CN109711934A - A kind of intelligent house leasing system based on big data - Google Patents
A kind of intelligent house leasing system based on big data Download PDFInfo
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- CN109711934A CN109711934A CN201811565074.4A CN201811565074A CN109711934A CN 109711934 A CN109711934 A CN 109711934A CN 201811565074 A CN201811565074 A CN 201811565074A CN 109711934 A CN109711934 A CN 109711934A
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Abstract
End, information of lease library, house to let end, data match module, matching rule unit, map office, processor, display unit, storage unit and data input cell are rented in the intelligent house leasing system based on big data that the invention discloses a kind of, including house；It is the personal portable terminals for needing rental estates child user that end is rented in the house；The present invention passes through the setting of data match module and matching rule unit, in data sample deficiency by the way that rent information and rental information will be asked to match by dependency rule, the rental information that matching is completed is transferred to house and rents end, it allows user independently to select rental information, is formed if lease process is completed and rent information；It is formed in the process according to the target position of user and working place and at heart anticipated price and recommends reciprocal value, determine that user may be inclined to the house of selection according to recommendation reciprocal value.
Description
Technical field
The invention belongs to house lease fields, are related to a kind of intelligent house leasing techniques, specifically a kind of to be based on big data
Intelligent house leasing system.
Background technique
House lease refers to the consumption that its house that is all or managing is given to house by the owner or operator in house
Person uses, house consumer by the rent of the certain number of delivering on term, obtain house occupy and using right behavior.Room
Room lease is a kind of commodity circulation mode that house use value is sporadicly sold.
House lease refers to lessor, generally housing ownership people, house to let is used to tenant, by tenant
Behavior to lessor's payment or rent.
The house lease time limit is more than that six months both parties must sign contract in writing.Should include in house-leasing contract
The main clause of following aspect: the range of rented house, area；The time limit of house lease, purposes；When the number of rent and delivery
Between；The responsibility of house repairing is subleted and liability for breach of contract etc..
But enough intelligence is not accomplished in current house lease, cannot realize accurate according to user demand and big data
Match；To solve drawbacks described above, a solution is now provided.
Summary of the invention
The intelligent house leasing system based on big data that the purpose of the present invention is to provide a kind of.
The technical problems to be solved by the invention are as follows:
(1) how in the case where no enough data samples, realize that accurately house lease matches；
(2) data sample after house lease is precisely recommended how is carried out to be formed；
(3) how in the case where getting enough data samples, the accurate match of house lease is carried out.
The purpose of the present invention can be achieved through the following technical solutions:
End, information of lease library, house to let are rented in a kind of intelligent house leasing system based on big data, including house
End, data match module, matching rule unit, map office, processor, display unit, storage unit and data input cell；
Wherein, it is the personal portable terminals for needing rental estates child user that end is rented in the house；End is rented in the house
For user publication ask rent information use, it is described ask rent information include target house type information, target position information, target price information
And personal information, the personal information include occupation and working place；
Wherein, the house to let end is the portable mobile termianl that house needs the user hired out to use, the house
It hires out end and uploads house for rent information use for house owner, the house for rent information includes house location, house rent and house tool
Body information, house specifying information are the pattern that house house type is several Rooms in several rooms；
End is rented for rent information will to be asked to be transferred to information of lease library in the house, and the house to let end will be for that will put out to lease
Information is transferred to information of lease library；The information of lease library is used to receive house and rents seeking rent information and carrying out real-time for end transmission
Storage, the information of lease library are used to receive the house for rent information of house to let end transmission and carry out real-time storage；The lease letter
Breath library, which is also used to store, rents information group, it is described rent information group and include several rent information, renting information is to be using this
What system was completed to rent a house rents situation, and renting information includes that corresponding ask rents information, house for rent information and rental period, rents defining for information
House is rented to stopping renting and renting information for one for user；
The data match module communication link is connected to information of lease library, matching rule unit and map office, the data
It is used to combine matching rule unit and map office to the house for rent information in information of lease library with module and rent information is asked to match,
The matching rule element memory contains rental matching rule, and specific rules show themselves in that
Step 1: it gets to rent in information group and rents information, the item number for renting information is counted；
Step 2: determining the item number for renting information, as required when renting information bar number less than preset value X1
Matched pair technique is matched；Need matched pair technique specific manifestation are as follows:
S1: get it is any seek rent information, and get this ask rent information in target house type information, target position letter
Breath, target price information and personal information；
S2: house location, house rent and the house specifying information in all house for rent information are got later；
S3: the house specifying information in house for rent information is got, is carried out according to target house type information and house specifying information
Target house type information and the consistent house for rent information flag of house specifying information are house for rent information to be confirmed by preliminary matches；
S4: being Di, i=1...n by house for rent information flag to be confirmed；Get the house position in house for rent information to be confirmed
It sets, it is poor that the target range between target position information and house location is calculated according to target position information combination map office；
