CN110909908B - Method and device for predicting item picking time - Google Patents
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Abstract
The invention discloses a method and a device for predicting article picking time length, and relates to the field of logistics storage. One embodiment of the method comprises the following steps: acquiring characteristic data and historical picking time of a sample object; determining a kernel matrix based on a preset kernel function and characteristic data of the sample object; and determining a picking time length prediction model according to the historical picking time length of the sample object and the nuclear matrix. The method solves the problem that characteristic information affecting object picking time is seriously insufficient, determines a picking time prediction model, improves prediction accuracy, reduces error rate and reduces workload in logistics simulation.
Description
Technical Field
The invention relates to the field of logistics storage, in particular to a method and a device for predicting article picking time.
Background
The picking operation is an important component of the warehousing operation, wherein the aging of the picking operation directly affects the aging of the ex-warehouse and in-warehouse operations. Therefore, in the logistics simulation, the picking time is required to be predicted according to the related information of the commodities, namely, the time period from the time that the pickers see the screen picking command device to the time that the pickers finish picking the last commodity of the same commodity and then scan the commodity.
For logistics simulation, firstly, the picking time length of each commodity needs to be determined, and then the picking time length and the corresponding commodity are input into a logistics simulation system. In the prior art, the picking time of the commodity is determined manually according to own experience, or historical picking data of the commodity is collected, and the picking time of the commodity is determined according to the historical picking data. Because the data needed by the logistics simulation is relatively large in the logistics simulation, if the sorting time of each commodity under various conditions is determined manually, the workload is very large, the accuracy is low, the error rate is high and the like.
And, for the picking time of the commodity, the extractable characteristic data is less, so that the problem of under fitting caused by insufficient characteristics is likely to occur when the predictive model is constructed, namely, the proper model is difficult to construct due to the owned information. Moreover, the existing models mostly need to assume a model form in advance, but the functional relationship between commodity picking time length and characteristic data is very complex, so that the model form is difficult to assume accurately.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method and a device for predicting the picking time length of an article, which overcome the problem that the characteristic information influencing the object picking time length is seriously insufficient, determine a picking time length prediction model, improve the prediction accuracy, reduce the error rate and reduce the workload in logistics simulation.
To achieve the above object, according to one aspect of an embodiment of the present invention, there is provided a method for item picking duration prediction.
The method for predicting the item picking time length comprises the following steps:
Acquiring characteristic data and historical picking time of a sample object;
determining a kernel matrix based on a preset kernel function and characteristic data of the sample object;
And determining a picking time length prediction model according to the historical picking time length of the sample object and the nuclear matrix.
Optionally, the step of determining the kernel matrix based on a preset kernel function and feature data of the sample object includes:
mapping the characteristic data of the sample object to an infinite dimension based on a preset kernel function; the preset kernel function is a Gaussian kernel function;
And determining a core matrix according to the mapping result.
Optionally, the step of mapping the feature data of the sample object to an infinite dimension based on a preset kernel function includes:
determining the regulation and control parameters of a preset kernel function through ten-fold cross validation;
and mapping the characteristic data of the sample object to infinite dimension according to the regulation parameters and a preset kernel function.
Optionally, the step of determining a picking time prediction model according to the historical picking time of the sample object and the kernel matrix comprises:
the model parameter α is determined by the following formula:
Wherein K is a kernel matrix, Historical picking time length for the sample object;
based on the model parameter alpha, determining a picking duration prediction model as follows:
y＝[1+k(x1,x),…,1+k(xn,x)]α
Wherein y is the picking time of the object, and x 1,...,xN is the characteristic data of n sample objects respectively; k (x i, x) is a kernel function, i=1, 2.
Optionally, after establishing the picking duration prediction model according to the historical picking duration of the sample object and the kernel matrix, the method further includes:
Reducing the number of the characteristic data according to a preset step length, and taking the reduced characteristic data as a characteristic data set to be calculated;
Determining the mean square error of each feature data set to be calculated based on the determined picking time length prediction model;
taking the feature data set to be calculated corresponding to the minimum mean square error as an adjustment feature data set;
And adjusting the picking time length prediction model based on the adjustment characteristic data set.
