CN107563702A - Commodity storage concocting method, device and storage medium - Google Patents

Commodity storage concocting method, device and storage medium Download PDF

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CN107563702A
CN107563702A CN201710824611.1A CN201710824611A CN107563702A CN 107563702 A CN107563702 A CN 107563702A CN 201710824611 A CN201710824611 A CN 201710824611A CN 107563702 A CN107563702 A CN 107563702A
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commodity
node
fusion
degree
association
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CN107563702B (en
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马腾飞
宋磊
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Abstract

The invention discloses a kind of commodity storage concocting method, device and storage medium, method therein to include：First degree of association between commodity is obtained according to commodity historical purchase data；Based on second degree of association between commodity historical purchase data and commodity stocks acquisition of information commodity and target warehouse；According to default commodity fusion rule and based on first degree of association and second degree of association, the fusion commodity that quantity merges quantity for commodity are filtered out from commodity；The fusion commodity filtered out are stored in target warehouse.The method, apparatus and storage medium of the present invention, realize that singulated rate significantly reduces, the logical more storehouse stocks of too small amount of commodity, the fractionation number after customer order is assigned is reduced, the workload of production order is reduced, improves packaging efficiency, reduce logistics operation cost, the increase of singulated caused packing material is also reduced simultaneously, is laid a good foundation for green operation, is improved the Experience Degree of client.

Description

Commodity storage concocting method, device and storage medium
Technical field
The present invention relates to technical field of electronic commerce, more particularly to a kind of commodity storage concocting method, device and storage Medium.
Background technology
At present, electric business establishes warehouse and carries out full dose commodity stock to chase good client's timeliness experience.Due to commodity Species is various, commodity broad covered area, and a warehouse or a Logistics Park can not meet.Electric business typically divides storehouse using based on category Storehouse is built, such as：Daily pin takes category product generally in one or more warehouses；The digital computer products of 3C are in one or more storehouses Storehouse；Dress ornament class is in one or more warehouses.Due to category expansion, customer order gathers single freight free in addition, and the same order of client is past Toward the commodity for including multiple categories, commodity are distributed in different warehouses, cause order to be split.
The existing method for reducing singulated rate has two kinds：First method is to find the pass between particular commodity and particular commodity Connection degree, as shown in figure 1, by analysis of history order, the degree of adhesion between the SKU of commodity and SKU is determined, lines are more thick, associate Degree is higher, so as to which the high SKU of degree of adhesion is placed in a same warehouse, reduces singulated rate.Second method is in commodity association On the basis of degree, the high commodity of sales volume and single storehouse incidence relation, analysis commodity and the set associative in single storehouse are found.As shown in Fig. 2 X Axle represents the quantity of shared product, and Y-axis represents the theoretical singulated rate between two storehouses, and shared product quantity is more, and theoretical singulated rate is lower, But diminishing marginal utility.2000 commodity of selection are needed to be shared with category storehouse for example, singulated rate is reduced to 10% if desired Fusion.There is shortage flexibility in existing both of which, same commodity are likely to be positioned at multiple storehouses, reduce carrying for singulated rate The shortcomings of ascending effect is poor.
The content of the invention
In view of this, the invention solves a technical problem be to provide a kind of commodity storage concocting method, device with And storage medium.
According to an aspect of the present invention, there is provided a kind of commodity storage concocting method, including：Number is bought according to commodity history According to first degree of association obtained between commodity；Target warehouse is determined, based on commodity historical purchase data and commodity stocks information Obtain second degree of association between the commodity and the target warehouse；Set commodity to merge quantity, melted according to default commodity Normally and based on first degree of association and second degree of association, quantity is filtered out from the commodity and is melted for the commodity Close the fusion commodity of quantity；The fusion commodity filtered out are stored in the target warehouse.
Alternatively, it is described to be associated according to default commodity fusion rule and based on first degree of association with described second Degree, filter out from the commodity quantity fusion commodity of quantity are merged for the commodity and include：Commodity fusion figure is built, its In, the commodity and the target warehouse are respectively the node in commodity fusion figure；Based on first degree of association and described Two degrees of association, the side for connecting the node is established in the commodity merge figure, and to weighted value corresponding to the imparting of the side；Root Condition and the weighted value are chosen according to default subgraph, is merged from the commodity and weight limit connected subgraph is obtained in figure, its In, the node in the weight limit connected subgraph includes：The target warehouse node, quantity are that the commodity merge quantity Merge commodity node.
