Warehouse siting method, apparatus, and medium

Abstract

The present disclosure relates to a warehouse site selection method, device and medium, the method comprising: obtaining a plurality of target first logistics orders and a plurality of target second logistics orders of at least one candidate warehouse; distributing the volume of products in each target first logistics order to a plurality of sub-regions according to the delivery address in the target second logistics order, and constructing a first volume matrix according to the volume of products in each target first logistics order distributed to each sub-region; constructing a second cost matrix according to the logistics cost of the plurality of target second logistics orders, the second cost matrix being used to reflect the logistics cost from each candidate warehouse to each sub-region; processing the first volume matrix and the second cost matrix according to a pre-constructed processing model to obtain target coverage information; and outputting the location information of the newly-built warehouse according to the target coverage information.

Description

Technical Field

[0001] This disclosure relates to the field of logistics technology, and in particular to a warehouse site selection method, apparatus and medium. Background Technology

[0002] The logistics and distribution process of a product may involve two stages of transportation, and the cost calculation methods differ between these stages. For example, cost calculation methods may include charging based on product volume or charging based on the number of products. However, due to the different cost calculation methods for different logistics stages, the relevant technology cannot effectively determine the location of a new warehouse for storing products that undergo two stages of transportation. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a warehouse site selection method, apparatus and medium.

[0004] According to a first aspect of the present disclosure, a warehouse location selection method is provided, including:

[0005] Obtain multiple target first logistics orders and multiple target second logistics orders for at least one candidate warehouse, wherein the target first logistics order refers to a logistics order that is billed based on product volume, and the target second logistics order refers to a logistics order that is billed based on the number of products.

[0006] Based on the delivery address in the target second logistics order, the volume of the product in each target first logistics order is allocated to multiple sub-regions, and a first volume matrix is ​​constructed based on the volume of the product of each target first logistics order allocated to each sub-region.

[0007] Based on the logistics costs of multiple target second logistics orders, a second cost matrix is ​​constructed, which reflects the logistics costs from each candidate warehouse to each sub-region.

[0008] The first volume matrix and the second cost matrix are processed according to a pre-built processing model to obtain target coverage information, which is used to reflect the target coverage relationship between the candidate warehouse and the sub-region.

[0009] Output the location information of the newly built warehouse based on the target coverage information.

[0010] In some embodiments, allocating the volume of products in each target first logistics order to multiple sub-regions based on the delivery address in the target second logistics order includes:

[0011] Based on the delivery address in the target second logistics order, multiple sub-regions are determined;

[0012] Based on the proportion of the products in each of the target first logistics orders in each of the sub-regions, the volume of the products in each of the target first logistics orders is allocated to multiple sub-regions, and the proportion of the products in each of the target first logistics orders is determined based on multiple target second logistics orders.

[0013] In some embodiments, the method further includes:

[0014] The quantity of the product in each of the sub-regions and the total quantity of the product are determined based on the multiple target second logistics orders.

[0015] The quantity percentage of the product in each sub-region is determined based on the quantity of the product in each sub-region and the total quantity of the product.

[0016] In some embodiments, the method further includes:

[0017] Based on the distance between the location of the candidate warehouse in each target second logistics order and the delivery address, and the number of products in each target second logistics order, the logistics cost of multiple target second logistics orders is determined;

[0018] The step of constructing a second cost matrix based on the logistics costs of multiple target second logistics orders includes:

[0019] The logistics costs of the target second logistics order are aggregated based on the candidate warehouses and the sub-regions to determine the logistics cost from each candidate warehouse to each sub-region.

[0020] The second cost matrix is ​​constructed based on the logistics costs from each candidate warehouse to each sub-region.

[0021] In some embodiments, the method further includes:

[0022] Based on multiple target second logistics orders, determine the shortest distance between each candidate warehouse and each sub-region;

[0023] An initial coverage matrix is ​​constructed based on the shortest distance, and the initial coverage matrix is ​​used to reflect the initial coverage relationship between the candidate warehouse and the sub-region;

[0024] The step of processing the first volume matrix and the second cost matrix according to the pre-built processing model to obtain target coverage information includes:

[0025] The initial coverage matrix, the first volume matrix, the second cost matrix, and the storage cost of each candidate warehouse are processed according to the processing model to determine the target coverage information.

[0026] In some embodiments, constructing the initial coverage matrix based on the shortest distance includes:

[0027] If the shortest distance is less than a preset threshold, it is determined that the candidate warehouse and the sub-region corresponding to the shortest distance have the initial coverage relationship.

[0028] If the shortest distance is greater than the preset threshold, it is determined that the candidate warehouse and the sub-region corresponding to the shortest distance do not have the initial coverage relationship;

[0029] In some embodiments, the processing model includes a constrained objective function, the constraints being used to constrain the coverage information between the candidate warehouse and the sub-region, and the objective function being used to determine, from the coverage information that satisfies the constraints, the target coverage information that minimizes the total cost corresponding to the candidate warehouse;

[0030] Wherein, for any of the coverage information, the total cost includes the sum of any one or more of the following:

[0031] The first logistics cost of the candidate warehouse determined based on the first volume matrix under the coverage relationship represented by the coverage information;

[0032] The second logistics cost of the candidate warehouse determined based on the second cost matrix under the coverage relationship represented by the coverage information.

[0033] In some embodiments, determining the first logistics cost includes:

[0034] Based on the first volume matrix, the delivery volume of each sub-region associated with the candidate warehouse under the coverage relationship represented by the coverage information is determined, and the tiered cost corresponding to the candidate warehouse is determined based on the delivery volume of each sub-region associated with the candidate warehouse.

[0035] The first logistics cost is determined based on the delivery volume of each sub-region and the tiered cost.

[0036] In some embodiments, outputting the location information of the newly built warehouse based on the target coverage information includes:

[0037] Determine the total cost of the candidate warehouse and the preset second logistics order under the coverage relationship represented by the target coverage information, wherein the preset second logistics order is a target second logistics order that matches the preset business among a plurality of target second logistics orders;

[0038] The new warehouse is determined from at least one of the candidate warehouses based on the total cost of the candidate warehouses and the preset second logistics order;

[0039] Output the location information of the newly created warehouse.

[0040] In some embodiments, determining the new warehouse from at least one of the candidate warehouses based on the total cost of the candidate warehouses and the preset second logistics order includes:

[0041] If the total cost of the candidate warehouse meets a first preset condition, and / or the proportion of the preset second logistics orders of the candidate warehouse in the multiple target second logistics orders meets a second preset condition, the candidate warehouse will be determined as the new warehouse.

[0042] In some embodiments, obtaining multiple target first logistics orders and multiple target second logistics orders for at least one candidate warehouse includes:

[0043] Obtain multiple historical logistics orders;

[0044] Based on order attributes, multiple historical logistics orders are classified to determine multiple initial first logistics orders and multiple initial second logistics orders. The initial first logistics order is a logistics order from the product's production address to the original warehouse, and the initial second logistics order is a logistics order from the original warehouse to the product's delivery address.

[0045] For each candidate warehouse, the original warehouse in each initial first logistics order and each initial second logistics order is replaced with the candidate warehouse to obtain multiple target first logistics orders and multiple target second logistics orders for each candidate warehouse.

[0046] According to a second aspect of the present disclosure, a warehouse location selection device is provided, comprising:

[0047] The acquisition module is configured to acquire multiple target first logistics orders and multiple target second logistics orders from at least one candidate warehouse, wherein the target first logistics order refers to a logistics order that is billed based on product volume, and the target second logistics order refers to a logistics order that is billed based on the number of products.

[0048] The first construction module is configured to allocate the volume of the product in each of the target first logistics orders to multiple sub-regions according to the delivery address in the target second logistics order, and to construct a first volume matrix according to the volume of the product of each of the target first logistics orders allocated to each of the sub-regions;

[0049] The second construction module is configured to construct a second cost matrix based on the logistics costs of multiple target second logistics orders, the second cost matrix being used to reflect the logistics costs from each candidate warehouse to each sub-region;

[0050] The processing module is configured to process the first volume matrix and the second cost matrix according to a pre-built processing model to obtain target coverage information, wherein the target coverage information is used to reflect the target coverage relationship between the candidate warehouse and the sub-region;

[0051] The output module is configured to output the location information of the newly created warehouse based on the target coverage information.

[0052] According to a third aspect of the present disclosure, a warehouse location selection device is provided, comprising:

[0053] processor;

[0054] Memory used to store processor-executable instructions;

[0055] The processor is configured as follows:

[0056] Obtain multiple target first logistics orders and multiple target second logistics orders for at least one candidate warehouse, wherein the target first logistics order refers to a logistics order that is billed based on product volume, and the target second logistics order refers to a logistics order that is billed based on the number of products.

[0057] Based on the delivery address in the target second logistics order, the volume of the product in each target first logistics order is allocated to multiple sub-regions, and a first volume matrix is ​​constructed based on the volume of the product of each target first logistics order allocated to each sub-region.

[0058] Based on the logistics costs of multiple target second logistics orders, a second cost matrix is ​​constructed, which reflects the logistics costs from each candidate warehouse to each sub-region.

[0059] The first volume matrix and the second cost matrix are processed according to a pre-built processing model to obtain target coverage information, which is used to reflect the target coverage relationship between the candidate warehouse and the sub-region.

[0060] Output the location information of the newly built warehouse based on the target coverage information.

[0061] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the warehouse location method provided in any of the first aspects of the present disclosure.

[0062] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: by allocating the volume of the products in each target first logistics order to multiple sub-regions, and constructing a first volume matrix based on the volume of the products of each target first logistics order allocated to each sub-region, the first volume matrix associates the volume with the sub-regions, so that the logistics cost of the target first logistics order can be calculated in the sub-regions. At the same time, a second cost matrix is ​​also associated with multiple sub-regions, so the logistics cost of the target second logistics order is also calculated in the sub-regions. Furthermore, the processing model can simultaneously optimize the logistics cost of the target first logistics order determined by the first volume matrix and the logistics cost of the target second logistics order determined by the second cost matrix, obtaining target coverage information. This disclosure enables the present disclosure to consider the costs of logistics orders with different billing methods in the same model, and thus can automatically output the location information of newly built warehouses based on the model.

[0063] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0065] Figure 1 This is a flowchart illustrating a warehouse location selection method according to an exemplary embodiment.

[0066] Figure 2 This is a flowchart illustrating the determination of quantity percentage according to an exemplary embodiment.

[0067] Figure 3 This is a block diagram illustrating a warehouse location selection device according to an exemplary embodiment.

[0068] Figure 4 This is a block diagram illustrating a warehouse location selection device according to an exemplary embodiment. Detailed Implementation

[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0070] As mentioned in the background section, product logistics and distribution may involve two stages of transportation, with different cost calculation methods for each stage. For example, taking electrical appliances as an example, the logistics and distribution process may include a stage where costs are calculated based on product volume and another stage where costs are calculated based on the number of products. Understandably, the cost calculation methods based on product volume and the number of products are different. Because the cost calculation methods differ for different logistics stages, related technologies cannot simultaneously consider the two-stage logistics and transportation costs of a warehouse in a single model, making it impossible to determine the location of a new warehouse for storing products requiring two-stage logistics and transportation.

[0071] Figure 1 This is a flowchart illustrating a warehouse location selection method according to an exemplary embodiment, such as... Figure 1 As shown, the method may include the following steps.

[0072] Step 110: Obtain multiple target first logistics orders and multiple target second logistics orders for at least one candidate warehouse. Target first logistics orders refer to logistics orders that are billed based on the volume of the items, and target second logistics orders refer to logistics orders that are billed based on the number of items.

[0073] In some embodiments, the candidate warehouse may be a pre-determined warehouse to be built, and the location of the candidate warehouse can be selected according to actual needs. In some embodiments, each of the at least one candidate warehouse may include multiple target first logistics orders and multiple target second logistics orders. Correspondingly, in some embodiments, multiple target first logistics orders and multiple target second logistics orders for each candidate warehouse can be obtained.

[0074] In some embodiments, obtaining multiple target first logistics orders and multiple target second logistics orders for at least one candidate warehouse may include: obtaining multiple historical logistics orders; classifying the multiple historical logistics orders according to order attributes to determine multiple initial first logistics orders and multiple initial second logistics orders, wherein the initial first logistics orders are logistics orders from the product's production address to the original warehouse, and the initial second logistics orders are logistics orders from the original warehouse to the product's delivery address; and for each candidate warehouse, replacing the original warehouse in each initial first logistics order and each initial second logistics order with that candidate warehouse to obtain multiple target first logistics orders and multiple target second logistics orders for each candidate warehouse.

[0075] In some embodiments, historical logistics orders can be logistics orders within a historical time period. The historical time period can be specifically determined according to the actual situation, for example, the historical time period can be 3 months or 1 year. Historical logistics orders can be logistics orders actually generated by the sale of products within the historical time period. In some embodiments, products can include electrical appliances, such as televisions, refrigerators, and washing machines. This disclosure does not impose any restrictions on the type of electrical appliances.

[0076] In some embodiments, the original warehouse in the initial first logistics order or the initial second logistics order is an existing warehouse. By replacing the existing warehouse with the warehouse to be built, logistics orders for the warehouse to be built after its actual use can be pre-built. In some embodiments, the target first logistics order can be a logistics order from the product's production address to the candidate warehouse, and the target second logistics order can be a logistics order from the candidate warehouse to the product's receiving address. In some embodiments, the product's production address can be the location of the product's manufacturing plant.

[0077] In some embodiments, a logistics order from the product’s manufacturing address to the candidate warehouse may be a logistics order billed based on the product’s volume, and a logistics order from the candidate warehouse to the product’s delivery address may be a logistics order billed based on the number of products.

[0078] In some embodiments, the initial first logistics order and the initial second logistics order may employ different logistics transportation methods. For example, the initial first logistics order may be transported by rail, ship, or road truck, while the initial second logistics order may be transported by freight truck.

[0079] In some embodiments, order attributes can be used to reflect whether a historical logistics order belongs to a first logistics order or a second logistics order. For example, taking a television set as an example, in a real-world scenario, if a user purchases the television set on an application platform, the television set may include an initial first logistics order 1 from factory A (i.e., the production address of the television set) to the original warehouse 1 used to store the television set, and an initial second logistics order 1 from the original warehouse 1 to the user's delivery address. Correspondingly, the historical logistics orders for the television set may include the initial first logistics order 1 and the initial second logistics order 1.

[0080] As mentioned above, the candidate warehouse can be a warehouse to be built. In some embodiments, the candidate warehouse may also include a warehouse to be built and the original warehouse in historical logistics orders. In some embodiments, when the candidate warehouse includes a warehouse to be built and the original warehouse in historical logistics orders, the initial first logistics order and the initial second logistics order may be determined as the target first logistics order and the target second logistics order, respectively.

[0081] Step 120: Based on the delivery address in the target second logistics order, allocate the volume of the product in each target first logistics order to multiple sub-regions, and construct a first volume matrix based on the volume of the product of each target first logistics order allocated to each sub-region.

[0082] In some embodiments, multiple sub-regions can be determined based on the delivery address in the target second logistics order. A sub-region can refer to the area to which the delivery address belongs. For example, if the delivery address is XXX in District B of City A, then the sub-region can refer to the district to which the delivery address belongs, i.e., District B.

[0083] In some embodiments, allocating the volume of products in each target first logistics order to multiple sub-regions based on the delivery address in the target second logistics order includes: determining multiple sub-regions based on the delivery address in the target second logistics order; and allocating the volume of products in each target first logistics order to multiple sub-regions based on the quantity percentage of products in each sub-region, wherein the quantity percentage is determined based on the multiple target second logistics orders. Specific details regarding the determination of the quantity percentage can be found below. Figure 2 The details and related descriptions will not be repeated here.

[0084] In some embodiments, each target first logistics order may include one or more product categories. If a target first logistics order includes multiple product categories, each product category can be allocated to multiple sub-regions based on its quantity proportion in each sub-region. In some embodiments, each target first logistics order may include one product category. The product categories can be divided according to actual circumstances; for example, in the home appliance delivery field, televisions and refrigerators can be classified as the same product category.

[0085] In some embodiments, the form of the first volume matrix can be specifically set according to actual needs. For example, taking the target first logistics order 1 as an example, the first volume matrix of the target first logistics order 1 with respect to multiple sub-regions can be as shown in Table 1 below:

[0086] Target First Logistics Order ID subregion volume 1 A1 <![CDATA[V 1,A1 ]]> 1 A2 <![CDATA[V 1,A2 ]]> 1 A3 <![CDATA[V 1,A3 ]]> 1 A4 <![CDATA[V 1,A3 ]]>

[0087] Table 1

[0088] Step 130: Based on the logistics costs of multiple target second logistics orders, construct a second cost matrix. The second cost matrix is ​​used to reflect the logistics costs from each candidate warehouse to each sub-region.

[0089] In some embodiments, the method further includes: determining the logistics cost of multiple target second logistics orders based on the distance between the location of the candidate warehouse and the delivery address in each target second logistics order, and the number of products in each target second logistics order.

[0090] In some embodiments, for each target second logistics order, the logistics cost of the target second logistics order can be determined based on a preset logistics charging rule, according to the distance between the location of the candidate warehouse in the target second logistics order and the delivery address, as well as the quantity of products in the target second logistics order, so as to obtain the logistics cost of multiple target second logistics orders.

[0091] For example, if the products include a television and a refrigerator, the preset logistics fee rules for the television and refrigerator can be as shown in Table 2 below.

[0092] Category model distance Price / unit television 55-inch (0,50) 40 television 55-inch [50,100) 50 television 55-inch [50,∞) 80 refrigerator 450L (0,50) 80 refrigerator 450 [50,∞) 150

[0093] Table 2

[0094] For example, taking the second target logistics order 1 as a logistics order for a 55-inch TV, if the distance between the candidate warehouse and the delivery address in the second target logistics order 1 is 30km, then according to the above-mentioned preset logistics charging rules, the logistics cost of the second target logistics order 1 can be obtained as 40 yuan.

[0095] In some embodiments, constructing a second cost matrix based on the logistics costs of multiple target second logistics orders includes: aggregating the logistics costs of the target second logistics orders based on candidate warehouses and sub-regions to determine the logistics cost from each candidate warehouse to each sub-region; and constructing a second cost matrix based on the logistics costs from each candidate warehouse to each sub-region. In some embodiments, the aggregation process refers to summing the logistics costs of target second logistics orders belonging to the same candidate warehouse and the same sub-region.

[0096] In some embodiments, the elements in the second cost matrix can be used to reflect the logistics costs from the corresponding candidate warehouse to the corresponding sub-region. The form of the second cost matrix is ​​determined specifically according to the actual situation. For example, taking a sub-region including A1-A4 and candidate warehouses including candidate warehouses 1-3 as an example, the second cost matrix can be in the form shown in Table 3 below:

[0097] subregion Candidate Repository 1 Candidate Repository 2 Candidate Warehouse 3 A1 <![CDATA[Cost 11 ]]> <![CDATA[Cost 12 ]]> <![CDATA[Cost 13 ]]> A2 <![CDATA[Cost 21 ]]> <![CDATA[Cost 22 ]]> <![CDATA[Cost 23 ]]> A3 <![CDATA[Cost 31 ]]> <![CDATA[Cost 32 ]]> <![CDATA[Cost 33 ]]> A4 <![CDATA[Cost 41 ]]> <![CDATA[Cost 42 ]]> <![CDATA[Cost 43 ]]>

[0098] Table 3

[0099] Cost 11 Cost represents the logistics cost from candidate warehouse 1 to sub-area A1. 21 This represents the logistics cost from candidate warehouse 1 to sub-area A2. The meanings of other elements are similar and will not be elaborated here.

[0100] Step 140: Process the first volume matrix and the second cost matrix according to the pre-built processing model to obtain target coverage information. The target coverage information is used to reflect the target coverage relationship between candidate warehouses and sub-regions.

[0101] This embodiment of the disclosure allocates the volume of products in each target first logistics order to multiple sub-regions, and constructs a first volume matrix based on the volume of products from each target first logistics order allocated to each sub-region. The first volume matrix associates volume with sub-regions, enabling the logistics cost of the target first logistics order to be calculated within each sub-region. Simultaneously, a second cost matrix is ​​also associated with multiple sub-regions. Furthermore, this allows the processing model to simultaneously optimize the logistics cost of the target first logistics order determined by the first volume matrix and the logistics cost of the target second logistics order determined by the second cost matrix, obtaining target coverage information. This disclosure allows the model to consider the costs of logistics orders with different billing methods within the same model, enabling the model to output the location information of the newly built warehouse based on the optimal target coverage relationship.

[0102] In some embodiments, the processing model includes a constrained objective function, the constraints being used to constrain the coverage information between candidate warehouses and sub-regions, and the objective function being used to determine, from the coverage information that satisfies the constraints, the target coverage information that minimizes the total cost corresponding to the candidate warehouse; wherein, for any type of coverage information, the total cost includes the sum of any one or more of the following: the first logistics cost of the candidate warehouse under the coverage relationship represented by the coverage information, determined based on the first volume matrix; and the second logistics cost of the candidate warehouse under the coverage relationship represented by the coverage information, determined based on the second cost matrix.

[0103] In some embodiments, the objective function can be determined according to the following formula (1):

[0104] H = Min(CP) amount +GX amount (1)

[0105] Where H represents the objective function, Min represents finding the minimum value, and CP amount Indicates the second logistics cost, GX amiunt This represents the first logistics cost.

[0106] In some embodiments, the total cost may also include the warehousing cost of the candidate warehouse, which may include at least one of the following: warehouse rent, warehouse depreciation, equipment depreciation, loading and unloading costs, cargo packaging material costs, and management fees.

[0107] In some embodiments, determining the first logistics cost includes: determining the delivery volume of each sub-region associated with the candidate warehouse under the coverage relationship represented by the coverage information based on the first volume matrix, and determining the tiered cost corresponding to the candidate warehouse based on the delivery volume of each sub-region associated with the candidate warehouse; and determining the first logistics cost based on the delivery volume of each sub-region and the tiered cost.

[0108] In some embodiments, determining the tiered cost corresponding to a candidate warehouse based on the delivery volume of each sub-region associated with the candidate warehouse may include: determining the delivery volume of the candidate warehouse based on the sum of the delivery volumes of each sub-region associated with the candidate warehouse; and determining the tiered cost corresponding to the candidate warehouse based on the delivery volume of the candidate warehouse. In some embodiments, the tiered cost of the candidate warehouse may be determined according to a preset mapping relationship between volume, volume tier, and tiered cost. For example, the tiered cost corresponding to the volume tier matching the delivery volume of the candidate warehouse may be determined as the tiered cost of the candidate warehouse. In some embodiments, the first logistics cost of the candidate warehouse may be determined according to the sum of the delivery volume of each sub-region and the volume of the tiered cost.

[0109] In some embodiments, the first logistics cost can be determined according to the following formula (2):

[0110]

[0111] Among them, GX amount Let Z represent the first logistics cost, p represent the p-th target first logistics order, p_n represent the number of target first logistics orders, j represent the j-th candidate warehouse, n represent the number of candidate warehouses, i represent the i-th sub-region, m represent the number of sub-regions, s represent the s-th volume step, k represent the number of volume steps, and Z represent the number of volume steps. p,j,i,s V represents the step indicator variable representing the aggregated delivery volume of the first logistics order for the p-th target assigned to the i-th sub-region and then to the j-th candidate warehouse. p,i Price represents the volume of the product allocated to the i-th sub-region in the first logistics order for the p-th target. p,j,s Let $\frac{p}{j}$ represent the step cost of the $s$-th tier after the first logistics order for the $p$-th target is allocated to the $j$-th candidate warehouse.

[0112] In some embodiments, z p,j,i,s ∈{0,1}, where z is the volume of the first logistics order for the p-th target assigned to the i-th sub-region, aggregated to the j-th candidate warehouse, when the delivery volume matches the s-th volume step. p,j,i,s =1, when the volume of the first logistics order for the p-th target assigned to the i-th sub-region aggregated to the j-th candidate warehouse does not match the s-th volume step, z p,j,i,s =1.

[0113] In some embodiments, the above V p,i Price can be obtained from the first volume matrix. p,j,s It can be a predetermined constant, Z p,j,i,s To handle the variables that the model needs to solve.

[0114] In some embodiments, the second logistics cost can be determined according to the following formula (3):

[0115]

[0116] Among them, CP amount This represents the second logistics cost, where i represents the i-th sub-region, m represents the number of sub-regions, j represents the j-th candidate warehouse, n represents the number of candidate warehouses, and cost is... i,j Let x represent the logistics cost from the j-th candidate warehouse to the i-th sub-region in the second cost matrix. i,j The indicator variable represents the coverage relationship between the j-th candidate warehouse and the i-th sub-region.

[0117] In some embodiments, x i,j ∈{0,1}, where,

[0118] x ij It can be obtained through the coverage information of the constraints in the constraints. Matching can refer to the fact that there is a coverage relationship between the candidate warehouse and the sub-region.

[0119] In some embodiments, based on the above formulas (2) and (3), the constraints of the processing model band may include at least one of the following formulas (4)-(9):

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] x i,j +Z p,j,s ≤Z p,j,i,s +1, x i,j +Z p,j,s ≥2*Z p,j,i,s (9);

[0126] The meanings of the elements in the above formula (4) can be found in the relevant description above, and will not be repeated here. Formula (4) is used to constrain the region to which the sub-region belongs to match a candidate warehouse. For example, if the sub-region is a city, then a candidate warehouse matches a city to which the city belongs.

[0127] In the above formula (5), y j It is an indicator variable for whether the candidate repository is enabled, y j ∈{0,1}, where, M represents the maximum value. For example, M can be 1,000,000. The meanings of the other elements in formula (5) can be found in the relevant descriptions above, and will not be repeated here. Formula (5) above is used to constrain whether the j-th candidate warehouse is enabled.

