CN109871494A - Querying method, device, equipment and the readable storage medium storing program for executing of urban house average price - Google Patents
Querying method, device, equipment and the readable storage medium storing program for executing of urban house average price Download PDFInfo
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Abstract
The present invention discloses querying method, device, equipment and the readable storage medium storing program for executing of a kind of urban house average price, the described method includes: when the urban house average price inquiry instruction that the terminal for receiving user is sent, read city keyword corresponding in urban house average price inquiry instruction, and according to city keyword, target cities are determined；According to the history location information of user, the city Chang Ju of user is determined, and detect the positioning city that user is currently located；Current average price data corresponding with target cities, the city Chang Ju and positioning city and average price trend graph are searched respectively, and the display interface that each current average price data and each average price trend graph are output to terminal is shown.This programme is inquired and is shown based on current average price data and price trend graph of the data analysis in big data technology to the target cities of inquiry required for user, the city Chang Ju of user and positioning city, facilitate checking and comparing for user, the operation for simplifying inquiry, improves search efficiency.
Description
Technical field
The invention mainly relates to technical field of data processing, specifically, being related to a kind of issuer of urban house average price
Method, device, equipment and readable storage medium storing program for executing.
Background technique
With the development of the city, urban population gradually increases, and increasing for population, can cause the increasing of house purchasing demand
Add, the factor of required consideration is numerous before buying house, such as city, price.Different cities have different average prices, mesh
The inquiry of preceding average price had for city can carry out artificial enquiry by artificial customer service or sale, can also pass through terminal
The mode of connection network carries out self-service query.Either artificial enquiry or self-service query are first to input one in query process
A city name, then using the corresponding city of the city name as inquiry city, and its average price is inquired；If
The average price to multiple related cities is needed to carry out inquiry comparison, as looked into average price possessed by inquiry city and city of residence
It sees comparison, then the title for inputting city one by one is needed to be inquired, it is cumbersome, search efficiency is low；Simultaneously because each inquiry is tied
Fruit shows one by one according to the city name inputted, and causes to be inconvenient to compare to check.
Summary of the invention
The main object of the present invention is to provide querying method, device, equipment and the readable storage of a kind of urban house average price
Medium, it is intended to solve in the prior art to the cumbersome of urban house average price inquiry, search efficiency is low, and is inconvenient to compare and look into
The problem of seeing.
To achieve the above object, the present invention provides a kind of querying method of urban house average price, the urban house average price
Querying method the following steps are included:
When the urban house average price inquiry instruction that the terminal for receiving user is sent, reads the urban house average price and look into
City keyword corresponding in instruction is ask, and according to the city keyword, determines target cities；
According to the history location information of the user grabbed from local storage unit, the Chang Jucheng of the user is determined
City, and detect the positioning city that the user is currently located；
The target cities, the city Chang Ju are searched from network respectively and positions current average price data corresponding to city
With average price trend graph, and by the target cities, the city Chang Ju and position city current average price data and average price trend graph
The display interface for being output to the terminal is shown.
Preferably, described to be searched from network corresponding to the target cities, the city Chang Ju and positioning city respectively
Include: before the step of current average price data and average price trend graph
Periodic detection current time, and when the current time reaches preset time, crawl and the mesh from network
Mark city, the city Chang Ju and the real time price data for positioning city building corresponding to city；
The current of the target cities, the city Chang Ju and positioning city is generated respectively according to each real time price data
Average price data, and call the target cities, the city Chang Ju respectively and position the current average price data in city to respective corresponding
The average price trend graph be updated.
Preferably, described to walk the target cities, the current average price data in the city Chang Ju and positioning city and average price
Include: after the step of display interface that gesture figure is output to the terminal is shown
When receiving price data of the user based on the current average price data input of display, search with it is described
The corresponding several buildings of price data, and grab the corresponding credit information of each building；
The identity information of the user is read from local storage unit, and according to the identity mark in the identity information
Know, grabs house-purchase information corresponding with the identity information；
According to each corresponding credit information of building and house-purchase information corresponding with the user, advised according to default screening
Then filtered out from the building with the matched building of the user, and building element will be set as with the matched building of the user
To form building sequence, the display interface that the building element in the building sequence is output to the terminal is shown.
Preferably, the step of each building of crawl corresponding credit information includes
The history loan data of each building is read from local storage unit, and from each history loan data
Filter out the loan application stroke count of each building, provide a loan successfully stroke count, each amount of the loan and with each pen loan gold
Volume is corresponding to make loans the time；
According to each loan application stroke count and the successfully stroke count of providing a loan, the loan success rate of each building is generated,
And each pen amount of the loan is compared, the difference between each pen amount of the loan is generated, by the difference default
Each pen amount of the loan in range is divided into same group of class；
It is made loans the time according to corresponding to each amount of the loan in each described group of class, when determining that each organized class is corresponding and making loans
Between section, and each loan success rate and each described group of class are determined as the corresponding credit information of each building and grabbed
It takes.
Preferably, the identity according in the identity information grabs house-purchase corresponding with the identity information
The step of information includes:
The identity in the identity information is read, and according to the identity, crawl and the identity information pair
Qualification data, history house property data, balance data and the kinsfolk's data answered；
When according to the qualification data, judging that the user has qualification, according to the history house property data, revenue and expenditure
Data and kinsfolk's data determine loan successful coefficient, loan limit data and the time demand data of the user respectively；
The loan successful coefficient, loan limit data and time demand data are determined as corresponding with the identity information
House-purchase information grabbed.
Preferably, described according to the history house property data, balance data and kinsfolk's data, the use is determined respectively
The step of loan successful coefficient at family, loan limit data and time demand data includes:
The first corresponding relationship between the history house property data and preset house property data and loan success rate is compared,
Determine loan successful coefficient corresponding with the history house property data；
The second corresponding relationship between the balance data and preset income and quota data is compared, it is determining with it is described
The corresponding loan limit data of balance data；
By kinsfolk's data and the third corresponding relationship comparison between preset member data and time of making loans, really
Fixed time demand data corresponding with kinsfolk's data.
