CN108108933A - Position distribution method of storing in a warehouse and device - Google Patents
Position distribution method of storing in a warehouse and device Download PDFInfo
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Abstract
The present invention provides a kind of storage position distribution method and device.This method includes：Obtain the historical transactional information of target user；According to the historical transactional information of the target user, trading volume ranking of the target user on the same day is assessed；According to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space.The method of the present invention, by the historical transactional information according to the target user got, assessment target user is in the trading volume ranking on the same day；According to target user the same day trading volume ranking, it is put for target user's distribution bin storage space, so as to realize when day trade transaction amount user in the top is distributed in outbound the shorter storage position of transportation range during cargo selection, transportation range when can greatly shorten outbound during cargo selection, the outbound time of cargo is shortened, improves the efficiency of cargo outbound.
Description
Technical field
The present invention relates to field of communication technology more particularly to a kind of storage position distribution method and devices.
Background technology
E-commerce is using information network technique as means, and the commercial activity centered on the exchange of commodities is traditional commerce
The electronization of each link of activity, networking, informationization.People's shopping on the web, then will be under the article line of purchase by express company
It transports.
User's shopping on the web submits order, and e-commerce platform is directed to each user's order packaged goods, and will be packaged
Good cargo is stored to the corresponding storage position of the user；It is completed by choosing personnel from the corresponding storage position discharging of goods of user
Outbound, and transfer to express company that goods handling to user is specified address.In modern warehouse logistics, cargo stops in warehouse
Time is short, and velocity of liquid assets is fast, in order to be more easily serve, accomplishes to deposit cache soon, improves cargo outbound efficiency,
The selection of warehouse location distribution method is particularly important.
Currently used storage position distribution method includes random distribution approach and nearest room distribution method, is randomly assigned
Method refers to the distribution method when the same product of certain batch is come in, being stored at random under the conditions of equiprobability on room, most
Near-space bit allocation method refers to, according into the sequencing in warehouse, cargo is stored in distance and exports closest storage
The distribution method in room.Current storage position distribution method, does not take into full account space availability ratio problem, can cause largely to deposit
The increase of transportation range during cargo is chosen when storing up the waste and outbound in space, the outbound time of cargo is long, cargo selection effect
Rate is low.
The content of the invention
The present invention provides a kind of storage position distribution method and device, to solve current storage position distribution method,
It does not take into full account space availability ratio problem, can cause to transport during cargo selection when the waste and outbound of a large amount of memory spaces
The problem of increase of distance, the outbound time of cargo is long, and cargo selection efficiency is low.
It is an aspect of the invention to provide it is a kind of storage position distribution method, including：
Obtain the historical transactional information of target user；
According to the historical transactional information of the target user, trading volume ranking of the target user on the same day is assessed；
According to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space.
Another aspect of the present invention is to provide a kind of storage position distributor, including：
Acquisition module, for obtaining the historical transactional information of target user；
Computing module for the historical transactional information according to the target user, assessed the target user on the same day
Trading volume ranking；
Distribution module, for being target user's distribution bin according to trading volume ranking of the target user on the same day
Storage space is put.
Storage position distribution method provided by the invention and device, pass through the historical trading according to the target user got
Information, assessment target user is in the trading volume ranking on the same day；It is target user according to trading volume ranking of the target user on the same day
Distribution bin storage space is put, so as to realize when day trade transaction amount user in the top is distributed in outbound during cargo selection
The shorter storage position of transportation range, transportation range when can greatly shorten outbound during cargo selection, shortens cargo
The outbound time, improve the efficiency of cargo outbound.
Description of the drawings
Attached drawing herein is merged in specification and forms the part of this specification, shows the implementation for meeting the present invention
Example, and the principle for explaining the present invention together with specification.
Fig. 1 is the storage position distribution method flow chart that the embodiment of the present invention one provides；
Fig. 2 is storage position distribution method flow chart provided by Embodiment 2 of the present invention；
Fig. 3 is the structure diagram for the storage position distributor that the embodiment of the present invention three provides；
Fig. 4 is the structure diagram for the storage position distributor that the embodiment of the present invention four provides.
Pass through above-mentioned attached drawing, it has been shown that the specific embodiment of the present invention will be hereinafter described in more detail.These attached drawings
It is not intended to limit the scope of present inventive concept by any mode with word description, but is by reference to specific embodiment
Those skilled in the art illustrate idea of the invention.
Specific embodiment
Here exemplary embodiment will be illustrated in detail, example is illustrated in the accompanying drawings.Following description is related to
During attached drawing, unless otherwise indicated, the same numbers in different attached drawings represent the same or similar element.Following exemplary embodiment
Described in embodiment do not represent and the consistent all embodiments of the present invention.On the contrary, they be only with it is such as appended
The example of the consistent apparatus and method of some aspects being described in detail in claims, of the invention.
Noun according to the present invention is explained first：
Linear regression：In statistics, linear regression (Linear Regression) is to utilize referred to as equation of linear regression
Least square function pair one or more independent variable and dependent variable between a kind of regression analysis for being modeled of relation.This letter
Number is the linear combination of one or more model parameters for being known as regression coefficient.Only the situation there are one independent variable is known as simple return
Return, be called multiple regression more than independent variable situation.
Gray model (grey models, abbreviation GM model)：It is to lead to too small amount of, imperfect information, it is micro- establishes grey
Divide prediction model, the long-term description of ambiguity is made to things development rule, be that theory, method are more complete in fuzzy prediction field
Kind prediction science branch.
These specific embodiments can be combined with each other below, may be at certain for the same or similar concept or process
It is repeated no more in a little embodiments.Below in conjunction with attached drawing, the embodiment of the present invention is described.
Embodiment one
Fig. 1 is the storage position distribution method flow chart that the embodiment of the present invention one provides.The embodiment of the present invention is for current
Storage position distribution method, do not take into full account space availability ratio problem, the waste and outbound of a large amount of memory spaces can be caused
When cargo selection during transportation range increase, the outbound time of cargo is long, cargo selection efficiency it is low the problem of, provide storehouse
Storage space puts distribution method.Such as Fig. 1, this method is as follows：
Step S101, the historical transactional information of target user is obtained.
Wherein, target user is the also unallocated user to storage position.
Historical transactional information in the present embodiment can be Transaction Information of the target user in nearest preset number of days.Its
In, preset number of days is longer, and the target user of assessment is higher in the accuracy rate of the trading volume ranking on the same day, but calculation amount is bigger；
Preset number of days can according to actual needs be set by technical staff, and the present embodiment is not specifically limited this.
Step S102, according to historical transactional information, assessment target user is in the trading volume ranking on the same day.
In the present embodiment, the process of exchange of each user is regarded as a system, in this system, whether is same day user
Participation transaction, trading volume size of the user on the same day are how many, trading volume ranking of the user on the same day, these are all not true
It is fixed, there is randomness, but historical transactional information is known, meets the feature of gray system, can be used gray model to
The trading volume at family is predicted.
Step S103, according to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space.
After trading volume ranking of the target user on the same day is predicted, arranged according to target user in the trading volume on the same day
Name is put for target user's distribution bin storage space, can be that cargo picks when day trade transaction amount user in the top is distributed in outbound
The shorter storage position of transportation range during choosing, for example, for trading volume user's distribution distance store exit in the top away from
From nearer storage position.
The embodiment of the present invention is by the historical transactional information according to the target user got, and target user was on the same day for assessment
Trading volume ranking；According to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space, so as to reality
Present day trade transaction amount user in the top distributes in outbound the shorter storage position of transportation range during cargo selection,
Transportation range when can greatly shorten outbound during cargo selection, shortens the outbound time of cargo, improves cargo and go out
The efficiency in storehouse.
