Branch site selection method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a website site selection method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting the index values of all land parcel attraction indexes and all commercial maturity indexes of selectable land parcels into a pre-constructed website site selection model, the website site selection model comprising a first sub-model, a second sub-model and a third sub-model; calculating the plot attraction score of the selectable plot through a first sub-model based on the index value of each plot attraction index; based on the index value of each commercial maturity index, calculating a commercial maturity score of the selectable plot through a second sub-model; based on the plot attraction score and the commercial maturity score, calculating a network site selection score of the selectable plot through a third sub-model; and determining the website location of the business website based on the website site selection score. According to the embodiment of the invention, the site selection of the business network can be efficiently, accurately and scientifically carried out.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a network site selection method, device, electronic equipment and storage medium. Background Art

[0002] Business outlets, such as bank outlets, express delivery outlets, and commodity sales outlets, can provide convenience for customers to handle business. By scientifically selecting the location of business outlets, the efficiency of outlets and customer experience can be improved. The existing methods for selecting outlets mainly include manual layout method and resource endowment method. The manual layout method has high labor costs, long cycles, uneven standards, single information sources, and low site selection efficiency and benefits; the resource endowment method has poor site selection accuracy. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, electronic device and storage medium for selecting a network point, which can select a network point efficiently, accurately and scientifically.

[0004] In a first aspect, an embodiment of the present invention provides a network site selection method, including:

[0005] Inputting the index values ​​of each plot attractiveness index and each commercial maturity index of the optional plots into a pre-built network site selection model, wherein the network site selection model includes: a first sub-model, a second sub-model and a third sub-model;

[0006] Based on the index values ​​of the attractiveness indexes of the respective plots, calculating the plot attractiveness score of the optional plots through the first sub-model;

[0007] Based on the indicator values ​​of the various business maturity indicators, calculating the business maturity score of the optional plot through a second sub-model;

[0008] Based on the plot attractiveness score and the business maturity score, calculating the network site selection score of the optional plot through a third sub-model; and

[0009] The deployment location of the business outlets is determined based on the outlet site selection score.

[0010] In a second aspect, an embodiment of the present invention provides a network point site selection device, including:

[0011] An input module, used to input the index values ​​of each plot attractiveness index and each commercial maturity index of the optional plot into a pre-built network site selection model, wherein the network site selection model includes: a first sub-model, a second sub-model and a third sub-model;

[0012] A plot attractiveness score calculation module, configured to calculate the plot attractiveness score of the optional plot through the first sub-model based on the index value of each plot attractiveness index;

[0013] A commercial maturity score calculation module, used to calculate the commercial maturity score of the optional plot through a second sub-model based on the indicator values ​​of each commercial maturity indicator;

[0014] a network site selection score calculation module, configured to calculate the network site selection score of the optional plot through a third sub-model based on the plot attractiveness score and the commercial maturity score; and

[0015] The network point location determination module is used to determine the deployment location of the business network point based on the network point site selection score.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a network site selection method as described in any one of the embodiments of the present invention is implemented.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a network site selection method as described in any one of the embodiments of the present invention.

[0018] The embodiments of the present invention provide a method, device, electronic device and storage medium for network site selection. By utilizing a pre-constructed network site selection model including a first sub-model, a second sub-model and a third sub-model, the first sub-model is used to accurately calculate the land attraction score of the optional land based on the index value of each land attraction index, and the second sub-model is used to accurately calculate the commercial maturity score of the optional land based on the index value of each commercial maturity index. Then, based on the land attraction score and the commercial maturity score, the third sub-model is used to accurately calculate the network site selection score of the optional land. Finally, based on the network site selection score, the deployment location of the business outlets is determined. The network site selection of the business outlets can be carried out efficiently, accurately and scientifically, and the network efficiency and customer experience can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of a method for selecting a network site provided by an embodiment of the present invention;

[0021] Figure 2 is another flow chart of the network site selection method provided by an embodiment of the present invention;

[0022] Figure 3 is another flow chart of the network site selection method provided by an embodiment of the present invention;

[0023] Figure 4 is a structural schematic diagram of a network point site selection device provided by an embodiment of the present invention;

[0024] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Figure 1 This is a flow chart of a method for selecting a branch site provided by an embodiment of the present invention. This embodiment is applicable to the scenario of selecting a branch site of a bank. The method can be executed by a branch site selection device provided by an embodiment of the present invention, and the device can be implemented in software and / or hardware. In a specific embodiment, the device can be integrated into an electronic device, such as a computer, a server, etc. The following embodiments will be described by taking the device integrated into an electronic device as an example. Reference Figure 1 , the method may specifically include the following steps:

[0028] Step 101, input the index values ​​of each plot attractiveness index and each commercial maturity index of the optional plot into a pre-built network site selection model, the network site selection model includes: a first sub-model, a second sub-model and a third sub-model. This step can be helpful for calculating the plot attractiveness score and the commercial maturity score of the optional plot based on the index values ​​of the plot attractiveness index and the commercial maturity index.

[0029] Specifically, the optional plots may include one plot or a plurality of plots, and the plots may be understood as geographical area blocks.

[0030] Specifically, the above-mentioned land attractiveness index and commercial maturity index can be determined by relevant professionals based on experience, or obtained through a large model.

[0031] Specifically, the attractiveness index of the above-mentioned optional plots may include supermarket index, convenience store index, market index, food index, drugstore index, cinema index and sports and fitness venue index within the scope of the optional plot.

[0032] Specifically, the commercial maturity indicators of the above-mentioned optional plots may include office building indicators, cultural media unit indicators, company indicators, shopping center indicators and other commercial maturity indicators.

[0033] Specifically, the index values ​​of the above-mentioned supermarket index, convenience store index, market index, beauty index, drugstore index, sports and fitness venue index and other land attractiveness indexes can be determined based on the corresponding quantity, or can be determined based on the corresponding type, scale and other characteristics.

[0034] Specifically, the index values ​​of the above-mentioned commercial maturity indicators such as office building indicators, cultural media unit indicators, company indicators, shopping center indicators, etc. can be determined based on the corresponding quantities, or based on the corresponding types, scales and other characteristics.

[0035] Optionally, before inputting the index values ​​of each plot attractiveness index and each commercial maturity index of the optional plots into a pre-built network site selection model, the original values ​​of each plot attractiveness index and each commercial maturity index are obtained; and the original values ​​of each plot attractiveness index and each commercial maturity index are standardized to obtain corresponding index values ​​of each plot attractiveness index and each commercial maturity index.

[0036] Optionally, the above process of standardizing the original values ​​of each plot attraction index and each commercial maturity index includes: performing Z standardization on the original values ​​of each plot attraction index and each commercial maturity index.

[0037] Specifically, the original values ​​of each plot attractiveness index and each commercial maturity index can be understood as the number of corresponding indicators, such as the number of supermarkets.

[0038] Specifically, the above process of performing Z-standardization on the original values ​​of each plot attractiveness index and each commercial maturity index can be performed based on the following formula:

[0039]

[0040] Wherein, ZP represents the index value of the land attractiveness index or the commercial maturity index, P represents the original value of the land attractiveness index or the commercial maturity index, σP represents the standard deviation of the original values ​​of the corresponding land attractiveness index or the commercial maturity index of each land parcel in the preselected geographical area, Represents the average value of the corresponding site attractiveness index or commercial maturity index for each site in the pre-selected geographical area.

[0041] Specifically, standardizing the raw data can eliminate the differences between data from different units and sources, so that the data can be compared and analyzed on the same scale.

[0042] Step 102, based on the index value of each plot attraction index, the plot attraction score of the optional plot is calculated by the first sub-model. This step can accurately obtain the plot attraction score of the optional plot, and is conducive to calculating the site selection score of the optional plot based on the plot attraction score, so as to accurately and scientifically determine the site selection of the business outlet under the premise of considering the plot attraction of the optional plot.

[0043] Optionally, the first sub-model mentioned above is a linear regression model.

[0044] Optionally, the first sub-model can be expressed as:

[0045]

[0046] Among them, x2 represents the attractiveness score of the plot; Z i Represents the index value of each plot's attractiveness index; α i It represents the model weight parameter corresponding to each plot attractiveness index; n represents the total number of plot attractiveness indexes.

[0047] Specifically, the first sub-model mentioned above may also be other types of machine learning models, such as a neural network model and an extreme gradient boosting model.

[0048] Step 103, based on the index value of each business maturity index, the business maturity score of the optional plot is calculated by the second sub-model. This step can accurately obtain the business maturity score of the optional plot, and is conducive to calculating the site selection score of the optional plot based on the business maturity score, so as to accurately and scientifically determine the site selection of the business outlet under the premise of considering the business maturity of the optional plot.

[0049] Optionally, the second sub-model is a linear regression model.

[0050] Optionally, the second sub-model can be expressed as:

[0051]

[0052] Among them, x3 represents the attractiveness score of the plot; Z j Represents the index value of each plot's attractiveness index; α j represents the model weight parameter corresponding to each plot attractiveness index; m represents the total number of plot attractiveness indexes.

[0053] Specifically, the first sub-model mentioned above may also be other types of machine learning models, such as a neural network model and an extreme gradient boosting model.

[0054] Step 104, based on the land attractiveness score and the commercial maturity score, the network site selection score of the optional land is calculated by the third sub-model. This step can accurately calculate the network site selection score of the optional land under the premise of considering the land attractiveness and commercial maturity of the optional land, and thus can help to scientifically and accurately determine the location of the business outlets.

[0055] Optionally, the third sub-model is an extreme gradient boosting model.

[0056] Specifically, the third sub-model mentioned above can also be other types of machine learning models, such as a neural network model and a linear regression model.

[0057] Optionally, the above extreme gradient boosting model can be expressed as:

[0058]

[0059] Where: obj (t) represents the site selection score, x k Indicates the index value of each network site selection related index, β k It represents the model weight parameter corresponding to each network site selection related index, β0 represents the model parameter, and λ represents the interference factor.

[0060] Specifically, since the extreme gradient boosting model can be calculated in parallel, setting the third sub-model as the extreme gradient boosting model can help improve the network site selection score and the efficiency of network site selection.

[0061] Optionally, the process of calculating the network site selection score of the optional plot through the third sub-model based on the plot attractiveness score and the commercial maturity score includes:

[0062] Based on the index values ​​of each network site selection related index of the optional plots, the network site selection score is calculated through the third sub-model. The network site selection related indicators include: plot attractiveness score, business maturity score, new first-tier city score, number of working and living population in the plot, number of existing competing outlets in the plot, and categories of existing competing outlets.

[0063] Specifically, the above-mentioned indicators related to branch site selection can be determined by professionals who have been engaged in the corresponding branch layout planning work for a long time, based on the impact of external factors on branch operations.

[0064] Specifically, the above-mentioned new first-tier city scores can be understood as the new first-tier city scores of the city where the plot is located, and the above-mentioned new first-tier city scores can be obtained from authoritative websites or authoritative channels.

[0065] Specifically, the number of employed and resident population within the above-mentioned plots can be understood as the population working and living within the optional plots, and can be determined based on officially published statistical data.

[0066] Specifically, the existing competing outlets in the above-mentioned plots can be understood as outlets that already exist in the optional plots and are in competition with the outlets that need to be located this time. For example, for bank outlets, the existing competing outlets in the above-mentioned plots can be understood as existing outlets of the bank that already exist in the optional plots.

[0067] Specifically, the above-mentioned existing competitive outlet categories can be understood as existing competitive outlet service mode categories or scale categories, etc. Specifically, for example, for bank outlets, the above-mentioned competitive outlet categories can be understood as: whether it is a branch business office.

[0068] Specifically, the index values ​​of the above-mentioned indicators related to the location of each branch can be determined based on the number and / or category of the corresponding indicators. For example, for the competitive branch category of a bank branch, when the competitive branch category is a branch business office, the corresponding index value is determined to be 1, otherwise the corresponding index value is determined to be 0.

[0069] Step 105, determine the deployment location of the business outlets based on the outlet site selection score. Based on steps 101 to 104, this step uses a pre-built outlet site selection model including a first sub-model, a second sub-model, and a third sub-model, and accurately calculates the land attraction score of the optional land through the first sub-model based on the index value of each land attraction index, and accurately calculates the commercial maturity score of the optional land through the second sub-model based on the index value of each commercial maturity index, and then accurately calculates the outlet site selection score of the optional land through the third sub-model based on the land attraction score and the commercial maturity score, and finally determines the deployment location of the business outlets based on the outlet site selection score, which can efficiently, accurately and scientifically select the location of the business outlets and improve the outlet efficiency and customer experience.

[0070] Optionally, the process of determining the deployment location of the business outlets based on the outlet site selection score includes:

[0071] The locations within one or more plots with the highest corresponding network point site selection scores among the multiple optional plots are determined as the network point locations.

[0072] Specifically, when the number of business outlets to be deployed is certain, a corresponding number of optional plots with the highest outlet site selection scores can be selected as outlet deployment plots, and appropriate locations within the outlet deployment plots can be determined as the above-mentioned outlet locations.

[0073] Optionally, the process of determining the deployment location of the business outlets based on the outlet site selection score includes:

[0074] A plurality of plots whose corresponding network point site selection scores are greater than a preset score threshold among the plurality of optional plots are determined as network point deployment plots, and appropriate positions within the network point deployment plots are determined as the above-mentioned network point positions.

[0075] The following further describes the network site selection method provided by the embodiment of the present invention. Specifically, Figure 2 The following steps may be included:

[0076] Step 201 , gridding a map of a target geographical area, and determining regional plots corresponding to each grid as optional plots.

[0077] Specifically, the map of the target geographical area may be obtained in advance from a map open platform.

[0078] Specifically, the target geographical area may be, for example, a province or a city.

[0079] Optionally, the process of determining the regional plots corresponding to each grid as optional plots includes: dividing the map of the target geographical area into grids of 1*1 square kilometer.

[0080] Step 202: Input the index values ​​of each plot attractiveness index and each commercial maturity index of each optional plot into a pre-built network site selection model.

[0081] Step 203, based on the index value of the corresponding land plot attraction index, the land plot attraction score of each optional land plot is calculated by the first sub-model.

[0082] Preferably, the first sub-model is a first linear regression model.

[0083] Optionally, the above-mentioned process of calculating the plot attractiveness score of each optional plot through the first sub-model based on the index value of the corresponding attractiveness index of each plot includes: based on the corresponding attractiveness index of each plot, calculating the plot attractiveness score of each optional plot in turn through the first linear regression model.

[0084] Step 204, based on the indicator values ​​of the corresponding commercial maturity indicators, the commercial maturity score of each optional plot is calculated by the second sub-model.

[0085] Preferably, the second sub-model is a second linear regression model.

[0086] Optionally, the above process of calculating the business maturity score of each optional plot through the second sub-model based on the indicator value of each corresponding business maturity indicator includes: calculating the business maturity score of each optional plot in turn through the second linear regression model based on the indicator value of each corresponding business maturity indicator.

[0087] Step 205: Based on the corresponding plot attractiveness score and business maturity score, the network site selection score of each optional plot is calculated by the third sub-model.

[0088] Preferably, the third sub-model is an extreme gradient boosting model.

[0089] Optionally, the above-mentioned project of calculating the network site selection score of each optional plot based on the corresponding plot attractiveness score and business maturity score through the third sub-model includes: calculating the network site selection score of each optional plot based on the corresponding plot attractiveness score and business maturity score through parallel processing of the extreme gradient boosting model.

[0090] Step 206: Determine the deployment location of the business outlets based on the outlet site selection scores of each optional plot.

[0091] The embodiment of the present invention determines the optional plots by dividing the map into grids based on the target geographical area, which can help avoid missing plots suitable for deploying business outlets, and uses the linear regression model as the model type of the first sub-model and the second sub-model to save computing power. At the same time, using the extreme gradient boosting model as the model type of the third sub-model can help improve the site selection efficiency.

[0092] The following further describes the network site selection method provided by the embodiment of the present invention. Figure 3 As shown, the steps of constructing the above network site selection model include:

[0093] Step 301: Establish an original first sub-model based on the attractiveness index of each plot of land.

[0094] Optionally, the process of establishing the original first sub-model based on the attractiveness index of each plot of land includes: determining a model function of the first sub-model based on the attractiveness index of each plot of land.

[0095] Specifically, when the first sub-model is a linear regression model, the number of independent variables of the corresponding model function is determined based on the total number of attractiveness indicators of each plot.

[0096] Step 302: Establish an original second sub-model based on each business maturity indicator.

[0097] Optionally, the process of establishing the original second sub-model based on each business maturity indicator includes: determining a model function of the second sub-model based on each business maturity indicator.

[0098] Step 303: Establishing an original third sub-model based on network site selection related indicators, where the network site selection related indicators include indicators representing the land attraction score and the commercial maturity score.

[0099] Optionally, the process of establishing the original third sub-model based on the network site selection related indicators includes: determining the model function of the third sub-model based on each network site selection related indicator.

[0100] Step 304: Establish a network site selection model to be trained based on the original first sub-model, the original second sub-model and the original third sub-model.

[0101] Optionally, the process of establishing the network site selection model to be trained based on the original first sub-model, the original second sub-model and the original third sub-model includes: setting the output of the original first sub-model and the original second sub-model as the input of the original third sub-model to obtain the network site selection model to be trained.

[0102] Step 305 , training the network site selection model to be trained based on the sample index values ​​of each plot attraction index and each commercial maturity index of each sample plot in the plurality of sample plots to obtain a network site selection model.

[0103] Optionally, the process of training the network site selection model to be trained based on the sample index values ​​of each plot attractiveness index and each commercial maturity index of each sample plot in the plurality of sample plots includes:

[0104] Based on the land attraction index and business maturity index of each sample land parcel, the training website site selection score corresponding to each sample land parcel is obtained through the network site selection model to be trained;

[0105] The training loss function of the plot location model is determined based on the training network point location scores, and the network parameters of the plot location model are adjusted based on the function value of the training loss function.

[0106] Specifically, the original first sub-model, the original second sub-model and the original third sub-model may be established respectively, and trained respectively, and then the above network site selection model may be constructed based on the trained first sub-model, the second sub-model and the third sub-model.

[0107] The embodiment of the present invention can train a network site selection model that does not need to rely too much on resource statistics results and can efficiently and accurately calculate network site selection scores, which can be beneficial to the efficient, accurate and scientific site selection of business outlets.

[0108] Figure 4 is a structural diagram of a network site selection device provided by an embodiment of the present invention, and the device is suitable for executing the network site selection method provided by an embodiment of the present invention. Figure 4 As shown, the device may specifically include:

[0109] The input module 401 is used to input the index values ​​of each land attraction index and each commercial maturity index of the optional land into the pre-built network site selection model, which includes: a first sub-model, a second sub-model and a third sub-model. It is beneficial to calculate the land attraction score and the commercial maturity score of the optional land based on the index values ​​of the land attraction index and the commercial maturity index.

[0110] Optionally, the above optional plots may include one plot or multiple plots, and the above plots may be understood as geographical area blocks.

[0111] The plot attractiveness score calculation module 402 is used to calculate the plot attractiveness score of the optional plot through the first sub-model based on the index value of each plot attractiveness index. The plot attractiveness score of the optional plot can be accurately obtained, and the site selection score of the optional plot can be calculated based on the plot attractiveness score, so as to accurately and scientifically determine the site selection of the business outlet under the premise of considering the plot attractiveness of the optional plot.

[0112] Optionally, the first sub-model mentioned above is a linear regression model.

[0113] The business maturity score calculation module 403 is used to calculate the business maturity score of the optional plot through the second sub-model based on the index value of each business maturity index. The business maturity score of the optional plot can be accurately obtained, and the site selection score of the optional plot can be calculated based on the business maturity score, so as to accurately and scientifically determine the site selection of the business outlet under the premise of considering the business maturity of the optional plot.

[0114] Optionally, the second sub-model is a linear regression model.

[0115] The network site selection score calculation module 404 is used to calculate the network site selection score of the optional plot through the third sub-model based on the plot attractiveness score and the commercial maturity score. The network site selection score of the optional plot can be accurately calculated under the premise of considering the plot attractiveness and commercial maturity of the optional plot, which can help to scientifically and accurately determine the location of the business outlets.

[0116] Optionally, the third sub-model is an extreme gradient boosting model.

[0117] Optionally, the above-mentioned network site selection score calculation module 404 can be specifically used to calculate the network site selection score based on the indicator values ​​of each network site selection related indicator of the optional plot through a third sub-model. The network site selection related indicators include: plot attractiveness score, business maturity score, new first-tier city score, number of working and living population in the plot, number of existing competing outlets in the plot, and category of existing competing outlets.

[0118] The outlet location determination module 405 is used to determine the deployment location of the business outlets based on the outlet location selection score. It can be combined with modules 401 to 404, by using the pre-built outlet location selection model including the first sub-model, the second sub-model and the third sub-model, accurately calculate the plot attractiveness score of the optional plot through the first sub-model based on the index value of each plot attractiveness index, and accurately calculate the commercial maturity score of the optional plot through the second sub-model based on the index value of each commercial maturity index, and then accurately calculate the outlet location score of the optional plot through the third sub-model based on the plot attractiveness score and the commercial maturity score, and finally determine the deployment location of the business outlets based on the outlet location score, so that the location of the business outlets can be selected efficiently, accurately and scientifically, and the outlet efficiency and customer experience can be improved.

[0119] Optionally, the network point location determination module 405 can be specifically configured to determine the location in one or more plots with the highest corresponding network point location scores among the multiple optional plots as the network point location.

[0120] Optionally, the network site selection device provided by the embodiment of the present invention also includes a data processing module, which is used to grid the map of the target geographical area and determine the regional plots corresponding to each grid as optional plots before inputting the index values ​​of each plot attractiveness index and each commercial maturity index of the optional plots into a pre-built network site selection model.

[0121] Optionally, the network site selection device provided by the embodiment of the present invention further includes a model building module, which is used to establish an original first sub-model based on the attractiveness index of each plot;

[0122] Establishing the original second sub-model based on each business maturity indicator;

[0123] The original third sub-model is established based on the indicators related to network site selection, which include the indicators representing the site attractiveness score and the commercial maturity score;

[0124] Establishing a network site selection model to be trained based on the original first sub-model, the original second sub-model and the original third sub-model; and

[0125] The network site selection model to be trained is trained based on the sample index values ​​of each plot attraction index and each commercial maturity index of each sample plot in multiple sample plots to obtain the network site selection model.

[0126] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0127] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the network site selection method provided in any of the above embodiments is implemented.

[0128] An embodiment of the present invention further provides a computer-readable medium on which a computer program is stored. When the program is executed by a processor, the network point site selection method provided in any of the above embodiments is implemented.

[0129] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the network site selection method as described in any one of the embodiments of the present invention.

[0130] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 of an electronic device suitable for implementing an embodiment of the present invention. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0131] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0132] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0133] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0134] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0135] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0136] The modules and / or units involved in the embodiments of the present invention may be implemented in software or hardware. The modules and / or units described may also be set in a processor, for example, it may be described as: a processor includes an input module, a plot attractiveness score calculation module, a commercial maturity score calculation module, a network site selection score calculation module, and a network site location determination module. The names of these modules do not, in some cases, constitute limitations on the modules themselves.

[0137] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: inputting the index values ​​of each plot attraction index and each business maturity index of the optional plot into a pre-built network site selection model, the network site selection model includes: a first sub-model, a second sub-model and a third sub-model; based on the index values ​​of each plot attraction index, calculating the plot attraction score of the optional plot through the first sub-model; based on the index values ​​of each business maturity index, calculating the business maturity score of the optional plot through the second sub-model; based on the plot attraction score and the business maturity score, calculating the network site selection score of the optional plot through the third sub-model; and determining the deployment location of the business outlets based on the network site selection score.

[0138] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for selecting a network site, characterized in that: include: Inputting the index values ​​of each plot attractiveness index and each commercial maturity index of the optional plots into a pre-built network site selection model, wherein the network site selection model includes: a first sub-model, a second sub-model and a third sub-model; Based on the index values ​​of the attractiveness indexes of the respective plots, calculating the plot attractiveness score of the optional plots through the first sub-model; Based on the indicator values ​​of the various business maturity indicators, calculating the business maturity score of the optional plot through a second sub-model; Based on the plot attractiveness score and the business maturity score, calculating the network site selection score of the optional plot through a third sub-model; and The deployment location of the business outlets is determined based on the outlet site selection score.

2. The network site selection method according to claim 1, characterized in that: The number of the optional plots is multiple; before inputting the index values ​​of the plot attraction index and the commercial maturity index of each optional plot into the pre-built network site selection model, the network site selection method further includes: The map of the target geographical area is divided into grids, and the regional plots corresponding to the respective grids are determined as the optional plots.

3. The network site selection method according to claim 1, characterized in that: The calculating the network site selection score of the optional plot by the third sub-model based on the plot attractiveness score and the commercial maturity score includes: Based on the index values ​​of each network site selection related index of the optional plot, the network site selection score is calculated by the third sub-model, and the network site selection related indexes include: the plot attractiveness score, the business maturity score, the new first-tier city score, the number of working and living population in the plot, the number of existing competing outlets in the plot, and the categories of existing competing outlets.

4. The network site selection method according to claim 2, characterized in that: The determining the deployment location of the business outlets based on the outlet site selection score includes: The locations within one or more plots with the highest corresponding network point site selection scores among the multiple optional plots are determined as the network point locations.

5. The network site selection method according to claim 1, characterized in that: Before inputting the index values ​​of each plot attraction index and each commercial maturity index of the selectable plots into the pre-built network site selection model, the network site selection method further includes: Obtaining the original values ​​of the respective land plot attractiveness indexes and the respective commercial maturity indexes; and The original values ​​of the attraction index of each plot of land and the commercial maturity index are standardized to obtain corresponding index values ​​of the attraction index of each plot of land and the commercial maturity index.

6. The network site selection method according to any one of claims 1 to 5, characterized in that: The first sub-model and the second sub-model are linear regression models, and the third sub-model is an extreme gradient boosting model.

7. The network site selection method according to claim 1, characterized in that: The steps of constructing the network site selection model include: Establishing an original first sub-model based on the attractiveness index of each plot of land; Establishing an original second sub-model based on each of the business maturity indicators; Establishing an original third sub-model based on network site selection related indicators, wherein the network site selection related indicators include the land attractiveness score and the commercial maturity score. Establishing a network site selection model to be trained based on the original first sub-model, the original second sub-model and the original third sub-model; and The network site selection model to be trained is trained based on the sample index values ​​of each plot attraction index and each commercial maturity index of each sample plot in multiple sample plots to obtain the network site selection model.

8. A network site selection device, characterized in that: include: An input module, used to input the index values ​​of each plot attractiveness index and each commercial maturity index of the optional plot into a pre-built network site selection model, wherein the network site selection model includes: a first sub-model, a second sub-model and a third sub-model; A plot attractiveness score calculation module, configured to calculate the plot attractiveness score of the optional plot through the first sub-model based on the index value of each plot attractiveness index; A commercial maturity score calculation module, used to calculate the commercial maturity score of the optional plot through a second sub-model based on the indicator values ​​of each commercial maturity indicator; a network site selection score calculation module, configured to calculate the network site selection score of the optional plot through a third sub-model based on the plot attractiveness score and the commercial maturity score; and The network point location determination module is used to determine the deployment location of the business network point based on the network point site selection score.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the network site selection method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the network site selection method as described in any one of claims 1 to 7 is implemented.

Images