CN113779163A - Method, device and equipment for processing geographical position information and storage medium - Google Patents
Method, device and equipment for processing geographical position information and storage medium Download PDFInfo
- Publication number
- CN113779163A CN113779163A CN202011222357.6A CN202011222357A CN113779163A CN 113779163 A CN113779163 A CN 113779163A CN 202011222357 A CN202011222357 A CN 202011222357A CN 113779163 A CN113779163 A CN 113779163A
- Authority
- CN
- China
- Prior art keywords
- target
- information
- shop
- store
- distance range
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012545 processing Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000008901 benefit Effects 0.000 claims description 31
- 230000002860 competitive effect Effects 0.000 claims description 24
- 238000011156 evaluation Methods 0.000 description 13
- 238000013461 design Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 12
- 238000004590 computer program Methods 0.000 description 9
- 235000013305 food Nutrition 0.000 description 7
- 238000001514 detection method Methods 0.000 description 6
- 238000003672 processing method Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 235000012055 fruits and vegetables Nutrition 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 235000019800 disodium phosphate Nutrition 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000012552 review Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0639—Item locations
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Finance (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a method, a device, equipment and a storage medium for processing geographic position information, which are characterized in that a position influence factor and a shop effectiveness factor when a target shop is set at a pre-selected geographic position are obtained by acquiring the pre-selected geographic position of a target shop and object information within a preset distance range with the pre-selected geographic position as the center, and then the object information within the preset distance range is processed, and finally whether the pre-selected geographic position meets the setting requirement of the target shop is determined according to the attribute information of the target shop, the position influence factor and the shop effectiveness factor when the target shop is set at the pre-selected geographic position. According to the technical scheme, whether the pre-selected geographic position meets the requirement set by the target shop can be accurately obtained without analyzing the collected information by special personnel, so that the labor cost is saved, and the site selection efficiency is improved.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing geographic location information.
Background
Compared with online shopping, a customer can obtain required articles at the first time through offline shopping, can conveniently check commodity objects on the spot and can also ensure fund safety to the maximum extent, so the offline shopping is the most indispensable part in the daily life of people.
At present, in order to ensure that a shop where a user shops can operate normally and a convenient service society, when the shop is selected, a large amount of data related to a preselected address needs to be collected, and expected benefits of the shop are artificially evaluated, so that whether the preselected address meets the requirements of the shop or not is determined.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: in the mode, a large amount of investigation on various information near the address is required by special personnel, the time and labor are consumed, the labor cost is high, scientific basis is not provided for comprehensive and detailed analysis in all aspects, whether the shop address is reasonable or not can not be judged efficiently and quickly, and the problem of low shop address selection efficiency exists.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for processing geographic position information, which are used for solving the problems of high labor cost and low efficiency in the existing shop address selecting process.
In a first aspect, an embodiment of the present application provides a method for processing geographic location information, including:
acquiring a pre-selected geographic position of a target store and object information within a preset distance range with the pre-selected geographic position as a center;
processing the object information within the preset distance range to obtain a position influence factor and a shop effectiveness factor when the target shop is opened at the pre-selected geographic position;
and determining whether the pre-selected geographic position meets the establishment requirement of the target store or not according to the attribute information of the target store, the position influence factor when the target store is established at the pre-selected geographic position and the store effectiveness factor.
In one possible design of the first aspect, the determining whether the pre-selected geographic location meets a target store establishment demand according to the attribute information of the target store, the location influence factor when the target store is established at the pre-selected geographic location, and the store effectiveness factor includes:
acquiring the people flow information quantity, the target store area and the target store cost of the target store laid in a preset time period according to the attribute information of the target store;
determining the transaction information of the target store laid in the preset time period according to the position influence factor, the shop effectiveness factor, preset per-person consumption information and the people flow information amount;
determining position benefit information of the target shop laid in the preset time period according to the transaction information and the area of the target shop;
according to the transaction information and the target shop cost, determining income information of the target shop laid in the preset time period;
and determining whether the pre-selected geographic position meets the establishment requirement of the target shop or not according to the position benefit information and the income information of the target shop laid in the preset time period.
In the above possible design of the first aspect, the transaction information of the target store in the preset time period is obtained by the following formula:
S＝a1*a2*b*c
wherein S is a trading value of a trading information preset time period of the target store paved in the preset time period, a1 represents the position influence factor, a2 represents the store effectiveness factor, b represents preset per-person consumption information, and c represents the number of users and the traffic information of the target users.
In another possible design of the first aspect, the object information includes: population information, cell information;
determining a position influence factor of the target shop according to the object information within a preset distance range with the preselected geographic position as the center, wherein the position influence factor comprises the following steps:
processing the object information within the preset distance range to obtain a position influence factor when the target shop is opened at the pre-selected geographic position, wherein the position influence factor comprises the following steps:
determining the cell position cost average value in the preset distance range and the population information and the cell information of a target area in the preset distance range according to the population information and the cell information in the preset distance range;
determining a cell location cost average value of the target area according to the population information and the cell information of the target area;
and determining the position influence factor of the target shop according to the cell position cost average value in the preset distance range and the cell position cost average value of the target area.
Optionally, the cell location cost average of the target area is obtained by the following formula:
wherein k1 is the cell location mean value of the target area, AiIs the location cost of the ith cell within the target area, BiThe number of users in the ith cell in the target area, n is the number of cells in the target area, and n is an integer greater than or equal to 1;
the cell location cost average k2 in the preset distance range is obtained by the following formula:
wherein k2 is the cell location mean value in the preset distance range, AiIs the location cost of the ith cell within the preset distance range, BiThe number of users of the ith cell in the preset distance range is m, and the number of the cells in the preset distance range is m;
both m and n are integers greater than or equal to 1, and m is greater than or equal to n;
the position influence factor of the target shop is obtained by the following formula:
a1＝k2/k1
wherein the a1 is the position influence factor, the k1 is a cell position cost average of the target area, and the k2 is a cell position average within the preset distance range.
In yet another possible design of the first aspect, the object information includes: competition object information of the target store;
processing the object information within the preset distance range to obtain a shop efficiency factor when the target shop is opened at the pre-selected geographic position, wherein the shop efficiency factor comprises:
determining at least one competitive object type of the target shop and the number of each competitive object type according to the competitive object information of the target shop within the preset distance range;
and determining the shop efficiency factor of the target shop according to at least one competition object type of the target shop, the number of each competition object type and a preset competition coefficient of each competition object type.
Optionally, the shop performance factor is obtained by the following formula:
wherein a2 is the store performance factor, xlThe number of the ith competition object type, the ylIs the competition coefficient of the ith competition object type, h is the number of types of the ith competition object, and h is an integer greater than or equal to 1.
In a second aspect, the present application provides a processing apparatus for geographic position information, comprising: the device comprises an acquisition module and a processing module;
the acquisition module is used for acquiring a pre-selected geographic position of a target store and object information within a preset distance range with the pre-selected geographic position as a center;
the processing module is used for processing the object information within the preset distance range to obtain a position influence factor and a shop efficiency factor when the target shop is set at the pre-selected geographic position, and determining whether the pre-selected geographic position meets the setting requirement of the target shop or not according to the attribute information of the target shop, the position influence factor and the shop efficiency factor when the target shop is set at the pre-selected geographic position.
In a possible design of the second aspect, the processing module is configured to determine whether the preselected geographic location meets a target store establishment requirement according to the attribute information of the target store, the location influence factor when the target store is established at the preselected geographic location, and the store effectiveness factor, and specifically:
the processing module is specifically configured to:
acquiring the people flow information quantity, the target store area and the target store cost of the target store laid in a preset time period according to the attribute information of the target store;
determining the transaction information of the target store laid in the preset time period according to the position influence factor, the shop effectiveness factor, preset per-person consumption information and the people flow information amount;
determining position benefit information of the target shop laid in the preset time period according to the transaction information and the area of the target shop;
according to the transaction information and the target shop cost, determining income information of the target shop laid in the preset time period;
and determining whether the pre-selected geographic position meets the establishment requirement of the target shop or not according to the position benefit information and the income information of the target shop laid in the preset time period.
Optionally, the transaction information of the target store spread in the preset time period is obtained through the following formula:
S＝a1*a2*b*c
wherein S is transaction information of the target store spread in the preset time period, a1 represents the location influence factor, a2 represents the store effectiveness factor, b represents preset per-person consumption information, and c represents the people flow information amount.
In another possible design of the second aspect, the object information includes: population information, cell information;
the processing module is configured to process the object information within the preset distance range to obtain a location influence factor when the target store is opened at the preselected geographic location, and specifically includes:
the processing module is specifically configured to:
determining the cell position cost average value in the preset distance range and the population information and the cell information of a target area in the preset distance range according to the population information and the cell information in the preset distance range;
determining a cell location cost average value of the target area according to the population information and the cell information of the target area;
and determining the position influence factor of the target shop according to the cell position cost average value in the preset distance range and the cell position cost average value of the target area.
Optionally, the cell location cost average of the target area is obtained by the following formula:
wherein k1 is the cell location mean value of the target area, AiIs the location cost of the ith cell within the target area, BiThe number of users in the ith cell in the target area, n is the number of cells in the target area, and n is an integer greater than or equal to 1;
the cell location cost average k2 in the preset distance range is obtained by the following formula:
wherein k2 is the cell location mean value in the preset distance range, AiIs the location cost of the ith cell within the preset distance range, BiThe number of users of the ith cell in the preset distance range is m, and the number of the cells in the preset distance range is m;
both m and n are integers greater than or equal to 1, and m is greater than or equal to n;
the position influence factor of the target shop is obtained by the following formula:
a1＝k2/k1
wherein the a1 is the position influence factor, the k1 is a cell position cost average of the target area, and the k2 is a cell position average within the preset distance range.
In yet another possible design of the second aspect, the object information includes: competition object information of the target store;
the processing module is configured to process the object information within the preset distance range to obtain a store efficiency factor when the target store is opened at the preselected geographic location, and specifically includes:
the processing module is specifically configured to:
determining at least one competitive object type of the target shop and the number of each competitive object type according to the competitive object information of the target shop within the preset distance range;
and determining the shop efficiency factor of the target shop according to at least one competition object type of the target shop, the number of each competition object type and a preset competition coefficient of each competition object type.
Optionally, the shop performance factor is obtained by the following formula:
wherein a2 is the store performance factor, xlThe number of the ith competition object type, the ylIs the competition coefficient of the ith competition object type, h is the number of the ith competition object types, and h is an integer greater than or equal to 1.
In a third aspect, the present application provides an electronic device, comprising: a processor, a memory, a display and a system bus;
the memory stores computer-executable instructions;
the processor, when executing the computer program instructions, implements the method provided by the first aspect and each of the possible designs.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer program instructions for implementing the method of the first aspect and of the various possible designs when executed by a processor.
According to the processing method, the device, the equipment and the storage medium of the geographic position information, the pre-selected geographic position of the target store and the object information in the preset distance range with the pre-selected geographic position as the center are obtained, the object information in the preset distance range is processed, the position influence factor and the store efficiency factor when the target store is opened in the pre-selected geographic position are obtained, and finally whether the pre-selected geographic position meets the opening requirement of the target store or not is determined according to the attribute information of the target store, the position influence factor and the store efficiency factor when the target store is opened in the pre-selected geographic position. According to the technical scheme, whether the requirement for opening the target shop is met when the target shop is opened at the pre-selected geographic position can be accurately obtained without analyzing the collected information by special personnel, so that labor cost is saved, and site selection efficiency is improved.
Drawings
Fig. 1 is a schematic view of an application scenario of a processing method for geographical location information according to an embodiment of the present application;
fig. 2 is a flowchart of a first embodiment of a method for processing geographical location information according to an embodiment of the present application;
FIG. 3 is a schematic view of monitoring data provided by an embodiment of the present application;
fig. 4A is a flowchart of a second method for processing geographical location information according to an embodiment of the present disclosure;
FIG. 4B is a schematic diagram illustrating an evaluation of preselected geographic locations provided by an embodiment of the present application;
FIG. 5 is a term interpretation diagram of a pre-selected geographic location evaluation decision rule provided by an embodiment of the present application;
fig. 6 is a flowchart of a third embodiment of a method for processing geographical location information according to the present application;
fig. 7 is a flowchart of a fourth embodiment of a method for processing geographical location information according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a processing apparatus for processing geographical location information according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a schematic application scenario diagram of a processing method for geographical location information according to an embodiment of the present application. As shown in fig. 1, the application scenario is explained by geographical location information of a target store presented on an interactive interface of the terminal device 11.
For the geographic position information processing scene of the target shop in the practical application, the type of the target shop, the pre-selected geographic position and the pre-set distance range are set on the initial interface of the terminal device 11. Exemplary, "target store type": a type indicating a pre-selected target store, for example, a fresh shop, a supermarket, a clothing store, etc.; "pre-selected geographic location": selecting from a map of the terminal device 11, and automatically generating detailed information of a target address; "preset distance range": a reference range around a preset target shop is set, and shops having a competitive relationship with the preset target shop are displayed in the reference range in real time.
For example, the present application describes the location of a fresh food store, and the predetermined distance range is referred to within a radius of 0.5 km. The terminal device 11 displays: within the preset distance range of 0.5km, the fruit and vegetable products comprise 1 good fruit and vegetable product, 1 new place and 1 fresh shop. After the user confirms the preset address for the fresh food store, the user may select "next" to perform a specific configuration, which is described in detail in the following embodiments.
It is understood that the "target store type", "pre-selected geographic location", "preset distance range", "next step", etc. presented in fig. 1 may be set according to actual situations, and are only examples, and do not limit the display content.
In the embodiment, a user selects a site for a preset target store through the terminal device, and generates detailed information whether a pre-selected geographic position meets the requirements of the target store, so that support is provided for judging whether the target store at the pre-selected geographic position meets the requirements of the target store, and the time and energy spent on analyzing relevant data laid at the pre-selected geographic position by the target store are saved for the user.
Aiming at the problems of time and labor consumption and low efficiency of site selection in the prior art, the embodiment of the application has the following technical conception processes: the inventor finds that the type and the address of a preset target store are selected on the terminal equipment through the related information of the preset target store, and related parameters are configured, so that whether the pre-selected geographic position meets the requirement set by the target store or not is determined according to the related parameters.
Based on the technical concept, the method for processing the geographic position information comprises the steps of obtaining a pre-selected geographic position of a target store, object information in a preset distance range with the pre-selected geographic position as a center, processing the object information in the preset distance range to obtain a position influence factor and a store efficiency factor when the target store is arranged at the pre-selected geographic position, and finally determining whether the pre-selected geographic position meets the arrangement requirement of the target store or not according to the attribute information of the target store, the position influence factor and the store efficiency factor when the target store is arranged at the pre-selected geographic position. According to the technical scheme, the collected information is not required to be analyzed by special personnel, whether the pre-selected geographic position meets the requirement set by the target shop or not can be accurately obtained, the labor cost is saved, and the site selection efficiency is improved.
The following describes the technical solution of the present application in detail through a specific embodiment in an application scenario shown in fig. 1. It should be noted that the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a first embodiment of a method for processing geographical location information according to an embodiment of the present application. As shown in fig. 2, a method for processing geographical location information provided in an embodiment of the present application may include the following steps:
and step 21, acquiring the pre-selected geographic position of the target shop and object information within a preset distance range with the pre-selected geographic position as the center.
In this embodiment, the description will be made with the type of the target store to be opened selected as a fresh food store and the preselected geographical position of the target store selected, and in this case, it is necessary to arrange information about the vicinity around the preselected geographical position.
Optionally, the object information within the preset distance range with the preselected geographic position as the center includes the number of target users in a preset time period, the number of the target users is determined by the people flow information with the preselected geographic position as the center, and the people flow information can be obtained through artificial monitoring and input into the terminal device; the measurement may also be obtained by the detection device, and the terminal device calls the detection device, which is not limited herein.
Specifically, the people flow information is shown in fig. 3, and fig. 3 is a schematic view of monitoring data provided in the embodiment of the present application, where the schematic view includes a detection date "XXXX year-XX month", a detection people flow (peak), a detection time length (peak), and a detection people flow (peak). Correspondingly, the target user number in the preset time period can be determined according to the people flow information. The embodiment of the application takes a preset time period of one month as an example, and the target user number is determined to be 20 thousands of people.
And step 22, processing the object information in the preset distance range to obtain a position influence factor and a shop effectiveness factor when the target shop is opened at the pre-selected geographic position.
In this embodiment, the establishment of the fresh food store needs to consider information of a cell, a resident and a related target store within a certain range with the preselected geographic location as a center, that is, since the range affected by one fresh food store has a certain geographical limitation, here, a 0.5km radius range with the preselected geographic location as a center is taken as an example for explanation as a preset distance range, and by determining information of the cell, the resident and the related target store within the preset distance range, information of the cell, the resident and the related store within the preset distance range is further processed, and a location influence factor and a store efficiency factor when the target store is established with the preselected geographic location are determined.
And step 23, determining whether the pre-selected geographic position meets the establishment requirement of the target store according to the attribute information of the target store, the position influence factor when the target store is established at the pre-selected geographic position and the store effectiveness factor.
Illustratively, the attribute information of the target store comprises a rent of the target store and an area of the target store. The attribute information may be determined by calling the relevant data recorded in the network database, or may be obtained by field investigation, which is not limited herein.
Optionally, the attribute information of the fresh food store is the influence of the tenant and community price in the radius range of 0.5km on the target store, and the influence of other competitive stores in the radius range of 0.5km on the target store, where the rent of the target store is 20 ten thousand per month, the area of the target store is 200 square meters, and the performance factor of the store is 0.5 km. Further, according to the attribute information, the location influence factor, and the store performance factor of the target store, after comprehensive review, it is determined whether the preselected geographic location meets the requirement set by the target store, and a specific review process is given by the following embodiments and will not be described herein again.
According to the processing method of the geographic position information, the pre-selected geographic position of the target store and the object information within the preset distance range with the pre-selected geographic position as the center are obtained, then the object information within the preset distance range is processed, the position influence factor and the store efficiency factor when the target store is arranged at the pre-selected geographic position are obtained, and finally whether the pre-selected geographic position meets the arrangement requirement of the target store is determined according to the attribute information of the target store, the position influence factor and the store efficiency factor when the target store is arranged at the pre-selected geographic position. According to the technical scheme, whether the pre-selected geographic position meets the requirement set by the target shop can be accurately obtained without analyzing the collected information by special personnel, so that the labor cost is saved, and the site selection efficiency is improved.
Based on the foregoing embodiments, fig. 4A is a flowchart of a second embodiment of a method for processing geographical location information according to the embodiments of the present application. Fig. 4B is a schematic diagram illustrating evaluation of preselected geographic locations provided by an embodiment of the present application. The above step 23 is explained below with reference to an evaluation diagram shown in fig. 4B. Optionally, referring to fig. 4B, the evaluation diagram includes XX street in XX district in XX city, an evaluation result, 0.5km target user number, 1km target user number, location benefit information of the target store, income information of the target store, 0.5km community population information, information of a competitive object, a trading value in a preset time period, and an evaluation result.
In this embodiment, as shown in fig. 4A, the step 23 may include the following steps:
and step 41, acquiring the people flow information amount, the area and the cost of the target store in a preset time period according to the attribute information of the target store.
Alternatively, the preset time period may be determined according to actual conditions. For example, the preset period is one month.
Optionally, the attribute information of the target store includes the target number of users of the target store, the area of the target store, the cost of the target store, and the like, and therefore, when the attribute information of the target store is acquired, the target number of users of the target store in the preset time period, the area of the target store, and the cost of the target store can be determined.
For example, referring to the introduction of the target store attribute information in the embodiment shown in fig. 2, it can be known that the number of the target users is 20 ten thousand, the rent of the target store is 20 ten thousand per month, and the area of the target store is 200 square meters, and the specific determination method is not described herein again.
And 42, determining the transaction information of the target shop in a preset time period according to the position influence factor, the shop efficiency factor, the preset per-person consumption information and the people flow information amount.
Optionally, the location influence factor is influence of community residents and community prices in a radius range of 0.5km of the target store, that is, a community factor value of the store in the radius range of 0.5km, the efficiency factor is influence of other stores which may have a competitive relationship in the radius range of 0.5km of the target store, that is, a market proportion of the store in the radius range of 0.5km, the preset per-person consumption information is determined according to the fresh consumption level of the market, the per-person consumption value range is between 40 and 60 yuan, and the calculation methods of the location influence factor and the store efficiency factor are provided in the following embodiments and are not described herein again.
Specifically, the transaction information, i.e., the transaction value, of the fresh food store in one month is obtained by the following formula:
S＝a1*a2*b*c
where a1 is a location influence factor, a2 is a shop performance factor, b is a per-person consumption amount, and c is a target number of users.
Illustratively, when a1 is 1.33, a2 is 60%, b is 50 yuan, c is 200000 persons/month, the fresh store has a trading value within one month, S is 1 a2 b c is 1.33 60%, 50 200000 is 8000000 yuan.
And 43, determining the position benefit information of the target store in a preset time period according to the transaction information and the area of the target store.
Optionally, the trading value of the target store within one month is determined, and according to the ratio of the trading value to the area of the target store, the benefit generated by each square meter of the target store, that is, the position benefit information of the target store is determined.
Specifically, the position benefit information of the target shop within one month is obtained through the following formula:
where i is the location benefit information, S is the targeted store trading value, and v is the targeted store area.
For example, when i 40000, S8000000, v 200 m,
and step 44, determining the income information of the target shop in a preset time period according to the transaction information and the cost of the target shop.
Optionally, the ratio of the rent to the trading value of the target store within one month, that is, the income information of the target store is determined according to the ratio of the trading value to the cost of the fresh store.
Specifically, the income information of the target store within one month is obtained through the following formula:
where j is benefit information, S is a targeted store trading value, and w is a rent of the targeted store.
Illustratively, when j is 4%, S is 8000000 yuan, and w is 200000 yuan, the profit information is obtained
And step 45, determining whether the pre-selected geographic position meets the set requirement of the target store or not according to the position benefit information and the income information of the target store spread in a preset time period.
Optionally, after the position benefit information of the target store and the income information of the target store are determined, whether the pre-selected geographic position meets the establishment requirement of the target store or not can be comprehensively evaluated. For example, whether the pre-selected geographic location satisfies the target store opening requirement may be indicated by the evaluation result, and displayed in a lighting manner, three lights indicate that a store is recommended to be opened, one light indicates that a store is not recommended to be opened, and two lights indicate that a store is considered to be opened.
Optionally, fig. 4B further includes: city analysis, perimeter analysis, crowd analysis, etc., and fig. 4B is an exemplary overview of the information for a city profile, e.g., XX city, second line city, etc., for the city in which the preselected geographic location is located.
For example, the user may derive the evaluation report information by selecting the option "derive pre-selected geographical location information evaluation information" for the user's reference to facilitate the user to view the evaluation report at any time.
Specifically, fig. 5 is a schematic view of a judgment rule term interpretation for pre-selected geographic location assessment. Referring to fig. 5, the term interpretation schematic includes: the system comprises a judgment rule, a lighting rule, a general population, the number of target users, the position benefit of a target store, the income information of the target store and the like.
Optionally, the factors influencing the "judgment rule" include "general population, number of target users, location benefit of the store, and revenue information of the store". The specific rule may be a "lighting rule", where the geographical location information evaluation diagram lights one lamp if one or more red lamps are used, and two lamps are lit if no red lamp or more yellow lamps are used, and three lamps are lit if all green lamps are used. The determination rule in practical application is not limited by the examples provided herein.
Alternatively, in fig. 5, the lighting rule (store availability) for the "location benefit information of the target store" is "red light: position benefit information is 4.5 ten thousand or less, yellow light: the position benefit information is 4.5 to 5 thousands, green light: the position benefit information is 5 ten thousand or more, and the lighting rule (store availability) for the "income information of the target store" is "red light: yield information is more than 8%, yellow light: yield information is 6% to 8%, green: the profit information is 6% or less.
Alternatively, as shown in fig. 4B, the "evaluation result" is displayed as a light, that is, a shop is not recommended, and specifically, although the profit information of the target shop is a green light, the location benefit is a red light, and the reason why the shop is not recommended may be that, compared to the location benefit of 40000 yuan/per month, the actual benefit of the target shop is not so great except for labor cost, commodity cost, property cost, finishing cost, and the like, and thus the location is not recommended as the best address for the fresh shop.
Through the schematic diagrams shown in fig. 4B and 5, the user can intuitively know the information related to the preselected geographical position and the suggestion whether the shop needs are met.
According to the method for processing the geographic position information, the people flow information amount, the target shop area and the target shop cost of a target shop in a preset time period are obtained according to the attribute information of the target shop, then the transaction information of the target shop in the preset time period is determined according to the position influence factor, the shop efficiency factor, the preset per-person consumption information and the people flow information amount, then the position benefit information of the target shop in the preset time period is determined according to the transaction information and the target shop area, the income information of the target shop in the preset time period is determined according to the transaction information and the target shop cost, and finally whether the preset geographic position meets the set requirement of the target shop or not is determined according to the position benefit information and the income information of the target shop in the preset time period. According to the technical scheme, whether the pre-selected geographic position meets the requirement set by the target store or not is accurately evaluated according to the trading value, the position benefit information and the income information of the target store.
Fig. 6 is a flowchart of a third embodiment of a processing method for geographical location information according to the present application. The object information includes: in the case of population information and cell information, as shown in fig. 6, the step 22 of processing the object information within the preset distance range to obtain the location influence factor when the target store is opened at the pre-selected geographic location may include the steps of:
and step 61, determining the cell position cost average value in the preset distance range and the population information and the cell information of the target area in the preset distance range according to the population information and the cell information in the preset distance range.
Illustratively, population information and cell information within a preset distance range are population information and cell information within a 0.5km radius range of the target store, and according to the population information and the cell information within the 0.5km radius range, the position cost and the number of the cells of all the cells within the 0.5km radius range can be obtained.
The target area in the preset distance range refers to a cell having the largest business circle of cells in the preset distance range.
Optionally, the position cost average of all cells in the preset distance range may be determined according to the following formula, the position costs of all cells in the preset distance range, and the number of users in the cell.
That is, the average value of the costs of all the cell locations within the preset distance range is obtained by the following formula:
wherein k2 is the mean value of the cell positions within a preset distance range, AiFor the location cost of the ith cell within a predetermined distance range, BiThe number of the users of the ith cell in the preset distance range is m, and the number of the cells in the preset distance range is m.
For example, assuming there are 4 cells in the 0.5km radius, the location cost of the 4 cells is: a. the1Is 2 ten thousand, A2Is 1.5 ten thousand, A3Is 1 ten thousand, A43.5 ten thousand, the number of the 4 cells is: b is1Is 1000 households, B2Is 1000 households, B3Is 1000 households, B4Is 1000 households.
Specifically, according to the location cost and the number of users of 4 cells within a 0.5km radius range, the mean value k2 of the location cost of 4 cells can be calculated as (ten thousand):
and step 62, determining the cell location cost average value of the target area according to the population information and the cell information of the target area.
For example, the population information and the cell information of the target area refer to the location cost of the cell having the largest business circle of cells within a preset distance range, the number of users of the cell, for example, the location cost of the cell having the largest business circle of cells within a 0.5km radius range, the number of users of the cell, that is, the average price of the target area, and the number of users of the cell.
Optionally, the cell location cost average of the target area within the preset distance range may be calculated according to the following formula, the location cost of the target area, and the number of users of the cell.
That is, the cell location cost average of the target area is obtained by the following formula:
where k1 is the cell location cost average of the target area, AiLocation cost of ith cell as target area, BiThe number of users of the ith cell of the target area, and n is the number of cells of the target area.
Optionally, A1、A2、A3Is the location cost of 3 cells within the target area, B1、B2、B3The number of users in 3 cells in the target area.
Specifically, the average cost of 3 cell locations within a 0.5km radius range is (ten):
for example, the number of cells within the preset distance range may be m, and the number of cells of the target area may be n, where m is an integer greater than or equal to n, and the minimum value of n is 1.
And 63, determining a position influence factor of the target shop according to the cell position cost average value in the preset distance range and the cell position cost average value of the target area.
Illustratively, according to the above steps, the cell location cost average value in the preset distance range and the cell location cost average value of the target area have been determined, and further, the position influence factor of the target store is determined according to the ratio of the cell location cost average value in the preset distance range to the cell location cost average value of the target area.
Optionally, the position influence factor of the target store is obtained by the following formula:
wherein, a1 position influence factor, k1 is the cell position cost average of the target area, and k2 is the cell position average within the preset distance range.
It is to be noted that the calculation result of the position influence factor of the target store is 1.5 at the maximum, more than 1.5 is calculated as 1.5, and 0.5 at the minimum, and less than 0.5 is calculated as 0.5, and therefore, the position influence factor of the target store takes a value between 0.5 and 1.5 according to the calculation result.
The method for processing geographic position information provided by the embodiment of the application determines a cell position cost mean value in a preset distance range and population information and cell information of a target area in the preset distance range according to population information and cell information in the preset distance range by using object information in the preset distance range with a preselected geographic position as a center, determines the cell position cost mean value of the target area according to the population information and the cell information of the target area in the preset distance range, and determines a position influence factor of a target store according to the cell position cost mean value in the preset distance range and the cell position cost mean value of the target area. In the technical scheme, the position influence factor provides a basis for determining the trading value of the preset target shop.
Fig. 7 is a flowchart of a fourth embodiment of a method for processing geographical location information according to the present application. Optionally, the object information includes: as shown in fig. 7, the step of processing the competition target information of the target store to obtain the store effectiveness factor when the target store is opened at the pre-selected geographic location may include:
and step 71, determining at least one competitive object type of the target shop and the number of each competitive object type according to the competitive object information of the target shop within the preset distance range.
Illustratively, according to the population information, the cell information and the information of the competitive objects within the radius range of the target address of 0.5km, it is determined that the competitive objects existing in the area comprise 1 fruit and vegetable good, 1 new place and 1 fresh shop.
