CN113191836A - Community tenant management method and system based on user habit analysis - Google Patents

Community tenant management method and system based on user habit analysis Download PDF

Info

Publication number
CN113191836A
CN113191836A CN202110314843.9A CN202110314843A CN113191836A CN 113191836 A CN113191836 A CN 113191836A CN 202110314843 A CN202110314843 A CN 202110314843A CN 113191836 A CN113191836 A CN 113191836A
Authority
CN
China
Prior art keywords
tenant
landlord
renting
acceptance
historical
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110314843.9A
Other languages
Chinese (zh)
Inventor
不公告发明人
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Terminus Technology Group Co Ltd
Original Assignee
Terminus Technology Group Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Terminus Technology Group Co Ltd filed Critical Terminus Technology Group Co Ltd
Priority to CN202110314843.9A priority Critical patent/CN113191836A/en
Publication of CN113191836A publication Critical patent/CN113191836A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0645—Rental transactions; Leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0605—Supply or demand aggregation

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Finance (AREA)
  • General Physics & Mathematics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The embodiment of the application provides a community tenant management method and system based on user habit analysis. The method comprises the following steps: obtaining the historical renting characteristics of the tenant by performing characteristic extraction on the historical renting records of the tenant; obtaining the current habit information of the tenant by carrying out questionnaire type survey on the tenant; performing feature extraction on the current habit information of the tenant to obtain the current renting feature of the tenant; performing feature fusion on the historical renting features and the current renting features to obtain tenant habit features; carrying out feature extraction on the tenant habits accepted by the landlord to obtain landlord acceptance features; matching the habit features of the tenant and the landlord acceptance features, and returning a matching result according to the matching degree. According to the community landlord matching method and the community landlord matching system, accuracy and efficiency of community landlords and tenants are improved through user habit analysis.