Alternatively, it is described that condition and the weighted value are chosen according to default subgraph, obtained from the commodity fusion figure Weight limit connected subgraph is taken to include：Calculate fusion goods weight value ∑i,j∈IZij*Wij, according to it is described fusion goods weight value with And the subgraph chooses condition and obtains weight limit connected subgraph；Wherein, the subgraph is chosen condition and included：∑i,j∈IZij*Wij Value be maximum；I and j is respectively the node in the commodity fusion figure, and I is all node sets in commodity fusion figure, ZijIdentify whether selection connecting node i and node j side, WijFor with the weighted value corresponding to connecting node i and node j side.
Alternatively, if node i and node j are commodity node, it is determined that Wij=Lij*(Ni+Nj)；Wherein, LijFor section First degree of association between commodity representated by point i and node j, NiFor the prediction quantity purchase of the commodity representated by node i, Nj For the prediction quantity purchase of the commodity representated by node j；If node i is commodity node and node j is target warehouse node, Then determine Wij=Mij*Nj；Wherein, MijFor second degree of association between the commodity representated by node i and the target warehouse.
Alternatively, first degree of association between the acquisition associated articles according to historical purchase data includes：Based on commodity Purchase order obtains the quantity on order D that have purchased the commodity representated by node ii, commodity representated by purchase node j order numbers Measure DjWhile it have purchased the quantity on order D of node i and the commodity representated by node jij；Determine representated by node i and node j First degree of association between commodity
Optionally it is determined that the second degree of association MijIt is currently stored with the target warehouse respectively for the commodity representated by node i The other commodity of whole between first degree of association summation.
Alternatively, the subgraph is chosen condition and included：For determining the constraints of fusion commodity node；Wherein, it is described Constraints includes：The commodity fusion quantity, the fusion commodity node include the target warehouse.
Alternatively, the constraints also includes：Two or more commodity can not be put into same warehouse simultaneously Storage mutual exclusion restriction, the storage capacity limit in the target warehouse.
Alternatively, the information of the fusion commodity filtered out and the target warehouse information are sent to commodity storage Function system, wherein, the commodity store function system includes：Replenishment system, allot system, stock warehouse system.
According to another aspect of the present invention, there is provided a kind of commodity storage deployment device, including：First degree of association obtains mould Block, for obtaining first degree of association between commodity according to commodity historical purchase data；Second degree of association acquisition module, for true Set the goal warehouse, based on described in commodity historical purchase data and commodity stocks acquisition of information between commodity and the target warehouse Second degree of association；Commodity processing module is merged, for setting commodity to merge quantity, according to default commodity fusion rule and base In first degree of association and second degree of association, quantity is filtered out from the commodity and merges melting for quantity for the commodity Commodity are closed, the fusion commodity filtered out are stored in the target warehouse.
Alternatively, the fusion commodity processing module, including：Model establishes unit, for building commodity fusion figure, its In, the commodity and the target warehouse are respectively the node in commodity fusion figure；Based on first degree of association and described Two degrees of association, the side for connecting the node is established in the commodity merge figure, and to weighted value corresponding to the imparting of the side；Melt Commodity determining unit is closed, for choosing condition and the weighted value according to default subgraph, is obtained from the commodity fusion figure Weight limit connected subgraph is taken, wherein, the node in the weight limit connected subgraph includes：The target warehouse node, number Measure the fusion commodity node that quantity is merged for the commodity.
Alternatively, the fusion commodity determining unit, goods weight value ∑ is merged for calculatingi,j∈IZij*Wij, according to institute State fusion goods weight value and the subgraph chooses condition and obtains weight limit connected subgraph；Wherein, the subgraph chooses bar Part includes：∑i,j∈IZij*WijValue be maximum；I and j is respectively the node in the commodity fusion figure, and I is commodity fusion figure In all node sets, ZijIdentify whether selection connecting node i and node j side, WijFor with connecting node i's and node j Weighted value corresponding to side.
