schematic structural view of some embodiments of an article information processing apparatus according to the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 is a schematic diagram of one application scenario of an item information processing method according to some embodiments of the present disclosure.

In the application scenario of fig. 1, first, the computing device 101 may perform the following processing steps for each item information 1021 in the item information group 102: determining whether an accumulated inventory amount included in the item information 1021 satisfies an optimal duality condition 103 corresponding to the item information, wherein the optimal duality condition 103 is a condition constructed based on the accumulated inventory amount; in response to determining that the accumulated inventory quantity satisfies the optimal dual-purpose condition 103, the item information 1021 is added to a preset item sample information set 104. In an actual application scenario, the item information 1021 may be "[ a item: cumulative stock 100 "). Here, the unit of the accumulated stock amount may not be limited, such as a unit of ton, number, tank, box, or the like. Then, the computing device 101 may determine the set of item sample information 104 as a set of target item sample information 105 in response to the target number being greater than or equal to a preset number, where the target number is a number of item sample information included in the set of item sample information 104. In an actual application scenario, the target item sample information set 105 may include "[ a item: cumulative stock 100]; [ article B: and accumulating the information of the articles such as 90 DEG. Here the number of the elements is the number, information about other items not shown is shown.

The computing device 101 may be hardware or software. When the computing device is hardware, the computing device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.

It should be understood that the number of computing devices in fig. 1 is merely illustrative. There may be any number of computing devices, as desired for an implementation.

With continued reference to fig. 2, a flow 200 of some embodiments of an item information processing method according to the present disclosure is shown. The article information processing method comprises the following steps:

step 201, for each item information in the item information group, the following processing steps are performed:

in step 2011, it is determined whether the accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information.

In some embodiments, an executing subject of the item information processing method (e.g., the computing device 101 shown in fig. 1) may determine whether the accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information. Wherein the optimal dual condition is a condition constructed based on the accumulated inventory. Here, the item information may refer to inventory information of a certain item. For example, the inventory information may include the item name, the cumulative inventory of the item. Optionally, the inventory information may also include daily circulation amounts (sales) of the items and/or item categories. Here, the accumulated stock quantity may be a sum of remaining stock quantities of respective time granularities of a certain item in a certain warehouse for a preset period of time. For example, the preset time period may be 9 months 1-9 months 3, item "a" in warehouse 1 having a remaining inventory of 30 for 9 months 1, 32 for 9 months 2, and 38 for 9 months 3. Thus, it can be calculated that the cumulative inventory amount of item "a" in warehouse No. 1 for the preset period of time "9 months No. 1 to 9 months No. 3" is 30+32+38=100. Here, the optimal dual condition corresponding to the item information may be a KKT (Karush-Kuhn-Tucker conditions) condition corresponding to the item information set according to the accumulated inventory amount included in the item information. Here, the optimal dual condition corresponding to the above item information may be used to screen the above item information. For example, the optimal dual condition may be "the accumulated inventory amount is equal to or greater than the preset inventory amount". The optimal dual condition may be that "the accumulated inventory amount included in the item information is equal to or greater than the preset inventory amount, and the item category is the preset item category". In practice, the setting of the optimal dual condition for each item information may be the same or different, and is not limited herein.

In step 2012, in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition, the item information is added to a preset item sample information set.

In some embodiments, the executing entity may add the item information to a predetermined set of item sample information in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition. Here, the preset article sample information set may be a set of article information satisfying the above-described optimal dual condition. That is, the article information satisfying the above-described optimal dual condition is added as article sample information to a preset article sample information set.

And step 202, determining the article sample information set as a target article sample information set in response to the target number being greater than or equal to a preset number.

In some embodiments, the executing entity may determine the set of item sample information as the set of target item sample information in response to the target number being equal to or greater than a preset number. Wherein the target number is the number of item sample information included in the item sample information set. Here, the preset number is not limited. For example, the preset number may be 30.

Optionally, clustering is performed on the target article sample information set to obtain a target article sample information set.

In some embodiments, the executing body may perform clustering on the target article sample information set to obtain a target article sample information set. Here, the target item sample information in the target item sample information set may include a warehouse identification. Here, the warehouse identifier may refer to an identifier of a warehouse in which an item corresponding to the target item sample information is located. For example, the warehouse identifier included in the target article sample information may be "number 1", and then the article "a" corresponding to the target article sample information is indicated to be stored in the "number 1" warehouse. In practice, the executing entity may group the target item sample information having the same warehouse identifier included in the target item sample information set into a group. Thus, a set of target item sample information sets is obtained.