Description

Community tenant management method and system based on user habit analysis
Technical Field
The application relates to the field of user habit analysis and community tenant management, in particular to a community tenant management method and system based on user habit analysis.
Background
The community tenant management is an important link of community safety, and as tenants belong to floating population, the daily activity habits, historical renting habits and the like of the tenants become important points concerned by landlords, so that the user habits of the tenants need to be analyzed when the tenants are selected, and the most appropriate landlords are matched. The current tenant management process is mainly matched manually, the accuracy is low, and the habit characteristics of tenants and landlords cannot be met comprehensively. Therefore, a method and a system for managing community tenants based on user habit analysis are needed.
Disclosure of Invention
In view of this, the present application aims to provide a community tenant management method and system based on user habit analysis, which improve the automation level of community security monitoring and solve the technical problems of low intelligence level, excessive dependence of manual participation and the like in the current community security monitoring process.
Based on the above purpose, the present application provides a community tenant management method based on user habit analysis, including:
obtaining the historical renting characteristics of the tenant by performing characteristic extraction on the historical renting records of the tenant; obtaining the current habit information of the tenant by carrying out questionnaire type survey on the tenant; performing feature extraction on the current habit information of the tenant to obtain the current renting feature of the tenant;
performing feature fusion on the historical renting features and the current renting features to obtain tenant habit features;
carrying out feature extraction on the tenant habits accepted by the landlord to obtain landlord acceptance features;
and matching the tenant habit characteristics with the landlord acceptance characteristics, and returning the matching result according to the matching degree.
In some embodiments, the method further comprises:
after checking the matching result, the landlord puts forward additional acceptance information; extracting the characteristics of the additional acceptance information to obtain additional acceptance characteristics;
merging the additional acceptance feature with the landlord acceptance feature to obtain an updated acceptance feature; and updating and matching the tenant habit characteristics and the updating and receiving characteristics, and returning the updating and matching result according to the matching degree.
In some embodiments, obtaining the historical rental characteristics of the tenant by performing characteristic extraction on the historical rental records of the tenant comprises:
according to characteristic dimensions including renting time, renting house size, renting house position and house owner evaluation, a text recognition method is adopted, and the initial selection historical characteristics are obtained by performing characteristic extraction on historical renting records of tenants;
and carrying out quantitative processing on the primarily selected historical characteristics to obtain the historical renting characteristics.
In some embodiments, performing feature fusion on the historical rental features and the current rental features to obtain tenant habit features includes:
according to each feature dimension of the current renting feature, performing feature decomposition on the historical renting feature to obtain a historical renting decomposition feature;
and performing weighted summation operation on the historical renting decomposition characteristics and the corresponding current renting characteristics in each characteristic dimension to obtain the tenant habit characteristics.
In some embodiments, the characteristic extraction of the tenant habits accepted by the landlord to obtain landlord acceptance characteristics comprises:
according to characteristic dimensions including renting time, house renting size, house renting position and tenant contraindication, characteristic extraction is carried out on historical renting records of the landlord to obtain a landlord positive acceptance characteristic and a landlord negative acceptance characteristic;
and carrying out quantization processing on the landlord positive acceptance characteristic and the landlord negative acceptance characteristic to obtain the landlord acceptance characteristic.
In some embodiments, matching the tenant habit characteristics and the landlord acceptance characteristics, and returning the matching result according to the matching degree includes:
setting a priority and a tolerance interval for the landlord acceptance characteristics, and matching the tenant habit characteristics with the landlord acceptance characteristics according to the priority sequence;
and in the matching process, if the deviation of the tenant habit characteristics and the landlord acceptance characteristics is within the range of the tolerance interval, the matching is considered to be successful.
In some embodiments, the historical lease resolution characteristics and the corresponding current lease characteristics are subjected to a weighted summation operation, which is calculated by the following formula:
Figure BDA0002990747670000031
where C is the result of the weighted sum, i is the feature number, ωiIs a weighting coefficient of the ith feature, fiM is the total number of the historical lease decomposition feature and the corresponding current lease feature.
Based on the above purpose, the present application further provides a community tenant management system based on user habit analysis, including:
the tenant characteristic extraction module is used for extracting characteristics of historical tenant records of tenants to obtain historical tenant characteristics of the tenants; obtaining the current habit information of the tenant by carrying out questionnaire type survey on the tenant; performing feature extraction on the current habit information of the tenant to obtain the current renting feature of the tenant;