And 72, determining the shop efficiency factor of the target shop according to at least one competition object type of the target shop, the number of each competition object type and a preset competition coefficient of each competition object type.
Illustratively, the competition coefficient is related to the type of the competition object, and is a value preset according to the survey.
Optionally, the store effectiveness factor is obtained by the following formula:
where a2 is the store performance factor, xlNumber of the I-th competing object type, ylIs the competition coefficient of the ith competition object type, h is the number of the ith competition object types, and h is an integer greater than or equal to 1.
For example, when the store performance factor is 60%, the competition coefficient of 1 fresh store is 100%, the competition coefficient of 1 fruit and vegetable is 50%, and the competition coefficient of 1 new place is 16.67%, the store performance factor within the radius range of 0.5km is obtained by the following formula:
notably, the value of the shop performance factor is between 0% and 100%.
According to the processing method of the geographic position information, at least one competitive object type of a target store and the number of each competitive object type are determined according to the competitive object information of the target store within a preset distance range; and determining the shop efficiency factor of the target shop according to at least one competition object type of the target shop, the number of each competition object type and a preset competition coefficient of each competition object type. In the technical scheme, the shop efficiency factor provides a basis for determining the trading value of the preset target shop.
Fig. 8 is a schematic structural diagram of a processing apparatus for geographic location information according to an embodiment of the present application. As shown in fig. 8, the apparatus includes an acquisition module 81 and a processing module 82.
The obtaining module 81 is configured to obtain a pre-selected geographic location of the target store and object information within a preset distance range with the pre-selected geographic location as a center.
The processing module 82 is configured to process the object information within the preset distance range to obtain a location influence factor and a store effectiveness factor when the target store is established at the preselected geographic location, and determine whether the preselected geographic location meets the establishment requirement of the target store according to the attribute information of the target store, the location influence factor and the store effectiveness factor when the target store is established at the preselected geographic location.
In one possible design of the embodiment of the present application, the processing module 82 is configured to determine whether the preselected geographic location meets the target store establishment requirement according to the attribute information of the target store, the location influence factor when the target store is established at the preselected geographic location, and the store effectiveness factor, specifically:
the processing module 82 is specifically configured to:
according to the attribute information of the target store, acquiring the people flow information quantity, the area and the cost of the target store in a preset time period;
determining the transaction information of the target shop in a preset time period according to the position influence factor, the shop efficiency factor, the preset per-person consumption information and the people flow information amount;
determining position benefit information of the target shop in a preset time period according to the transaction information and the area of the target shop;
according to the transaction information and the cost of the target store, determining income information of the target store in a preset time period;
and determining whether the pre-selected geographic position meets the establishment requirement of the target shop or not according to the position benefit information and the income information of the target shop spread in the preset time period.
Optionally, the transaction information of the target store in the preset time period is obtained by the following formula:
S＝a1*a2*b*c
wherein S is transaction information of a target store laid in a preset time period, a1 represents a position influence factor, a2 represents a store effectiveness factor, b represents preset per-person consumption information, and c represents a people flow information amount.
In another possible design of the embodiment of the present application, the object information includes: population information, cell information;
the processing module 82 is configured to process the object information within the preset distance range to obtain a location influence factor when the target store is opened at the pre-selected geographic location, and specifically includes:
the processing module 82 is specifically configured to:
determining a cell position cost average value in a preset distance range and population information and cell information of a target area in the preset distance range according to population information and cell information in the preset distance range;
determining a cell location cost average value of a target area according to population information and cell information of the target area;
and determining the position influence factor of the target shop according to the cell position cost mean value in the preset distance range and the cell position cost mean value of the target area.
Optionally, the cell location cost average of the target area is obtained by the following formula:
where k1 is the cell location mean of the target area, AiIs the location cost of the ith cell in the target area, BiThe number of users in the ith cell in the target area, n is the number of cells in the target area, and n is an integer greater than or equal to 1;
the cell location cost average k2 in the preset distance range is obtained by the following formula:
wherein k2 is the mean value of the cell positions within a preset distance range, AiFor the location cost of the ith cell within a predetermined distance range, BiThe number of the users of the ith cell in the preset distance range is m, and the number of the cells in the preset distance range is m;
m and n are integers greater than or equal to 1, and m is greater than or equal to n;
the location impact factor of the targeted store is obtained by the following formula:
a1＝k2/k1
wherein, a1 position influence factor, k1 is the cell position cost average of the target area, and k2 is the cell position average within the preset distance range.
In yet another possible design of the embodiment of the present application, the object information includes: competition object information of the target store;
the processing module 82 is configured to process the object information within the preset distance range to obtain a shop efficiency factor when the target shop is set at the pre-selected geographic location, and specifically includes:
the processing module 82 is specifically configured to:
determining at least one competitive object type of the target shop and the number of each competitive object type according to the competitive object information of the target shop within the preset distance range;
and determining the shop efficiency factor of the target shop according to at least one competition object type of the target shop, the number of each competition object type and a preset competition coefficient of each competition object type.
Optionally, the store effectiveness factor is obtained by the following formula:
where a2 is the store performance factor, xlNumber of the I-th competing object type, ylIs the competition coefficient of the ith competition object type, h is the number of the ith competition object types, and h is an integer greater than or equal to 1.
The processing apparatus for geographic location information provided in this embodiment may be used to execute the schemes in the foregoing embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the processing module may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the above determination module. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element here may be an integrated circuit with signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 9, the apparatus may include: a processor 91, a memory 92, a display 93, and a system bus 94.
The processor 91 executes computer-executable instructions stored in the memory, so that the processor 91 executes the scheme in the above-described embodiment.
The processor 91 may be a general-purpose processor including a central processing unit CPU, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
The memory 92 stores computer-executable instructions, the display 93 is used for displaying processing results of the processor 91 and interacting with a human machine, and the memory 92 and the display 93 are connected with the processor 92 through a system bus 94 and are used for achieving mutual communication.