[0128] In the above formula (6), w is the upper limit of the number of warehouses to be built. Formula (6) is used to constrain the number of warehouses in the candidate warehouse area to be less than the upper limit of the number of warehouses to be built.

[0129] Z in the above formula (7) p,j,s z represents the quotation ladder indicator variable for the allocation of the first logistics order of the p-th target to the j-th candidate warehouse. p,j,s ∈{0,1}, where z is the volume of the first logistics order of the p-th target assigned to the j-th candidate warehouse when it matches the s-th volume step. p,j,s =1; when the volume of the first logistics order for the p-th target assigned to the j-th candidate warehouse does not match the volume step of the s-th volume, z p,j,s =0.

[0130] Formula (7) is used to constrain the distribution volume of the candidate warehouse to match a volume step after the target first logistics order is assigned to the candidate warehouse.

[0131] V in the above formula (8) p,j Let u represent the volume of the first logistics order for the p-th target assigned to the j-th candidate warehouse. s This represents the boundary value of the transport volume interval corresponding to the s-th volume step. There are k transport volume intervals, such as {(0,u1],(u1,u2],(u2,u3],…,(u...}. k-1 ,u k ],(u k For example, taking s = 1 as an example, then u s =u1. The meanings of the other elements in formula (8) can be found in the relevant descriptions above, and will not be repeated here. Formula (8) above is used to constrain the volume of the target first logistics order allocated to the candidate warehouse.

[0132] The meanings of the elements in formula (9) above can be found in the relevant descriptions above, and will not be repeated here. Formula (9) is used to constrain x. i,jWith z p,j,s When both are 1, the volume of the first logistics order for the p-th target allocated to the j-th candidate warehouse must conform to the s-th volume step for the condition to be met.

[0133] In some embodiments, the method further includes: determining the shortest distance between each candidate warehouse and each sub-region based on multiple target second logistics orders; constructing an initial coverage matrix based on the shortest distance, the initial coverage matrix being used to reflect the initial coverage relationship between the candidate warehouse and the sub-region; and processing the first volume matrix and the second cost matrix according to a pre-constructed processing model to obtain target coverage information, including: processing the initial coverage matrix, the first volume matrix, the second cost matrix, and the storage cost of each candidate warehouse according to the processing model to determine the target coverage information.

[0134] As mentioned earlier, each delivery address can belong to a corresponding sub-region. Correspondingly, based on the distance between the location of the candidate warehouse in each target second logistics order and the delivery address, the distance between each candidate warehouse and each sub-region can be determined. Furthermore, based on this distance, the shortest distance between each candidate warehouse and each sub-region can be determined.

[0135] In some embodiments, constructing an initial coverage matrix based on the shortest distance may include: determining that the candidate warehouse corresponding to the shortest distance and the sub-region have an initial coverage relationship when the shortest distance is less than a preset threshold; determining that the candidate warehouse corresponding to the shortest distance and the sub-region do not have an initial coverage relationship when the shortest distance is greater than the preset threshold; and constructing an initial coverage matrix based on the initial coverage relationship between the candidate warehouse and the sub-region.

[0136] In some embodiments, the shortest distance can be specifically determined based on actual circumstances. For example, the shortest distance can be determined based on the distance in a preset logistics charging rule. In some embodiments, the element values ​​in the initial coverage matrix can include 0 and 1. When the candidate warehouse represented by the element in the initial coverage matrix has an initial coverage relationship with the sub-region, the value of the element is 1; otherwise, the value of the element is 0. For example, using z ij Taking an element in the initial coverage matrix as an example, this element can represent the initial coverage relationship between candidate warehouse j and sub-region i. If the two have an initial coverage relationship, then z ij =1, if there is no initial covering relationship, then z ij =0.

[0137] As mentioned earlier, the logistics cost of the target second logistics order can be obtained based on the number of products and the distance between the candidate warehouse and the delivery address in the target second logistics order. Therefore, for the same number of products, the greater the distance, the higher the transportation cost. The revenue of the candidate warehouse comes from the delivery distance from the candidate warehouse to the delivery address. Since the candidate warehouse will divide the volume of the products in the target first logistics order, this may lead to an increase in the logistics cost of the target first logistics order. If there is an overlap relationship between the candidate warehouse and the sub-region, the revenue generated by the warehousing and distribution logistics transportation stage of the sub-region caused by the candidate warehouse (i.e., logistics transportation from the candidate warehouse to the sub-region) will be greater than the increase in the first logistics cost allocated to the sub-region. Therefore, if the shortest distance is greater than a preset threshold, it is determined that the candidate warehouse corresponding to the shortest distance does not have an initial overlap relationship with the sub-region, thereby reducing the solution space of the processing model.

[0138] Therefore, by determining the initial coverage matrix, the approximate coverage relationship between candidate warehouses and sub-regions can be determined. This allows the processing model to reduce the range of the model's solution space based on the initial coverage matrix during the determination of target coverage information, thereby improving the efficiency of the processing model.

[0139] Step 150: Output the location information of the newly created warehouse based on the target coverage information.

[0140] In some embodiments, at least one candidate warehouse can be identified as a new warehouse, and the location information of the new warehouse can be output based on the target coverage information. The location information of the new warehouse can reflect the sub-area covered by the new warehouse. The sub-area covered by the new warehouse can characterize that the new warehouse is used to store products that will be delivered to the sub-area it covers.

[0141] In some embodiments, a new warehouse can be selected from at least one candidate warehouse based on the target coverage information, and the location information of the selected new warehouse can be output. In some embodiments, outputting the location information of the new warehouse based on the target coverage information may include: determining the total cost of the candidate warehouse and a preset second logistics order under the coverage relationship represented by the target coverage information, wherein the preset second logistics order is a target second logistics order that matches a preset business among a plurality of target second logistics orders; and determining the new warehouse from at least one candidate warehouse based on the total cost of the candidate warehouse and the preset second logistics order.

[0142] In some embodiments, under the coverage relationship represented by the target coverage information, the total cost of a candidate warehouse may include at least one of the following: a first logistics cost of the candidate warehouse under the coverage relationship, a second logistics cost of the candidate warehouse under the coverage relationship, and the warehousing cost of the candidate warehouse. The methods for determining the first and second logistics costs can be found in the relevant descriptions above, and will not be repeated here.

[0143] Since the processing model constructs an objective function with the goal of minimizing total cost, in some embodiments, the processing model can directly output the total cost of the candidate warehouses under the coverage relationship represented by the target coverage information. In some embodiments, determining a new warehouse from at least one candidate warehouse based on the total cost of the candidate warehouses and preset second logistics orders may include: determining the candidate warehouse as a new warehouse if the total cost of the candidate warehouse meets a first preset condition, and / or the proportion of the preset second logistics orders of the candidate warehouses in the multiple target second logistics orders meets a second preset condition.

