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Intelligent house renting recommendation system based on big data and working method thereof

A recommendation system and working method technology, applied in data processing applications, sales/lease transactions, instruments, etc., can solve problems such as difficult to verify the number of residents, and achieve the effect of preventing being cheated

Inactive Publication Date: 2020-09-11
苏州商信宝信息科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the tenants of the shared house may bring couples or friends to live in. The actual number of people living in the shared house may be greater than the number agreed in the contract, and the landlord cannot go to the house to check in real time. Even after the landlord has visited, it is difficult to verify the actual number of residents

Method used

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  • Intelligent house renting recommendation system based on big data and working method thereof
  • Intelligent house renting recommendation system based on big data and working method thereof
  • Intelligent house renting recommendation system based on big data and working method thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0084] Such as figure 1 As shown, the present embodiment provides a working method of a big data-based smart renting recommendation system, including the following working steps:

[0085] S101: Receive a renting request submitted by a renting user, the renting request includes the renting area, the renting area, the number of co-tenants and the renting price;

[0086] S102: Extract the registered housing information, the housing information includes housing location, housing area, number of residents and rental price;

[0087] S103: Determine whether the location of the house is within the rental area;

[0088] S104: If yes, determine whether the area of ​​the house is greater than or equal to the area of ​​the rental house;

[0089] S105: If yes, determine whether the rental price is less than or equal to the rental price;

[0090] S106: If yes, determine whether the number of residents is smaller than the number of shared tenants;

[0091] S107: If yes, recommend the hou...

Embodiment 2

[0117] Such as figure 2 As shown, S109 also includes:

[0118] dividing the preset time into a first preset time period and a second preset time period;

[0119] calculating the first frequency of each unique number of people entering and exiting the house within the first preset time period;

[0120] judging whether the first frequency is greater than or equal to a preset frequency;

[0121] If so, calculate the second frequency of each unique number of people entering and leaving the house within the second preset time period;

[0122] judging whether the second frequency is greater than or equal to a preset frequency;

[0123] If yes, execute S111.

[0124] Such as image 3 As shown, within the second preset period, if there is a person who does not appear in the first preset period, the second frequency of the person entering and leaving the house within the second preset period is calculated separately;

[0125] judging whether the second frequency is greater than ...

Embodiment 3

[0132] Such as Figure 4 As shown, after S113, it also includes:

[0133] Extract housing information marked as suspicious information;

[0134] Extract the contact information of the owner or agent who released the house information;

[0135] Send a check-up form to said owner or agent, said check-up form including the true number of occupants;

[0136] A checklist for obtaining responses from said property owner or agent;

[0137] Judging whether the number of residents in the checklist is smaller than the number of shared tenants;

[0138]If so, remove the flag for suspicious information.

[0139] Specifically, when the house information is marked as available information, in order to avoid false marking, the system will contact the owner or intermediary who released the house information to check the actual number of occupants of the house.

[0140] The system sends a check form to the owner or intermediary. The check form requires the owner or intermediary to fill in...

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Abstract

The invention discloses an intelligent house renting recommendation system based on big data and a working method thereof, and the system comprises: a first extraction module which is configured to extract registered house information; a first acquisition module which is configured to acquire house information selected by a house renting user; a personnel detection module which is configured to detect the number of people going in and out of the house; a second extraction module which is configured to extract the number of people who are not repeated; a first calculation module which is configured to calculate the frequency of the non-repetitive people going in and out of the house within a preset time; a fifth judgment module which is configured to judge whether the frequency is greater than or equal to a preset frequency or not; a sixth judgment module which is configured to judge whether the number of non-repetitive people whose in-out frequency is greater than or equal to the preset frequency within the preset time is less than the number of shared renters; and a marking module which is configured to mark the house information of which the in-out frequency is greater than or equal to the preset frequency and the number of non-repetitive people is greater than or equal to the number of joint tenants in the preset time as suspicious information.

Description

technical field [0001] The invention relates to the field of real estate, in particular to a big data-based intelligent house rental recommendation system and its working method. Background technique [0002] With the migration of the population to large and medium-sized cities, the demand for rental housing in large and medium-sized cities continues to expand, and young people will choose shared housing in order to reduce the financial burden. The management of shared housing is difficult, and it is inevitable that different people live in the same apartment Contradictions, especially in shared houses with a large number of people, so renting users will focus on the number of shared renters before renting a house. [0003] However, the tenants of the shared house may bring couples or friends to live in. The actual number of people living in the shared house may be greater than the number agreed in the contract, and the landlord cannot go to the house to check in real time. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06Q50/16
CPCG06Q30/0631G06Q30/0645G06Q50/16
Inventor 徐建红
Owner 苏州商信宝信息科技有限公司
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