CN115222342A

CN115222342A - Intelligent allocation and compensation method, system, equipment and medium

Info

Publication number: CN115222342A
Application number: CN202211141509.9A
Authority: CN
Inventors: 严嘉星; 杨涛; 万华; 廖波涛; 陆艳贞; 陈彬; 李智威
Original assignee: Guangzhou Hengkang Information Technology Co ltd
Current assignee: Guangzhou Hengkang Information Technology Co ltd
Priority date: 2022-09-20
Filing date: 2022-09-20
Publication date: 2022-10-21

Abstract

The embodiment of the application provides an intelligent compensation method, system, equipment and medium, and belongs to the technical field of computers. The method comprises the steps of collecting basic information of goods distribution, wherein the basic information of goods distribution comprises a commodity order or a shop code distribution table; acquiring the goods distribution information input by the user based on the goods distribution basic information; acquiring delivery warehouse information input by a user based on the goods allocation information; acquiring distribution priority information input by a user; determining the distribution sequence of each shop according to the distribution priority information; acquiring distribution rule information input by a user; and generating a distribution list according to the distribution commodity information, the delivery warehouse information, the distribution sequence and the distribution rule information. The method and the device can realize intelligent distribution, manual distribution is not needed by a user, and the precision of distribution is improved.

Description

Intelligent compensation and adjustment method, system, equipment and medium

Technical Field

The application relates to the technical field of computers, in particular to an intelligent compensation method, system, equipment and medium.

Background

The traditional commodity management mode comprises a manual Excel allocation mode, a mode of setting sales one for one, fixing the ratio for goods supplement, a mode of laying goods plan, a mode of importing the allocation plan into a system to generate warehouse allocation instructions and the like. The method comprises the steps of introducing in Excel, manually inputting, manually distributing, reading manual data, inputting into a system through subjective judgment of personnel in a department of goods and shop personnel, and carrying out a goods distribution instruction on the basis of safe stock.

However, with the continuous development of enterprises, the defects are continuously shown, and the manual operation of preparing and replenishing goods is obviously not practical.

Therefore, how to solve the above problems is a problem that needs to be solved at present.

Disclosure of Invention

The application provides an intelligent matching and supplementing method, system, equipment and medium, aiming at solving the problems.

In a first aspect, the application provides an intelligent distribution and compensation method, which collects basic information of distribution, wherein the basic information of distribution comprises a commodity order or a shop code matching table;

acquiring the goods distribution information input by the user based on the goods distribution basic information;

acquiring delivery warehouse information input by a user based on the goods allocation information;

acquiring distribution priority information input by a user;

determining the distribution sequence of each shop according to the distribution priority information;

acquiring distribution rule information input by a user;

and generating a distribution bill according to the distribution commodity information, the delivery warehouse information, the distribution sequence and the distribution rule information.

In one possible embodiment, the distribution priority information includes calculation dimension information and store tag information; the calculation dimension information comprises a new product level and a running level;

the step of determining the distribution sequence of each shop according to the distribution priority information comprises the following steps:

acquiring the shop label information corresponding to each shop;

acquiring the new product level and the running level corresponding to each store label information;

determining a first distribution sequence among a plurality of shops according to the new product level;

and adjusting the first distribution sequence according to the flow level to generate the distribution sequence of each shop.

In a possible embodiment, the distribution priority information includes the weight information, and the calculation dimension information includes the store level and the sales amount;

the determining the distribution sequence of each shop according to the distribution priority information comprises the following steps:

respectively determining a grade rank of each store according to the store grade, wherein the grade rank is opposite to the store grade;

determining a sales ranking corresponding to each store according to the sales amount, wherein the larger the sales amount is, the larger the number corresponding to the sales ranking is;

determining a score for each of the stores based on the weight information, the ranking, and the sales ranking;

and determining the distribution sequence of each shop according to the scores.

In a possible embodiment, the weight information comprises a first sub-weight and a second sub-weight, the sum of the first sub-weight and the second sub-weight is equal to 1;

the fraction F = a1 × D + a2 × C;

wherein a1 represents the first sub-weight, a2 represents the second sub-weight, D is the ranking, and C is the sales ranking.