the characteristic fusion module is used for carrying out characteristic fusion on the historical renting characteristics and the current renting characteristics to obtain tenant habit characteristics;
the landlord feature extraction module is used for extracting features of the tenant habits accepted by landlords to obtain landlord acceptance features;
and the characteristic matching module is used for matching the tenant habit characteristics with the landlord acceptance characteristics and returning the matching result according to the matching degree.
In some embodiments, the system further comprises:
the additional characteristic acquisition module is used for proposing additional acceptance information after the landlord checks the matching result; extracting the characteristics of the additional acceptance information to obtain additional acceptance characteristics;
the characteristic merging module is used for merging the additional acceptance characteristic and the landlord acceptance characteristic to obtain an updated acceptance characteristic; and updating and matching the tenant habit characteristics and the updating and receiving characteristics, and returning the updating and matching result according to the matching degree.
In some embodiments, the tenant feature extraction module comprises:
the system comprises a primary selection unit, a database unit and a database unit, wherein the primary selection unit is used for extracting the characteristics of the historical rental records of tenants by adopting a text recognition method according to the characteristic dimensions including the rental time, the rental size, the rental position and the landlord evaluation to obtain the primary selection historical characteristics;
and the quantization unit is used for performing quantization processing on the primarily selected historical characteristics to obtain the historical renting characteristics.
In general, the advantages of the present application and the experience brought to the user are: through the user habit analysis method, the historical renting habits of the tenants and the current renting characteristics are fused and matched with the acceptance standard of the landlord, the tenants and the landlord are more accurately paired, and the accuracy and the intelligence of house renting recommendation management are improved.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 illustrates a flowchart of a community tenant management method based on user habit analysis according to an embodiment of the present invention.
Fig. 2 illustrates a flowchart of a community tenant management method based on user habit analysis according to an embodiment of the present invention.
Fig. 3 illustrates a constitutional diagram of a community tenant management system based on user habit analysis according to an embodiment of the present invention.
Fig. 4 illustrates a constitutional diagram of a community tenant management system based on user habit analysis according to an embodiment of the present invention.
Fig. 5 is a block diagram showing a tenant feature extraction module according to an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 illustrates a flowchart of a community tenant management method based on user habit analysis according to an embodiment of the present invention. As shown in fig. 1, the community tenant management method based on user habit analysis includes:
step S11, extracting characteristics of the historical rental records of the tenants to obtain the historical rental characteristics of the tenants; obtaining the current habit information of the tenant by carrying out questionnaire type survey on the tenant; and performing feature extraction on the current habit information of the tenant to obtain the current renting feature of the tenant.
Specifically, the historical lease record of the tenant reflects the lease habit of the tenant for a long time, but when the tenant searches for a new house, some new requirements, which are called current habits, may be provided according to the past lease experience or the current new requirements. Under the condition that the historical renting characteristics of the tenant are combined with the current renting characteristics, the renting habits of the tenant can be more accurately evaluated, and therefore more suitable landlors and houses can be matched.
In one embodiment, the obtaining of the historical rental characteristics of the tenant through characteristic extraction of the historical rental records of the tenant comprises:
according to characteristic dimensions including renting time, renting house size, renting house position and house owner evaluation, a text recognition method is adopted, and the initial selection historical characteristics are obtained by performing characteristic extraction on historical renting records of tenants;
and carrying out quantitative processing on the primarily selected historical characteristics to obtain the historical renting characteristics.
And step S12, performing feature fusion on the historical renting features and the current renting features to obtain tenant habit features.
Specifically, the purpose of feature fusion is to combine the historical rental features and the current rental features of the tenant together, and comprehensively evaluate the living habits and requirements of the tenant. By the weighting method, the proportion of different characteristics can be flexibly adjusted according to different requirements.
In one embodiment, the performing feature fusion on the historical rental feature and the current rental feature to obtain the tenant habit feature includes:
according to each feature dimension of the current renting feature, performing feature decomposition on the historical renting feature to obtain a historical renting decomposition feature;
and performing weighted summation operation on the historical renting decomposition characteristics and the corresponding current renting characteristics in each characteristic dimension to obtain the tenant habit characteristics.
In one embodiment, the historical lease decomposition characteristics and the corresponding current lease characteristics are subjected to weighted summation operation, and the weighted summation operation is calculated by the following formula:
Figure BDA0002990747670000051