Preferably, described according to each corresponding credit information of building and house-purchase information corresponding with the user, it presses
The step of filtering out building matched with the user from the building according to default screening rule include:
By the comparison of the loan success rate of the loan successful coefficient and each building, by each loan success rate
In be higher than it is described loan successful coefficient building screening be target building；
By the described group of class comparison in the loan limit data and each target building, the loan value degree is determined
According to the target group class at place；
It reads the corresponding target of each target group class to make loans time interval, and by the time demand data and each described
Target is made loans time interval comparison, and each target where the time demand data is made loans the corresponding target of time interval
Building screening be and the matched building of the user.
In addition, to achieve the above object, the present invention also proposes a kind of inquiry unit of urban house average price, the city room
The inquiry unit of room average price includes:
Read module, when urban house average price inquiry instruction for being sent when the terminal for receiving user, described in reading
Corresponding city keyword in urban house average price inquiry instruction, and according to the city keyword, determine target cities；
Determining module determines institute for the history location information according to the user grabbed from local storage unit
The city Chang Ju of user is stated, and detects the positioning city that the user is currently located；
Searching module is right with the target cities, the city Chang Ju and positioning city institute for searching from network respectively
The current average price data and average price trend graph answered, and by the target cities, the city Chang Ju and position city current average price
The display interface that data and average price trend graph are output to the terminal is shown.
In addition, to achieve the above object, the present invention also proposes a kind of query facility of urban house average price, the city room
The query facility of room average price includes: that memory, processor, communication bus and the urban house that is stored on the memory are equal
The polling routine of valence；
The communication bus is for realizing the connection communication between processor and memory；
The processor is used to execute the polling routine of the urban house average price, to perform the steps of
When the urban house average price inquiry instruction that the terminal for receiving user is sent, reads the urban house average price and look into
City keyword corresponding in instruction is ask, and according to the city keyword, determines target cities；
According to the history location information of the user grabbed from local storage unit, the Chang Jucheng of the user is determined
City, and detect the positioning city that the user is currently located；
The target cities, the city Chang Ju are searched from network respectively and positions current average price data corresponding to city
With average price trend graph, and by the target cities, the city Chang Ju and position city current average price data and average price trend graph
The display interface for being output to the terminal is shown.
In addition, to achieve the above object, the present invention also provides a kind of readable storage medium storing program for executing, the readable storage medium storing program for executing storage
Have one perhaps more than one program the one or more programs can be held by one or more than one processor
Row is to be used for:
When the urban house average price inquiry instruction that the terminal for receiving user is sent, reads the urban house average price and look into
City keyword corresponding in instruction is ask, and according to the city keyword, determines target cities；
According to the history location information of the user grabbed from local storage unit, the Chang Jucheng of the user is determined
City, and detect the positioning city that the user is currently located；
The target cities, the city Chang Ju are searched from network respectively and positions current average price data corresponding to city
With average price trend graph, and by the target cities, the city Chang Ju and position city current average price data and average price trend graph
The display interface for being output to the terminal is shown.
The querying method of the urban house average price of the present embodiment, when user has the query demand to urban house average price,
Urban house average price inquiry instruction is sent by the terminal that it is held；When receiving the urban house average price inquiry instruction,
Wherein corresponding city keyword is read, and determines target cities according to the city keyword, needed for which is
The city to be inquired；The history location information of user is grabbed from local storage unit simultaneously, according to the history location information, really
Determine the city Chang Ju of user, and detects the positioning city that user is currently located；For the target cities, the city Chang Ju and positioning
City, searches respective current average price data and price trend graph from network respectively, and then by each current equal valence mumber
It is shown according to the display interface for being output to terminal with each average price trend graph.This programme is realizing the city to inquiry required for user
While house average price data in city are inquired, to the urban house average price data and its average price tendency in city associated with user
Figure is inquired, and query result is output to terminal together and is shown, facilitates checking for user, and compare each related city it
Between average price difference and average price tendency variation, avoid user from inputting city name one by one and inquire, simplify the behaviour of inquiry
Make, improves search efficiency.
Detailed description of the invention
Fig. 1 is the flow diagram of the querying method first embodiment of urban house average price of the invention；
Fig. 2 is the functional block diagram of the inquiry unit first embodiment of urban house average price of the invention；
Fig. 3 is the device structure schematic diagram for the hardware running environment that present invention method is related to.
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 invention provides a kind of querying method of urban house average price.
Fig. 1 is please referred to, Fig. 1 is the flow diagram of the querying method first embodiment of urban house average price of the present invention.?
In the present embodiment, the querying method of the urban house average price includes:
Step S10 reads the city room when the urban house average price inquiry instruction that the terminal for receiving user is sent
Corresponding city keyword in room average price inquiry instruction, and according to the city keyword, determine target cities；
The querying method of urban house average price of the invention is applied to server, suitable for being had by server to city
There is the average price of selling of building to be inquired；The terminals such as server and smart phone, tablet computer communicate to connect, and are equipped in terminal
It may be connected to the application software of server, the user of terminal in application software by registering user account to the number in server
According to accessing.When user has the query demand to urban house average price, starting application software is shown in display circle of terminal
In face, there is the input of inquiry city keyword required for virtual key and the input for triggering inquiry in the interface of display
Frame；User inputs city keyword in input frame in the form of voice or text, and by double-click to the virtual key or
Long press operation sends urban house average price inquiry instruction to server.When the urban house for receiving user and being sent based on terminal
When average price inquiry instruction, then the city keyword received corresponding with the urban house average price inquiry instruction is read, which closes
Keyword is the city inquired required for characterizing user.All cities pass for inquiry is stored in the storage unit of server
Keyword compares the city keyword of reading and each city keyword of storage, in each city keyword for determining storage
With the consistent city keyword of city keyword of reading；The city that the consistent city keyword is characterized is required for user
The city that the consistent city keyword is characterized is determined as target cities, to grab its corresponding information by the city of inquiry
It is shown to user, realizes the query demand of user.
In view of user is in city keyword input process, it is understood that there may be the city of input error or storage is crucial
The incomplete reason of word, so that during the comparison of each city keyword for the city keyword and storage that will be read, no
In the presence of the city keyword completely the same with the city keyword of reading, need to obscure the city keyword of reading at this time
Comparison；Specifically, according to city keyword, the step of determining target cities, includes:
The city keyword and default city keyword are compared, judge each default city keyword by step S11
In with the presence or absence of and the consistent target cities keyword of the city keyword；
Using each city keyword of storage as default city keyword, the city keyword of reading and default city are closed
Keyword comparison is compared with each city keyword of storage；Judge to close in each default city keyword with the presence or absence of with city
The consistent target cities keyword of keyword.