Alternatively, the fusion commodity determining unit, if being all commodity node for node i and node j, it is determined that Wij =Lij*(Ni+Nj)；Wherein, LijFor first degree of association between the commodity representated by node i and node j, NiFor node i institute's generation The prediction quantity purchase of the commodity of table, NjFor the prediction quantity purchase of the commodity representated by node j；The fusion commodity determine single Member, if being commodity node for node i and node j is target warehouse node, it is determined that Wij=Mij*Nj；Wherein, MijFor Second degree of association between commodity and the target warehouse representated by node i.
Alternatively, first degree of association acquisition module, node i institute's generation is have purchased for being obtained based on commodity purchasing order The quantity on order D of the commodity of tablei, commodity representated by purchase node j quantity on order DjWhile it have purchased node i and node j The quantity on order D of representative commodityij；Determine first degree of association between node i and commodity representated by node j
Alternatively, second degree of association acquisition module, for determining the second degree of association MijFor the commodity representated by node i The summation of first degree of association between the other commodity of whole currently stored with the target warehouse respectively.
Alternatively, the subgraph is chosen condition and included：For determining the constraints of fusion commodity node；Wherein, it is described Constraints includes：The commodity fusion quantity, the fusion commodity node include the target warehouse.
Alternatively, the constraints also includes：Two or more commodity can not be put into same warehouse simultaneously Storage mutual exclusion restriction, the storage capacity limit in the target warehouse.
Alternatively, the fusion commodity processing module, including：Merchandise news transmitting element is merged, for that will filter out The information and the target warehouse information of the fusion commodity are sent to commodity store function system, wherein, the commodity are deposited Storage function system includes：Replenishment system, allot system, stock warehouse system.
According to another aspect of the invention, there is provided a kind of commodity storage deployment device, including：Memory；And it is coupled to The processor of the memory, the processor are configured as, based on the instruction being stored in the memory, performing as above institute The commodity storage concocting method stated.
In accordance with a further aspect of the present invention, there is provided a kind of computer-readable recording medium, the computer-readable storage medium Matter is stored with computer instruction, and commodity storage concocting method as described above is realized in the instruction when being executed by processor.
The commodity storage concocting method of the present invention, device and storage medium, according between commodity and commodity and target The degree of association between warehouse, fusion commodity are filtered out from commodity and fusion commodity are stored in target warehouse, are realized singulated Rate significantly reduces, and leads to the more storehouse stocks of too small amount of commodity, reduces the fractionation number after customer order is assigned, reduce production and order Single workload, packaging efficiency is improved, reduce logistics operation cost.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only Some embodiments of the present invention, for those of ordinary skill in the art, without having to pay creative labor, also Other accompanying drawings can be obtained according to these accompanying drawings.
The schematic diagram of degree of adhesions of the Fig. 1 between commodity of the prior art；
Fig. 2 is shared product quantity of the prior art schematic diagram corresponding with singulated rate；
Fig. 3 is the flow chart according to one embodiment of the commodity storage concocting method of the present invention；
Fig. 4 is to establish showing for commodity fusion figure in another embodiment according to the commodity storage concocting method of the present invention It is intended to；
Fig. 5 be according to the present invention commodity store in a warehouse concocting method another embodiment in fusion commodity amount with it is singulated Schematic diagram corresponding to rate；
Fig. 6 is the module diagram according to one embodiment of the commodity storage deployment device of the present invention；
Fig. 7 is the mould of the fusion commodity processing module in one embodiment according to the commodity storage deployment device of the present invention Block schematic diagram；
Fig. 8 is the module diagram according to another embodiment of the commodity storage deployment device of the present invention.
Embodiment
The present invention is described more fully with reference to the accompanying drawings, wherein illustrating the exemplary embodiment of the present invention.Under The accompanying drawing that face will be combined in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, and shows So, described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.Based on the reality in the present invention Example is applied, the every other embodiment that those of ordinary skill in the art are obtained under the premise of creative work is not made, is all belonged to In the scope of protection of the invention.
" first " hereinafter, " second " etc. are only used for distinguishing in description, not other special implications.
Fig. 3 be according to the present invention commodity store in a warehouse concocting method one embodiment flow chart, as shown in Figure 3：
Step 101, first degree of association between commodity is obtained according to commodity historical purchase data.
First degree of association can have a variety of computational methods.For example, calculate SKU (Stock Keeping Unit, the storehouse of commodity Storage unit) between first degree of association be：For commodity A and B, it is assumed that the quantity on order for buying A is 100, buys B order numbers Measure as 200, while the quantity on order for buying A and B is 50, then the degree of association between A and B is 50/ (100+200).
