CN110322114A - Cell recommended method, device, equipment and storage medium based on big data - Google Patents
Cell recommended method, device, equipment and storage medium based on big data Download PDFInfo
- Publication number
- CN110322114A CN110322114A CN201910438261.4A CN201910438261A CN110322114A CN 110322114 A CN110322114 A CN 110322114A CN 201910438261 A CN201910438261 A CN 201910438261A CN 110322114 A CN110322114 A CN 110322114A
- Authority
- CN
- China
- Prior art keywords
- facility
- target
- cell
- big data
- distance
- 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
- 238000000034 method Methods 0.000 title claims abstract description 58
- 230000005540 biological transmission Effects 0.000 claims abstract description 4
- 230000002776 aggregation Effects 0.000 claims description 8
- 238000004220 aggregation Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 abstract description 5
- 238000013139 quantization Methods 0.000 abstract 1
- 230000000875 corresponding effect Effects 0.000 description 76
- 238000010586 diagram Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 239000000039 congener Substances 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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]
- G06Q30/0631—Item recommendations
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/16—Real estate
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Accounting & Taxation (AREA)
- Educational Administration (AREA)
- Finance (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a kind of cell recommended method, device, equipment and storage medium based on big data.The method includes the steps: receive the geographical location of user's transmission；It obtains away from each cell in the geographical location pre-determined distance from map software as Target cell；Target facility will be set as away from each default facility in Target cell pre-determined distance, and obtain the facilities information of target facility, multiple facility parameters corresponding with each facilities information are obtained from preset address, using the product of the corresponding multiple facility indexs of the facilities information of each target facility as the corresponding facility score value of each target facility；Using the summation of the facility score value of each target facility as the score of Target cell, the corresponding Target cell of the score for meeting default recommendation rules is recommended into the user.In above-mentioned technical proposal, it is based on big data, realizes the quantization of cell living quality, to improve the effect that cell is recommended according to numerical recommendation cell.
Description
Technical field
The present invention relates to data analysis technique field more particularly to a kind of cell recommended method based on big data, device,
Equipment and computer readable storage medium.
Background technique
When selecting room to purchase house, the living quality of cell is an important factor for influencing to purchase house or rent a house decision.It is all in the past root
According to subjective assessment or the living quality of the quantitative assessment cell based on low volume data, so recommended according to evaluation result cell to
House purchaser, but either still the quantitative assessment based on low volume data all cannot objectively reflect the inhabitation matter of cell for subjective assessment
Amount, causes recommendation effect poor.
Summary of the invention
The main purpose of the present invention is to provide a kind of cell recommended method, device, equipment and calculating based on big data
Machine readable storage medium storing program for executing, it is intended to solve the technology for the effect difference recommended based on the cell of subjective assessment or low volume data quantitative assessment
Problem.
To achieve the above object, the present invention provides a kind of cell recommended method based on big data, and the method includes steps
It is rapid:
Receive the geographical location that user sends；
It obtains away from each cell in the geographical location pre-determined distance from map software as Target cell；
It will be set as target facility away from each default facility in the Target cell pre-determined distance, and obtains the target facility
Facilities information, the facilities information include the corresponding facility type of each target facility and the target facility with it is corresponding
Road distance between Target cell；
Multiple facility parameters corresponding with each facilities information are obtained from preset address, the facility, which calculates, to be referred to
Mark includes and the corresponding facility coefficient of the facility type of each target facility and with the road is apart from corresponding distance
Number；
Using the product of the corresponding multiple facility parameters of the facilities information of each target facility as each target
The corresponding facility score value of facility；
Using the summation of the facility score value of each target facility as the score of the Target cell；
The corresponding Target cell of the score for meeting default recommendation rules is recommended into the user.
Preferably, described to be set as target facility away from each default facility in the Target cell pre-determined distance, and obtain
The step of facilities information of the target facility includes:
The facility name away from each target facility in the Target cell pre-determined distance is obtained from map software；
Each facility name and the facility type phrase in default facility dictionary are compared, judge the facility name
In whether have continuous field identical as the facility type phrase；
If having continuous field identical as the facility type phrase in the facility name, by the facility name pair
The facility answered is as target facility, and using facility type phrase identical with the continuation field in the facility name as described in
The facility type of target facility.
Preferably, described to be set as target facility away from each default facility in the Target cell pre-determined distance, and obtain
The step of facilities information of the target facility further include:
Obtain the walking distance in each walking path between each target facility and the Target cell；
To be preset in the corresponding walking distance of each target facility the average value of the smallest walking distance as pair
Road distance between the target facility answered and the Target cell.
Preferably, the step that multiple facility parameters corresponding with each facilities information are obtained from preset address
Suddenly include:
Facility corresponding with the facility type of the target facility is inquired from the first default file of the first preset address
Coefficient includes the facility coefficient of various types of facility in first default file；
The road distance is compared in section at a distance from the second default file of the second preset address, by the road
Distance fall into apart from the corresponding distance coefficient in section as the road apart from corresponding distance coefficient.