Step S12 then will be with the target cities if it exists with the consistent target cities keyword of the city keyword
The corresponding city of keyword is determined as target cities；
Further, server storage unit is also crucial to each city while storing to each city keyword
The city that word is characterized carries out corresponding storage, i.e., each default city keyword is corresponding with a certain city.It is each pre- when being judged
If existing in the keyword of city and the consistent target cities keyword of city keyword, because target cities keyword is default city
One of keyword, it is corresponding with a certain city, and the corresponding city is determined as to the target cities of inquiry required for user.
Step S13, it is if it does not exist with the consistent target cities keyword of the city keyword, then crucial to the city
Word is identified, determines approximate city keyword corresponding with the city keyword, and will be with the approximate city keyword
Corresponding city is determined as target cities.
When judged in each default city keyword there is no and the consistent target cities keyword of city keyword, then
City keyword is identified, determines that approximate city corresponding with the city keyword is crucial in each default city keyword
Word.The process wherein identified, which can be, is split as single word for city keyword, and by each word and default city keyword pair
Than, the determining and highest default city keyword of city keyword degree of closeness, the highest default city key of the degree of closeness
Word is approximate city keyword；And then city corresponding with approximate city keyword is determined as target cities, in maximum
The query demand of user is realized in degree.The service mechanism of artificial customer service is set simultaneously, shows in display interface and does not find
The completely the same target cities with city keyword, if require connect to the prompt information of artificial customer service；If user's foundation mentions
Show information, selection requires connect to artificial customer service, then establishes the connection between user terminal and artificial customer service, be by artificial customer service
User provides service.
Step S20 determines the user according to the history location information of the user grabbed from local storage unit
The city Chang Ju, and detect the positioning city that the user is currently located；
Understandably, the user that target cities are carried out with average price inquiry, may have and purchase to building in target cities
The demand bought, and it may need the average price to multiple city buildings to inquire and compare before purchase.Multiple city building
A possibility that city and the user city Chang Ju for being currently located for user, is larger；To the inquiry in order to better meet user
Demand, it is thus necessary to determine that its city Chang Ju and the city being currently located, with while inquiring the average price of target cities, also inquiry is normal
It occupies city and positions the average price in city, check comparison convenient for user.Wherein the city Chang Ju can be by the user's collected in the past
History location information is determined, and server is correspondingly arranged on database or memory for storing data, by the database
Or memory, as local storage unit, the history location information collected is stored in the local storage unit in the past.Work as needs
When determining the city Chang Ju, the historical location data of user is grabbed out from local storage unit；User is big in history location information
Positioning city where part-time, the as city Chang Ju of user.Being currently located city can be by GPS (Global
Positioning System, global positioning system) positioning or base station location real-time positioning information determine.
Step S30 is searched from network work as with corresponding to the target cities, the city Chang Ju and positioning city respectively
Preceding average price data and average price trend graph, and by the target cities, the city Chang Ju and position city current average price data and
The display interface that average price trend graph is output to the terminal is shown.
Further, it is searched from network respectively for target cities, the city Chang Ju and positioning city corresponding
Current average price data and average price trend graph, wherein current average price data characterization receives working as urban house average price inquiry instruction
The preceding time, target cities, the city Chang Ju and positioning city possessed by average price；It is united by house property mechanism possessed in each city
Family planning is at server is by network and each house property institutional communication, therefrom to search corresponding current average price data.Equally
Ground, average price trend graph is by possessed house property mechanism in target cities, the city Chang Ju and positioning city to previous each period
Interior average price carries out statistics generation, characterizes the average price situation of change of different time.Target cities, the positioning city that inquiry is obtained
And the current average price data and average price trend graph in the city Chang Ju are output to the display interface of terminal and compare display, with
It is checked convenient for user.The advantages of can also grabbing between three cities simultaneously and disadvantage are shown that the merits and demerits derives from
Other users are to the evaluation in the city and the planning trend of government etc.；It additionally can be according to user in login account during institute
The characteristics of identity information of offer grabs the characteristic of user, characterized by characteristic carries out between three cities
Match；If user is good at the work in the field A, target cities are more flourishing in the field A, and position city and be more short of in the field A；It will
The matching generates matching result and is shown, with for reference.
Understandably, it is influenced because of building possessed by city by factors such as market, policies, price has fluctuation, makes
Obtaining urban house average price has otherness in different time.In order to ensure inquire target cities, the city Chang Ju with
And the urban house average price data in city and the accuracy of average price trend graph are positioned, it is provided with update mechanism；Specifically, respectively
It is searched from network and current average price data and average price trend graph corresponding to target cities, the city Chang Ju and positioning city
Include: before step
Step a1, periodic detection current time, and when the current time reach preset time when, from network crawl with
The real time price data of city building corresponding to the target cities, the city Chang Ju and positioning city；
Step a2 generates the target cities, the city Chang Ju and positioning city according to each real time price data respectively
The current average price data in city, and the target cities, the city Chang Ju and the current average price data pair for positioning city are called respectively
Corresponding average price trend graph is updated.
Further, the preset time for update, such as one week, two weeks are set with previously according to demand, and to working as
The preceding time carries out periodic detection；When detecting that current time reaches the preset time, i.e., current time distance is last updates
When time reaches preset time, then target cities, the city Chang Ju and the real time price for positioning the city city Zhong Ge building are read
Data, and confluence analysis is carried out based on the classification of target cities, the city Chang Ju and positioning city to every real time price data,
The interval average price of target cities, the city Chang Ju and positioning city within the interval time is generated respectively.The interval average price is available
The mean value of each city building real time price data characterizes, i.e., to the real time price for reading had city building in city of all categories
Data add up, and do ratio, the knot of ratio with the quantity of city building had in accumulated result and city of all categories
Fruit is the interval average price in city of all categories.Such as read target cities, the city Chang Ju and positioning city in relate separately to m,
N, k city building, wherein the real time price of had city building is respectively A1, A2, A3Am in target cities, then
Interval average price M=(A1+A2+A3++Am)/m of the target cities within interval time.Target cities, Chang Jucheng will be directed to
City and positioning city respectively interval average price generated stored as respective current average price data, while with this respectively when
Preceding average price data are updated respective average price trend graph, to accurately reflect target cities, the city Chang Ju and positioning city
Average price variation tendency within each time；So that user is in inquiry, the current average price data and average price trend graph of inquiry are more
Accurately reflect the average price and average price situation of change in city of all categories.