where C is the result of the weighted sum, i is the feature number, ωiIs a weighting coefficient of the ith feature, fiM is the total number of the historical lease decomposition feature and the corresponding current lease feature.
And step S13, performing feature extraction on the tenant habits accepted by the landlord to obtain landlord acceptance features.
In one embodiment, the characteristic extraction of the tenant habits accepted by the landlord to obtain landlord acceptance characteristics comprises the following steps:
according to characteristic dimensions including renting time, house renting size, house renting position and tenant contraindication, characteristic extraction is carried out on historical renting records of the landlord to obtain a landlord positive acceptance characteristic and a landlord negative acceptance characteristic;
and carrying out quantization processing on the landlord positive acceptance characteristic and the landlord negative acceptance characteristic to obtain the landlord acceptance characteristic.
In particular, the landlord positive acceptance feature and the landlord negative acceptance feature differ in the direction of subjective inclination of the landlord. For example, if the landlord likes clean and tidy tenants, the landlord considers the feature of positive acceptance; the landlord finds that the landlord belongs to the negative acceptance characteristic when the landlord dislikes the tenants with complex social relations.
And step S14, matching the tenant habit characteristics and the landlord acceptance characteristics, and returning the matching result according to the matching degree.
In one embodiment, matching the tenant habit characteristics and the landlord acceptance characteristics, and returning the matching result according to the matching degree, includes:
setting a priority and a tolerance interval for the landlord acceptance characteristics, and matching the tenant habit characteristics with the landlord acceptance characteristics according to the priority sequence;
and in the matching process, if the deviation of the tenant habit characteristics and the landlord acceptance characteristics is within the range of the tolerance interval, the matching is considered to be successful.
In particular, the purpose of setting the priorities is that not all of the landlords may be able to match a tenant one hundred percent, and therefore it is necessary to prioritize different features, which should be considered more during the matching process because the landlord or tenant is more looking heavily.
Fig. 2 illustrates a flowchart of a community tenant management method based on user habit analysis according to an embodiment of the present invention. As shown in fig. 2, the community tenant management method based on user habit analysis further includes:
step S15, after checking the matching result, the landlord puts forward additional acceptance information; and performing feature extraction on the additional acceptance information to obtain additional acceptance features.
In particular, the present invention relates to a method for producing,
step S16, combining the additional acceptance feature and the landlord acceptance feature to obtain an updated acceptance feature; and updating and matching the tenant habit characteristics and the updating and receiving characteristics, and returning the updating and matching result according to the matching degree.
In particular, the present invention relates to a method for producing,
fig. 3 illustrates a constitutional diagram of a community tenant management system based on user habit analysis according to an embodiment of the present invention. As shown in fig. 3, the community tenant management system based on user habit analysis may be divided into:
the tenant characteristic extraction module 31 is configured to perform characteristic extraction on historical tenant records of tenants to obtain historical tenant characteristics of the tenants; obtaining the current habit information of the tenant by carrying out questionnaire type survey on the tenant; performing feature extraction on the current habit information of the tenant to obtain the current renting feature of the tenant;
a feature fusion module 32, configured to perform feature fusion on the historical rental features and the current rental features to obtain tenant habit features;
the landlord feature extraction module 33 is configured to perform feature extraction on a tenant habit received by a landlord to obtain landlord receiving features;
and the feature matching module 34 is configured to match the tenant habit features and the landlord acceptance features, and return the matching result according to the matching degree.
Fig. 4 is a constitutional diagram of a community tenant management system based on user habit analysis according to an embodiment of the present invention. As shown in fig. 4, the community tenant management system based on user habit analysis further includes:
an additional feature obtaining module 35, configured to provide additional acceptance information after the landlord checks the matching result; extracting the characteristics of the additional acceptance information to obtain additional acceptance characteristics;
a feature merging module 36, configured to merge the additional acceptance feature with the landlord acceptance feature to obtain an updated acceptance feature; and updating and matching the tenant habit characteristics and the updating and receiving characteristics, and returning the updating and matching result according to the matching degree.
Fig. 5 is a block diagram showing a tenant feature extraction module according to an embodiment of the present invention. As shown in fig. 5, the tenant feature extraction module 31 includes:
the primary selection unit 311 is configured to obtain primary selection historical characteristics by performing characteristic extraction on historical rental records of tenants by adopting a text recognition method according to characteristic dimensions including rental time, rental size, rental position, and landlord evaluation;
and a quantizing unit 312, configured to perform quantization processing on the primarily selected historical features to obtain the historical renting features.
The functions of the modules in the systems in the embodiments of the present application may refer to the corresponding descriptions in the above methods, and are not described herein again.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (10)

1. A community tenant management method based on user habit analysis is characterized by comprising the following steps:
obtaining the historical renting characteristics of the tenant by performing characteristic extraction on the historical renting records of the tenant; obtaining the current habit information of the tenant by carrying out questionnaire type survey on the tenant; performing feature extraction on the current habit information of the tenant to obtain the current renting feature of the tenant;
performing feature fusion on the historical renting features and the current renting features to obtain tenant habit features;
carrying out feature extraction on the tenant habits accepted by the landlord to obtain landlord acceptance features;
and matching the tenant habit characteristics with the landlord acceptance characteristics, and returning the matching result according to the matching degree.
2. The method of claim 1, further comprising:
after checking the matching result, the landlord puts forward additional acceptance information; extracting the characteristics of the additional acceptance information to obtain additional acceptance characteristics;
merging the additional acceptance feature with the landlord acceptance feature to obtain an updated acceptance feature; and updating and matching the tenant habit characteristics and the updating and receiving characteristics, and returning the updating and matching result according to the matching degree.
3. The method according to claim 1, wherein obtaining the historical rental characteristics of the tenant by performing characteristic extraction on historical rental records of the tenant comprises:
according to characteristic dimensions including renting time, renting house size, renting house position and house owner evaluation, a text recognition method is adopted, and the initial selection historical characteristics are obtained by performing characteristic extraction on historical renting records of tenants;
and carrying out quantitative processing on the primarily selected historical characteristics to obtain the historical renting characteristics.
4. The method according to claim 1, wherein performing feature fusion on the historical rental features and the current rental features to obtain tenant habit features comprises:
according to each feature dimension of the current renting feature, performing feature decomposition on the historical renting feature to obtain a historical renting decomposition feature;
and performing weighted summation operation on the historical renting decomposition characteristics and the corresponding current renting characteristics in each characteristic dimension to obtain the tenant habit characteristics.
5. The method of claim 1, wherein the step of performing feature extraction on the tenant habits accepted by the landlord to obtain landlord acceptance features comprises the following steps:
according to characteristic dimensions including renting time, house renting size, house renting position and tenant contraindication, characteristic extraction is carried out on historical renting records of the landlord to obtain a landlord positive acceptance characteristic and a landlord negative acceptance characteristic;
and carrying out quantization processing on the landlord positive acceptance characteristic and the landlord negative acceptance characteristic to obtain the landlord acceptance characteristic.
6. The method according to claim 1, wherein matching the tenant habit characteristics and the landlord acceptance characteristics, and returning the matching result according to the matching degree comprises:
setting a priority and a tolerance interval for the landlord acceptance characteristics, and matching the tenant habit characteristics with the landlord acceptance characteristics according to the priority sequence;
and in the matching process, if the deviation of the tenant habit characteristics and the landlord acceptance characteristics is within the range of the tolerance interval, the matching is considered to be successful.
7. The method of claim 4, wherein the historical lease resolution characteristics and the corresponding current lease characteristics are subjected to a weighted summation operation, which is calculated by the following formula:
Figure FDA0002990747660000021
where C is the result of the weighted sum, i is the feature number, ωiIs a weighting coefficient of the ith feature, fiM is the total number of the historical lease decomposition feature and the corresponding current lease feature.
8. A community tenant management system based on user habit analysis, comprising:
the tenant characteristic extraction module is used for extracting characteristics of historical tenant records of tenants to obtain historical tenant characteristics of the tenants; obtaining the current habit information of the tenant by carrying out questionnaire type survey on the tenant; performing feature extraction on the current habit information of the tenant to obtain the current renting feature of the tenant;
the characteristic fusion module is used for carrying out characteristic fusion on the historical renting characteristics and the current renting characteristics to obtain tenant habit characteristics;
the landlord feature extraction module is used for extracting features of the tenant habits accepted by landlords to obtain landlord acceptance features;
and the characteristic matching module is used for matching the tenant habit characteristics with the landlord acceptance characteristics and returning the matching result according to the matching degree.
9. The system of claim 8, further comprising:
the additional characteristic acquisition module is used for proposing additional acceptance information after the landlord checks the matching result; extracting the characteristics of the additional acceptance information to obtain additional acceptance characteristics;
the characteristic merging module is used for merging the additional acceptance characteristic and the landlord acceptance characteristic to obtain an updated acceptance characteristic; and updating and matching the tenant habit characteristics and the updating and receiving characteristics, and returning the updating and matching result according to the matching degree.
10. The system of claim 8, wherein the tenant feature extraction module comprises:
the system comprises a primary selection unit, a database unit and a database unit, wherein the primary selection unit is used for extracting the characteristics of the historical rental records of tenants by adopting a text recognition method according to the characteristic dimensions including the rental time, the rental size, the rental position and the landlord evaluation to obtain the primary selection historical characteristics;
and the quantization unit is used for performing quantization processing on the primarily selected historical characteristics to obtain the historical renting characteristics.
CN202110314843.9A 2021-03-24 2021-03-24 Community tenant management method and system based on user habit analysis Pending CN113191836A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110314843.9A CN113191836A (en) 2021-03-24 2021-03-24 Community tenant management method and system based on user habit analysis

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110314843.9A CN113191836A (en) 2021-03-24 2021-03-24 Community tenant management method and system based on user habit analysis

Publications (1)

Publication Number Publication Date
CN113191836A true CN113191836A (en) 2021-07-30

Family

ID=76973684

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110314843.9A Pending CN113191836A (en) 2021-03-24 2021-03-24 Community tenant management method and system based on user habit analysis

Country Status (1)

Country Link
CN (1) CN113191836A (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103700003A (en) * 2013-12-30 2014-04-02 陶鹏 House online direct renting method and system based on wish conformity matching
CN109087056A (en) * 2018-06-15 2018-12-25 平安科技（深圳）有限公司 Electronic contract signs method, apparatus and server
CN111311378A (en) * 2020-03-22 2020-06-19 厦门造艺科技有限公司 Rental system of idle house
CN112395515A (en) * 2021-01-19 2021-02-23 腾讯科技（深圳）有限公司 Information recommendation method and device, computer equipment and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103700003A (en) * 2013-12-30 2014-04-02 陶鹏 House online direct renting method and system based on wish conformity matching
CN109087056A (en) * 2018-06-15 2018-12-25 平安科技（深圳）有限公司 Electronic contract signs method, apparatus and server
CN111311378A (en) * 2020-03-22 2020-06-19 厦门造艺科技有限公司 Rental system of idle house
CN112395515A (en) * 2021-01-19 2021-02-23 腾讯科技（深圳）有限公司 Information recommendation method and device, computer equipment and storage medium

Similar Documents

Publication Publication Date Title
CN102792332B (en) Image management apparatus, image management method and integrated circuit
CN111709613A (en) Task automatic allocation method and device based on data statistics and computer equipment
CN102656553A (en) Enterprise level data management
CN111445121A (en) Risk assessment method and apparatus, storage medium, and electronic apparatus
Sólymos et al. Evaluating time-removal models for estimating availability of boreal birds during point count surveys: Sample size requirements and model complexity
CN110679114A (en) Method for estimating deletability of data object
CN113487359A (en) Multi-modal feature-based commodity sales prediction method and device and related equipment
KR20210082109A (en) An apparatus for evaluating the value of real estates based on estimations of real transactions of the real estates
CN101908057A (en) Information processing apparatus and information processing method
Davis et al. Errors in aerial survey count data: Identifying pitfalls and solutions
Papakosta et al. Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data
CN113191836A (en) Community tenant management method and system based on user habit analysis
CN116701772A (en) Data recommendation method and device, computer readable storage medium and electronic equipment
CN109766440B (en) Method and system for determining default classification information for object text description
CN116501979A (en) Information recommendation method, information recommendation device, computer equipment and computer readable storage medium
CN109902129A (en) Insurance agent's classifying method and relevant device based on big data analysis
CN116187675A (en) Task allocation method, device, equipment and storage medium
CN115878877A (en) Concept drift-based visual detection method for access crawler of aviation server
CN113935788A (en) Model evaluation method, device, equipment and computer readable storage medium
CN113688206A (en) Text recognition-based trend analysis method, device, equipment and medium
CN110334277B (en) User search behavior identification method and device
CN111104609A (en) Interpersonal relationship prediction method, interpersonal relationship prediction device, computer program, and storage medium
CN114495301B (en) Subway ticket business processing method, device, equipment and storage medium
US10997248B2 (en) Data association using complete lists
Edriss Determinants Of Annual Salary In Canada: Ordered Probit Regression Approach

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination