CN106384219B - Storage sub-warehouse auxiliary analysis method and device - Google Patents

Storage sub-warehouse auxiliary analysis method and device Download PDF

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CN106384219B
CN106384219B CN201610896578.9A CN201610896578A CN106384219B CN 106384219 B CN106384219 B CN 106384219B CN 201610896578 A CN201610896578 A CN 201610896578A CN 106384219 B CN106384219 B CN 106384219B
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CN106384219A (en
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刘旭
徐卓然
孙旭锋
武海龙
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Beijing Jingbangda Trade Co Ltd
Beijing Jingdong Zhenshi Information Technology Co Ltd
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    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/2323—Non-hierarchical techniques based on graph theory, e.g. minimum spanning trees [MST] or graph cuts

Abstract

The present disclosure relates to a warehouseA bin assisted analysis method comprising: obtaining order information of an area in a historical time period and obtaining single item information in the historical time period from the order information; establishing a historical three-dimensional incidence matrix, wherein any point H of the historical three-dimensional incidence matrixi(xi,yi,zi) For indicating singlets xiAnd singleton yiAt historical time ziThe correlation of (A) is gi(ii) a Fitting the time series of each relevance of any two single products, and calculating a future three-dimensional relevance matrix in a future time period according to a fitting result; adding the future three-dimensional incidence matrix on the historical three-dimensional incidence matrix, and fusing the added three-dimensional incidence matrix on a time dimension to obtain a two-dimensional incidence matrix; performing spectral clustering on the two-dimensional incidence matrix, and performing graph segmentation on the two-dimensional incidence matrix according to a spectral clustering result; and distributing the single product set to each warehouse according to the graph segmentation result. The present disclosure can reduce the operation cost.

Description

Storage sub-warehouse auxiliary analysis method and device
Technical Field
The disclosure relates to the technical field of warehousing, in particular to a warehousing sub-warehouse auxiliary analysis method and a warehousing sub-warehouse auxiliary analysis device.
Background
In the development process of the e-commerce, along with the market expansion and the continuous expansion of the user population, the sales volume of a single sku (stock keeping unit) is increased sharply. In order to ensure the stock-in-stock ratio (i.e. the stock in the warehouse is available when the customer places an order), the stock-taking and selling system has to increase the stock-in amount of sku, which puts unprecedented pressure on the warehouse operation. Under the limitation of the warehouse capacity, some warehouses have to store the existing stored goods separately, so-called sub-warehouse. In the warehouse-dividing process, an important index in operation production is induced, namely, the order splitting rate (the number of order splitting orders/the total number of order splitting rate), which refers to: a customer order comprises N commodities, which are respectively stored in M (M is less than or equal to N) warehouses, and M times of production cost is needed for completing warehouse production and distribution of the order. For example, in the above process, if M >1, then a "ticket splitting" is required; that is, one customer order is split into a plurality of sub-orders; thus, the cost is increased; after a customer order is split into M sub-orders, the e-commerce has to pay M times of cost to complete warehousing production and distribution; but the customer pays only 1 cost, which greatly increases the overall cost.
At present, when e-commerce plans storage and storage, the common method is as follows: placing the same type of commodities in the same warehouse as much as possible; the commodity category may include, for example: mother and infant articles, clothing articles, outdoor articles, personal care, 3C electronic products, book audio and video and the like. However, when the warehouse is split, the traditional working warehouse planning mode is not applicable any more, because the splitting of the warehouse inevitably needs to carry out the grade splitting. Therefore, at present, classification during warehouse separation is often required to be forcibly classified according to manual experience, and the same classification in the same warehouse is classified into different warehouses for storage.
In the current warehouse category planning, when the warehouse division problem is faced, a large amount of manual experience is needed to carry out category splitting, and the conditions of different warehouses in different regions are different, so that the requirement on the quality of personnel is high; if the warehouse category is unreasonably split, the order splitting rate of customer orders is greatly increased, and the operation cost of warehousing is increased. Therefore, it is desirable to provide a new warehouse sub-warehouse analysis method and device.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a method and an apparatus for auxiliary analysis of storage, which overcome one or more of the problems due to the limitations and disadvantages of the related art, at least to some extent.
According to one aspect of the present disclosure, there is provided a method for auxiliary analysis of storage sub-warehouse, comprising:
obtaining order information of an area in a historical time period and obtaining single item information in the historical time period from the order information;
establishing a historical three-dimensional incidence matrix, wherein any point H of the historical three-dimensional incidence matrixi(xi,yi,zi) For indicating singlets xiAnd singleton yiAt historical time ziThe correlation of (A) is gi(ii) a Wherein, giIs based on said singlets xiAnd singleton yiAt historical time ziCalculating the single item information;
fitting the time series of each relevance of any two single products, and calculating a future three-dimensional relevance matrix in a future time period according to a fitting result;
adding the future three-dimensional incidence matrix on the historical three-dimensional incidence matrix, and fusing the added three-dimensional incidence matrix on a time dimension to obtain a two-dimensional incidence matrix;
performing spectral clustering on the two-dimensional incidence matrix, and performing graph segmentation on the two-dimensional incidence matrix by taking the minimum relevance between the segmented subgraphs as a target according to a spectral clustering result;
and distributing the single product set to each warehouse according to the graph segmentation result.