The querying method of the urban house average price of the present embodiment, when user has the query demand to urban house average price,
Urban house average price inquiry instruction is sent by the terminal that it is held；When receiving the urban house average price inquiry instruction,
Wherein corresponding city keyword is read, and determines target cities according to the city keyword, needed for which is
The city to be inquired；The history location information of user is grabbed from local storage unit simultaneously, according to the history location information, really
Determine the city Chang Ju of user, and detects the positioning city that user is currently located；For the target cities, the city Chang Ju and positioning
City, searches respective current average price data and price trend graph from network respectively, and then by each current equal valence mumber
It is shown according to the display interface for being output to terminal with each average price trend graph.This programme is realizing the city to inquiry required for user
While house average price data in city are inquired, to the urban house average price data and its average price tendency in city associated with user
Figure is inquired, and query result is output to terminal together and is shown, facilitates checking for user, and compare each related city it
Between average price difference and average price tendency variation, avoid user from inputting city name one by one and inquire, simplify the behaviour of inquiry
Make, improves search efficiency.
Further, described by the target in another embodiment of the querying method of urban house average price of the present invention
City, the city Chang Ju and position the current average price data in city and average price trend graph be output to the display interface of the terminal into
Include: after the step of row display
Step S40 is looked into when receiving price data of the user based on the current average price data input of display
Several buildings corresponding with the price data are looked for, and grab the corresponding credit information of each building；
Understandably, it is shown after display interface by the current average price data in city of all categories, user can be based on display
Current average price data input its receptible price data of institute, the price number that such as input is higher or lower than current average price data
According to being the building for meeting user demand to characterize present price and the consistent building of the price data in city of all categories.When connecing
When receiving the price data, by the building valence of each building in the price data and target cities, the city Chang Ju and positioning city
Lattice compare, and find out several buildings corresponding with price data.During the comparison process, by the Building Checks Price and valence of each building
Lattice data do difference, whether within a preset range the result of difference are judged, if within a preset range, determining Building Checks Price and valence
Lattice data are corresponding, otherwise not correspond to.It is the building for only meeting user demand on Building Checks Price because finding out corresponding building,
In order to determine that each building, with the matching of user demand, needs further to grab loan corresponding to each building in terms of loan
Money information, the credit information characterize the previous loan profile of building, to determine that building and user are borrowing by loan profile
Matching in money demand.
Specifically, the step of grabbing each building corresponding credit information include
Step S41 reads the history loan data of each building from local storage unit, and borrows from each history
Amount of money according in filter out the loan application stroke count of each building, provide a loan successfully stroke count, each amount of the loan and with each institute
State that the amount of the loan is corresponding to make loans the time；
Understandably, the time of making loans of different buildings are supported loan types and corresponding loan is different, reads
History loan data of each building corresponding with price data stored in local storage unit within the scope of certain time, and
Classification type by each history loan data according to Accumulation Fund Loan and commercial loans carries out loan application stroke count, loan respectively
It makes loans the screening of time, i.e., is borrowed from the history of each building corresponding to success stroke count, each amount of the loan and each amount of the loan
Amount of money filters out loan application stroke count in, provides a loan and put corresponding to successfully stroke count, each amount of the loan and each amount of the loan
The money time.Wherein loan application stroke count characterization is directed to the applied loan stroke count of the building within this time range, provides a loan successfully
Stroke count, which characterizes, applies for successful stroke count in applied loan stroke count, each amount of the loan characterization applies for each pen in successful stroke count
Amount of money numerical value, each amount of the loan corresponding time of making loans then characterizes application, and successfully each pen is provided a loan the time made loans.
Step S42 generates the loan of each building according to each loan application stroke count and the successfully stroke count of providing a loan
Success rate, and each pen amount of the loan is compared, the difference between each pen amount of the loan is generated, by the difference
Each pen amount of the loan within a preset range is divided into same group of class；
Further, the loan success stroke count and loan application stroke count of each building are done into ratio, obtained ratio result is
The loan success rate of each building, the probability of success size that characterization is provided a loan for each building.Simultaneously by each amount of the loan base
It is compared in numerical values recited, obtains the difference of amount of money numerical value between each amount of the loan；And it will be between each amount of the loan
It the difference of amount of money numerical value and is compared previously according to preset range set by demand, determines difference within a preset range each
The amount of the loan, and each amount of the loan within a preset range is divided into same group of class.As preset range be -5 to 50,000 it
Between, wherein being related to 500,000,530,000,600,000 and 650,000 in each amount of the loan, then the difference between 500,000 and 530,000 is default
In range, 500,000 and 530,000 also within a preset range, are then divided into a group class by the difference between 600,000 and 650,000, and by 60
Ten thousand and 650,000 are divided into another group of class.
Step S43 makes loans the time according to corresponding to each amount of the loan in each described group of class, determines that each organized class is corresponding
Time interval of making loans, and each loan success rate and each described group of class are determined as the corresponding credit information of each building
It is grabbed.
Further, the possessed each amount of the loan be not identical in different group classes, and each amount of the loan institute is right
Answer different make loans the time；The length for basis of time time of making loans corresponding to each amount of the loan in each group class is carried out pair
Than determining in each group class the time span longest time and time span is shortest makes loans the time of making loans；It will be put by longest
Money time and shortest time of making loans are formed by section as time interval of making loans corresponding to each group class.Such as above-mentioned 50
Ten thousand and 530,000 are formed by a group class, if 500,000 corresponding times of making loans were 3 months, 550,000 corresponding times of making loans were 4 months, then
The corresponding time interval of making loans of this group of class is set as between 3 months to 4 months.By the loan success of each building of the generation
Rate and each group of class are grabbed as the credit information of each building, to characterize the big of each building loan probability of success respectively
It makes loans corresponding to small and each amount of the loan the length of time.