Preferably, the facility parameter further includes bulkfactor, and described obtain from preset address sets with each described
Apply the corresponding multiple facility parameters of information comprising steps of
The number of target facility described in each type is counted, and calculates the facility density of target facility described in each type；
By the density region in the third default file of the facility density of the target facility of each type and third preset address
Between compare, the corresponding bulkfactor in density section that the facility density is fallen into is close as each target facility of the type
Coefficient is spent, the bulkfactor is greater than 0 and is less than or equal to 1.
Preferably, the facility parameter further includes bulkfactor, and described obtain from preset address sets with each described
Before the step of applying information corresponding multiple facility parameters, the cell recommended method based on big data further includes step
It is rapid:
Focusing solutions analysis is utilized according to the number of target facility described in each road distance and each type
The aggregation extent of the target facility of each type；
The bulkfactor is determined according to the aggregation extent and the bulkfactor is stored in the preset address, it is described
Bulkfactor is greater than 0 and is less than or equal to 1.
Preferably, the facility parameter further includes classification weight set by user, it is described from preset address obtain with
Before the step of each facilities information corresponding multiple facility parameters, the cell recommended method based on big data
It further comprises the steps of:
It obtains the classification weight of the various types of target facility set by user and is stored in the preset address.
The present invention also provides a kind of cell recommendation apparatus based on big data, the cell recommendation apparatus based on big data
Include:
Receiving module, for receiving the geographical location of user's transmission；
First obtains module, for obtaining away from each cell in the geographical location pre-determined distance from map software as target
Cell；
Setting module for will be set as target facility away from each default facility in the Target cell pre-determined distance, and obtains
Take the facilities information of the target facility, the facilities information includes the corresponding facility type of each target facility and described
Road distance between target facility and the Target cell；
Second obtains module, refers to for obtaining multiple facilities corresponding with each facilities information from preset address and calculating
Mark, the facility parameter include facility coefficient corresponding with the facility type of each target facility and with the road
Apart from corresponding distance coefficient；
First computing module, for using the product of the corresponding multiple facility indexs of the facilities information of each target facility as
The corresponding facility score value of each target facility；
Second computing module, for using the summation of the facility score value of each target facility as point of the Target cell
Number；
Pushing module, for the corresponding Target cell of the score for meeting default recommendation rules to be recommended the user.
The present invention also provides a kind of equipment, the equipment includes processor, memory and is stored on the memory
And the cell recommended program based on big data that can be executed by the processor, wherein the cell based on big data recommends journey
When sequence is executed by the processor, realize as above described in any item cell recommended methods based on big data the step of.
The present invention also provides a kind of computer readable storage medium, it is stored with and is based on the computer readable storage medium
The cell recommended program of big data, wherein being realized as above when the cell recommended program based on big data is executed by processor
The step of described in any item cell recommended methods based on big data.
The cell recommended method based on big data of the embodiment of the present invention receives the geographical location that user sends；From map
Software is obtained away from each cell in the geographical location pre-determined distance as Target cell；It will be away from each in Target cell pre-determined distance
Default facility is set as target facility, and obtains the facilities information of target facility, obtains and each facilities information pair from preset address
The multiple facility parameters answered, using the product of the corresponding multiple facility indexs of the facilities information of each target facility as each
The corresponding facility score value of target facility；Using the summation of the facility score value of each target facility as the score of Target cell, will accord with
The corresponding Target cell of score for closing default recommendation rules recommends the user.In this way, first calculating cell using big data
The facility score value of each target facility of surrounding, then sum the facility score value of each target facility to obtain the score of Target cell,
It is achieved and quantifies the living quality of Target cell, and then the living quality of Target cell is measured in realization with numerical value, thus
Improve the effect that cell is recommended.
Detailed description of the invention
Fig. 1 is the hardware structural diagram of equipment involved in the embodiment of the present invention；
Fig. 2 is that the present invention is based on the flow diagrams of the cell recommended method first embodiment of big data；
Fig. 3 is that the present invention is based on the flow diagrams of the cell recommended method second embodiment of big data；
Fig. 4 is that the present invention is based on the flow diagrams of the cell recommended method 3rd embodiment of big data；
Fig. 5 is that the present invention is based on the flow diagrams of the cell recommended method fourth embodiment of big data；
Fig. 6 is that the present invention is based on the flow diagrams of the 5th embodiment of cell recommended method of big data；
Fig. 7 is that the present invention is based on the flow diagrams of the cell recommended method sixth embodiment of big data；
Fig. 8 is that the present invention is based on the flow diagrams of the 7th embodiment of cell recommended method of big data.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The present embodiments relate to the cell recommended method based on big data be mainly used in equipment, which can be
The equipment that PC, portable computer, mobile device etc. have display and processing function.
Referring to Fig.1, Fig. 1 is device structure schematic diagram involved in the embodiment of the present invention.In the embodiment of the present invention, if
Standby may include processor 1001 (such as CPU), communication bus 1002, user interface 1003, network interface 1004, memory
1005.Wherein, communication bus 1002 is for realizing the connection communication between these components；User interface 1003 may include display
Shield (Display), input unit such as keyboard (Keyboard)；Network interface 1004 optionally may include that the wired of standard connects
Mouth, wireless interface (such as WI-FI interface)；Memory 1005 can be high speed RAM memory, be also possible to stable memory
(non-volatile memory), such as magnetic disk storage, memory 1005 optionally can also be independently of aforementioned processor
1001 storage device.
It will be understood by those skilled in the art that hardware configuration shown in Fig. 1 does not constitute the restriction to equipment, can wrap
It includes than illustrating more or fewer components, perhaps combines certain components or different component layouts.
With continued reference to Fig. 1, the memory 1005 in Fig. 1 as a kind of computer readable storage medium may include operation system
The acquisition program that system, network communication module and the cell based on big data are recommended.
In Fig. 1, network communication module is mainly used for connecting server, carries out data communication with server；And processor
The 1001 acquisition programs that the cell based on big data stored in memory 1005 can be called to recommend, and execute below based on big
The step of each embodiment of the cell recommended method of data.
Hardware configuration based on above equipment proposes that the present invention is based on each implementations of the cell recommended method of big data
Example.
The present invention provides a kind of cell recommended method based on big data.
Referring to Fig. 2, in an embodiment of the present invention, the cell recommended method based on big data the following steps are included:
S11: the geographical location that user sends is received；
The cell recommended method based on big data of the embodiment of the present invention can be executed by the equipment of the embodiment of the present invention, if
Standby can may be server for terminal.The geographical location that user sends is received by cable network or wireless network, such as
User is sent to the cell recommendation apparatus based on big data by way of terminal inputs geographical location in APP program.
S12: it obtains away from each cell in the geographical location pre-determined distance from map software as Target cell；
It can be obtained from map software small as target away from each cell in the pre-determined distance of geographical location with invocation map software
Area.Its pre-determined distance can be the default distance being arranged by application developer, can also need self-setting according to oneself with user
The distance.
Certainly, administrative area name can also be had jurisdiction over to receive the city in the city that user sends for obtaining the process of Target cell
Claim, then using all cells in city linchpin administrative area as Target cell.
S13: it will be set as target facility away from each default facility in Target cell pre-determined distance, and obtains setting for target facility
Apply information, facilities information include road between the corresponding facility type of each target facility and target facility and Target cell away from
From；
Living quality is usually measured with the accessibility facility of walking, therefore pre-determined distance can feel comfortable according to people
Daily walking distance determine, such as 0.8km, 1km, 1.2km, 1.5km etc., can by user according to itself habit setting also with
It is arranged by developer.Default facility include school, hospital, park, dining room, hotel, convenience store, shopping center, bus station,
Iron station etc. is on the influential facility of the living quality of Target cell.
S14: multiple facility parameters corresponding with each facilities information, facility parameter packet are obtained from preset address
Include and the corresponding facility coefficient of the facility type of each target facility and with road apart from corresponding distance coefficient；
Preset address has facility parameter corresponding with each facilities information.Such as facility corresponding with facility type
Coefficient and with road apart from corresponding distance coefficient.It can be inquired and be obtained and facility type from preset address according to facility type
Corresponding facility coefficient is inquired from preset address according to road distance and is obtained with road apart from corresponding distance coefficient.
S15: using the product of the corresponding multiple facility parameters of the facilities information of each target facility as each target
The corresponding facility score value of facility；
S16: using the summation of the facility score value of each target facility as the score of Target cell.
For example, the facility score value of each target facility is the mesh when facility index includes facility coefficient and distance coefficient
Mark the facility coefficient of facility and the product of distance coefficient.Target facility has multiple, first passes through step S13-S15 and calculates each mesh
The facility score value for marking facility, then sums the facility score value of each target facility to obtain the score of Target cell.
S17: the corresponding Target cell of the score for meeting default recommendation rules is recommended into the user.
For example, recommendation rules can be one recommender score threshold value of setting, such as recommender score threshold value is 90, then by score
Target cell more than 90 recommends user.Recommendation rules can also be for each Target cell scoring height to be ranked up, then
From high to low according to scoring, it selects the Target cell of preset quantity to recommend user, such as when preset quantity is 3, will score most
3 high Target cells recommend user.