The system bus 94 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The transceiver is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries). The memory may comprise Random Access Memory (RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The embodiment of the present application further provides a computer-readable storage medium, in which computer instructions are stored, and when the computer instructions are run on a computer, the computer is caused to execute the scheme of the foregoing embodiment
The embodiment of the application also provides a chip for running the instructions, and the chip is used for executing the scheme in the embodiment.
Embodiments of the present application also provide a computer program product, which includes a computer program stored in a computer-readable storage medium, where the computer program can be read by at least one processor from the computer-readable storage medium, and the at least one processor can implement the solutions in the above embodiments when executing the computer program.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method for processing geographical location information, comprising:
acquiring a pre-selected geographic position of a target store and object information within a preset distance range with the pre-selected geographic position as a center;
processing the object information within the preset distance range to obtain a position influence factor and a shop effectiveness factor when the target shop is opened at the pre-selected geographic position;
and determining whether the pre-selected geographic position meets the establishment requirement of the target store or not according to the attribute information of the target store, the position influence factor when the target store is established at the pre-selected geographic position and the store effectiveness factor.
2. The method as claimed in claim 1, wherein said determining whether the pre-selected geographic location meets a target store establishment demand based on the attribute information of the target store, the location impact factor and the store effectiveness factor at the time of establishing the target store at the pre-selected geographic location comprises:
acquiring the people flow information quantity, the target store area and the target store cost of the target store laid in a preset time period according to the attribute information of the target store;
determining the transaction information of the target store laid in the preset time period according to the position influence factor, the shop effectiveness factor, preset per-person consumption information and the people flow information amount;
determining position benefit information of the target shop laid in the preset time period according to the transaction information and the area of the target shop;
according to the transaction information and the target shop cost, determining income information of the target shop laid in the preset time period;
and determining whether the target position meets the shop establishment requirement or not according to the position benefit information and the income information of the target shop laid in the preset time period.
3. The method of claim 2, wherein the transaction information of the target store spread in the preset time period is obtained by the following formula:
S＝a1*a2*b*c
wherein S is transaction information of the target store spread in the preset time period, a1 represents the location influence factor, a2 represents the store effectiveness factor, b represents preset per-person consumption information, and c represents the people flow information amount.
4. The method according to any one of claims 1 to 3, wherein the object information includes: population information, cell information;
processing the object information within the preset distance range to obtain a position influence factor for setting the target shop at the pre-selected geographic position, including:
determining the cell position cost average value in the preset distance range and the population information and the cell information of a target area in the preset distance range according to the population information and the cell information in the preset distance range;
determining a cell location cost average value of the target area according to population information and cell information of the target area within the preset distance range;
and determining the position influence factor of the target shop according to the cell position cost average value in the preset distance range and the cell position cost average value of the target area.
5. The method of claim 4, wherein the mean cell location cost of the target area is obtained by using the following formula:
wherein k1 is the cell location mean value of the target area, AiIs the location cost of the ith cell within the target area, BiThe number of users in the ith cell in the target area, n is the number of cells in the target area, and n is an integer greater than or equal to 1;
the cell location cost average k2 in the preset distance range is obtained by using the following formula:
wherein k2 is the cell location mean value in the preset distance range, AiIs the location cost of the ith cell within the preset distance range, BiThe number of the users of the ith cell in the preset distance range is m, the number of the cells in the preset distance range is m, both m and n are integers greater than or equal to 1, and m is greater than or equal to n;
the position influence factor of the target store is obtained by the following formula:
a1＝k2/k1
wherein a1 is the position influence factor, k1 is the cell position cost average of the target area, and k2 is the cell position average within the preset distance range.
6. The method according to any one of claims 1-3, wherein the object information includes: competition object information of the target store;
processing the object information within the preset distance range to obtain a shop effectiveness factor for setting the target shop at the pre-selected geographic position, wherein the shop effectiveness factor comprises:
determining at least one competitive object type of the target shop and the number of each competitive object type according to the competitive object information of the target shop within the preset distance range;
and determining the shop efficiency factor of the target shop according to at least one competition object type of the target shop, the number of each competition object type and a preset competition coefficient of each competition object type.
7. The method of claim 6, wherein the store performance factor is derived by the formula:
wherein a2 is the store performance factor, xlThe number of competing objects of the l-th type, the ylIs the competition coefficient of the ith competition object type, h is the number of types of the ith competition object, and h is an integer greater than or equal to 1.
8. A device for processing geolocation information, comprising: the device comprises an acquisition module and a processing module;
the acquisition module is used for acquiring a pre-selected geographic position of a target store and object information within a preset distance range with the pre-selected geographic position as a center;
the processing module is used for processing the object information within the preset distance range to obtain a position influence factor and a shop efficiency factor when the target shop is set at the pre-selected geographic position, and determining whether the pre-selected geographic position meets the setting requirement of the target shop or not according to the attribute information of the target shop, the position influence factor and the shop efficiency factor when the target shop is set at the pre-selected geographic position.
