Method and device for entering and exiting storage

Abstract

The present invention discloses a method and device for entering and exiting a warehouse, and relates to the field of warehousing and logistics technology. A specific implementation of the method includes: based on the data of a target warehouse and at least one non-target warehouse, and using a variable selection method, a second variable set is selected from a first variable set; the data includes the historical value of the first variable set, and the entry and exit efficiency corresponding to the historical value of the first variable set; based on the historical value of the second variable set of the target warehouse, and the entry and exit efficiency corresponding to the historical value of the second variable set of the target warehouse, a model to be analyzed is constructed; according to the coefficient of each second variable in the model to be analyzed, the parameter value of at least one second variable of the target warehouse is set; according to the parameter value of at least one second variable of the target warehouse after setting, items are entered or exited. This implementation significantly improves the entry and exit efficiency of the target warehouse.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing logistics, and in particular to a method and device for entering and exiting a warehouse. Background Art

[0002] The following is a specific example to illustrate the existing warehousing and outbound process: the warehousing and outbound efficiency of warehouse a (target warehouse) is low, and the warehousing and outbound efficiency of warehouse b is high. The historical value of the variable of warehouse a is subtracted from the historical value of the variable of warehouse b, and it is empirically determined whether the low warehousing and outbound efficiency of warehouse a is caused by the variable based on the size of the difference. If so, the historical value of the variable is changed, and the changed warehouse a will take items out of or into the warehouse, thereby improving the warehousing and outbound efficiency of warehouse a.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0004] First, warehouse a is only compared with one warehouse b. If the historical value of the variable of warehouse b is the same as the historical value of the variable of warehouse a, the prior art often ignores the influence of the variable on the in-and-out efficiency of warehouse a. Therefore, the prior art has the problem of one-sided data comparison. Second, it is impossible to determine the degree of influence of the variable on the in-and-out efficiency of warehouse a by empirically determining which variable causes the in-and-out efficiency of warehouse a. Therefore, the prior art has the problem of not being able to significantly improve the in-and-out efficiency of the target warehouse. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for warehousing in and out, which can reduce the problem of one-sided data comparison and significantly improve the efficiency of warehousing in and out of a target warehouse.

[0006] To achieve the above objective, according to one aspect of an embodiment of the present invention, a method for entering and exiting a warehouse is provided.

[0007] The method for entering and exiting a warehouse according to an embodiment of the present invention includes:

[0008] Based on the data of the target warehouse and at least one non-target warehouse, and using a variable selection method, a second variable set is selected from the first variable set; the data includes historical values ​​of the first variable set, and the storage efficiency corresponding to the historical values ​​of the first variable set;

[0009] Based on the historical values ​​of the second variable set of the target warehouse and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse, construct a model to be analyzed;

[0010] Setting a parameter value of at least one second variable of the target warehouse according to the coefficient of each second variable in the model to be analyzed;

[0011] Items are put into or taken out of the warehouse according to the parameter value of at least one second variable of the target warehouse after being set.

[0012] In one embodiment, based on the data of the target warehouse and at least one non-target warehouse, and using a variable selection method, a second variable set is selected from the first variable set, including:

[0013] Based on the data of the target warehouse and at least one non-target warehouse, a second variable set is selected from the first variable set by adopting a stepwise regression method or a full subset regression method.

[0014] In one embodiment, based on the historical values ​​of the second variable set of the target warehouse and the inbound and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, a model to be analyzed is constructed, including:

[0015] Determine whether there is collinearity between the second variables of the target warehouse; if so, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the ridge estimation method, construct the model to be analyzed; if not, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the least squares method, construct the model to be analyzed.

[0016] In one embodiment, determining whether there is collinearity between the second variables of the target warehouse includes:

[0017] Calculate the correlation coefficient or variance inflation factor of the second variable of the target warehouse according to the historical values ​​of the second variable set of the target warehouse;

[0018] Determine whether the absolute value of the correlation coefficient of the second variable of the target warehouse is greater than 0.5;

[0019] Alternatively, determine whether the variance inflation factor of the second variable of the target warehouse is greater than 4.

[0020] In one embodiment, setting the parameter value of at least one second variable of the target warehouse according to the coefficient of each second variable in the model to be analyzed includes:

[0021] For the coefficient of each second variable in the model to be analyzed, the parameter value of the second variable of the target warehouse is set in the following manner:

[0022] Determining whether the absolute value of the coefficient of the second variable is greater than a preset value;

[0023] If not, setting the parameter value of the second variable of the target warehouse equal to the historical value of the second variable;

[0024] If so, determine whether the coefficient of the second variable is negative. If so, set the parameter value of the second variable of the target warehouse to be smaller than the historical value of the second variable. If not, set the parameter value of the second variable of the target warehouse to be larger than the historical value of the second variable.

[0025] To achieve the above objective, according to another aspect of an embodiment of the present invention, a device for entering and exiting a warehouse is provided.

[0026] The storage device of the embodiment of the present invention includes:

[0027] A selection unit is used to select a second variable set from the first variable set based on data of a target warehouse and at least one non-target warehouse and using a variable selection method; the data includes historical values ​​of the first variable set and storage efficiency corresponding to the historical values ​​of the first variable set;

[0028] A construction unit, configured to construct a model to be analyzed based on historical values ​​of the second variable set of the target warehouse and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse;

[0029] A setting unit, configured to set a parameter value of at least one second variable of the target warehouse according to a coefficient of each second variable in the model to be analyzed;

[0030] A processing unit is used to warehouse or remove items according to the parameter value of at least one second variable of the target warehouse after setting.