Optionally, the feature data of the sample object at least includes: number of single picks, weight, volume, number of shelf layers where the sample object is located.
To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided an apparatus for item picking duration prediction.
The device for predicting the item picking time length comprises the following components:
the sample data acquisition module is used for acquiring characteristic data and historical picking time length of the sample object;
the kernel matrix determining module is used for determining a kernel matrix based on a preset kernel function and characteristic data of the sample object;
And the model determining module is used for determining a picking time length prediction model according to the historical picking time length of the sample object and the nuclear matrix.
Optionally, the kernel matrix determining module is further configured to map the feature data of the sample object to an infinite dimension based on a preset kernel function; the preset kernel function is a Gaussian kernel function; and determining a core matrix according to the mapping result.
Optionally, the kernel matrix determining module is further configured to determine a regulatory parameter of a preset kernel function through ten-fold cross validation; and mapping the characteristic data of the sample object to infinite dimensions according to the regulation parameters and a preset kernel function.
Optionally, the model determination module is further configured to determine the model parameter α by the following formula:
Wherein K is a kernel matrix, Historical picking time length for the sample object;
based on the model parameter alpha, determining a picking duration prediction model as follows:
y＝[1+k(x1,x),...,1+k(xn,x)]α
Wherein y is the picking time of the object, and x 1,...,xN is the characteristic data of n sample objects respectively; k (x i, x) is a kernel function, i=1, 2.
Optionally, the device further comprises an adjustment module, which is used for reducing the number of the feature data according to a preset step length, and taking the reduced feature data as a feature data set to be calculated; determining the mean square error of each feature data set to be calculated based on the determined picking time length prediction model; taking the feature data set to be calculated corresponding to the minimum mean square error as an adjustment feature data set; and adjusting the picking time length prediction model based on the adjustment characteristic data set.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus.
The electronic equipment of the embodiment of the invention comprises: one or more processors; storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method for item picking duration prediction of any of the above.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a computer-readable medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the method for item picking duration prediction of any one of the above.
One embodiment of the above invention has the following advantages or benefits: the kernel function can expand the information in the limited dimension into the information in the infinite dimension, so the technical scheme solves the problem of insufficient characteristic information by using the kernel function. And based on the kernel function, the function relation between the picking time length and the characteristic data can be conveniently determined, and the problem that the existing function model needs to be provided with a model form in advance is solved. The embodiment of the invention not only improves the prediction accuracy and reduces the error rate, but also reduces the workload in logistics simulation.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic illustration of the main flow of a method for item picking duration prediction according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for item picking duration prediction according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the major modules of an apparatus for item picking duration prediction in accordance with an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
Fig. 5 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
FIG. 1 is a schematic diagram of the main flow of a method for item picking duration prediction according to an embodiment of the present invention, as shown in FIG. 1, the method for item picking duration prediction according to an embodiment of the present invention mainly includes:
Step S101: feature data and historical picking time of the sample object are obtained. The characteristic data of the sample object at least comprises: number of single picks, weight, volume, number of shelf layers where the sample object is located. Wherein, the number of single picking items refers to the number of objects to be picked in each picking operation; weight refers to the total weight of the subject during this pick; volume refers to the total volume of the subject during this pick; the number of shelf layers in which a sample object is located indicates the location of the shelf in which the object is located. In order to improve the accuracy of the determined model, the picking time of the object is the real picking time in the historical picking data, namely, the difference value y between the time point y1 when the picking personnel sees the screen picking command and the time point y2 when the picking personnel picks the last article of the same article and scans the article, and the picking time y=y2-y 1. In another embodiment of the invention, the picking time of the sample object may also be determined manually based on experience, and is not necessarily real picking time data.
Step S102: and determining a kernel matrix based on the preset kernel function and the characteristic data of the sample object. Kernel functions include linear kernel functions, polynomial kernel functions, gaussian kernel functions, etc., where gaussian kernel functions are most commonly used, which map data to infinity, also called radial basis functions (Radial Basis Function abbreviated RBFs), and are some sort of scalar function that is radially symmetric. A monotonic function, typically defined as the euclidean distance between any point x in space to some center xc, can be denoted as k (||x-xc|). The original existing feature data of the object can be mapped from European space to high-dimensional (even infinite-dimensional) regenerated kernel Hilbert space, namely x i→Φ(xi),φ(xi, into x i mapped feature vectors through a kernel function.
Specifically, based on a preset kernel function, mapping the characteristic data of the sample object to infinite dimensions; the preset kernel function is a gaussian kernel function. And determining a core matrix according to the mapping result. And determining the regulation and control parameters of a preset kernel function through ten-fold cross validation; and mapping the characteristic data of the sample object to infinite dimension according to the regulation parameters and a preset kernel function. The most commonly used radial basis function is the gaussian kernel function, the radial basis function (Radial Basis Function abbreviated RBF), which is some kind of radially symmetric scalar function. The Gaussian kernel function is to directly map the variable of the input space into an infinite dimensional space, so that the problem of insufficient information quantity of the input space can be effectively solved, and a specific mapping function relation is not needed to be known. The gaussian kernel function is:
Wherein sigma is the width parameter (regulation parameter) of the function, which controls the radial action range of the function. Bringing the taylor expansion into the gaussian kernel yields a mapping of infinite dimensions:
Then the inner product form for x 1 and x 2 fits the inner product calculation at infinity in the SVM, i.e. the gaussian kernel maps the data to an infinitely high dimension.
And, ten-fold cross-validation, called 10-fold cross-validation, is used to test algorithm accuracy. Is a common test method. The data set was divided into ten parts, 9 parts of which were used as training data and 1 part as test data in turn, and the test was performed. Each test gives a corresponding correct rate (or error rate). As an estimation of the accuracy of the algorithm, an average value of the accuracy (or error rate) of the result of 10 times is generally required to perform 10-fold cross-validation (e.g., 10 times 10-fold cross-validation), and then the average value is obtained as an estimation of the accuracy of the algorithm.
Step S103: and determining a picking time prediction model according to the historical picking time of the sample object and the nuclear matrix. Specifically, the model parameter α is determined by the following formula:
Wherein K is a kernel matrix, Is the historical picking time period of the sample object.
Building a picking time model, which is easy to know
Wherein,
(X′X)-2＝(X′X)-1(X′X)-1,α＝X(X′X)-2X′y
Considering the mapping phi, let
Where X 1,x2,……,xn is n sample points in the sample object feature data set X.
And
X(X′X)X′α＝XX′y
Accordingly, there are
Let the core matrix
Thus there is
KKα＝Ky
Thus, there are
α＝K-1y
Based on the model parameter alpha, determining a picking duration prediction model as follows:
y＝[1+k(x1,x),…,1+k(xn,x)]α
Wherein y is the picking time of the object, and x 1,...,xN is the characteristic data of n sample objects respectively; k (x i, x) is a kernel function, i=1, 2.
From the above, the embodiment of the invention is based on the least square method and the quadratic loss function, and has a clear solving expression. Compared with the support vector machine which is a range loss function, the method has the advantages that an explicit solving expression is not available, the calculation speed is low, variable selection is not easy to realize, and the calculation speed is high.
After the process, the number of the feature data is reduced according to a preset step length, and the reduced feature data is used as a feature data set to be calculated. All variables may be tried stepwise by reducing 1 variable at a time until reducing to 1 variable, and the mean square error over the test set is calculated each time (the square of the actual and predicted value differences for each sample point is calculated and then averaged). Therefore, based on the determined picking time length prediction model, the mean square error of each feature data set to be calculated is determined. And taking the feature data set to be calculated corresponding to the minimum mean square error as an adjustment feature data set, and adjusting the picking time length prediction model based on the adjustment feature data set. Since the gaussian kernel is chosen, as described above, a few variables in the original input space are mapped to an infinite number of variables in the hilbert space, at which point the under-fitting problem may become an over-fitting problem.
Assuming a sample size of 100, there are 4 raw independent variables (weight, volume, number of pieces, number of layers) and the dependent variable is the picking duration. First, 70 sample points were randomly selected as training sets and 30 as test sets. Prior to calculation, the total sample data is divided into a training set and a test set, the latter typically representing 30% of the total sample size. Then, a variable, such as a volume, is removed, and the corresponding mse1 (mean square error) is calculated; the volume is reserved, the weight is removed, the corresponding mse2 is calculated, and the method is performed by analogy, so that 4 mean square errors mse can be obtained. The smallest mse is found, assuming mse1. And then, one of the weight, number of pieces, and number of layers is removed, in a manner similar to that previously described. Until only one variable remains. Then comparing which mse is smaller when 1, 2 and 3 variables are removed, the corresponding variable set is the last variable, and finally substituting the last variable into formula calculation to adjust the model. For example, after the calculation, it is determined that the mean square error of the feature data set "weight, number of pieces" is the smallest among all the feature data combinations, the adjusted model is the two features related to the weight and the number of pieces, when the picking time of the commodity is predicted based on the model, the weight and the number of pieces of the commodity are directly acquired (other feature data related to the training process are not acquired), and based on the adjusted model, the picking time of the commodity can be calculated.
Taking an unmanned warehouse with an automatic guided vehicle AGV as a main robot as an example, the method for predicting the article picking time length is further described. FIG. 2 is a schematic diagram of a method for item picking duration prediction according to an embodiment of the present invention.
For the embodiment of the invention, the kernel function can expand the information in the limited dimension into the information in the infinite dimension, so the technical scheme solves the problem of insufficient characteristic information by using the kernel function. And based on the kernel function, the function relation between the picking time length and the characteristic data can be conveniently determined, and the problem that the existing function model needs to be provided with a model form in advance is solved. The embodiment of the invention not only improves the prediction accuracy and reduces the error rate, but also reduces the workload in logistics simulation. Also, to prevent overfitting, embodiments of the present invention are controlled by way of variable selection.
As shown in fig. 2, a method for item picking duration prediction according to an embodiment of the present invention includes:
Step S201: and acquiring characteristic data of the sample commodity. In the unmanned bin, goods are stored on shelves, each shelf has a plurality of layers, and each layer is divided into different goods lattices. After the user orders, the AGV trolley receives the corresponding instruction, firstly finds a proper goods shelf and then carries the goods shelf to a workstation. Then, the sorting personnel will sort out a certain amount of goods from a certain goods lattice of a certain layer of the goods shelf according to the instruction on the computer screen. The number of single picks, the weight and the volume of the same commodity and the number of shelf layers where the commodity is located can be extracted only under the influence of the availability of the data, and the characteristic data matrix is marked as X.
Step S202: and collecting the historical picking time of the sample commodity. And (3) recording the time point when the picking personnel sees the screen picking command, setting the time point as y1, recording the time for scanning the goods lattice after the picking personnel picks the last piece of the same goods as y2, and taking the y=y2-y 1 as the historical picking time length y of the sample goods.
Step S203: feature data of the original existing commodity is mapped from European space to an infinite-dimensional regeneration kernel Hilbert space through a Gaussian kernel function. Hilbert space refers to a complete inner product space. It can be regarded as a generalization of the European space. In European space, vectors are finite dimensions and define inner products. If the inner product space of the finite dimension is generalized to the inner product space of the infinite dimension, the inner product space is Hilbert space. Whether the space is complete is determined by convergence of all Cauchy (Cauchy) columns in the space.
Step S204: calculating a kernel matrix K:
and, the unknown parameter sigma 2 in the kernel function is determined by means of 10-fold cross-validation.
Step S205: determining a model parameter alpha according to the historical picking time length of the sample object and the nuclear matrix K:
Step S206: the model is adjusted by techniques of backward variable selection. All the independent variables in the original input space are substituted into calculation, and the mean square error on the test set is calculated. Every time 1 variable is reduced, all variables are tried stepwise until 1 variable is reduced, and the mean square error over the test set is calculated each time. And selecting the variable set corresponding to the minimum mean square error as the finally selected variable set.
The embodiment of the invention solves the problem in prediction by means of a kernel function. The kernel function can extend information in a finite dimension to information in an infinite dimension. Because of the mapping of data to the high-dimensional regenerated kernel hilbert space (Reproducing Kernel Hilbert Space, RKHS), there may be overfitting issues, and to prevent overfitting, embodiments of the present invention are controlled by way of variable selection.
Fig. 3 is a schematic diagram of main modules of an apparatus for item picking duration prediction according to an embodiment of the present invention, and as shown in fig. 3, an apparatus 300 for item picking duration prediction according to an embodiment of the present invention includes a sample data acquisition module 301, a core matrix determination module 302, and a model determination module 303.
The sample data obtaining module 301 is configured to obtain feature data and a historical picking duration of a sample object.
The kernel matrix determining module 302 is configured to determine a kernel matrix based on a preset kernel function and feature data of the sample object. The kernel matrix determining module is further used for mapping the characteristic data of the sample object to infinite dimensions based on a preset kernel function; the preset kernel function is a Gaussian kernel function; and determining a core matrix according to the mapping result. The nuclear matrix determining module is also used for determining the regulation and control parameters of a preset nuclear function through ten-fold cross validation; and mapping the characteristic data of the sample object to infinite dimensions according to the regulation parameters and a preset kernel function.
The model determining module 303 is configured to determine a picking duration prediction model according to the historical picking duration of the sample object and the kernel matrix. The model determination module is further configured to determine the model parameter α by the following formula:
Wherein K is a kernel matrix, Historical picking time length for the sample object;
Based on the model parameter alpha, determining a picking duration prediction model as follows:
y＝[1+k(x1,x),...,1+k(xn,x)]α
Wherein y is the picking time of the object, and x 1,...,xN is the characteristic data of n sample objects respectively; k (x i, x) is a kernel function, i=1, 2.
The device for predicting the picking time length of the article further comprises an adjusting module, wherein the adjusting module is used for reducing the number of the characteristic data according to a preset step length, and taking the reduced characteristic data as a characteristic data set to be calculated; determining the mean square error of each feature data set to be calculated based on the determined picking time length prediction model; taking the feature data set to be calculated corresponding to the minimum mean square error as an adjustment feature data set; and adjusting the picking time length prediction model based on the adjustment characteristic data set.
For the embodiment of the invention, the kernel function can expand the information in the limited dimension into the information in the infinite dimension, so the technical scheme solves the problem of insufficient characteristic information by using the kernel function. And based on the kernel function, the function relation between the picking time length and the characteristic data can be conveniently determined, and the problem that the existing function model needs to be provided with a model form in advance is solved. The embodiment of the invention not only improves the prediction accuracy and reduces the error rate, but also reduces the workload in logistics simulation. Also, to prevent overfitting, embodiments of the present invention are controlled by way of variable selection.
FIG. 4 illustrates an exemplary system architecture 400 to which a method for item picking duration prediction or an apparatus for item picking duration prediction of embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 is used as a medium to provide communication links between the terminal devices 401, 402, 403 and the server 405. The network 404 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 405 via the network 404 using the terminal devices 401, 402, 403 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 401, 402, 403.
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (by way of example only) providing support for shopping-type websites browsed by users using the terminal devices 401, 402, 403. The background management server can analyze and other data of the received product information inquiry request and feed back the processing result to the terminal equipment.
It should be noted that, the method for predicting the picking time length of an item according to the embodiment of the present invention is generally performed by the server 405, and accordingly, the device for predicting the picking time length of an item is generally provided in the server 405.
It should be understood that the number of terminal devices, networks and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, there is illustrated a schematic diagram of a computer system 500 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 501.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes an acquisition sample data acquisition module, a nuclear matrix determination module, and a model determination module. The names of these modules do not in any way limit the module itself, and for example, the sample data acquisition module may also be described as a "module that acquires characteristic data of a sample object and a historical picking time period".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: acquiring characteristic data and historical picking time of a sample object; determining a kernel matrix based on a preset kernel function and characteristic data of the sample object; and determining a picking time length prediction model according to the historical picking time length of the sample object and the nuclear matrix.