And marking target range difference is i=1...n；And Mi and Di is corresponded；
S5: getting the working place in personal information, and base area picture library obtains the upper of working distance location house location
Headway period is from Si, i=1...n；And Si and Di is corresponded；
S6: distance is calculated according to target range difference and working distance and is satisfied with reciprocal value Q1i, i=1...n, distance is satisfied with reciprocal value
It is more big, it indicates the position more does not meet user demand；Because target range difference and working distance adjust the distance and are satisfied with the influence of reciprocal value
Degree is different, for the prominent influence degree, is now not so good as correction value X2 and X3, wherein X2 < X3, and X2+X3=1；It is specific to calculate public affairs
Formula is Q1i=Mi*X3+Si*X2；And Q1i and Di is corresponded；
S7: getting the target price information asked and rented in information, while getting the house of institute's subject to confirmation house for rent information
Rent；Using formula anticipated price difference=target price information-house rent, anticipated price difference is obtained and by anticipated price
Difference is labeled as Q2i, i=1...n；And Ci and Di is corresponded；
S8: it is satisfied with reciprocal value Q1i and anticipated price difference Q2i according to distance and calculates and recommend reciprocal value Qti；Because inverse apart from satisfaction
Value Q1i and anticipated price difference Q2i is different to the influence degree for recommending reciprocal value, for its prominent influence, now introduces complement value X4 and X5,
Wherein X4+X5=1, X4 < X5；X4 and X5 is preset value；
S9: assigning X4 to Q1i, assigns X5 to Q2i, is calculated according to formula Qti=Q1i*X4-Q2i*X5, i=1...n
To recommendation reciprocal value Qti；Qti and Di is corresponded；
S10: reciprocal value Qti will be recommended to be ranked up according to ascending sequence, by the recommendation reciprocal value Qti of ranking front three
Corresponding house for rent information flag to be confirmed is to recommend house for rent information；
S11: house for rent information will be recommended to be pushed to publication, and this asks the house for renting information to rent on end, pushes away when user confirms to rent
It is to have rented information, while information will have been rented and be transferred to pair for the house for rent information flag when recommending any house for rent information in house for rent information
It should upload on the house to let end of the house for rent information, and rent information corresponding house for rent information deletion this；When user does not rent
With recommending when any house for rent information in house for rent information, then according to reciprocal value Qti is recommended, sequence is successively recommended from small to large；
S12: the house for rent information can be equally included in calculating as long as house for rent information intentionally uploads in above-mentioned steps and examined
Consider range；
S13: optionally it is next ask to rent information and repeat step S1-S13 all seek rent information until having handled；
Step 3: recommended when renting information bar number more than or equal to preset value X1 according to inertia matched pair technique；Inertia is matched
To method specific manifestation are as follows:
S1: all correspondences rented in information are got and ask rent information and house for rent information, information will be rented and believed according to house for rent
Breath is classified, and renting the house for rent information pair in the same house information and should be summarized as renting group information obtains several rent
Group information；
S2: optional house for rent information；
S3: it gets its and passing rents group information；
S4: getting all of its inside and rent information, will rent in information and rent information being asked to extract, according to individual
Occupation and working place of the information extraction to the lodger；
S5: the occupation of all lodgers being summarized to come, and calculates each occupation in all duties rented in information
Industry accounting rate, by professional accounting rate ranking first three occupation label for occupation；
S6: according to the house location in the working place of all lodgers and corresponding house for rent information, house position is calculated
The mean value in working place is set, and the mean value is labeled as renting away from mean value；
S7: according to the house specifying information in house for rent information to asking rent information to carry out preliminary screening, will specifically believe with house
Ceasing unmatched target house type information correspondence asks rent information to be rejected for the first time；
S8: get it is remaining after progress is rejected for the first time seek rent information, it is surplus after being rejected for the first time according to tendency occupation
It is remaining ask to rent personal information in information and reject the unmatched progress of occupation again obtain to be confirmed seeking rent information；It is asked to be confirmed
Rent information flag is Ai, i=1...n；
S9: getting the working place to be confirmed asked and rented in information in personal information, gets working ground in conjunction with map office
Point arrives the working distance of house location；Working distance is subtracted into rent and obtains distance difference group away from mean value, by distance difference group echo
For Jci, i=1...n；Jci and Ai is corresponded；
S10: it is pre- that the house rent to be confirmed for asking the target price information for renting information to subtract the house for rent information is obtained into rent
Phase difference；And the expected difference of rent is labeled as Zci, i=1...n；Zci and Ai is corresponded；
S11: it calculates and recommends reciprocal value Tni=Jci-Zci, i=1...n；Reciprocal value will be recommended to be ranked up from small to large, will be pushed away
It recommends reciprocal value the smallest three to be confirmed ask and rents information flag to recommend to seek rent information；
S12: the house for rent information is transferred to the corresponding house rental end for recommending to ask rent information user, when any user selects
When renting then by the house for rent information removing and formed lease information；Then continue when there is no user's selection according to recommend reciprocal value from small
Recommended to big sequence；
S13: optionally next house for rent information repeats step S3-S13, until all house for rent information recommendations are completed；
The data match module is transferred to processor for will rent information and leased information, and the processor is used for will
It has rented information and has leased information and be transferred to display unit and shown, the processor is for will rent information and leased information biography
It is defeated to carry out real-time storage to storage unit.