In one possible embodiment, a1 is 0.3 and a2 is 0.7.

In a possible embodiment, the method further comprises:

acquiring replenishment information;

and generating a replenishment scheme according to the replenishment information.

In a possible embodiment, the replenishment information comprises a delivery warehouse, a replenishment store, replenishment commodities, replenishment rate and replenishment rules; the replenishment multiplying power comprises a replenishment multiplying power influence factor, a final multiplying power calculation mode and a maximum multiplying power; the influence factors of the replenishment multiplying power comprise shop-by-shop, commodity-by-commodity and festival-by-festival, and the final multiplying power calculation mode comprises proportional calculation; the replenishment rule comprises the daily average sales volume of the nearly N days;

the generating of the replenishment scheme according to the replenishment information comprises:

generating an initial replenishment scheme according to the delivery warehouse, the replenishment store and the replenishment commodity;

obtaining a target final multiplying power according to the shop-by-shop, the commodity-by-commodity, the festival-by-festival and the proportional calculation mode;

generating an intermediate replenishment scheme according to the target final multiplying power and the initial replenishment scheme;

acquiring the daily average sales volume of the near N days;

and adding the average sales volume in the near N days into the intermediate replenishment scheme to generate a target replenishment scheme, wherein the target replenishment scheme is used as a replenishment scheme.

In a second aspect, the present application provides an intelligent compensation and adjustment system, the system comprising: a store distribution unit to perform the steps of:

acquiring basic information of goods allocation, wherein the basic information of goods allocation comprises a commodity order or a shop code allocation table;

acquiring distribution priority information input by a user;

acquiring distribution rule information input by a user;

In a third aspect, the present application provides an electronic device, comprising:

a memory for storing executable instructions;

a processor, configured to implement the intelligent patch tuning method according to any one of the first aspect when executing the executable instructions stored in the memory.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processing device, performs the steps of the intelligent patch tuning method according to any one of the first aspect.

According to the intelligent distribution and compensation method, the system, the equipment and the medium, the distribution basic information is acquired, and the distribution basic information comprises a commodity order or a shop code distribution table; acquiring the information of the goods to be distributed input by the user based on the basic information of the goods to be distributed; acquiring delivery warehouse information input by a user based on the goods allocation information; acquiring distribution priority information input by a user; determining the distribution sequence of each shop according to the distribution priority information; acquiring distribution rule information input by a user; and generating a distribution bill according to the distribution commodity information, the delivery warehouse information, the distribution sequence and the distribution rule information. The method and the device can realize intelligent distribution, a user is not required to perform pure manual distribution, and the precision of distribution is improved.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a schematic structural diagram of an electronic device according to a first embodiment of the present application;

fig. 2 is a flowchart of an intelligent patch adjustment method according to a second embodiment of the present application;

fig. 3 is a schematic view of an operation interface for collecting basic information of cargo allocation in the intelligent allocation and compensation method shown in fig. 2;

fig. 4 is a schematic view of a goods allocation commodity operation interface in the intelligent allocation and compensation method shown in fig. 2;

fig. 5 is a schematic view of an operation interface for information collection of a shipping warehouse in the intelligent replenishment and adjustment method shown in fig. 2;

fig. 6 is a schematic view of an operation interface for collecting distribution priority information in the intelligent distribution and adjustment method shown in fig. 2;

fig. 7 is a schematic view of an operation interface for collecting cargo allocation rule information in the intelligent allocation and reconditioning method shown in fig. 2;

fig. 8 is a functional module schematic diagram of an intelligent patch system according to a third embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.

First embodiment

Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and in the present application, an electronic device 100 for implementing an example of an intelligent patch method, an example of an intelligent patch system, and an example of a medium according to the embodiment of the present application may be described with reference to the schematic diagram shown in fig. 1.

As shown in FIG. 1, an electronic device 100 includes one or more processors 102, one or more memory devices 104, and an input device 106, which are interconnected via a bus system and/or other form of connection (not shown). It should be noted that the components and structure of the electronic device 100 shown in fig. 1 are only exemplary and not limiting, and the electronic device may have some of the components shown in fig. 1 and may also have other components and structures not shown in fig. 1 as needed.