Step S50 reads the identity information of the user from local storage unit, and according in the identity information
Identity grabs house-purchase information corresponding with the identity information；
Further, in order to embody demand of the user in terms of loan, user is first read from local storage unit and is being infused
Identity information provided by during volume account, and according to the identity in the identity information, it grabs corresponding with identity information
House-purchase information；The house-purchase information corresponding to the identity information is the house-purchase information of user, is related to house-purchase qualification, loan needs
Seek the data of the various aspects such as amount, demand for loan time.
Specifically, according to the identity in identity information, the step of grabbing house-purchase information corresponding to the identity information, is wrapped
It includes:
Step S51 reads the identity in the identity information, and according to the identity, crawl and the body
The corresponding qualification data of part information, history house property data, balance data and kinsfolk's data；
Understandably, the house-purchase demand of different user is different, and server and multiple and different third-party institutions are with logical
Letter connection, first reads the identity that user identity uniqueness is characterized in identity information, then by the identity from each the
Tripartite mechanism grabs the data of user in all respects；The data of the user in all respects are various aspects corresponding to the identity information
Data embody the house-purchase demand of user.Wherein, the data of the various aspects may include user's gender, whether the age, wedding is no, have
Child, child's age, social security information, account information, whether with house property information, whether there is house property loan information, economical receive
Enter, the consumption expenditure etc.；Wherein social security information, account information characterize whether user has to the building for meeting its price request
Qualification is bought, and using such data as qualification data；Whether with house property information and whether with house property loan information table
House property situation possessed by user is levied, using such data as history house property data；Income and the consumption expenditure characterize
The case where between the income and expenditure of user, using such data as balance data；Gender, the age, wedding is no, whether has child,
Child characterizes subscriber household member's situation the age, using such data as kinsfolk's data.
Step S52, when according to the qualification data, judging that the user has qualification, according to the history house property
Data, balance data and kinsfolk's data determine loan successful coefficient, loan limit data and the time of the user respectively
Demand data；
Further, the qualification data of user and preset building are bought into qualification data comparison, judges user couple
Whether the building for meeting its price request has purchase qualification；If not having purchase qualification, terminate follow-up process；If having purchase
Buy qualification, then determined respectively according to history house property data, balance data and kinsfolk's data user loan successful coefficient,
Loan limit data and time demand data.The loan successful coefficient that user is determined with history house property data, with revenue and expenditure number
According to characterizing loan limit data, and time demand data is embodied with kinsfolk's data.Specifically, according to history house property number
According to, balance data and kinsfolk's data, loan successful coefficient, loan limit data and the time demand number of user are determined respectively
According to the step of include:
Step S521, first between the history house property data and preset house property data and loan success rate is corresponding
Relationship comparison, determines loan successful coefficient corresponding with the history house property data；
Further, multinomial history loan data is grabbed in advance, and multinomial history loan data is analyzed, and is established
Corresponding relationship between house property data in history loan data and loan success rate, is determined as house property data for the corresponding relationship
With the first corresponding relationship between loan success rate.In 1000 history loan datas, user corresponding to 800 has room
Credit information is produced, user corresponding to 200 does not have house property information and house property loan information；And 200 loans in 800
It is successful, it provides a loan successfully for 180 in 200；Then explanation has the loan success rate of house property loan information corresponding 25%, without
The loan success rate of house property information and house property loan information corresponding 90%, will be between the loan success rate and all kinds of house property data
Corresponding relationship as the first corresponding relationship.The user's history house property data of crawl and first corresponding relationship are compared, are determined
First corresponding relationship neutralizes the consistent house property data of history house property data；The consistent house property data are in the first corresponding relationship
Corresponding loan success rate, loan successful coefficient as corresponding with history loan data, characterization user's loan are successfully general
Rate size.
Step S522 compares the second corresponding relationship between the balance data and preset income and quota data,
Determine loan limit data corresponding with the balance data；
Similarly, grabbed from history loan data in advance each pen provide a loan income data that corresponding user has and with
The corresponding quota data of each income data, and classify to each income data and its corresponding quota data, establish income
The second corresponding relationship between quota data characterizes the loan limit that there is the user of each income may need.It will grab
The user's balance data taken and second corresponding relationship comparison, determine that the second corresponding relationship neutralizes the consistent receipts of the income data
Enter；Consistent income quota data corresponding in the second corresponding relationship, loan limit as corresponding with balance data
Data, the required amount provided a loan of characterization user.
Step S523 is closed kinsfolk's data and third between preset member data and time of making loans are corresponding
System's comparison, determines time demand data corresponding with kinsfolk's data.
Further, grabbed from history loan data in advance each pen provide a loan member data that corresponding user has and
It makes loans the time with corresponding to each member data, and classifies to each member data and its corresponding time of making loans, build
Vertical member data and the third corresponding relationship between the time of making loans characterize what the user with all kinds of kinsfolks may need
It makes loans the time；If any child and child's age is 5 years old, then illustrates that the time made loans required for the user is shorter.By the use of crawl
Family kinsfolk's data and the third corresponding relationship compare, determine third corresponding relationship neutralize kinsfolk's data it is consistent at
Member's data；The consistent member data is corresponding in third corresponding relationship to make loans the time, as with kinsfolk's data pair
The time demand data answered characterizes the time made loans required for user.
The loan successful coefficient, loan limit data and time demand data are determined as and the identity by step S53
The corresponding house-purchase information of information is grabbed.
Further, using identified loan successful coefficient, loan limit data and time demand data as user
Required house-purchase information, i.e., house-purchase information corresponding to the identity information are grabbed, successful to characterize user's loan respectively
Probability size is made loans the time required for loan limit and user required for user.
Step S60 believes according to each corresponding credit information of building and the house-purchase corresponding with the user
Breath, filtered out from the building according to default screening rule with the matched building of the user, and will be matched with the user
Building be set as building element to form building sequence, the building element in the building sequence is output to the aobvious of the terminal
Show that interface is shown.
Further, grab credit information corresponding to each building and the how corresponding house-purchase information of user it
Afterwards, it is filtered out from each building according to default screening rule and the matched building of user；It should be carried out according to default screening rule
The process of screening is that the process of purchase house information and the comparison of each credit information is met user demand to filter out from each building
Building；It is arranged using each building for meeting user demand as building element, forms building sequence.Arrangement when according to
According to each credit information and house-purchase information between matching degree carry out, when credit information and house-purchase information degree it is higher, characterize
The degree that building with the credit information meets user demand is higher, and using the building as building arrangement of elements in building sequence
The forefront of column；And when credit information is lower with the degree for information of purchasing house, characterizing, there is the building of the credit information to meet user's need
The degree asked is lower, then using the building as building arrangement of elements building sequence rank rear.After generating building sequence, the building
Each building in disk sequence is all satisfied user demand, so that each building element in building sequence is output to end according to arrangement
The display interface at end is shown, in order to which user checks the building for meeting its demand.
In view of the credit information of each building and the house-purchase information of user all refer to multinomial information, carried out by the two
When comparison, this is the comparison between information included in the two in fact.Specifically, according to the corresponding credit information of each building and
House-purchase information corresponding to the user, the step of building matched with user is filtered out from building according to default screening rule packet
It includes:
Step S61 compares the loan success rate of the loan successful coefficient and each building, by each loan
The building screening for being higher than the loan successful coefficient in money success rate is target building；
Further, successfully it is by the loan in the house-purchase information of loan success rate and user in each building credit information
Number compares, and the loan success rate for being higher than loan successful coefficient is determined from each loan success rate；It should be higher than to provide a loan and successfully be
Building corresponding to several loan success rates is user for its loan probability of success with higher, so that this is corresponding
Building be determined as target building.
Described group of class in the loan limit data and each target building is compared, determines the loan by step S62
Target group class where money quota data；
It further, will be each in the credit information of loan limit data and each target building in user's house-purchase information
Group class comparison, the group class where determining loan limit data in each target building, and by group where each loan limit data
Class is as target group class.It include wherein group class p1 and p2 in the credit information of W1, and p1 is by gold such as target building W1 and W2
Each amount of the loan composition of the volume between 50 ten thousand to 60 ten thousand, each amount of the loan of the p2 by the amount of money between 80 ten thousand to 100 ten thousand
Composition；And in the credit information of W2 include group class p3 and p4, each amount of the loan group of the p3 by the amount of money between 45 ten thousand to 55 ten thousand
At p4 is made of each amount of the loan of the amount of money between 80 ten thousand to 90 ten thousand；And loan limit data are 850,000, then by it and respectively
Group class compares, it may be determined that the group class where it is p2 and p4.Group class where loan limit data is determined as target group
Class, to be distinguished with other group of class in credit information.
Step S63 reads the corresponding target of each target group class and makes loans time interval, and by the time demand data
It makes loans time interval comparison with each target, each target where the time demand data is made loans time interval pair
Answer target building screening be and the matched building of the user.
Understandably, the target because time interval of making loans corresponding to different groups of classes is not identical, where loan limit data
Time interval of making loans corresponding to group class can be able to satisfy demand of the user to the time of making loans, it is also possible to when being unsatisfactory for user to making loans
Between demand.The time interval that will make loans corresponding to each target group class is read as target time interval of making loans, and user is purchased
Time demand data and each target in room information are made loans time interval comparison, and the target where determining time demand data puts
Money time interval.Target where this make loans target building corresponding to time interval be user have purchase qualification, and price,
Loan success rate and time of making loans are all satisfied the building of user demand, which is matched with user
Building, and then this is formed as into the building sequence recommended as building element with the matched building of user, in order to quasi- to user
Really recommend the building for meeting its demand.
In addition, referring to figure 2., the present invention provides a kind of inquiry unit of urban house average price, in urban house of the present invention
In the inquiry unit first embodiment of average price, the inquiry unit of the urban house average price includes:
Read module 10, for reading institute when receiving the urban house average price inquiry instruction of terminal transmission of user
City keyword corresponding in urban house average price inquiry instruction is stated, and according to the city keyword, determines target cities；
Determining module 20 is determined for the history location information according to the user grabbed from local storage unit
The city Chang Ju of the user, and detect the positioning city that the user is currently located；
Searching module 30, for being searched from network respectively and the target cities, the city Chang Ju and positioning city institute
Corresponding current average price data and average price trend graph, and by the target cities, the city Chang Ju and position the current equal of city
The display interface that valence mumber evidence and average price trend graph are output to the terminal is shown.
The inquiry unit of the urban house average price of the present embodiment, when user has the query demand to urban house average price,
Urban house average price inquiry instruction is sent by the terminal that it is held；It is looked into when read module 10 receives the urban house average price
When asking instruction, wherein corresponding city keyword is read, and determine target cities according to the city keyword, the target cities
The city inquired required for as；Determining module 20 grabs the history location information of user, root from local storage unit simultaneously
According to the history location information, the city Chang Ju of user is determined, and detect the positioning city that user is currently located；For the target city
City, the city Chang Ju and positioning city, searching module 30 search respective current average price data and price from network respectively
Trend graph, and then the display interface that each current average price data and each average price trend graph are output to terminal is shown.This
Scheme is while the urban house average price data of inquiry required for user are inquired in realization, to city associated with user
Urban house average price data and its average price trend graph inquired, and query result is output to terminal together and is shown, it is convenient
User's checks, and compares average price difference and the variation of average price tendency between each related city, and user is avoided to input one by one
City name is inquired, and is simplified the operation of inquiry, is improved search efficiency.
Further, in another embodiment of inquiry unit of urban house average price of the present invention, the urban house average price
Inquiry unit further include:
Handling module is used for periodic detection current time, and when the current time reaches preset time, from network
The real time price data of crawl and city building corresponding to the target cities, the city Chang Ju and positioning city；
Calling module, for according to each real time price data generate respectively the target cities, the city Chang Ju and
The current average price data in city are positioned, and call the target cities, the city Chang Ju and the current average price for positioning city respectively
Data are updated corresponding average price trend graph.
Further, in another embodiment of inquiry unit of urban house average price of the present invention, the urban house average price
Inquiry unit further include:
Receiving module, for when the price data for receiving the current average price data input of the user based on display
When, several buildings corresponding with the price data are searched, and grab the corresponding credit information of each building；
The read module is also used to read the identity information of the user from local storage unit, and according to the body
Identity in part information, grabs house-purchase information corresponding with the identity information；
Screening module is used for according to each corresponding credit information of building and house-purchase information corresponding with the user,
Filtered out from the building according to default screening rule with the matched building of the user, and will be with the matched building of the user
Disk is set as building element to form building sequence, and the building element in the building sequence is output to display circle of the terminal
Face is shown.
Further, in another embodiment of inquiry unit of urban house average price of the present invention, the receiving module is also used
In:
The history loan data of each building is read from local storage unit, and from each history loan data
Filter out the loan application stroke count of each building, provide a loan successfully stroke count, each amount of the loan and with each pen loan gold
Volume is corresponding to make loans the time；
According to each loan application stroke count and the successfully stroke count of providing a loan, the loan success rate of each building is generated,
And each pen amount of the loan is compared, the difference between each pen amount of the loan is generated, by the difference default
Each pen amount of the loan in range is divided into same group of class；
It is made loans the time according to corresponding to each amount of the loan in each described group of class, when determining that each organized class is corresponding and making loans
Between section, and each loan success rate and each described group of class are determined as the corresponding credit information of each building and grabbed
It takes.
Further, in another embodiment of inquiry unit of urban house average price of the present invention, the read module is also used
In:
The identity in the identity information is read, and according to the identity, crawl and the identity information pair
Qualification data, history house property data, balance data and the kinsfolk's data answered；
When according to the qualification data, judging that the user has qualification, according to the history house property data, revenue and expenditure
Data and kinsfolk's data determine loan successful coefficient, loan limit data and the time demand data of the user respectively；
The loan successful coefficient, loan limit data and time demand data are determined as corresponding with the identity information
House-purchase information grabbed.
Further, in another embodiment of inquiry unit of urban house average price of the present invention, the read module is also used
In:
The first corresponding relationship between the history house property data and preset house property data and loan success rate is compared,
Determine loan successful coefficient corresponding with the history house property data；
The second corresponding relationship between the balance data and preset income and quota data is compared, it is determining with it is described
The corresponding loan limit data of balance data；
By kinsfolk's data and the third corresponding relationship comparison between preset member data and time of making loans, really
Fixed time demand data corresponding with kinsfolk's data.
Further, in another embodiment of inquiry unit of urban house average price of the present invention, the screening module is also wrapped
It includes:
Screening unit will be each for comparing the loan success rate of the loan successful coefficient and each building
The building screening for being higher than the loan successful coefficient in the loan success rate is target building；
Comparison unit is determined for comparing described group of class in the loan limit data and each target building
Target group class where the loan limit data；
Reading unit is made loans time interval for reading the corresponding target of each target group class, and the time is needed
Ask data and each target make loans time interval comparison, each target where the time demand data is made loans the time
Section corresponding target building screening is and the matched building of the user.
Wherein, each virtual functions module of the inquiry unit of above-mentioned urban house average price is stored in urban house shown in Fig. 3
In the memory 1005 of the query facility of average price, when processor 1001 executes the polling routine of urban house average price, Fig. 2 institute is realized
Show the function of modules in embodiment.
Referring to Fig. 3, Fig. 3 is the device structure schematic diagram for the hardware running environment that present invention method is related to.
The query facility of urban house average price of the embodiment of the present invention can be PC (personal computer, individual calculus
Machine), it is also possible to the terminal devices such as smart phone, tablet computer, E-book reader, portable computer.
As shown in figure 3, the query facility of the urban house average price may include: processor 1001, such as CPU (Central
Processing Unit, central processing unit), memory 1005, communication bus 1002.Wherein, communication bus 1002 for realizing
Connection communication between processor 1001 and memory 1005.Memory 1005 can be high-speed RAM (random access
Memory, random access memory), it is also possible to stable memory (non-volatile memory), such as disk storage
Device.Memory 1005 optionally can also be the storage device independently of aforementioned processor 1001.
Optionally, the query facility of the urban house average price can also include user interface, network interface, camera, RF
(Radio Frequency, radio frequency) circuit, sensor, voicefrequency circuit, WiFi (Wireless Fidelity, WiMAX) mould
Block etc..User interface may include display screen (Display), input unit such as keyboard (Keyboard), and optional user connects
Mouth can also include standard wireline interface and wireless interface.Network interface optionally may include the wireline interface, wireless of standard
Interface (such as WI-FI interface).
It will be understood by those skilled in the art that the query facility structure of urban house average price shown in Fig. 3 is not constituted
Restriction to the query facility of urban house average price may include than illustrating more or fewer components, or the certain portions of combination
Part or different component layouts.
As shown in figure 3, as may include operating system, network communication in a kind of memory 1005 of readable storage medium storing program for executing
The polling routine of module and urban house average price.Operating system is the query facility hardware for managing and controlling urban house average price
With the program of software resource, the polling routine of urban house average price and the operation of other softwares and/or program are supported.Network is logical
Believe module for realizing the communication between each component in the inside of memory 1005, and with its in the query facility of urban house average price
It is communicated between its hardware and software.
In the query facility of urban house average price shown in Fig. 3, processor 1001 is deposited in memory 1005 for executing
The polling routine of the urban house average price of storage realizes the step in each embodiment of querying method of above-mentioned urban house average price.
The present invention provides a kind of readable storage medium storing program for executing, the readable storage medium storing program for executing is stored with one or more than one journey
Sequence, the one or more programs can also be executed by one or more than one processor for realizing above-mentioned city
Step in each embodiment of querying method of city house average price.
It should also be noted that, herein, the terms "include", "comprise" or its any other variant are intended to non-
It is exclusive to include, so that the process, method, article or the device that include a series of elements not only include those elements,
It but also including other elements that are not explicitly listed, or further include solid by this process, method, article or device
Some elements.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including
There is also other identical elements in the process, method of the element, article or device.
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 readable storage medium storing program for executing (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be hand
Machine, computer, server or network equipment etc.) execute method described in each embodiment of the present invention.
The above description is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all at this
Under the design of invention, using equivalent structure transformation made by description of the invention and accompanying drawing content, or directly/it is used in it indirectly
He is included in scope of patent protection of the invention relevant technical field.
Claims (10)
1. a kind of querying method of urban house average price, which is characterized in that the querying method of the urban house average price include with
Lower step:
When the urban house average price inquiry instruction that the terminal for receiving user is sent, reads the urban house average price inquiry and refer to
Corresponding city keyword in order, and according to the city keyword, determine target cities；
According to the history location information of the user grabbed from local storage unit, the city Chang Ju of the user is determined,
And detect the positioning city that the user is currently located；
Searched from network respectively the target cities, the city Chang Ju and positioning city corresponding to current average price data and
Valence trend graph, and the target cities, the current average price data in the city Chang Ju and positioning city and average price trend graph is defeated
The display interface for arriving the terminal out is shown.
2. the querying method of urban house average price as described in claim 1, which is characterized in that described to be searched from network respectively
It is wrapped before the step of current average price data corresponding to the target cities, the city Chang Ju and positioning city and average price trend graph
It includes:
Periodic detection current time, and when the current time reaches preset time, crawl and the target city from network
The real time price data of city building corresponding to city, the city Chang Ju and positioning city；
The target cities, the city Chang Ju and the current average price for positioning city are generated respectively according to each real time price data
Data, and call the target cities, the city Chang Ju respectively and position the current average price data in city to corresponding institute
Average price trend graph is stated to be updated.
3. the querying method of urban house average price as described in claim 1, which is characterized in that it is described by the target cities,
The display interface that the current average price data and average price trend graph in the city Chang Ju and positioning city are output to the terminal is shown
Include: after the step of showing
When receiving price data of the user based on the current average price data input of display, search and the price
The corresponding several buildings of data, and grab the corresponding credit information of each building；
The identity information of the user is read from local storage unit, and according to the identity in the identity information, is grabbed
Take house-purchase information corresponding with the identity information；
According to each corresponding credit information of building and house-purchase information corresponding with the user, according to default screening rule from
Filtered out in the building with the matched building of the user, and building element will be set as with the matched building of the user with shape
At building sequence, the display interface that the building element in the building sequence is output to the terminal is shown.
4. the querying method of urban house average price as claimed in claim 3, which is characterized in that each building pair of crawl
The step of credit information answered includes
The history loan data of each building is read from local storage unit, and is screened from each history loan data
Out the loan application stroke count of each building, provide a loan successfully stroke count, each amount of the loan and with each pen amount of the loan pair
That answers makes loans the time；
According to each loan application stroke count and the successfully stroke count of providing a loan, the loan success rate of each building is generated, and right
Each pen amount of the loan compares, and the difference between each pen amount of the loan is generated, by the difference in preset range
Interior each pen amount of the loan is divided into same group of class；
It is made loans the time according to corresponding to each amount of the loan in each described group of class, determines the corresponding time zone of making loans of each organized class
Between, and each loan success rate and each described group of class are determined as the corresponding credit information of each building and grabbed.
5. the querying method of urban house average price as claimed in claim 4, which is characterized in that described according to the identity information
In identity, grab house-purchase information corresponding with the identity information the step of include:
The identity in the identity information is read, and according to the identity, is grabbed corresponding with the identity information
Qualification data, history house property data, balance data and kinsfolk's data；
When according to the qualification data, judging that the user has qualification, according to the history house property data, balance data
With kinsfolk's data, loan successful coefficient, loan limit data and the time demand data of the user are determined respectively；
The loan successful coefficient, loan limit data and time demand data are determined as purchase corresponding with the identity information
Room information is grabbed.
6. the querying method of urban house average price as claimed in claim 5, which is characterized in that described according to the history house property
Data, balance data and kinsfolk's data determine loan successful coefficient, loan limit data and the time of the user respectively
The step of demand data includes:
The first corresponding relationship between the history house property data and preset house property data and loan success rate is compared, is determined
Loan successful coefficient corresponding with the history house property data；
The second corresponding relationship between the balance data and preset income and quota data is compared, the determining and revenue and expenditure
The corresponding loan limit data of data；
By kinsfolk's data and between preset member data and time of making loans third corresponding relationship comparison, determine with
The corresponding time demand data of kinsfolk's data.
7. the querying method of urban house average price as claimed in claim 5, which is characterized in that described according to each building pair
The credit information answered and house-purchase information corresponding with the user, filter out from the building and institute according to default screening rule
The step of stating user's matched building include:
It, will be high in each loan success rate by the comparison of the loan success rate of the loan successful coefficient and each building
Screening in the building of the loan successful coefficient is target building；
By the described group of class comparison in the loan limit data and each target building, the loan limit data institute is determined
Target group class；
It reads the corresponding target of each target group class to make loans time interval, and by the time demand data and each target
Time interval of making loans comparison, each target where the time demand data is made loans the corresponding target building of time interval
Screening be and the matched building of the user.
8. a kind of inquiry unit of urban house average price, which is characterized in that the inquiry unit of the urban house average price includes:
Read module, for reading the city when receiving the urban house average price inquiry instruction of terminal transmission of user
Corresponding city keyword in house average price inquiry instruction, and according to the city keyword, determine target cities；
Determining module determines the use for the history location information according to the user grabbed from local storage unit
The city Chang Ju at family, and detect the positioning city that the user is currently located；
Searching module, for respectively from network search with the target cities, the city Chang Ju and positioning city corresponding to
Current average price data and average price trend graph, and by the target cities, the city Chang Ju and the current average price data for positioning city
The display interface for being output to the terminal with average price trend graph is shown.
9. a kind of query facility of urban house average price, which is characterized in that the query facility of the urban house average price includes: to deposit
Reservoir, processor, communication bus and the urban house average price being stored on the memory polling routine；
The communication bus is for realizing the connection communication between processor and memory；
The processor is used to execute the polling routine of the urban house average price, to realize such as any one of claim 1-7
The step of querying method of the urban house average price.
10. a kind of readable storage medium storing program for executing, which is characterized in that be stored with the inquiry of urban house average price on the readable storage medium storing program for executing
It realizes when the polling routine of program, the urban house average price is executed by processor as of any of claims 1-7
The step of querying method of urban house average price.
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| CN110738565A (en) * | 2019-10-11 | 2020-01-31 | 中山市银鹿金科信息科技有限公司 | Real estate finance artificial intelligence composite wind control model based on data set |
| CN112686703A (en) * | 2020-12-31 | 2021-04-20 | 长沙市到家悠享网络科技有限公司 | Automatic generation and query method for national household industry price and electronic equipment |
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