The cell recommended method based on big data of the embodiment of the present invention receives the geographical location that user sends；From map
Software is obtained away from each cell in the geographical location pre-determined distance as Target cell；It will be away from each in Target cell pre-determined distance
Default facility is set as target facility, and obtains the facilities information of target facility, obtains and each facilities information pair from preset address
The multiple facility parameters answered, using the product of the corresponding multiple facility indexs of the facilities information of each target facility as each
The corresponding facility score value of target facility；Using the summation of the facility score value of each target facility as the score of Target cell, will accord with
The corresponding Target cell of score for closing default recommendation rules recommends the user.In this way, first calculating cell using big data
The facility score value of each target facility of surrounding, then sum the facility score value of each target facility to obtain the score of Target cell,
It is achieved and quantifies the living quality of Target cell, and then the living quality of Target cell is measured in realization with numerical value, thus
Improve the effect that cell is recommended.
Referring to Fig. 3, based on the above embodiment, step S13 comprising steps of
S131: the facility name away from each facility in Target cell pre-determined distance is obtained from map software；
S132: each facility name and the facility type phrase in default facility dictionary are compared, judged in facility name
Whether there is continuous field identical as facility type phrase；
The facility name away from each facility in Target cell pre-determined distance can be obtained from map software with invocation map software.
It may include but be not limited to " school ", " hospital ", " dining room ", " hotel ", " convenience store ", " shopping center " etc. in default facility dictionary
Facility type phrase.
S133: if having in facility name, continuous field is identical as facility type phrase, sets facility name is corresponding
It applies for target facility, and using facility type phrase identical with the continuation field in facility name as the facility of target facility
Type.
For example, when the facility name of target facility is " Concord Hospital ", it will be in " Concord Hospital " and default facility dictionary
Facility type phrase compare, have the facility type word in continuous field " hospital " and default facility dictionary in " Concord Hospital "
Group " hospital " is identical, and the facility type that the target facility that facility name is " Concord Hospital " can be obtained is hospital.
If identical as facility type phrase without continuous field in facility name, illustrate that the facility name is corresponding and set
Applying not is default facility, and the facility is on the smaller without influencing or influencing of the living quality of Target cell, to can also be set
For target facility.
Further, referring to Fig. 4, based on the above embodiment, step S13 is further comprised the steps of:
S134: the walking distance in each walking path between each target facility and Target cell is obtained；
Step S134 can be executed after step S133.It can use map software object of planning facility and Target cell
Between walking path, and obtain the walking distance in each walking path.
S135: will be preset in the corresponding walking distance of each target facility the average value of the smallest walking distance as pair
Road distance between the target facility answered and Target cell.
Usually there are multiple walking paths between Target cell and target facility, but consider from convenience, lives in mesh
The walking path that the resident for marking cell often only can select 1 therein or several roads are closer is as small from target
Area to target facility practical Walking Route.In order to preferably measure living quality, can be selected from multiple walking paths pre-
If a the smallest walking path of walking distance, sets the average value of the walking distance in these walking paths as corresponding target
Grant the road distance between Target cell.For example, default road distance is 3 minimums in the corresponding walking distance of target facility
Walking distance average value, then when target facility is to cultivate people of ability middle school, middle school of cultivating people of ability is away from there is 5 pedestrian-ways between Target cell
When diameter, by 5 walking paths, the average value of the walking distance in the least 3 walking paths of walking distance is as road distance.
In this way, can accurately obtain road between the facility type and target facility and Target cell of target facility away from
From to facilitate the living quality for more accurately measuring Target cell.
Referring to Fig. 5, based on the above embodiment, step S14 comprising steps of
S141: facility corresponding with the facility type of target facility is inquired from the first default file of the first preset address
Coefficient includes the facility coefficient of various types of facility in the first default file；
Preset address includes the first preset address and the second preset address.There is the first default text in first preset address
Part includes the corresponding facility coefficient of each facility type in the first default file, and the corresponding facility coefficient of each facility type can be by opening
Hair personnel determine the influence degree of daily life according to facility, such as can set the facility coefficient of school as 0.5, convenience store
Facility coefficient is 0.75, and the facility coefficient in park is 1, and the facility coefficient of hospital is 1.Can according to the facility type of target facility from
Facility coefficient corresponding with the facility type of target facility is inquired in first default file of the first preset address.Certainly, at it
In his embodiment, each facility coefficient that type is arranged is not limited to above-mentioned value, also may be set to other values.
S142: road distance is compared in section at a distance from the second default file of the second preset address, by road away from
The corresponding distance coefficient in section is as road apart from corresponding distance coefficient with a distance from falling into.
There is the second default file in second preset address, includes respectively with multiple apart from section pair in the second default file
The distance coefficient answered can compare in section road distance, by road at a distance from the second default file of the second preset address
Distance fall into apart from the corresponding distance coefficient in section as road apart from corresponding distance coefficient.
Preferably, the influence to user's walking trip is bigger since distance is remoter.It is multiple to be apart from the corresponding distance in section
Number gradually successively decreases from small to large according to the distance value apart from section.For example, can be in the second default file by the distance of 0-0.5km
The distance coefficient that coefficient is set as 0.95,1km is set as 0.75, and the distance coefficient of 1-1.5km is set as 0.1.In this way, can be more accurately
Measure the living quality of Target cell.