The processor 102 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 100 to perform desired functions.

It should be understood that the processor 102 in the embodiments of the present application may be a Central Processing Unit (CPU), and the processor may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 104 may include one or more computer program products that may include various forms of computer-readable storage media.

It should be appreciated that the storage 104 in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example, but not limitation, many forms of Random Access Memory (RAM) are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct bus RAM (DR RAM).

On which one or more computer program instructions may be stored that may be executed by processor 102 to implement the client functionality (implemented by the processor) in the embodiments of the application described below, and/or other desired functionality. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer-readable storage medium.

The input device 106 may be a device used by a user to input distribution commodity information, delivery warehouse information, distribution priority information, and distribution rule information, and may include one or more of a keyboard, a mouse, a microphone, a touch screen, and the like.

Second embodiment:

referring to a flowchart of an intelligent supplementary tone allocation method shown in fig. 2, the method specifically includes the following steps:

step S201, collecting basic information of goods distribution.

The goods distribution basic information comprises a commodity order or a shop code distribution table.

Wherein, the commodity order is the order placed by the shop.

The shop code matching table is an order of a shop according to the number of codes, and the format of the shop code matching table is as follows:

26	27	28	29	30	31	32
							20	20	20	20	20	20	20

in the above table, the first row represents the code, and the second row represents the order quantity corresponding to each code.

As a specific embodiment, as shown in fig. 3, the distribution basic information may further include other information besides the goods order or the shop code table, such as a plan code, a plan name, a distribution time, an organization, an execution time, a distribution object, and the like.

The source of the allocation amount in fig. 3 is a product order or a store allocation table.

It should be understood that the operator interface shown in FIG. 3 is exemplary only, and not limiting.

That is, the user may input the distribution basis information through the operation interface shown in fig. 3, so as to facilitate background collection.

Alternatively, the product order or the store code list may be of one store or of a plurality of different stores, and is not particularly limited herein.

And step S202, acquiring the goods distribution information input by the user based on the goods distribution basic information.

It should be noted that, after the user inputs the basic information of the goods distribution, the operation interface displays the operation interface of the goods distribution on the operation interface for the user to configure.

For example, as shown in fig. 4, the user may input a commodity condition, such as a certain brand (e.g., test brand a, test brand B shown in the figure), on the operation interface to complete the input of the information of the matched commodity.

Of course, in actual use, the user can also select a specific commodity to complete the input of the information of the goods for distribution.

It should be understood that the operation interface shown in fig. 4 is only an example, and not a limitation.

The term "commodity condition" means that the commodity range is filtered by the common component according to the commodity attribute.

As another embodiment, after the product order/store code table is collected in step S201, the corresponding product is automatically displayed for the user to select.

And step S203, acquiring the information of the delivery warehouse input by the user based on the information of the goods to be delivered.

In other words, after the user completes the input of the information of the goods to be delivered, the system displays the next operation interface to collect the information of the delivery warehouse input by the user.

Optionally, the shipping warehouse information includes a shipping warehouse mode and a shipping warehouse.

Alternatively, the shipping warehouse model includes, but is not limited to, sharing a branch warehouse by branch companies, setting a branch warehouse by business region where a store is located, and dividing by store.

For example, as shown in fig. 5, the user completes the input of the shipping warehouse information on the operation interface shown in fig. 5, so as to facilitate the collection by the system.

Optionally, the shipping warehouse uses a pull-down option for user selection to collect the target warehouse selected by the user.

And step S204, acquiring the distribution priority information input by the user.

The distribution priority information comprises time range information, calculation dimension information, shop label information, shop area information and/or weight information.

The time range information may refer to a specific time shown in fig. 6, and is not specifically limited herein.

The calculation dimension information comprises shop label information, area, new product flow, new product level, flow level, label matching, shop level, sales amount, sales quantity, plateau effect and/or human effect.

The following briefly introduces a partial dimension calculation method in calculating dimension information:

store tag dimensions: and displaying a selection component of the shop label, a necessary shop label, multiple selections and a sequence.