Step 102, target warehouse is determined, based on commodity historical purchase data and commodity stocks acquisition of information commodity and mesh Mark second degree of association between warehouse.
Second degree of association can have a variety of computational methods.For example, second degree of association between the SKU and warehouse of commodity is equal to The summation of the SKU of the SKU of this commodity and all other commodity in the warehouse first degree of association.The degree of association between warehouse can Be all commodity in two warehouses SKU between first degree of association summation.For example, the commodity in warehouse 1 have A and B, storehouse The C and D of the commodity in storehouse 2, then the degree of association in warehouse 1 and warehouse 2 is first degree of association between A and C, A and D, B and C, B and D Summation.
Step 103, set commodity to merge quantity, according to default commodity fusion rule and be based on first degree of association and second The degree of association, the fusion commodity that quantity merges quantity for commodity are filtered out from commodity.Commodity fusion rule can have it is a variety of, can be with It is configured according to specific demand.
Step 104, the fusion commodity filtered out are stored in target warehouse.
Fusion commodity are stored in same warehouse, and the information and target warehouse information that merge commodity that will be filtered out Commodity store function system is sent to, to carry out purchase of merchandise, allot, commodity store function system includes：Replenishment system, Allot system, stock warehouse system etc..
Based on the commodity storage concocting method in above-described embodiment, the more storehouses of commodity can be passed through and got ready the goods, reduce customer order Fractionation number after assigning, the workload of production order is reduced, improve packaging efficiency, reduce logistics operation cost, while also drop It is low it is singulated caused by packaging increase.
In one embodiment, a variety of methods can be had for the fusion commodity that commodity merge quantity by filtering out quantity.For example, Commodity fusion figure is built, commodity and target warehouse are respectively the node in commodity fusion figure.Closed based on first degree of association and second Connection degree, the side of connecting node, and weighted value corresponding to opposite side imparting are established in commodity fusion figure.Chosen according to default subgraph Condition and weighted value, merged from commodity and weight limit connected subgraph is obtained in figure, the node bag in weight limit connected subgraph Include target warehouse node, the fusion commodity node that quantity is commodity fusion quantity.
Weight limit connected subgraph is obtained from commodity fusion figure can a variety of methods, such as can use integer programming Method etc..Integer programming refers to that the variable (all or part) in planning is limited to integer, if in linear model, variable limit Integer is made as, then referred to as integral linear programming, can be subdivided into linearly from the composition of constraints, secondary and nonlinear paced beat Draw.
For example, calculate fusion goods weight value ∑i,j∈IZij*Wij, condition is chosen according to fusion goods weight value and subgraph Obtain weight limit connected subgraph.Subgraph, which chooses condition, to be included：∑i,j∈IZij*WijValue be maximum, i and j are respectively commodity The node in figure is merged, I merges all node sets in figure, Z for commodityijIdentify whether to select connecting node i's and node j Side, WijFor with the weighted value corresponding to connecting node i and node j side.
If node i and node j are commodity node, it is determined that Wij=Lij*(Ni+Nj), LijFor node i and node j institutes First degree of association between the commodity of representative, NiFor the prediction quantity purchase of the commodity representated by node i, NjFor representated by node j Commodity prediction quantity purchase.It can be the shipment amount in a week, the time such as one month to predict quantity purchase.If node I is commodity node and node j is target warehouse node, it is determined that Wij=Mij*Nj, MijFor the commodity and mesh representated by node i Mark second degree of association between warehouse.
The quantity on order D that have purchased the commodity representated by node i can be obtained based on commodity purchasing orderi, purchase node j The quantity on order D of representative commodityjWhile it have purchased the quantity on order D of node i and the commodity representated by node jij.It is determined that First degree of association between node i and commodity representated by node j
The second degree of association M can be determinedijFor the commodity representated by node i respectively with target warehouse it is currently stored all its The summation of first degree of association between its commodity.
Subgraph, which chooses condition, to be included being used for the constraints for determining fusion commodity node, and constraints includes commodity fusion number Amount, fusion commodity node include target warehouse etc..Constraints can also include：Two or more commodity can not be simultaneously It is put into storage mutual exclusion restriction in same warehouse, the storage capacity limit etc. in target warehouse.