In an exemplary embodiment of the present disclosure, the method further comprises:
and performing outlier removal operation on the time series of the relevance through the first moment and the second moment of the time series of the relevance.
In an exemplary embodiment of the present disclosure, the method further comprises:
and further clustering the two-dimensional incidence matrix according to the spectral clustering result, and further evaluating the clustering result.
In an exemplary embodiment of the disclosure, fitting the time series of each correlation of any two of the singlets includes:
Figure BDA0001130351380000031
wherein x isiIs a value in time series, wiT is the weight of the time sequence, and T is the size of the time window.
In an exemplary embodiment of the present disclosure, fusing the added three-dimensional correlation matrix in the time dimension to obtain a two-dimensional correlation matrix includes:
Figure BDA0001130351380000032
wherein n is the length of the time series, H*(xi,yi) For any point on the two-dimensional correlation matrix,
Figure BDA0001130351380000033
is any point on the future three-dimensional correlation matrix.
In an exemplary embodiment of the present disclosure, spectrally clustering the two-dimensional incidence matrix comprises:
establishing a similar graph, wherein a weighted adjacent matrix of the similar graph is W, wherein W is H, and H is the two-dimensional incidence matrix;
calculating a Laplace matrix L, wherein
Figure BDA0001130351380000034
wijJ is any point in the weighted adjacent matrix W, and j is the number of columns in the weighted adjacent matrix W;
decomposing the Laplace matrix L ═ UΛ U-1Wherein U is [ U ═ U1,u2,...,ur]，
Figure BDA0001130351380000035
[u1,u2,...,ur]A characteristic vector value of L, λiIs a characteristic value of L, and in Λ, λ1≤λ2≤...≤λrR is the rank of W;
selecting the first k eigenvector values of the Laplace matrix L to form an r x k matrix, taking each row in the matrix as one vector in a k-dimensional space, and clustering the eigenvectors by using a clustering algorithm, wherein k is the pre-clustering number of spectral clustering.
In an exemplary embodiment of the present disclosure, further clustering the two-dimensional incidence matrix according to the spectral clustering result includes:
Figure BDA0001130351380000041
wherein S (k, m) is the number of schemes distributed to the single item set, C is a binomial coefficient, m is the number of warehouses needing to participate in calculation, k is the pre-clustering number of spectral clustering, and m is less than k.
According to another aspect of the present disclosure, there is provided a device for auxiliary analysis of warehouses, including:
an information acquisition module: the system comprises a data processing system and a data processing system, wherein the data processing system is used for acquiring order information of an area in a historical time period and acquiring single item information in the historical time period from the order information;
a first incidence matrix module: is used for establishing a historical three-dimensional incidence matrix, and any point H of the historical three-dimensional incidence matrixi(xi,yi,zi) For indicating singlets xiAnd singleton yiAt historical time ziThe correlation of (A) is gi(ii) a Wherein, giIs based on said singlets xiAnd singleton yiAt historical time ziCalculating the single item information;
a fitting module: fitting a time series of each said association for any two of said singlets;
a second incidence matrix module: the three-dimensional incidence matrix calculation module is used for calculating a future three-dimensional incidence matrix in a future time period according to the fitting result;
a third correlation matrix module: for adding the future three-dimensional correlation matrix to the historical three-dimensional correlation matrix;
a fusion module: the system is used for fusing the added three-dimensional incidence matrix on a time dimension to obtain a two-dimensional incidence matrix;
a spectral clustering module: the system is used for performing spectral clustering on the two-dimensional incidence matrix;
a graph partitioning module: the two-dimensional incidence matrix is used for carrying out graph segmentation on the two-dimensional incidence matrix according to a spectral clustering result by taking the minimum relevance between the segmented subgraphs as a target;
a unit dispensing module: the system is used for distributing the single product set to each warehouse according to the result of the graph segmentation.
In an exemplary embodiment of the present disclosure, the apparatus further includes:
outlier removal module: for performing a de-outlier operation on the time-series of correlations by the first and second moments of the time-series of correlations.
In an exemplary embodiment of the present disclosure, the apparatus further includes:
the first clustering module: the two-dimensional incidence matrix is used for further clustering according to the spectral clustering result;
an evaluation module: for further evaluation of the clustering results.
In an exemplary embodiment of the present disclosure, fitting the time series of each correlation of any two of the singlets includes:
Figure BDA0001130351380000051
wherein x isiIs a value in time series, wiT is the weight of the time sequence, and T is the size of the time window.
In an exemplary embodiment of the present disclosure, the added three-dimensional correlation matrix is fused in the time dimension to obtain a two-dimensional correlation matrix, where the fusing includes:
Figure BDA0001130351380000052
wherein n is the length of the time series, H*(xi,yi) For any point on the two-dimensional correlation matrix,
Figure BDA0001130351380000053
is any point on the future three-dimensional correlation matrix.
In an exemplary embodiment of the present disclosure, the spectral clustering module includes:
the similar graph establishing module: the method comprises the steps of establishing a similarity graph, wherein a weighted adjacency matrix of the similarity graph is W, wherein W is H, and H is the two-dimensional incidence matrix;
a Laplace matrix calculation module: for calculating the laplacian matrix L, wherein,
Figure BDA0001130351380000054
wijj is any point in the weighted adjacent matrix W, and j is the number of columns in the weighted adjacent matrix W;
a Laplace matrix decomposition module: for decomposing the Laplace matrix L ═ UΛ U-1Wherein U is [ U ═ U1,u2,...,ur]，
Figure BDA0001130351380000055
[u1,u2,...,ur]A characteristic vector value of L, λiIs a characteristic value of L, and in Λ, λ1≤λ2≤...≤λrR is the rank of W;
a second type of module: and the method is used for selecting the first k characteristic vector values of the Laplace matrix L to form an r x k matrix, taking each row in the matrix as a vector in a k-dimensional space, and clustering the characteristic vectors by using a clustering algorithm, wherein k is the pre-clustering number of the spectral clustering.
In an exemplary embodiment of the present disclosure, the two-dimensional incidence matrix is further clustered according to the spectral clustering result, and the clustering includes:
Figure BDA0001130351380000061
wherein S (k, m) is the number of schemes distributed to the single item set, C is a binomial coefficient, m is the number of warehouses needing to participate in calculation, k is the pre-clustering number of spectral clustering, and m is less than k.
In the warehousing warehouse-by-warehouse auxiliary analysis method and device of the exemplary embodiment, on one hand, the order information of the user is abstracted into the commodity incidence matrix, so that the calculation of commodity distribution is completely based on the incidence matrix, the user order is not required to be traversed, and the calculation efficiency is improved; on the other hand, the incidence matrix in a short period can be predicted through analysis of the time sequence, so that the bill splitting condition after commodity warehouse splitting can be predicted, an enterprise is assisted to realize better warehouse splitting, and the cost brought to enterprise operation by high bill splitting rate can be reduced to a great extent.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically shows a flow chart of a storage binning auxiliary analysis method in an exemplary embodiment of the present disclosure.
Fig. 2 schematically shows a flowchart of an individual product information acquisition section in an exemplary embodiment of the present disclosure.
Fig. 3(a) schematically illustrates a historical three-dimensional correlation matrix diagram in an exemplary embodiment of the disclosure.
Fig. 3(b) schematically illustrates a historical three-dimensional correlation matrix diagram in an exemplary embodiment of the disclosure.
Fig. 4(a) schematically illustrates an association time series in an exemplary embodiment of the present disclosure.
Fig. 4(b) schematically shows a fitting result of one correlation time series in an exemplary embodiment of the present disclosure.
Fig. 5 schematically illustrates a three-dimensional correlation matrix map after adding a future three-dimensional correlation matrix in an exemplary embodiment of the present disclosure.
Fig. 6 schematically illustrates a flowchart of a spectral clustering method in an exemplary embodiment of the present disclosure.
Fig. 7 schematically illustrates a flow chart of a method of outlier removal in an exemplary embodiment of the present disclosure.
Fig. 8 schematically illustrates a two-dimensional correlation matrix diagram in an exemplary embodiment of the present disclosure.
Fig. 9 schematically illustrates a block diagram of a storage sub-binning auxiliary analysis apparatus in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In the present exemplary embodiment, a warehouse and warehouse separation auxiliary analysis method is provided, where the warehouse and warehouse separation auxiliary analysis method may start from data analysis of a customer order, start from a purchasing behavior of a user for different regions and different time periods, analyze a purchasing relevance of a commodity, and split n warehouses into m warehouses by planning a commodity layout. Referring to fig. 1, the warehouse sub-warehouse auxiliary analysis method may include the following steps:
in step S110, order information of a region in a historical time period is obtained and item information in the historical time period is obtained from the order information. As shown in fig. 2, step S110 in the present exemplary embodiment may include, for example, steps S112 to S116 described below. Wherein:
in step S112, order information within a period of time is acquired from the hadoop cluster.
The Hadoop cluster is a distributed system infrastructure developed by the Apache foundation, and a user can develop a distributed program without knowing details of a distributed bottom layer, and fully utilizes the advantages of the cluster to carry out high-speed operation and storage. In this example embodiment, the order information obtained from the hadoop cluster may include: parent order number, child order number, purchase order (sku or class) information, warehouse information (warehouse information includes warehouse name, the area and the time of delivery), order placing time, order valid identifier, etc., but the disclosure is not limited thereto. In addition, in other exemplary embodiments of the present disclosure, the order information may also be obtained in other manners according to circumstances, and this is not particularly limited in this exemplary embodiment.
In step S114, the order information is sorted.
In this example embodiment, the sorting of the order information may include, for example, an invalid order exclusion, an order type identifier, a primary attribute information identifier, and the like, and the disclosure is not limited thereto. In this exemplary embodiment, step S114 can implement: filtering out invalid orders (order valid identification is invalid); filtering out orders without warehouse information; identifying the order according to the order service type: such as may be identified as: self-operated orders, third-party orders, self-operated and third-party mixed orders and the like, wherein the order service type can be deduced from the order number, or order service type identification is added into the order information; and adding attribute information to the order: for example, attribute information can be added to distinguish cross-regional orders from cross-product orders.
In step S116, the individual information is collated.
In this exemplary embodiment, the singleton itemiMay be the goods in the order, which may be sku (store ordering unit) or categories, etc.; sku is the minimum storage unit in warehouse management, and the categories can be divided into multiple stages according to different services: for example Yanjing beer and Qingdao beer, the two single products are calculated according to sku, but the two single products are both beer according to three grades and are single products; for another example, Yanjing beer, Qingdao beer and Feitian Maotai, three single products are provided according to sku, but the three-grade product is two single products of beer and white spirit, and one single product is provided according to the second-grade product. And selecting the processing granularity (sku or class) according to the actual application requirement when the single-product information is arranged.
After obtaining the order information of each region, in this exemplary embodiment, the order information of the region may be combined into one item set I for each region, where I includes all items appearing in the order in the region for a period of time, such as: i ═ itemi}. In addition, in the present exemplary embodiment, order information may also be integrated, and the sub-orders of the same parent order need to be merged to generate an order set; such as order o1Including sub-orders
Figure BDA0001130351380000091
And sub-order
Figure BDA0001130351380000092
Wherein
Figure BDA0001130351380000093
The order includes an item1,item2,item3)，
Figure BDA0001130351380000094
The order comprises an item4,item5) I.e. by
Figure BDA0001130351380000095
Figure BDA0001130351380000096
Post-consolidation order o1Is o1{item1,item2,item3,item4,item5}; wherein item1,item2,item3,item4,item5Is a single product.
In step S120, a historical three-dimensional correlation matrix is established, and any point H of the historical three-dimensional correlation matrixi(xi,yi,zi) For indicating singlets xiAnd singleton yiAt historical time ziThe correlation of (A) is gi. Wherein, giIs based on said singlets xiAnd singleton yiAt historical time ziAnd calculating the single item information.
Referring to fig. 3(a), in the present exemplary embodiment, for each region, correlation analysis may be performed starting from order information, and statistics may be performed on a daily basis; obtaining a three-dimensional incidence matrix, namely a data cube H (X, Y, Z), wherein the data cube records the incidence of the commodities by taking days as granularity; referring to fig. 3(b), each XY layer H (, z) of each correlation matrix represents a point at the same time.
In step S130, a time series of each of the correlations of any two of the singlets is fitted, and a future three-dimensional correlation matrix in a future time period is calculated according to the fitting result.
Referring to FIG. 4(a), each point H (x) on the matrix1,y1,z1) (i.e., A in the figure) represents the correlation of two singles at a certain time point, and the data of the point along the z-axis is the time sequence H (x) of the correlation of the two singles1,y1,*). In this exemplary embodiment, fitting the time series of correlations may be calculated by the following formula:
Figure BDA0001130351380000097
wherein x isiIs a value in time series, wiT is the weight of the time sequence, and T is the size of the time window.
Referring to fig. 4(b), fig. 4(b) shows a time series of any two-commodity correlation, in which N1 represents true data, N2 represents fitted data, and N3 region represents prediction expectation. In step S140, the future three-dimensional correlation matrix is added to the historical three-dimensional correlation matrix, and the added three-dimensional correlation matrices are fused in the time dimension to obtain a two-dimensional correlation matrix. However, it is easily understood by those skilled in the art that in other exemplary embodiments of the present disclosure, the future three-dimensional correlation matrix in the future time period may be calculated in other manners, which is not particularly limited in the present exemplary embodiment.
Referring to fig. 5, fig. 5 is different from fig. 3(a) in that a correlation matrix of prediction time periods is added; wherein C1 is a future three-dimensional correlation matrix, a historical three-dimensional correlation matrix at C2; the three-dimensional correlation matrix in fig. 5 is fused in the time dimension to form a new two-dimensional correlation matrix H*. In this exemplary embodiment, the fusion mode may be as follows:
Figure BDA0001130351380000102
where n is the length of the time series.
The above type will be squareThe arrays are summed in the time dimension to form a new matrix H*Unlike matrix H, matrix H*Is a two-dimensional matrix, H*Each element in the matrix represents the overall relevance of some two singlets over a period of time.
In step S150, performing spectral clustering on the two-dimensional incidence matrix, and performing graph segmentation on the two-dimensional incidence matrix according to a spectral clustering result with the minimum relevance between the segmented subgraphs as a target. Referring to fig. 6, in the present exemplary embodiment, spectral clustering may include steps S602 to S608. Wherein:
in step S602, a similarity graph (similarity graph) is created. In this exemplary embodiment, the weighted adjacency matrix of the similarity graph is W, where W is H, and H is the above two-dimensional correlation matrix.