For example, assume that the user has selected a new product level label (first rank), a water level (second rank), where:

the new product level label comprises: a (10 min), B (9 min) and C (8 min);

the running water grade label comprises: high (10 points), medium (9 points) and low (8 points), then

Store data are shown in table one below:

shop	New product level label	Running water grade label	Priority level
				Shop A	A	High (a)	1
Shop B	A	Is low with	2
				Shop C	C	Is low in	6
Shop D	B	In	3
				Shop E	C	Height of	5
Shop F	B	Is low in	4

Watch 1

As shown in Table I, the new product level label has the highest priority on level A, i.e., store A and store B, and then the running level label is used to rank A and B who have the higher priority.

For example, as shown in fig. 6, when the calculation dimension is area: and when the area of the shop is larger than or equal to the reference value, the goods are distributed preferentially.

Wherein, when the area is greater than or equal to 120m, whether 120m or 180m has had their priorities identical. If the area is not set in the shop, the priority is the lowest.

The new product runs: the new product is distributed with high priority.

New product flow = sale amount x sale new product rate;

the sale new product rate = store new product sale amount ÷ store sale amount;

sales amount of new product in store: total sales of the item tagged with "new product".

Sales amount of shop: regardless of the total sales of the new, old, and store.

Matching the labels: the shop label is matched with the commodity label, and the goods are distributed preferentially.

For example, the items to be matched are listed in the following 2 tables:

the sales amount is a statistical value in the "calculation time" range in the shop priority rule.

Matching labels: the shop label is matched with the commodity label, and the goods are distributed preferentially.

For example, the items to be distributed and the stores are shown in Table two below:

goods of commerce	Commodity label
		15S08343	Leisure time
100534WE	Formal dress
		78953434	Business affairs
Shop	Shop label
		Shop A	Leisure time
Shop B	Formal dress
		Shop C	Formal dress
Shop D	Leisure chair
		Shop E	Leisure business

Watch two

Then, when the product 15S08343 is distributed, the product labels "leisure" can be matched to the stores a, D, and E, and the priority of the three stores is higher.

When the product 100534WE is shipped, the product label "front-loaded" can be matched to the stores B and C, and the two stores have higher priority.

When the item 78953434 is shipped, store E can be matched based on the item label "business", and the priority of this store is higher.

Optionally, the calculation time gives quick setting items of about 3 months, about 6 months, about 9 months, about 12 months and about 24 months, and if the use by the user is not met, the user can select to define and enter about N months by himself.

The items for calculating the weight are dynamically generated according to the selection of the calculation dimension, and the set rule is stored in a json format.

And S205, determining the distribution sequence of each shop according to the distribution priority information.

In one embodiment, the distribution priority information includes the weight information, and the calculation dimension information includes the store level and the sales amount; step S205, including: respectively determining a grade ranking of each store according to the store grades, wherein the grade ranking is opposite to the store grades; determining a sales ranking corresponding to each store according to the sales amount, wherein the larger the sales amount is, the larger the number corresponding to the sales ranking is; determining a score for each of the stores based on the weight information, the ranking, and the sales ranking; and determining the distribution sequence of each shop according to the scores.

Optionally, the weight information includes a first sub-weight and a second sub-weight, and a sum of the first sub-weight and the second sub-weight is equal to 1;

the fraction F = a1 × D + a2 × C;

Alternatively, a1 is 0.3 and a2 is 0.7.

For example, as shown in table three below:

shop	Shop grade	Rank ranking	Amount of sale	Sales ranking
					Store A	1	3	20w	4
Shop B	3	1	5w	1
					C store	1	3	10w	2
D store	2	2	11w	3

Watch III

Assuming a1 is 0.3 and a2 is 0.7, the order of distribution is as follows:

shop	Score of	Ranking
			Store A	3.7	1
Shop B	1	4
			C store	2.3	3
D store	2.7	2

Watch four

The order of distribution is first since store A has the highest score, and last since store B has the lowest score.