Further, it includes renting information uploading unit, second processor and second display that end is rented in the house；
Wherein, the rental information uploading unit will be for that will ask rent information to be transferred to information of lease library；The Data Matching
Module is transferred to second processor for the information that will put out to lease, and the second processor is transferred to the second display for the information that will put out to lease
Device carries out real-time display, and user uploads echo message to second processor by renting information uploading unit, and echo message includes
Selection rents the corresponding house of house for rent information and does not rent the corresponding house of house for rent information, and the second processor will be for that will return
Answer information back to data match module；
The data match module is also used to that house for rent information is recommended to be transferred to second processor, and the second processor is used for
House for rent information will be recommended to be transferred to second display and carry out real-time display, user uploads reaction letter by renting information uploading unit
Second processor is ceased, reaction information includes that selection rents which corresponding corresponding house of recommendation house for rent information and do not receive institute
Some recommendation house for rent information, the second processor are used to reaction information returning to data match module.
Further, the house to let end includes rental information uploading unit, third processor and third display；
Wherein, the rental information uploading unit is transferred to information of lease library for the information that will put out to lease；The Data Matching
Module is transferred to third processor for will rent information, and the third processor is transferred to third for will rent information and shows
Device carries out real-time display.
Beneficial effects of the present invention:
(1) present invention is by the setting of data match module and matching rule unit, pass through in data sample deficiency by
It asks rent information and rental information to be matched by dependency rule, the rental information that matching is completed is transferred to house and rents end,
It allows user independently to select rental information, is formed if lease process is completed and rent information；In the process according to the mesh of user
Cursor position and working place and at heart anticipated price, which are formed, recommends reciprocal value, determines that user may be inclined to choosing according to recommendation reciprocal value
The house selected；
(2) setting for passing through information of lease library simultaneously, can store house lease situation of the passing user in this system,
Obtain several and rent information, and asked according to all correspondences rented in information and rent information and house for rent information, will rent information by
Classify according to house for rent information, renting the house for rent information pair in the same house information and should be summarized as renting group information obtains
It is several to rent group information, it forms data and analyzes sample；
(3) occupation of all lodgers is summarized by the present invention according to after the data sample of formation, and is calculated often
One occupation is in all professional accounting rates rented in information, and by professional accounting rate ranking, first three occupation being marked as duty
Industry；Later according to the house location in the working place of all lodgers and corresponding house for rent information, house location is calculated and arrives
The mean value in working place, and the mean value is labeled as renting away from mean value；Further according to house for rent information in house specifying information to ask rent
Information carries out preliminary screening, and rent information corresponding with the unmatched target house type information of house specifying information will be asked to be picked for the first time
It removes；Get later progress for the first time reject after it is remaining seek rent information, according to tendency occupation will reject for the first time after it is remaining
It asks to rent personal information in information and reject the unmatched progress of occupation again and obtains to be confirmed seeking rent information；Finally in conjunction with correlation
Algorithm and rule obtain the recommendation reciprocal value to be confirmed asked and rent information, and the house for rent information for recommending reciprocal value small is transferred to corresponding recommendation and is asked
End is rented in the house for renting information user, and user is allowed independently to select to rent house, to complete to rent process；The present invention simply has
Effect, and it is easy to practical.
Detailed description of the invention
In order to facilitate the understanding of those skilled in the art, the present invention will be further described below with reference to the drawings.
Fig. 1 is system block diagram of the invention.