Embodiment two
Fig. 2 is storage position distribution method flow chart provided by Embodiment 2 of the present invention.On the basis of above-described embodiment one
On, in the present embodiment, according to historical transactional information, trading volume ranking of the target user on the same day is assessed, including：Target is obtained to use
The trading volume prediction model at family and the linear equation of trading volume ranking and trading volume；It is used according to historical transactional information and target
The trading volume prediction model at family, assessment target user is in the trading volume on the same day；According to target user the same day trading volume and
The trading volume ranking of target user and the linear equation of trading volume, assessment target user is in the trading volume ranking on the same day.Such as Fig. 2 institutes
Show, this method is as follows：
Step S201, sequence information is received, sequence information includes at least user identifier.
The executive agent of storage position distribution method provided by the invention can be put down with various types of e-commerce
The big data platform that platform interacts, for example, e-commerce platform can be shopping at network platform or auction platform etc..Example
Such as, user can install auction client in the terminal that can carry out internet communication, and it is clear to carry out product in auction client
The sequence information of auction trade is look at, supplements with money and submits, in process of exchange, using the phone number of user as user identifier.Electricity
Sequence information is sent to big data platform by sub- business platform after the order that user submits is received, and being by big data platform should
User's distribution bin storage space is put.Sequence information can include all information or partial information of the original order that user submits.
Wherein, user identifier refers to for the mark of one user of unique mark.User identifier can be the mobile phone of user
Number, E-mail address etc..Sequence information includes at least user identifier, and sequence information can also include：At least one shipment identifier,
And the corresponding quantity of goods of each shipment identifier, order submission date, the Shipping Date of requirement, payment information, user's other information
Deng.
Step S202, the user identifier in sequence information, it is determined whether be the corresponding user point of user identifier
Storage position is matched somebody with somebody, if being put not yet for the corresponding user's distribution bin storage space of user identifier, the corresponding user of user identifier has been made
For target user.
After the sequence information of user is received, it is determined whether there is storehouse corresponding with the user identifier in sequence information
Storage space is put, if in the presence of storage position corresponding with the user identifier in sequence information, can determine to be user identifier pair
The user answered is assigned with storage position, then without being put again for user's distribution bin storage space.
If there is no storage positions corresponding with the user identifier in sequence information, can determine to mark for user not yet
Know corresponding user's distribution bin storage space to put, then using user as target user, subsequently through step S203-S207, used for target
Family distribution bin storage space is put.
Target user in the present embodiment can be any user, after receiving any user submission sequence information, according to
User identifier in sequence information, however, it is determined that there are no being put for the corresponding user's distribution bin storage space of user identifier, then by the user
Corresponding user is identified as target user, obtains the historical transactional information of target user, to be believed according to the historical trading of user
The trading volume ranking on breath prediction user's same day, and be target user's distribution bin according to trading volume ranking of the target user on the same day
Storage space is put, and the waste for causing warehouse memory space can be so put to avoid the user's distribution bin storage space for not having order for the same day, also
The allocative efficiency of storage position can be improved.
In the present embodiment, each day of trade puts again for user's distribution bin storage space, after user submits sequence information,
It is that user had trading volume on the same day and then put for user's distribution bin storage space, can divides to avoid for the user of no trading volume
Waste with storage space caused by storage space.
Step S203, the historical transactional information of target user is obtained.
Historical transactional information in the present embodiment can be Transaction Information of the target user in nearest preset number of days.Its
In, preset number of days is longer, and the target user of assessment is higher in the accuracy rate of the trading volume ranking on the same day, but calculation amount is bigger；
Preset number of days can according to actual needs be set by technical staff, and the present embodiment is not specifically limited this.
In addition, the present embodiment can be based on big data transaction platform, the magnanimity Transaction Information of each user is obtained.
Step S204, the trading volume prediction model of target user and the linear equation of trading volume ranking and trading volume are obtained.
In the present embodiment, the trading volume prediction model of target user and trading volume ranking and trading volume can be previously generated
Linear equation, and store, the trading volume prediction model of stored target user and trading volume row are directly acquired in the step
The linear equation of name and trading volume.
In the present embodiment, the process of exchange of each user is regarded as a system, in this system, whether is same day user
Participation transaction, trading volume size of the user on the same day are how many, trading volume ranking of the user on the same day, these are all not true
It is fixed, there is randomness, but historical transactional information is known, meets the feature of gray system, can be used gray model to
The trading volume at family is predicted.Grey prediction of data sequence refers to using dynamic GM models, to the time series of gray system into line number
Measure the prediction of size, i.e. the principal act characteristic quantity to system or a certain index, the number that development and change occur to following particular moment
Value is predicted.