Optionally, for each target item sample information set in the set of target item sample information sets, performing the following verification steps:

the method comprises the steps of firstly, obtaining an item inventory information set of each target item in a target item set in a simulation time period, and obtaining an item inventory information set. Wherein the target items in the target item group correspond to the target item sample information in the target item sample information group. Here, the setting of the simulation period is not limited. Here, the target item in the target item group is an item characterized by target item sample information in the target item sample information group. Here, the item inventory information in the item inventory information group may include a unit inventory amount and a unit circulation amount. Here, the unit stock quantity and the unit circulation quantity may refer to a stock quantity and a circulation quantity (sales quantity) of a certain time granularity within the simulation period. For example, the simulation time period may be 9 months 5-9.8, and the time granularity of the simulation time period may be 1 day.

And step two, generating a simulated total turnover number and a simulated total spot rate corresponding to the target object group according to the object inventory information group set.

In practice, the second step may comprise the following sub-steps:

a first sub-step of generating a simulated accumulated inventory and a simulated accumulated circulation according to the respective unit inventory and unit circulation included in the item inventory information group for each item inventory information group in the item inventory information group set. In practice, first, the sum of the individual unit stock amounts included in the above item stock information group may be determined as the simulated cumulative stock amount. Then, the sum of the individual unit flow amounts included in the item inventory information group may be determined as the simulation cumulative flow amount.

And a second sub-step of generating a simulation total turnover number according to the generated simulation accumulated inventory and simulation accumulated circulation. In practice, first, the sum of the above-described individual simulation accumulated stock amounts may be determined as a simulation total stock amount. Then, the sum of the above-described respective simulation cumulative flow amounts may be determined as a simulation total flow amount. Finally, the ratio of the simulated total stock quantity to the simulated total circulation quantity can be determined as the simulated total turnover number of days.

A third sub-step of determining, for each item inventory information group in the item inventory information group, the number of individual unit inventory amounts included in the item inventory information group as a unit simulation total number of days. Here, the total number of days per unit simulation may refer to the number of time granularity in the simulation time period corresponding to each item inventory information in the item inventory information group.

A fourth sub-step of determining, for each item inventory information group in the item inventory information group set, a unit inventory amount greater than 0 out of the unit inventory amounts included in the item inventory information group as a simulated inventory amount, to obtain a simulated inventory amount group;

and a fifth sub-step of determining, for each of the obtained simulated inventory groups, the number of simulated inventory included in the simulated inventory group as simulated inventory days.

A sixth substep of determining a sum of the determined individual simulated days of goods as a total simulated days of goods.

And a seventh sub-step of determining the sum of the determined total number of unit simulation days as the total number of simulation days.

And an eighth substep, determining the ratio of the total simulated number of days to the total simulated number of days as a simulated total spot rate.

And thirdly, determining whether the absolute value of the difference between the target turnover number and the simulated total turnover number is smaller than or equal to a preset number of days error, and determining whether the absolute value of the difference between the target spot rate and the simulated total spot rate is smaller than or equal to a preset spot rate error. The target turnover number of days is the total turnover number of days corresponding to the target article sample information group, and the target spot rate is the total spot rate corresponding to the target article sample information group. Here, the target turnover number may be a total turnover number calculated and generated by each item corresponding to the target item sample information set, each unit stock amount and unit circulation amount in a preset period. The generation mode of the target turnover number can be specifically referred to the implementation mode of the simulation total turnover number. The generation mode of the target spot rate can be specifically referred to as an implementation mode of simulating the total spot rate.

And a fourth step of storing the target article sample information set in a preset warehouse terminal in response to the absolute value of the difference of the days being less than or equal to the preset number of days and the absolute value of the difference of the spot rate being less than or equal to the preset spot rate. Here, the warehouse terminal may refer to a device terminal of a warehouse. Here, the warehouse identifier corresponding to the preset warehouse terminal may be the same as the warehouse identifier corresponding to the above-mentioned target article sample information group.

Optionally, the associated vehicle is controlled to schedule the articles according to each target article sample information group stored in the warehouse terminal.