As another embodiment, the distribution priority information includes calculation dimension information and store tag information; the calculation dimension information includes a new product level and a pipeline level, and step S205 includes: the determining the distribution sequence of each shop according to the distribution priority information comprises the following steps: obtaining the shop label information corresponding to each shop; acquiring the new product level and the running water level corresponding to each shop label information; determining a first distribution sequence among a plurality of shops according to the new product level; and adjusting the first distribution sequence according to the flow grade to generate a distribution sequence of each shop.

And step S206, acquiring the distribution rule information input by the user.

Optionally, the allocation rule information includes, but is not limited to, a bookable store proportion, a commodity allocation proportion, and/or a code break no-allocation.

Wherein, the shop can be laid: and limiting the stores which can meet the distribution to generate a distribution list when reaching a certain proportion, otherwise, not automatically carrying out distribution arrangement. It is necessary to limit the number to 0 or more and 100 or less.

Shop occupancy = number of shops that can be allocated this time/total number of shops that need the product (i.e. number of shops with new product order).

For example, assuming a distribution plan with a commodity allocation ratio =80%, a distribution by store bin:

the available inventory for the guangzhou warehouse of 001 commodity =100, then the available inventory for the formula =80;

the available stock of the foshan warehouse for goods 001 =50, then the available inventory for the allotment =40;

goods of commerce	Shop	Priority of distribution	Amount of orders	Delivery warehouse	Amount of goods dispensed
						001	Store A	1	30	Guangzhou warehouse	30
001	Shop B	2	30	Guangzhou warehouse	30
						001	C store	3	30	Guangzhou warehouse	20
001	D store	4	20	Guangzhou warehouse	0
						001	E store	5	10	Guangzhou warehouse	0
001	F shop	6	20	Buddha mountain storehouse	20
						001	G store	7	20	Buddha mountain storehouse	20

Watch five

As shown in table five, the number of shops available is = 4.67/7 × 100% =67%,4.7= a (30/30 = 1) + B (30/30 = 1) + F (20/20 = 1) + G (20/20 = 1) +0.67 (20/30 reserved 2 decimal for C). If the proportion of the shop can not meet the requirement of the distribution scheme, the distribution amount of the commodity is totally reduced to 0 and the commodity is not distributed.

The commodity distribution ratio is as follows: and taking N% of the stock as the distribution amount of the customer, wherein N is a positive integer greater than zero.

Alternatively, the commodity allocation ratio can be manually input, or a threshold value can be set, so that the customer allocation amount can be calculated in real time according to the current inventory.

For example, if N is 50 and the current inventory is 2000, the customer allocation volume is 1000 and the allocation percentage of the goods is equal to N%, which is 50%.

And (4) code breaking and no cargo allocation: and determining whether the commodity is broken according to the code alignment rule, wherein if the commodity is marked, the broken commodity does not need to be matched. Default no check.

For example, as shown in fig. 7, the user may input a shop-able shop ratio and a product allocation ratio on the operation interface, such as 50% for shop-able shop ratio and 60% for product allocation ratio.

In a possible embodiment, the distribution rule information further comprises additional shop information.

Wherein the additional paving information is used for indicating whether to continue paving goods when the paving goods do not meet the planned listing quantity. For example, assume that store A requires 39245 with an order size of 50, but the warehouse has only 30 inventory, and the first allocation to store A has 30, and then there are 20 outstanding allocations. If the additional paving information is configured in the goods allocation rule information, the rest 20 goods allocation can be continuously arranged after the goods come from the warehouse; if no additional pallets are added, the quantity of goods will not be given to store A20.

And step S207, generating a distribution list according to the distribution commodity information, the delivery warehouse information, the distribution sequence and the distribution rule information.

It should be understood that the order sheet has recorded thereon at least a delivery warehouse, an order of delivery, and a delivery item for each store.

In a possible embodiment, the intelligent compensation method further includes: acquiring replenishment information; and generating a replenishment scheme according to the replenishment information.

Optionally, the replenishment information includes, but is not limited to: delivery warehouse, replenishment store, replenishment commodity, replenishment rate, replenishment priority and replenishment rule.