For the embodiment of the invention, the kernel function can expand the information in the limited dimension into the information in the infinite dimension, so the technical scheme solves the problem of insufficient characteristic information by using the kernel function. And based on the kernel function, the function relation between the picking time length and the characteristic data can be conveniently determined, and the problem that the existing function model needs to be provided with a model form in advance is solved. The embodiment of the invention not only improves the prediction accuracy and reduces the error rate, but also reduces the workload in logistics simulation.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method for item picking duration prediction, comprising:
Acquiring characteristic data and historical picking time of a sample object;
Determining a kernel matrix based on a preset kernel function and characteristic data of the sample object, wherein the determining comprises the following steps: mapping the characteristic data of the sample object to an infinite dimension based on a preset kernel function; the preset kernel function is a Gaussian kernel function; according to the mapping result, determining a core matrix;
determining a picking duration prediction model according to the historical picking duration of the sample object and the nuclear matrix, wherein the method comprises the following steps of: the model parameter α is determined by the following formula:
Wherein K is a kernel matrix, Historical picking time length for the sample object;
based on the model parameter alpha, determining a picking duration prediction model as follows:
y＝[1+k(x1,x),...,1+k(xn,x)]α
Wherein y is the picking time of the object, and x 1,...,xn is the characteristic data of n sample objects respectively; k (x i, x) is a kernel function, i=1, 2.
2. The method of claim 1, wherein the step of mapping the feature data of the sample object to an infinite dimension based on a preset kernel function comprises:
determining the regulation and control parameters of a preset kernel function through ten-fold cross validation;
and mapping the characteristic data of the sample object to infinite dimension according to the regulation parameters and a preset kernel function.
3. The method of claim 1, further comprising, after establishing a picking duration prediction model based on the historical picking durations of the sample objects and the kernel matrix:
Reducing the number of the characteristic data according to a preset step length, and taking the reduced characteristic data as a characteristic data set to be calculated;
Determining the mean square error of each feature data set to be calculated based on the determined picking time length prediction model;
taking the feature data set to be calculated corresponding to the minimum mean square error as an adjustment feature data set;
And adjusting the picking time length prediction model based on the adjustment characteristic data set.
4. The method according to claim 1, wherein the characteristic data of the sample object comprises at least: number of single picks, weight, volume, number of shelf layers where the sample object is located.
5. An apparatus for item picking duration prediction, comprising:
the sample data acquisition module is used for acquiring characteristic data and historical picking time length of the sample object;
The kernel matrix determining module is configured to determine a kernel matrix based on a preset kernel function and feature data of the sample object, and includes: mapping the characteristic data of the sample object to an infinite dimension based on a preset kernel function; the preset kernel function is a Gaussian kernel function; according to the mapping result, determining a core matrix;
The model determining module is used for determining a picking time length prediction model according to the historical picking time length of the sample object and the nuclear matrix, and comprises the following steps: the model parameter α is determined by the following formula:
Wherein K is a kernel matrix, Historical picking time length for the sample object;
based on the model parameter alpha, determining a picking duration prediction model as follows:
y＝[1+k(x1,x),...,1+k(xn,x)]α
Wherein y is the picking time of the object, and x 1,...,xn is the characteristic data of n sample objects respectively; k (x i, x) is a kernel function, i=1, 2.
6. The device according to claim 5, wherein the kernel matrix determining module is further configured to determine a regulatory parameter of a preset kernel function through ten-fold cross-validation; and mapping the characteristic data of the sample object to infinite dimensions according to the regulation parameters and a preset kernel function.
7. The apparatus of claim 5, further comprising an adjustment module configured to reduce the number of feature data according to a preset step size, and take the reduced feature data as a feature data set to be calculated; determining the mean square error of each feature data set to be calculated based on the determined picking time length prediction model; taking the feature data set to be calculated corresponding to the minimum mean square error as an adjustment feature data set; and adjusting the picking time length prediction model based on the adjustment characteristic data set.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-4.
9. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-4.
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