Specific embodiment
As shown in Figure 1, a kind of intelligent house leasing system based on big data, including house rent end, information of lease library,
House to let end, data match module, matching rule unit, map office, processor, display unit, storage unit and data are defeated
Enter unit；
Wherein, it is the personal portable terminals for needing rental estates child user that end is rented in the house；End is rented in the house
For user publication ask rent information use, it is described ask rent information include target house type information, target position information, target price information
And personal information, the personal information include occupation and working place；
Wherein, the house to let end is the portable mobile termianl that house needs the user hired out to use, the house
It hires out end and uploads house for rent information use for house owner, the house for rent information includes house location, house rent and house tool
Body information, house specifying information are the pattern that house house type is several Rooms in several rooms；
End is rented for rent information will to be asked to be transferred to information of lease library in the house, and the house to let end will be for that will put out to lease
Information is transferred to information of lease library；The information of lease library is used to receive house and rents seeking rent information and carrying out real-time for end transmission
Storage, the information of lease library are used to receive the house for rent information of house to let end transmission and carry out real-time storage；The lease letter
Breath library, which is also used to store, rents information group, it is described rent information group and include several rent information, renting information is to be using this
What system was completed to rent a house rents situation, and renting information includes that corresponding ask rents information, house for rent information and rental period, rents defining for information
House is rented to stopping renting and renting information for one for user；
The data match module communication link is connected to information of lease library, matching rule unit and map office, the data
It is used to combine matching rule unit and map office to the house for rent information in information of lease library with module and rent information is asked to match,
The matching rule element memory contains rental matching rule, and specific rules show themselves in that
Step 1: it gets to rent in information group and rents information, the item number for renting information is counted；
Step 2: determining the item number for renting information, as required when renting information bar number less than preset value X1
Matched pair technique is matched；Need matched pair technique specific manifestation are as follows:
S1: get it is any seek rent information, and get this ask rent information in target house type information, target position letter
Breath, target price information and personal information；
S2: house location, house rent and the house specifying information in all house for rent information are got later；
S3: the house specifying information in house for rent information is got, is carried out according to target house type information and house specifying information
Target house type information and the consistent house for rent information flag of house specifying information are house for rent information to be confirmed by preliminary matches；
S4: being Di, i=1...n by house for rent information flag to be confirmed；Get the house position in house for rent information to be confirmed
It sets, it is poor that the target range between target position information and house location is calculated according to target position information combination map office；
And marking target range difference is i=1...n；And Mi and Di is corresponded；
S5: getting the working place in personal information, and base area picture library obtains the upper of working distance location house location
Headway period is from Si, i=1...n；And Si and Di is corresponded；
S6: distance is calculated according to target range difference and working distance and is satisfied with reciprocal value Q1i, i=1...n, distance is satisfied with reciprocal value
It is more big, it indicates the position more does not meet user demand；Because target range difference and working distance adjust the distance and are satisfied with the influence of reciprocal value
Degree is different, for the prominent influence degree, is now not so good as correction value X2 and X3, wherein X2 < X3, and X2+X3=1；It is specific to calculate public affairs
Formula is Q1i=Mi*X3+Si*X2；And Q1i and Di is corresponded；
S7: getting the target price information asked and rented in information, while getting the house of institute's subject to confirmation house for rent information
Rent；Using formula anticipated price difference=target price information-house rent, anticipated price difference is obtained and by anticipated price
Difference is labeled as Q2i, i=1...n；And Ci and Di is corresponded；
S8: it is satisfied with reciprocal value Q1i and anticipated price difference Q2i according to distance and calculates and recommend reciprocal value Qti；Because inverse apart from satisfaction
Value Q1i and anticipated price difference Q2i is different to the influence degree for recommending reciprocal value, for its prominent influence, now introduces complement value X4 and X5,
Wherein X4+X5=1, X4 < X5；X4 and X5 is preset value；
S9: assigning X4 to Q1i, assigns X5 to Q2i, is calculated according to formula Qti=Q1i*X4-Q2i*X5, i=1...n
To recommendation reciprocal value Qti；Qti and Di is corresponded；
S10: reciprocal value Qti will be recommended to be ranked up according to ascending sequence, by the recommendation reciprocal value Qti of ranking front three
Corresponding house for rent information flag to be confirmed is to recommend house for rent information；
S11: house for rent information will be recommended to be pushed to publication, and this asks the house for renting information to rent on end, pushes away when user confirms to rent
It is to have rented information, while information will have been rented and be transferred to pair for the house for rent information flag when recommending any house for rent information in house for rent information
It should upload on the house to let end of the house for rent information, and rent information corresponding house for rent information deletion this；When user does not rent