In some embodiments, the executing entity may control the associated vehicle to schedule the article according to each target article sample information set stored in the warehouse terminal. In practice, the execution subject may calculate sales or stock of each item in a future time period according to each target item sample information set stored in the warehouse terminal and the stock of each item corresponding to each target item sample information set stored in the warehouse corresponding to the warehouse terminal. Thus, the associated transport vehicle may be controlled to perform a transport schedule of the item. Here, the manner of calculating the sales or the sales of each item in the future time period is not limited.

With further reference to fig. 3, further embodiments of article information processing methods according to the present disclosure are shown. The article information processing method comprises the following steps:

step 301, for each item information in the item information group, performing the following processing steps:

step 3011, determining whether the accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information.

In some embodiments, the specific implementation of step 3011 and the technical effects thereof may refer to step 2011 in those embodiments corresponding to fig. 2, which are not described herein.

Step 3012, in response to determining that the accumulated inventory quantity satisfies the optimal dual-purpose condition, setting a value of an adjustment variable corresponding to the item information as a first adjustment variable value.

In some embodiments, an execution subject of the item information processing method (e.g., the computing device 101 shown in fig. 1) may set a value of an adjustment variable corresponding to the item information to a first adjustment variable value in response to determining that the accumulated inventory amount satisfies the optimal dual-purpose condition. Wherein the adjustment variable characterizes whether the item information is selected as item sample information. The first adjustment variable value indicates that the item information is selected as item sample information. For example, the first adjustment variable value may be 1.1 indicates that the above article information is selected as article sample information.

Step 3013, adding the article information to a preset article sample information set.

In some embodiments, the specific implementation of step 3013 and the technical effects thereof may refer to step 2012 in those embodiments corresponding to fig. 2, which is not described herein.

And step 302, determining the article sample information set as a target article sample information set in response to the target number being greater than or equal to a preset number.

Step 303, in response to the target number being smaller than the preset number, updating the optimal dual-purpose condition according to the first adjustment variable value, taking the updated optimal dual-purpose condition as the optimal dual-purpose condition, taking the article information group with the article information deleted as the article information group, and executing the processing step again.

In some embodiments, the execution body may update the optimal dual-purpose condition according to the first adjustment variable value in response to the target number being smaller than the preset number, take the updated optimal dual-purpose condition as the optimal dual-purpose condition, and take the item information group from which the item information is deleted as the item information group, and execute the processing step again. Wherein the optimal dual-affinity condition comprises constraint parameters. Here, the constraint parameter may refer to a set constraint parameter that constructs an optimal dual condition corresponding to each item information. For example, the constraint parameter may be an array of elements all equal to 0 [0, 0] when item information is first screened. Here, the item information may further include an accumulated amount of flow and a spot rate. Here, the cumulative circulation amount may be a sum of circulation amounts (sales) of respective time granularities of a certain item in a certain warehouse for a preset period of time.

In practice, according to the first adjustment variable value, the execution subject may update the optimal dual condition by:

and a first step of generating updated constraint parameters according to the constraint parameters, the step length parameters and the first adjustment variable values. In practice, the update constraint parameters may be generated by the following formula:

wherein P is _i+1 Representing updating constraint parameters. i represents the number of execution times of the processing step. P (P) _i Constraint parameters representing item information corresponding to the ith processing step. Gamma denotes the step size parameter described above.

a _i ＝[I _i -(1+α)×ITO×S _i ，-I _i +(1-α)×S _i ，r _i -R-β，-r _i +R-β]。I _i The accumulated inventory amount included in the item information corresponding to the i-th processing step is indicated. Alpha represents a preset turnaround day constraint factor. ITO represents the number of turnaround days. S is S _i The accumulated circulation amount included in the item information corresponding to the ith processing step is indicated. r is (r) _i And the stock ratio included in the item information corresponding to the ith processing step is indicated. R represents the total spot rate. Beta represents a preset spot rate constraint coefficient. T represents the matrix transpose. X is x _i Indicating the adjustment variable corresponding to the ith processing step. When the constraint parameters are updated this time, x _i Is the first adjustment variable value. Here, the turnaround days may be total turnaround days of the corresponding item information group generated by calculation in advance. Here, the total spot rate may be a pre-calculated total spot rate of the generated corresponding item information set. Gamma is Here, n represents the number of execution times of the processing steps.

And secondly, updating the optimal dual-sex condition according to the updating constraint parameters.

In practice, the update constraint parameter may be substituted into the optimal dual condition to complete the update of the optimal dual condition. For example, the optimal dual condition may be:

for the item information corresponding to the (i+1) -th processing step, updating the constraint parameter and the accumulated inventory amount included in the (i+1) -th item information into the optimal dual condition to obtain an updated optimal dual condition:

optionally, in response to determining that the accumulated inventory does not satisfy the optimal dual-purpose condition, setting a value of an adjustment variable corresponding to the item information to a second adjustment variable value.

In some embodiments, the execution body may set an adjustment variable corresponding to the item information to a second adjustment variable value in response to determining that the accumulated inventory amount does not satisfy the optimal dual-purpose condition. Here, the second adjustment variable value may indicate that the above item information is not selected as the item sample information. For example, the second adjustment variable value may be 0.

Optionally, the optimal duality condition is updated according to the second adjustment variable value, the updated optimal duality condition is taken as the optimal duality condition, and the item information group from which the item information is deleted is taken as the item information group, and the processing step is performed again.

In some embodiments, the execution body may update the optimal dual-matching condition according to the second adjustment variable value, take the updated optimal dual-matching condition as the optimal dual-matching condition, and take the item information group from which the item information is deleted as the item information group, and execute the processing step again. Here, the specific implementation manner of updating the optimal dual-purpose condition according to the second adjustment variable value may refer to the description of the embodiment of "updating the optimal dual-purpose condition according to the first adjustment variable value" in step 303, which is not repeated herein.

As can be seen from fig. 3, compared with the description of some embodiments corresponding to fig. 2, the process 300 in some embodiments corresponding to fig. 3 can continuously update the optimal dual condition, so that the item information selected each time meets the condition of sample selection, so that the final obtained item sample information set is representative, and can accurately represent the inventory state of the items in the warehouse, thereby facilitating the subsequent scheduling of the items.

With further reference to fig. 4, still further embodiments of the item information processing method according to the present disclosure are shown. The article information processing method comprises the following steps:

step 401, acquiring an item circulation information set of each item in the item groups in a preset time period, and obtaining an item circulation information set.

In some embodiments, the execution body of the item information processing method (for example, the computing device 101 shown in fig. 1) may obtain, by using a wired connection or a wireless connection, an item circulation information set of each item in the item group in a preset period from the terminal device, so as to obtain an item circulation information set. Wherein, the article circulation information in the article circulation information group set comprises a unit stock quantity and a unit circulation quantity. Here, the setting of the preset time period is not limited. Here, the unit stock quantity and the unit circulation quantity may refer to a stock quantity and a circulation quantity (sales quantity) of a certain time granularity within a preset period of time. For example, the simulation time period may be 9 months 5-9.8, and the time granularity of the preset time period may be 1 day. In practice, the execution body can acquire the article circulation information sets of the articles in different warehouses in a certain area within a preset time period to obtain an article circulation information set. Here, the item circulation information group may refer to circulation information of each time granularity of a certain item in a certain warehouse within a preset time period. For example, the preset time period may be 9 months 1-9 months 3, the item circulation information of item "a" in warehouse 1 at 9 months 1 may be "unit stock quantity 20, unit circulation quantity 19", the item circulation information at 9 months 2 may be "unit stock quantity 21, unit circulation quantity 18", and the item circulation information at 9 months 3 may be "unit stock quantity 19, unit circulation quantity 23".

Step 402, for each item circulation information group in the item circulation information group, generating an accumulated stock quantity and an accumulated circulation quantity according to each unit stock quantity and unit circulation quantity included in the item circulation information group.

In some embodiments, for each item circulation information set in the item circulation information set, the executing entity may generate an accumulated inventory amount and an accumulated circulation amount according to respective unit inventory amounts and unit circulation amounts included in the item circulation information set. In practice, the sum of the individual unit stock amounts included in the above item flow information group may be determined as the accumulated stock amount. The sum of the individual unit circulation amounts included in the above item circulation information group may be determined as the accumulated circulation amount.

Step 403, generating the turnover days according to the generated accumulated stock quantity and accumulated circulation quantity.

In some embodiments, the executing entity may generate the turnover number of days according to the generated respective accumulated inventory amounts and accumulated circulation amounts. In practice, first, the sum of the respective generated accumulated amounts of stock may be determined as the accumulated total amount of stock. Then, the sum of the generated respective accumulated flow amounts may be determined as an accumulated total flow amount. Finally, the ratio of the accumulated total stock quantity to the accumulated total circulation quantity can be determined as the turnover number of days.

Step 404, generating a spot rate group and a total spot rate corresponding to the article group according to the article circulation information group set.

In some embodiments, the executing entity may generate a spot rate group and a total spot rate corresponding to the item group according to the item circulation information group set. Wherein the items in the item group correspond to the spot rates in the spot rate group.

In practice, according to the above-mentioned article circulation information group set, the spot rate group and the total spot rate corresponding to the above-mentioned article group can be generated by:

first, for each item circulation information group in the item circulation information group set, the following determination steps are executed:

and a first step of determining the number of the unit stock quantity included in the item circulation information group as a single item circulation day number. Here, the number of days of single item circulation may refer to the number of time granularity in a preset time period corresponding to each item circulation information in the item circulation information group.

And a second step of determining a unit stock quantity greater than 0 of the unit stock quantities as a stock quantity to obtain a stock quantity group.

And a third step of determining the number of inventory levels included in the inventory level group as the number of days on inventory.

And a fourth step of determining a ratio of the number of days of stock and the number of days of single-item circulation as a stock-in-stock rate.

Thus, the determined individual spot rates may be determined as the spot rate group corresponding to the above-described item group.

And a second step of determining the sum of the determined individual item circulation days as the total number of days.

Third, the sum of the determined individual days of goods is determined as the total days of goods.

And step four, determining the ratio of the total stock-keeping days to the total days as the total stock-keeping rate.

And 405, combining the accumulated stock quantity, the accumulated flow quantity and the spot rate corresponding to each item in the item group to generate item information, thereby obtaining an item information group.

In some embodiments, the execution body may perform a combination process on the accumulated inventory, the accumulated flow rate, and the spot rate corresponding to each item in the item group to generate item information, so as to obtain an item information group. Here, the combining process may refer to a splicing process.

Step 406, for each item information in the item information group, performing the following processing steps:

step 4061, determining whether the accumulated inventory included in the item information satisfies an optimal dual condition corresponding to the item information.

In some embodiments, the executing entity may determine whether the accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information. Wherein the optimal dual condition is a condition constructed based on the accumulated inventory.

In practice, the optimal dual-purpose condition corresponding to the above item information is constructed by the following steps:

first, constructing a turnover number constraint parameter according to the turnover number, the accumulated stock quantity included in the article information, the accumulated circulation quantity and a preset turnover number constraint coefficient. In order to make the relative deviation of the turnover number of the selected target article sample information set from the turnover number of days corresponding to the article circulation information set smaller than or equal to the preset turnover number constraint coefficient, in practice, the turnover number constraint parameter may be expressed as follows:

|I _i -ITO×S _i |-ITO×S _i ≤0。

wherein I _i -(1+α)×ITO×S _i I represents the turnaround day constraint parameter. I _i The accumulated amount of stock included in the item information corresponding to the i-th processing step (i.e., the accumulated amount of stock included in the item information in the present processing step) is indicated. Alpha represents a preset turnaround day constraint factor. ITO represents the number of turnover days described above. S is S _i The accumulated flow amount included in the item information corresponding to the i-th processing step (i.e., the accumulated flow amount included in the item information in the present processing step) is indicated.

And a second step of constructing a spot rate constraint parameter according to the total spot rate, the spot rate included in the article information and a preset spot rate constraint coefficient. In order to make the relative deviation between the total spot rate of the selected target article sample information set and the total spot rate corresponding to the article circulation information group set smaller than or equal to the preset spot rate constraint coefficient, in practice, the spot rate constraint parameter may be represented by the following formula:

|r _i -R|-β≤0。

wherein r _i -r| - β represents the spot rate constraint parameter. r is (r) _i The spot rate included in the item information corresponding to the i-th processing step (in this processing step, the spot rate included in the item information) is indicated. R represents the total spot rate. Beta represents a preset spot rate constraint coefficient.

And thirdly, constructing an optimal dual condition according to the initial constraint parameter, the turnover number constraint parameter and the spot rate constraint parameter. Here, the initial constraint parameter may refer to a set constraint parameter that constructs an optimal dual condition corresponding to the first item information. For example, at the first screening of item information, the initial constraint parameter may be an array of elements 0,0 that are all equal to 0]. In practice, first, the absolute sign of the constraint parameter of the turnaround days can be removed to obtain I _i -(1+α)×ITO×S _i and-I _i +(1-α)×S _i . Then, the absolute value sign of the spot rate constraint parameter can be removed to obtain r _i -R-beta and-R _i +R-beta. Next, according to I _i -(1+α)×ITO×S _i 、-I _i +(1-α)×S _i 、r _i -R-beta and-R _i +R-beta, constructing a condition vector of the article information: a, a _i ＝[I _i -(1+α)×ITO×S _i ，-I _i +(1-α)×S _i ，r _i -R-β，-r _i +R-β]. Finally, deducing the optimal dual condition by utilizing the dual theory in operation research:

wherein P is _i Representing the ith pair of processing stepsConstraint parameters of the corresponding item information (P in this processing step _i As an initial constraint parameter). I _i Indicating the accumulated stock quantity included in the item information corresponding to the ith processing step (I in this processing step _i A cumulative inventory included for the item information). T represents the matrix transpose.

Step 4062, in response to determining that the accumulated inventory satisfies the optimal dual condition, adding the item information to a preset item sample information set.

In some embodiments, the specific implementation of step 4062 and the technical effects thereof may refer to step 2012 in those embodiments corresponding to fig. 2, which is not described herein.

In step 407, in response to the target number being greater than or equal to the preset number, the set of item sample information is determined as a set of target item sample information.

In some embodiments, the specific implementation of step 407 and the technical effects thereof may refer to step 202 in those embodiments corresponding to fig. 2, which are not described herein.

As can be seen from fig. 4, compared with the description of some embodiments corresponding to fig. 2, the process 400 in some embodiments corresponding to fig. 4 may construct an optimal dual condition corresponding to each item information, and the item information may be rapidly screened using the constructed optimal dual condition, thereby shortening the time for screening the item information.

With further reference to fig. 5, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of an article information processing apparatus, which apparatus embodiments correspond to those method embodiments shown in fig. 2, and which apparatus is particularly applicable in various electronic devices.

As shown in fig. 5, the article information processing apparatus 500 of some embodiments includes: an information processing unit 501 and a determination unit 502. Wherein the information processing unit 501 is configured to perform the following processing steps for each item information in the item information group: determining whether an accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information, wherein the optimal dual condition is a condition constructed according to the accumulated inventory amount; in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition, adding the item information to a preset item sample information set; and a determining unit 502 configured to determine the article sample information set as a target article sample information set in response to a target number being equal to or greater than a preset number, wherein the target number is a number of article sample information included in the article sample information set.

Optionally, before adding the item information to a preset item sample information set, the information processing unit 501 is further configured to: and setting the value of an adjustment variable corresponding to the item information as a first adjustment variable value, wherein the adjustment variable represents whether the item information is selected as item sample information, and the first adjustment variable value represents that the item information is selected as item sample information.

Optionally, the apparatus 500 further comprises: a first condition updating unit configured to update the optimal duality condition according to the first adjustment variable value in response to the target number being smaller than the preset number, take the updated optimal duality condition as the optimal duality condition, and take an item information group from which the item information is deleted as an item information group, and execute the processing step again.

Optionally, the apparatus 500 further comprises: a setting unit configured to set a value of an adjustment variable corresponding to the item information to a second adjustment variable value in response to determining that the accumulated inventory amount does not satisfy the optimal dual-purpose condition; and a second condition updating unit configured to update the optimal duality condition based on the second adjustment variable value, take the updated optimal duality condition as the optimal duality condition, and take the item information group from which the item information is deleted as the item information group, and execute the processing step again.

Optionally, before the information processing unit 501, the apparatus 500 further includes: the device comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is configured to acquire an article circulation information set of each article in an article set in a preset time period to obtain an article circulation information set, and the article circulation information in the article circulation information set comprises a unit inventory and a unit circulation; a first generation unit configured to generate, for each of the item circulation information groups in the item circulation information group, an accumulated inventory amount and an accumulated circulation amount from respective unit inventory amounts and unit circulation amounts included in the item circulation information group; a second generation unit configured to generate a turnover number of days from the generated respective accumulated stock amounts and accumulated circulation amounts; a third generation unit configured to generate a spot rate group and a total spot rate corresponding to the article group according to the article circulation information group set, wherein the articles in the article group correspond to the spot rates in the spot rate group; and the combining unit is configured to combine the accumulated stock quantity, the accumulated flow quantity and the spot rate corresponding to each article in the article group so as to generate article information and obtain an article information group.

Optionally, the optimal dual condition includes constraint parameters.

Optionally, the apparatus 500 further comprises: the clustering unit is configured to perform clustering processing on the target article sample information set to obtain a target article sample information set; a verification unit configured to perform, for each target item sample information set in the set of target item sample information sets, the following verification steps: acquiring an article inventory information group of each target article in a target article group in a simulation time period to obtain an article inventory information group set, wherein the target articles in the target article group correspond to target article sample information in the target article sample information group; generating a simulated total turnover number and a simulated total spot rate corresponding to the target object group according to the object inventory information group set; determining whether the absolute value of the difference between the number of target turnover days and the simulated total turnover number is smaller than or equal to a preset number of days error, and determining whether the absolute value of the difference between the target spot rate and the simulated total spot rate is smaller than or equal to a preset spot rate error, wherein the target turnover number is the total turnover number corresponding to the target article sample information group, and the target spot rate is the total spot rate corresponding to the target article sample information group; and storing the target object sample information set to a preset warehouse terminal in response to the absolute value of the difference of the days being smaller than or equal to the preset day error and the absolute value of the difference of the stock-in-stock rate being smaller than or equal to the preset stock-in-stock error.

Optionally, the apparatus 500 further comprises: and the control unit is configured to control the associated vehicle to schedule the articles according to each target article sample information group stored in the warehouse terminal.

It will be appreciated that the elements described in the apparatus 500 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting benefits described above with respect to the method are equally applicable to the apparatus 500 and the units contained therein, and are not described in detail herein.

Referring now to FIG. 6, a schematic diagram of an electronic device 600 (e.g., computing device 101 of FIG. 1) suitable for use in implementing some embodiments of the disclosure is shown. The electronic device in some embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.

As shown in fig. 6, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 6 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 609, or from storage device 608, or from ROM 602. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.

It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: for each item information in the item information set, the following processing steps are performed: determining whether an accumulated inventory amount included in the item information satisfies an optimal dual condition corresponding to the item information, wherein the optimal dual condition is a condition constructed according to the accumulated inventory amount; in response to determining that the accumulated inventory satisfies the optimal dual-purpose condition, adding the item information to a preset item sample information set; and determining the article sample information set as a target article sample information set in response to the target number being greater than or equal to a preset number, wherein the target number is the number of article sample information included in the article sample information set.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an information processing unit and a determination unit. The names of the units do not limit the unit itself in some cases, and for example, the determining unit may be further described as "a unit that determines the above-described article sample information set as a target article sample information set in response to a target number of the number of article sample information included in the above-described article sample information set being equal to or greater than a preset number".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. An article information processing method comprising:

for each item information in the item information set, the following processing steps are performed:

determining whether an accumulated inventory quantity included in the item information meets an optimal dual condition corresponding to the item information, wherein the optimal dual condition is a condition constructed according to the accumulated inventory quantity;

in response to determining that the accumulated inventory satisfies the optimal dual-affinity condition, adding the item information to a preset item sample information set;

and determining the article sample information set as a target article sample information set in response to the target number being greater than or equal to a preset number, wherein the target number is the number of article sample information included in the article sample information set.

2. The method of claim 1, wherein prior to the adding the item information to the set of preset item sample information, the processing step further comprises:

setting the value of an adjustment variable corresponding to the item information as a first adjustment variable value, wherein the adjustment variable represents whether the item information is selected as item sample information, and the first adjustment variable value represents that the item information is selected as item sample information.

3. The method of claim 2, wherein the method further comprises:

and in response to the target number being smaller than the preset number, updating the optimal dual-activity condition according to the first adjustment variable value, taking the updated optimal dual-activity condition as the optimal dual-activity condition, taking the item information group with the item information deleted as the item information group, and executing the processing step again.

4. The method of claim 1, wherein the method further comprises:

in response to determining that the accumulated inventory quantity does not satisfy the optimal dual-purpose condition, setting a value of an adjustment variable corresponding to the item information to a second adjustment variable value;

and updating the optimal dual-activity condition according to the second adjustment variable value, taking the updated optimal dual-activity condition as the optimal dual-activity condition, taking the article information group with the article information deleted as the article information group, and executing the processing step again.

