CN108108933B - Storage position distribution method and device - Google Patents
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Abstract
The invention provides a storage position distribution method and device. The method comprises the following steps: acquiring historical transaction information of a target user; evaluating the transaction amount ranking of the target user on the current day according to the historical transaction information of the target user; and allocating warehousing positions for the target users according to the transaction amount ranking of the target users on the current day. According to the method, the transaction amount ranking of the target user on the current day is evaluated according to the acquired historical transaction information of the target user; according to the trading volume ranking of the target users on the same day, the warehousing positions are distributed for the target users, so that the warehousing positions with shorter transportation distances in the goods sorting process can be distributed to the users with the highest trading volume ranking on the same day when the goods are delivered out of the warehouse, the transportation distances in the goods sorting process when the goods are delivered out of the warehouse can be greatly shortened, the delivery time of the goods is shortened, and the delivery efficiency of the goods is improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for allocating storage locations.
Background
Electronic commerce is a business activity taking an information network technology as a means and taking commodity exchange as a center, and is electronization, networking and informatization of each link of the traditional business activity. People shop on the internet and then the goods purchased are transported by express companies off-line.
A user purchases on the internet to submit orders, the electronic commerce platform packs goods according to each user order, and the packed goods are stored in a storage position corresponding to the user; and picking the goods from the storage position corresponding to the user by a picker to finish the delivery, and delivering the goods to the user designated address by an express company. In modern warehouse logistics, the retention time of goods in a warehouse is short, the circulation speed is high, in order to provide services for customers more conveniently, the goods can be stored and taken quickly, the goods delivery efficiency is improved, and the selection of a warehouse position distribution method is particularly important.
The current common storage position allocation method comprises a random allocation method and a nearest vacancy allocation method, wherein the random allocation method refers to an allocation method of randomly storing goods on vacancies under an equal probability condition when a certain batch of the same products come in, and the nearest vacancy allocation method refers to an allocation method of storing the goods in a storage vacancy which is nearest to an exit according to the sequence of entering the warehouse. The existing storage position distribution method does not fully consider the problem of space utilization rate, can cause the waste of a large amount of storage space and the increase of the transportation distance in the goods sorting process when the goods are delivered out of the warehouse, and has long delivery time and low goods sorting efficiency.
Disclosure of Invention
The invention provides a storage position distribution method and a storage position distribution device, which are used for solving the problems that the space utilization rate is not fully considered in the conventional storage position distribution method, a large amount of storage space is wasted, the transportation distance is increased in the goods sorting process during delivery, the delivery time of goods is long, and the goods sorting efficiency is low.
One aspect of the present invention provides a method for allocating storage locations, comprising:
acquiring historical transaction information of a target user;
evaluating the transaction amount ranking of the target user on the current day according to the historical transaction information of the target user;
and allocating warehousing positions for the target users according to the transaction amount ranking of the target users on the current day.
Another aspect of the present invention provides a storage position distribution device, including:
the acquisition module is used for acquiring historical transaction information of a target user;
the calculation module is used for evaluating the transaction amount ranking of the target user on the current day according to the historical transaction information of the target user;
and the distribution module is used for distributing the warehousing positions for the target users according to the transaction amount ranking of the target users on the current day.
According to the warehousing position distribution method and device, the transaction amount ranking of the target user on the current day is evaluated according to the acquired historical transaction information of the target user; according to the trading volume ranking of the target users on the same day, the warehousing positions are distributed for the target users, so that the warehousing positions with shorter transportation distances in the goods sorting process can be distributed to the users with the highest trading volume ranking on the same day when the goods are delivered out of the warehouse, the transportation distances in the goods sorting process when the goods are delivered out of the warehouse can be greatly shortened, the delivery time of the goods is shortened, and the delivery efficiency of the goods is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart of a warehousing location allocation method according to an embodiment of the present invention;
fig. 2 is a flowchart of a warehousing location allocation method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a warehouse location distribution device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a warehouse location distribution device according to a fourth embodiment of the present invention.