In the present embodiment, if not storing the trading volume prediction model of target user, the present embodiment uses the list of GM (1,1)
The modeling pattern of variable linear first-order differential equation generates the trading volume prediction model of target user by following steps：：
Step 1: according to historical transactional information, target user is obtained in nearest n days in daily trading volume, obtains original
Beginning time series：Q0=[Q0(1), Q0(2) ..., Q0(n)], wherein, Q0(k) transaction in kth day target user before is represented
Amount, Q0(k) >=0, k=1,2 ..., n.Wherein n is positive integer.
Step 2: to original time series Q0Data conversion is carried out, obtains new time series：
X0=[X0(1), X0(2) ..., X0(n)]。
Wherein, X0(k)=A (Q0(k)), A (Q0(k)) represent by kth item in time series new after data variation
Value, k=1,2 ..., n.Wherein n is positive integer.
The essence of gray model is predicted that the precision of model and the gradation law of prediction data have based on index
Close relationship, if original time series with index or close to exponential law be changed when, the precision of prediction result will
It can be fine.But actual prediction data is few to be changed with approaching exponential law, exemplified by conform to original time series
It asks, carrying out appropriate data to original time series in step 2 converts, such as logarithmic transformation, instruction conversion, power function transformation,
Antitrigonometric function converts, translation transformation etc..Optionally, exponential transform is carried out to original time series in step 2.
Step 3: the new time series X obtained after transforming the data into0As new original time series, data are become
The new time series X obtained after changing0One-accumulate, generation single order add up sequence X1。
X1=[X1(1), X1(2) ..., X1(n)]。
Wherein
Step 4: determine accumulated matrix B and constant item vector Y：
The differential equation of first order model for establishing GM (1,1) is：Wherein, a and u is undetermined parameter.
Determine that accumulated matrix B and constant item vector Y are as follows：
Wherein, Z1(k+1) it isIn the background value at (k+1) moment, i.e.,：
Step 5: the undetermined parameter a and u in linear first-order differential equation are obtained using least square method.
φ=[a, u]TFor parameter vector to be identified, the match value of a and u are asked for by least square method：Wherein,Represent the match value of a,Represent the match value of u.
Step 6: the match value of a acquired and u is updated in the differential equation of first order in step 4, it is discrete that its is obtained
Solution：
Due to calculatingIt is the predicted value after one-accumulate, it is therefore desirable to can just be obtained after regressive
The predicted value of original time series：
The data that original time series carries out are converted according in step 2, it is rightCorresponding data convert is carried out,
Obtain the trading volume prediction model of target user：
In k=n,As target user is in the predicted value of the trading volume on the same day.
In the present embodiment, if not storing the trading volume ranking of target user and the linear equation of trading volume, basis is gone through
History Transaction Information generates the trading volume ranking of target user and the linear equation of trading volume, and following manner reality specifically may be employed
It is existing：
According to historical transactional information, target user's trading volume daily in first n days and trading volume ranking, wherein n are calculated
For positive integer；According to target user's trading volume daily in first n days and trading volume ranking, using linear regression method, calculate
The trading volume ranking of target user and the linear equation of trading volume.
Specifically, according to target user's trading volume daily in first n days and trading volume ranking, using linear regression side
Method calculates the trading volume ranking of target user and the linear relationship of trading volume, and following steps realization specifically may be employed：
The linear relation model of step 1, the trading volume for establishing target user's same day and trading volume ranking：
Y=ax+b, wherein x are trading volume, and y is the corresponding trading volume ranking of trading volume, and a and b are undetermined parameter.
Step 2 utilizes least square method, the match value of solution undetermined parameter a and bWith
Wherein,WithThe average of the average of the trading volume of target user and trading volume ranking in n days respectively preceding.
