CN106651392A - Intelligent business location selection method, apparatus and system - Google Patents
Intelligent business location selection method, apparatus and system Download PDFInfo
- Publication number
- CN106651392A CN106651392A CN201611118332.5A CN201611118332A CN106651392A CN 106651392 A CN106651392 A CN 106651392A CN 201611118332 A CN201611118332 A CN 201611118332A CN 106651392 A CN106651392 A CN 106651392A
- Authority
- CN
- China
- Prior art keywords
- data
- target
- subregion
- user
- target subregion
- 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
Classifications
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Finance (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent business location selection method, apparatus and system, which relates to the technical field of communications and can improve business location selection accuracy of users, shorten cycle of a location selection process and reduce labor cost. The method comprises the steps of obtaining business geographic data of a target region and obtaining user information data of the target region from a telecom operator; performing grid division on the target region to obtain a plurality of target sub-regions; calculating recommendation values of the target sub-regions by utilizing the business geographic data and the user information data; and recommending the target sub-regions to the users according to the recommendation values of the target sub-regions, so that the users perform location selection according to the recommended target sub-regions. The intelligent business location selection method, apparatus and system is mainly used in the business location selection process.
Description
Technical field
The present invention relates to communication technical field, more particularly to a kind of Intelligent Business site selecting method, apparatus and system.
Background technology
Traditionally user typically carries out Market Site Selection using manual type, and addressing flow process is, for example,：1) clear and definite business type,
Location condition；2) rule of thumb roughly select partial target region (addressing scope is larger)；3) crowd's letter in artificial investigation target area
Breath, geographical facilities information, transport information further screens target area, reduces addressing scope；4) determine behind target area, seek
Look for tenantable StoreFront in target area；On-the-spot investigation StoreFront.This traditional addressing mode whole process expends a large amount of manpowers
Cost and time, and manually investigate the information that obtains may be inaccurate, easily cause project failure.
Therefore, some Intelligent Business addressing schemes are occurred in that at present, and one of which Intelligent Business addressing scheme is：Build bag
Address database containing a large amount of addresses and with the related business information data storehouse of the address database, and can be to above-mentioned database
In data carry out inductive statistics analysis；User can be input into the address to be inquired about to system, obtain the business with the address information
Information data, these data for example, export in graphical form Query Result or analysis report；Afterwards user can be according to inquiry knot
Fruit or analysis report are determined whether in the address addressing.
Present inventor has found that in actual applications above-mentioned prior art is actually by the information of a large amount of artificial collections
It is synthesized in unified system for user's inquiry, although it incorporates the part steps of traditional addressing, the information of early stage is adopted
Collection process is remained by what is manually carried out, still can expend substantial amounts of human cost and time.And due to the letter of artificial collection
To subjectivity, confidence level and accuracy are relatively low, it is thus possible to cause the analysis result to information inaccurate, and then cause for manner of breathing
The Query Result of user is inaccurate, misleads user, even results in the failure of user's business.
The content of the invention
The embodiment of the present invention provides a kind of Intelligent Business site selecting method, apparatus and system, it is possible to increase user's Market Site Selection
Accuracy, and shorten cycle of addressing process, reduce cost of labor.
To reach above-mentioned purpose, embodiments of the invention are adopted the following technical scheme that：
A kind of Intelligent Business site selecting method, including：
Obtain the applied geography data of target area and the user profile of the target area is obtained from telecom operators
Data；
The target area gridding is divided into into multiple target subregions；
The recommendation of each target subregion is calculated using the applied geography data and user profile data；
Target subregion is recommended to user according to the recommendation of each target subregion so that user is according to the target recommended
Subregion carries out addressing.
A kind of Intelligent Business addressing device, including：
Acquiring unit, the acquiring unit is used to obtain the applied geography data of target area and obtain from telecom operators
Take the user profile data of the target area；
Stress and strain model unit, the stress and strain model unit is used to for the target area gridding to be divided into multiple target
Region；
Computing unit, the computing unit is used to calculate each using the applied geography data and user profile data
The recommendation of target subregion；
Addressing recommendation unit, the recommendation selected cell is used for each the target sub-district calculated according to the computing unit
The recommendation in domain to user recommends target subregion so that user carries out addressing according to the target subregion recommended.
A kind of Intelligent Business site selection system, including Intelligent Business addressing device as above.
Intelligent Business site selecting method, apparatus and system that the present invention is provided, by the direct access institute from telecom operators
State the user profile data of target area, can save in prior art obtained by artificial investigation this kind of data it is artificial into
This, and the addressing cycle is greatly shortened, while the relatively low defect of the data reliability and accuracy that avoid artificial collection.And
And, big target area is subdivided into less target subregion by the application by way of gridding division, calculates every afterwards
The recommendation of individual target subregion, target subregion is recommended according to the recommendation of each target subregion to user, is enabled to
The addressing address recommended to user is more accurate.
Description of the drawings
In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, below will be to use needed for embodiment description
Accompanying drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for
For those of ordinary skill in the art, on the premise of not paying creative work, can be obtaining other according to these accompanying drawings
Accompanying drawing.
Fig. 1 is the flow chart of the Intelligent Business site selecting method that the embodiment of the present invention one is provided；
Fig. 2 is another flow chart of the Intelligent Business site selecting method that the embodiment of the present invention one is provided；
The structural representation of the Intelligent Business addressing device that Fig. 3 embodiment of the present invention two is provided；
Fig. 4 is another structural representation of the Intelligent Business addressing device that the embodiment of the present invention two is provided.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than the embodiment of whole.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Embodiment one
In order to improve the accuracy of user's Market Site Selection, and shorten cycle of addressing process, reduce cost of labor, such as Fig. 1
Shown, the Intelligent Business site selecting method that the embodiment of the present invention one is provided includes：
S1, the applied geography data for obtaining target area simultaneously obtain the user of the target area from telecom operators at and believe
Breath data.
Wherein, the target area is the desired addressing place of user, such as certain city.
Wherein, the applied geography data of the target area include geographyization infrastructure data and ground physics and chemistry business number
According to.The applied geography data of the acquisition target area include：Geographyization basis is crawled from map using data crawler technology
Facility data and ground physics and chemistry business data.
The data list structure of the ground physics and chemistry infrastructure data in the application is as shown in table 1：
Table 1：
The enumerated value that TYPE (types of infrastructures) field in table 1 is included is as shown in table 2：
Table 2：
The data list structure of the ground physics and chemistry business data in the application is as shown in table 3：
Table 3：
The enumerated value that TYPE (retail shop's big class) field is included in table 3 is as shown in table 4：
Table 4：
Wherein, retail shop's subclass SUB_TYPE refers to the concrete retail shop under retail shop's big class, such as retail shop's big class is " super in table 4
Retail shop's subclass under city " can be Wal-Mart, Carrefour, Hua Lian etc..Due to retail shop's subclass SUB_TYPE enumerated value it is more,
Do not enumerate herein.
As described above, above-mentioned ground physics and chemistry infrastructure data and ground physics and chemistry business data all availability data crawler technologies
Crawl from map, for example, crawl the basis containing latitude and longitude information from high moral map open platform using data crawler technology and set
Apply data.
It may be noted that over the ground the acquisition modes of physics and chemistry basis facility data and ground physics and chemistry business data are not limited in the application
It is fixed, in addition to crawling these data from high moral map open platform using data crawler technology, prior art can also be utilized
In other modes obtaining these data, as long as comprising data listed in the application table 1 and table 3 in the data for being obtained
Full dose information.
Wherein, the user profile data include user base information data and user service data.It is described from telecommunications
The user profile data of the target area are obtained at operator to be included：From the business operation support system of telecom operators
The user base information data of the target area are obtained in (Business＆Operation Support System, BOSS)；
From telecom operators mobile network Gn/S1-U interfaces the user service data of the target area is obtained (for example, by moving
The Gn/S1-U interfaces of network core net side carry out the business datum that pocket watch gathers networked users, based on interface protocol specification/business
Classification resolution rules carry out data parsing, generating structure data after parsing, by joining data correlation with mobile network cell work
Backfill latitude and longitude information, user service data needed for generating).
The data list structure of the user base information data in the application is as shown in table 5：
Table 5：
The tables of data of the user service data in the application is as shown in table 6：
Table 6：
The enumerated value of the SERVICE_CLASS (class of service) in table 6 is as shown in table 7：
Table 7：
S2, the target area gridding is divided into into multiple target subregions.
Specifically, " gridding division " refer on the basis of the central point of the target area, by the target area draw
It is divided into the multiple square nets with the default length of side, each of which square net is a target subregion.
It is noted that described below " grid " and " target subregion " all feeling the pulse with the finger-tip mark subregions.
Wherein, the default length of side can need to be set according to the addressing of user, such as 200 meters/500 meters/1000 meters.
Whether the length of side of each grid is not equally limited in the application.In actual applications, can be by each grid
The length of side be all set to the same, the length of side of such as each grid is 200 meters；Or the grid for marking off can have different side
Long, the length of side of such as some grids can be 200 meters, and the length of side of other grid is 500 meters.
In the application, in order to analyze the dependency relation between retail shop's addressing and each influence factor, need attribution data
Quantum chemical method is carried out in unified computing unit, therefore, in step s 2, target area is divided, afterwards can be by
Each the foursquare grid (i.e. target subregion) for obtaining carries out the calculating and analysis of addressing data as a unit.
As shown in table 8, wherein the position of each grid (i.e. target subregion) is by two pairs for the related data of each grid
Longitude and latitude represents, other 4 class data (i.e. physics and chemistry infrastructure data, physics and chemistry business data, user base information data with
And user service data) can be associated with grid longitude and latitude by itself longitude and latitude, so, so that it may obtain each grid
Various types of data.
Table 8：
Wherein, each grid has a unique sign GRID_ID, such as GRID_1, GRID_2, GRID_3 etc..
S3, the recommendation that each target subregion is calculated using the applied geography data and user profile data.
In actual applications, as shown in Fig. 2 this step may include step S31-S34：
S31, using the applied geography data and user profile data calculate in each target subregion it is each affect because
Influence value of the element to Market Site Selection.
Wherein, the influence factor includes population agglomeration degree, transportation accessibility, population income level, retail shop's wide variety
Property, similar retail shop's concentration class；Wherein, the influence value of population agglomeration degree density of population P of target subregionaverageTable
Show；The influence value of transportation accessibility quantity N of the traffic infrastructure in target subregiontrafficRepresent；The people
The population level of relative income V of the influence value in target subregion of mouth income levelincomeRepresent；Retail shop's wide variety
Property with quantity N of existing retail shop big class in target subregiontypeRepresent；Similar retail shop's concentration class is with same in target subregion
Density P of class retail shopsub_typeRepresent.
Below to Paverage、Ntraffic、Vincome、NtypeAnd Psub_typeCircular be described：
P average ：
In actual applications, density of population P of target subregionaverageCan be each grid (i.e. each target sub-district
Domain) many days (either 10 days or one month etc. a such as week) the density of population mean value.It is concrete to calculate thinking for example
For：For the grid for being denoted as GRID_1, count duplicate removal number N of appearance in one day, using N divided by grid GRID_1 face
Product obtains density of population P on the grid GRID_1 same day, further according to the P of nearest a week (either 10 days or one month etc.), calculates
Mean value, just obtains density of population P of grid GRID_1average.The people of each grid can be calculated using same method
Mouth density.
N traffic ：
In actual applications, the traffic infrastructure in target subregion for example includes：Subway station, bus station, parking lot.
Wherein subway station quantity is Nsubway, bus station is Nbusstop, parking lot is Nparking.Comprehensive three, obtains transportation accessibility
Expression formula Ntraffic=α × Nsubway+β×Nbusstop+γ×Nparking, wherein, α, β, γ are respectively three kinds of traffic infrastructures
Weight shared by (subway station, bus station, parking lot), in this application, the recommendation of three weights is α=0.5, β=0.2,
γ=0.3.