Referring to Fig. 6, based on the above embodiment, in certain embodiments, facility parameter further includes bulkfactor, step
Rapid S14 is further comprised the steps of:
S143: counting the number of each type target facility, and calculates the facility density of each type target facility；
It can will be interpreted as convenient region away from the region in Target cell pre-determined distance L, the area in convenient region is π L2.?
There may be multiple congener target facilities in convenient region.The number of each type target facility can be first counted, then will
The number of each type target facility is to obtain the facility density of target facility divided by the area in convenient region.
S144: by the facility density of the target facility of each type with it is close in the third default file of third preset address
It spends section to compare, the corresponding bulkfactor in density section that facility density is fallen into is as the density system of each target facility of type
Number.
Preset address further includes third preset address, has third default file, the default text of third in third preset address
It, can be by the of the density of target facility and third preset address comprising bulkfactor corresponding with multiple density sections respectively in part
Density section in three default files compares, and the corresponding bulkfactor in density section that the density of target facility is fallen into is as mesh
The bulkfactor of facility is marked, bulkfactor is greater than 0 and is less than or equal to 1.
It is appreciated that when the facility density for having congener target facility in convenient region is greater than to a certain degree, this kind
The quantity of the target facility of class changes the influence to the actual living quality of Target cell and little.For example, having in convenient region
When 30 hospitals and when convenient region Nei You25Jia hospital, actually there is no very big differences for the living quality of Target cell, such as
The facility score value of each facility is directly superimposed by fruit, then the score under will lead to both of these case differs greatly, with actual conditions
It is not inconsistent.And in the present embodiment, using bulkfactor as facility parameter, when there is congener target facility in convenient region
When facility density is larger, when calculating the facility score value of each target facility of the type, the facility multiplied by the target facility of the type is close
The corresponding bulkfactor in density section fallen into is spent, the score being finally calculated is aloowed more accurately to reflect target
The living quality of cell.
Referring to Fig. 7, based on the above embodiment, in certain embodiments, facility parameter further includes bulkfactor, step
Before rapid S14, the cell recommended method based on big data is further comprised the steps of:
S18: each type of focusing solutions analysis is utilized according to the number of each road distance and each type target facility
Target facility aggregation extent；
S19: determining bulkfactor according to aggregation extent and bulkfactor be stored in preset address, bulkfactor be greater than 0 and
Less than or equal to 1.
Specifically, the target facility of each type can be divided by multiple clusters by clustering algorithm in step S18, and united
The quantity of target facility included by each cluster is counted, then in step s 16, according to target facility included by each cluster
The average value of quantity determines that the bulkfactor of the target facility of the type, average value and bulkfactor are negatively correlated.Average value is bigger,
Bulkfactor is smaller.Can the average value of the quantity of target facility included by each cluster and density system be established in preset address in advance
Several mapping tables, after then obtaining the average value of the quantity of target facility included by each cluster in step S19, from
The mapping table inquires corresponding bulkfactor, and bulkfactor is stored in preset address.It is finally calculated in this way, may make
To score can more accurately reflect the living quality of Target cell.
Referring to Fig. 8, based on the above embodiment, before step S14, facility parameter further includes classification set by user
Weight, the cell recommended method based on big data are further comprising the steps of:
S20: it obtains the classification weight of various types of target facility set by user and is stored in preset address.
Developer determines facility coefficient according to influence degree of the various types of facility to daily life, but user is thought
Various types of facility it is to the influence degree of daily life and the viewpoint of developer inconsistent, for example, when user is middle school student
When parent, more focus on the educational resource near cell, therefore the classification weight of school can be set as by user according to voluntarily needs
2, when user does not need often to do shopping, the classification weight of convenience store can be set as 0.5.
Preferably, the product of the classification weight of various types of target facility set by user is 1.The inhabitation matter obtained in this way
The total score of living quality when the total score of amount is with without setting classification weight is consistent, easily facilitates user and is compared using score
The living quality of multiple Target cells.
Certainly, in other embodiments, the product of the classification weight of various types of target facility set by user be can also
Not for 1.Because because the user is to each Target cell when the living quality of the more multiple Target cells of same user
Demand be it is the same, therefore, user can be various types of mesh with same standard when obtaining the score of multiple Target cells
The classification weight for marking facility setting, in this way it is also possible that the total score of the living quality of multiple Target cells is consistent.