With recommending when any house for rent information in house for rent information, then according to reciprocal value Qti is recommended, sequence is successively recommended from small to large；
S12: the house for rent information can be equally included in calculating as long as house for rent information intentionally uploads in above-mentioned steps and examined
Consider range；
S13: optionally it is next ask to rent information and repeat step S1-S13 all seek rent information until having handled；
Step 3: recommended when renting information bar number more than or equal to preset value X1 according to inertia matched pair technique；Inertia is matched
To method specific manifestation are as follows:
S1: all correspondences rented in information are got and ask rent information and house for rent information, information will be rented and believed according to house for rent
Breath is classified, and renting the house for rent information pair in the same house information and should be summarized as renting group information obtains several rent
Group information；
S2: optional house for rent information；
S3: it gets its and passing rents group information；
S4: getting all of its inside and rent information, will rent in information and rent information being asked to extract, according to individual
Occupation and working place of the information extraction to the lodger；
S5: the occupation of all lodgers being summarized to come, and calculates each occupation in all duties rented in information
Industry accounting rate, by professional accounting rate ranking first three occupation label for occupation；
S6: according to the house location in the working place of all lodgers and corresponding house for rent information, house position is calculated
The mean value in working place is set, and the mean value is labeled as renting away from mean value；
S7: according to the house specifying information in house for rent information to asking rent information to carry out preliminary screening, will specifically believe with house
Ceasing unmatched target house type information correspondence asks rent information to be rejected for the first time；
S8: get it is remaining after progress is rejected for the first time seek rent information, it is surplus after being rejected for the first time according to tendency occupation
It is remaining ask to rent personal information in information and reject the unmatched progress of occupation again obtain to be confirmed seeking rent information；It is asked to be confirmed
Rent information flag is Ai, i=1...n；
S9: getting the working place to be confirmed asked and rented in information in personal information, gets working ground in conjunction with map office
Point arrives the working distance of house location；Working distance is subtracted into rent and obtains distance difference group away from mean value, by distance difference group echo
For Jci, i=1...n；Jci and Ai is corresponded；
S10: it is pre- that the house rent to be confirmed for asking the target price information for renting information to subtract the house for rent information is obtained into rent
Phase difference；And the expected difference of rent is labeled as Zci, i=1...n；Zci and Ai is corresponded；
S11: it calculates and recommends reciprocal value Tni=Jci-Zci, i=1...n；Reciprocal value will be recommended to be ranked up from small to large, will be pushed away
It recommends reciprocal value the smallest three to be confirmed ask and rents information flag to recommend to seek rent information；
S12: the house for rent information is transferred to the corresponding house rental end for recommending to ask rent information user, when any user selects
When renting then by the house for rent information removing and formed lease information；Then continue when there is no user's selection according to recommend reciprocal value from small
Recommended to big sequence；
S13: optionally next house for rent information repeats step S3-S13, until all house for rent information recommendations are completed；
The data match module is transferred to processor for will rent information and leased information, and the processor is used for will
It has rented information and has leased information and be transferred to display unit and shown, the processor is for will rent information and leased information biography
It is defeated to carry out real-time storage to storage unit.
Further, it includes renting information uploading unit, second processor and second display that end is rented in the house；
Wherein, the rental information uploading unit will be for that will ask rent information to be transferred to information of lease library；The Data Matching
Module is transferred to second processor for the information that will put out to lease, and the second processor is transferred to the second display for the information that will put out to lease
Device carries out real-time display, and user uploads echo message to second processor by renting information uploading unit, and echo message includes
Selection rents the corresponding house of house for rent information and does not rent the corresponding house of house for rent information, and the second processor will be for that will return
Answer information back to data match module；
The data match module is also used to that house for rent information is recommended to be transferred to second processor, and the second processor is used for
House for rent information will be recommended to be transferred to second display and carry out real-time display, user uploads reaction letter by renting information uploading unit
Second processor is ceased, reaction information includes that selection rents which corresponding corresponding house of recommendation house for rent information and do not receive institute
Some recommendation house for rent information, the second processor are used to reaction information returning to data match module.
Further, the house to let end includes rental information uploading unit, third processor and third display；
Wherein, the rental information uploading unit is transferred to information of lease library for the information that will put out to lease；The Data Matching
Module is transferred to third processor for will rent information, and the third processor is transferred to third for will rent information and shows
Device carries out real-time display.