In one embodiment, based on History Order, calculated using big data correlation technique between the SKU of commodity, business The degree of association between the SKU and warehouse of product.Can be according to business demand, Automatic sieve selects what is had a great influence for reducing singulated rate Warehouse, the warehouse merged as needs.After determining and needing the warehouse that merges, using integer programming technology, according to counting The degree of association automatically select respective numbers commodity SKU and target warehouse merged.
A commodity fusion figure can be established according to the SKU of commodity and the relation in warehouse, node is SKU or the storehouse of commodity Storehouse, weight is the degree of association between two nodes on side, and target is one weight limit connected subgraph of selection so that weight limit The degree of association sum on the side in connected subgraph is maximum, i.e. the sub- order of reduction is most.
For example, having two warehouses H1 and H2, the SKU of 5 commodity is respectively S1、S2、S3、S4、S5.The mesh of commodity storage allotment Mark is that the SKU for selecting 3 commodity is placed in H1 or H2 so that singulated rate reduces most.H1 is arranged to target bin first Storehouse, I={ H 1, S are set1、S2、S3、S4、S5}。
Constraints is set：
Constraints 1：
Constraints 2：
Constraints 3：
Constraints 4：
Constraints 5：
Constraints 6：
Above-mentioned constraints 1-3 is used to limit node side corresponding with its while is selected or is not selected.Constraint Condition 4 represents that most 3 nodes of multiselect, the constant 3 of constraints 4 can be changed to other numbers.Constraints 5 represents corresponding warehouse H1 It is certain selected.Constraints 6 limits the value non-zero i.e. 1 of all variables.Yi represents whether select i-node, constraints 1,2 It is configurable with 3.Maximized fusion goods weight value ∑ is calculated according to 6 above-mentioned constraints conditionsi,j∈IZij*Wij, choose 3 fusion commodity, the fusion commodity filtered out are stored in warehouse H1.
In one embodiment, as shown in figure 4, structure commodity fusion figure, node A represent storehouse, node b, c, d represent 3 kinds Commodity.I={ A, b, c, d }, the weight on W_ij representative edges, such as W_Ab=10.Each edge corresponding one in commodity fusion figure Individual variable z_ij, value 0 or 1, represents whether this side is selected.Each corresponding variable y_i of node represents that this is saved Whether point is selected.
It is to select weight limit connected subgraph to merge target so that the weight summation on its side is maximum.If limitation selection The SKU maximum quantities of commodity be 2 (constant behind constraints 4 is changed to 2), then it is y_A=to solve the result come Y_c=y_d=1, y_b=0；Z_Ab=z_bc=z_Ac=0, z_cd=z_Ad=z_Ac=1, i.e. commodity c and d are selected, It is stored in as fusion commodity in warehouse A.
In one embodiment, stored in a warehouse concocting method using the commodity of the present invention, consider between the SKU of commodity and The degree of association between SKU and warehouse, a variety of constraintss of flexibly configurable, mutex relation between constraints such as capacity, SKU, SKU limited proportions etc., it can be achieved to participate in fusion with less SKU, realize that singulated rate significantly reduces, as shown in figure 5, using The commodity storage concocting method of the present invention chooses 1000 commodity as commodity are merged, and can reduce by 20% singulated rate.
In one embodiment, as shown in fig. 6, the present invention provides a kind of commodity storage deployment device 60, including：First closes Connection degree acquisition module 61, the second degree of association acquisition module 62, fusion commodity processing module 63.First degree of association acquisition module 61 First degree of association between commodity is obtained according to commodity historical purchase data.Second degree of association acquisition module 62 determines target warehouse, Based on second degree of association between commodity historical purchase data and commodity stocks acquisition of information commodity and target warehouse.Merge business Product processing module 63 sets commodity to merge quantity, is associated according to default commodity fusion rule and based on first degree of association with second Degree, the fusion commodity that quantity merges quantity for commodity are filtered out from commodity, the fusion commodity filtered out are stored in target bin In storehouse.