9. An electronic device, comprising:
a processor, a memory, a display and a system bus;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory, causing the processor to perform the method of any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011222357.6A CN113779163A (en) | 2020-11-05 | 2020-11-05 | Method, device and equipment for processing geographical position information and storage medium |
| PCT/CN2021/091778 WO2022095387A1 (en) | 2020-11-05 | 2021-04-30 | Geographic location information processing method and apparatus, and device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011222357.6A CN113779163A (en) | 2020-11-05 | 2020-11-05 | Method, device and equipment for processing geographical position information and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113779163A true CN113779163A (en) | 2021-12-10 |
Family
ID=78835128
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011222357.6A Pending CN113779163A (en) | 2020-11-05 | 2020-11-05 | Method, device and equipment for processing geographical position information and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113779163A (en) |
| WO (1) | WO2022095387A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114596127A (en) * | 2022-05-10 | 2022-06-07 | 北京百炼智能科技有限公司 | Store site selection analysis method and device, electronic equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003288459A (en) * | 2002-03-28 | 2003-10-10 | Kumagai Gumi Co Ltd | Method and system for estimating commercial zone demand |
| US20090076888A1 (en) * | 2007-09-14 | 2009-03-19 | Mark Oster | Method and system for evaluating retail and related real estate development opportunities |
| CN104965920A (en) * | 2015-07-08 | 2015-10-07 | 百度在线网络技术（北京）有限公司 | Method and apparatus for determining target address |
| CN106384250A (en) * | 2016-09-13 | 2017-02-08 | 百度在线网络技术（北京）有限公司 | Site selection method and device |
| CN108876032A (en) * | 2018-06-12 | 2018-11-23 | 阿里巴巴集团控股有限公司 | A kind of data processing method, device, equipment and the system of object addressing |
| CN110111150A (en) * | 2019-05-08 | 2019-08-09 | 拉扎斯网络科技（上海）有限公司 | Information processing method, device, storage medium and electronic equipment |
| CN110288390A (en) * | 2019-06-17 | 2019-09-27 | 杭州火小二科技有限公司 | Determine the method and device of address |
| JP2020013510A (en) * | 2018-07-20 | 2020-01-23 | Ｚホールディングス株式会社 | Delivery management device, delivery management method, and delivery management program |
| CN111242666A (en) * | 2018-11-29 | 2020-06-05 | Tcl集团股份有限公司 | Store site selection method based on big data analysis, storage medium and server |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101784005A (en) * | 2009-12-17 | 2010-07-21 | 华为终端有限公司 | Method for retrieving point of interest and terminal thereof |
| CN103440589A (en) * | 2013-09-17 | 2013-12-11 | 上海商学院 | Store site selection system and method |
| CN107766984A (en) * | 2017-10-27 | 2018-03-06 | 郑州云海信息技术有限公司 | The optimal addressing system and method for businessman based on cloud computing |
-
2020
- 2020-11-05 CN CN202011222357.6A patent/CN113779163A/en active Pending
-
2021
- 2021-04-30 WO PCT/CN2021/091778 patent/WO2022095387A1/en active Application Filing
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003288459A (en) * | 2002-03-28 | 2003-10-10 | Kumagai Gumi Co Ltd | Method and system for estimating commercial zone demand |
| US20090076888A1 (en) * | 2007-09-14 | 2009-03-19 | Mark Oster | Method and system for evaluating retail and related real estate development opportunities |
| CN104965920A (en) * | 2015-07-08 | 2015-10-07 | 百度在线网络技术（北京）有限公司 | Method and apparatus for determining target address |
| CN106384250A (en) * | 2016-09-13 | 2017-02-08 | 百度在线网络技术（北京）有限公司 | Site selection method and device |
| CN108876032A (en) * | 2018-06-12 | 2018-11-23 | 阿里巴巴集团控股有限公司 | A kind of data processing method, device, equipment and the system of object addressing |
| JP2020013510A (en) * | 2018-07-20 | 2020-01-23 | Ｚホールディングス株式会社 | Delivery management device, delivery management method, and delivery management program |
| CN111242666A (en) * | 2018-11-29 | 2020-06-05 | Tcl集团股份有限公司 | Store site selection method based on big data analysis, storage medium and server |
| CN110111150A (en) * | 2019-05-08 | 2019-08-09 | 拉扎斯网络科技（上海）有限公司 | Information processing method, device, storage medium and electronic equipment |
| CN110288390A (en) * | 2019-06-17 | 2019-09-27 | 杭州火小二科技有限公司 | Determine the method and device of address |
Non-Patent Citations (3)
| Title |
|---|
| 刘宁;: "餐饮连锁机构选址策略分析", 中国科技信息, no. 04, 15 February 2009 (2009-02-15), pages 192 - 193 * |
| 王海燕;: "层次分析法在连锁便利店选址评价中的应用研究", 洛阳师范学院学报, no. 04, 25 April 2016 (2016-04-25), pages 85 - 88 * |
| 王金超;谢明;: "连锁药店选址影响因素与收益的关联分析", 中国卫生产业, no. 24, 25 August 2013 (2013-08-25), pages 67 - 68 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114596127A (en) * | 2022-05-10 | 2022-06-07 | 北京百炼智能科技有限公司 | Store site selection analysis method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022095387A1 (en) | 2022-05-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104050187B (en) | Search result methods of exhibiting and system | |
| CN109656967B (en) | Big data mining processing method, device, medium and electronic equipment based on space | |
| CN107993144A (en) | Customer risk grade determines method, apparatus, equipment and readable storage medium storing program for executing | |
| CN111709756A (en) | Method and device for identifying suspicious communities, storage medium and computer equipment | |
| KR20140079652A (en) | Method of providing helpful information for inauguration and server thereof | |
| JP2018139036A (en) | Analysis device | |
| JP2009169698A (en) | Sales information analysis device | |
| CN115033766A (en) | Data processing method, life circle display method and device, medium and equipment | |
| CN113779163A (en) | Method, device and equipment for processing geographical position information and storage medium | |
| KR20120124680A (en) | Automation system and automation method for analyzing commercial supremacy based on web-gis | |
| KR101729194B1 (en) | System for providing of trading area information | |
| CN114444985A (en) | Unmanned vehicle-based dynamic adjustment method and device for mobile selling route | |
| Januário et al. | Transport infrastructure, accessibility, and spillover effects: An empirical analysis of the Portuguese real estate market from 2000 to 2018 | |
| CN110335084A (en) | One kind being based on b2c online shopping mall Development control area system | |
| KR20100123408A (en) | System for providing a marketing information to a company based on gis and the method thereof | |
| CN111369284B (en) | Target object type determining method and device | |
| Lord et al. | Competition and location strategy in branch banking: spatial avoidance or clustering | |
| US11037133B2 (en) | System, method, and computer program product for selectively displaying information regarding activity in a geographic area | |
| CN115713345A (en) | Variable screening method and device, nonvolatile storage medium and processor | |
| CN111709750B (en) | Bill management system and method based on block chain | |
| CN114219547A (en) | Method, device, equipment and storage medium for determining store ordering amount | |
| CN110517063A (en) | Method, apparatus and server are determined into shop consumption person-time | |
| JP6027711B1 (en) | Information processing apparatus, information processing method, and information processing program | |
| CN106228313A (en) | A kind of telecommunications shops based on Balanced scorecard performance evaluation method | |
| RU2770568C1 (en) | Method and system for analysing the financial activity of sales outlets |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |