CN111598603A - Warehouse site selection method, device, equipment and storage medium - Google Patents

Warehouse site selection method, device, equipment and storage medium Download PDF

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CN111598603A
CN111598603A CN202010142327.8A CN202010142327A CN111598603A CN 111598603 A CN111598603 A CN 111598603A CN 202010142327 A CN202010142327 A CN 202010142327A CN 111598603 A CN111598603 A CN 111598603A
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汤春峰
刘强强
李�权
黎治伟
郑明华
魏帅超
詹子知
陈天健
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WeBank Co Ltd
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Abstract

The invention discloses a warehouse address selection method, a device, equipment and a storage medium, wherein the method comprises the following steps: receiving a warehouse location request, wherein the warehouse location request comprises a target area; calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm; and generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list. Therefore, on the basis of artificial intelligence, a warehouse list is generated according to the operation cost of warehouse construction in the target area, and then a warehouse site selection scheme corresponding to the type of the warehouse site selection scheme is generated on the basis of the warehouse list, so that the workload of warehouse site selection is reduced, and the intelligent decision of warehouse site selection is realized.

Description

Warehouse site selection method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of financial science and technology, in particular to a warehouse site selection method, device, equipment and storage medium.
Background
With the development of computer technology, more and more technologies (big data, distributed, Blockchain, artificial intelligence, etc.) are applied to the financial field, and the traditional financial industry is gradually changing to financial technology (Fintech), but higher requirements are also put forward on the technologies due to the requirements of security and real-time performance of the financial industry.
Along with the development of the logistics industry and the retail industry, the selection of the warehouse location can greatly affect the operation cost and efficiency. At present, people generally manually obtain related information, and select the information according to experience after simple arrangement and induction. Therefore, the site selection workload of the warehouse is huge, the intelligent decision is lacked, and the optimal warehouse is difficult to select.
Disclosure of Invention
The invention provides a warehouse site selection method, device, equipment and storage medium, aiming at reducing the workload of warehouse site selection and realizing intelligent decision of warehouse site selection.
In order to achieve the above object, the present invention provides a warehouse address selecting method, which comprises:
receiving a warehouse location request, wherein the warehouse location request comprises a target area;
calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm;
and generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list.
Preferably, the step of calculating an operation cost for building a warehouse in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm includes:
calculating a business circle transportation cost of each warehouse to the business circle, a wholesale market transportation cost of each warehouse to the wholesale market, and a highway entrance transportation cost of each warehouse to the highway entrance;
calculating the warehouse construction operation cost of each warehouse based on the business circle transportation cost, the cargo occupation ratio from the warehouse to the business circle, the wholesale market transportation cost, the warehouse opening cost of each warehouse and a preset operation cost function;
and selecting warehouses with the operation cost lower than a threshold value, and generating a warehouse list according to the screening result.
Preferably, the step of calculating the business turn transportation cost of each warehouse to the business turn, the wholesale market transportation cost of each warehouse to the wholesale market, and the highway entrance transportation cost of each warehouse to the highway entrance comprises:
acquiring the driving cost and the distance from the warehouse to each reference object;
calculating the business circle transportation cost from the warehouse to the business circle based on the driving cost and the distance from the warehouse to each business circle;
(ii) a highway entrance transportation cost of warehouse to highway population based on the driving cost and a distance of each of the highway entrances of the warehouse;
calculating the wholesale market transportation cost from the warehouse to the wholesale market based on the driving cost and the distance of each wholesale market of the warehouse.
Preferably, the type of the warehouse location scheme includes an insight location scheme, and the step of generating the warehouse location scheme according to the type of the warehouse location scheme based on the warehouse list includes:
if the type of the warehouse site selection scheme is an insight site selection scheme, acquiring traffic conditions from each warehouse to each reference object in the warehouse list based on a warehouse visual graph, wherein the traffic conditions comprise path information and congestion indexes;
and comparing the warehouses based on the path and the congestion index to obtain a comparison result, and generating an insight site selection scheme based on the comparison result.
Preferably, before the step of obtaining traffic conditions from each warehouse in the warehouse list to each reference object based on the warehouse visualization, the method further includes:
acquiring spatiotemporal information from each warehouse to each reference point, wherein the spatiotemporal information comprises the distance from each warehouse to each reference point and time consumption information;
and generating a warehouse visual graph based on the distance information and the time consumption information.
Preferably, the type of the warehouse addressing scheme includes creating an addressing scheme, and the step of generating the warehouse addressing scheme according to the type of the warehouse addressing scheme based on the warehouse list includes:
if the type of the warehouse site selection scheme is the establishment site selection scheme, acquiring the physical attribute of each warehouse in the warehouse list, and screening one or more target warehouses based on the physical attribute;
and generating a created addressing scheme according to the one or more target warehouses, and setting the name of the created addressing scheme, wherein the created addressing scheme comprises warehouse information of the one or more target warehouses.
Preferably, before the step of calculating the business turn transportation cost of each warehouse to the business turn, the wholesale market transportation cost of each warehouse to the wholesale market, and the highway entrance transportation cost of each warehouse to the highway entrance, the method further comprises:
collecting merchant data in the target area, and clustering merchants in the target area based on the merchant data to obtain a clustering result;
and marking the clusters with the number of the merchants larger than the threshold value of the merchants in the clustering result as a quotient circle.
In addition, in order to achieve the above object, the present invention further provides a warehouse site selection device, including:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a warehouse address selection request, and the warehouse address selection request comprises a target area;
the calculation module is used for calculating the operation cost of warehouse building in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm;
and the generating module is used for generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list.
In addition, in order to achieve the above object, the present invention further provides a warehouse addressing device, where the warehouse addressing device includes a processor, a memory, and a warehouse addressing program stored in the memory, and when the warehouse addressing program is executed by the processor, the steps of the warehouse addressing method are implemented.
In addition, to achieve the above object, the present invention further provides a computer storage medium, on which a warehouse addressing program is stored, and when the warehouse addressing program is executed by a processor, the steps of the warehouse addressing method are implemented.
Compared with the prior art, the invention provides a warehouse site selection method, a device, equipment and a storage medium, which are used for receiving a warehouse site selection request, wherein the warehouse site selection request comprises a target area; calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm; and generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list. Therefore, on the basis of artificial intelligence, a warehouse list is generated according to the operation cost of warehouse construction in the target area, and then a warehouse site selection scheme corresponding to the type of the warehouse site selection scheme is generated on the basis of the warehouse list, so that the workload of warehouse site selection is reduced, and the intelligent decision of warehouse site selection is realized.