With the above figures, certain embodiments of the invention have been illustrated and described in more detail below. The drawings and the description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate it by those skilled in the art with reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terms to which the present invention relates will be explained first:
linear regression: in statistics, Linear Regression (Linear Regression) is a Regression analysis that models the relationship between one or more independent and dependent variables using a least squares function called a Linear Regression equation. Such a function is a linear combination of one or more model parameters called regression coefficients. The case of only one independent variable is called simple regression, and the case of more than one independent variable is called multiple regression.
Gray models (grey models, abbreviated GM model): a grey differential prediction model is established through a small amount of incomplete information, the long-term description of the ambiguity of the development rule of things is made, and the method is a predictive branch with relatively perfect theory and method in the fuzzy prediction field.
The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Example one
Fig. 1 is a flowchart of a warehousing location allocation method according to an embodiment of the present invention. The embodiment of the invention provides a storage position distribution method aiming at the problems that the space utilization rate is not fully considered in the existing storage position distribution method, a large amount of storage space is wasted, the transportation distance in the goods sorting process is increased during delivery, the delivery time of the goods is long, and the goods sorting efficiency is low. As shown in fig. 1, the method comprises the following specific steps:
and step S101, acquiring historical transaction information of the target user.
Wherein the target user is a user that has not been assigned to a warehousing location.
The historical transaction information in this embodiment may be the transaction information of the target user within the last preset number of days. The longer the preset days are, the higher the accuracy of the transaction amount ranking of the evaluated target user on the current day is, but the larger the calculation amount is; the preset number of days may be set by a technician according to actual needs, and this embodiment does not specifically limit this.
And S102, evaluating the trading volume ranking of the target user on the current day according to the historical trading information.
In this embodiment, the transaction process of each user is considered as a system, in this system, whether the user participates in the transaction on the current day, what the transaction amount of the user is on the current day, and the ranking of the transaction amount of the user on the current day are uncertain and random, but the historical transaction information is known and conforms to the characteristics of a gray system, and the transaction amount of the user can be predicted by adopting a gray model.
And S103, distributing storage positions for the target users according to the transaction amount ranking of the target users on the current day.
After the rank of the transaction amount of the target user on the current day is predicted, the warehousing position is allocated to the target user according to the rank of the transaction amount of the target user on the current day, the warehousing position with shorter transportation distance in the goods picking process during the delivery of the goods can be allocated to the user with the rank of the transaction amount on the current day, and for example, the warehousing position with the closer distance to the warehouse exit is allocated to the user with the rank of the transaction amount on the front.
According to the embodiment of the invention, the transaction amount ranking of the target user on the current day is evaluated according to the acquired historical transaction information of the target user; according to the trading volume ranking of the target users on the same day, the warehousing positions are distributed for the target users, so that the warehousing positions with shorter transportation distances in the goods sorting process can be distributed to the users with the highest trading volume ranking on the same day when the goods are delivered out of the warehouse, the transportation distances in the goods sorting process when the goods are delivered out of the warehouse can be greatly shortened, the delivery time of the goods is shortened, and the delivery efficiency of the goods is improved.
Example two
Fig. 2 is a flowchart of a warehousing location allocation method according to a second embodiment of the present invention. On the basis of the first embodiment, in this embodiment, the evaluating the transaction amount ranking of the target user on the current day according to the historical transaction information includes: acquiring a transaction amount prediction model and a linear equation of transaction amount ranking and transaction amount of a target user; evaluating the transaction amount of the target user on the current day according to the historical transaction information and the transaction amount prediction model of the target user; and evaluating the trading volume ranking of the target user on the day according to the trading volume of the target user on the day and a linear equation of the trading volume ranking and the trading volume of the target user. As shown in fig. 2, the method comprises the following specific steps:
step S201, receiving order information, wherein the order information at least comprises a user identification.
The execution main body of the warehousing position allocation method provided by the invention can be a big data platform capable of interacting with various electronic commerce platforms, for example, the electronic commerce platform can be an online shopping platform, an auction platform or the like. For example, a user may install an auction client at a terminal capable of performing internet communication, browse a product, recharge and submit order information for auction transaction at the auction client, and in the transaction process, the mobile phone number of the user may be used as the user identifier. After receiving an order submitted by a user, the electronic commerce platform sends order information to the big data platform, and the big data platform allocates a storage position for the user. The order information may include all or part of the information of the original order submitted by the user.
The user identifier is an identifier for uniquely identifying a user. The user identification can be a mobile phone number, an email box and the like of the user. The order information at least comprises a user identification, and the order information can further comprise: at least one goods identification, and the quantity of goods, order submission date, delivery date required, payment information, other information of the user and the like corresponding to each goods identification.
Step S202, according to the user identification in the order information, whether a warehousing position is allocated to the user corresponding to the user identification is determined, and if the warehousing position is not allocated to the user corresponding to the user identification, the user corresponding to the user identification is used as a target user.
After receiving the order information of the user, determining whether a warehousing position corresponding to the user identifier in the order information exists, if so, determining that the warehousing position is allocated to the user corresponding to the user identifier, and then, not allocating the warehousing position to the user.
If the warehousing position corresponding to the user identifier in the order information does not exist, it can be determined that the warehousing position is not allocated to the user corresponding to the user identifier, the user is taken as a target user, and the warehousing position is allocated to the target user through steps S203-S207.
The target user in this embodiment may be any user, after receiving order information submitted by any user, according to a user identifier in the order information, if it is determined that a warehousing location has not been allocated to a user corresponding to the user identifier, the user corresponding to the user identifier is taken as the target user, historical transaction information of the target user is obtained, a transaction amount ranking of the user on the current day is predicted according to the historical transaction information of the user, and a warehousing location is allocated to the target user according to the transaction amount ranking of the target user on the current day, so that waste of warehouse storage space caused by allocation of a warehousing location to a user who has no order on the current day can be avoided, and allocation efficiency of the warehousing location can also be improved.
In this embodiment, the warehousing positions are allocated to the users again every trading day, and after the users submit order information, that is, after the users have the trading volume on the same day, the warehousing positions are allocated to the users again, so that the waste of the warehousing space caused by allocating the warehousing space to the users without the trading volume can be avoided.
And step S203, acquiring historical transaction information of the target user.
The historical transaction information in this embodiment may be the transaction information of the target user within the last preset number of days. The longer the preset days are, the higher the accuracy of the transaction amount ranking of the evaluated target user on the current day is, but the larger the calculation amount is; the preset number of days may be set by a technician according to actual needs, and this embodiment does not specifically limit this.
In addition, the embodiment can acquire mass transaction information of each user based on a big data transaction platform.
And S204, acquiring a transaction amount prediction model and a linear equation of the transaction amount ranking and the transaction amount of the target user.
In this embodiment, the transaction amount prediction model and the linear equation of the transaction amount ranking and the transaction amount of the target user may be generated in advance and stored, and in this step, the stored transaction amount prediction model and the stored linear equation of the transaction amount ranking and the transaction amount of the target user may be directly obtained.
In this embodiment, the transaction process of each user is considered as a system, in this system, whether the user participates in the transaction on the current day, what the transaction amount of the user is on the current day, and the ranking of the transaction amount of the user on the current day are uncertain and random, but the historical transaction information is known and conforms to the characteristics of a gray system, and the transaction amount of the user can be predicted by adopting a gray model. The grey number series prediction refers to the prediction of the number of the time series of a grey system by using a dynamic GM model, namely the prediction of the main behavior characteristic quantity or a numerical value of an index of the system which is developed and changed to a future specific moment.
In this embodiment, if the traffic prediction model of the target user is not stored, the traffic prediction model of the target user is generated by using a modeling manner of a univariate first-order linear differential equation of GM (1,1) through the following steps: :
step one, acquiring the daily transaction amount of a target user in the last n days according to historical transaction information to obtain an original time sequence: q0＝[Q0(1)，Q0(2)，…，Q0(n)]Wherein Q is0(k) Representing the amount of transactions, Q, of the target user on the previous k-th day0(k) And k is equal to or more than 0, 1,2, … and n. Wherein n is a positive integer.
Step two, the original time sequence Q0And (3) carrying out data transformation to obtain a new time sequence:
X0＝[X0(1)，X0(2)，…，X0(n)]。
wherein, X0(k)＝A(Q0(k))，A(Q0(k) Is) indicates the value of the k-th term in a new time series after a change in data, k being 1,2, …, n. Wherein n is a positive integer.
The nature of the gray model is predicated based on an index, the accuracy of the model has a close relation with the gradient rule of the predication data, and if the original time sequence is changed by the index or the approximate index rule, the accuracy of the predication result is good. However, the actual prediction data rarely changes in a nearly exponential manner, for example, to make the original time series meet the requirements, and in the second step, the original time series is subjected to appropriate data transformation, such as logarithmic transformation, indicator transformation, power function transformation, inverse trigonometric function transformation, translation transformation, and the like. Optionally, in step two, the original time series is subjected to exponential transformation.
Step three, obtaining a new time sequence X after data transformation0As new original time sequence, new time sequence X obtained after data conversion0Once accumulating to generate a first-order accumulation sequence X1。
X1＝[X1(1)，X1(2)，…，X1(n)]。
Wherein
Step four, determining an accumulation matrix B and a constant term vector Y:
the first order differential equation model of GM (1,1) is established as follows:
wherein a and u are undetermined parameters.
Determining the accumulation matrix B and the constant term vector Y as follows:
wherein Z is1(k +1) is
Background value at time (k +1), i.e.:
and step five, solving undetermined parameters a and u in the first-order linear differential equation by adopting a least square method.
φ＝[a,u]TIs to be treatedIdentifying the parameter vector, and solving the fitting value of a and u by a least square method:
wherein the content of the first and second substances,
the fit value of a is shown as,
the fit value of u is indicated.
Step six, substituting the obtained fitting values of a and u into a first order differential equation in step four to obtain a discrete solution:
due to calculation
Is a predicted value after one accumulation, so the predicted value of the original time sequence can be obtained after the accumulation:
according to the data transformation performed on the original time sequence in the step two, the
And (3) carrying out corresponding data reduction to obtain a transaction amount prediction model of the target user:
when k is equal to n, the number of n,
i.e. the amount of the target user's transaction on the current dayAnd (5) predicting the value.
In this embodiment, if the linear equation of the transaction amount ranking and the transaction amount of the target user is not stored, the linear equation of the transaction amount ranking and the transaction amount of the target user is generated according to the historical transaction information, and the linear equation of the transaction amount ranking and the transaction amount of the target user may be specifically implemented in the following manner:
calculating the daily transaction amount and transaction amount ranking of the target user in the previous n days according to the historical transaction information, wherein n is a positive integer; and calculating a linear equation of the transaction quantity ranking and the transaction quantity of the target user by adopting a linear regression method according to the transaction quantity and the transaction quantity ranking of the target user in the previous n days.
Specifically, a linear regression method is adopted to calculate a linear relationship between the transaction amount ranking and the transaction amount of the target user according to the transaction amount and the transaction amount ranking of the target user in the previous n days, and the method can be specifically realized by adopting the following steps:
step 1, establishing a linear relation model of the daily transaction amount and the transaction amount ranking of a target user:
and y is ax + b, wherein x is the transaction amount, y is the ranking of the transaction amount corresponding to the transaction amount, and a and b are undetermined parameters.
Step 2, solving fitting values of undetermined parameters a and b by using a least square method
And
wherein the content of the first and second substances,
and
the average value of the transaction amount of the target user in the previous n days and the average value of the ranking of the transaction amount are respectively.
Step 3, fitting values of a and b obtained in the step 2 are calculated
And
and (3) the linear relation model in the step (1) is substituted, so that a linear equation of the trading volume ranking and the trading volume of the target user can be obtained:
alternatively, the transaction amount prediction model and the linear equation of the transaction amount ranking and the transaction amount of the target user may be periodically updated according to the latest historical transaction information of the target user. For example, the transaction amount prediction model and the linear equation of the transaction amount ranking and the transaction amount of the target user may be regenerated periodically according to the latest historical transaction information of the target user.
In addition, in another embodiment of the invention, a pre-stored linear equation between the transaction quantity prediction model and the transaction quantity ranking of the target user and the transaction quantity may not be directly obtained, but the transaction quantity prediction model and the linear equation between the transaction quantity ranking and the transaction quantity of the target user are generated according to the latest historical transaction information each time the warehousing location is allocated to the target user, and the transaction quantity ranking of the target user on the day are evaluated according to the newly generated linear equation between the transaction quantity prediction model and the transaction quantity ranking and the transaction quantity of the target user on the day, so that the predicted transaction quantity and the transaction quantity ranking of the target user on the day are more accurate.
And S205, evaluating the transaction amount of the target user on the current day according to the historical transaction information and the transaction amount prediction model of the target user.
Specifically, the transaction amount of the target user on the current day is evaluated according to the historical transaction information and the transaction amount prediction model of the target user, and the following method is specifically adopted:
determining the transaction amount of the target user on the nth day before the current day according to the historical transaction information; substituting the transaction amount of the target user on the nth day before the current day into the transaction amount prediction model of the target user shown in the formula (one) can calculate the transaction amount of the target user on the current day:
wherein Q is0(1) Is the transaction amount of the target user on the nth day prior to the current day,
a fitting value representing a calculated in generating a traffic prediction model of the target user,
representing the fitting value of u calculated in the generation of the target user's transaction amount prediction model.
And S206, evaluating the trading volume ranking of the target user on the day according to the trading volume of the target user on the day and a linear equation of the trading volume ranking and the trading volume of the target user.
In this step, the trading volume of the target user on the current day is substituted into the linear equation of the trading volume ranking and the trading volume of the target user represented by the formula (II), so that the trading volume ranking of the target user on the current day can be calculated.
Alternatively, if the calculated rank of the transaction amount of the target user on the current day is not an integer, the target user may be rounded.
Optionally, after calculating the trading volume and the trading volume ranking of the target user on the current day, the trading volume and the trading volume ranking of the target user on the current day may be stored.
And step S207, distributing warehousing positions for the target users according to the transaction amount ranking of the target users on the current day.
In this embodiment, the warehousing positions are allocated to the target users according to the rank of the transaction amount of the target users on the current day, and the method may specifically be implemented as follows:
according to the transaction amount ranking of the target users on the current day and the distance between each warehousing position and the warehouse outlet, allocating the warehousing position close to the warehouse outlet for the target user with the highest transaction amount ranking, so that the distance between the warehousing position of the target user and the warehouse outlet is smaller than or equal to the distance between the warehousing position of the user with the transaction amount ranking behind the target user and the warehouse outlet, and is larger than or equal to the distance between the warehousing position of the user with the transaction amount ranking in front of the target user and the warehouse outlet.
Optionally, if the predicted transaction amount ranking of the other user and the target user is the same before the target user, the next free warehousing location of the warehousing locations corresponding to the transaction amount ranking of the target user on the current day is selected for the target user in this step.
Optionally, the distance between each warehousing position and the warehouse exit may be calculated in advance, and the warehousing positions are sorted according to the distance from the warehouse exit, so that after the transaction amount ranking of the target user on the current day is obtained through calculation, the sorting may be performed according to the calculated warehousing positions, the warehousing positions are directly allocated to the target user, and the efficiency of warehousing position allocation may be improved.
In practical application, a secondary goods distribution area of a warehouse is usually located near an outlet of the warehouse, in this embodiment, a storage position close to the outlet of the warehouse is allocated to a target user with a front rank of a transaction amount, and the storage position of the target user with the front rank of the transaction amount can be close to the secondary goods distribution area, so that the moving distance and time of goods pickers during delivery of goods can be reduced.
Optionally, after the allocation of the warehousing location of the target user is completed, the warehousing location of the target user may be pushed to the picker corresponding to the order information, so as to inform the picker in advance.
In another embodiment of the present invention, the daily transaction amount and the ranking of the transaction amount of each user can be calculated according to the historical transaction information of each user
According to the embodiment of the invention, after the order information of the target user is received, namely the target user has the transaction amount on the same day, the warehousing position is allocated to the target user, so that the waste of the warehousing space caused by allocating the warehousing space to the user without the transaction amount can be avoided; evaluating the trading volume ranking of the target user on the current day according to the historical trading information; according to the trading volume ranking of the target users on the same day, the warehousing positions are distributed for the target users, so that the warehousing positions with shorter transportation distances in the goods sorting process can be distributed to the users with the highest trading volume ranking on the same day when the goods are delivered out of the warehouse, the transportation distances in the goods sorting process when the goods are delivered out of the warehouse can be greatly shortened, the delivery time of the goods is shortened, and the delivery efficiency of the goods is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a warehouse location distribution device according to a third embodiment of the present invention. The warehousing position distribution device provided by the embodiment of the invention can execute the processing flow provided by the warehousing position distribution method. As shown in fig. 3, the apparatus 30 includes: an acquisition module 301, a calculation module 302 and an assignment module 303.
Specifically, the obtaining module 301 is configured to obtain historical transaction information of the target user.
The calculation module 302 is configured to evaluate a transaction amount ranking of the target user on the current day according to the historical transaction information of the target user.
The allocation module 303 is configured to allocate the warehousing location to the target user according to the transaction amount ranking of the target user on the current day.
The apparatus provided in the embodiment of the present invention may be specifically configured to execute the method embodiment provided in the first embodiment, and specific functions are not described herein again.
According to the embodiment of the invention, the transaction amount ranking of the target user on the current day is evaluated according to the acquired historical transaction information of the target user; according to the trading volume ranking of the target users on the same day, the warehousing positions are distributed for the target users, so that the warehousing positions with shorter transportation distances in the goods sorting process can be distributed to the users with the highest trading volume ranking on the same day when the goods are delivered out of the warehouse, the transportation distances in the goods sorting process when the goods are delivered out of the warehouse can be greatly shortened, the delivery time of the goods is shortened, and the delivery efficiency of the goods is improved.
Example four
Fig. 4 is a schematic structural diagram of a warehouse location distribution device according to a fourth embodiment of the present invention. On the basis of the third embodiment, in this embodiment, the calculating module 302 includes: the device comprises an acquisition submodule, a first calculation submodule and a second calculation submodule.
The acquisition submodule is used for acquiring a transaction amount prediction model and a linear equation of transaction amount ranking and transaction amount of the target user.
The first calculation submodule is used for evaluating the transaction amount of the target user on the current day according to the historical transaction information and the transaction amount prediction model of the target user.
And the second calculation submodule is used for evaluating the transaction amount ranking of the target user on the current day according to the transaction amount of the target user on the current day and a linear equation of the transaction amount ranking and the transaction amount of the target user.
In this embodiment, the allocating module 303 is further configured to: according to the transaction amount ranking of the target users on the current day and the distance between each warehousing position and the warehouse outlet, allocating the warehousing position close to the warehouse outlet for the target user with the highest transaction amount ranking, so that the distance between the warehousing position of the target user and the warehouse outlet is smaller than or equal to the distance between the warehousing position of the user with the transaction amount ranking behind the target user and the warehouse outlet, and is larger than or equal to the distance between the warehousing position of the user with the transaction amount ranking in front of the target user and the warehouse outlet.
Optionally, as shown in fig. 4, the apparatus 30 may further include a generating module 304. The generating module 304 is configured to generate and store a transaction amount prediction model and a linear equation between the transaction amount ranking and the transaction amount of the target user according to the historical transaction information.
The generation module 304 is further configured to: calculating the daily transaction amount and transaction amount ranking of the target user in the last n days according to the historical transaction information, wherein n is a positive integer; and generating a linear equation of the transaction amount ranking and the transaction amount of the target user by adopting a linear regression method according to the transaction amount and the transaction amount ranking of the target user in the last n days.
Optionally, the apparatus 30 may further comprise an update module 305. The update module 305 is used to periodically update the transaction amount prediction model and the linear equation of the transaction amount ranking and transaction amount for the target user.
The apparatus 30 may further comprise: a pre-processing module 306.
The pre-processing module 306 is configured to: receiving order information, wherein the order information at least comprises a user identifier; determining whether a warehousing position is allocated to a user corresponding to the user identification according to the user identification in the order information; and if the warehousing position is not distributed to the user corresponding to the user identification, taking the user corresponding to the user identification as a target user.
The apparatus provided in the embodiment of the present invention may be specifically configured to execute the method embodiment provided in the second embodiment, and specific functions are not described herein again.
According to the embodiment of the invention, after the order information of the target user is received, namely the target user has the transaction amount on the same day, the warehousing position is allocated to the target user, so that the waste of the warehousing space caused by allocating the warehousing space to the user without the transaction amount can be avoided; evaluating the trading volume ranking of the target user on the current day according to the historical trading information; according to the trading volume ranking of the target users on the same day, the warehousing positions are distributed for the target users, so that the warehousing positions with shorter transportation distances in the goods sorting process can be distributed to the users with the highest trading volume ranking on the same day when the goods are delivered out of the warehouse, the transportation distances in the goods sorting process when the goods are delivered out of the warehouse can be greatly shortened, the delivery time of the goods is shortened, and the delivery efficiency of the goods is improved.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A method of allocating storage locations, comprising:
acquiring historical transaction information of a target user;
evaluating the transaction amount ranking of the target user on the current day according to the historical transaction information of the target user;
according to the rank of the transaction amount of the target user on the current day, distributing a warehousing position for the target user;
the evaluating the transaction amount ranking of the target user on the current day according to the historical transaction information comprises:
acquiring a transaction amount prediction model and a linear equation of the transaction amount ranking and the transaction amount of the target user;
evaluating the transaction amount of the target user on the current day according to the historical transaction information and the transaction amount prediction model of the target user;
evaluating the transaction amount ranking of the target user on the current day according to the transaction amount of the target user on the current day and a linear equation of the transaction amount ranking and the transaction amount of the target user;
the transaction amount prediction model is generated in a modeling mode of a univariate first-order linear differential equation of GM (1, 1);
the step of allocating warehousing positions to the target users according to the rank of the transaction amount of the target users on the current day comprises the following steps:
according to the transaction amount ranking of the target users on the current day and the distance between each warehousing position and the warehouse outlet, allocating the warehousing position close to the warehouse outlet for the target user with the highest transaction amount ranking, so that the distance between the warehousing position of the target user and the warehouse outlet is smaller than or equal to the distance between the warehousing position of the user with the transaction amount ranking behind the target user and the warehouse outlet, and is larger than or equal to the distance between the warehousing position of the user with the transaction amount ranking ahead the target user and the warehouse outlet.
2. The method of claim 1, wherein before obtaining the target user's transaction volume prediction model and the linear equation of transaction volume ranking and transaction volume, further comprising:
and generating and storing a transaction amount prediction model and a linear equation of the transaction amount ranking and the transaction amount of the target user according to the historical transaction information.
3. The method of claim 2, wherein generating a linear equation of the transaction amount ranking and transaction amount of the target user based on the historical transaction information comprises:
calculating the daily transaction amount and transaction amount ranking of the target user in the last n days according to the historical transaction information, wherein n is a positive integer;
and generating a linear equation of the transaction amount ranking and the transaction amount of the target user by adopting a linear regression method according to the transaction amount and the transaction amount ranking of the target user in the last n days.
4. The method of claim 2, wherein after generating a transaction amount prediction model corresponding to the target user and a linear equation between the transaction amount ranking and the transaction amount of the target user according to the historical transaction information, further comprising:
periodically updating the transaction amount prediction model and the linear equation of transaction amount ranking and transaction amount of the target user.
5. The method of claim 1, prior to said obtaining historical transaction information of the target user, further comprising:
receiving order information, wherein the order information at least comprises a user identifier;
determining whether a warehousing position is allocated to a user corresponding to the user identification or not according to the user identification in the order information;
and if the warehousing position is not allocated to the user corresponding to the user identification, taking the user corresponding to the user identification as the target user.
6. A storage position dispensing apparatus, comprising:
the acquisition module is used for acquiring historical transaction information of a target user;
the calculation module is used for evaluating the transaction amount ranking of the target user on the current day according to the historical transaction information of the target user;
the distribution module is used for distributing the warehousing positions for the target users according to the transaction amount ranking of the target users on the current day;
the calculation module comprises:
the acquisition submodule is used for acquiring a transaction amount prediction model and a linear equation of the transaction amount ranking and the transaction amount of the target user;
the first calculation submodule is used for evaluating the transaction amount of the target user on the current day according to the historical transaction information and the transaction amount prediction model of the target user;
the second calculation submodule is used for evaluating the transaction amount ranking of the target user on the current day according to the transaction amount of the target user on the current day and a linear equation of the transaction amount ranking and the transaction amount of the target user;
the transaction amount prediction model is generated in a modeling mode of a univariate first-order linear differential equation of GM (1, 1);
the allocation module is further configured to:
according to the transaction amount ranking of the target users on the current day and the distance between each warehousing position and the warehouse outlet, allocating the warehousing position close to the warehouse outlet for the target user with the highest transaction amount ranking, so that the distance between the warehousing position of the target user and the warehouse outlet is smaller than or equal to the distance between the warehousing position of the user with the transaction amount ranking behind the target user and the warehouse outlet, and is larger than or equal to the distance between the warehousing position of the user with the transaction amount ranking ahead the target user and the warehouse outlet.
7. The apparatus of claim 6, further comprising:
and the generating module is used for generating and storing a transaction amount prediction model and a linear equation of the transaction amount ranking and the transaction amount of the target user according to the historical transaction information.
8. The apparatus of claim 7, wherein the generating module is further configured to:
calculating the daily transaction amount and transaction amount ranking of the target user in the last n days according to the historical transaction information, wherein n is a positive integer;
and generating a linear equation of the transaction amount ranking and the transaction amount of the target user by adopting a linear regression method according to the transaction amount and the transaction amount ranking of the target user in the last n days.
9. The apparatus of claim 7, further comprising:
and the updating module is used for periodically updating the transaction amount prediction model and the linear equation of the transaction amount ranking and the transaction amount of the target user.
10. The apparatus of claim 6, further comprising:
the system comprises a preprocessing module, a storage module and a processing module, wherein the preprocessing module is used for receiving order information, and the order information at least comprises a user identifier;
determining whether a warehousing position is allocated to a user corresponding to the user identification or not according to the user identification in the order information;
and if the warehousing position is not allocated to the user corresponding to the user identification, taking the user corresponding to the user identification as the target user.
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| CN110615226B (en) * | 2018-06-19 | 2021-10-15 | 北京京东振世信息技术有限公司 | Storage bit allocation method, device and computer readable storage medium |
| TWI684145B (en) * | 2018-08-31 | 2020-02-01 | 財團法人工業技術研究院 | A system for configuring storage spaces of warehouse |
| CN109359905B (en) * | 2018-09-14 | 2021-01-19 | 广东飞库科技有限公司 | Automatic unmanned warehouse goods space allocation method and device and storage medium |
| CN109515911B (en) * | 2018-09-30 | 2020-09-22 | 浙江百世技术有限公司 | Box type design method for express delivery |
| CN115099525B (en) * | 2022-07-28 | 2022-11-25 | 国连科技(浙江)有限公司 | Cargo warehousing management method and system |
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