A kind of intelligent house leasing system based on big data rents end by house first and house goes out at work
Rent end, which uploads to ask respectively, rents information and house for rent information to information of lease library, is also stored in information of lease library and rents information, is renting
It, will first by the way that rent information and rental information will be asked to match by dependency rule in the case that firmly message sample number is inadequate
The rental information that matching is completed is transferred to house and rents end, and user is allowed independently to select rental information, if lease process is completed
Information is rented in formation；In the case where renting the enough situations of message sample, analyzed by the information of renting to a certain house, in conjunction with
The rental information and personal information of the passing user for renting the house carries out sample analysis, obtains the potential user in the house, it
End is rented into the house that the rental information in corresponding house is transferred to potential user afterwards, is independently selected for user, if rented
It is then formed afterwards and leases information；
Beneficial effects of the present invention are as follows:
(2) present invention is by the setting of data match module and matching rule unit, pass through in data sample deficiency by
It asks rent information and rental information to be matched by dependency rule, the rental information that matching is completed is transferred to house and rents end,
It allows user independently to select rental information, is formed if lease process is completed and rent information；In the process according to the mesh of user
Cursor position and working place and at heart anticipated price, which are formed, recommends reciprocal value, determines that user may be inclined to choosing according to recommendation reciprocal value
The house selected；
(2) setting for passing through information of lease library simultaneously, can store house lease situation of the passing user in this system,
Obtain several and rent information, and asked according to all correspondences rented in information and rent information and house for rent information, will rent information by
Classify according to house for rent information, renting the house for rent information pair in the same house information and should be summarized as renting group information obtains
It is several to rent group information, it forms data and analyzes sample；
(3) occupation of all lodgers is summarized by the present invention according to after the data sample of formation, and is calculated often
One occupation is in all professional accounting rates rented in information, and by professional accounting rate ranking, first three occupation being marked as duty
Industry；Later according to the house location in the working place of all lodgers and corresponding house for rent information, house location is calculated and arrives
The mean value in working place, and the mean value is labeled as renting away from mean value；Further according to house for rent information in house specifying information to ask rent
Information carries out preliminary screening, and rent information corresponding with the unmatched target house type information of house specifying information will be asked to be picked for the first time
It removes；Get later progress for the first time reject after it is remaining seek rent information, according to tendency occupation will reject for the first time after it is remaining
It asks to rent personal information in information and reject the unmatched progress of occupation again and obtains to be confirmed seeking rent information；Finally in conjunction with correlation
Algorithm and rule obtain the recommendation reciprocal value to be confirmed asked and rent information, and the house for rent information for recommending reciprocal value small is transferred to corresponding recommendation and is asked
End is rented in the house for renting information user, and user is allowed independently to select to rent house, to complete to rent process；The present invention simply has
Effect, and it is easy to practical.
Above content is only to structure of the invention example and explanation, affiliated those skilled in the art couple
Described specific embodiment does various modifications or additions or is substituted in a similar manner, without departing from invention
Structure or beyond the scope defined by this claim, is within the scope of protection of the invention.
Claims (3)
1. a kind of intelligent house leasing system based on big data, which is characterized in that including house rent end, information of lease library,
House to let end, data match module, matching rule unit, map office, processor, display unit, storage unit and data are defeated
Enter unit；
Wherein, it is the personal portable terminals for needing rental estates child user that end is rented in the house；The house is rented end and is used for
User's publication, which is asked, rents information and uses, described to ask that rent information include target house type information, target position information, target price information and a
People's information, the personal information include occupation and working place；
Wherein, the house to let end is the portable mobile termianl that house needs the user hired out to use, the house to let
End uploads house for rent information for house owner and uses, and the house for rent information includes that house location, house rent and house are specifically believed
Breath, house specifying information is the pattern that house house type is several Rooms in several rooms；
End is rented for rent information will to be asked to be transferred to information of lease library in the house, and the house to let end is for the information that will put out to lease
It is transferred to information of lease library；The information of lease library is used to receive house and rents seeking rent information and being deposited in real time for end transmission
Storage, the information of lease library are used to receive the house for rent information of house to let end transmission and carry out real-time storage；The information of lease
Library, which is also used to store, rents information group, it is described rent information group and include several rent information, renting information is using this system