In step S604, a non-normalized graph (unnormalized graph) Laplace matrix L is calculated, wherein
Figure BDA0001130351380000101
J is an arbitrary point in the weighted adjacent matrix W, and j is the number of columns in the weighted adjacent matrix W.
In step S606, the laplacian matrix L is subjected to SVD decomposition. For example:
L＝UΛU-1；
wherein the content of the first and second substances,
Figure BDA0001130351380000111
[u1,u2,...,ur]a characteristic vector value of L, λiIs a characteristic value of L, and in Λ, λ1≤λ2≤...≤λrAnd r is the rank of W.
In step S608, the first k eigenvector values of the laplacian matrix L are selected to form an r × k matrix, each row in the matrix is used as one vector in a k-dimensional space, the eigenvectors are clustered by using a clustering algorithm, and k is the pre-clustering number of spectral clustering.
Selecting the first k minimum feature vectors (the same as the pre-clustering number k of the spectral clustering), wherein the k value selection needs to fully consider the bin dividing condition; for example, if 2 bins are divided into 3 bins (i.e., a new bin is created, and the number of bins to be involved in calculation is 3), then the k value is selected to be 15; for another example, if 4 bins are to be changed into 6 bins (i.e. 2 bins are newly built, and 6 bins are needed to participate in the calculation), the k value needs to be set to about 30; typically, the k value is selected to be at least 5 times the number of bins and not greater than H, but the disclosure is not so limited.
Arranging the k eigenvectors together to form an r x k matrix, taking each row as one vector in a k-dimensional space, and clustering by using a k-means algorithm; in other exemplary embodiments of the present disclosure, clustering may also be performed according to other manners, which is not particularly limited in this exemplary embodiment.
In step S160, the singleton sets are assigned to the respective warehouses according to the result of the graph division.
According to the storage sub-warehouse auxiliary analysis method and device, on one hand, the order information of the user is abstracted into the commodity incidence matrix, so that the calculation of commodity distribution is completely based on the incidence matrix, the user order is not required to be traversed, and the calculation efficiency is improved; on the other hand, the incidence matrix in a short term can be predicted through the analysis of the time sequence, so that the bill splitting condition after the commodities are separated in bins is predicted.
According to the other storage warehouse-dividing auxiliary analysis method and device, the influence of sales promotion on the relevance of the single product is filtered through the outlier of the time sequence, the accuracy of the relevance is improved, the rate of splitting the single product is reduced, and the cost is greatly reduced.
In other embodiments of the present disclosure, the warehouse sub-warehouse auxiliary analysis method further includes: and performing outlier removal operation on the time series of the relevance through the first moment and the second moment of the time series of the relevance.
Referring to fig. 7, the step of removing outliers may include:
in step S702, the correlation T distribution of any two items is used to calculate the time series of any two items in the unit timeFirst order matrix M1And a second order matrix M2。
In step S704, a confidence level of 95%, that is, α is 0.05, and the degree of freedom is the number of samples minus 1, that is, p-1(p is the range of the time series, in days).
In the above step S704, α is selected to be 0.05, but the present disclosure is not limited thereto, and may be selected according to the actual situation.
In step S706, the critical value c ═ t is calculated(1-α/2)(p-1) wherein t(1-α/2)(p-1) can be obtained by looking up the t distribution table.
In step S708, selection is performed
Figure BDA0001130351380000121
For the confidence interval, and for each value x in the time series,
Figure BDA0001130351380000122
if x' is in the interval
Figure BDA0001130351380000123
If not, the data is retained, otherwise, the data is removed.
By removing outliers, order information about promotions is removed, increasing the accuracy of the correlation. Furthermore, the above-mentioned outlier removing method is merely exemplary, and outlier removing schemes implemented by other means are also within the scope of the present disclosure.
In other embodiments of the present disclosure, the warehouse sub-warehouse auxiliary analysis method further includes: and cleaning the two-dimensional incidence matrix. For example:
referring to fig. 8, the first action is the correlation between the item a and other items (a, b, c, d, e, f), and the darker the color represents the strength of the correlation, the stronger the correlation; at this time, the scheme only reserves the first m associated data (m) of each row>k, k is the number of spectral clusters to be clustered), and the rest is set to zero. Further, H after washing*Still a symmetric matrix.
And the clustering effect is ensured by cleaning the data with weak relevance.
In other embodiments of the present disclosure, the warehouse sub-warehouse auxiliary analysis method further includes: and further clustering the two-dimensional incidence matrix according to the spectral clustering result, and further evaluating the clustering result.
In the scheme, the related single item sets are divided into k types, and the number of warehouses needing to participate in calculation is m; since k is larger than m, further clustering is needed, and if k is clustered into m classes, there are S (k, m) kinds of possibilities, as shown in the following formula, each class in the last m classes represents the class stored in the warehouse after warehouse separation.