[0031] In one embodiment, the selection unit is used to:

[0032] Based on the data of the target warehouse and at least one non-target warehouse, a second variable set is selected from the first variable set by adopting a stepwise regression method or a full subset regression method.

[0033] In one embodiment, the building block is used to:

[0034] Determine whether there is collinearity between the second variables of the target warehouse; if so, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the ridge estimation method, construct the model to be analyzed; if not, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the least squares method, construct the model to be analyzed.

[0035] In one embodiment, the building block is used to:

[0036] Calculate the correlation coefficient or variance inflation factor of the second variable of the target warehouse according to the historical values ​​of the second variable set of the target warehouse;

[0037] Determine whether the absolute value of the correlation coefficient of the second variable of the target warehouse is greater than 0.5;

[0038] Alternatively, determine whether the variance inflation factor of the second variable of the target warehouse is greater than 4.

[0039] In one embodiment, the setting unit is used to:

[0040] For the coefficient of each second variable in the model to be analyzed, the parameter value of the second variable of the target warehouse is set in the following manner:

[0041] Determining whether the absolute value of the coefficient of the second variable is greater than a preset value;

[0042] If not, setting the parameter value of the second variable of the target warehouse equal to the historical value of the second variable;

[0043] If so, determine whether the coefficient of the second variable is negative. If so, set the parameter value of the second variable of the target warehouse to be smaller than the historical value of the second variable. If not, set the parameter value of the second variable of the target warehouse to be larger than the historical value of the second variable.

[0044] To achieve the above objective, according to another aspect of an embodiment of the present invention, an electronic device is provided.

[0045] An electronic device according to an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for entering and exiting storage provided by the embodiment of the present invention.

[0046] To achieve the above objective, according to another aspect of an embodiment of the present invention, a computer readable medium is provided.

[0047] A computer-readable medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements the storage-in / out method provided by the embodiment of the present invention.

[0048] One embodiment of the above invention has the following advantages or beneficial effects: based on the data of the target warehouse and at least one non-target warehouse, and using the variable selection method, a second variable set is selected from the first variable set; the data includes the historical values ​​of the first variable set, and the storage efficiency corresponding to the historical values ​​of the first variable set, thereby reducing the problem of one-sided data comparison. Based on the historical values ​​of the second variable set of the target warehouse, and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse, a model to be analyzed is constructed; according to the coefficient of each second variable in the model to be analyzed, the parameter value of at least one second variable of the target warehouse is set; and the items are put into or taken out of the warehouse according to the parameter value of at least one second variable of the target warehouse after setting. Since the coefficient of the second variable represents the influence of the second variable on the storage efficiency of the target warehouse, the parameter value of the second variable with a strong influence is set, thereby significantly improving the storage efficiency of the target warehouse.

[0049] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0051] Figure 1 is a schematic diagram of the main process of the storage and retrieval method according to an embodiment of the present invention;

[0052] Figure 2 is a schematic diagram of the main process of a warehousing method according to another embodiment of the present invention;

[0053] Figure 3 is a schematic diagram of main units of a storage and retrieval device according to an embodiment of the present invention;

[0054] Figure 4 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0055] Figure 5 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0057] It should be pointed out that the embodiments of the present invention and the features in the embodiments may be combined with each other if there is no conflict.

[0058] In order to solve the problems existing in the prior art, an embodiment of the present invention provides a method for entering and exiting a warehouse, such as Figure 1 As shown, the method includes:

[0059] Step S101: Based on the data of the target warehouse and at least one non-target warehouse, and using a variable selection method, a second variable set is selected from the first variable set; the data includes historical values ​​of the first variable set, and the warehousing efficiency corresponding to the historical values ​​of the first variable set.

[0060] In this step, it should be understood that the in-and-out efficiency can be the out-of-warehouse efficiency or the in-warehouse efficiency. If it is the out-of-warehouse efficiency, the embodiment of the present invention provides a method for out-of-warehouse; if it is the in-warehouse efficiency, the embodiment of the present invention provides a method for in-warehouse. The following is a specific example to illustrate the calculation process of the out-of-warehouse efficiency: the out-of-warehouse efficiency of the warehouse = the number of out-of-warehouse items in one day / the actual working time of the warehouse in one day. For example, a warehouse only takes out items from the warehouse in the time period from 9:15 to 10:00, and a total of 500 items are taken out of the warehouse. Therefore, the out-of-warehouse efficiency of the warehouse = 300 / 0.75 = 400 items / hour. The reason why the out-of-warehouse efficiency of the warehouse is calculated in this way is that the time when the warehouse is not out of the warehouse is not counted in the actual working time of the warehouse in one day, and the warehouses are comparable. It should be understood that without affecting the embodiments of the present invention, those skilled in the art can flexibly calculate the out-of-warehouse efficiency of the warehouse or the in-warehouse efficiency of the warehouse.

[0061] It should be noted that if this embodiment is to be implemented, factors that may affect the warehouse's in-and-out efficiency must be collected first, and the factors that may affect the warehouse's in-and-out efficiency should be considered from multiple aspects, and the factors that may affect the warehouse's in-and-out efficiency should be used as the first variable.

[0062] In addition, when selecting variables, it is necessary to select variables with a small number of variables, good fitting degree (the model to be analyzed and the historical values ​​of the second variable set of the target warehouse, and the warehouse-in and warehouse-out efficiency corresponding to the historical values ​​of the second variable set of the target warehouse have a high fitting degree. Only with a high fitting degree can the constructed model be closer to the actual situation) and variables with practical reference significance (it means that the constructed model can truly reflect the relationship between the historical values ​​of the second variable set of the target warehouse and the warehouse-in and warehouse-out efficiency corresponding to the historical values ​​of the second variable set of the target warehouse) as the second variable.

[0063] Furthermore, the most preferred method is: based on multiple data of the target warehouse and at least one non-target warehouse, and using the variable selection method, the second variable set is selected from the first variable set; each data includes the historical value of the first variable set, and the storage efficiency corresponding to the historical value of the first variable set. The more data, the more reasonable the selected second variable set is, and the storage efficiency of the target warehouse is further improved. It should be understood that the historical value of the first variable set refers to the historical value of each first variable in the first variable set.

[0064] Step S102: construct a model to be analyzed based on the historical values ​​of the second variable set of the target warehouse and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse.

[0065] In this step, during the specific implementation, the ridge estimation method or the least squares method is adopted to construct the model to be analyzed based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse.

[0066] It should be noted that if there are multiple data in step S101, there are also multiple historical values ​​of the second variable set of the target warehouse, and there are also multiple inbound and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse.

[0067] Step S103: setting a parameter value of at least one second variable of the target warehouse according to the coefficient of each second variable in the model to be analyzed.

[0068] Step S104: warehousing or unwarehousing items according to the set parameter value of at least one second variable of the target warehouse.

[0069] In this step, a specific example is used to illustrate this step below: the number of open outbound workstations and the proportion of single-piece orders are the second variables. Based on the historical values ​​of the second variable set of the target warehouse and the inbound and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, a model to be analyzed is constructed. In the model to be analyzed, the coefficient of the number of open outbound workstations is 12, and the coefficient of the proportion of single-piece orders is 1. Since the absolute value of the coefficient of the number of open outbound workstations is greater than the absolute value of the coefficient of the proportion of single-piece orders, that is, 12 is greater than 1, the number of open outbound workstations in the target warehouse is increased from the existing 3 to 7. After the number of open outbound workstations in the target warehouse increases, the target warehouse can ship out items, and the outbound efficiency of the target warehouse is improved.

[0070] It should be noted that the target warehouse is a warehouse with low efficiency in warehousing and unloading, and the non-target warehouse is a warehouse of the same type as the target warehouse (if the type is the same, the second variable set selected is more reasonable, so the efficiency of warehousing and unloading of the target warehouse can be further improved). The non-target warehouse can be a warehouse with high efficiency in warehousing and unloading, or a warehouse with low efficiency in warehousing and unloading. The following is a specific example to illustrate the target warehouse and the non-target warehouse: the target warehouse is an ordinary warehouse, so the non-target warehouse must also be an ordinary warehouse, and cannot be a luxury warehouse or a spare warehouse without timeliness requirements.

[0071] In the embodiment of the present invention, step S101 may include:

[0072] Based on the data of the target warehouse and at least one non-target warehouse, a second variable set is selected from the first variable set by adopting a stepwise regression method or a full subset regression method.

[0073] It should be noted that the method of selecting the second variable set from the first variable set is not only the stepwise regression method or the full subset regression method, but also the coefficient compression method or the dimensionality reduction method.

[0074] In this embodiment, based on the data of the target warehouse and at least one non-target warehouse, a second variable set is selected from the first variable set by using a stepwise regression method or a full subset regression method, thereby further reducing the problem of one-sided data comparison.

[0075] In the embodiment of the present invention, step S102 may include:

[0076] Determine whether there is collinearity between the second variables of the target warehouse; if so, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the ridge estimation method, construct the model to be analyzed; if not, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the least squares method, construct the model to be analyzed.

[0077] In this embodiment, the expression of the model to be analyzed includes:

[0078] Y i =α i +β 1i X 1i +β 2i X 2i +β 3i X 3i +β 4i X 4i +β 5i X 5i i=1,2,3,…,n

[0079] Among them, i represents the warehouse, X represents the second variable, β represents the coefficient of the second variable, and α represents a constant.

[0080] In this embodiment, it is determined whether there is collinearity between the second variables of the target warehouse; if so, the ridge estimation method is used to construct the model to be analyzed; if not, the least square method is used to construct the model to be analyzed. This makes the constructed model to be analyzed more accurate, and further makes the influence of the second variable on the storage efficiency of the target warehouse more accurately determined, that is, the coefficient of the second variable is more accurately determined, and the storage efficiency of the target warehouse is further improved.

[0081] In an embodiment of the present invention, determining whether there is collinearity between the second variables of the target warehouse includes:

[0082] Calculate the correlation coefficient or variance inflation factor of the second variable of the target warehouse according to the historical values ​​of the second variable set of the target warehouse;

[0083] Determine whether the absolute value of the correlation coefficient of the second variable of the target warehouse is greater than 0.5;

[0084] Alternatively, determine whether the variance inflation factor of the second variable of the target warehouse is greater than 4.

[0085] In this embodiment, it should be understood that when the absolute value of the correlation coefficient of the second variables of the target warehouse is equal to 0, there is no collinearity between the second variables of the target warehouse, and when the absolute value of the correlation coefficient of the second variables of the target warehouse is equal to 1, the linear relationship between the second variables of the target warehouse is the closest. Therefore, when the absolute value of the correlation coefficient of the second variables of the target warehouse is greater than 0.5, it is determined that there is collinearity between the second variables of the target warehouse, and when the absolute value of the correlation coefficient of the second variables of the target warehouse is between [0,0.5], it is determined that there is no collinearity between the second variables of the target warehouse.

[0086] In this embodiment, by calculating the correlation coefficient or variance inflation factor of the second variable of the target warehouse, it is determined whether there is collinearity between the second variables of the target warehouse, so that the constructed model to be analyzed is more accurate, and then the influence of the second variable on the warehousing efficiency of the target warehouse is determined more accurately, that is, the coefficient of the second variable is determined more accurately, thereby further improving the warehousing efficiency of the target warehouse.

[0087] In the embodiment of the present invention, step S103 may include:

[0088] For the coefficient of each second variable in the model to be analyzed, the parameter value of the second variable of the target warehouse is set in the following manner:

[0089] Determining whether the absolute value of the coefficient of the second variable is greater than a preset value;

[0090] If not, setting the parameter value of the second variable of the target warehouse equal to the historical value of the second variable;

[0091] If so, determine whether the coefficient of the second variable is negative. If so, set the parameter value of the second variable of the target warehouse to be smaller than the historical value of the second variable. If not, set the parameter value of the second variable of the target warehouse to be larger than the historical value of the second variable.

[0092] In this embodiment, since the coefficient of the second variable represents the influence of the second variable on the in-and-out efficiency of the target warehouse, the second variable with a strong influence on the in-and-out efficiency of the target warehouse is selected by comparing the absolute value of the coefficient of the second variable with the preset value, and the parameter value of the second variable with a strong influence is set, thereby further improving the in-and-out efficiency of the target warehouse. The positive or negative value of the coefficient of the second variable represents the correlation between the second variable and the in-and-out efficiency of the target warehouse: if the coefficient is negative, it is negatively correlated, and accordingly, the set parameter value is less than the historical value; if the coefficient is positive, it is positively correlated, and accordingly, the set parameter value is greater than the historical value, thereby further improving the in-and-out efficiency of the target warehouse.

[0093] In order to solve the problems existing in the prior art, another embodiment of the present invention provides a method for warehousing. In this embodiment, warehouse 1, warehouse 2 and warehouse 3 are warehouses of the same type, warehouse 1 is a target warehouse, and warehouse 2 and warehouse 3 are non-target warehouses. Figure 2 As shown, the method includes:

[0094] It should be noted that the factors that may affect the warehouse's warehousing efficiency include: R1, R2, R3 and R4, and R1, R2, R3 and R4 are taken as the first variable set.

[0095] Step S201, obtain the historical values ​​of the first variable set of warehouse 1 and the warehousing efficiency corresponding to the historical values ​​of the first variable set of warehouse 1; obtain the historical values ​​of the first variable set of warehouse 2 and the warehousing efficiency corresponding to the historical values ​​of the first variable set of warehouse 2; obtain the historical values ​​of the first variable set of warehouse 3 and the warehousing efficiency corresponding to the historical values ​​of the first variable set of warehouse 3.

[0096] Specifically, for each of Warehouse 1, Warehouse 2, and Warehouse 3, the historical values ​​of R1, R2, R3, and R4 of the warehouse every day during the period from January 1 to January 10, and the daily warehousing efficiency are collected, and a first data table is prepared based on the data, as shown in Table 1:

[0097]

[0098] Table 1 First data table

[0099] Step S202: Based on the historical values ​​of the first variable set of warehouse 1, the warehousing efficiency of warehouse 1, the historical values ​​of the first variable set of warehouse 2, the warehousing efficiency of warehouse 2, the historical values ​​of the first variable set of warehouse 3 and the warehousing efficiency of warehouse 3, and using the stepwise regression method, a second variable set is selected from R1, R2, R3 and R4, and the second variable set includes: R1 and R2.

[0100] In this step, when implemented, according to Table 1, a stepwise regression method is used to select a second variable from R1, R2, R3 and R4.

[0101] The following uses the stepwise regression method to illustrate the process of selecting the second variable set from the first variable set: the historical value of a first variable of warehouse 1, the warehousing efficiency of warehouse 1, the historical value of the first variable of warehouse 2, the warehousing efficiency of warehouse 2, the historical value of the first variable of warehouse 3 and the warehousing efficiency of warehouse 3 are taken as the first set, and a first model is constructed based on the first set, and the fit of the first model is calculated based on the first model and the first set; the historical values ​​of the two first variables of warehouse 1, the warehousing efficiency of warehouse 1, the historical values ​​of the two first variables of warehouse 2, the warehousing efficiency of warehouse 2, the historical values ​​of the two first variables of warehouse 3 and the warehousing efficiency of warehouse 3 are taken as the second set, and a second model is constructed based on the second set, and the fit of the second model is calculated based on the second model and the second set, ..., the fit is constantly changing. When the fit tends to be stable, the variables included in the model corresponding to the fit at the stable state are taken as the second variable set.