Step 3, the match value by a being calculated in step 2 and bWithThe linear relationship being brought into above-mentioned steps 1
In model, you can obtain the trading volume ranking of target user and the linear equation of trading volume：
It is alternatively possible to regularly it is updated periodically target user's according to the newest historical transactional information of target user
Trading volume prediction model and the linear equation of trading volume ranking and trading volume.It for example, can be regularly newest according to target user
Historical transactional information, regenerate the trading volume prediction model of target user and the linear side of trading volume ranking and trading volume
Journey.
In addition, in another embodiment of the invention, the transaction of pre-stored target user can not also be directly acquired
Prediction model and the linear equation of trading volume ranking and trading volume are measured, but when being put every time for target user's distribution bin storage space,
According to newest historical transactional information, generate target user trading volume prediction model and trading volume ranking and trading volume it is linear
Equation assesses mesh according to the trading volume prediction model of newly-generated target user and the linear equation of trading volume ranking and trading volume
User is marked in the trading volume on the same day and trading volume ranking so that the target user of prediction is in the trading volume on the same day and trading volume ranking
It is more accurate.
Step S205, according to historical transactional information and the trading volume prediction model of target user, assessment target user exists
The trading volume on the same day.
Specifically, according to historical transactional information and the trading volume prediction model of target user, assessment target user is working as
Following manner realization specifically may be employed in the trading volume of day：
According to historical transactional information, the trading volume of n-th day of the target user before the same day is determined；Target user is being worked as
The trading volume of n-th day before day is substituted into the trading volume prediction model of the target user shown in above-mentioned formula (one), Ke Yiji
Calculation obtains trading volume of the target user on the same day：
Wherein, Q0(1) it is the trading volume of n-th day of the target user before the same day,Represent the friendship in generation target user
The match value for a being easily calculated in amount prediction model,It represents to calculate in the trading volume prediction model of generation target user
The match value of the u arrived.
Step S206, according to target user in the trading volume on the same day and the trading volume ranking and trading volume of target user
Linear equation, assessment target user the same day trading volume ranking.
In the step, the trading volume that trading volume of the target user on the same day is substituted into the target user that formula (two) represents is arranged
Name in the linear equation of trading volume, you can calculate trading volume ranking of the target user on the same day.
Optionally, if trading volume ranking of the target user calculated on the same day is not integer, it may be employed and round up
Mode handled.
Optionally, target user is being calculated after the trading volume on the same day and trading volume ranking, target can be stored
User is in the trading volume on the same day and trading volume ranking.
Step S207, according to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space.
In the present embodiment, according to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space, specifically
Following manner realization may be employed：
According to target user in the trading volume ranking on the same day and the distance of the distance of each storage position and store exit,
For the storage position that trading volume target user's distribution distance store exit in the top is near, so that the storage position of target user
The storage position for being less than or equal to user of the trading volume ranking after target user with the distance of store exit goes out with warehouse
Mouthful distance, and more than or equal to the storage position of user of the trading volume ranking before target user and store exit
Distance.
Optionally, if before target user, another user is identical with the trading volume ranking predicted of target user,
Then in the step for target user select with target user the same day trading volume ranking it is corresponding storage position next sky
Not busy storage position.
It is alternatively possible to precalculate it is each storage position and store exit distance, and according to store exit away from
From size to it is each storage position be ranked up, so after trading volume ranking of the target user on the same day is calculated,
It can be ranked up according to each storage position calculated, directly be put for target user's distribution bin storage space, storage can be improved
The efficiency of position distribution.
In practical applications, the secondary distribution of goods region in usual warehouse is located near store exit, is transaction in the present embodiment
The near storage position of target user's distribution distance store exit in the top is measured, it is also possible that the mesh that trading volume is in the top
Mark that the secondary goods-distributing area of storage positional distance of user is nearer, during so as to reduce outbound the distance of cargo selection personnel's movement and
Time.
It optionally, can be by the storage location push of target user after being assigned in the storage position of target user
It gives sequence information corresponding selection personnel, personnel is chosen with advance notice.