Figure BDA0001130351380000131
Where C is a binomial coefficient.
In the above step, through clustering again, S (k, m) possible single-item layout schemes are generated, and in this step, S (k, m) schemes need to be evaluated to select an optimal scheme; the excellent single-product layout scheme can effectively reduce the occurrence of the single-product splitting situation, so the contribution degree of the commodity layout scheme to the single-product splitting is used as an evaluation criterion.
The evaluation step comprises:
S(k,m)＝{s1,s2,s3...,sll represents the length of the set S (k, m) with l combination schemes;
si＝{m1,m2,m3,...,mm}；
Figure BDA0001130351380000132
wherein s isiFor a singleton layout scheme of S (k, m) schemes, each SiIn which there are m classes, each of which is miConsisting of single articles, and one siThe individual items in the m classifications of (1) are not duplicative, i.e., are not duplicated
Figure BDA0001130351380000133
Figure BDA0001130351380000134
Representative itemi,itemjThe relevance of (a) to (b),
Figure BDA0001130351380000135
represents H*One line above, representing itemiAssociation with all merchandise;
as mentioned above, itemiAt H*In which there is a corresponding row
Figure BDA0001130351380000136
Value representation item on corresponding lineiAssociation with all merchandise; if itemiAnd itemjThe bill dismantling can be reduced when the warehouse is placed in the same warehouse; on the contrary, if itemiAnd itemjPlacing in different warehouses necessarily increases the quantity of orders broken.
Thus, evaluation scheme siWhen the quality of s is good, s needs to be calculatediEach class m ofiContribution score _ m to the sheet splittingiTo m, to miChinese single itemiCorresponding to
Figure BDA0001130351380000137
The rows are summed. itemiAnd itemjAre all in miIn (1), then: score _ miPlus with
Figure BDA0001130351380000138
The two single products are put together to reduce the number of the split single products;
on the contrary, if itemiAnd itemjIs not in miIn (1), then: score _ miPlus with
Figure BDA0001130351380000139
Indicating that the two singlets are not put together, the rate of singling is increased.
Will calculate the score _ miSumming to obtain siIs evaluated byi_score；si_scoreRepresenting the influence of the distribution of the single products on the order, wherein the larger the value is, the more the commodity layout scheme contributes to the order splitting; otherwise, the better the scheme is, the lower the contribution to the bill splitting is; if s isi_scoreThe negative value of (A) indicates that the scheme can effectively reduce the bill breaking. At all siTo select the minimum si_scoreNamely the final single-product layout scheme. Through the re-clustering and the combination of the evaluation parameters, the binning scheme can be further optimized, and the optimal binning scheme is further realized.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
The present exemplary embodiment further provides a warehouse and warehouse separation auxiliary analysis device, which starts from data analysis of a customer order, starts from a purchasing behavior of a user for different regions and different time periods, analyzes a purchasing relevance of a commodity, and splits n warehouses into m warehouses by planning a commodity layout. Referring to fig. 9, the warehousing sub-warehouse auxiliary analysis device includes: the system comprises an information acquisition module 900, a first incidence matrix module 910, a second incidence matrix module 920, a third incidence matrix module 930, a fusion module 940, a spectral clustering module 950, a graph segmentation module 960 and a single item distribution module 970; wherein the content of the first and second substances,
the information acquisition module 900: the system comprises a data processing system and a data processing system, wherein the data processing system is used for acquiring order information of an area in a historical time period and acquiring single item information in the historical time period from the order information;
the first incidence matrix module 910: is used for establishing a historical three-dimensional incidence matrix, and any point H of the historical three-dimensional incidence matrixi(xi,yi,zi) For indicating singlets xiAnd singleton yiAt historical time ziThe correlation of (A) is gi(ii) a Wherein, giIs based on said singlets xiAnd singleton yiAt historical time ziCalculating the single item information;
a fitting module: fitting a time series of each said association for any two of said singlets;
the second incidence matrix module 920: the three-dimensional incidence matrix calculation module is used for calculating a future three-dimensional incidence matrix in a future time period according to the fitting result;
the third correlation matrix module 930: for adding the future three-dimensional correlation matrix to the historical three-dimensional correlation matrix;
a fusion module 940: the system is used for fusing the added three-dimensional incidence matrix on a time dimension to obtain a two-dimensional incidence matrix;
the spectral clustering module 950: the system is used for performing spectral clustering on the two-dimensional incidence matrix;
the graph partitioning module 960: the two-dimensional incidence matrix is used for carrying out graph segmentation on the two-dimensional incidence matrix according to a spectral clustering result by taking the minimum relevance between the segmented subgraphs as a target;
the individual product dispensing module 970: the system is used for distributing the single product set to each warehouse according to the result of the graph segmentation.
In other embodiments of the present disclosure, the apparatus further comprises:
outlier removal module: for performing a de-outlier operation on the time-series of correlations by the first and second moments of the time-series of correlations.