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Fig. 1 is a schematic hardware configuration diagram of a warehouse addressing device according to embodiments of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a warehouse location method of the present invention;
fig. 3 is a schematic diagram of a first embodiment of the warehouse location method of the present invention;
fig. 4 is a schematic flow chart of a warehouse location method according to a second embodiment of the present invention;
fig. 5 is a schematic diagram of a scenario of a second embodiment of the warehouse location method of the present invention;
FIG. 6 is a flowchart illustrating a warehouse location method according to a third embodiment of the present invention
FIG. 7 is a comparative view of an exemplary warehouse location method of the present invention
FIG. 8 is an exemplary timeline for a warehouse location method of the present invention;
FIG. 9 is an exemplary warehouse insight view of the warehouse location method of the present invention;
FIG. 10 is an exemplary congestion index view of the warehouse location method of the present invention;
fig. 11 is a schematic flow chart of a warehouse location method according to a fourth embodiment of the present invention;
fig. 12 is a schematic diagram of a scenario of a warehouse location method according to a fourth embodiment of the present invention;
FIG. 13 is a schematic diagram of another scenario of the warehouse location method according to the fourth embodiment of the present invention
Fig. 14 is a functional block diagram of the warehouse site selection device according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The warehouse site selection equipment mainly related to the embodiment of the invention is network connection equipment capable of realizing network connection, and the warehouse site selection equipment can be a server, a cloud platform and the like. In addition, the mobile terminal related to the embodiment of the invention can be mobile network equipment such as a mobile phone, a tablet personal computer and the like.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of a warehouse address selecting device according to embodiments of the present invention. In this embodiment of the present invention, the warehouse addressing device may include a processor 1001 (e.g., a Central processing unit, CPU), a communication bus 1002, an input port 1003, an output port 1004, and a memory 1005. The communication bus 1002 is used for realizing connection communication among the components; the input port 1003 is used for data input; the output port 1004 is used for data output, the memory 1005 may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as a magnetic disk memory, and the memory 1005 may optionally be a storage device independent of the processor 1001. Those skilled in the art will appreciate that the hardware configuration depicted in FIG. 1 is not intended to be limiting of the present invention, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
With continued reference to fig. 1, the memory 1005 of fig. 1, which is one type of readable storage medium, may include an operating system, a network communication module, an application program module, and a repository addressing program. In fig. 1, the network communication module is mainly used for connecting to a server and performing data communication with the server; and processor 1001 may call a warehouse addressing program stored in memory 1005 and perform the warehouse addressing method provided by embodiments of the present invention.
The embodiment of the invention provides a warehouse address selection method.
Referring to fig. 2, fig. 2 is a schematic flow chart of a warehouse location method according to a first embodiment of the present invention.
In this embodiment, the warehouse location method is applied to a warehouse location device, and the method includes:
step S101, receiving a warehouse site selection request, wherein the warehouse site selection request comprises a target area;
the warehouse site selection device monitors received voice and/or action operation requests and analyzes the voice and/or action operation requests, and if the voice and/or action operation requests are warehouse site selection requests, the warehouse site selection requests are received, and the warehouse site selection requests comprise target areas and warehouse site selection scheme types.
In this embodiment, the target area may be a selected area selected on a map displayed on the screen of the warehouse addressing device, and specifically, the selected area may be obtained at least in the following manner:
the first method is as follows: after receiving a region setting request, acquiring a click position of a user on the screen, taking the click position as a central point, and marking a region within a preset radius taking the central point as a center as the selected region;
the second method comprises the following steps: acquiring a click position of a user on the screen, identifying an administrative area where the click position is located, and marking the administrative area as the selected area;
the third method comprises the following steps: and acquiring a region boundary manually drawn on the screen by a user, and determining a region in the region boundary as the selected region.
Step S102: calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm;
it will be appreciated that transportation costs are one of the expenses associated with transporting goods to and from the warehouse, and that transportation costs are one of the considerations in addressing the warehouse. The opening cost includes a rent. In addition, the operation cost can also comprise various expenses such as labor cost, water and electricity cost, network cost, property cost and the like.
In this embodiment, the reference objects include a business district, a wholesale market, and a highway entrance.
Specifically, the step S102: calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm comprises the following steps:
step S102a, calculating the transportation cost of the business circle from each warehouse to the business circle, the transportation cost of the wholesale market from each warehouse to the wholesale market and the transportation cost of the high-speed entrance from each warehouse to the highway entrance;
specifically, the step S102a includes:
step S102a: acquiring the driving cost and the distance from the warehouse to each reference object;
the driving cost is obtained according to experience, and comprises oil consumption, vehicle breakage, driver wages and the like. The distances of the warehouse to the respective reference objects may be obtained from a navigation platform.
Step S102b: calculating the business circle transportation cost from the warehouse to the business circle based on the driving cost and the distance from the warehouse to each business circle;
step S102c: (ii) a highway entrance transportation cost to a highway population based on the driving cost and a distance from the warehouse to each of the highway intersections;
step S102 d: and the wholesale market transportation cost from the warehouse to the wholesale market is based on the driving cost and the distance between each high-speed intersection of the warehouse.
Specifically, expressing the quotient circle set as I and the warehouse set as J, first, a binary variable is defined:
xj1 (select warehouse j), xj0 (warehouse j is not selected), and the warehouse transportation cost of warehouse j is defined as fj；
Then, the warehouse-to-business district transportation cost c is calculatedij，cijIs the business turn transportation cost from warehouse j to business turn i, usually the warehouse-to-business turn distance dijIn proportion: c. Cij＝adijA is the driving cost per kilometer;
secondly, calculating the transportation cost e of the highway entrance from the warehouse to the highway entrancekjUsually at a distance f from the warehouse to the entrance of the highwaykjIn proportion: e.g. of the typekj＝afkj；
And then calculating the wholesale market transportation cost g from the warehouse to the wholesale marketmjUsually from warehouse to wholesale markethmjIn proportion: gmj＝ahmj。
Step S102b, calculating the operation cost of warehouse construction of each warehouse based on the transportation cost of the trade district, the cargo occupation ratio from the warehouse to the trade district, the wholesale market transportation cost, the warehouse opening cost of each warehouse and a preset operation cost function;
in this example, y is definedijIs the ratio of goods transported from warehouse j to business circle i, and yijNot less than 0; expressing the opening cost as fj；
Representing the preset operation cost function as:
Figure BDA0002398999270000071
wherein
Figure BDA0002398999270000072
Figure BDA0002398999270000073
Figure BDA0002398999270000074
Figure BDA0002398999270000075
And calculating the operation cost of each warehouse building bin based on the preset operation cost function.
And step S102c, selecting warehouses with operation costs lower than a threshold value, and generating a warehouse list according to the screening result.
In this embodiment, the warehouse recommendation algorithm is an algorithm that recommends that the operating cost of the warehouse is lower than a threshold value,
the warehouse recommendation algorithm may be a minimization objective function. If the warehouse recommendation algorithm is to minimize the preset operation cost function, one or more warehouses with operation costs lower than a threshold value are obtained according to a minimization result, and the warehouse list is generated by the one or more warehouses. The warehouse list displays at least a warehouse name. Further, information such as warehouse area, warehouse use condition, warehouse rent and the like can be displayed through the warehouse list.
Further, the step S102a: before the step of calculating the business turn transportation cost of each warehouse to the business turn, the wholesale market transportation cost of each warehouse to the wholesale market, and the highway entrance transportation cost of each warehouse to the highway entrance, the method further comprises the following steps:
step S102 a-1: collecting merchant data in the target area, and clustering merchants in the target area based on the merchant data to obtain a clustering result;
specifically, the merchant data includes a merchant address, a merchant ID (identification), a merchant name, a merchant type, a contact way, and the like; the warehouse data comprises a warehouse name, a warehouse ID, a warehouse location, a warehouse area, a price, a warehouse advantage, a warehouse use condition, an owner, a contact way and the like; the wholesale market data includes a wholesale market name, a wholesale market ID, a wholesale market location, a wholesale market type, and the like.
The clustering algorithm is a statistical analysis method for researching classification problems and is an important algorithm for data mining. The clustering (Cluster) analysis is composed of several patterns (patterns), which are vectors of a metric (measure) or a point in a multidimensional space. Cluster analysis is based on similarity, with patterns in one cluster having directly more similarity than between patterns not in the same cluster.
Generally, the clustering algorithms include a K-Means clustering algorithm, a Means-Shift clustering, an expected maximum value clustering based on a gaussian mixture model, a coacervation hierarchical clustering, and the like, which have advantages, and the K-Means clustering algorithm is taken as an example for specific explanation in this embodiment.
Specifically, the steps of the K-Means clustering algorithm include:
firstly, randomly selecting K merchants from the merchants in the target area as initial clustering centers, and for the rest other merchants, respectively allocating the other merchants to a first cluster represented by the initial clustering center with the maximum similarity according to the similarity (distance) between the other merchants and each initial clustering center; and the K value is determined by the clustering number corresponding to the inflection point in the relation curve of the clustering number and the average inter-class distance. Specifically, referring to fig. 3, fig. 3 is a schematic view of a scenario of the warehouse address selecting method according to the first embodiment of the present invention, in fig. 3, when the number of clusters is 220, a first inflection point appears, so that the K value is determined to be 220.
Then, calculating a first clustering center of the first cluster, continuously repeating the process of distributing and calculating the clustering centers until the obtained standard measure function is converged, and marking each clustering center in the finally obtained clustering result as a target clustering center; wherein the cluster center is an average value of all merchants in the corresponding cluster, and the standard measure function may be an average variance.
Step S102 a-2: and marking the clusters with the number of the merchants larger than the threshold value of the merchants in the clustering result as a quotient circle.
And after the clustering is finished, counting the number of the commercial tenants in each cluster in the clustering result, and marking the clusters with the number of the commercial tenants larger than the threshold value of the commercial tenants in the clustering result as a quotient circle. The merchant threshold may be determined empirically. For example, the merchant threshold is set to 100, 200, etc. In this embodiment, after the business circle is determined, the candidate warehouse list needs to be obtained.
And step S103, generating a warehouse location scheme according to the type of the warehouse location scheme based on the warehouse list.
After the warehouse list is obtained, a part of suitable warehouses are screened out. In order to obtain a more satisfactory warehouse, the screening of the warehouses in the warehouse list is continued, so as to further reduce the workload of warehouse site selection.
Specifically, the warehouses in the warehouse list are screened according to preset screening conditions, wherein the screening conditions can be traffic conditions, transportation costs, time consumption, distances and the like. In this embodiment, the traffic condition may be assisted by a traffic navigation platform, which provides the traffic condition, and the traffic navigation platform may be a Baidu map, a Gauder map, an Tencent map, an Apple map, or the like. For example, if the traffic conditions from each warehouse to the reference object need to be obtained, the first traffic conditions from each warehouse to each business district may be obtained respectively, the second traffic conditions from each candidate warehouse to each wholesale market may be obtained respectively, and the first traffic conditions and the second traffic conditions of the same candidate warehouse may be stored correspondingly for subsequent reading and use.
In this embodiment, the traffic condition corresponds to a time point. That is, the traffic situation at a plurality of time points can be taken, and the time points can be 8 points, 10 points, 14 points and 17 points.
And then screening the warehouse list based on the traffic condition to obtain a warehouse meeting the requirement, and generating a warehouse site selection scheme.
Further, a hard screening condition may also be set. The hard screening conditions comprise warehouse area, warehouse floor, warehouse rent and the like. And adding the warehouse meeting the hard screening condition to the warehouse list.
According to the scheme, the warehouse address selection request is received, and the warehouse address selection request comprises a target area; calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm; and generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list. Therefore, on the basis of artificial intelligence, a warehouse list is generated according to the operation cost of warehouse construction in the target area, and then a warehouse site selection scheme corresponding to the type of the warehouse site selection scheme is generated on the basis of the warehouse list, so that the workload of warehouse site selection is reduced, and the intelligent decision of warehouse site selection is realized.
A second embodiment of the present invention is proposed based on the first embodiment shown in fig. 2 described above. Fig. 4 is a schematic flow chart of a warehouse addressing method according to a second embodiment of the present invention, as shown in fig. 4. The warehouse site selection scheme type comprises an insight site selection scheme, and the step of generating the warehouse site selection scheme according to the warehouse site selection scheme type based on the warehouse list comprises the following steps:
step S201: if the type of the warehouse site selection scheme is an insight site selection scheme, acquiring traffic conditions from each warehouse to each reference object in the warehouse list based on a warehouse visual graph, wherein the traffic conditions comprise path information and congestion indexes;
in this embodiment, the pre-generated warehouse visualization map is analyzed to obtain the traffic condition. The warehouse visualization graph comprises a warehouse insight view and a congestion index view. In addition, related information can be obtained according to the warehouse comparison view.
Specifically, traffic conditions from each reference object to each reference object in the warehouse list are obtained according to information in the warehouse insight view and the congestion index view, and the traffic conditions include paths and congestion conditions.
Step S202: and comparing the warehouses based on the path information and the traffic condition to obtain a comparison result, and generating an insight site selection scheme based on the comparison result.
And comparing the traffic conditions from each warehouse to the reference object to obtain the optimal warehouse, and taking the optimal warehouse as an addressing scheme and storing the optimal warehouse as an insight addressing scheme.
Specifically, referring to fig. 5, fig. 5 is a schematic view of a scenario of the second embodiment of the present invention. Fig. 5 shows an interface of the insight addressing scheme displayed on the screen of the warehouse addressing device. Acquiring each warehouse from a warehouse list area of a target area, and adjusting a time axis to acquire a target time point; viewing the instant traffic condition through navigation preview; acquiring a congestion index and time consumption from the warehouse to a business district at the target time point from a warehouse visualization graph; and then obtaining comparison results from each warehouse in the target area to the business district and the wholesale market based on comparison results of the warehouse comparison views, screening an optimal warehouse by integrating the traffic condition, the comparison results and the comparison results, and storing the optimal warehouse as an insight site selection scheme. The insight addressing scheme may further include a congestion index and a time consumption of the best warehouse, a comparison result based on a comparison view of warehouses, and a comparison result of each warehouse in the target area to the business district and the wholesale market.
According to the scheme, if the type of the warehouse location scheme is an insight location scheme, the traffic condition from each warehouse to each reference object in the warehouse list is obtained based on a warehouse visual map, and the traffic condition comprises path information and a congestion index; and comparing the warehouses based on the path and the congestion index to obtain a comparison result, and generating an insight site selection scheme based on the comparison result. Therefore, the traffic condition is taken as a consideration factor of an insight site selection scheme, and intelligent decision of warehouse site selection is realized.
A third embodiment of the present invention is proposed based on the second embodiment shown in fig. 4 described above. Fig. 6 is a schematic flow chart of a warehouse location method according to a third embodiment of the present invention, as shown in fig. 6. Before the step of obtaining the traffic condition from each warehouse to each reference object in the warehouse list based on the warehouse visualization map, the method further includes:
step S301: acquiring spatiotemporal information from each warehouse to each reference point, wherein the spatiotemporal information comprises the distance from each warehouse to each reference point and time consumption information;
step S302: and generating a warehouse visual graph based on the distance information and the time consumption information.
Specifically, the step 301 includes:
step S301a, obtaining basic information of each warehouse, wherein the basic information comprises a warehouse name, an ID (identity) and a warehouse location;
there are currently a number of warehouse platforms that provide warehouse information, and some house rental vending platforms also have associated warehouse information. In this embodiment, an information acquisition protocol is signed with a warehouse platform, and the warehouse platform sends basic information of each warehouse, where the basic information includes a name, an ID (Identification), a location, an area, a price, a advantage, a use condition, an owner, a contact manner, and the like of the warehouse.
Step S301b, acquiring reference object information within a preset range based on the warehouse location, wherein the types of the reference objects comprise a trade area and a wholesale market;
in this embodiment, after the warehouse location of the subsequent warehouse is obtained, the information of the relevant reference object in the preset range is examined with the warehouse location as a center. The preset range may be set as required, for example, the preset range is set to 10km, 30km, 50km, and the like.
For the site selection of the warehouse, hard conditions such as the area and price of the warehouse need to be considered, and reference objects which have certain influence on the warehouse need to be considered, for example, the actual storage function of the warehouse needs to be considered, the surrounding commercial circle and the wholesale market need to be referred to, the fire safety of the warehouse needs to be considered, and the surrounding gas stations, and flammable factories need to be considered. In this embodiment, the reference objects may be classified into a plurality of categories according to the attributes of the objects, wherein the categories of the reference objects include a trade center, a wholesale market, a gas station, an exhibition center, a station, a dock, an airport, a flammable factory, and the like.
Step S301c, obtaining, by a third-party platform, reference distances from the warehouses to the reference objects, and obtaining reference elapsed time for the warehouses to reach the reference objects at a preset time point;
after all the reference objects are obtained, the reference distance from each warehouse to each reference object and the reference time consumed when the candidate warehouse reaches each reference object at a preset time point are obtained. The third party platform may be a Baidu map, a Gade map, an Tencent map, an Apple map, or the like.
In this embodiment, a relatively representative time point in one day is selected as the preset time point, for example, 6:00, 6:30, 7:00, 7:30, 8:00, 8:30, 9:00, 9:30, and the like. And the number of the preset time points can also be specifically set according to the requirement. It can be understood that, because the traffic congestion conditions at different time points are different, the recommended routes of the third-party platform at different time points may be different for the same reference object, and the corresponding distances and time consumption may also be different.
In other embodiments, a reference distance and a reference time consumption of the reference object to each warehouse in the warehouse list at a preset time point may also be obtained.
Step S301d, correspondingly saving the reference distance and the reference elapsed time as the spatiotemporal information between the warehouse and the reference object according to the type of the reference object;
and correspondingly storing the space-time information for subsequent retrieval and use. In this embodiment, a reference distance and reference time consumption of a warehouse and a reference object are stored as a set of information, where the set of information includes reference distances and reference time consumption information of a plurality of preset time points. Since the same reference object may contain a plurality of such reference objects, the spatiotemporal information contains multiple sets of information. For example, for a reference object whose category is business circles, there may be 100 business circles within the preset range, and the spatiotemporal information of a candidate warehouse includes 100 sets of information.
Step S301e, one or more of the spatiotemporal information is displayed in spatiotemporal coordinates, and a warehouse comparison view is generated.
Specifically, the step S301 e: presenting one or more of the spatiotemporal information in spatiotemporal coordinates, the step of generating the warehouse comparison view comprising:
step S301e 1: setting the reference distance as a vertical coordinate of the space-time coordinate, and setting a time-consuming reference as a horizontal coordinate of the space-time coordinate;
it will be appreciated that in other embodiments, the distance may be set as the abscissa of the spatiotemporal coordinate and the elapsed time may be set as the ordinate of the spatiotemporal coordinate.
Step S301e 2: determining coordinate values of each target reference object according to the reference distance and the reference time;
in this embodiment, the reference distance is used as a coordinate y value of the target reference object, and the accumulated consumed time of each preset time point is used as a coordinate x value of the target reference object; the reference time consumption is the accumulated time consumption from each preset time point of the candidate warehouse to each reference object.
Wherein the coordinate y value of the target reference object is the distance between the candidate warehouse and the target reference object;
coordinate x-value t of target reference object1+t2+…+tiWherein t represents time consumption, and i is the number of the preset time points.
Thus, for the target reference object a1, if the distance from the candidate warehouse to the target reference object a1 is 16km and the accumulated time consumption of each preset time point is 252min, the coordinate value thereof is (16km,252 min). Each coordinate value is a point in the space-time coordinate.
Step S301e 3: and respectively representing each target reference object in the space-time coordinate according to the coordinate values, and connecting coordinate points representing the target reference objects to obtain the warehouse comparison view. In this embodiment, different candidate warehouses may be identified by different shapes, styles and colors. For example, blue represents warehouse A, yellow represents warehouse B, and green represents warehouse C.
It is understood that the spatio-temporal coordinate may be a common coordinate graph with one abscissa on the ordinate, or may be a graph with one ordinate on a plurality of abscissas, or a graph with a plurality of ordinates on the abscissa, or a graph with a plurality of ordinates on a plurality of abscissas on a plurality of ordinates. For example, the time consumed by the trade circle and the wholesale market is taken as two abscissas of the space-time coordinate, and the distance is set as an ordinate.
Specifically, referring to fig. 7, fig. 7 is a comparative view of an exemplary warehouse of the warehouse location method of the present invention. In the example warehouse comparison view shown in FIG. 7, there are three warehouses, warehouse A, warehouse B, and warehouse C; the example warehouse comparison view takes distance as a vertical coordinate, a target reference object business circle as a first horizontal coordinate, and a target reference object wholesale market as a second horizontal coordinate. After the space-time coordinates of the example warehouse comparison view are set, first reference distances from the warehouse A, the warehouse B and the warehouse C to each business circle are respectively obtained through a third-party platform, and second reference distances from the warehouse A, the warehouse B and the warehouse C to each wholesale market are respectively obtained; respectively acquiring first reference consumed time when the warehouse A, the warehouse B and the warehouse C reach each business district at a preset time point, and respectively acquiring second consumed time when the warehouse A, the warehouse B and the warehouse C reach each wholesale market at the preset time point; then, determining a trade circle coordinate value of each trade circle according to the first reference distance and the first reference consumed time, and determining a wholesale market coordinate value of each wholesale market according to the second reference distance and the second reference consumed time, wherein the first reference distance is used as a coordinate y value of the trade circle, the second reference distance is used as a coordinate y value of the wholesale market, the accumulated first reference consumed time of each time point is used as a coordinate x value of the trade circle, and the accumulated second reference consumed time of each time point is used as a coordinate x value of the wholesale market; saving the first reference distance, the first reference elapsed time, the second reference distance, and the second reference elapsed time as the spatiotemporal information, and displaying the spatiotemporal information in spatiotemporal coordinates of the example warehouse comparison view to generate the example warehouse comparison view. Each point in the example warehouse comparison view represents a business turn or a wholesale market. The example warehouse comparison view clearly reflects that warehouse C spends less time to the business circle, and the distance is relatively shortest; the warehouse C takes the least time to the wholesale market; the longest time is spent from warehouse a to the wholesale market.
In order to display the spatiotemporal information more intuitively, after the warehouse comparison view is generated, display attributes of each coordinate point are set, wherein the display attributes comprise display trigger conditions and display contents. For example, the display trigger condition may be a mouse hover, a brush box selection, a single or double click, and the like. The display content may be a reference object name, an ID, an area of a reference object, a distance, a time consumption, and the like. Continuing with fig. 6, after the display triggering condition is triggered, the displayed related information includes reference object ID, distance, time consumption, etc., that is, business district ID is 700, distance is 9km, and time consumption is 14 min. It should be noted that the elapsed time displayed in the example warehouse comparison view is a reference elapsed time at a preset time point, and may also be set to display a cumulative reference elapsed time corresponding to the abscissa.
Further, the step of generating a warehouse comparison view according to the spatiotemporal information further comprises:
step S3011: counting the number of the target reference objects and identifying the number in the spatiotemporal coordinates.
In order to display information of the reference objects more intuitively, it is also necessary to count the number of the target reference objects and identify the number in the spatiotemporal coordinates by distance. The number of the target reference objects may be identified by lines, graphics, text, etc. If the space-time coordinates contain multiple target reference objects, different types of reference objects can be identified in the same or different ways.
With continued reference to FIG. 7, the quantities of the business circles and the wholesale markets are identified in the spatio-temporal coordinates based on distance in FIG. 7 in the form of histograms. In FIG. 7, the number of pens and wholesale markets are identified at distances of 0-5km, 5-10km,10-15km,20-25km,25-30km, respectively. As shown in fig. 3, within a distance of 0-5km, there are about 5 business circles and 7 wholesale markets; within a distance of 25-30km, there are 0 business circles, about 16 wholesale markets.
Thus, the user can clearly know the relevant information according to the warehouse comparison view.
Step S302: generating a warehouse visualization graph based on the spatiotemporal information.
Due to the differences in traffic conditions at different times, the time consumption at different points in time may not be the same for the same route. It is to be understood that the elapsed time is related to a route, and the warehouse visualization may include not only the elapsed time but also route information corresponding to the elapsed time.
The warehouse visualization may include a warehouse insight view, a congestion index view, a navigation view, a time-consuming statistics view, and the like.