It should be noted that in another embodiment of the present invention, can be counted according to the historical transactional information of each user
The user is calculated in daily trading volume and trading volume ranking
The embodiment of the present invention is by after the sequence information of target user is received, that is to say that target user had on the same day
It trading volume and then puts for target user's distribution bin storage space, can be led to avoid storage space is distributed for the user of no trading volume
The waste of the storage space of cause；By the way that according to historical transactional information, assessment target user is in the trading volume ranking on the same day；According to mesh
Trading volume ranking of the user on the same day is marked, is put for target user's distribution bin storage space, so as to realize in day trade transaction amount ranking
Forward user distributes in outbound the shorter storage position of transportation range during cargo selection, when can greatly shorten outbound
Transportation range during cargo selection shortens the outbound time of cargo, improves the efficiency of cargo outbound.
Embodiment three
Fig. 3 is the structure diagram for the storage position distributor that the embodiment of the present invention three provides.The embodiment of the present invention carries
The storage position distributor of confession can perform the process flow that storage position distribution method embodiment provides.It as shown in figure 3, should
Device 30 includes：Acquisition module 301, computing module 302 and distribution module 303.
Specifically, acquisition module 301 is used to obtain the historical transactional information of target user.
Computing module 302 is used for the historical transactional information according to target user, and assessment target user is in the trading volume on the same day
Ranking.
Distribution module 303 is used to, according to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space.
Device provided in an embodiment of the present invention can be specifically used for performing the embodiment of the method that above-described embodiment one is provided,
Details are not described herein again for concrete function.
The embodiment of the present invention is by the historical transactional information according to the target user got, and target user was on the same day for assessment
Trading volume ranking；According to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space, so as to reality
Present day trade transaction amount user in the top distributes in outbound the shorter storage position of transportation range during cargo selection,
Transportation range when can greatly shorten outbound during cargo selection, shortens the outbound time of cargo, improves cargo and go out
The efficiency in storehouse.
Example IV
Fig. 4 is the structure diagram for the storage position distributor that the embodiment of the present invention four provides.In above-described embodiment three
On the basis of, in the present embodiment, computing module 302 includes：Acquisition submodule, the first computational submodule and second calculate submodule
Block.
Wherein, acquisition submodule is used to obtaining the trading volume prediction model of target user and trading volume ranking and trading volume
Linear equation.
First computational submodule is used for the trading volume prediction model according to historical transactional information and target user, assessment
Target user is in the trading volume on the same day.
Second computational submodule is used for according to target user in the trading volume on the same day and the trading volume ranking of target user
With the linear equation of trading volume, assessment target user is in the trading volume ranking on the same day.
In the present embodiment, distribution module 303 is additionally operable to：According to trading volume ranking of the target user on the same day and each storehouse
Storage space puts the distance with the distance of store exit, is the near storehouse of trading volume target user's distribution distance store exit in the top
Storage space is put, so that the storage position of target user and the distance of store exit are less than or equal to trading volume ranking in target user
The storage position of user afterwards and the distance of store exit, and more than or equal to trading volume ranking before target user
User storage position and store exit distance.
Optionally, as shown in figure 4, device 30 can also include generation module 304.Generation module 304 is used for according to history
Transaction Information generates and stores the trading volume prediction model of target user and the linear equation of trading volume ranking and trading volume.
Generation module 304 is additionally operable to：According to historical transactional information, target user's friendship daily in nearest n days is calculated
Easily amount and trading volume ranking, wherein n are positive integer；According to target user trading volume and trading volume daily in nearest n days
Ranking using linear regression method, generates the trading volume ranking of target user and the linear equation of trading volume.
Optionally, device 30 can also include update module 305.Update module 305 is used to be updated periodically target user
Trading volume prediction model and the linear equation of trading volume ranking and trading volume.
Device 30 can also include：Preprocessing module 306.
Preprocessing module 306 is used for：Sequence information is received, sequence information includes at least user identifier；According to sequence information
In user identifier, it is determined whether for the corresponding user of user identifier be assigned with storage position；If it is marked not yet for user
Know corresponding user's distribution bin storage space to put, then using the corresponding user of user identifier as target user.
Device provided in an embodiment of the present invention can be specifically used for performing the embodiment of the method that above-described embodiment two is provided,
Details are not described herein again for concrete function.
The embodiment of the present invention is by after the sequence information of target user is received, that is to say that target user had on the same day
It trading volume and then puts for target user's distribution bin storage space, can be led to avoid storage space is distributed for the user of no trading volume
The waste of the storage space of cause；By the way that according to historical transactional information, assessment target user is in the trading volume ranking on the same day；According to mesh
Trading volume ranking of the user on the same day is marked, is put for target user's distribution bin storage space, so as to realize in day trade transaction amount ranking
Forward user distributes in outbound the shorter storage position of transportation range during cargo selection, when can greatly shorten outbound
Transportation range during cargo selection shortens the outbound time of cargo, improves the efficiency of cargo outbound.
In several embodiments provided by the present invention, it should be understood that disclosed apparatus and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of the unit, only
Only a kind of division of logic function can have other dividing mode in actual implementation, such as multiple units or component can be tied
It closes or is desirably integrated into another system or some features can be ignored or does not perform.It is another, it is shown or discussed
Mutual coupling, direct-coupling or communication connection can be the INDIRECT COUPLING or logical by some interfaces, device or unit
Letter connection can be electrical, machinery or other forms.
The unit illustrated as separating component may or may not be physically separate, be shown as unit
The component shown may or may not be physical location, you can be located at a place or can also be distributed to multiple
In network element.Some or all of unit therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it can also
That unit is individually physically present, can also two or more units integrate in a unit.Above-mentioned integrated list
The form that hardware had both may be employed in member is realized, can also be realized in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit realized in the form of SFU software functional unit, can be stored in one and computer-readable deposit
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, is used including some instructions so that a computer
It is each that equipment (can be personal computer, server or the network equipment etc.) or processor (processor) perform the present invention
The part steps of embodiment the method.And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only memory (Read-
Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disc or CD etc. it is various
The medium of program code can be stored.
Those skilled in the art can be understood that, for convenience and simplicity of description, only with above-mentioned each function module
Division progress for example, in practical application, can be complete by different function modules by above-mentioned function distribution as needed
Into the internal structure of device being divided into different function modules, to complete all or part of function described above.On
The specific work process of the device of description is stated, may be referred to the corresponding process in preceding method embodiment, details are not described herein.
Those skilled in the art will readily occur to the present invention its after considering specification and putting into practice invention disclosed herein
Its embodiment.It is contemplated that cover the present invention any variations, uses, or adaptations, these modifications, purposes or
Person's adaptive change follows the general principle of the present invention and including undocumented common knowledge in the art of the invention
Or conventional techniques.Description and embodiments are considered only as illustratively, and true scope and spirit of the invention are by following
Claims are pointed out.
It should be appreciated that the invention is not limited in the precision architecture for being described above and being shown in the drawings, and
And various modifications and changes may be made without departing from the scope thereof.The scope of the present invention is only limited by appended claims
System.
Claims (14)
1. a kind of storage position distribution method, which is characterized in that including：
Obtain the historical transactional information of target user；
According to the historical transactional information of the target user, trading volume ranking of the target user on the same day is assessed；
According to trading volume ranking of the target user on the same day, put for target user's distribution bin storage space.
2. according to the method described in claim 1, it is characterized in that, described arrange according to the target user in the trading volume on the same day
Name is put for target user's distribution bin storage space, including：
According to the target user in the trading volume ranking on the same day and the distance of the distance of each storage position and store exit,
For the storage position that trading volume target user's distribution distance store exit in the top is near, so that the storage of the target user
The distance of position and store exit is less than or equal to the storage position of user of the trading volume ranking after the target user
With the distance of store exit, and more than or equal to user of the trading volume ranking before the target user storage position
With the distance of store exit.
3. according to the method described in claim 1, it is characterized in that, described according to the historical transactional information, the mesh is assessed
Trading volume ranking of the user on the same day is marked, including：
Obtain the trading volume prediction model of the target user and the linear equation of trading volume ranking and trading volume；
According to the historical transactional information and the trading volume prediction model of the target user, assess the target user and exist
The trading volume on the same day；
According to the target user in the linear of the trading volume on the same day and the trading volume ranking of the target user and trading volume
Equation assesses trading volume ranking of the target user on the same day.