V income ：
In actual applications, the population level of relative income V in target subregionincomeCan be by user base information table (table
5) monthly average consumption value ARPU and user in is held terminal price PHONEPRICE and is comprehensively obtained, specifically, Vincome=λ ×
ARPU+μ×PHONEPRICE.Wherein, λ, μ are respectively monthly average consumption value ARPU and user holds terminal price PHONEPRICE power
Weight, in this application, the recommendation of two weights is λ=0.8, μ=0.2.
N type ：
In actual applications, in target subregion in the enumerated value such as table 4 of existing retail shop's big class (being represented with TYPE in table 3)
It is shown, in table 4, quantity N of existing retail shop big class in target subregiontype=10.
P sub_type ：
In actual applications, first, retail shop's big class (being represented with TYPE in table 3) of location condition is met in statistics grid
Retail shop's quantity Ntype, then, statistics meets retail shop's quantity of retail shop's subclass (being represented with SUB_TYPE in table 3) of location condition
Nsub_type, finally give similar retail shop's concentration class expression formula Psub_type=Nsub_type/Ntype。
If that is, user is needed for clothes shop's addressing, (i.e. retail shop of clothes shop in each grid is counted first
Big class) quantity Ntype, then count (i.e. retail shop's subclass) quantity of the specific clothes shop under this retail shop's big class of clothes shop
Nsub_type, then using similar retail shop's concentration class formula Psub_type=Nsub_type/NtypeCalculate clothes shop in each grid
Concentration class.
It should be noted that each influence factor is calculated in each target subregion in step S31 to Market Site Selection
After influence value, because each index that above-mentioned computational algorithm is selected has different dimensions, so before being further analyzed
Each achievement data is standardized, will index value be transformed into a certain scope specified, such as [0,1].
That is, after the influence value of each factor of each grid is calculated, needing the impact to each factor
Value is standardized.
Therefore, after step S31, methods described may also include Intelligent Business site selecting method of the invention：
S32, each influence factor in each target subregion for calculating is standardized to the influence value of Market Site Selection.
In actual applications, using min-max data normalizations method to each in each target subregion for calculating
Influence factor is standardized to the influence value of Market Site Selection.
Specifically, min-max data normalizations method is：If minAAnd maxAIt is respectively the minimum of a value in attribute A original values
And maximum, then it is by the v ' that min-max standardized methods map by the original value v of attribute A：
V '=(v-minA)/(maxA-minA) (equation 1)
Wherein, " attribute " in min-max data normalizations method refers to 5 influence factors in the application：Population collection
Poly- degree, transportation accessibility, population income level, retail shop's specific diversity, similar retail shop's concentration class.The value of " attribute " affect because
The influence value of element, the original value v of such as attribute A can be the influence value P of population aggregation degreeaverage。minATo belong in all grids
The minimum of a value of property A, maxAFor the maximum of attribute A in all grids.V ' is the original value v of attribute A after standardization
Value.
As a example by below using the population agglomeration degree of the application as attribute A, to the population agglomeration degree that calculates in step S31
The standardization of influence value is illustrated.
The influence value of the population agglomeration degree of all grids is calculated first.To the population agglomeration degree to grid GRID_1
Influence value is standardized, it assumes that the influence value P of the population agglomeration degree of the grid GRID_1 calculated in step S31average1
=5 (i.e. original value v=5), and the minimum of a value in all grids in the influence value of population agglomeration degree is the 2 (nets for for example calculating
The influence value of the population agglomeration degree of lattice GRID_6 is minimum, i.e. Paverage6=2, namely minA=2), population agglomeration in all grids
Maximum in the influence value of degree is that 12 (influence value of the population agglomeration degree of the grid GRID_15 for for example calculating is maximum, i.e.,
Paverage15=12, namely maxA=12), then the value after the population agglomeration degree of grid GRID_1 is normalized is：Paverage1'=
(Paverage1-Paverage6)/(Paverage15-Paverage6)=(5-2)/(12-2)=0.3.Can be counted using same method afterwards
Calculate the value that the influence value of each influence factor of each grid is passed through after standardization.
Data interval after standardization is [0,1], and the method remains down the correlation of initial data
Come.
Although it should be noted that in the application in example in min-max data normalizations method to each mesh for calculating
Each influence factor is standardized to the influence value of Market Site Selection in mark subregion, but in actual applications, can also adopt
Other standardized methods, the application is not limited this, as long as the data interval after standardization is [0,1], will
The correlation of initial data is remained.
S33, the weight factor for calculating each influence factor in each target subregion.
In actual applications, the power of each influence factor in each target subregion can be calculated using gray relative analysis method
Repeated factor.Gray relative analysis method need not know the relation between variable, can according to the similarity degree of its time-serial position come
Judge the size of its correlation degree, if the shape of two curves is similar, the degree of association is then bigger, and weight is also bigger.
The present invention can pass through retail shop's quantity N for calculating existing retail shop subclass SUB_TYPEsub_typeBetween 5 influence factors
Dependency relation calculating weight factor.Circular is as follows：
Assume that reference factor ordered series of numbers is X, be designated as X={ X1, X2, X3..., Xk..., Xn, wherein n be number of grid, XkDeng
In the N of k-th gridsub_type。
Relatively factor ordered series of numbers Y, each Y represent respectively an influence factor ordered series of numbers, are designated as Yi={ Yi1, Yi2, Yi3...,
Yik... Yin, wherein, in this application i represents influence factor, and i=1,2,3 ... 5.
Now, X and YiThe degree of association beWherein riReflection be
YiDisturbance degree to X.
In equation 2, ξ is resolution ratio, typically between 0～1, generally takes 0.5.△min
For ordered series of numbers X (1 row n row) and ordered series of numbers YiMinimum of a value in (1 row n row) ordered series of numbers (1 row n row) resulting after subtracting each other；For ordered series of numbers X (1 row n row) and ordered series of numbers Yi(1 row n row) ordered series of numbers resulting after subtracting each other
Maximum in (1 row n row).