[0102] Step S203, calculate the variance inflation factor of the second variable of warehouse 1 according to the historical values ​​of the second variable set of warehouse 1, determine that the variance inflation factor of the second variable of warehouse 1 is greater than 4, based on the historical values ​​of the second variable set of warehouse 1, and the warehousing efficiency corresponding to the historical values ​​of the second variable set of warehouse 1, and use the ridge estimation method to construct the model to be analyzed.

[0103] In this step, when it is specifically implemented, a second data table is prepared according to the historical values ​​of the second variable set of warehouse 1 and the warehousing efficiency corresponding to the historical values ​​of the second variable set of warehouse 1, as shown in Table 2:

[0104]

[0105] Table 2 Second data table

[0106] The variance inflation factor of the second variable of warehouse 1 is calculated according to the historical values ​​of the second variable set of warehouse 1, and it is determined that the variance inflation factor of the second variable of warehouse 1 is greater than 4. Based on Table 2 and using the ridge estimation method, the model to be analyzed is constructed.

[0107] Step S204: Set the parameter value of at least one second variable of warehouse 1 according to the coefficient of R1 and the coefficient of R2 in the model to be analyzed.

[0108] Step S205: After the parameter value of at least one second variable of warehouse 1 is set, warehouse 1 puts the items into storage.

[0109] It should be understood that the embodiments of the present invention can not only be used for warehousing, but also provide guidance for the initial planning of a new warehouse.

[0110] In order to solve the problems existing in the prior art, another embodiment of the present invention provides a method for outbound delivery. In this embodiment, the specific application scenario is an unmanned warehouse, unmanned warehouse a is the target warehouse, and unmanned warehouse b and unmanned warehouse c are non-target warehouses. The method includes:

[0111] It should be noted that, considering the characteristics of unmanned warehouses, factors that may affect the warehouse's outbound efficiency are collected from three aspects: planning, algorithm, and operation. The details are as follows:

[0112] Planning factors include: warehouse area (referring to the floor space of the warehouse); average number of categories per order (referring to the average number of commodity types per order); average number of pieces per order (referring to the average number of commodity pieces per order); average number of pieces per row (referring to the average number of pieces per order / average number of categories per order); proportion of single orders (referring to the proportion of single orders to the total number of orders); number of working AGVs (referring to the number of AGVs performing outbound tasks); number of inventory SKU categories (referring to the number of SKU types in stock); proportion of SKU categories stored on each shelf surface (referring to the proportion of the average number of SKU types stored on each shelf surface to the total number of SKU types).

[0113] Factors related to the algorithm include: the number of items picked per shelf surface (the average number of items picked per shelf surface); the number of categories picked per shelf surface (the average number of categories picked per shelf surface); the number of shelf surfaces for each SKU (the average number of shelf surfaces for each SKU); the number of SKU categories stored per shelf surface (the average number of SKU types stored on each shelf surface).

[0114] Operational factors include: the number of orders received by WMS (referring to the number of orders received by WMS); the residence time of AGVs at the picking location (referring to the average residence time of AGVs at the picking location); the average picking time per collection order (referring to the average completion time of picking each collection order); the number of outbound workstations opened; the number of orders received by XWMS (referring to the number of orders received by XWMS); the number of single-piece orders (referring to the number of single-piece orders); the number of trains (referring to the number of trains arriving at each workstation per hour); and the utilization rate of AGVs.

[0115] Among them, Automated Guided Vehicle (AGV); Stock Keeping Unit (SKU); Warehouse Management System (WMS); A collection order refers to an order collection formed by grouping together highly similar orders, for example, an order collection containing the same product.

[0116] The English names of the above factors are: X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, and they are used as the first variable set. The third data table is made according to the first variable set, as shown in Table 3:

[0117] Chinese name English name unit Warehouse area X1 Square meter Average number of categories per order (EN) X2 Type / Single Average number of items per order (EQ) X3 Piece / Order Line average number of pieces X4 Item / Order Line Percentage of single orders X5 ％ Number of working AGVs X6 Vehicle Number of SKU categories in stock X7 kind The proportion of SKU categories stored on each shelf X8 ％ Number of pieces picked per shelf surface X9 Pieces / Shelf Surface Number of categories picked per shelf X10 Type / Shelf Surface Number of shelves for each SKU X11 Shelf surface / type Number of SKU categories stored on each shelf X12 Type / Shelf Surface WMS receives order quantity X13 one AGV picking location residence time X14 Second Average picking time per set of orders X15 Second Number of outbound workstations opened X16 indivual XWMS receives order quantity X17 one Unit Order Quantity X18 one Train X19 Second-rate AGV Utilization X20 ％

[0118] Table 3 The third data table

[0119] The first step is to obtain from the warehouse management system the historical values ​​of the above 20 first variables of unmanned warehouse a and the corresponding outbound efficiency of the above 20 first variables of unmanned warehouse a; obtain from the warehouse management system the historical values ​​of the above 20 first variables of unmanned warehouse b and the corresponding outbound efficiency of the above 20 first variables of unmanned warehouse b; obtain from the warehouse management system the historical values ​​of the above 20 first variables of unmanned warehouse c and the corresponding outbound efficiency of the above 20 first variables of unmanned warehouse c.

[0120] In the second step, based on the historical values ​​of the above 20 first variables of unmanned warehouse a, the outbound efficiency of unmanned warehouse a, the historical values ​​of the above 20 first variables of unmanned warehouse b, the outbound efficiency of unmanned warehouse b, the historical values ​​of the above 20 first variables of unmanned warehouse c and the outbound efficiency of unmanned warehouse c, and using the full subset regression method, a second variable set is selected from the above 20 first variables. The second variable set includes: average number of pieces per row X4, number of pieces picked per shelf surface X9, number of categories picked per shelf surface X10, average picking time per set order X15, number of open outbound workstations X16 and single-piece order quantity X18.

[0121] It should be noted that, in order to ensure the accuracy of the construction of the model to be analyzed, each first variable should have multiple historical values, and the corresponding outbound efficiency should also have multiple historical values. Each first variable can have 30 historical values. In addition, X17 and X18 are correlated. Generally, as the historical value of X17 increases, the historical value of X18 will increase accordingly. Then, whether to choose X17 or X18 as the second variable is determined by the following content: Use the full subset regression method to select the variable with a strong influence on the outbound efficiency of unmanned warehouse a.

[0122] The third step is to calculate the correlation coefficient of the second variable of unmanned warehouse a according to the historical values ​​of the second variable set of unmanned warehouse a, determine that the absolute value of the correlation coefficient of the second variable of unmanned warehouse a is greater than 0.5, and construct the model to be analyzed based on the historical values ​​of the second variable set of unmanned warehouse a and the outbound efficiency corresponding to the historical values ​​of the second variable set of unmanned warehouse a and the ridge estimation method. The expression of the model to be analyzed includes:

[0123] Y=2.13X16+0.34X9+1.06X10-0.25X15+0.68X4+0.08X18

[0124] In specific implementation, a fourth data table is prepared according to the second variable in the model to be analyzed and the coefficient of the second variable, as shown in Table 4:

[0125] Second variable The coefficient of the second variable X16 2.13 X9 0.34 X10 1.06 X15 -0.25 X4 0.68 X18 0.08

[0126] It should be noted that before building the model to be analyzed, the historical values ​​of the second variable set of unmanned warehouse a need to be standardized or logarithmized to eliminate the impact of the dimension. For example, the unit of X15 is seconds, and the unit of X18 is ten thousand orders. Since the magnitude difference between X15 and X18 is particularly large, it is necessary to use the existing logarithm method to process the historical values ​​of X15 and X18 of unmanned warehouse a.

[0127] The fourth step is to set the parameter value of at least one second variable of the unmanned warehouse a according to the coefficients of X16, X10, X4, X9, X15 and X18 in the model to be analyzed.

[0128] Specifically, according to Table 4, the coefficient of X15 is negative, and X15 is negatively correlated with the outbound efficiency of unmanned warehouse a. The coefficients of X4, X9, X10, X16 and X18 are positive, and X4, X9, X10, X16 and X18 are positively correlated with the outbound efficiency of unmanned warehouse a. The absolute values ​​of the coefficients of the second variable in the model to be analyzed are in the order from large to small: X16, X10, X4, X9, X15, X18, and the order of the influence of the second variable on the outbound efficiency of unmanned warehouse a from strong to weak is: X16, X10, X4, X9, X15, X18. The following is a specific example to illustrate the positive correlation: the larger the historical value of X4, the higher the outbound efficiency of unmanned warehouse a. Of course, other conditions remain unchanged, for every unit increase in X4, the outbound efficiency of unmanned warehouse a increases by 0.68 units on average. The following is a specific example to illustrate the negative correlation: the larger the historical value of X15, the lower the outbound efficiency of unmanned warehouse a.

[0129] The first implementation method of this step: the preset value is 1. Since the absolute value of the coefficient of X10, 1.06, is greater than 1, the parameter value of X10 is set to be greater than the historical value of X10. Since the absolute value of the coefficient of X16, 2.13, is greater than 1, the parameter value of X16 is set to be greater than the historical value of X16. The absolute values ​​of the coefficients of X4, X9, X15, and X18 are less than 1, so the parameter value of X4 is set to the historical value of X4, the parameter value of X9 is set to the historical value of X9, the parameter value of X15 is set to the historical value of X15, and the parameter value of the coefficient of X18 is set to the historical value of X18.

[0130] The second specific implementation method: Since X16 has the strongest influence on the outbound efficiency of unmanned warehouse a, the parameter value of X16 can be directly set to be greater than the historical value of X16, so that the outbound efficiency of unmanned warehouse a can be significantly improved. In addition, the layout planning or algorithm strategy of unmanned warehouse a can also be changed, and the unmanned warehouse a after the change will outbound the items, and the outbound efficiency of unmanned warehouse a can be improved.

[0131] In the fifth step, after the parameter value of at least one second variable of the unmanned warehouse a is set, the unmanned warehouse a takes out the items.

[0132] It should be noted that in the prior art, the efficiency of warehouse a in and out is low. Due to limited manpower and material resources, only one warehouse with high efficiency in and out is often selected (by comparing the efficiency of in and out, the warehouse with high efficiency in and out is obtained) to be compared with warehouse a. This causes the problem of one-sided data comparison in the prior art. The following is a specific example to illustrate the empirical determination of whether the variable causes the low efficiency of warehouse a in and out: the time for warehouse a to pick a single item is 6 hours, and the time for warehouse b to pick a single item is 1 hour. The difference between the two is 5 hours. The reason for the low efficiency of warehouse a in outbound delivery may be the lack of proficiency of the pickers and the difference in item types. Therefore, the time for picking a single item is identified as the variable causing the low efficiency of warehouse a in and out.

[0133] Combination of the above Figure 1-Figure 2 The method of entering and exiting the warehouse is explained below. Figure 3 Describe the equipment for entering and exiting the warehouse.

[0134] In order to solve the problems existing in the prior art, the embodiment of the present invention provides a device for entering and exiting a warehouse, such as Figure 3 As shown, the device comprises:

[0135] The selection unit 301 is used to select a second variable set from the first variable set based on the data of the target warehouse and at least one non-target warehouse and using a variable selection method; the data includes historical values ​​of the first variable set and the warehousing efficiency corresponding to the historical values ​​of the first variable set.

[0136] The construction unit 302 is used to construct a model to be analyzed based on the historical values ​​of the second variable set of the target warehouse and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse.

[0137] The setting unit 303 is used to set the parameter value of at least one second variable of the target warehouse according to the coefficient of each second variable in the model to be analyzed.

[0138] The processing unit 304 is used to warehouse or remove items according to the set parameter value of at least one second variable of the target warehouse.

[0139] In the embodiment of the present invention, the selection unit 301 is used to:

[0140] Based on the data of the target warehouse and at least one non-target warehouse, a second variable set is selected from the first variable set by adopting a stepwise regression method or a full subset regression method.

[0141] In the embodiment of the present invention, the construction unit 302 is used to:

[0142] Determine whether there is collinearity between the second variables of the target warehouse; if so, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the ridge estimation method, construct the model to be analyzed; if not, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the least squares method, construct the model to be analyzed.

[0143] In the embodiment of the present invention, the construction unit 302 is used to:

[0144] Calculate the correlation coefficient or variance inflation factor of the second variable of the target warehouse according to the historical values ​​of the second variable set of the target warehouse;

[0145] Determine whether the absolute value of the correlation coefficient of the second variable of the target warehouse is greater than 0.5;

[0146] Alternatively, determine whether the variance inflation factor of the second variable of the target warehouse is greater than 4.

[0147] In the embodiment of the present invention, the setting unit 303 is used to:

[0148] For the coefficient of each second variable in the model to be analyzed, the parameter value of the second variable of the target warehouse is set in the following manner:

[0149] Determining whether the absolute value of the coefficient of the second variable is greater than a preset value;

[0150] If not, setting the parameter value of the second variable of the target warehouse equal to the historical value of the second variable;

[0151] If so, determine whether the coefficient of the second variable is negative. If so, set the parameter value of the second variable of the target warehouse to be smaller than the historical value of the second variable. If not, set the parameter value of the second variable of the target warehouse to be larger than the historical value of the second variable.

[0152] It should be understood that the functions performed by the various components of the storage and warehousing device provided in the embodiment of the present invention have been introduced in detail in the storage and warehousing method of the above embodiment, and will not be repeated here.

[0153] Figure 4 An exemplary system architecture 400 is shown to which the method or apparatus for entering and exiting a warehouse according to the embodiments of the present invention may be applied.

[0154] like Figure 4As shown, system architecture 400 may include terminal devices 401, 402, 403, network 404 and server 405. Network 404 is used to provide a medium for communication links between terminal devices 401, 402, 403 and server 405. Network 404 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0155] Users can use terminal devices 401, 402, 403 to interact with server 405 through network 404 to receive or send messages, etc.

[0156] The terminal devices 401 , 402 , and 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0157] The server 405 may be a server that provides various services, such as a backend management server (only an example) that provides support for shopping websites browsed by users using the terminal devices 401, 402, and 403. The backend management server may analyze and process the received data such as product information query requests, and feed back the processing results (such as target push information, product information - only an example) to the terminal device.

[0158] It should be noted that the method for entering and exiting a warehouse provided in the embodiment of the present invention is generally executed by the server 405 , and accordingly, the device for entering and exiting a warehouse is generally arranged in the server 405 .

[0159] It should be understood that Figure 4 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0160] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 of a terminal device suitable for implementing an embodiment of the present invention. Figure 5 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0161] like Figure 5As shown, 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 part 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, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0162] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A 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, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0163] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0164] It should be noted that 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 above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0165] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a unit, a program segment, or a part of a code, and the above-mentioned unit, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0166] The units involved in the embodiments of the present invention may be implemented by software or hardware. The units described may also be set in a processor, for example, may be described as: a processor includes a selection unit, a construction unit, a setting unit and a processing unit. The names of these units do not constitute a limitation on the unit itself in some cases, for example, the setting unit may also be described as "a unit for setting the parameter value of at least one second variable of the target warehouse according to the coefficient of each second variable in the model to be analyzed".

[0167] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: based on the data of the target warehouse and at least one non-target warehouse, and using a variable selection method, a second variable set is selected from a first variable set; the data includes the historical value of the first variable set, and the storage efficiency corresponding to the historical value of the first variable set; based on the historical value of the second variable set of the target warehouse, and the storage efficiency corresponding to the historical value of the second variable set of the target warehouse, a model to be analyzed is constructed; according to the coefficient of each second variable in the model to be analyzed, the parameter value of at least one second variable of the target warehouse is set; according to the parameter value of at least one second variable of the target warehouse after setting, the items are put into or taken out of the warehouse.