5. The method of claim 1, wherein the method further comprises, prior to the performing the following processing steps for each item information in the set of item information:

acquiring an item circulation information set of each item in an item group in a preset time period to obtain an item circulation information set, wherein the item circulation information in the item circulation information set comprises a unit stock quantity and a unit circulation quantity;

For each item circulation information group in the item circulation information group, generating an accumulated stock quantity and an accumulated circulation quantity according to each unit stock quantity and unit circulation quantity included in the item circulation information group;

generating turnover days according to the generated accumulated stock quantity and accumulated circulation quantity;

generating a spot rate group and a total spot rate corresponding to the article group according to the article circulation information group set, wherein articles in the article group correspond to the spot rate in the spot rate group;

and combining the accumulated stock quantity, the accumulated flow quantity and the spot rate corresponding to each item in the item group to generate item information, so as to obtain an item information group.

6. The method of claim 5, wherein the generating a spot rate group and a total spot rate corresponding to the group of items from the group of item flow information sets comprises:

for each item circulation information group in the item circulation information group set, performing the following determination steps:

determining the number of each unit stock quantity included in the item circulation information group as a single item circulation day;

determining the unit stock quantity greater than 0 in the unit stock quantities as a stock quantity to obtain a stock quantity group;

Determining the number of inventory levels included in the inventory level group as the number of days on inventory;

and determining the ratio of the number of the stock days to the number of the single-item circulation days as the stock-in-stock rate.

7. The method of claim 6, wherein the generating a spot rate group and a total spot rate corresponding to the group of items from the group of item flow information sets further comprises:

determining the sum of the determined individual item circulation days as a total number of days;

determining the sum of the determined individual days of goods as the total days of goods;

the ratio of the total number of stock days to the total number of days is determined as a total stock-in-process rate.

8. The method of claim 5, wherein the optimal dual-affinity condition corresponding to the item information is constructed by:

constructing a turnover number constraint parameter according to the turnover number, the accumulated inventory quantity included in the article information, the accumulated circulation quantity and a preset turnover number constraint coefficient;

constructing spot rate constraint parameters according to the total spot rate, the spot rate included in the article information and a preset spot rate constraint coefficient;

and constructing an optimal dual condition according to the initial constraint parameter, the turnover number about parameter and the spot rate constraint parameter.

9. A method according to claim 3, wherein the optimal dual-connectivity condition comprises constraint parameters; and

said updating said optimal dual-connectivity condition according to said first adjustment variable value, comprising:

generating updated constraint parameters according to the constraint parameters, the step size parameters and the first adjustment variable values;

and updating the optimal dual-coupling condition according to the updating constraint parameters.

10. The method of claim 1, wherein the method further comprises:

clustering the target article sample information set to obtain a target article sample information set;

for each target item sample information set in the set of target item sample information sets, performing the following verification steps:

acquiring an article inventory information group of each target article in a target article group in a simulation time period to obtain an article inventory information group set, wherein the target articles in the target article group correspond to target article sample information in the target article sample information group;

generating a simulated total turnover number and a simulated total spot rate corresponding to the target article group according to the article inventory information group set;

determining whether the absolute value of the difference between the number of target turnover days and the simulated total turnover number is smaller than or equal to a preset number of days error, and determining whether the absolute value of the difference between the target spot rate and the simulated total spot rate is smaller than or equal to a preset spot rate error, wherein the target turnover number is the total turnover number corresponding to the target article sample information group, and the target spot rate is the total spot rate corresponding to the target article sample information group;

And responding to the absolute value of the number of days difference being smaller than or equal to the preset number of days error and the absolute value of the spot rate difference being smaller than or equal to the preset spot rate error, and storing the target article sample information set into a preset warehouse terminal.

11. The method of claim 10, wherein the method further comprises:

and controlling the associated vehicle to schedule the articles according to each target article sample information group stored in the warehouse terminal.

12. An article information processing apparatus comprising:

an information processing unit configured to perform, for each item information in the item information group, the following processing steps: determining whether an accumulated inventory quantity included in the item information meets an optimal dual condition corresponding to the item information, wherein the optimal dual condition is a condition constructed according to the accumulated inventory quantity; in response to determining that the accumulated inventory satisfies the optimal dual-affinity condition, adding the item information to a preset item sample information set;

and a determining unit configured to determine the article sample information set as a target article sample information set in response to a target number being equal to or greater than a preset number, wherein the target number is a number of article sample information included in the article sample information set.

13. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon;

when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-11.

14. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-11.