[0144] By combining the total cost with the proportion of pre-set second logistics orders to determine whether a candidate warehouse should be a new warehouse, the accuracy of the new warehouse decision can be improved.

[0145] The first preset condition can be specifically determined according to the actual situation. In some embodiments, the first preset condition may be that the decrease in total cost is greater than a first preset value. This decrease may refer to the reduction in total cost compared to the total cost of the original warehouse. It is understood that the total cost of the original warehouse can be calculated through historical logistics orders, and the total cost of the original warehouse may be a known amount.

[0146] The second preset condition can be specifically determined according to the actual situation. In some embodiments, the second preset condition may be that the proportion of preset second logistics orders is greater than a second preset value. The preset second logistics orders are target second logistics orders that match the preset business. The preset business can be specifically determined according to actual needs. For example, the preset business may be next-day delivery, which means that the product ordered by the user will be delivered to the user within 24 hours after the user places an order.

[0147] The percentage of pre-set second logistics orders can be used to characterize the increase in pre-set business. By screening new warehouses based on the percentage of pre-set second logistics orders, the pre-set business is taken into account during the process of determining new warehouses, making the new warehouses more in line with business needs and improving the rationality of the decision to build new warehouses.

[0148] Figure 2 This is a flowchart illustrating the determination of quantity proportions according to an exemplary embodiment, such as... Figure 2 As shown, the process may include the following steps.

[0149] Step 210: Determine the quantity of products in each sub-region and the total quantity of products based on multiple target second logistics orders.

[0150] Step 220: Determine the percentage of products in each sub-region based on the quantity of products in each sub-region and the total quantity of products.

[0151] In some embodiments, the target first logistics order and the target second logistics order can reflect the SKU attributes of their respective products. SKU (Stock Keeping Unit) is the unit for measuring inventory inflows and outflows. SKU attributes can be characteristics of a product; for example, the SKU attributes of electrical appliances can include color, size, style, and model. In some embodiments, products with the same SKU attributes belong to the same product category, and each product category has a unique SKU code. Correspondingly, the product category can be determined through the SKU code in the target first logistics order.

[0152] For products in the target first logistics order, the corresponding target second logistics order can be selected from multiple target second logistics orders using the product's SKU code. Then, the quantity of the product in each sub-region and the total product quantity can be determined based on these target second logistics orders. For example, taking the r-th SKU from multiple target first logistics orders and sub-region 1 as an example, the proportion of the r-th SKU in sub-region 1 can be determined by the ratio of the quantity of the r-th SKU in sub-region 1 to the total product quantity of the r-th SKU in multiple target second logistics orders.

[0153] In some embodiments, the proportion of a product in each sub-region can be determined based on the ratio of the quantity of the product in each sub-region to the total quantity of the product.

[0154] Figure 3 This is a block diagram illustrating a warehouse location selection device according to an exemplary embodiment. (Refer to...) Figure 3 The device 300 includes an acquisition module 310, a first construction module 320, a second construction module 330, a processing module 340, and an output module 350.

[0155] The acquisition module 310 is configured to acquire multiple target first logistics orders and multiple target second logistics orders for at least one candidate warehouse, wherein the target first logistics order refers to a logistics order that is billed based on product volume, and the target second logistics order refers to a logistics order that is billed based on the number of products.

[0156] The first construction module 320 is configured to allocate the volume of the product in each of the target first logistics orders to multiple sub-regions according to the delivery address in the target second logistics order, and to construct a first volume matrix according to the volume of the product of each of the target first logistics orders allocated to each of the sub-regions;

[0157] The second construction module 330 is configured to construct a second cost matrix based on the logistics costs of the multiple target second logistics orders, the second cost matrix being used to reflect the logistics costs from each of the candidate warehouses to each of the sub-regions;

[0158] The processing module 340 is configured to process the first volume matrix and the second cost matrix according to a pre-built processing model to obtain target coverage information, wherein the target coverage information is used to reflect the target coverage relationship between the candidate warehouse and the sub-region;

[0159] The output module 350 is configured to output the location information of the newly built warehouse based on the target coverage information.

[0160] In some embodiments, the first construction module 320 is further configured to:

[0161] Based on the delivery address in the target second logistics order, multiple sub-regions are determined;

[0162] Based on the proportion of the products in each of the target first logistics orders in each of the sub-regions, the volume of the products in each of the target first logistics orders is allocated to multiple sub-regions, and the proportion of the products in each of the target first logistics orders is determined based on multiple target second logistics orders.

[0163] In some embodiments, the apparatus further includes:

[0164] The first determining module is configured to determine the quantity of the product in each of the sub-regions and the total quantity of the product based on the multiple target second logistics orders;

[0165] The second determining module is configured to determine the proportion of the quantity of the product in each of the sub-regions based on the quantity of the product in each of the sub-regions and the total quantity of the product.

[0166] In some embodiments, the apparatus further includes:

[0167] The third determining module is configured to determine the logistics cost of multiple target second logistics orders based on the distance between the location of the candidate warehouse in each target second logistics order and the delivery address, and the number of products in each target second logistics order;

[0168] The second building module 330 is further configured as follows:

[0169] The logistics costs of the target second logistics order are aggregated based on the candidate warehouses and the sub-regions to determine the logistics cost from each candidate warehouse to each sub-region.

[0170] The second cost matrix is ​​constructed based on the logistics costs from each candidate warehouse to each sub-region.

[0171] In some embodiments, the apparatus further includes:

[0172] The fourth determining module is configured to determine the shortest distance between each of the candidate warehouses and each of the sub-regions based on multiple target second logistics orders;

[0173] The third construction module is configured to construct an initial coverage matrix based on the shortest distance, the initial coverage matrix being used to reflect the initial coverage relationship between the candidate warehouse and the sub-region;

[0174] The processing module 340 is further configured to:

[0175] The initial coverage matrix, the first volume matrix, the second cost matrix, and the storage cost of each candidate warehouse are processed according to the processing model to determine the target coverage information.

[0176] In some embodiments, the third building module is further configured to:

[0177] If the shortest distance is less than a preset threshold, it is determined that the candidate warehouse and the sub-region corresponding to the shortest distance have the initial coverage relationship.

[0178] If the shortest distance is greater than the preset threshold, it is determined that the candidate warehouse and the sub-region corresponding to the shortest distance do not have the initial coverage relationship;

[0179] The initial coverage matrix is ​​constructed based on the initial coverage relationship between the candidate warehouse and the sub-region.