In this embodiment, the warehouse visualization includes a warehouse insight view, the step of obtaining the time consumption of the warehouse of interest to reach each reference object at the target time point, and the step of generating the warehouse visualization includes:
step S302a, acquiring a reference time point received by a preset time axis, and marking a plurality of preset time points adjacent to the reference time point as target time points;
the time axis is used for receiving the reference time point and marking a plurality of preset time points adjacent to the reference time point as target time points. It is understood that the number of the target time points can be set autonomously, for example, four or five, but generally does not exceed the number of the preset time points. The time axis may be specifically set according to a scene. The time axis may be a horizontal axis, a vertical axis, a circular axis, etc.
Referring to fig. 8, fig. 8 is an exemplary timeline for addressing a warehouse of an embodiment of the present invention. The example time axis illustrates 7 preset time points of 06:00 to 09:00 in a horizontal axis for the user to select the reference time point, and in fig. 7, the example time axis indicates a position of 06:30, so that the reference time point is marked as 06:30, and if the number of the preset time points is three, three adjacent time points 06:00, 07:00 and 06:30 are marked as the target time points.
Step S302b, obtaining a plurality of guidance time durations that the warehouse of interest reaches each reference object at a plurality of target time points and a guidance distance corresponding to the guidance time durations from the spatiotemporal information of the warehouse of interest;
in this embodiment, the time consumed by the interested warehouse to reach each reference object is marked as guidance time, and the corresponding distance is marked as guidance distance. It is understood that if there are three target time points, there are a guiding time and a guiding distance corresponding to the three time points. Since the traffic conditions are different at different time points, the time consumption may be different at different time points, and the route recommended by the third platform may be different, and thus the distance may be different.
Step S302c, saving the corresponding guidance time and guidance distance as reference object-distance-time information according to each reference object;
for example, if the warehouse a is the warehouse of interest, the reference object is a quotient circle, n quotient circles are included in the preset range, and the target time points are 3, t being respectively1,t2,t3Then the corresponding reference object-distance-time-consuming information includes the quotient circle 1 corresponding to the time point t1,t2,t3Three groups of information: trade circle 1-distance t1-time of consumption t1Quotient circle 1-distance t2-time of consumption t2Quotient circle 1-distance t3-time of consumption t3. The same applies to other quotient circles, corresponding to the time t1,t2,t3Three sets of information.
Step S302d, marking each of the reference object-distance-time-consuming information in a preset time-distance map, and generating a warehouse insight view.
In this embodiment, one reference object may be represented by a form, color or symbol, for example, orange represents the reference object 1, red represents the reference object 2, and then the reference object-distance-time consumption information of each reference object is marked in the preset time-distance map to generate the warehouse insight view.
The time-distance map may be a circular map, time being marked in turn on a line representing the circle, distance being represented by the radius of the circle. In particular, referring to FIG. 9, FIG. 9 is an exemplary warehouse insight view of the warehouse location method of the present invention. In fig. 8, the time of the whole circle is set to be 32min and divided evenly, each grid is divided into 4min, the time is marked from 0min in sequence, the initial distance at the original point is set to be 6km, and the time is 7km, 8km and 9km respectively from the original point outwards in sequence. Two business circles are illustrated in fig. 7, located at a distance of about 6.8km and at a distance of 8.5km, respectively. The business turn information indicated by the arrows in fig. 7 is: the mall ID742, at a time point of 6:30, the distance from the warehouse of interest to the mall is 8.5km, which takes 13.7 min.
Further, the warehouse visualization map includes a congestion index view, the step of marking each of the reference object-distance-time-consuming information in the generated time-distance map further includes, after the step of generating a warehouse insight view:
a1, respectively acquiring graphs surrounded by a view origin and the guide time consumption and the guide distance of each reference object based on the warehouse insight view, and calculating the area of the graphs;
a step a2 of calculating a congestion index of the reference object based on the area;
specifically, whether a plurality of guidance distances corresponding to a plurality of guidance time consumptions in the reference object are the same is judged; since the time consumption and the distance at different points in time may not be the same. If a plurality of guiding distances corresponding to a plurality of guiding consumed times in the reference object are the same, marking the area as the congestion index; referring to fig. 10, fig. 10 is an exemplary congestion index view of one embodiment of warehouse addressing of the present invention. In fig. 10A, if the distances of the 6 o 'clock, and the 7 o' clock are the same at the time point, the area of the sector surrounded by the positions of the 6 o 'clock and the 7 o' clock and the origin is directly calculated, and the area is used as the congestion index. And if the plurality of guiding distances corresponding to the plurality of guiding consumed time in the reference object are different, marking a result obtained by dividing the area of the graph surrounded by the adjacent target time points by a preset numerical value as the congestion index.
And a step a3, marking the congestion coordinate value in a coordinate graph, and generating the congestion index view.
In this embodiment, the congestion index is taken as a vertical coordinate, the distance is taken as a horizontal coordinate, and then the congestion index view is generated according to the distance of each reference object and the corresponding congestion index. It is understood that the area may be normalized to serve as the congestion index in order to better reflect congestion conditions. Further, a cumulative congestion index may also be reflected in the congestion index view. With continued reference to fig. 10, in fig. 10B, the normalized area is taken as the congestion index. And the curve a represents the congestion index of each reference object, and the curve b represents the cumulative congestion index. Therefore, the congestion condition is visually reflected, the user can conveniently and quickly obtain related information, and the decision is made according to the information so as to select the optimal warehouse.
According to the scheme, the spatiotemporal information from each warehouse to each reference point is obtained, and the spatiotemporal information comprises the distance from each warehouse to each reference point and time consumption information; and generating a warehouse visual graph based on the spatiotemporal information, thereby realizing the visualization of the spatiotemporal information of the warehouse, providing multi-dimensional warehouse information and being beneficial to the intelligent site selection of the warehouse according to the visual information.
A fourth embodiment of the present invention is proposed based on the first embodiment shown in fig. 2 described above. As shown in fig. 11, fig. 11 is a schematic flow chart of the warehouse addressing method according to the fourth embodiment of the present invention. The type of the warehouse addressing scheme comprises a created addressing scheme, and the step of generating the warehouse addressing scheme according to the type of the warehouse addressing scheme based on the warehouse list comprises the following steps:
step S401: if the type of the warehouse site selection scheme is the establishment site selection scheme, acquiring the physical attribute of each warehouse in the warehouse list, and screening one or more target warehouses based on the physical attribute;
and acquiring the physical attributes of each warehouse in the warehouse list after receiving a request for creating the addressing scheme. The physical attributes include shape, floor, area, orientation, and the like. And presetting physical attribute screening conditions, and screening one or more target warehouses based on the physical attribute screening conditions. The physical attribute screening condition is set according to actual needs, for example, the area is greater than or equal to 1000 square meters.
Step S402: and generating a created addressing scheme according to the one or more target warehouses.
And storing the one or more screened target warehouses, and generating a created addressing scheme according to the one or more target warehouses. The created addressing scheme includes warehouse information of the one or more target warehouses, such as warehouse names, warehouse specific locations, warehouse characteristics, and the like.
And further, judging whether repeated warehouses exist in the created addressing scheme, and if so, performing secondary screening on the repeated warehouses and reserving only one warehouse.
Further, after the created addressing scheme is successfully obtained, the name of the created addressing scheme is set. And after setting the name of the created addressing scheme, the name can be modified.
Specifically, referring to fig. 12, fig. 12 is a schematic view of a scenario of the warehouse location selecting method according to the fourth embodiment of the present invention. Fig. 12 shows an interface for creating an addressing scheme displayed on a screen of a warehouse addressing device, where a new area of the scheme is used to trigger a request for creating an addressing scheme as shown in fig. 12, after the new scheme area triggers the request for creating an addressing scheme, a target area selected by the map area is obtained, transportation costs from a warehouse to each reference object in the target area are then calculated, a warehouse list is generated according to the transportation costs by using a warehouse recommendation algorithm, and the warehouse list is displayed in a warehouse list area; selecting the warehouse in the warehouse list, selecting the related information of the warehouse to be displayed in a preview area, obtaining physical attributes based on the preview area, selecting the warehouse meeting the physical attribute screening condition, generating a created addressing scheme, and displaying the warehouse in the created addressing scheme in a target warehouse display area.
Further, referring to fig. 13, fig. 13 is a schematic view of another scenario of the warehouse site selection method according to the fourth embodiment of the present invention. As shown in fig. 13, after initialization, a new proposal is created according to a new proposal request; then selecting an area from the map so as to obtain a target area; clustering according to the business circle data in the target area to obtain a business circle; then, according to the operation cost, carrying out algorithm recommendation on warehouses based on a preset operation cost function to obtain a warehouse list; based on the warehouse list, a candidate warehouse, namely an interested warehouse is selected; checking whether a repeated warehouse exists in the interested warehouse, and if so, only keeping one repeated warehouse; and if no repeated warehouse exists, directly saving the candidate warehouse as the created addressing scheme. Further, the warehouse in the created address selection scheme can be deleted or added; further, the name of the created addressing scheme may also be modified.
With continued reference to fig. 13, as shown in fig. 13, after a warehouse is selected and an interested warehouse is added into a candidate warehouse, the candidate warehouse may be subjected to comparative insight, an optimal path and a congestion index from the warehouse to each reference object are calculated, a congestion condition is determined based on the congestion index, and the candidate warehouse is adjusted according to the calculation result; further, checking the congestion indexes of the paths and the time ranges through the warehouse visual graph, comparing the path information, the time ranges and the congestion indexes from the warehouse to the business district and the wholesale market, screening out the optimal warehouse according to the comparison result, and ending the process after obtaining the optimal warehouse.
In this embodiment, according to the above scheme, if the type of the warehouse location scheme is the creation location scheme, the physical attributes of each warehouse in the warehouse list are obtained, and one or more target warehouses are screened out based on the physical attributes; and generating a created addressing scheme according to the one or more target warehouses, and setting the name of the created addressing scheme, wherein the created addressing scheme comprises warehouse information of the one or more target warehouses. Therefore, only address selection of the warehouse is realized based on the physical attributes of the warehouse.
In addition, the embodiment also provides a warehouse address selection device. Referring to fig. 14, fig. 14 is a functional block diagram of a warehouse site selection device according to a first embodiment of the present invention.
In this embodiment, the warehouse addressing device is a virtual device, and is stored in the memory 1005 of the warehouse addressing device shown in fig. 1, so as to implement all functions of the warehouse addressing program: the business circle is used for obtaining the target area from the merchant data based on a clustering algorithm; the warehouse data processing system is used for screening out candidate warehouses from the warehouse data according to the transportation cost from warehouses in the area to the commercial district, the wholesale market and the entrance of the expressway; and the system is used for screening out the target warehouse by insights of the traffic conditions from the candidate warehouse to the business district and the wholesale market according to the warehouse site selection request.
Specifically, the warehouse site selection device includes:
a receiving module 10, configured to receive a warehouse location request, where the warehouse location request includes a target area;
a calculating module 20, configured to calculate, based on the reference object in the target area, an operation cost for building a warehouse in each warehouse in the target area, and generate a warehouse list according to the operation cost by using a warehouse recommendation algorithm;
and a generating module 30, configured to generate a warehouse location scheme according to a type of the warehouse location scheme based on the warehouse list.
Further, the calculation module includes:
a first calculation unit for calculating a business turn transportation cost of each warehouse to the business turn, a wholesale market transportation cost of each warehouse to the wholesale market, and an expressway entrance transportation cost of each warehouse to the expressway entrance;
the second calculation unit is used for calculating the operation cost of warehouse construction of each warehouse based on the transportation cost of the commercial district, the cargo occupation ratio from the warehouse to the commercial district, the wholesale market transportation cost, the warehouse opening cost of each warehouse and a preset operation cost function;
and the generating unit is used for selecting the warehouse with the operation cost lower than the threshold value and generating a warehouse list according to the screening result.
Further, the first calculation unit includes:
the acquisition subunit is used for acquiring the driving cost and the distance from the warehouse to each reference object;
the first calculating subunit is used for calculating the business circle transportation cost from the warehouse to the business circle based on the driving cost and the distance from the warehouse to each business circle;
a second calculation subunit, configured to calculate, based on the driving cost and a highway entrance transportation cost from the warehouse to a highway population at each of the highway intersections of the warehouse;
and the third calculation subunit is used for calculating the wholesale market transportation cost from the warehouse to the wholesale market based on the driving cost and the distance between each high-speed intersection of the warehouse.
Further, the generating module includes:
a first obtaining unit, configured to obtain, based on a warehouse visualization map, traffic conditions from each warehouse to each reference object in the warehouse list if the type of the warehouse addressing scheme is an insight addressing scheme, where the traffic conditions include path information and congestion indexes;
and the first generating unit is used for comparing the warehouses based on the path and the congestion index to obtain a comparison result, and generating an insight addressing scheme based on the comparison result.
Further, the first obtaining unit further includes:
the first acquisition subunit is configured to acquire spatiotemporal information from each warehouse to each reference point, where the spatiotemporal information includes a distance from each warehouse to each reference point and time consumption information;
and the first generation subunit is used for generating a warehouse visualization graph based on the distance information and the time consumption information.
Further, the generating module includes:
a second obtaining unit, configured to obtain a physical attribute of each warehouse in the warehouse list if the type of the warehouse addressing scheme is a create addressing scheme, and screen out one or more target warehouses based on the physical attribute;
and the second generating unit is used for generating and creating an addressing scheme according to the one or more target warehouses.
Further, the first calculation unit further includes:
the collecting subunit is used for collecting the merchant data in the target area, and clustering the merchants in the target area based on the merchant data to obtain a clustering result;
and the clustering subunit is used for marking the clusters with the number of the commercial tenants larger than the commercial tenant threshold value in the clustering result as the quotient circle.
In addition, an embodiment of the present invention further provides a computer storage medium, where a warehouse addressing program is stored on the computer storage medium, and when the warehouse addressing program is executed by a processor, the steps of the warehouse addressing method are implemented.
Compared with the prior art, the warehouse address selection method, device, equipment and storage medium provided by the invention comprise the following steps: receiving a warehouse location request, wherein the warehouse location request comprises a target area; calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm; and generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list. Therefore, on the basis of artificial intelligence, a warehouse list is generated according to the operation cost of warehouse construction in the target area, and then a warehouse site selection scheme corresponding to the type of the warehouse site selection scheme is generated on the basis of the warehouse list, so that the workload of warehouse site selection is reduced, and the intelligent decision of warehouse site selection is realized.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for causing a terminal device to execute the method according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention and is not intended to limit the scope of the present invention, and all equivalent structures or flow transformations made by the present specification and drawings, or applied directly or indirectly to other related arts, are included in the scope of the present invention.

Claims (10)

1. A method for addressing a warehouse, the method comprising:
receiving a warehouse location request, wherein the warehouse location request comprises a target area;
calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm;
and generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list.
2. The method of claim 1, wherein the reference objects include trade circles, wholesale markets, and highway entrances;
the step of calculating the operation cost of warehouse construction in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm comprises the following steps:
calculating a business circle transportation cost of each warehouse to the business circle, a wholesale market transportation cost of each warehouse to the wholesale market, and a highway entrance transportation cost of each warehouse to the highway entrance;
calculating the operation cost of warehouse construction of each warehouse based on the transportation cost of the trade district, the cargo occupation ratio from the warehouse to the trade district, the transportation cost of the wholesale market, the warehouse opening cost of each warehouse and a preset operation cost function;
and selecting warehouses with the operation cost lower than a threshold value, and generating a warehouse list according to the screening result.
3. The method of claim 2, wherein the step of calculating the business turn transportation cost of each warehouse to the business turn, the wholesale market transportation cost of each warehouse to the wholesale market, and the highway entrance transportation cost of each warehouse to the highway entrance comprises:
acquiring the driving cost and the distance from the warehouse to each reference object;
calculating the business circle transportation cost from the warehouse to the business circle based on the driving cost and the distance from the warehouse to each business circle;
calculating highway entrance transportation costs from the warehouse to each highway entrance based on the driving costs and the distance of each highway entrance of the warehouse;
and calculating the transportation cost of the wholesale market from the warehouse to the wholesale market based on the driving cost and the distance from the warehouse to each wholesale market.
4. The method of claim 1, wherein the step of generating a warehouse addressing plan by warehouse addressing plan type based on the warehouse list comprises:
if the type of the warehouse site selection scheme is an insight site selection scheme, acquiring traffic conditions from each warehouse to each reference object in the warehouse list based on a warehouse visual graph, wherein the traffic conditions comprise path information and congestion indexes;
and comparing the warehouses based on the path information and the congestion index to obtain a comparison result, and generating an insight site selection scheme based on the comparison result.
5. The method of claim 4, wherein the step of obtaining traffic conditions from each warehouse in the warehouse list to each reference object based on the warehouse visualization map further comprises:
acquiring spatiotemporal information from each warehouse to each reference point, wherein the spatiotemporal information comprises distance information and time consumption information from each warehouse to each reference point;
and generating a warehouse visual graph based on the distance information and the time consumption information.
6. The method of claim 1, wherein the step of generating a warehouse addressing plan by warehouse addressing plan type based on the warehouse list comprises:
if the type of the warehouse site selection scheme is the establishment site selection scheme, acquiring the physical attribute of each warehouse in the warehouse list, and screening one or more target warehouses based on the physical attribute;
and generating a created address selection scheme according to the warehouse information corresponding to the one or more target warehouses.
7. The method of claim 2, wherein the step of calculating the business turn transportation cost of each warehouse to the business turn, the wholesale market transportation cost of each warehouse to the wholesale market, and the highway entrance transportation cost of each warehouse to the highway entrance is preceded by the step of:
collecting merchant data in the target area, and clustering merchants in the target area based on the merchant data to obtain a clustering result;
and marking the clusters with the number of the merchants larger than the threshold value of the merchants in the clustering result as a quotient circle.
8. A warehouse site selection device, the warehouse site selection device comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a warehouse address selection request, and the warehouse address selection request comprises a target area;
the calculation module is used for calculating the operation cost of warehouse building in each warehouse in the target area based on the reference object of the target area, and generating a warehouse list according to the operation cost by using a warehouse recommendation algorithm;
and the generating module is used for generating a warehouse site selection scheme according to the type of the warehouse site selection scheme based on the warehouse list.
9. A warehouse addressing device, characterized in that the warehouse addressing device comprises a processor, a memory and a warehouse addressing program stored in the memory, which when executed by the processor implements the steps of the warehouse addressing method as claimed in any one of claims 1 to 7.
10. A computer storage medium having stored thereon a warehouse addressing program, which when executed by a processor performs the steps of the warehouse addressing method as claimed in any one of claims 1 to 7.
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