That completes to rent a house rents situation, and renting information includes that corresponding ask rents information, house for rent information and rental period, rents being defined as information
User rents house to stopping renting and renting information for one；
The data match module communication link is connected to information of lease library, matching rule unit and map office, the Data Matching mould
Block is used to combine matching rule unit and map office to the house for rent information in information of lease library and rent information is asked to match, described
Matching rule element memory contains rental matching rule, and specific rules show themselves in that
Step 1: it gets to rent in information group and rents information, the item number for renting information is counted；
Step 2: determining the item number for renting information, matches as required when renting information bar number less than preset value X1
Method is matched；Need matched pair technique specific manifestation are as follows:
S1: get it is any seek rent information, and get this ask rent information in target house type information, target position information, mesh
Mark pricing information and personal information；
S2: house location, house rent and the house specifying information in all house for rent information are got later；
S3: getting the house specifying information in house for rent information, is carried out according to target house type information and house specifying information preliminary
Target house type information and the consistent house for rent information flag of house specifying information are house for rent information to be confirmed by matching；
S4: being Di, i=1...n by house for rent information flag to be confirmed；Get the house location in house for rent information to be confirmed, root
It is poor that the target range between target position information and house location is calculated according to target position information combination map office；And by mesh
Gauge length deviation is labeled as Mi, i=1...n；And Mi and Di is corresponded；
S5: getting the working place in personal information, and base area picture library obtains the upper headway period of working distance location house location
From Si, i=1...n；And Si and Di is corresponded；
S6: distance is calculated according to target range difference and working distance and is satisfied with reciprocal value Q1i, i=1...n, it is bigger that distance is satisfied with reciprocal value
Then indicate that the position does not meet user demand more；Because target range difference and working distance adjust the distance and are satisfied with the influence degree of reciprocal value
It is different, for the prominent influence degree, now it is not so good as correction value X2 and X3, wherein X2 < X3, and X2+X3=1；Specific formula for calculation is
Q1i=Mi*X3+Si*X2；And Q1i and Di is corresponded；
S7: getting the target price information asked and rented in information, while getting the house rent of institute's subject to confirmation house for rent information；
Using formula anticipated price difference=target price information-house rent, anticipated price difference is obtained and by anticipated price difference
Labeled as Q2i, i=1...n；And Ci and Di is corresponded；
S8: it is satisfied with reciprocal value Q1i and anticipated price difference Q2i according to distance and calculates and recommend reciprocal value Qti；Because distance is satisfied with reciprocal value
Q1i and anticipated price difference Q2i is different to the influence degree for recommending reciprocal value, for its prominent influence, now introduces complement value X4 and X5,
Middle X4+X5=1, X4 < X5；X4 and X5 is preset value；
S9: assigning X4 to Q1i, assigns X5 to Q2i, is calculated and is pushed away according to formula Qti=Q1i*X4-Q2i*X5, i=1...n
Recommend reciprocal value Qti；Qti and Di is corresponded；
S10: will recommend reciprocal value Qti to be ranked up according to ascending sequence, and the recommendation reciprocal value Qti of ranking front three is corresponding
House for rent information flag to be confirmed be recommend house for rent information；
S11: house for rent information will be recommended to be pushed to publication, and this asks the house for renting information to rent on end, recommends to recruit when user confirms to rent
It is to have rented information, while information will have been rented and be transferred on corresponding for the house for rent information flag when any house for rent information rented in information
It passes on the house to let end of the house for rent information, and has rented information corresponding house for rent information deletion this；It is pushed away when user does not rent
Then according to reciprocal value Qti is recommended, sequence is successively recommended from small to large when recommending any house for rent information in house for rent information；
S12: the house for rent information can be equally included in as long as house for rent information intentionally uploads in above-mentioned steps and model is calculated and considered
It encloses；
S13: optionally it is next ask to rent information and repeat step S1-S13 all seek rent information until having handled；
Step 3: recommended when renting information bar number more than or equal to preset value X1 according to inertia matched pair technique；Inertia matched pair technique
Specific manifestation are as follows:
S1: get all correspondences rented in information ask rent information and house for rent information, will rent information according to house for rent information into
Row classification, renting the house for rent information pair in the same house information and should be summarized as renting group information obtain several rent and group believe
Breath；
S2: optional house for rent information；
S3: it gets its and passing rents group information；
S4: getting all of its inside and rent information, will rent in information and rent information being asked to extract, according to personal information
Extract occupation and the working place of the lodger；
S5: the occupation of all lodgers being summarized to come, and is calculated each occupation and accounted in all occupations rented in information
Ratio, by professional accounting rate ranking first three occupation label for occupation；
S6: according to the house location in the working place of all lodgers and corresponding house for rent information, house location is calculated and arrives