[0180] In some embodiments, the processing model includes a constrained objective function, the constraints being used to constrain the coverage information between the candidate warehouse and the sub-region, and the objective function being used to determine, from the coverage information that satisfies the constraints, the target coverage information that minimizes the total cost corresponding to the candidate warehouse;

[0181] Wherein, for any of the coverage information, the total cost includes the sum of any one or more of the following:

[0182] The first logistics cost of the candidate warehouse determined based on the first volume matrix under the coverage relationship represented by the coverage information;

[0183] The second logistics cost of the candidate warehouse determined based on the second cost matrix under the coverage relationship represented by the coverage information.

[0184] In some embodiments, determining the first logistics cost includes:

[0185] Based on the first volume matrix, the delivery volume of each sub-region associated with the candidate warehouse under the coverage relationship represented by the coverage information is determined, and the tiered cost corresponding to the candidate warehouse is determined based on the delivery volume of each sub-region associated with the candidate warehouse.

[0186] The first logistics cost is determined based on the delivery volume of each sub-region and the tiered cost.

[0187] In some embodiments, the output module 350 is further configured to:

[0188] Determine the total cost of the candidate warehouse and the preset second logistics order under the coverage relationship represented by the target coverage information, wherein the preset second logistics order is a target second logistics order that matches the preset business among a plurality of target second logistics orders;

[0189] The new warehouse is determined from at least one of the candidate warehouses based on the total cost of the candidate warehouses and the preset second logistics order;

[0190] Output the location information of the newly created warehouse.

[0191] In some embodiments, the output module 350 is further configured to:

[0192] If the total cost of the candidate warehouse meets a first preset condition, and / or the proportion of the preset second logistics orders of the candidate warehouse in the multiple target second logistics orders meets a second preset condition, the candidate warehouse will be determined as the new warehouse.

[0193] In some embodiments, the acquisition module 310 is further configured to:

[0194] Obtain multiple historical logistics orders;

[0195] Based on order attributes, multiple historical logistics orders are classified to determine multiple initial first logistics orders and multiple initial second logistics orders. The initial first logistics order is a logistics order from the product's production address to the original warehouse, and the initial second logistics order is a logistics order from the original warehouse to the product's delivery address.

[0196] For each candidate warehouse, the original warehouse in each initial first logistics order and each initial second logistics order is replaced with the candidate warehouse to obtain multiple target first logistics orders and multiple target second logistics orders for each candidate warehouse.

[0197] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0198] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the warehouse location method provided in this disclosure.

[0199] Figure 4 This is a block diagram illustrating a warehouse location device 400 according to an exemplary embodiment. For example, device 400 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0200] Reference Figure 4 The device 400 may include one or more of the following components: processing component 402, memory 404, power supply component 406, multimedia component 408, audio component 410, input / output interface 412, sensor component 414, and communication component 416.

[0201] Processing component 402 typically controls the overall operation of device 400, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include a multimedia module to facilitate interaction between multimedia component 408 and processing component 402.

[0202] Memory 404 is configured to store various types of data to support the operation of device 400. Examples of such data include instructions for any application or method operating on device 400, contact data, phonebook data, messages, pictures, videos, etc. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0203] Power supply component 406 provides power to various components of device 400. Power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 400.

[0204] Multimedia component 408 includes a screen that provides an output interface between the device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 408 includes a front-facing camera and / or a rear-facing camera. When the device 400 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0205] Audio component 410 is configured to output and / or input audio signals. For example, audio component 410 includes a microphone (MIC) configured to receive external audio signals when device 400 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 404 or transmitted via communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

[0206] Input / output interface 412 provides an interface between processing component 402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0207] Sensor assembly 414 includes one or more sensors for providing state assessments of various aspects of device 400. For example, sensor assembly 414 may detect the on / off state of device 400, the relative positioning of components such as the display and keypad of device 400, changes in position of device 400 or one of its components, the presence or absence of user contact with device 400, orientation or acceleration / deceleration of device 400, and temperature changes of device 400. Sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 414 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0208] Communication component 416 is configured to facilitate wired or wireless communication between device 400 and other devices. Device 400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0209] In an exemplary embodiment, the apparatus 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the warehouse location method described above.

[0210] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of the device 400 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0211] In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable device, the computer program having a code portion for performing the warehouse location method described above when executed by the programmable device.

[0212] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0213] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A warehouse siting method characterized by, The method comprises: obtaining a plurality of target first logistics orders and a plurality of target second logistics orders of at least one candidate warehouse, the target first logistics order being a logistics order charged by product volume, and the target second logistics order being a logistics order charged by product quantity; the target first logistics order being a logistics order from a product production address to the candidate warehouse, and the target second logistics order being a logistics order from the candidate warehouse to a product receiving address; allocating the volume of the product in each target first logistics order to a plurality of sub-regions according to the receiving address in the target second logistics order, and constructing a first volume matrix according to the volume of the product in each target first logistics order allocated to each sub-region; constructing a second cost matrix according to the logistics cost of a plurality of target second logistics orders, the second cost matrix reflecting the logistics cost from each candidate warehouse to each sub-region; processing the first volume matrix and the second cost matrix according to a pre-constructed processing model to obtain target coverage information, the target coverage information reflecting the target coverage relationship between the candidate warehouse and the sub-region; outputting the location information of a newly-built warehouse according to the target coverage information, wherein the newly-built warehouse is determined from at least one candidate warehouse; the method further comprises: determining a plurality of sub-regions according to the receiving address in the target second logistics order; allocating the volume of the product in each target first logistics order to a plurality of sub-regions according to the quantity proportion of the product in each target first logistics order in each sub-region, the quantity proportion being determined according to a plurality of target second logistics orders.

2. The method of claim 1, wherein, The method further comprises: determining the quantity of the product in each sub-region and the total quantity of the product according to a plurality of target second logistics orders; determining the quantity proportion of the product in each sub-region according to the quantity of the product in each sub-region and the total quantity of the product.

3. The method of claim 1, wherein, The method further comprises: determining the logistics cost of a plurality of target second logistics orders according to the distance between the location of the candidate warehouse in each target second logistics order and the receiving address, and the quantity of the product in each target second logistics order; the method further comprises: aggregating and processing the logistics cost of the target second logistics order according to the candidate warehouse and the sub-region to determine the logistics cost from each candidate warehouse to each sub-region; constructing the second cost matrix according to the logistics cost from each candidate warehouse to each sub-region.