In addition, the present invention also provides a kind of cell recommendation apparatus based on big data.Any of the above-described embodiment based on big
The cell recommended method of data can be realized by the cell recommendation apparatus based on big data of the present embodiment, based on the small of big data
Area's recommendation apparatus includes:
Receiving module, for receiving the geographical location of user's transmission；
First obtains module, for obtaining away from each cell in the geographical location pre-determined distance from map software as target
Cell；
Setting module for will be set as target facility away from each default facility in the Target cell pre-determined distance, and obtains
Take the facilities information of the target facility, the facilities information includes the corresponding facility type of each target facility and described
Road distance between target facility and the Target cell；
Second obtains module, refers to for obtaining multiple facilities corresponding with each facilities information from preset address and calculating
Mark, the facility parameter include facility coefficient corresponding with the facility type of each target facility and with the road
Apart from corresponding distance coefficient；
First computing module, for using the product of the corresponding multiple facility indexs of the facilities information of each target facility as
The corresponding facility score value of each target facility；
Second computing module, for using the summation of the facility score value of each target facility as point of the Target cell
Number；
Pushing module, for the corresponding Target cell of the score for meeting default recommendation rules to be recommended the user.
Further, setting module includes:
Noun acquiring unit, for obtaining the facility name away from target facility each in Target cell pre-determined distance from map software
Claim；
Comparing unit, for comparing each facility name and the facility type phrase in default facility dictionary, judgement is set
Whether apply in title has continuous field identical as facility type phrase；
First setup unit, if for there is continuous field identical as facility type phrase in facility name, by facility
The corresponding facility of title is as target facility, and using facility type phrase identical with the continuation field in facility name as mesh
Mark the facility type of facility.
Further, setting module further include:
Distance acquiring unit, for obtain the walking in each walking path between each target facility and Target cell away from
From；
Second setup unit will preset being averaged for a smallest walking distance in the corresponding walking distance of each target facility
Value is as the road distance between corresponding target facility and Target cell.
Further, the second acquisition module includes:
Facility coefficient acquiring unit is set for inquiring from the first default file of the first preset address with target facility
The corresponding facility coefficient of type is applied, includes the facility coefficient of various types of facility in the first default file；
Distance coefficient acquiring unit, for by road distance and the distance regions in the second default file of the second preset address
Between compare, using road distance fall into apart from the corresponding distance coefficient in section as road apart from corresponding distance coefficient.
Further, facility parameter further includes bulkfactor, obtains module further include:
Statistic unit for counting the number of each type target facility, and calculates the facility of each type target facility
Density；
Density acquiring unit, for the facility density of the target facility of each type and the third of third preset address is pre-
If the density section in file compares, the corresponding bulkfactor in density section that facility density is fallen into is as each target of type
The bulkfactor of facility, bulkfactor are greater than 0 and are less than or equal to 1.
Further, facility parameter further includes bulkfactor, the cell recommendation apparatus based on big data further include:
Analysis module, for utilizing focusing solutions analysis according to the number of each road distance and each type target facility
The aggregation extent of the target facility of each type；
Bulkfactor obtains module, for determining bulkfactor according to aggregation extent and bulkfactor being stored in default ground
Location, bulkfactor are greater than 0 and are less than or equal to 1.
Further, facility parameter further includes classification weight set by user, and the cell based on big data recommends dress
It sets further include:
Classification weight obtains module, for obtaining the classification weight of various types of target facility set by user and being stored in pre-
If address.
Wherein, the function of modules is realized with above-mentioned based on big data in the above-mentioned cell recommendation apparatus based on big data
Cell recommended method embodiment in each step it is corresponding, function and realization process no longer repeat one by one here.
In addition, the present invention also provides a kind of computer readable storage mediums.
The cell recommended program based on big data is stored on computer readable storage medium of the present invention, it is computer-readable to deposit
The cell recommended program based on big data is stored on storage media, wherein the cell recommended program based on big data is held by processor
When row, realize as above-mentioned any embodiment the cell recommended method based on big data the step of.
Wherein, the cell recommended program based on big data, which is performed realized method, can refer to that the present invention is based on big numbers
According to cell recommended method each embodiment, details are not described herein again.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the system that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or system institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or system.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that an equipment (can be mobile phone, calculate
Machine, server, air conditioner or network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of cell recommended method based on big data, which is characterized in that the cell recommended method packet based on big data
Include step:
Receive the geographical location that user sends；
It obtains away from each cell in the geographical location pre-determined distance from map software as Target cell；
It will be set as target facility away from each default facility in the Target cell pre-determined distance, and obtains setting for the target facility
Information is applied, the facilities information includes the corresponding facility type of each target facility and the target facility and corresponding target
Road distance between cell；
Multiple facility parameters corresponding with each facilities information, the facility parameter packet are obtained from preset address
Include and the corresponding facility coefficient of the facility type of each target facility and with the road apart from corresponding distance coefficient；
Using the product of the corresponding multiple facility parameters of the facilities information of each target facility as each target facility
Corresponding facility score value；
Using the summation of the facility score value of each target facility as the score of the Target cell；
The corresponding Target cell of the score for meeting default recommendation rules is recommended into the user.
2. the cell recommended method according to claim 1 based on big data, which is characterized in that it is described will be away from the target
Each default facility in cell pre-determined distance is set as target facility, and the step of obtaining the facilities information of target facility packet
It includes:
The facility name away from each target facility in the Target cell pre-determined distance is obtained from map software；
Each facility name and the facility type phrase in default facility dictionary are compared, judge be in the facility name
It is no to have continuous field identical as the facility type phrase；
If having in the facility name, continuous field is identical as the facility type phrase, and the facility name is corresponding
Facility is as target facility, and using facility type phrase identical with the continuation field in the facility name as the target
The facility type of facility.
3. the cell recommended method according to claim 1 or 2 based on big data, which is characterized in that it is described will be away from described
Each default facility in Target cell pre-determined distance is set as target facility, and the step of obtaining the facilities information of the target facility
Further include:
Obtain the walking distance in each walking path between each target facility and the Target cell；
The average value of a smallest walking distance is preset as corresponding using in the corresponding walking distance of each target facility
Road distance between the target facility and the Target cell.
4. the cell recommended method according to claim 1 based on big data, which is characterized in that described to be obtained from preset address
The step of taking multiple facility parameters corresponding with each facilities information include:
Facility coefficient corresponding with the facility type of the target facility is inquired from the first default file of the first preset address,
It include the facility coefficient of various types of facility in first default file；
The road distance is compared in section at a distance from the second default file of the second preset address, by the road distance
Fall into apart from the corresponding distance coefficient in section as the road apart from corresponding distance coefficient.
5. the cell recommended method according to claim 1 or 4 based on big data, which is characterized in that the facility calculates
Index further includes bulkfactor, described to obtain multiple facility parameters corresponding with each facilities information from preset address
Comprising steps of
The number of target facility described in each type is counted, and calculates the facility density of target facility described in each type；
By the density section ratio in the third default file of the facility density of the target facility of each type and third preset address
Right, the corresponding bulkfactor in density section that the facility density is fallen into is as the density system of each target facility of the type
Number, the bulkfactor are greater than 0 and are less than or equal to 1.
6. the cell recommended method according to claim 1 or 4 based on big data, which is characterized in that the facility calculates
Index further includes bulkfactor, described to obtain multiple facility parameters corresponding with each facilities information from preset address
The step of before, the cell recommended method based on big data further comprises the steps of:
It is each using focusing solutions analysis according to the number of target facility described in each road distance and each type
The aggregation extent of the target facility of type；
The bulkfactor is determined according to the aggregation extent and the bulkfactor is stored in the preset address, the density
Coefficient is greater than 0 and is less than or equal to 1.
7. the cell recommended method according to claim 1 based on big data, which is characterized in that the facility parameter
It further include classification weight set by user, it is described to obtain multiple facility meters corresponding with each facilities information from preset address
Before the step of calculating index, the cell recommended method based on big data is further comprised the steps of:
It obtains the classification weight of the various types of target facility set by user and is stored in the preset address.
8. a kind of cell recommendation apparatus based on big data, for obtaining the score of Target cell, which is characterized in that described to be based on
The cell recommendation apparatus of big data includes:
Receiving module, for receiving the geographical location of user's transmission；
First obtains module, small as target away from each cell in the geographical location pre-determined distance for obtaining from map software
Area；
Setting module for will be set as target facility away from each default facility in the Target cell pre-determined distance, and obtains institute
The facilities information of target facility is stated, the facilities information includes each corresponding facility type of the target facility and the target
Road distance between facility and the Target cell；
Second obtains module, for obtaining multiple facility parameters corresponding with each facilities information from preset address,
The facility parameter include facility coefficient corresponding with the facility type of each target facility and with the road away from
From corresponding distance coefficient；
First computing module, for using the product of the corresponding multiple facility indexs of the facilities information of each target facility as each
The corresponding facility score value of the target facility；
Second computing module, for using the summation of the facility score value of each target facility as the score of the Target cell；
Pushing module, for the corresponding Target cell of the score for meeting default recommendation rules to be recommended the user.
9. a kind of equipment, which is characterized in that the equipment includes processor, memory and is stored on the memory simultaneously
The cell recommended program based on big data that can be executed by the processor, wherein the cell recommended program based on big data
When being executed by the processor, the step of the cell recommended method based on big data described in any one of claims 1 to 7 is realized
Suddenly.
