CN110321399A - The method and apparatus for selecting address - Google Patents
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Abstract
The invention discloses the method and apparatus of selection address, are related to field of computer technology.One specific embodiment of this method includes: that predeterminable area is divided into multiple grids, extracts the attribute data of each grid；Wherein, attribute data includes the first property value and the second attribute value of each existing object in grid；Using the attribute data training regression model of at least one grid, object module is obtained；According to the first property value of object to be selected, the attribute data and object module of the affiliated grid of object to be selected, the second attribute value of each object to be selected is obtained；The third attribute value that each object to be selected is respectively obtained according to the first property value of each object to be selected and the second attribute value determines destination address according to the third attribute value of each object to be selected.The embodiment reduces the problem of the not high caused user experience difference of matching degree of destination address and user demand.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for selecting an address.
Background
Currently, the existing method for selecting an address is to select a target address from addresses of objects to be selected by minimizing the sum of distances among the existing object, the object to be selected, and the current object.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
because the address selection only considers the distance between the objects and the considered factors are single, the prior art has the problem of poor user experience caused by low matching degree of the target address and the user requirements.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for selecting an address, which can reduce the problem of poor user experience caused by a low matching degree between a target address and a user requirement.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of selecting an address.
The method for selecting the address of the embodiment of the invention comprises the following steps:
dividing a preset area into a plurality of grids, and extracting attribute data of each grid; wherein the attribute data comprises a first attribute value and a second attribute value for each existing object located within a grid;
training a regression model by using the attribute data of at least one grid to obtain a target model;
obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data of a grid to which the object to be selected belongs and the target model;
and respectively obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected, and determining a target address according to the third attribute value of each object to be selected.
In one embodiment, obtaining the second attribute value of each object to be selected according to the first attribute value of the object to be selected, the attribute data of the grid to which the object to be selected belongs, and the target model includes:
for each object to be selected, obtaining a second attribute value of the object to be selected according to the following method:
acquiring the address of the object to be selected, and determining the grid to which the object to be selected belongs according to the address of the object to be selected;
acquiring attribute data of the grids to which the objects to be selected belong from the attribute data of the grids;
and inputting the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs into the target model to obtain a second attribute value of the object to be selected.
In one embodiment, the first attribute value of the candidate object comprises a distance from the candidate object to a transportation junction, a distance from the candidate object to a current object, a current amount from the candidate object to the current object, a land price of the candidate object and the number of existing objects within a preset range of the candidate object;
or the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object and the land price of the object to be selected; the attribute data also includes the number of existing objects located within the grid.
In one embodiment, obtaining the third attribute value of each of the objects to be selected according to the first attribute value and the second attribute value of each of the objects to be selected respectively includes:
for each object to be selected, obtaining a third attribute value of the object to be selected according to the following method:
and subtracting the land price of the object to be selected from the second attribute value of the object to be selected to obtain a difference serving as a third attribute value of the object to be selected.
In one embodiment, determining the target address according to the third attribute value of each of the objects to be selected includes:
and selecting a target address from the addresses of the objects to be selected according to the third attribute value of each object to be selected, the land area of each object to be selected and the geological condition of each object to be selected.
To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided an apparatus for selecting an address.
The device for selecting the address of the embodiment of the invention comprises:
the device comprises a preprocessing unit, a data processing unit and a data processing unit, wherein the preprocessing unit is used for dividing a preset area into a plurality of grids and extracting attribute data of each grid; wherein the attribute data comprises a first attribute value and a second attribute value for each existing object located within a grid;
the training unit is used for training a regression model by utilizing the attribute data of at least one grid to obtain a target model;
the processing unit is used for obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data of a grid to which the object to be selected belongs and the target model;
and the selecting unit is used for respectively obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected, and determining a target address according to the third attribute value of each object to be selected.
In one embodiment, the processing unit is to:
for each object to be selected, obtaining a second attribute value of the object to be selected according to the following method:
acquiring the address of the object to be selected, and determining the grid to which the object to be selected belongs according to the address of the object to be selected;
acquiring attribute data of the grids to which the objects to be selected belong from the attribute data of the grids;
and inputting the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs into the target model to obtain a second attribute value of the object to be selected.
In one embodiment, the first attribute value of the candidate object comprises a distance from the candidate object to a transportation junction, a distance from the candidate object to a current object, a current amount from the candidate object to the current object, a land price of the candidate object and the number of existing objects within a preset range of the candidate object;
or the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object and the land price of the object to be selected; the attribute data also includes the number of existing objects located within the grid.
In one embodiment, the selection unit is configured to:
for each object to be selected, obtaining a third attribute value of the object to be selected according to the following method:
and subtracting the land price of the object to be selected from the second attribute value of the object to be selected to obtain a difference serving as a third attribute value of the object to be selected.
In one embodiment, the selection unit is configured to:
and selecting a target address from the addresses of the objects to be selected according to the third attribute value of each object to be selected, the land area of each object to be selected and the geological condition of each object to be selected.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus.
An electronic device of an embodiment of the present invention includes: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the method for selecting the address provided by the embodiment of the invention.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a computer-readable medium.
A computer-readable medium of an embodiment of the present invention stores thereon a computer program, which when executed by a processor implements the method for selecting an address provided by an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: extracting attribute data of each grid by dividing a preset area into a plurality of grids; wherein the attribute data comprises a first attribute value and a second attribute value of each existing object located within the grid; and training a regression model by using the attribute data of at least one grid to obtain a target model. Thereby determining the correlation of the first attribute value and the second attribute value through the grid based on the existing object. The method comprises the steps of obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data and correlation of a grid to which the object to be selected belongs, obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected respectively, and determining a target address according to the third attribute value of each object to be selected, wherein the larger the third attribute value is, the more the target address meets the user requirements, so that the problem of poor user experience caused by low matching degree of the target address and the user requirements is solved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a method of selecting an address according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a main flow of a method of selecting an address according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of a main flow of a method of selecting an address according to yet another embodiment of the present invention;
FIG. 4 is an application scenario of a method of selecting an address according to yet another embodiment of the present invention;
fig. 5 is a schematic diagram of a warehouse to be selected in the method of selecting an address according to still another embodiment of the present invention;
FIG. 6 is a diagram illustrating second attribute values in a method of selecting an address according to yet another embodiment of the present invention;
fig. 7 is a schematic diagram illustrating a combination of a warehouse to be selected and a second attribute value in a method of selecting an address according to still another embodiment of the present invention;
FIG. 8 is a schematic diagram of the main units of an apparatus for selecting an address according to an embodiment of the present invention;
FIG. 9 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 10 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The step of selecting the address to construct the warehouse refers to the step of determining the geographic position of the warehouse by a scientific method, so that the warehouse is organically combined with the overall operation planning of the enterprise, and the enterprise can develop at a higher speed. Two main aspects are considered for selecting addresses: firstly, selecting a proper region; second, a specific location is selected from a suitable region. The influencing factors for selecting the address include: economic factors, environmental factors, engineering and competitive factors, etc., and thus, addresses should be selected based on economic principles, near-user principles, long-term development principles, adaptability principles, feasibility principles, and strategic principles. In addition, the warehouse location is determined by the customer, the manufacturing enterprise, the raw material supplier, and the product itself. According to traditional taxonomies, warehouses are broadly divided into: 1 warehouse located in the market, 2 warehouse located in the manufacturing, 3 warehouse located in the middle. The general warehouse must have sufficient article sources and convenient transportation, the environment of the warehouse should meet the requirement of safe storage of the articles, and the warehouse should have a good sanitary environment. In addition, there should be infrastructure for water, drainage and power supply. Storage and transportation warehouses should be close to highways, railways and waterways, wholesale retail warehouses should be close to urban areas, and the like.
The method for selecting the address is divided into qualitative and quantitative methods, and the qualitative method comprises the following steps: the brain storm method and the delphire method. The quantitative method comprises the following steps: a quantitative benliry analysis method, a weighted scoring method, and a center of gravity method. Of course, ant colony algorithm, artificial neural network algorithm, genetic algorithm, simulated annealing algorithm, analytic hierarchy process, simulation technique, etc. may be used to select the address.
The basic function of a warehouse is to store items, and when determining the location of a warehouse, no single tool gives a perfect developmental blueprint, and the growth of traffic around the warehouse will lead to a corresponding growth in the size, construction and labor requirements of the warehouse. The location of the warehouse is typically chosen to be relatively close to the market center (which refers to downstream distributors, retailers, or customers), where the land price is high and the cost of building the warehouse is high, but the transportation speed is high.
The address is selected to minimize the cost by minimizing the transportation route, number and size of the warehouse based on the geographic location of the customer and the supply and demand relationship of the product. Specifically, the Alred Weber proposes an address selection method, which minimizes the sum of distances among the existing object, the object to be selected and the current object, thereby selecting a target address from the addresses of the object to be selected. Because the address selection only considers the distance between the objects, the considered factors are single, and only one target address can be selected, the problem of poor user experience caused by low matching degree of the target address and the user requirements exists in the prior art. The P-median problem and the P-center problem refer to modeling of transfer problems and selection of distribution paths in distribution, and then, Aikebs provides linear programming, dynamic and overall programming site selection models in different environments aiming at the problem of minimization of logistics and warehousing expenses. And then, organically combining the site selection problem of the logistics storage with a geographic information system by using a modern information technology, and developing a corresponding decision system for site selection. However, the problems of the prior art are still not solved.
In order to solve the problems in the prior art, an embodiment of the present invention provides a method for selecting an address, as shown in fig. 1, where the method includes:
step S101, dividing a preset area into a plurality of grids, and extracting attribute data of each grid; wherein the attribute data comprises a first attribute value and a second attribute value for each existing object located within the grid.
In this step, the preset area may be the whole china, province, city, or city in direct prefecture, etc. In addition, the dividing mode can be uniform dividing or non-uniform dividing, but the uniform dividing mode further reduces the problem of poor user experience caused by low matching degree of the target address and the user requirement. Furthermore, the existing object and the candidate object are the same type of object, for example, if the existing object is an existing warehouse (the existing warehouse is a warehouse which has been built and used now), the candidate object is a candidate warehouse (the candidate warehouse is a warehouse which has not been built yet); for another example, if the existing object is an existing store, the candidate object is a candidate store. It should be noted that the present invention aims to select a target address from addresses of objects to be selected, and construct an object at the target address.
The problems involved in transportation are relatively fixed in different cities. Railways and highways are major factors if the items are to be carried by rail and received by trucks; air traffic is a major factor if the items are in the amount of just one truck, or even less than one truck, the package is too heavy to ship, or requires time of arrival. Thus, the first attribute value of the existing object includes a distance of the existing object to the transportation junction.
It should be noted that, if the existing object is located in the grid (that is, the address of the existing object belongs to the area range of the grid), the attribute data of the grid includes the first attribute value and the second attribute value of the existing object. In addition, the method for extracting the attribute data of the grid may refer to the embodiment shown in fig. 3, and is not described herein again.
And S102, training a regression model by using the attribute data of at least one grid to obtain a target model.
In the step, in specific implementation, a linear regression model is trained by using the attribute data of at least one grid to obtain parameters of the linear regression model, and a target model is obtained based on the parameters of the linear regression model. Although the attribute data of a plurality of grids can be obtained in step S101, and the object model can be obtained using the attribute data of all the grids in step S101 or the attribute data of a part of the grids in step S101 in step S102, the object model obtained using the attribute data of all the grids is more accurate than the object model obtained using the attribute data of a part of the grids, and therefore, the problem of poor user experience due to a low degree of matching between the target address and the user' S request is further reduced.
Step S103, obtaining a second attribute value of each object to be selected according to the first attribute value of the object to be selected, the attribute data of the grid to which the object to be selected belongs and the target model.
In this step, when the specific implementation is performed, reference may be made to the embodiment shown in fig. 2 or fig. 3, which is not described herein again.
It should be noted that:
the first attribute values of the existing object include: the distance from the existing object to the transportation hub, the distance from the existing object to the current object, the amount of traffic between the existing object and the current object (the current object is the current object), the land price of the existing object, and the number of the existing objects within a preset range of the existing object. The first attribute value of the object to be selected comprises: the distance between the candidate object and the transportation hub, the distance between the candidate object and the current object, the current amount between the candidate object and the current object (the current object is the current object of the candidate object), the land price of the candidate object and the number of the existing objects within a preset range of the candidate object.
Or,
the first attribute values of the existing object include: the distance from the existing object to the transportation junction, the distance from the existing object to the current object, the traffic volume from the existing object to the current object and the land price of the existing object. The first attribute value of the object to be selected comprises: the distance between the object to be selected and the traffic hub, the distance between the object to be selected and the current object, the current amount between the object to be selected and the current object and the land price of the object to be selected. The attribute data also includes the number of existing objects located within the grid.
Wherein, the transportation junction comprises airports, trains, motor cars, high-speed trains and the like. If the existing object is the existing warehouse, the object to be selected is the warehouse to be selected, the current object can be a sorting center, and the current amount can be the order amount of the sorting center. The second attribute value of the existing object may be a rent of the existing warehouse, and the second attribute value of the object to be selected may be a rent of the warehouse to be selected. If the existing object is the existing store, the object to be selected is the store to be selected, the current object can be a supplier or a client, and the current quantity can be the supply quantity of the supplier. The preset range may be 3 km.
The sorting center is not a warehouse, and the function of the sorting center is: providing delivery services. In the logistics supply chain link, a logistics node is used for performing distribution procedures for downstream logistics distributors, retailers and clients. The circulation facility and the information system platform are used for inverting, classifying, circulating and processing, matching and designing a transportation route and a transportation mode for goods which are handed in the logistics, and the self-distribution service is provided for customers. The purpose is to save the transportation cost and ensure the customer satisfaction.
It should be noted that the preset range is set to 3 kilometers, which is more beneficial to selecting a target address, and the selected target address better meets the user requirements. Of course, the preset range can be flexibly set by a person skilled in the art without affecting the embodiment of the present invention.
Step S104, respectively obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected, and determining a target address according to the third attribute value of each object to be selected.
In this step, in a specific implementation, the third attribute values of the objects to be selected are arranged in descending order, and the address of the first object to be selected is selected as the target address. It should be understood that the maximum third attribute value may be multiple, and thus, the address of the candidate object with the maximum third attribute value is multiple, that is, the target address is multiple, so that a warehouse may be built at each target address.
Or selecting a target address from the addresses of the objects to be selected according to the third attribute value of each object to be selected, the land area of each object to be selected and the geological condition of each object to be selected.
In the embodiment of the present invention, step S103 may include:
for each object to be selected, obtaining a second attribute value of the object to be selected according to the following method:
acquiring the address of the object to be selected, and determining the grid to which the object to be selected belongs according to the address of the object to be selected;
acquiring attribute data of a grid to which the object to be selected belongs from attribute data of a plurality of grids;
and inputting the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs into the target model to obtain a second attribute value of the object to be selected.
In this embodiment, in implementation, each grid is distinguished by a different grid ID, and the attribute data of the grid is stored in a preset area according to the grid ID. And acquiring the boundary of the object to be selected from government published information, and converting the boundary of the object to be selected into the longitude and latitude of the object to be selected by using a desktop geographic information system (QGIS), wherein the longitude and latitude of the object to be selected is the address of the object to be selected. Determining the grids to which the objects to be selected belong according to the addresses of the objects to be selected (the determination process specifically comprises the steps of obtaining the area range of each grid, taking the grid in the area range to which the addresses of the objects to be selected belong as the grid to which the objects to be selected belong), obtaining the ID of the grid to which the objects to be selected belong, and obtaining the attribute data of the grid to which the objects to be selected belong from a preset area according to the ID of the grid to which the objects to be selected belong. And inputting the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs into the target model to obtain a second attribute value of the object to be selected.
In the embodiment, the address of the object to be selected is obtained, the grid to which the object to be selected belongs is determined according to the address of the object to be selected, the attribute data of the grid to which the object to be selected belongs is obtained from the attribute data of the multiple grids, the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs are input into the target model, and the second attribute value of the object to be selected is obtained. And obtaining the second attribute value of each object to be selected according to the above manner, so as to obtain the third attribute value of each object to be selected according to the second attribute value of each object to be selected, and selecting the address of the object to be selected with the largest third attribute value as the target address, wherein the larger the third attribute value is, the more the target address meets the user requirement, and the problem of poor user experience caused by low matching degree of the target address and the user requirement is further reduced.
In the embodiment of the invention, the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object, the land price of the object to be selected and the number of the existing objects within a preset range of the object to be selected;
or the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object and the land price of the object to be selected; the attribute data also includes the number of existing objects located within the grid.
It should be noted that the reason why the first attribute value of the candidate object includes the amount of the interaction between the candidate object and the current object is that the amount of the interaction between the candidate object and the current object affects the second attribute value of the candidate object, and the larger the amount of the interaction between the candidate object and the current object is, the larger the second attribute value of the candidate object is. The reason why the first attribute value of the candidate object includes the number of the existing objects is that the number of the existing objects affects the second attribute value of the candidate object, and the larger the number of the existing objects is, the smaller the second attribute value of the candidate object is (as described in a specific example below, the more the second attribute value of the candidate object is, the more the number of the existing warehouses is, the more the rentable warehouses can be selected for the tenant, the less the tenant naturally selects the warehouse with the rent for renting, and thus, the less the rent of the warehouse is). The reason why the first attribute value of the candidate object includes the distance is that the distance affects the second attribute value of the candidate object, and the larger the distance is, the smaller the second attribute value of the candidate object is.
In the embodiment, the address is selected by the content included in the first attribute value of the object to be selected and the content included in the attribute data, so that the distance between the objects is not considered, but the distance between the object to be selected and a transportation junction, the distance between the object to be selected and a current object, the current quantity, the land price of the object to be selected and the number of the existing objects are also considered, the considered factors are comprehensive, and the problem of poor user experience caused by low matching degree of the target address and the user requirement is further reduced.
In the embodiment of the present invention, obtaining the third attribute value of each candidate object according to the first attribute value and the second attribute value of each candidate object respectively includes:
for each object to be selected, obtaining a third attribute value of the object to be selected according to the following method:
and subtracting the land price of the object to be selected from the second attribute value of the object to be selected to obtain a difference serving as a third attribute value of the object to be selected.
In this embodiment, the second attribute value of the candidate object may be a rent of the candidate warehouse, and at this time, the third attribute value of the candidate object may be a benefit of the candidate warehouse. It should be noted that, since the object to be selected is an object that is not yet constructed, the first attribute value of the object to be selected can be directly obtained, so the second attribute value of the object to be selected is obtained by target model prediction, and the third attribute value of the object to be selected is obtained by the second attribute value of the object to be selected.
In this embodiment, the difference obtained by subtracting the land price of the candidate object from the second attribute value of the candidate object is used as the third attribute value of the candidate object. And obtaining the third attribute value of each object to be selected according to the above manner, so that the address of the object to be selected with the largest third attribute value is selected as the target address, and the larger the third attribute value is, the more the target address meets the user requirement, thereby further reducing the problem of poor user experience caused by low matching degree of the target address and the user requirement.
In this embodiment of the present invention, determining a target address according to the third attribute value of each candidate object includes:
and selecting a target address from the addresses of the objects to be selected according to the third attribute value of each object to be selected, the land area of each object to be selected and the geological condition of each object to be selected.
This embodiment is carried out as a specific example below:
the third attribute value of the first object to be selected is 10, the land area of the first object to be selected is 110 square meters, and the geological condition of the first object to be selected is that the engineering geology is good;
the third attribute value of the second object to be selected is 5, the land area of the second object to be selected is 150 square meters, and the geological condition of the second object to be selected is that the geological disaster develops strongly;
the third attribute value of the third candidate object is 12, the land area of the third candidate object is 50 square meters, and the geological condition of the third candidate object is poor hydrogeological condition.
The third attribute value of the second object to be selected is smaller and the geological condition is poorer, and the land area of the third object to be selected is smaller and the geological condition is poorer, so that the address of the first object to be selected is selected as the target address.
To solve the problems in the prior art, another embodiment of the present invention provides a method for selecting an address. In this embodiment, the specific application scenario is as follows: and selecting a target address from addresses of the stores to be selected so as to construct the stores. As shown in fig. 2, the method includes:
step S201, uniformly dividing Nanjing city into a plurality of grids, and extracting attribute data of each grid; wherein the attribute data includes a first attribute value and a second attribute value for each existing store located within the grid.
In this step, when embodied, the first attribute value of the existing store includes: the distance from the existing store to the transportation hub, the distance from the existing store to the supplier, the supply volume from the existing store to the supplier, the land price of the existing store, and the number of existing stores within 3 kilometers of the existing store. In addition, the number of existing stores located within the grid may be one, two, multiple, or even none.
Step S202, performing deduplication processing, missing value processing and data normalization processing on the attribute data of each grid to obtain the processed attribute data of each grid.
In this step, in concrete implementation, the missing value processing method includes: filling the missing value or deleting the missing value by the mean value; data normalization: formats of data of a plurality of grids are unified.
Step S203, training a linear regression model by using the processed attribute data of at least one grid to obtain a target model.
Step S204, for each store to be selected, obtaining a second attribute value of the store to be selected according to the following method: acquiring the address of the shop to be selected, and determining the grid to which the shop to be selected belongs according to the address of the shop to be selected; acquiring attribute data of a grid to which the shop to be selected belongs from the processed attribute data of the plurality of grids; and inputting the first attribute value of the store to be selected and the attribute data of the grid to which the store to be selected belongs into the target model to obtain a second attribute value of the store to be selected.
In this step, in implementation, the first attribute value of the store to be selected includes: the distance from the store to be selected to the transportation hub, the distance from the store to be selected to the supplier, the supply quantity of the store to be selected to the supplier, the land price of the store to be selected, and the number of existing stores within 3 kilometers of the store to be selected.
Step S205, for each store to be selected, obtaining a third attribute value of the store to be selected according to the following method: subtracting the land price of the shop to be selected from the second attribute value of the shop to be selected to obtain a difference serving as a third attribute value of the shop to be selected; wherein the first attribute value of the candidate store comprises a land price of the candidate store.
And S206, determining a target address according to the third attribute value of each store to be selected.
To solve the problems of the prior art, another embodiment of the present invention provides a method for selecting an address. In this embodiment, the specific application scenario is as follows: and selecting a target address from the addresses of the warehouse to be selected so as to construct the warehouse. As shown in fig. 3, the method includes:
step S301, uniformly dividing Nanjing city into a plurality of grids, and extracting attribute data of each grid; wherein the attribute data includes a first attribute value and a second attribute value for each existing warehouse located within the grid.
In this step, when implemented, the first attribute value of the existing warehouse includes: the distance from the existing warehouse to the transportation hub, the distance from the existing warehouse to the sorting center, the order quantity of the existing warehouse and the sorting center and the land price of the existing warehouse. The attribute data also includes the number of existing objects located within the grid.
As shown in fig. 4, the following describes a specific example of the process of obtaining the first attribute value of the existing warehouse: acquiring distances from the existing warehouse to an airport, a high-speed rail and a high-speed entrance from traffic data; calculating the distance from the existing warehouse to the sorting center according to the address of the existing warehouse (namely the longitude and latitude of the existing warehouse, which is resolved by the existing application program interface) and the address of the sorting center (the position of the sorting center of the existing warehouse can be obtained by crawling from the network); the order quantity of the existing warehouse and the sorting center is obtained by crawling from the network; the land prices of existing warehouses are obtained from government published information. The attribute data also includes the number of existing warehouses located within the grid, which may be obtained from an existing map.
Step S302, training a linear regression model by using the attribute data of at least one grid to obtain a target model.
Step S303, for each warehouse to be selected, obtaining a second attribute value of the warehouse to be selected according to the following method: acquiring the address of the warehouse to be selected, and determining the grid to which the warehouse to be selected belongs according to the address of the warehouse to be selected; acquiring attribute data of a grid to which the warehouse to be selected belongs from attribute data of a plurality of grids; and inputting the first attribute value of the warehouse to be selected and the attribute data of the grid to which the warehouse to be selected belongs into the target model to obtain a second attribute value of the warehouse to be selected.
In this step, in implementation, the first attribute value of the to-be-selected warehouse includes: the distance between the warehouse to be selected and the transportation hub, the distance between the warehouse to be selected and the sorting center, the order quantity between the warehouse to be selected and the sorting center and the land price of the warehouse to be selected. It should be noted that the process of obtaining the first attribute value of the to-be-selected warehouse is the same as the process of obtaining the first attribute value of the existing warehouse, and details are not described herein again. In addition, if the multiple warehouses to be selected are located in the same grid, the first attribute values of the multiple warehouses to be selected and the attribute data of the same grid are input into the target model, and the second attribute value of each warehouse to be selected can be obtained at the same time.
Residential areas, city centers, mountain areas or areas with inconvenient traffic are not suitable for building warehouses; determining according to the urban land general plan provided by each urban land resource bureau: the warehouse can be constructed by urban and rural construction land, other construction land, general agricultural development area, urban and rural development area, allowed construction land, conditional construction area and independent mining area, and considering that the land area of only the independent mining area and the general agricultural development area is relatively large, only the independent mining area and the general agricultural development area are taken as the warehouse to be selected, which is specifically shown in fig. 5.
And acquiring the boundary of the warehouse to be selected from government published information, and converting the boundary of the warehouse to be selected into the longitude and latitude of the warehouse to be selected by using a desktop geographic information system (QGIS), wherein the longitude and latitude of the warehouse to be selected is the address of the warehouse to be selected. And obtaining a second attribute value of each warehouse to be selected according to the first attribute value of the warehouse to be selected, the attribute data of the grid to which the warehouse to be selected belongs and the target model, which is specifically shown in fig. 6.
Fig. 7 is obtained by combining fig. 5 and fig. 6, and fig. 7 shows the candidate warehouse and the second attribute value of the candidate warehouse.
Step S304, for each warehouse to be selected, obtaining a third attribute value of the warehouse to be selected according to the following method: subtracting the land price of the warehouse to be selected from the second attribute value of the warehouse to be selected, and taking the obtained difference as a third attribute value of the warehouse to be selected; wherein the first attribute value of the warehouse to be selected comprises a land price of the warehouse to be selected.
And S305, determining a target address according to the third attribute value of each warehouse to be selected.
In the embodiment of the invention, based on the quantitative benefit analysis method, any address selection scheme has fixed cost and variable cost, and the cost and income of different address selection schemes can change along with the change of warehouse reserves. And comparing the warehouse reserves of the profit-loss balance points of each scheme with the warehouse reserves of each scheme assembly which are basically the same by using a quantity bengal analysis method through mapping or numerical calculation. When the profit and loss balance points are the same, the lower the warehouse reserve is, the more reasonable the scheme is; when the assembly is equal, the scheme is more reasonable when the warehouse reserves are higher, and when the warehouse reserves are the same, the scheme with the maximum profit (namely the third attribute value) is selected. Specifically, according to the logistics relationship between the existing warehouse and the sorting center, the total cost of the system (traffic cost and land price) and the total income (rent) of the system are calculated, if the total cost of the system can be reduced by adding one warehouse or replacing one warehouse (replacing one warehouse means replacing the warehouse rented by a newly-built warehouse), and the total income of the system is increased, then adding one warehouse or replacing one warehouse is considered to be feasible.
The process of selecting an address is described above in connection with fig. 1-7, and the means of selecting an address is described below in connection with fig. 8.
In order to solve the problems in the prior art, an embodiment of the present invention provides an apparatus for selecting an address, as shown in fig. 8, the apparatus including:
a preprocessing unit 801, configured to divide a preset region into multiple grids, and extract attribute data of each grid; wherein the attribute data comprises a first attribute value and a second attribute value for each existing object located within the grid.
The training unit 802 is configured to train a regression model using the attribute data of at least one grid to obtain a target model.
The processing unit 803 is configured to obtain a second attribute value of each object to be selected according to the first attribute value of the object to be selected, the attribute data of the grid to which the object to be selected belongs, and the target model.
The selecting unit 804 is configured to obtain a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected, and determine a target address according to the third attribute value of each object to be selected.
In this embodiment of the present invention, the processing unit 803 is configured to:
for each object to be selected, obtaining a second attribute value of the object to be selected according to the following method:
acquiring the address of the object to be selected, and determining the grid to which the object to be selected belongs according to the address of the object to be selected;
acquiring attribute data of a grid to which the object to be selected belongs from attribute data of a plurality of grids;
and inputting the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs into the target model to obtain a second attribute value of the object to be selected.
In the embodiment of the invention, the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object, the land price of the object to be selected and the number of the existing objects within a preset range of the object to be selected;
or the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object and the land price of the object to be selected; the attribute data also includes the number of existing objects located within the grid.
In this embodiment of the present invention, the selecting unit 804 is configured to:
for each object to be selected, obtaining a third attribute value of the object to be selected according to the following method:
and subtracting the land price of the object to be selected from the second attribute value of the object to be selected to obtain a difference serving as a third attribute value of the object to be selected.
In this embodiment of the present invention, the selecting unit 804 is configured to:
and selecting a target address from the addresses of the objects to be selected according to the third attribute value of each object to be selected, the land area of each object to be selected and the geological condition of each object to be selected.
It should be understood that the functions performed by the components of the address selecting apparatus provided in the embodiments of the present invention have been described in detail in the address selecting method in the above embodiments, and are not described herein again.
Fig. 9 illustrates an exemplary system architecture 900 to which the method of selecting an address or the apparatus of selecting an address of embodiments of the present invention may be applied.
As shown in fig. 9, the system architecture 900 may include end devices 901, 902, 903, a network 904, and a server 905. Network 904 is the medium used to provide communication links between terminal devices 901, 902, 903 and server 905. Network 904 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 901, 902, 903 to interact with a server 905 over a network 904 to receive or send messages and the like. The terminal devices 901, 902, 903 may have installed thereon various messenger client applications such as, for example only, a shopping-like application, a web browser application, a search-like application, an instant messaging tool, a mailbox client, social platform software, etc.
The terminal devices 901, 902, 903 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 905 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 901, 902, 903. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the method for selecting an address provided by the embodiment of the present invention is generally executed by the server 905, and accordingly, the apparatus for selecting an address is generally disposed in the server 905.
It should be understood that the number of terminal devices, networks, and servers in fig. 9 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 10, a block diagram of a computer system 1000 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 10, the computer system 1000 includes a Central Processing Unit (CPU)1001 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)1002 or a program loaded from a storage section 1008 into a Random Access Memory (RAM) 1003. In the RAM 1003, various programs and data necessary for the operation of the system 1000 are also stored. The CPU 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.
The following components are connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 1008 including a hard disk and the like; and a communication section 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The driver 1010 is also connected to the I/O interface 1005 as necessary. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1010 as necessary, so that a computer program read out therefrom is mounted into the storage section 1008 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication part 1009 and/or installed from the removable medium 1011. The computer program executes the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 1001.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a unit, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a preprocessing unit, a training unit, a processing unit, and a selection unit. Where the names of the cells do not in some cases constitute a limitation on the cells themselves, for example, a training cell may also be described as "a cell that trains a regression model using the attribute data of at least one grid, resulting in a target model".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: dividing a preset area into a plurality of grids, and extracting attribute data of each grid; wherein the attribute data comprises a first attribute value and a second attribute value for each existing object located within a grid; training a regression model by using the attribute data of at least one grid to obtain a target model; obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data of a grid to which the object to be selected belongs and the target model; and respectively obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected, and determining a target address according to the third attribute value of each object to be selected.
According to the technical scheme of the embodiment of the invention, the preset area is divided into a plurality of grids, and the attribute data of each grid is extracted; wherein the attribute data comprises a first attribute value and a second attribute value of each existing object located within the grid; and training a regression model by using the attribute data of at least one grid to obtain a target model. Thereby determining the correlation of the first attribute value and the second attribute value through the grid based on the existing object. The method comprises the steps of obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data and correlation of a grid to which the object to be selected belongs, obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected respectively, and determining a target address according to the third attribute value of each object to be selected, wherein the larger the third attribute value is, the more the target address meets the user requirements, so that the problem of poor user experience caused by low matching degree of the target address and the user requirements is solved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method of selecting an address, comprising:
dividing a preset area into a plurality of grids, and extracting attribute data of each grid; wherein the attribute data comprises a first attribute value and a second attribute value for each existing object located within a grid;
training a regression model by using the attribute data of at least one grid to obtain a target model;
obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data of a grid to which the object to be selected belongs and the target model;
and respectively obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected, and determining a target address according to the third attribute value of each object to be selected.
2. The method of claim 1, wherein obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data of a grid to which the object to be selected belongs, and the target model comprises:
for each object to be selected, obtaining a second attribute value of the object to be selected according to the following method:
acquiring the address of the object to be selected, and determining the grid to which the object to be selected belongs according to the address of the object to be selected;
acquiring attribute data of the grids to which the objects to be selected belong from the attribute data of the grids;
and inputting the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs into the target model to obtain a second attribute value of the object to be selected.
3. The method according to claim 2, wherein the first attribute value of the candidate object comprises a distance from the candidate object to a transportation junction, a distance from the candidate object to a current object, a current amount of the candidate object to the current object, a land price of the candidate object, and a number of existing objects within a preset range of the candidate object;
or the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object and the land price of the object to be selected; the attribute data also includes the number of existing objects located within the grid.
4. The method according to claim 3, wherein obtaining a third attribute value of each of the objects to be selected according to the first attribute value and the second attribute value of each of the objects to be selected respectively comprises:
for each object to be selected, obtaining a third attribute value of the object to be selected according to the following method:
and subtracting the land price of the object to be selected from the second attribute value of the object to be selected to obtain a difference serving as a third attribute value of the object to be selected.
5. The method of claim 1, wherein determining the target address according to the third attribute value of each of the objects to be selected comprises:
and selecting a target address from the addresses of the objects to be selected according to the third attribute value of each object to be selected, the land area of each object to be selected and the geological condition of each object to be selected.
6. An apparatus for selecting an address, comprising:
the device comprises a preprocessing unit, a data processing unit and a data processing unit, wherein the preprocessing unit is used for dividing a preset area into a plurality of grids and extracting attribute data of each grid; wherein the attribute data comprises a first attribute value and a second attribute value for each existing object located within a grid;
the training unit is used for training a regression model by utilizing the attribute data of at least one grid to obtain a target model;
the processing unit is used for obtaining a second attribute value of each object to be selected according to a first attribute value of the object to be selected, attribute data of a grid to which the object to be selected belongs and the target model;
and the selecting unit is used for respectively obtaining a third attribute value of each object to be selected according to the first attribute value and the second attribute value of each object to be selected, and determining a target address according to the third attribute value of each object to be selected.
7. The apparatus of claim 6, wherein the processing unit is configured to:
for each object to be selected, obtaining a second attribute value of the object to be selected according to the following method:
acquiring the address of the object to be selected, and determining the grid to which the object to be selected belongs according to the address of the object to be selected;
acquiring attribute data of the grids to which the objects to be selected belong from the attribute data of the grids;
and inputting the first attribute value of the object to be selected and the attribute data of the grid to which the object to be selected belongs into the target model to obtain a second attribute value of the object to be selected.
8. The apparatus according to claim 7, wherein the first attribute value of the candidate object comprises a distance from the candidate object to a transportation junction, a distance from the candidate object to a current object, a current amount of the candidate object to the current object, a land price of the candidate object, and a number of existing objects within a preset range of the candidate object;
or the first attribute value of the object to be selected comprises the distance from the object to be selected to a transportation junction, the distance from the object to be selected to a current object, the current amount from the object to be selected to the current object and the land price of the object to be selected; the attribute data also includes the number of existing objects located within the grid.
9. The apparatus of claim 8, wherein the selection unit is configured to:
for each object to be selected, obtaining a third attribute value of the object to be selected according to the following method:
and subtracting the land price of the object to be selected from the second attribute value of the object to be selected to obtain a difference serving as a third attribute value of the object to be selected.
10. The apparatus of claim 6, wherein the selection unit is configured to:
and selecting a target address from the addresses of the objects to be selected according to the third attribute value of each object to be selected, the land area of each object to be selected and the geological condition of each object to be selected.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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