4. The method of claim 1, wherein, The method further comprises: determining the shortest distance between each candidate warehouse and each sub-region according to a plurality of target second logistics orders. constructing an initial coverage matrix according to the shortest distances, the initial coverage matrix being used to reflect initial coverage relationships between the candidate warehouses and the sub-regions; processing the first volume matrix and the second cost matrix according to a pre-constructed processing model to obtain target coverage information, including: processing the initial coverage matrix, the first volume matrix, the second cost matrix and the warehouse storage cost of each candidate warehouse according to the processing model to determine the target coverage information.

5. The method of claim 4, wherein, constructing the initial coverage matrix according to the shortest distances, including: in a case where the shortest distance is less than a preset threshold, determining that the candidate warehouse corresponding to the shortest distance and the sub-region have the initial coverage relationship; in a case where the shortest distance is greater than the preset threshold, determining that the candidate warehouse corresponding to the shortest distance and the sub-region do not have the initial coverage relationship; constructing the initial coverage matrix according to the initial coverage relationship between the candidate warehouse and the sub-region.

6. The method according to any one of claims 1 to 5, characterized in that, the processing model includes a target function with a constraint condition, the constraint condition being used to constrain coverage information between the candidate warehouse and the sub-region, and the target function being used to determine target coverage information that minimizes a total cost of the candidate warehouse from the coverage information satisfying the constraint condition; wherein, for any one of the coverage information, the total cost includes a sum of any one or more of: a first logistics cost of the candidate warehouse in a coverage relationship represented by the coverage information, which is determined based on the first volume matrix; a second logistics cost of the candidate warehouse in the coverage relationship represented by the coverage information, which is determined based on the second cost matrix.

7. The method of claim 6, wherein, the determination of the first logistics cost includes: determining, based on the first volume matrix, a delivery volume of each sub-region associated with the candidate warehouse in the coverage relationship represented by the coverage information, and determining a step cost corresponding to the candidate warehouse based on the delivery volume of each sub-region associated with the candidate warehouse; determining the first logistics cost according to the delivery volume of each sub-region and the step cost.

8. The method of claim 1, wherein, the outputting of the position information of the newly-built warehouse according to the target coverage information includes: determining a total cost of the candidate warehouse and a preset second logistics order in the coverage relationship represented by the target coverage information, the preset second logistics order being a target second logistics order matching a preset business among a plurality of target second logistics orders; determining the newly-built warehouse from at least one of the candidate warehouses according to the total cost of the candidate warehouse and the preset second logistics order; outputting the position information of the newly-built warehouse.

9. The method of claim 8, wherein, the determining of the newly-built warehouse from at least one of the candidate warehouses according to the total cost of the candidate warehouse and the preset second logistics order includes: in a case where the total cost of the candidate warehouse satisfies a first preset condition and / or a proportion of the preset second logistics order of the candidate warehouse in the plurality of target second logistics orders satisfies a second preset condition, determining the candidate warehouse as the newly-built warehouse.

10. The method of claim 1, wherein, The obtaining of the plurality of target first logistics orders and the plurality of target second logistics orders of the at least one candidate warehouse comprises: obtaining a plurality of historical logistics orders; classifying the plurality of historical logistics orders according to order attributes, to determine a plurality of initial first logistics orders and a plurality of initial second logistics orders, the initial first logistics orders being logistics orders from a production address of a product to an original warehouse, and the initial second logistics orders being logistics orders from the original warehouse to a consignee address of the product; for each candidate warehouse, replacing the original warehouse in each of the initial first logistics orders and the initial second logistics orders with the candidate warehouse, to obtain the plurality of target first logistics orders and the plurality of target second logistics orders of the candidate warehouse.

11. A warehouse siting apparatus, characterized by, comprise: an obtaining module configured to obtain a plurality of target first logistics orders and a plurality of target second logistics orders of at least one candidate warehouse, the target first logistics orders being logistics orders charged according to product volume, and the target second logistics orders being logistics orders charged according to product quantity; the target first logistics orders being logistics orders from a production address of a product to the candidate warehouse, and the target second logistics orders being logistics orders from the candidate warehouse to a consignee address of the product; a first constructing module configured to, according to a consignee address in the target second logistics orders, allocate the volume of the product in each of the target first logistics orders to a plurality of sub-regions, and construct a first volume matrix according to the volume of the product in each of the target first logistics orders allocated to each of the sub-regions; a second constructing module configured to construct a second cost matrix according to logistics costs of the plurality of target second logistics orders, the second cost matrix being used to reflect logistics costs from each of the candidate warehouses to each of the sub-regions; a processing module configured to process the first volume matrix and the second cost matrix according to a pre-constructed processing model, to obtain target coverage information, the target coverage information being used to reflect target coverage relationships between the candidate warehouses and the sub-regions; an output module configured to output location information of a newly-built warehouse according to the target coverage information, wherein the newly-built warehouse is determined from the at least one candidate warehouse; the first constructing module is further configured to: determine a plurality of sub-regions according to the consignee address in the target second logistics orders; allocate the volume of the product in each of the target first logistics orders to a plurality of sub-regions according to a quantity proportion of the product in each of the target first logistics orders in each of the sub-regions, the quantity proportion being determined according to the plurality of target second logistics orders.

12. A warehouse siting apparatus, characterized by, comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: Obtaining a plurality of target first logistics orders and a plurality of target second logistics orders of at least one candidate warehouse, the target first logistics orders refer to logistics orders charged by product volume, and the target second logistics orders refer to logistics orders charged by product quantity; the target first logistics orders are logistics orders from a production address of a product to the candidate warehouse, and the target second logistics orders are logistics orders from the candidate warehouse to a consignee address of the product; According to a consignee address in the target second logistics order, a volume of a product in each target first logistics order is allocated to a plurality of sub-regions, and a first volume matrix is constructed according to the volume of the product in each target first logistics order allocated to each sub-region; According to a logistics cost of a plurality of target second logistics orders, a second cost matrix is constructed, the second cost matrix is used to reflect a logistics cost from each candidate warehouse to each sub-region; According to a pre-constructed processing model, the first volume matrix and the second cost matrix are processed to obtain target coverage information, the target coverage information is used to reflect a target coverage relationship between the candidate warehouse and the sub-region; According to the target coverage information, position information of a newly-built warehouse is output, wherein the newly-built warehouse is determined from at least one candidate warehouse; The processor is further configured to: According to the consignee address in the target second logistics order, a plurality of sub-regions are determined; According to a quantity proportion of a product in each target first logistics order in each sub-region, a volume of the product in each target first logistics order is allocated to a plurality of sub-regions, the quantity proportion is determined according to a plurality of target second logistics orders.

13. A computer-readable storage medium having stored thereon computer program instructions, wherein, The program instruction is executed by the processor to implement the steps of the method in any one of claims 1-10.

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