Through normalized, factor Y can be tried to achieveiWeight (i.e. factor of influence) be：
Population agglomeration degree, transportation accessibility, population income level, the retail shop's species calculated using gray relative analysis method
Diversity, the weight factor of similar this five influence factors of retail shop's concentration class can for example be respectively 0.3,0.2,0.2,0.1,
0.2。
Although it should be noted that upper example gray relative analysis method calculates each shadow in each target subregion in the application
The weight factor of the factor of sound, but in actual applications, other methods for calculating weight factor, the application couple can also be adopted
This is not limited, as long as the weight factor of each influence factor can be calculated.
S34, the recommendation that each target subregion is calculated according to the influence value after standardization and the weight factor for calculating
Value.
This step is actually according to 5 influence factors (population agglomeration degree, transportation accessibility, population income level, retail shop
Specific diversity, similar retail shop's concentration class) standardized data and corresponding factor of influence value to each grid (i.e. target
Subregion) carry out comprehensive marking.For example, each factor is merged by way of linear, additive, so as to draw each net
The final recommendation of lattice.Recommendation computing formula is as follows：
Wherein, k represents the sequence number of each grid, wiFor the weight factor (i.e. in step S33 of the influence factor of serial number i
The weight factor for calculating)；PkiIt is value of influence factor i after data normalization (i.e. step S31 in k-th grid
In v ' value), P hereinkiSubscript ki and step S33 equation 2 in YikSubscript ik refer to identical content, i.e., all
Refer to i-th factor of k-th grid；PkThe recommendation of the grid k to obtain.
S4, target subregion is recommended to user according to the recommendation of each target subregion so that user is according to recommending
Target subregion carries out addressing.
In actual applications, this step can be：
The recommendation of each target subregion is ranked up by order from big to small；By the target subregion after sequence
List recommend user so that user carries out addressing according to the list.
For example, grid (target subregion) can be recommended into user according to recommendation order from big to small.The recommendation of grid
Value is bigger, then more suggestion user carries out addressing in this grid.
Therefore, the Intelligent Business site selecting method that the embodiment of the present invention one is provided, by direct from telecom operators
The user profile data of the target area are obtained, can be saved in prior art and this kind of data are obtained by artificial investigation
Cost of labor, and the addressing cycle is greatly shortened, while the data reliability and accuracy that avoid artificial collection are relatively low
Defect.Also, big target area is subdivided into less target subregion by the application by way of gridding division, afterwards
The recommendation of each target subregion is calculated, target subregion, energy are recommended to user according to the recommendation of each target subregion
Enough addressing addresses caused to user's recommendation are more accurate.
Embodiment two
As shown in figure 3, the embodiment of the present invention two provides a kind of Intelligent Business addressing device 20, including：Acquiring unit 21,
The acquiring unit 21 is used to obtain the applied geography data of target area and the acquisition target area from telecom operators
User profile data；Stress and strain model unit 22, the stress and strain model unit is used to be divided into the target area gridding
Multiple target subregions；Computing unit 23, the computing unit 23 is used for the applied geography number obtained using the acquiring unit
According to this and user profile data calculate the recommendation of each target subregion；Addressing recommendation unit 24, the recommendation selected cell
The recommendation of 24 each the target subregion for being calculated according to the computing unit 23 recommends target subregion to user, makes
Obtain user carries out addressing according to the target subregion recommended.
Wherein, the applied geography data of the target area that the acquiring unit 21 is obtained include geographyization infrastructure data
And ground physics and chemistry business data, the user profile data include user base information data and user service data.Now,
The acquiring unit 21 specifically for：Geographyization infrastructure data and geography are crawled from map using data crawler technology
Change business data, the user base information of the target area is obtained from business operation support system BOSS of telecom operators
Data；The user service data of the target area is obtained from telecom operators mobile network Gn/S1-U interfaces.
Wherein, the stress and strain model unit 22 specifically for：On the basis of the central point of the target area, by the mesh
Mark region division is the multiple square nets with the default length of side, and each of which square net is a target sub-district
Domain.
Wherein, as shown in figure 4, the computing unit 23 includes：First computation subunit 231, described first calculates son list
Unit 231 is used to calculate each influence factor pair in each target subregion using the applied geography data and user profile data
The influence value of Market Site Selection；Data normalization subelement 232, the data normalization subelement is used to calculate son to described first
Each influence factor is standardized to the influence value of Market Site Selection in each target subregion that unit 231 is calculated；Second meter
Operator unit 233, second computation subunit 233 be used to calculating the weight of each influence factor in each target subregion because
Son；3rd computation subunit 234, the 3rd computation subunit 234 is used for according to the standard of data normalization subelement 232
The weight factor that influence value and second computation subunit 233 after change is calculated calculates the recommendation of each target subregion
Value.
The influence factor includes population agglomeration degree, transportation accessibility, population income level, retail shop's specific diversity, same
Class retail shop concentration class；Wherein, the influence value of population agglomeration degree density of population P of target subregionaverageRepresent；It is described
The influence value of transportation accessibility quantity N of the traffic infrastructure in target subregiontrafficRepresent；The population takes in water
Population level of relative income V of the flat influence value in target subregionincomeRepresent；Retail shop's specific diversity target
Quantity N of existing retail shop big class in subregiontypeRepresent；Similar retail shop's concentration class similar retail shop in target subregion
Density Psub_typeRepresent.
Above-mentioned Paverage、Ntraffic、Vincome、Ntype、Psub_typeComputational methods can refer in previous methods embodiment
Description, will not be described here.
The data normalization subelement 232 is particularly used in using min-max data normalizations method to described first
Each influence factor is standardized to the influence value of Market Site Selection in each target subregion that computation subunit 231 is calculated.
Second computation subunit 233 specifically calculates each shadow in each target subregion using gray relative analysis method
The weight factor of the factor of sound.
Wherein, the addressing recommendation unit 24 specifically for：Each target sub-district that the computing unit 23 is calculated
The recommendation in domain is ranked up by order from big to small；User is recommended in the list of the target subregion after sequence so that
User carries out addressing according to the list.
The operation principle of said apparatus can refer to the description in preceding method embodiment, will not be described here.
Therefore, the Intelligent Business addressing device that the embodiment of the present invention two is provided, by direct from telecom operators
The user profile data of the target area are obtained, can be saved in prior art and this kind of data are obtained by artificial investigation
Cost of labor, and the addressing cycle is greatly shortened, while the data reliability and accuracy that avoid artificial collection are relatively low
Defect.Also, big target area is subdivided into less target subregion by the application by way of gridding division, afterwards
The recommendation of each target subregion is calculated, target subregion, energy are recommended to user according to the recommendation of each target subregion
Enough addressing addresses caused to user's recommendation are more accurate.
Additionally, the embodiment of the present invention additionally provides a kind of Intelligent Business site selection system, the system includes intelligence as above
Can Market Site Selection device 20.
Each embodiment in this specification is described by the way of progressive, identical similar portion between each embodiment
Divide mutually referring to what each embodiment was stressed is the part with other embodiment.Especially for device embodiment
For, because it is substantially similar to embodiment of the method, so describe fairly simple, portion of the related part referring to embodiment of the method
Defend oneself bright.
It should be noted that, device embodiment described above is only schematic, wherein illustrating as separating component
Unit can be or may not be physically separate, can be as the part that unit shows or may not be
Physical location, you can be located at a place, or can also be distributed on multiple NEs.Can be according to the actual needs
Select some or all of module therein to realize the purpose of this embodiment scheme.In addition, the device that the present invention is provided is implemented
In example accompanying drawing, the annexation between module is represented and have between them communication connection, specifically can be implemented as one or more
Communication bus or holding wire.Those of ordinary skill in the art are not in the case where creative work is paid, you can simultaneously real to understand
Apply.
The above, the only specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, all should contain
Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be defined by the scope of the claims.
Claims (13)
1. a kind of Intelligent Business site selecting method, it is characterised in that include：
Obtain the applied geography data of target area and the user profile data of the target area are obtained from telecom operators；
The target area gridding is divided into into multiple target subregions；
The recommendation of each target subregion is calculated using the applied geography data and user profile data；
Target subregion is recommended to user according to the recommendation of each target subregion so that user is according to the target sub-district recommended
Domain carries out addressing.
2. Intelligent Business site selecting method according to claim 1, it is characterised in that the applied geography number of the target area
According to including ground physics and chemistry infrastructure data and ground physics and chemistry business data, the user profile data include user base information number
According to this and user service data；
The applied geography data of the acquisition target area include：Geographyization basis is crawled from map using data crawler technology
Facility data and ground physics and chemistry business data；
The user profile data that the target area is obtained from telecom operators include：Transport from the business of telecom operators
The user base information data of the target area are obtained in battalion's support system, from telecom operators mobile network interface institute is obtained
State the user service data of target area.
3. Intelligent Business site selecting method according to claim 1 and 2, it is characterised in that by the target area gridding
Being divided into multiple target subregions includes：
On the basis of the central point of the target area, the target area is divided into into the multiple squares with the default length of side
Grid, each of which square net is a target subregion.
4. Intelligent Business site selecting method according to claim 1 and 2, it is characterised in that described using the applied geography
Data and user profile data calculate the recommendation of each target subregion to be included：
Using the applied geography data and user profile data each influence factor is calculated in each target subregion to business
The influence value of addressing；
Each influence factor in each target subregion for calculating is standardized to the influence value of Market Site Selection；
Calculate the weight factor of each influence factor in each target subregion；
The recommendation of each target subregion is calculated according to the influence value after standardization and the weight factor for calculating.
5. Intelligent Business site selecting method according to claim 4, it is characterised in that the influence factor includes population agglomeration
Degree, transportation accessibility, population income level, retail shop's specific diversity, similar retail shop's concentration class；Wherein, the population agglomeration degree
The density of population of influence value target subregion represent；Traffic of the influence value of the transportation accessibility in target subregion
The quantity of infrastructure is represented；Population level of relative income table of the influence value of the population income level in target subregion
Show；The quantity of existing retail shop big class is represented in retail shop's specific diversity target subregion；Similar retail shop's concentration class
Represented with the density of similar retail shop in target subregion.
6. Intelligent Business site selecting method according to claim 1, it is characterised in that described according to each target subregion
Recommendation to user recommends target subregion so that user carries out addressing according to the target subregion recommended to be included：
The recommendation of each target subregion is ranked up by order from big to small；
User is recommended in the list of the target subregion after sequence so that user carries out addressing according to the list.
7. a kind of Intelligent Business addressing device, it is characterised in that include：
Acquiring unit, the acquiring unit is used to obtain the applied geography data of target area and the acquisition institute from telecom operators
State the user profile data of target area；
Stress and strain model unit, the stress and strain model unit is used to for the target area gridding to be divided into multiple target sub-districts
Domain；
Computing unit, the computing unit is used to calculate each target using the applied geography data and user profile data
The recommendation of subregion；
Addressing recommendation unit, the recommendation selected cell is used for each the target subregion calculated according to the computing unit
Recommendation to user recommends target subregion so that user carries out addressing according to the target subregion recommended.
8. Intelligent Business addressing device according to claim 7, it is characterised in that the target area that the acquiring unit is obtained
The applied geography data in domain include geographyization infrastructure data and ground physics and chemistry business data, and the user profile data include
User base information data and user service data；
The acquiring unit specifically for：Geographyization infrastructure data and ground are crawled from map using data crawler technology
Physics and chemistry business data；The user base information number of the target area is obtained from the business operation support system of telecom operators
According to from the user service data of the telecom operators mobile network interface acquisition target area.
9. the Intelligent Business addressing device according to claim 7 or 8, it is characterised in that the stress and strain model unit is concrete
For：
On the basis of the central point of the target area, the target area is divided into into the multiple squares with the default length of side
Grid, each of which square net is a target subregion.
10. the Intelligent Business addressing device according to claim 7 or 8, it is characterised in that the computing unit includes：
First computation subunit, first computation subunit is used for using the applied geography data and user profile data
Calculate influence value of each influence factor to Market Site Selection in each target subregion；
Data normalization subelement, the data normalization subelement is used for each calculated to first computation subunit
Each influence factor is standardized to the influence value of Market Site Selection in target subregion；
Second computation subunit, second computation subunit is used to calculate the weight of each influence factor in each target subregion
The factor；
3rd computation subunit, the 3rd computation subunit is used for according to the shadow after data normalization subelement standardization
The weight factor that sound value and second computation subunit are calculated calculates the recommendation of each target subregion.
11. Intelligent Business addressing devices according to claim 10, it is characterised in that
The influence factor includes population agglomeration degree, transportation accessibility, population income level, retail shop's specific diversity, similar business
Paving concentration class；Wherein, the density of population of the influence value target subregion of the population agglomeration degree is represented；The transportation accessibility
Influence value target subregion in the quantity of traffic infrastructure represent；The influence value target of the population income level
Population level of relative income in subregion is represented；Retail shop's specific diversity existing retail shop big class in target subregion
Quantity is represented；The density of similar retail shop is represented in similar retail shop's concentration class target subregion.
12. Intelligent Business addressing devices according to claim 7, it is characterised in that the addressing recommendation unit is specifically used
In：The recommendation of each target subregion that the computing unit is calculated is ranked up by order from big to small；Will row
User is recommended in the list of the target subregion after sequence so that user carries out addressing according to the list.
13. a kind of Intelligent Business site selection systems, it is characterised in that including the arbitrary described Intelligent Business addressing of claim 7-12
Device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611118332.5A CN106651392A (en) | 2016-12-07 | 2016-12-07 | Intelligent business location selection method, apparatus and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611118332.5A CN106651392A (en) | 2016-12-07 | 2016-12-07 | Intelligent business location selection method, apparatus and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106651392A true CN106651392A (en) | 2017-05-10 |
Family
ID=58819146
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611118332.5A Pending CN106651392A (en) | 2016-12-07 | 2016-12-07 | Intelligent business location selection method, apparatus and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106651392A (en) |
Cited By (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563789A (en) * | 2017-07-31 | 2018-01-09 | 石河子大学 | Data processing method, system, terminal and computer-readable recording medium |
| 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 |
| CN107886241A (en) * | 2017-11-10 | 2018-04-06 | 北京三快在线科技有限公司 | Resource analysis method, apparatus, medium and electronic equipment |
| CN108009677A (en) * | 2017-11-29 | 2018-05-08 | 黑龙江鑫联华信息股份有限公司 | A kind of site selecting method |
| CN108053096A (en) * | 2017-11-22 | 2018-05-18 | 链家网（北京）科技有限公司 | Items in commerce real estate address approach and device |
| CN108446800A (en) * | 2018-03-12 | 2018-08-24 | 西北工业大学 | A kind of site selecting method and system of trans-city chain |
| CN108921605A (en) * | 2018-06-25 | 2018-11-30 | 阿里巴巴集团控股有限公司 | It is a kind of that framework and method are expanded based on the trade company of LBS and big data |
| CN109241234A (en) * | 2018-09-27 | 2019-01-18 | 北京京东尚科信息技术有限公司 | The determination method, apparatus and computer readable storage medium of address |
| CN109345130A (en) * | 2018-10-12 | 2019-02-15 | 深圳市和讯华谷信息技术有限公司 | Method, apparatus, computer equipment and the storage medium of Market Site Selection |
| CN109446244A (en) * | 2018-12-06 | 2019-03-08 | 重庆协洽科技有限公司 | It is a kind of that Site Selection method is optimized by big data excavation |
| CN109447374A (en) * | 2018-11-17 | 2019-03-08 | 朱学庆 | A kind of new shop quick site selection method and apparatus based on big data analysis |
| CN109657883A (en) * | 2019-01-28 | 2019-04-19 | 重庆邮电大学 | A kind of bank branches recommended method based on multi-source data driving |
| CN109800940A (en) * | 2018-12-03 | 2019-05-24 | 北京掌行通信息技术有限公司 | A kind of charging pile site selecting method and device |
| CN109961197A (en) * | 2017-12-22 | 2019-07-02 | 中国电力科学研究院有限公司 | A kind of regional complex electricity consumption evaluation method and system based on power consumer label |
| CN110020226A (en) * | 2018-08-20 | 2019-07-16 | 中国平安人寿保险股份有限公司 | Method for exhibiting data, user equipment, storage medium and device based on big data |
| CN110019568A (en) * | 2019-04-12 | 2019-07-16 | 深圳市和讯华谷信息技术有限公司 | Site selecting method, device, computer equipment and storage medium based on space clustering |
| CN110414751A (en) * | 2018-04-26 | 2019-11-05 | 观相科技（上海）有限公司 | A kind of hotel industry addressing evaluation system and evaluation method based on geographical location |
| CN110503463A (en) * | 2019-08-08 | 2019-11-26 | 江苏荣泽信息科技股份有限公司 | A kind of shops's Site planning method based on chain industry situation |
| CN110648161A (en) * | 2019-08-05 | 2020-01-03 | 中移（杭州）信息技术有限公司 | Benefit evaluation method and device for commercial site selection and storage medium |
| CN110955820A (en) * | 2018-09-22 | 2020-04-03 | 北京微播视界科技有限公司 | Media information interest point recommendation method, device, server and storage medium |
| CN111193998A (en) * | 2020-01-02 | 2020-05-22 | 湖北工业大学 | Business address recommendation system and method based on multidimensional aggregation flow analysis user demand |
| CN111274500A (en) * | 2020-01-15 | 2020-06-12 | 平安医疗健康管理股份有限公司 | Position information recommendation method and device, computer equipment and storage medium |
| CN111400417A (en) * | 2018-12-28 | 2020-07-10 | 航天信息股份有限公司 | Self-service tax service hall site selection method, device, medium and equipment |
| CN111598603A (en) * | 2020-03-03 | 2020-08-28 | 深圳前海微众银行股份有限公司 | Warehouse site selection method, device, equipment and storage medium |
| CN111882185A (en) * | 2020-07-15 | 2020-11-03 | 上海丙晟科技有限公司 | Shopping center site selection device and method based on big data and expert experience |
| CN112016969A (en) * | 2020-08-31 | 2020-12-01 | 中国银联股份有限公司 | Method and device for determining target area |
| CN112488441A (en) * | 2020-10-23 | 2021-03-12 | 湖南大学 | Intelligent dispatching method and system for strip mine truck |
| CN112561550A (en) * | 2019-09-26 | 2021-03-26 | 中移动信息技术有限公司 | Method, device, equipment and storage medium for classifying health degrees of merchants |
| CN112685658A (en) * | 2020-12-25 | 2021-04-20 | 江苏满运物流信息有限公司 | Goods source gathering place display method and device, electronic equipment and storage medium |
| CN112967109A (en) * | 2021-03-15 | 2021-06-15 | 杭州邻汇网络科技有限公司 | Intelligent flash shop product selection method |
| CN112990976A (en) * | 2021-03-29 | 2021-06-18 | 华南理工大学 | Commercial network site selection method, system, equipment and medium based on open source data mining |
| CN113449922A (en) * | 2021-07-09 | 2021-09-28 | 中国银行股份有限公司 | Bank branch site selection method and device |
| CN113487341A (en) * | 2021-06-02 | 2021-10-08 | 广州数说故事信息科技有限公司 | Urban business strategy data processing method |
| CN113837813A (en) * | 2021-10-15 | 2021-12-24 | 浪潮云信息技术股份公司 | Business site selection recommendation method based on population library and legal population library |
| CN113888213A (en) * | 2021-09-29 | 2022-01-04 | 平安银行股份有限公司 | Address selection method, device, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103578045A (en) * | 2013-11-11 | 2014-02-12 | 广东工业大学 | Method for evaluating health of water supply pipelines |
| 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 |
| CN105404942A (en) * | 2015-12-07 | 2016-03-16 | 山东科技大学 | Optimal weight combination prediction model of tourism data |
| EP2785095B1 (en) * | 2011-11-16 | 2016-09-07 | Huawei Technologies Co., Ltd. | Site selection method and device |
-
2016
- 2016-12-07 CN CN201611118332.5A patent/CN106651392A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2785095B1 (en) * | 2011-11-16 | 2016-09-07 | Huawei Technologies Co., Ltd. | Site selection method and device |
| CN103578045A (en) * | 2013-11-11 | 2014-02-12 | 广东工业大学 | Method for evaluating health of water supply pipelines |
| 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 |
| CN105404942A (en) * | 2015-12-07 | 2016-03-16 | 山东科技大学 | Optimal weight combination prediction model of tourism data |
Cited By (47)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563789A (en) * | 2017-07-31 | 2018-01-09 | 石河子大学 | Data processing method, system, terminal and computer-readable recording medium |
| 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 |
| CN107886241A (en) * | 2017-11-10 | 2018-04-06 | 北京三快在线科技有限公司 | Resource analysis method, apparatus, medium and electronic equipment |
| CN108053096A (en) * | 2017-11-22 | 2018-05-18 | 链家网（北京）科技有限公司 | Items in commerce real estate address approach and device |
| CN108009677A (en) * | 2017-11-29 | 2018-05-08 | 黑龙江鑫联华信息股份有限公司 | A kind of site selecting method |
| CN108009677B (en) * | 2017-11-29 | 2021-11-30 | 黑龙江鑫联华信息股份有限公司 | Site selection method |
| CN109961197A (en) * | 2017-12-22 | 2019-07-02 | 中国电力科学研究院有限公司 | A kind of regional complex electricity consumption evaluation method and system based on power consumer label |
| CN109961197B (en) * | 2017-12-22 | 2021-08-27 | 中国电力科学研究院有限公司 | Regional comprehensive power utilization evaluation method and system based on power user label |
| CN108446800A (en) * | 2018-03-12 | 2018-08-24 | 西北工业大学 | A kind of site selecting method and system of trans-city chain |
| CN110414751A (en) * | 2018-04-26 | 2019-11-05 | 观相科技（上海）有限公司 | A kind of hotel industry addressing evaluation system and evaluation method based on geographical location |
| CN108921605A (en) * | 2018-06-25 | 2018-11-30 | 阿里巴巴集团控股有限公司 | It is a kind of that framework and method are expanded based on the trade company of LBS and big data |
| CN110020226A (en) * | 2018-08-20 | 2019-07-16 | 中国平安人寿保险股份有限公司 | Method for exhibiting data, user equipment, storage medium and device based on big data |
| CN110955820A (en) * | 2018-09-22 | 2020-04-03 | 北京微播视界科技有限公司 | Media information interest point recommendation method, device, server and storage medium |
| CN109241234A (en) * | 2018-09-27 | 2019-01-18 | 北京京东尚科信息技术有限公司 | The determination method, apparatus and computer readable storage medium of address |
| CN109345130A (en) * | 2018-10-12 | 2019-02-15 | 深圳市和讯华谷信息技术有限公司 | Method, apparatus, computer equipment and the storage medium of Market Site Selection |
| CN109345130B (en) * | 2018-10-12 | 2021-03-16 | 深圳市和讯华谷信息技术有限公司 | Method and device for commercial site selection, computer equipment and storage medium |
| CN109447374A (en) * | 2018-11-17 | 2019-03-08 | 朱学庆 | A kind of new shop quick site selection method and apparatus based on big data analysis |
| CN109800940B (en) * | 2018-12-03 | 2022-02-18 | 北京掌行通信息技术有限公司 | Charging pile site selection method and device |
| CN109800940A (en) * | 2018-12-03 | 2019-05-24 | 北京掌行通信息技术有限公司 | A kind of charging pile site selecting method and device |
| CN109446244A (en) * | 2018-12-06 | 2019-03-08 | 重庆协洽科技有限公司 | It is a kind of that Site Selection method is optimized by big data excavation |
| CN109446244B (en) * | 2018-12-06 | 2021-06-25 | 重庆协洽科技有限公司 | Optimized site selection working method through big data mining |
| CN111400417A (en) * | 2018-12-28 | 2020-07-10 | 航天信息股份有限公司 | Self-service tax service hall site selection method, device, medium and equipment |
| CN109657883A (en) * | 2019-01-28 | 2019-04-19 | 重庆邮电大学 | A kind of bank branches recommended method based on multi-source data driving |
| CN110019568B (en) * | 2019-04-12 | 2022-03-11 | 深圳市和讯华谷信息技术有限公司 | Spatial clustering-based addressing method and device, computer equipment and storage medium |
| CN110019568A (en) * | 2019-04-12 | 2019-07-16 | 深圳市和讯华谷信息技术有限公司 | Site selecting method, device, computer equipment and storage medium based on space clustering |
| CN110648161A (en) * | 2019-08-05 | 2020-01-03 | 中移（杭州）信息技术有限公司 | Benefit evaluation method and device for commercial site selection and storage medium |
| CN110503463B (en) * | 2019-08-08 | 2022-06-24 | 江苏荣泽信息科技股份有限公司 | Shop site selection planning method based on chain business state |
| CN110503463A (en) * | 2019-08-08 | 2019-11-26 | 江苏荣泽信息科技股份有限公司 | A kind of shops's Site planning method based on chain industry situation |
| CN112561550A (en) * | 2019-09-26 | 2021-03-26 | 中移动信息技术有限公司 | Method, device, equipment and storage medium for classifying health degrees of merchants |
| CN111193998A (en) * | 2020-01-02 | 2020-05-22 | 湖北工业大学 | Business address recommendation system and method based on multidimensional aggregation flow analysis user demand |
| CN111274500A (en) * | 2020-01-15 | 2020-06-12 | 平安医疗健康管理股份有限公司 | Position information recommendation method and device, computer equipment and storage medium |
| CN111598603A (en) * | 2020-03-03 | 2020-08-28 | 深圳前海微众银行股份有限公司 | Warehouse site selection method, device, equipment and storage medium |
| CN111882185A (en) * | 2020-07-15 | 2020-11-03 | 上海丙晟科技有限公司 | Shopping center site selection device and method based on big data and expert experience |
| CN112016969A (en) * | 2020-08-31 | 2020-12-01 | 中国银联股份有限公司 | Method and device for determining target area |
| CN112488441A (en) * | 2020-10-23 | 2021-03-12 | 湖南大学 | Intelligent dispatching method and system for strip mine truck |
| CN112488441B (en) * | 2020-10-23 | 2024-05-14 | 湖南大学 | Intelligent scheduling method and system for strip mine trucks |
| CN112685658A (en) * | 2020-12-25 | 2021-04-20 | 江苏满运物流信息有限公司 | Goods source gathering place display method and device, electronic equipment and storage medium |
| CN112685658B (en) * | 2020-12-25 | 2022-07-26 | 江苏满运物流信息有限公司 | Goods source gathering place display method and device, electronic equipment and storage medium |
| CN112967109A (en) * | 2021-03-15 | 2021-06-15 | 杭州邻汇网络科技有限公司 | Intelligent flash shop product selection method |
| CN112990976B (en) * | 2021-03-29 | 2024-06-14 | 华南理工大学 | Commercial site selection method, system, equipment and medium based on open source data mining |
| CN112990976A (en) * | 2021-03-29 | 2021-06-18 | 华南理工大学 | Commercial network site selection method, system, equipment and medium based on open source data mining |
| CN113487341A (en) * | 2021-06-02 | 2021-10-08 | 广州数说故事信息科技有限公司 | Urban business strategy data processing method |
| CN113487341B (en) * | 2021-06-02 | 2024-04-26 | 广州数说故事信息科技有限公司 | Urban business strategy data processing method |
| CN113449922A (en) * | 2021-07-09 | 2021-09-28 | 中国银行股份有限公司 | Bank branch site selection method and device |
| CN113449922B (en) * | 2021-07-09 | 2024-06-28 | 中国银行股份有限公司 | Bank outlet address selection method and device |
| CN113888213A (en) * | 2021-09-29 | 2022-01-04 | 平安银行股份有限公司 | Address selection method, device, equipment and storage medium |
| CN113837813A (en) * | 2021-10-15 | 2021-12-24 | 浪潮云信息技术股份公司 | Business site selection recommendation method based on population library and legal population library |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106651392A (en) | Intelligent business location selection method, apparatus and system | |
| Visser et al. | The map comparison kit | |
| Laffan et al. | Assessing endemism at multiple spatial scales, with an example from the Australian vascular flora | |
| Zhang et al. | Urban street cleanliness assessment using mobile edge computing and deep learning | |
| CN108876027A (en) | A kind of addressing of rural residential area centralized residential district and optimization method based on GIS | |
| Liu et al. | A generalized framework for measuring pedestrian accessibility around the world using open data | |
| CN109064000A (en) | The methods, devices and systems of natural resources audit | |
| CN107239846B (en) | Parking lot berth prediction processing method and device | |
| Zhang et al. | Characterizing scientific production and consumption in physics | |
| CN109508363A (en) | Water conservancy big data service platform and its working method based on GIS | |
| Wang et al. | A spatial econometric modeling of online social interactions using microblogs | |
| CN108062366A (en) | Public culture information recommendation system | |
| CN106557869A (en) | A kind of air pollutant emission inventory space allocation method and device based on POI points | |
| CN109389358A (en) | Project management system and method based on GIS | |
| Van Dessel et al. | Sensitivity analysis of logistic regression parameterization for land use and land cover probability estimation | |
| CN109669999A (en) | The generation method and device in interest region, storage medium, electronic device | |
| Preston et al. | Not all brownfields are equal: A typological assessment reveals hidden green space in the city | |
| CN113888045A (en) | Island ecological environment management and evaluation platform based on big data analysis | |
| CN103218411B (en) | Website related information acquisition methods and device | |
| CN108921425A (en) | A kind of method, system and the server of asset item classifcation of investment | |
| CN106844626B (en) | Method and system for simulating air quality by using microblog keywords and position information | |
| CN108875032A (en) | Area type determines method and device | |
| Wu et al. | Econometric modelling and forecasting of tourism demand | |
| CN103365900A (en) | Method and device for throwing on-line material | |
| Chakraborti et al. | Assessing dynamism of urban built-up growth and landuse change through spatial metrics: a study on Siliguri and its surroundings |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170510 |
|
| RJ01 | Rejection of invention patent application after publication |