As shown in fig. 7, fusion commodity processing module 63 includes：Model establish unit 631, fusion commodity determining unit 632, Merge merchandise news transmitting element 633.Model establishes unit 631 and builds commodity fusion figure, and commodity and target warehouse are respectively business Node in product fusion figure.Model establishes unit 631 and is based on first degree of association and second degree of association, is established in commodity fusion figure The side of connecting node, and weighted value corresponding to opposite side imparting.Merge commodity determining unit 632 and condition is chosen according to default subgraph And weighted value, merged from commodity and weight limit connected subgraph is obtained in figure, the node in weight limit connected subgraph includes：Mesh Mark warehouse node, the fusion commodity node that quantity is commodity fusion quantity.
Merge commodity determining unit 632 and calculate fusion goods weight value ∑i,j∈IZij*Wij, according to fusion goods weight value with And subgraph chooses condition and obtains weight limit connected subgraph, subgraph, which chooses condition, to be included：∑i,j∈IZij*WijValue be maximum；i It is respectively the node in commodity fusion figure with j, I merges all node sets in figure, Z for commodityijIdentify whether selection connection The side of node i and node j, WijFor with the weighted value corresponding to connecting node i and node j side.
If node i and node j are commodity node, fusion commodity determining unit 632 determines Wij=Lij*(Ni+Nj), LijFor first degree of association between the commodity representated by node i and node j, NiBought for the prediction of the commodity representated by node i Quantity, NjFor the prediction quantity purchase of the commodity representated by node j.If node i is commodity node and node j is target bin Storehouse node, then merge commodity determining unit 632 and determine Wij=Mij*Nj, MijFor the commodity representated by node i and target warehouse it Between second degree of association.
First degree of association acquisition module 61 obtains the order that have purchased the commodity representated by node i based on commodity purchasing order Quantity Di, commodity representated by purchase node j quantity on order DjWhile it have purchased node i and the commodity representated by node j Quantity on order Dij, determine first degree of association between node i and commodity representated by node j
Second degree of association acquisition module 62 determines the second degree of association MijFor the commodity representated by node i respectively with target warehouse The summation of first degree of association between the other commodity of currently stored whole.
Fig. 8 is the module diagram according to another embodiment of the commodity storage deployment device of the present invention.Such as Fig. 8 institutes Show, the device may include memory 81, processor 82, communication interface 83 and bus 84.Memory 81 is used for store instruction, place Reason device 82 is coupled to memory 81, and the instruction that processor 82 is configured as storing based on memory 81, which performs, realizes above-mentioned commodity Storage concocting method.
Memory 81 can be high-speed RAM memory, nonvolatile memory (non-volatile memory) etc., deposit Reservoir 81 can also be memory array.Memory 81 is also possible to by piecemeal, and block can be combined into virtually by certain rule Volume.Processor 82 can be central processor CPU, or application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or it is arranged to implement one or more collection of the commodity storage concocting method of the present invention Into circuit.
In one embodiment, the present invention provides a kind of computer-readable recording medium, and computer-readable recording medium is deposited Computer instruction is contained, the commodity storage concocting method in as above any one embodiment is realized when instruction is executed by processor.
Commodity storage concocting method that above-described embodiment provides, device and storage medium, according between the SKU of commodity with And the degree of association between SKU and target warehouse, fusion commodity are filtered out from commodity and fusion commodity are stored in target warehouse In, a variety of constraintss of flexibly configurable, realize that singulated rate significantly reduces, lead to the more storehouse stocks of too small amount of commodity, reduce Customer order assign after fractionation number, reduce production order workload, improve packaging efficiency, reduce logistics operation cost, The increase of singulated caused packing material is also reduced simultaneously, is laid a good foundation for green operation, is improved the experience of client Degree.
The method and system of the present invention may be achieved in many ways.For example, can by software, hardware, firmware or Software, hardware, firmware any combinations come realize the present invention method and system.The said sequence of the step of for method is only Order described in detail above is not limited in order to illustrate, the step of method of the invention, is especially said unless otherwise It is bright.In addition, in certain embodiments, the present invention can be also embodied as recording program in the recording medium, these programs include For realizing the machine readable instructions of the method according to the invention.Thus, the present invention also covering storage is used to perform according to this hair The recording medium of the program of bright method.
Description of the invention provides for the sake of example and description, and is not exhaustively or by the present invention It is limited to disclosed form.Many modifications and variations are obvious for the ordinary skill in the art.Select and retouch State embodiment and be to more preferably illustrate the principle and practical application of the present invention, and one of ordinary skill in the art is managed The present invention is solved so as to design the various embodiments with various modifications suitable for special-purpose.

Claims (20)

  1. The concocting method 1. a kind of commodity are stored in a warehouse, it is characterised in that including：
    First degree of association between commodity is obtained according to commodity historical purchase data；
    Determine target warehouse, based on commodity historical purchase data and commodity stocks acquisition of information commodity and the target warehouse it Between second degree of association；
    Set commodity to merge quantity, associated according to default commodity fusion rule and based on first degree of association with described second Degree, the fusion commodity that quantity merges quantity for the commodity are filtered out from the commodity；
    The fusion commodity filtered out are stored in the target warehouse.
  2. 2. the method as described in claim 1, it is characterised in that described according to default commodity fusion rule and based on described the One degree of association filters out the fusion commodity bundle that quantity merges quantity for the commodity with second degree of association, from the commodity Include：
    Commodity fusion figure is built, wherein, the commodity and the target warehouse are respectively the node in commodity fusion figure；
    Based on first degree of association and second degree of association, established in the commodity merge figure and connect the node Side, and to weighted value corresponding to the imparting of the side；
    Condition and the weighted value are chosen according to default subgraph, is merged from the commodity and weight limit connection is obtained in figure Figure, wherein, the node in the weight limit connected subgraph includes：The target warehouse node, quantity merge for the commodity The fusion commodity node of quantity.
  3. 3. method as claimed in claim 2, it is characterised in that described that condition and the weight are chosen according to default subgraph Value, acquisition weight limit connected subgraph includes from the commodity fusion figure：
    Calculate fusion goods weight value ∑i,j∈IZij*Wij, condition is chosen according to the fusion goods weight value and the subgraph Obtain weight limit connected subgraph；
    Wherein, the subgraph is chosen condition and included：∑i,j∈IZij*WijValue be maximum；I and j is respectively the commodity fusion Node in figure, I merge all node sets in figure, Z for commodityijSelection connecting node i and node j side is identified whether, WijFor with the weighted value corresponding to connecting node i and node j side.
  4. 4. method as claimed in claim 3, it is characterised in that also include：
    If node i and node j are commodity node, it is determined that Wij=Lij*(Ni+Nj)；
    Wherein, LijFor first degree of association between the commodity representated by node i and node j, NiFor the commodity representated by node i Predict quantity purchase, NjFor the prediction quantity purchase of the commodity representated by node j；
    If node i is commodity node and node j is target warehouse node, it is determined that Wij=Mij*Nj；
    Wherein, MijFor second degree of association between the commodity representated by node i and the target warehouse.
  5. 5. method as claimed in claim 4, it is characterised in that between the acquisition associated articles according to historical purchase data First degree of association includes：
    The quantity on order D that have purchased the commodity representated by node i is obtained based on commodity purchasing orderi, representated by purchase node j The quantity on order D of commodityjWhile it have purchased the quantity on order D of node i and the commodity representated by node jij；
    Determine first degree of association between node i and commodity representated by node j
    <mrow> <msub> <mi>L</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>D</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>D</mi> <mi>j</mi> </msub> </mrow> </mfrac> <mo>.</mo> </mrow>
  6. 6. method as claimed in claim 4, it is characterised in that also include：
    Determine the second degree of association MijFor the whole other business currently stored with the target warehouse respectively of the commodity representated by node i The summation of first degree of association between product.
  7. 7. method as claimed in claim 3, it is characterised in that
    The subgraph, which chooses condition, to be included：For determining the constraints of fusion commodity node；
    Wherein, the constraints includes：The commodity fusion quantity, the fusion commodity node include the target warehouse.
  8. 8. method as claimed in claim 7, it is characterised in that
    The constraints also includes：Two or more commodity can not be put into the storage mutual exclusion limit in same warehouse simultaneously Restrict beam, the storage capacity limit in the target warehouse.
  9. 9. the method as described in claim 1, it is characterised in that also include：
    The information of the fusion commodity filtered out and the target warehouse information are sent to commodity store function system, its In, the commodity store function system includes：Replenishment system, allot system, stock warehouse system.
  10. The deployment device 10. a kind of commodity are stored in a warehouse, it is characterised in that including：
    First degree of association acquisition module, for obtaining first degree of association between commodity according to commodity historical purchase data；
    Second degree of association acquisition module, for determining target warehouse, based on commodity historical purchase data and commodity stocks information Obtain second degree of association between the commodity and the target warehouse；
    Commodity processing module is merged, for setting commodity to merge quantity, according to default commodity fusion rule and based on described the One degree of association and second degree of association, the fusion commodity that quantity merges quantity for the commodity are filtered out from the commodity, The fusion commodity filtered out are stored in the target warehouse.
  11. 11. device as claimed in claim 10, it is characterised in that
    The fusion commodity processing module, including：
    Model establishes unit, for building commodity fusion figure, wherein, the commodity and the target warehouse are respectively commodity fusion Node in figure；Based on first degree of association and second degree of association, established in the commodity merge figure described in connection The side of node, and to weighted value corresponding to the imparting of the side；
    Commodity determining unit is merged, for choosing condition and the weighted value according to default subgraph, is merged from the commodity Weight limit connected subgraph is obtained in figure, wherein, the node in the weight limit connected subgraph includes：Target warehouse section Point, quantity are the fusion commodity node that the commodity merge quantity.
  12. 12. device as claimed in claim 11, it is characterised in that
    The fusion commodity determining unit, goods weight value ∑ is merged for calculatingi,j∈IZij*Wij, according to the fusion commodity power Weight values and the subgraph choose condition and obtain weight limit connected subgraph；Wherein, the subgraph is chosen condition and included：∑i,j∈ IZij*WijValue be maximum；I and j is respectively the node in the commodity fusion figure, and I is all nodes in commodity fusion figure Set, ZijIdentify whether selection connecting node i and node j side, WijFor with the power corresponding to connecting node i and node j side Weight values.
  13. 13. device as claimed in claim 12, it is characterised in that
    The fusion commodity determining unit, if being all commodity node for node i and node j, it is determined that Wij=Lij*(Ni+ Nj)；Wherein, LijFor first degree of association between the commodity representated by node i and node j, NiFor the commodity representated by node i Predict quantity purchase, NjFor the prediction quantity purchase of the commodity representated by node j；
    The fusion commodity determining unit, if being commodity node for node i and node j is target warehouse node, really Determine Wij=Mij*Nj；Wherein, MijFor second degree of association between the commodity representated by node i and the target warehouse.
  14. 14. device as claimed in claim 13, it is characterised in that
    First degree of association acquisition module, for have purchased the commodity representated by node i based on the acquisition of commodity purchasing order Quantity on order Di, commodity representated by purchase node j quantity on order DjWhile it have purchased node i and the business representated by node j The quantity on order D of productij；Determine first degree of association between node i and commodity representated by node j
    <mrow> <msub> <mi>L</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>D</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>D</mi> <mi>j</mi> </msub> </mrow> </mfrac> <mo>.</mo> </mrow>
  15. 15. device as claimed in claim 13, it is characterised in that
    Second degree of association acquisition module, for determining the second degree of association MijFor the commodity representated by node i respectively with it is described The summation of first degree of association between the other commodity of the currently stored whole in target warehouse.
  16. 16. device as claimed in claim 12, it is characterised in that
    The subgraph, which chooses condition, to be included：For determining the constraints of fusion commodity node；
    Wherein, the constraints includes：The commodity fusion quantity, the fusion commodity node include the target warehouse.
  17. 17. device as claimed in claim 16, it is characterised in that
    The constraints also includes：Two or more commodity can not be put into the storage mutual exclusion limit in same warehouse simultaneously Restrict beam, the storage capacity limit in the target warehouse.
  18. 18. device as claimed in claim 10, it is characterised in that
    The fusion commodity processing module, including：
    Merchandise news transmitting element is merged, for by the information of the fusion commodity filtered out and the target warehouse information Commodity store function system is sent to, wherein, the commodity store function system includes：Replenishment system, allot system, stock storehouse Storehouse system.
  19. The deployment device 19. a kind of commodity are stored in a warehouse, it is characterised in that including：
    Memory；And
    The processor of the memory is coupled to, the processor is configured as based on the instruction being stored in the memory, Perform commodity storage concocting method as claimed in any one of claims 1-9 wherein.
  20. 20. a kind of computer-readable recording medium, it is characterised in that the computer-readable recording medium storage has computer to refer to Commodity storage concocting method as claimed in any one of claims 1-9 wherein is realized in order, the instruction when being executed by processor.
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