In one embodiment, the replenishment information is information input by the user based on the replenishment interface.

Optionally, the replenishment factor includes a replenishment factor influence factor, a final factor calculation mode, a maximum factor, and a store factor.

Wherein, the influence factors of the replenishment multiplying power comprise shop-by-shop, commodity-by-commodity, holiday-by-weather, lost sales and sales promotion.

Optionally, the final multiplying power calculation mode includes at least one of the following modes:

1) Taking the maximum value: if the conditions of meeting a plurality of influence factors exist, the maximum multiplying power is selected;

2) Taking an average value: if the conditions of multiple influencing factors are met, taking the average value of the multiplying power;

3) According to the ratio: it is necessary to set the weight coefficients of the respective factors.

As an embodiment, the influence factors of the replenishment multiplying power are assumed to comprise the calculation mode of the final multiplying power in proportion according to shops, commodities and festivals; then final magnification = store x1% + commodity x2% + holiday x3%; wherein, the final magnification is less than or equal to the preset maximum magnification (that is, when the final magnification is calculated, the final magnification is less than or equal to the preset maximum magnification, the final magnification at this time is the target final magnification, and if the final magnification is greater than the preset maximum magnification, the target final magnification is equal to the preset maximum magnification); x1% + x2% + x3% =1.

The replenishment priority mode may refer to the priority in the distribution scheme, which is not described herein again.

The replenishment rule comprises at least one of the following modes:

1) And (4) replenishment threshold: controlling the upper limit of the repaired stock, popping up an input box after a user can select a custom threshold, and inputting the threshold, wherein only positive integers of 1 to 9999 are allowed to be input;

2) Replenishing the goods due to shortage;

3) Generating documents according to categories;

4) Average sales volume on nearly N days: default number of days is 30;

wherein the predicted daily sales is: (sales for last 1 week 0.6+ sales for last 2 weeks 0.2+ sales for last 3 weeks 0.1+ sales for last 4 weeks 0.1)/7.

Wherein the sum of the weight coefficients is 1.

As an embodiment, the replenishment rule is a daily average sales amount in near N days, and the obtaining replenishment information includes: and acquiring a delivery warehouse, a replenishment store, replenishment commodities, replenishment multiplying power, replenishment priority and the average sales volume in the near N days.

Wherein the average daily sales in N days last = predicted daily sales 30= (daily sales in 1 week last × 0.6+ daily sales in 2 weeks last × 0.2+ daily sales in 3 weeks last 0.1+ daily sales in 4 weeks last × 0.1)/7/30.

As one embodiment, the restocking information includes: a delivery warehouse, a replenishment store, replenishment commodities, replenishment multiplying power and replenishment rules; the replenishment multiplying power comprises a replenishment multiplying power influence factor, a final multiplying power calculation mode and a maximum multiplying power; the replenishment multiplying power influence factors comprise the steps of calculating according to a shop, a commodity and a festival, and the final multiplying power calculation mode comprises proportional calculation; the replenishment rule comprises the daily average sales volume of nearly N days;

generating a replenishment scheme according to the replenishment information, comprising: generating an initial replenishment scheme according to the delivery warehouse, the replenishment store and the replenishment commodity; obtaining a target final multiplying power according to the modes of calculating according to shops, commodities, festivals and proportions; generating an intermediate replenishment scheme according to the target final multiplying power and the initial replenishment scheme; acquiring the daily average sales volume of the near N days; and adding the daily average sales amount of the nearly N days into an intermediate replenishment scheme to generate a target replenishment scheme.

In a possible embodiment, the method further comprises: and generating a replenishment bill according to the replenishment scheme.

As one embodiment, generating a restocking order according to a restocking plan includes: the method comprises the steps of obtaining shops with replenishment execution dates less than or equal to the current date and the next replenishment period of each shop; generating a first Cartesian set A of stores and dates of replenishment periods; acquiring a replenishment commodity range; generating a second Cartesian set B from the first Cartesian set A and the good; calculating the final multiplying power according to the influence factors of the replenishment multiplying power; calculating the average multiplying power of the shops and the commodities according to the second Cartesian set B to obtain a new set C; calculating the suggested replenishment quantity of each commodity of each shop according to the replenishment mode; calculating the replenishment priority of the shop, and updating the set C; acquiring available stock of an delivery warehouse, calculating the actual replenishment quantity, and generating a replenishment set D; if the minimum replenishment quantity is set in the stores, calculating the total replenishment quantity of each store, removing the data of the stores smaller than the minimum replenishment quantity, and not generating a replenishment order; and grouping according to the delivery warehouse and the bill generation type to generate a replenishment bill.

The replenishment execution date refers to the date when replenishment was last executed; the next replenishment period is the set deadline after the last replenishment.

That is, the replenishment execution date and the next replenishment cycle are historical data.

For example, if the replenishment mode is replenishment per store order, replenishment is performed per amount of unsoldered store orders, assuming that the order demand of the store a for the item 1001 is as shown in the following table six:

36	37	38	39	40	41
						10	10	10	10	10	10

watch six

2021/11/29, inventory of warehouse 1001 is as follows:

36	37	38	39	40	41
						20	20	20	20	0	10

watch seven

Replenishment according to the store order is as follows:

36	37	38	39	40	41
						10	10	10	10	0	10

table eight

2021/11/30, inventory of warehouse with 1001 is as follows nine:

36	37	38	39	40	41
						10	10	10	10	20	10

watch nine

Replenishment according to store order is as follows:

36	37	38	39	40	41
						0	0	0	0	10	0

watch ten

In tables six to ten, the first row of the tables represents the number of codes, and the second row represents the quantity of goods (such as stock or order quantity) corresponding to the number of codes.

2) Otherwise, the recommended replenishment quantity is calculated according to a replenishment formula.

For example, assume that the minimum replenishment quantity for all stores is 30, as shown in Table eleven:

shop	SKU	Warehouse inventory	Amount of advice	Amount of replenishment
					Shop A	1001001	20	10	10
Shop A	1001002	20	10	10
					Shop B	1001001	20	10	10
Shop B	1001002	20	10	10
					Shop B	2001001	40	20	20
Shop B	2001002	40	20	20
					Shop C	1001002	20	10	0
Shop B	2001001	40	20	20
					Shop B	2001002	40	20	20

Watch eleven

In the above table, the total replenishment quantity of the store a is 20, and the minimum replenishment quantity is not satisfied, and no replenishment slip is generated and no replenishment is performed. However, the replenishment quantity 10 of 1001002, which is occupied in the calculation of the algorithm, is not automatically released and adjusted to the store C, and is manually adjusted only by the user.

It is understood that by obtaining replenishment information; the replenishment scheme is generated according to the replenishment information, so that replenishment can be performed for different stores in different warehouses, different replenishment modes (replenishment according to sales, replenishment according to inventory days, replenishment forecast and replenishment according to a marketing plan) can be adopted for commodities in different life cycles (lead-in period, growth period, maturity period and decline period), different replenishment multiplying powers can be set according to different influence factors, such as specific commodities, weather conditions, sales promotion activities, holidays and the like, and priorities can be set for the replenishment of the stores according to different calculation rules. Different stores may have different execution plans.

In a possible embodiment, the intelligent patch adjustment method further includes: acquiring the dispatching information; and generating a goods adjusting scheme according to the goods adjusting information.

Optionally, the diversion information includes, but is not limited to, a diversion rule, a diversion priority, and a diversion rule.

The calling-out rule comprises changing shops for sale or withdrawing shops for calling out.

Wherein the call-in rule is used for indicating the store quota, such as calling in the store upper limit. I.e., an upper limit on the number of stores into which the product called out by a single store can be called.

For example, the store quota is a limit for limiting the number of the stores a calling out the commodities to the target stores B, C, and the like, and is used for controlling the delivery cost of the stores.

The order priority includes, but is not limited to, removing shop priority, changing shop and selling, breaking code, losing sale, distance, regional priority, etc.

The third embodiment:

referring to fig. 8, an intelligent blending and supplementing system 500 includes: the store distribution unit 510.

The store distribution unit 510 is configured to perform the following steps:

collecting basic information of distribution, wherein the basic information of distribution comprises a commodity order or a shop code distribution table;

acquiring delivery warehouse information input by a user based on the information of the goods to be delivered;

acquiring distribution priority information input by a user;

acquiring distribution rule information input by a user;

and generating a distribution list according to the distribution commodity information, the delivery warehouse information, the distribution sequence and the distribution rule information.

In a possible embodiment, as shown in fig. 8, the intelligent patch system 500 further includes: a store replenishment unit 520 and a store adjustment unit 530;

a store replenishment unit 520 for acquiring replenishment information; and generating a replenishment scheme according to the replenishment information.

A shop scheduling unit 530 for acquiring scheduling information; and generating a goods adjusting scheme according to the goods adjusting information.

Optionally, the shunting information includes, but is not limited to, shunting rules, shunting priorities, and shunting rules.

The calling rule comprises a shop changing sale or a shop withdrawing calling.

For example, the store quota is a limit for limiting the number of sales pulled from store a to a target store such as store B or store C, and is used to control the delivery cost of the store.

It should be noted that, for the specific functions of the intelligent configuration and supplementary tuning system 500, reference is made to the description of the method embodiment, and no further description is given here.

Further, the present embodiment also provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processing device, the steps of any one of the intelligent patch tuning methods provided in the foregoing embodiments are executed.

The computer program product of the intelligent patch and call method and system provided in the embodiment of the present application includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.

It should be noted that the above embodiments may be implemented in whole or in part by software, hardware (e.g., a circuit), firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, data center, etc., that contains one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

It should be understood that the term "and/or" herein is only one kind of association relationship describing the association object, and means that there may be three kinds of relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. In addition, the "/" in this document generally indicates that the former and latter associated objects are in an "or" relationship, but may also indicate an "and/or" relationship, which may be understood with particular reference to the former and latter text.

In the present application, "at least one" means one or more, "a plurality" means two or more. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.

It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

Claims

1. An intelligent compensation method is characterized by comprising the following steps:

acquiring distribution priority information input by a user;

acquiring distribution rule information input by a user;

2. The method of claim 1, wherein the shipment priority information includes calculation dimension information and store tag information; the calculation dimension information comprises a new product level and a running level;

obtaining the shop label information corresponding to each shop;

acquiring the new product level and the running water level corresponding to each shop label information;

3. The method of claim 1, wherein the distribution priority information includes weight information and calculation dimension information, the calculation dimension information including store level and sales amount;

and determining the distribution sequence of each shop according to the scores.

4. The method according to claim 3, wherein the weight information comprises a first sub-weight and a second sub-weight, and the sum of the first sub-weight and the second sub-weight is equal to 1;

the fraction F = a1 × D + a2 × C;

5. The method of claim 4, wherein a1 is 0.3 and a2 is 0.7.

6. The method of claim 1, further comprising:

acquiring replenishment information;

7. The method according to claim 6, wherein the replenishment information includes a delivery warehouse, a replenishment store, replenishment goods, a replenishment magnification, and a replenishment rule; the replenishment multiplying power comprises a replenishment multiplying power influence factor, a final multiplying power calculation mode and a maximum multiplying power; the influence factors of the replenishment multiplying power comprise shop-by-shop, commodity-by-commodity and festival-by-festival, and the final multiplying power calculation mode comprises proportional calculation; the replenishment rule comprises the average sales volume in N days;

acquiring the daily average sales volume of the near N days;

and adding the daily average sales amount of the nearly N days into the intermediate replenishment scheme to generate a target replenishment scheme, wherein the target replenishment scheme is used as a replenishment scheme.

8. An intelligent allotment system, characterized in that the system comprises: a store distribution unit to perform the steps of:

acquiring the information of the goods to be distributed input by the user based on the basic information of the goods to be distributed;

acquiring distribution priority information input by a user;

acquiring distribution rule information input by a user;

9. An electronic device, comprising:

a memory for storing executable instructions;

a processor for implementing the intelligent patch method according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processing device, performs the steps of the intelligent patch method according to any one of claims 1-7.