In other embodiments of the present disclosure, the apparatus further comprises:
the first clustering module: the two-dimensional incidence matrix is used for further clustering according to the spectral clustering result;
an evaluation module: for further evaluation of the clustering results.
In other embodiments of the present disclosure, fitting the time series of each correlation for any two of the singlets comprises:
Figure BDA0001130351380000151
wherein x isiIs a value in time series, wiT is the weight of the time sequence, and T is the size of the time window.
In other embodiments of the present disclosure, the added three-dimensional correlation matrix is fused in the time dimension to obtain a two-dimensional correlation matrix, where the fusing includes:
Figure BDA0001130351380000152
wherein n is the length of the time series, H*(xi,yi) For any point on the two-dimensional correlation matrix,
Figure BDA0001130351380000153
is any point on the future three-dimensional correlation matrix.
In other embodiments of the present disclosure, the spectral clustering module includes:
the similar graph establishing module: the method comprises the steps of establishing a similarity graph, wherein a weighted adjacency matrix of the similarity graph is W, wherein W is H, and H is the two-dimensional incidence matrix;
a Laplace matrix calculation module: for calculating the laplacian matrix L, wherein,
Figure BDA0001130351380000154
wijj is any point in the weighted adjacent matrix W, and j is the number of columns in the weighted adjacent matrix W;
a Laplace matrix decomposition module: for decomposing the Laplace matrix L ═ UΛ U-1Wherein U is [ U ═ U1,u2,...,ur]，
Figure BDA0001130351380000161
[u1,u2,...,ur]A characteristic vector value of L, λiIs a characteristic value of L, and in Λ, λ1≤λ2≤...≤λrR is the rank of W;
a second type of module: and the method is used for selecting the first k characteristic vector values of the Laplace matrix L to form an r x k matrix, taking each row in the matrix as a vector in a k-dimensional space, and clustering the characteristic vectors by using a clustering algorithm, wherein k is the pre-clustering number of the spectral clustering.
In other embodiments of the present disclosure, the two-dimensional incidence matrix is further clustered according to the spectral clustering result, where the clustering includes:
Figure BDA0001130351380000162
wherein S (k, m) is the number of schemes distributed to the single item set, C is a binomial coefficient, m is the number of warehouses needing to participate in calculation, k is the pre-clustering number of spectral clustering, and m is less than k.
Since each functional module of the warehousing sub-warehouse auxiliary analysis device in the embodiment of the invention is the same as that in the embodiment of the method, the description is omitted.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (14)

1. A storage sub-warehouse auxiliary analysis method is characterized by comprising the following steps:
acquiring order information of an area in a historical time period from a hadoop cluster and acquiring single item information in the historical time period from the order information;
establishing a historical three-dimensional incidence matrix according to the singleness information, wherein any point H of the historical three-dimensional incidence matrixi(xi,yi,zi) For indicating singlets xiAnd singleton yiAt historical time ziThe correlation of (A) is gi(ii) a Wherein, giIs based on said singlets xiAnd singleton yiAt historical time ziCalculating the single item information;
fitting the time series of each relevance of any two single products, and calculating a future three-dimensional relevance matrix in a future time period according to a fitting result;
adding the future three-dimensional incidence matrix on the historical three-dimensional incidence matrix, and fusing the added three-dimensional incidence matrix on a time dimension to obtain a two-dimensional incidence matrix;
cleaning the two-dimensional incidence matrix, performing spectral clustering on the cleaned two-dimensional incidence matrix, and performing graph segmentation on the two-dimensional incidence matrix according to a spectral clustering result by taking the minimum relevance between the segmented subgraphs as a target;
and distributing the single product set to each warehouse according to the graph segmentation result.
2. The method of claim 1, further comprising:
and performing outlier removal operation on the time series of the relevance through the first moment and the second moment of the time series of the relevance.
3. The method of claim 1, further comprising:
and further clustering the two-dimensional incidence matrix according to the spectral clustering result, and further evaluating the clustering result.
4. The method of claim 1, wherein fitting the time series of each of the correlations for any two of the singlets comprises:
Figure FDF0000007312860000011
wherein x isiIs a value in time series, wiT is the weight of the time sequence, and T is the size of the time window.
5. The method of claim 1, wherein fusing the added three-dimensional correlation matrix in the time dimension to obtain a two-dimensional correlation matrix comprises:
Figure FDF0000007312860000021
wherein n is the length of the time series, H*(xi,yi) For any point on the two-dimensional correlation matrix,
Figure FDF0000007312860000022
is any point on the future three-dimensional correlation matrix.
6. The method of claim 1, wherein spectrally clustering the two-dimensional incidence matrix comprises:
establishing a similar graph, wherein a weighted adjacent matrix of the similar graph is W, wherein W is H, and H is the two-dimensional incidence matrix;
calculating a laplacian matrix L, wherein L ═ D-W,
Figure FDF0000007312860000023
wijj is any point in the weighted adjacent matrix W, and j is the number of columns in the weighted adjacent matrix W;
decomposing the Laplace matrix L ═ UΛ U-1Wherein U is [ U ═ U1,u2,...,ur]，
Figure FDF0000007312860000024
[u1,u2,...,ur]A characteristic vector value of L, λiIs a characteristic value of L, and in Λ, λ1≤λ2≤...≤λrR is the rank of W;
selecting the first k eigenvector values of the Laplace matrix L to form an r x k matrix, taking each row in the matrix as one vector in a k-dimensional space, and clustering the eigenvectors by using a clustering algorithm, wherein k is the pre-clustering number of spectral clustering.
7. The method of claim 3, wherein further clustering the two-dimensional incidence matrix according to the spectral clustering result comprises:
Figure FDF0000007312860000025
wherein S (k, m) is the number of schemes distributed to the single item set, C is a binomial coefficient, m is the number of warehouses needing to participate in calculation, k is the pre-clustering number of spectral clustering, and m is provided<k。
8. The utility model provides a storage divides storehouse auxiliary analysis device which characterized in that includes:
an information acquisition module: the system comprises a data processing system and a data processing system, wherein the data processing system is used for acquiring order information of a region in a historical time period from a hadoop cluster and acquiring single item information in the historical time period from the order information;
a first incidence matrix module: is used for establishing a historical three-dimensional incidence matrix, and any point H of the historical three-dimensional incidence matrixi(xi,yi,zi) For indicating singlets xiAnd singleton yiAt historical time ziThe correlation of (A) is gi(ii) a Wherein, giIs based on said singlets xiAnd singleton yiAt historical time ziCalculating the single item information;
a fitting module: fitting a time series of each said association for any two of said singlets;
a second incidence matrix module: the three-dimensional incidence matrix calculation module is used for calculating a future three-dimensional incidence matrix in a future time period according to the fitting result;
a third correlation matrix module: for adding the future three-dimensional correlation matrix to the historical three-dimensional correlation matrix;
a fusion module: the system is used for fusing the added three-dimensional incidence matrix on a time dimension to obtain a two-dimensional incidence matrix;
a spectral clustering module: the system is used for performing spectral clustering on the two-dimensional incidence matrix;
a graph partitioning module: the two-dimensional incidence matrix is used for carrying out graph segmentation on the two-dimensional incidence matrix according to a spectral clustering result by taking the minimum relevance between the segmented subgraphs as a target;
a unit dispensing module: the system is used for distributing the single product set to each warehouse according to the result of the graph segmentation.
9. The apparatus of claim 8, further comprising:
outlier removal module: for performing a de-outlier operation on the time-series of correlations by the first and second moments of the time-series of correlations.
10. The apparatus of claim 8, further comprising:
the first clustering module: the two-dimensional incidence matrix is used for further clustering according to the spectral clustering result;
an evaluation module: for further evaluation of the clustering results.
11. The apparatus of claim 8, wherein fitting is performed on each of said correlated time series of any two of said singlets, said fitting comprising:
Figure FDF0000007312860000031
wherein x isiIs a value in time series, wiT is the weight of the time sequence, and T is the size of the time window.
12. The apparatus according to claim 8, wherein the added three-dimensional correlation matrix is fused in the time dimension to obtain a two-dimensional correlation matrix, and the fusing comprises:
Figure FDF0000007312860000041
wherein n is the length of the time series, H*(xi,yi) For any point on the two-dimensional correlation matrix,
Figure FDF0000007312860000042
is any point on the future three-dimensional correlation matrix.
13. The apparatus of claim 8, wherein the spectral clustering module comprises:
the similar graph establishing module: the method comprises the steps of establishing a similarity graph, wherein a weighted adjacency matrix of the similarity graph is W, wherein W is H, and H is the two-dimensional incidence matrix;
a Laplace matrix calculation module: for calculating a laplacian matrix L, where L ═ D-W,
Figure FDF0000007312860000043
wijj is any point in the weighted adjacent matrix W, and j is the number of columns in the weighted adjacent matrix W;
a Laplace matrix decomposition module: for decomposing the Laplace matrix L ═ UΛ U-1Wherein U is [ U ═ U1,u2,...,ur]，
Figure FDF0000007312860000044
[u1,u2,...,ur]A characteristic vector value of L, λiIs a characteristic value of L, and in Λ, λ1≤λ2≤...≤λrR is the rank of W;
a second type of module: and the method is used for selecting the first k characteristic vector values of the Laplace matrix L to form an r x k matrix, taking each row in the matrix as a vector in a k-dimensional space, and clustering the characteristic vectors by using a clustering algorithm, wherein k is the pre-clustering number of the spectral clustering.
14. The apparatus of claim 10, wherein the two-dimensional incidence matrix is further clustered according to the spectral clustering result, the clustering comprising:
Figure FDF0000007312860000045
wherein S (k, m) is the number of schemes distributed to the single item set, C is a binomial coefficient, m is the number of warehouses needing to participate in calculation, k is the pre-clustering number of spectral clustering, and m is provided<k。
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