[0168] According to the technical solution of the embodiment of the present invention, based on the data of the target warehouse and at least one non-target warehouse, and using the variable selection method, a second variable set is selected from the first variable set; the data includes the historical values ​​of the first variable set, and the storage efficiency corresponding to the historical values ​​of the first variable set, thereby reducing the problem of one-sided data comparison. Based on the historical values ​​of the second variable set of the target warehouse, and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse, a model to be analyzed is constructed; the parameter value of at least one second variable of the target warehouse is set according to the coefficient of each second variable in the model to be analyzed; and items are put into or out of the warehouse according to the parameter value of at least one second variable of the target warehouse after setting. Since the coefficient of the second variable represents the degree of influence of the second variable on the storage efficiency of the target warehouse, the parameter value of the second variable with a strong degree of influence is set, thereby significantly improving the storage efficiency of the target warehouse.

[0169] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for warehousing, characterized in that: include: Based on the data of the target warehouse and at least one non-target warehouse, and using the variable selection method, a second variable set is selected from the first variable set; the data includes historical values ​​of the first variable set, and the storage efficiency corresponding to the historical values ​​of the first variable set; wherein the target warehouse is a warehouse with low storage efficiency; the non-target warehouse is of the same warehouse type as the target warehouse; wherein the first variable included in the first variable set is a factor affecting the storage efficiency of the warehouse; Based on the historical values ​​of the second variable set of the target warehouse and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse, construct a model to be analyzed; Setting a parameter value of at least one second variable of the target warehouse according to the coefficient of each second variable in the model to be analyzed; For the coefficient of each second variable in the model to be analyzed, the parameter value of the second variable of the target warehouse is set in the following manner: Determining whether the absolute value of the coefficient of the second variable is greater than a preset value; If not, set the parameter value of the second variable of the target warehouse equal to the historical value of the second variable; if yes, determine whether the coefficient of the second variable is negative, if yes, set the parameter value of the second variable of the target warehouse less than the historical value of the second variable, if not, set the parameter value of the second variable of the target warehouse greater than the historical value of the second variable; Items are put into or taken out of the warehouse according to the parameter value of at least one second variable of the target warehouse after being set.

2. The method according to claim 1, characterized in that: Based on the data of the target warehouse and at least one non-target warehouse, and using a variable selection method, a second variable set is selected from the first variable set, including: Based on the data of the target warehouse and at least one non-target warehouse, a second variable set is selected from the first variable set by adopting a stepwise regression method or a full subset regression method.

3. The method according to claim 1, characterized in that Based on the historical values ​​of the second variable set of the target warehouse and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse, a model to be analyzed is constructed, including: Determine whether there is collinearity between the second variables of the target warehouse; if so, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the ridge estimation method, construct the model to be analyzed; if not, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the least squares method, construct the model to be analyzed.

4. The method according to claim 3, characterized in that Determining whether there is collinearity between the second variables of the target warehouse includes: Calculate the correlation coefficient or variance inflation factor of the second variable of the target warehouse according to the historical values ​​of the second variable set of the target warehouse; Determine whether the absolute value of the correlation coefficient of the second variable of the target warehouse is greater than 0.5; Alternatively, determine whether the variance inflation factor of the second variable of the target warehouse is greater than 4.

5. A device for entering and exiting a warehouse, characterized in that: include: A selection unit is used to select a second variable set from a first variable set based on data of a target warehouse and at least one non-target warehouse and using a variable selection method; the data includes historical values ​​of the first variable set and storage efficiency corresponding to the historical values ​​of the first variable set; wherein the target warehouse is a warehouse with low storage efficiency; the non-target warehouse is of the same warehouse type as the target warehouse; wherein the first variable included in the first variable set is a factor affecting the storage efficiency of the warehouse; A construction unit, configured to construct a model to be analyzed based on historical values ​​of the second variable set of the target warehouse and the storage efficiency corresponding to the historical values ​​of the second variable set of the target warehouse; A setting unit, configured to set a parameter value of at least one second variable of the target warehouse according to a coefficient of each second variable in the model to be analyzed; The setting unit is used to: for each coefficient of the second variable in the model to be analyzed, set the parameter value of the second variable of the target warehouse in the following manner: determine whether the absolute value of the coefficient of the second variable is greater than a preset value; if not, set the parameter value of the second variable of the target warehouse equal to the historical value of the second variable; if so, determine whether the coefficient of the second variable is negative, if so, set the parameter value of the second variable of the target warehouse to be less than the historical value of the second variable, if not, set the parameter value of the second variable of the target warehouse to be greater than the historical value of the second variable; A processing unit is used to warehouse or remove items according to the parameter value of at least one second variable of the target warehouse after setting.

6. The device according to claim 5, characterized in that The selection unit is used to: Based on the data of the target warehouse and at least one non-target warehouse, a second variable set is selected from the first variable set by adopting a stepwise regression method or a full subset regression method.

7. The device according to claim 5, characterized in that The building blocks are used to: Determine whether there is collinearity between the second variables of the target warehouse; if so, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the ridge estimation method, construct the model to be analyzed; if not, based on the historical values ​​of the second variable set of the target warehouse and the warehousing and outbound efficiencies corresponding to the historical values ​​of the second variable set of the target warehouse, and using the least squares method, construct the model to be analyzed.

8. The device according to claim 7, characterized in that The building blocks are used to: Calculate the correlation coefficient or variance inflation factor of the second variable of the target warehouse according to the historical values ​​of the second variable set of the target warehouse; Determine whether the absolute value of the correlation coefficient of the second variable of the target warehouse is greater than 0.5; Alternatively, determine whether the variance inflation factor of the second variable of the target warehouse is greater than 4.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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