4. the according to the method described in claim 3, it is characterized in that, trading volume prediction model for obtaining the target user
With before the linear equation of trading volume ranking and trading volume, further include：
According to the historical transactional information, generate and store the target user trading volume prediction model and trading volume ranking with
The linear equation of trading volume.
5. according to the method described in claim 4, it is characterized in that, according to the historical transactional information, generate the target and use
The trading volume ranking at family and the linear equation of trading volume, including：
According to the historical transactional information, target user trading volume daily in nearest n days and trading volume row are calculated
Name, wherein n are positive integer；
It is raw using linear regression method according to target user trading volume daily in nearest n days and trading volume ranking
Into the trading volume ranking of the target user and the linear equation of trading volume.
6. according to the method described in claim 4, it is characterized in that, according to the historical transactional information, generate the target and use
After the corresponding trading volume prediction model in family and the trading volume ranking of the target user and the linear equation of trading volume, also
Including：
It is updated periodically the trading volume prediction model of the target user and the linear equation of trading volume ranking and trading volume.
7. according to the method described in claim 1, it is characterized in that, it is described acquisition target user historical transactional information it
Before, it further includes：
Sequence information is received, the sequence information includes at least user identifier；
The user identifier in the sequence information, it is determined whether be the corresponding user's distribution of the user identifier
Storage position；
If being put not yet for the corresponding user's distribution bin storage space of the user identifier, the corresponding user of the user identifier is made
For the target user.
8. a kind of storage position distributor, which is characterized in that including：
Acquisition module, for obtaining the historical transactional information of target user；
Computing module for the historical transactional information according to the target user, assesses transaction of the target user on the same day
Measure ranking；
Distribution module, for being target user's distribution bin storage space according to trading volume ranking of the target user on the same day
It puts.
9. device according to claim 8, which is characterized in that the distribution module is additionally operable to：
According to the target user in the trading volume ranking on the same day and the distance of the distance of each storage position and store exit,
For the storage position that trading volume target user's distribution distance store exit in the top is near, so that the storage of the target user
The distance of position and store exit is less than or equal to the storage position of user of the trading volume ranking after the target user
With the distance of store exit, and more than or equal to user of the trading volume ranking before the target user storage position
With the distance of store exit.
10. device according to claim 8, which is characterized in that the computing module includes：
Acquisition submodule, for obtaining the linear of the trading volume prediction model of the target user and trading volume ranking and trading volume
Equation；
First computational submodule, for the trading volume prediction model according to the historical transactional information and the target user,
Assess trading volume of the target user on the same day；
Second computational submodule, for according to the target user in the trading volume on the same day and the transaction of the target user
The linear equation of ranking and trading volume is measured, assesses trading volume ranking of the target user on the same day.
11. device according to claim 10, which is characterized in that further include：
Generation module, for according to the historical transactional information, generating and storing the trading volume prediction model of the target user
With the linear equation of trading volume ranking and trading volume.
12. according to the devices described in claim 11, which is characterized in that the generation module is additionally operable to：
According to the historical transactional information, target user trading volume daily in nearest n days and trading volume row are calculated
Name, wherein n are positive integer；
It is raw using linear regression method according to target user trading volume daily in nearest n days and trading volume ranking
Into the trading volume ranking of the target user and the linear equation of trading volume.
13. according to the devices described in claim 11, which is characterized in that further include：
Update module, for being updated periodically the trading volume prediction model of the target user and trading volume ranking and trading volume
Linear equation.
14. device according to claim 8, which is characterized in that further include：
Preprocessing module, for receiving sequence information, the sequence information includes at least user identifier；
The user identifier in the sequence information, it is determined whether be the corresponding user's distribution of the user identifier
Storage position；
If being put not yet for the corresponding user's distribution bin storage space of the user identifier, the corresponding user of the user identifier is made
For the target user.
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