The mean value in working place, and the mean value is labeled as renting away from mean value；
S7:, will be with house specifying information not according to the house specifying information in house for rent information to asking rent information to carry out preliminary screening
Matched target house type information is corresponding to ask rent information to be rejected for the first time；
S8: get progress for the first time reject after it is remaining seek rent information, according to tendency occupation will reject for the first time after it is remaining
It asks to rent personal information in information and reject the unmatched progress of occupation again and obtains to be confirmed seeking rent information；Rent is asked to believe by be confirmed
Breath is labeled as Ai, i=1...n；
S9: getting the working place to be confirmed asked and rented in information in personal information, gets working place in conjunction with map office and arrives
The working distance of house location；Working distance is subtracted into rent and obtains distance difference group away from mean value, is by distance difference group echo
Jci, i=1...n；Jci and Ai is corresponded；
S10: it is poor that the house rent to be confirmed for asking the target price information for renting information to subtract the house for rent information is obtained into rent expection
Value；And the expected difference of rent is labeled as Zci, i=1...n；Zci and Ai is corresponded；
S11: it calculates and recommends reciprocal value Tni=Jci-Zci, i=1...n；Reciprocal value will be recommended to be ranked up from small to large, will be recommended inverse
It is worth the smallest three to be confirmed ask and rents information flag to recommend to seek rent information；
S12: the house for rent information is transferred to the corresponding house rental end for recommending to ask rent information user, when any user selection is rented
Shi Ze is by the house for rent information removing and information is leased in formation；Then continue according to recommendation reciprocal value from small to large when there is no user's selection
Sequence is recommended；
S13: optionally next house for rent information repeats step S3-S13, until all house for rent information recommendations are completed；
The data match module is transferred to processor for will rent information and leased information, and the processor is for will rent
It information and leases information and is transferred to display unit and shown, the processor is transferred to for will rent information and leased information
Storage unit carries out real-time storage.
2. a kind of intelligent house leasing system based on big data according to claim 1, which is characterized in that the house
Renting end includes renting information uploading unit, second processor and second display；
Wherein, the rental information uploading unit will be for that will ask rent information to be transferred to information of lease library；The data match module
Be transferred to second processor for the information that will put out to lease, the second processor for the information that will put out to lease be transferred to second display into
Row real-time display, user upload echo message to second processor by renting information uploading unit, and echo message includes selection
It rents the corresponding house of house for rent information and does not rent the corresponding house of house for rent information, the second processor will be for that will respond letter
Breath returns to data match module；
The data match module is also used to that house for rent information is recommended to be transferred to second processor, and the second processor will be for that will push away
It recommends house for rent information and is transferred to second display progress real-time display, user is arrived by renting information uploading unit upload reaction information
Second processor, reaction information include that selection is rented which corresponding corresponding house of recommendation house for rent information and do not received all
Recommend house for rent information, the second processor is used to reaction information returning to data match module.
3. a kind of intelligent house leasing system based on big data according to claim 1, which is characterized in that the house
Hiring out end includes rental information uploading unit, third processor and third display；
Wherein, the rental information uploading unit is transferred to information of lease library for the information that will put out to lease；The data match module
Be transferred to third processor for information will to have been rented, the third processor for will rent information be transferred to third display into
Row real-time display.
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| CN201811565074.4A CN109711934A (en) | 2018-12-20 | 2018-12-20 | A kind of intelligent house leasing system based on big data |
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| CN201811565074.4A CN109711934A (en) | 2018-12-20 | 2018-12-20 | A kind of intelligent house leasing system based on big data |
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| CN201811565074.4A Pending CN109711934A (en) | 2018-12-20 | 2018-12-20 | A kind of intelligent house leasing system based on big data |
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| CN110111216A (en) * | 2019-05-13 | 2019-08-09 | 西南民族大学 | A kind of service platform in the school based on cloud computing |
| CN110619027A (en) * | 2019-06-18 | 2019-12-27 | 北京无限光场科技有限公司 | House source information recommendation method and device, terminal equipment and medium |
| CN112114725A (en) * | 2019-11-05 | 2020-12-22 | 上海旅家网络信息技术有限公司 | Customizable house finding software operation method |
| CN112561625A (en) * | 2020-11-13 | 2021-03-26 | 广州市美果科技有限公司 | Unmanned house renting management system and operation method |
| CN112667687A (en) * | 2020-12-31 | 2021-04-16 | 中国建设银行股份有限公司 | House rental data processing method, device, equipment and readable storage medium |
| CN115204991A (en) * | 2022-09-14 | 2022-10-18 | 深圳市房帮帮互联网科技有限公司 | Real estate information visualization analysis system and method based on web crawler |
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| CN115204991A (en) * | 2022-09-14 | 2022-10-18 | 深圳市房帮帮互联网科技有限公司 | Real estate information visualization analysis system and method based on web crawler |
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Application publication date: 20190503 |