10. a kind of computer readable storage medium, which is characterized in that be stored on the computer readable storage medium based on big
The cell recommended program of data, wherein realizing that right is wanted when the cell recommended program based on big data is executed by processor
The step of cell recommended method described in asking any one of 1 to 7 based on big data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910438261.4A CN110322114A (en) | 2019-05-23 | 2019-05-23 | Cell recommended method, device, equipment and storage medium based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910438261.4A CN110322114A (en) | 2019-05-23 | 2019-05-23 | Cell recommended method, device, equipment and storage medium based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110322114A true CN110322114A (en) | 2019-10-11 |
Family
ID=68119055
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910438261.4A Pending CN110322114A (en) | 2019-05-23 | 2019-05-23 | Cell recommended method, device, equipment and storage medium based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110322114A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909100A (en) * | 2019-11-20 | 2020-03-24 | 口口相传(北京)网络技术有限公司 | Shop display method and device based on walking distance |
| CN113592401A (en) * | 2021-07-30 | 2021-11-02 | 上海寻梦信息技术有限公司 | Address recommendation method, system, device and storage medium |
| CN114898593A (en) * | 2022-04-11 | 2022-08-12 | 珠海云洲智能科技股份有限公司 | Track acquisition method, track acquisition system and server |
| CN117313335A (en) * | 2023-09-14 | 2023-12-29 | 苏州圣蒙莱科技有限公司 | Digital twinning-based smart city componentization construction method and construction platform |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002163265A (en) * | 2000-11-22 | 2002-06-07 | Nissan Motor Co Ltd | Area searching device |
| JP2011257195A (en) * | 2010-06-07 | 2011-12-22 | Honda Motor Co Ltd | Facility name assignment device |
| CN107239967A (en) * | 2017-05-10 | 2017-10-10 | 平安科技（深圳）有限公司 | House property information processing method, device, computer equipment and storage medium |
-
2019
- 2019-05-23 CN CN201910438261.4A patent/CN110322114A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002163265A (en) * | 2000-11-22 | 2002-06-07 | Nissan Motor Co Ltd | Area searching device |
| JP2011257195A (en) * | 2010-06-07 | 2011-12-22 | Honda Motor Co Ltd | Facility name assignment device |
| CN107239967A (en) * | 2017-05-10 | 2017-10-10 | 平安科技（深圳）有限公司 | House property information processing method, device, computer equipment and storage medium |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909100A (en) * | 2019-11-20 | 2020-03-24 | 口口相传(北京)网络技术有限公司 | Shop display method and device based on walking distance |
| CN110909100B (en) * | 2019-11-20 | 2021-07-20 | 口口相传(北京)网络技术有限公司 | Shop display method and device based on walking distance |
| CN113592401A (en) * | 2021-07-30 | 2021-11-02 | 上海寻梦信息技术有限公司 | Address recommendation method, system, device and storage medium |
| CN114898593A (en) * | 2022-04-11 | 2022-08-12 | 珠海云洲智能科技股份有限公司 | Track acquisition method, track acquisition system and server |
| CN114898593B (en) * | 2022-04-11 | 2024-02-02 | 珠海云洲智能科技股份有限公司 | Track acquisition method, track acquisition system and server |
| CN117313335A (en) * | 2023-09-14 | 2023-12-29 | 苏州圣蒙莱科技有限公司 | Digital twinning-based smart city componentization construction method and construction platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110322114A (en) | Cell recommended method, device, equipment and storage medium based on big data | |
| Wang et al. | A comparison of perceived and geographic access to predict urban park use | |
| US20170206204A1 (en) | System, method, and device for generating a geographic area heat map | |
| KR102340463B1 (en) | Sample weight setting method and device, electronic device | |
| Pan | Socioeconomic predictors of smoking and smoking frequency in urban China: evidence of smoking as a social function | |
| CN104111938B (en) | A kind of method and device of information recommendation | |
| JP2009511991A5 (en) | ||
| CA2919551A1 (en) | Managing reviews | |
| CN109902713A (en) | Building recommended method, equipment, storage medium and device based on data analysis | |
| CN110472995A (en) | To shop prediction technique, device, readable storage medium storing program for executing and electronic equipment | |
| Page et al. | An evaluation of alternative measures of accessibility for investigating potential ‘deprivation amplification’in service provision | |
| KR20160135451A (en) | System of fixing department and method thereof | |
| CN110188120A (en) | A kind of personalized screens recommended method based on collaborative filtering | |
| CN106447425A (en) | Life service information recommendation method and apparatus | |
| CN113918806A (en) | Method for automatically recommending training courses and related equipment | |
| CN109636530B (en) | Product determination method, product determination device, electronic equipment and computer-readable storage medium | |
| CN107679887A (en) | A kind for the treatment of method and apparatus of trade company's scoring | |
| Sari et al. | Measurement and Analysis of Tourism Website User Experience Using Usability Techniques | |
| US20210200753A1 (en) | Strain Recommendation System and Method | |
| CN106878938A (en) | A kind of information-pushing method, location positioning method and equipment | |
| CN113360790A (en) | Information recommendation method and device and electronic equipment | |
| CN102567425B (en) | Method and device for processing data | |
| GB2517358A (en) | Recommendation creation system | |
| KR20200065754A (en) | Method for recommending book and service device supporting the same | |
| CN108335242A (en) | Student's differentiating method and device |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20191011 |
|
| WD01 | Invention patent application deemed withdrawn after publication |