CN111652698A - Intelligent house renting system based on block chain - Google Patents
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Abstract
The invention discloses an intelligent housing leasing system based on a block chain, which comprises a data entry module, a data acquisition module, an access statistic module, a data analysis module, a transaction evaluation module, a survey module, a database, a controller, a display module and a post-leasing management module, wherein the data entry module is used for recording data; the method comprises the steps of obtaining basic information of a house and a house basic image through a data entry module, a data acquisition module and a database, counting the number of times of being consulted and the time of being consulted of the house within 10 days before the current time of a system through an access counting module, counting the trading information of the system within 10 days before the current time of the system through a trading module, monitoring the communication condition between a lessor of the house and a user at preset time intervals through a surveying module, and obtaining a value of interest of the house, a house renting attraction value of a corresponding region, a selling price bias value of the house, an active credit value of the house lessor and a renting reference value of the house according to a related algorithm.
Description
Technical Field
The invention relates to the technical field of house leasing, in particular to an intelligent house leasing system based on a block chain.
Background
The blockchain technology is a technical scheme for collectively maintaining a reliable database in a decentralized and trust-removing mode, and is a bottom-layer technology of digital currencies such as bitcoin, Ethernet currency and the like. In popular terms, the block chain technology refers to a way for people to participate in accounting. And the transaction confirmation on the blockchain is completed by the consensus of all the nodes on the blockchain, and the block is packed and written after the consensus is successful. The blockchain maintains a public account book for storing all transactions on the blockchain network, a database is arranged behind all systems, and a user can regard the database as a big account book. It becomes important who remembers this ledger. At present, who is who the system accounts, the account book of WeChat is in Tencent, and the account book of Taobao is in Ali. This approach we call it blockchain technique.
Since the reform was open, the rapidly developing economy created a large number of employment and entrepreneurship opportunities. A large number of graduates and agricultural and civil projects seek to develop towards cities every year. To survive in a city, the living problem is solved first. Due to the urgent needs of the people, most of demanders of the house leasing industry are formed, with the cultivation development of the long house leasing market, the house leasing market will show a rapid growth situation in the future, and the general current situation of the house leasing industry is that information is inundated and is opaque, the market is disordered, links are loose, and supervision is difficult.
The existing house leasing management efficiency is low, mistakes are easy to make, the fund is unsafe, the market renting is disordered, the transaction cost is high, the reading of the water and electricity meter is complex, and a plurality of industrial problems and pain points exist. Past renting modes have no management chapter sequence, the property management efficiency is low, the house value is not convenient to know, the transaction price of the house has no measurement scale and reference standard, so that the transaction is not guaranteed, a user cannot easily identify the real quality of the house, and the house with poor quality is easily rented at high price.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent housing renting system based on a block chain.
The technical problem to be solved by the invention is as follows:
(1) how to obtain the house basic image avoids the trouble that tenants need to examine on the spot, and the whole house selecting process can be finished on a house renting platform;
(2) how to obtain a rental reference value of the house through analyzing the concerned value, a rental housing attraction value and a selling price bias value of the corresponding area and an active credit value of a house lessor, and reasonably recommending the house to a user through the rental reference value, thereby solving the problem that the existing house rental system cannot reasonably recommend house rental to the user;
(3) how to track and urge related personnel to process tenant demands in time can be achieved, tenant satisfaction is improved, and property management efficiency is improved.
The purpose of the invention can be realized by the following technical scheme: an intelligent housing renting system based on a block chain runs on each node of a bottom block chain platform, and participating nodes comprise government institutions, financial institutions, house property brokerage companies, property agencies and community service institutions; the system is characterized by comprising a data entry module, a data acquisition module, an access statistic module, a data analysis module, a transaction evaluation module, a survey module, a database, a controller, a display module and a post-lease management module;
the data entry module is used for entering basic information of each house and transmitting the basic information of each house to the database, and the database generates an acquisition instruction after receiving the basic information of each house and transmits the acquisition instruction to the data acquisition module; the data acquisition module acquires a basic image corresponding to the house after receiving the acquisition instruction and transmits the acquired house basic image to the database for storage;
the access counting module is used for counting the number of times of being referred and the time of being referred of the house within 10 days before the current time of the system and transmitting the number of times of being referred and the time of being referred to the data analysis module;
the transaction module is used for counting transaction information of the system within 10 days before the current time and transmitting the transaction information to the data analysis module;
the data analysis module is used for processing the referred times, referred time and transaction information, and comprises the following specific processing steps:
the method comprises the following steps: and calculating the concerned value of the referred times and the referred time, wherein the specific calculation process is as follows:
s1: marking the number of times of being referred to of the house within 10 days before the current time of the system as A, and marking the time of being referred to of the house within 10 days before the current time of the system as B;
s2: using formulas
Calculating a value E of interest of the house, wherein a1, a2, a3, b1, b2 and b3 are coefficient factors;
step two: acquiring the area and the bargaining price corresponding to the trading house in the trading information within 10 days before the current time of the system; calculating a house renting attraction value Q of the area corresponding to the house;
step three: acquiring the house price of the house, marking the house price of the house as G, and marking the house contract price of the same area as Gi according to the area; n ═ 1.. n;
step four: using formulas
Calculating to obtain a selling price bias value H of the house;
step five: the data analysis module transmits the concerned value E of the house, the rent room attraction value Q of the corresponding region and the selling price bias value H of the house to the transaction evaluation module;
the investigation module is used for monitoring the communication condition between the lessor and the user of the house at preset time intervals and transmitting the communication condition to the transaction evaluation module;
the transaction evaluation module is used for analyzing the concerned value E of the house, the rent attraction value Q of the corresponding area, the selling price bias value H of the house and the communication condition transmitted by the investigation module, and the specific analysis process is as follows:
x1: marking the service evaluation coefficient as Kx; averaging the service evaluation coefficients Kx to obtain a service evaluation mean value K;
x2: marking the time of a user for proposing a question as T1x, marking the time of a lessor for answering the question as T2x, marking the number of words of the lessor for answering the question as Fx, marking the reaction time of the lessor as T3x, and marking T3x as T2x-T1 x; averaging the reaction time T3x to obtain an average reaction time T; averaging the answer word number Fx to obtain an average answer word number F; m 1.. m;
x3: marking the total number of rented houses under the name of the house renter as P1; the number of houses that have been committed under the house lessor name is marked as P2;
x4: using formulas
Calculating to obtain an active reputation value R of the house lessor, wherein c1, c2, c3, c4 and c5 are all coefficient factors;
x5: carrying out weight distribution on the concerned value E, the rented house attraction value Q of the corresponding region, the selling price bias value H and the active credit value R of the house lessor, wherein the weight of the concerned value E is marked as d1, and the weight of the rented house attraction value Q of the corresponding region is marked as d 2; the weight of the selling price bias value H is marked as d 3; the weight of the house lessor's active reputation value R is marked as d4, and d1+ d2+ d3+ d4 is 1;
x6: and calculating a leasing reference value Y of the house by using the formula Y-E × d1+ Q × d2-H × d3+ R × d 4.
Further, the specific calculation process of the house renting attraction value Q of the corresponding area in the step two is as follows:
SS 1: marking the total transaction number of 10 days before the current time of the system as C; marking the transaction assembly price within 10 days before the current time of the system as D;
SS 2: accumulating the bargaining prices of houses in the same region according to the region to form a regional total bargaining price, and marking the regional total bargaining price as D1; accumulating the number of trades of the houses in the same region according to the region to form the total number of regional trades, and marking the total number of regional trades as C1; the regional total transaction price D1 corresponds to the regional total transaction number C1;
SS 3: using formulas
And calculating a rental housing attraction value Q of the corresponding region, wherein β 1, β 2, β 3, η 1 and η 2 are all coefficient factors.
Further, the data analysis module is further used for transmitting the basic information of the house and the house basic image to the controller, the transaction evaluation module is further used for transmitting the lease reference value Y of the house to the controller, and the controller arranges the basic information of the corresponding house and the house basic image in the sequence from high to low according to the lease reference value Y and transmits the basic information and the house basic image to the display module for real-time display.
Further, the demand input module is used for transmitting selected information to the controller by a user, wherein the selected information is basic information of a certain house; the controller transmits the selected information to the display module for real-time display when receiving the selected information transmitted by the demand input module;
furthermore, the post-rental management module comprises a repair reporting unit, a maintenance unit, a monitoring unit and an evaluation unit, wherein the repair reporting unit is used for tenants to report house repair reporting information, repair reporting emergency degree and responsibility parties; the maintenance unit is used for recording maintenance progress; the monitoring unit is used for detecting and tracking maintenance progress; the evaluation unit is used for recording the evaluation of the tenant on the repair processing;
the monitoring unit comprises a detection unit, a judgment unit, a reminding unit and an information receiving and sending unit, wherein the detection unit is used for periodically detecting the reporting time of the uncounted reporting and repairing information in the management module, and the judgment unit is used for judging whether the reporting time of the uncounted reporting and repairing information and the current detection time interval are larger than a preset reminding threshold value or not; when the time interval is larger than a reminding threshold value, triggering a reminding unit; the reminding unit constructs and displays a reminding window body, reminds the current property personnel to process the non-settlement repair information displayed in the window body and larger than a reminding threshold value, and triggers the information receiving and sending unit; the information receiving and transmitting unit starts to send short messages to inform maintenance personnel and managers;
the emergency degree of the repair is divided into mild degree, moderate degree and severe degree, and different emergency degrees respectively correspond to different reminding threshold values;
the detection unit is also used for detecting newly-added repair information, when the detection unit detects that the newly-added repair information exists, the reminding unit is triggered to trigger the reminding unit, the reminding unit constructs and displays a reminding window body to remind current property personnel to display the newly-added repair information in the window body and distribute the newly-added repair information to maintenance personnel, the information receiving and sending unit is triggered after the newly-added repair information is distributed to the maintenance personnel, and the information receiving and sending unit starts to send corresponding repair information to the maintenance personnel;
further, the data acquisition module comprises a scene modeling device, and the scene modeling device comprises an image acquisition unit, an image analysis unit, a 3D MAX modeling unit, a URL texture mapping unit and a model release unit; the image acquisition unit is used for acquiring a house acquisition image in the visual field range of a user, and is a camera integrated on AR eyes or an AR helmet; after the image analysis unit acquires the house acquisition image, analyzing the light and shadow characteristics in the house acquisition image to obtain the size information of a house space boundary line and the ground and the wall surface; the 3D MAX modeling unit and the URL texture mapping unit are used for generating a furniture three-dimensional image; the model publishing unit integrates the house basic image and the furniture three-dimensional image to form a house basic image and uploads the house basic image to the database;
the data acquisition module distributes corresponding staff to carry out image acquisition after receiving an acquisition instruction, and the specific distribution steps are as follows:
s61: marking the house corresponding to the acquisition instruction as a house to be collected; sending a position acquisition instruction to a mobile phone terminal of a worker to acquire the position of the worker, and calculating the distance difference between the position of the worker and the position of a house to be mined to obtain a worker distance RG;
s62: setting the working age of a worker as RF; setting the collection frequency of a worker as RC;
s63: using formulas
s64: selecting the worker with the largest value to be mined as the collector of the house to be mined, and sending the position of the house to be mined to the mobile phone terminal of the collector; meanwhile, the collection times of the collection personnel are increased once;
s65, collecting the house to be collected after the collector receives the position of the house to be collected, and uploading the image to a database, wherein the method comprises the following steps:
s651: collecting personnel enters a house to be collected by carrying a scene modeling device, and an image collecting unit obtains a house collecting image in the visual field range of a user;
s652: the image analysis unit analyzes the light and shadow characteristics in the house collected image to obtain a house basic image;
s653: the method comprises the following steps that an acquisition worker generates a furniture three-dimensional image through a 3D MAX modeling unit and a URL texture mapping unit;
and S654, the acquisition personnel synthesizes the furniture three-dimensional image and the house basic image to form a house basic image and uploads the house basic image to the database through the model issuing unit.
Further, the house basic information data comprises house type, area, orientation, decoration, building type, floor, address and price, and each house basic information is associated with a house basic image; the transaction information comprises regions corresponding to transaction houses and bargaining prices; the communication condition comprises the time for the user to ask a question, the time for the lessor to answer the question, the number of words for the lessor to answer the question, a service evaluation coefficient, the total number of rented houses under the house lessor name and the number of houses already paid under the house lessor name; the service evaluation coefficient rule is as follows: the lessor service is scored as 100 points full.
The invention has the beneficial effects that:
(1) the data acquisition module receives an acquisition instruction and then allocates corresponding workers to acquire images, the data acquisition module comprises a scene modeling device, and the scene modeling device comprises an image acquisition unit, an image analysis unit, a 3D MAX modeling unit, a URL texture mapping unit and a model release unit; the method comprises the steps that a scene modeling device is used for collecting a house basic image and a three-dimensional image of furniture in a house, the three-dimensional image of the furniture and the house basic image are synthesized to form a house basic image, and then the house basic image is uploaded to a house database through a model publishing unit, so that the trouble that a tenant needs to investigate on site is avoided, and the whole house selecting process can be completed on a house renting platform;
(2) the system comprises an access counting module, a transaction module, a data analysis module and a monitoring module, wherein the access counting module is used for counting the number of times of being consulted and the consulted time of the house within 10 days before the current time of the system, the transaction module is used for counting the transaction information within 10 days before the current time of the system, the consulted times and the consulted time of the house within 10 days before the current time of the system, the transaction information within 10 days before the current time of the system, the basic information of the house and the basic image of the house, performing specified processing, and obtaining a value E of interest of the house, a house renting attraction value Q of a corresponding region and a selling price bias value H of the house according to a related; the investigation module is used for monitoring the communication condition between the lessor and the user of the house at preset time intervals; the transaction evaluation module can acquire an active credit value R of the house lessor and a lease reference value Y of the house through related processing and an algorithm; the data analysis module is further used for transmitting the basic information of the house and the house basic image to the controller, the transaction evaluation module is further used for transmitting the lease reference value Y of the house to the controller, and the controller arranges the basic information of the corresponding house and the house basic image in sequence from high to low according to the lease reference value Y and transmits the basic information and the house basic image to the display module for real-time display, so that a user can conveniently select a favorite house;
(3) the post-renting management module comprises a repair reporting unit, a maintenance unit, a monitoring unit and an evaluation unit, wherein the repair reporting unit is used for tenants to report house repair reporting information, repair reporting emergency degree and responsibility parties; the maintenance unit is used for recording maintenance progress; the monitoring unit is used for detecting and tracking maintenance progress; the evaluation unit is used for recording evaluation of the tenants on repair processing, and tracking and urging related personnel to process tenant requirements in time and improve tenant satisfaction by arranging the post-tenant management module.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a system block diagram of a data acquisition module according to the present invention;
FIG. 3 is a system block diagram of a post-rental management module of the present invention;
fig. 4 is a system block diagram of a monitoring unit in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-4, an intelligent housing renting system based on a block chain is operated on each node of a block chain platform at the bottom layer, and participating nodes comprise government institutions, financial institutions, house property brokerages, property agencies and community service type institutions; the house renting system comprises a data entry module, a data acquisition module, an access statistic module, a data analysis module, a transaction evaluation module, a survey module, a database, a controller and a display module;
the data entry module is used for entering basic information of each house and transmitting the basic information of each house to the database, and the database generates an acquisition instruction after receiving the basic information of each house and transmits the acquisition instruction to the data acquisition module; the data acquisition module acquires a basic image corresponding to the house after receiving the acquisition instruction and transmits the acquired house basic image to the database for storage;
the house basic information data comprises house type, area, orientation, decoration, building type, floor, address and price, and each house basic information is associated with a house basic image;
the data acquisition module comprises a scene modeling device, and the scene modeling device comprises an image acquisition unit, an image analysis unit, a 3D MAX modeling unit, a URL texture mapping unit and a model release unit; the image acquisition unit is used for acquiring a house acquisition image in the visual field range of a user, and is a camera integrated on AR eyes or an AR helmet; after the image analysis unit acquires the house acquisition image, analyzing the light and shadow characteristics in the house acquisition image to obtain the size information of a house space boundary line and the ground and the wall surface; the 3D MAX modeling unit and the URL texture mapping unit are used for generating a furniture three-dimensional image; the model publishing unit integrates the house basic image and the furniture three-dimensional image to form a house basic image and uploads the house basic image to the database;
the data acquisition module distributes corresponding staff to carry out image acquisition after receiving an acquisition instruction, and the specific distribution steps are as follows:
s61: marking the house corresponding to the acquisition instruction as a house to be collected; sending a position acquisition instruction to a mobile phone terminal of a worker to acquire the position of the worker, and calculating the distance difference between the position of the worker and the position of a house to be mined to obtain a worker distance RG;
s62: setting the working age of a worker as RF; setting the collection frequency of a worker as RC;
s63: using formulas
s64: selecting the worker with the largest value to be mined as the collector of the house to be mined, and sending the position of the house to be mined to the mobile phone terminal of the collector; meanwhile, the collection times of the collection personnel are increased once;
s65, collecting the house to be collected after the collector receives the position of the house to be collected, and uploading the image to a database, wherein the method comprises the following steps:
s651: collecting personnel enters a house to be collected by carrying a scene modeling device, and an image collecting unit obtains a house collecting image in the visual field range of a user;
s652: the image analysis unit analyzes the light and shadow characteristics in the house collected image to obtain a house basic image;
s653: the method comprises the following steps that an acquisition worker generates a furniture three-dimensional image through a 3D MAX modeling unit and a URL texture mapping unit;
s654, the collecting personnel synthesizes the furniture three-dimensional image and the house basic image to form a house basic image and uploads the house basic image to the database through the model issuing unit;
the access counting module is used for counting the number of times of being referred and the time of being referred of the house within 10 days before the current time of the system and transmitting the number of times of being referred and the time of being referred to the data analysis module;
the transaction module is used for counting transaction information of the system within 10 days before the current time and transmitting the transaction information to the data analysis module; the transaction information comprises regions corresponding to transaction houses and bargaining prices;
the data analysis module is used for processing the referred times, referred time and transaction information, and comprises the following specific processing steps:
the method comprises the following steps: according to the number of times of being referred to and the time of being referred to of the house within 10 days before the current time of the system transmitted by the access statistic module, the data analysis module calculates the value of interest of the house, and the specific calculation process is as follows:
s1: marking the number of times of being referred to of the house within 10 days before the current time of the system as A, and marking the time of being referred to of the house within 10 days before the current time of the system as B;
s2: using formulas
Calculating to obtain the houseA value of interest E for the house, where a1, a2, a3, b1, b2, and b3 are all coefficient factors;
step two: acquiring the area and the bargaining price corresponding to the trading house in the trading information within 10 days before the current time of the system; calculating the house renting attraction value of the area corresponding to the house, wherein the specific calculation process is as follows:
SS 1: marking the total transaction number of 10 days before the current time of the system as C; marking the transaction assembly price within 10 days before the current time of the system as D;
SS 2: accumulating the bargaining prices of houses in the same region according to the region to form a regional total bargaining price, and marking the regional total bargaining price as D1; accumulating the number of trades of the houses in the same region according to the region to form the total number of regional trades, and marking the total number of regional trades as C1; the regional total transaction price D1 corresponds to the regional total transaction number C1,
SS 3: using formulas
Calculating a rental housing attraction value Q of the corresponding region, wherein β 1, β 2, β 3, η 1 and η 2 are all coefficient factors;
step three: acquiring the house price of the house, marking the house price of the house as G, and marking the house contract price of the same area as Gi according to the area; n ═ 1.. n;
step four: using formulas
Calculating to obtain a selling price bias value H of the house;
step five: the data analysis module transmits the concerned value E of the house, the rent room attraction value Q of the corresponding region and the selling price bias value H of the house to the transaction evaluation module;
the investigation module is used for monitoring the communication situation between the renters of the house and the users at preset time intervals and transmitting the communication situation to the transaction evaluation module, wherein the communication situation comprises the time for the users to ask questions, the time for the renters to answer the questions, the word number of the renters to answer the questions, the service evaluation coefficient, the total number of rented houses under the house renter name and the number of houses which have been committed under the house renter name; the service evaluation coefficient rule is as follows: scoring the service of the lessor, wherein the full score is 100;
the transaction evaluation module is used for analyzing the concerned value E of the house, the rent attraction value Q of the corresponding area, the selling price bias value H of the house and the communication condition transmitted by the investigation module, and the specific analysis process is as follows:
x1: marking the service evaluation coefficient as Kx; averaging the service evaluation coefficients Kx to obtain a service evaluation mean value K;
x2: marking the time of a user for proposing a question as T1x, marking the time of a lessor for answering the question as T2x, marking the number of words of the lessor for answering the question as Fx, marking the reaction time of the lessor as T3x, and marking T3x as T2x-T1 x; averaging the reaction time T3x to obtain an average reaction time T; averaging the answer word number Fx to obtain an average answer word number F; m 1.. m;
x3: marking the total number of rented houses under the name of the house renter as P1; the number of houses that have been committed under the house lessor name is marked as P2;
x4: using formulas
Calculating to obtain an active reputation value R of the house lessor, wherein c1, c2, c3, c4 and c5 are coefficient factors;
x5: carrying out weight distribution on the concerned value E, the rented house attraction value Q of the corresponding region, the selling price bias value H and the active credit value R of the house lessor, wherein the weight of the concerned value E is marked as d1, and the weight of the rented house attraction value Q of the corresponding region is marked as d 2; the weight of the selling price bias value H is marked as d 3; the weight of the house lessor's active reputation value R is marked as d4, and d1+ d2+ d3+ d4 is 1;
x6: calculating a leasing reference value Y of the house by using a formula Y which is E multiplied by d1+ Q multiplied by d2-H multiplied by d3+ R multiplied by d 4;
the data analysis module is further used for transmitting basic information of the house and the house basic image to the controller, the transaction evaluation module is further used for transmitting a lease reference value Y of the house to the controller, and the controller arranges the basic information of the corresponding house and the house basic image in a descending order according to the lease reference value Y and transmits the basic information and the house basic image to the display module for real-time display;
the system also comprises a demand input module, wherein the demand input module is used for transmitting selected information to the controller by a user, and the selected information is basic information of a certain house; the controller transmits the selected information to the display module for real-time display when receiving the selected information transmitted by the demand input module;
the system also comprises a post-renting management module, wherein the post-renting management module comprises a repair reporting unit, a maintenance unit, a monitoring unit and an evaluation unit, and the repair reporting unit is used for the tenant to report the house repair reporting information, the repair reporting emergency degree and the responsible party; the maintenance unit is used for recording maintenance progress; the monitoring unit is used for detecting and tracking maintenance progress; the evaluation unit is used for recording the evaluation of the tenant on the repair processing;
the monitoring unit comprises a detection unit, a judgment unit, a reminding unit and an information receiving and sending unit, wherein the detection unit is used for periodically detecting the reporting time of the uncounted reporting and repairing information in the management module, and the judgment unit is used for judging whether the reporting time of the uncounted reporting and repairing information and the current detection time interval are larger than a preset reminding threshold value or not; when the time interval is larger than a reminding threshold value, triggering a reminding unit; the reminding unit constructs and displays a reminding window body, reminds the current property personnel to process the non-settlement repair information displayed in the window body and larger than a reminding threshold value, and triggers the information receiving and sending unit; the information receiving and transmitting unit starts to send short messages to inform maintenance personnel and managers;
the emergency degree of the repair is divided into mild degree, moderate degree and severe degree, and different emergency degrees respectively correspond to different reminding threshold values.
The maintenance unit is maintained by a maintenance worker, fills in a maintenance progress and a maintenance result, and uploads pictures before/after maintenance;
the detection unit is further used for detecting newly-increased repair information, the reminding unit is triggered when the detection unit detects the newly-increased repair information, the reminding unit constructs and displays the reminding window body to remind current property personnel to display the newly-increased repair information in the window body to be distributed to maintenance personnel, the information receiving and sending unit is triggered after the newly-increased repair information is distributed to the maintenance personnel, and the information receiving and sending unit is started to send corresponding repair information to the maintenance personnel.
A block chain-based intelligent house renting system is characterized in that when the system works, basic information of each house is input through a data input module, the basic information of each house is associated with a house basic image, the data acquisition module receives an acquisition instruction and then distributes corresponding workers to acquire images, meanwhile, the data acquisition module comprises a scene modeling device, the scene modeling device acquires house basic images and three-dimensional images of furniture in the house, the three-dimensional images of the furniture and the house basic images are synthesized to form house basic images, and then the house basic images are uploaded to a database through a model issuing unit, so that the trouble that tenants need to investigate on site is avoided, and the whole house selecting process can be completed on a house renting platform;
the access counting module is used for counting the number of times of being referred and the referred time of the house within 10 days before the current time of the system, the transaction module is used for counting the transaction information within 10 days before the current time of the system, the data analysis module is used for receiving the referred number of times of the house within 10 days before the current time of the system, the referred time, the transaction information within 10 days before the current time of the system, the basic information of the house and the house basic image, performing specified processing, and obtaining a value E of interest of the house, a house renting attraction value Q of a corresponding region and a sale price bias value H of the house according to a related algorithm; the investigation module is used for monitoring the communication condition between the lessor and the user of the house at preset time intervals; the transaction evaluation module can acquire an active credit value R of the house lessor and a lease reference value Y of the house through related processing and an algorithm;
the data analysis module is further used for transmitting basic information of the house and the house basic image to the controller, the transaction evaluation module is further used for transmitting a lease reference value Y of the house to the controller, and the controller arranges the basic information of the corresponding house and the house basic image in a descending order according to the lease reference value Y and transmits the basic information and the house basic image to the display module for real-time display;
a user transmits selected information to a controller through a demand input module, wherein the selected information is basic information of a certain house; the controller transmits the selected information to the display module for real-time display when receiving the selected information transmitted by the demand input module;
when a tenant has a problem in a house and needs to be repaired, the tenant can call the property personnel to repair, the property personnel fills repair information in the system, and the tenant can log in the system by using the identity card number to fill corresponding repair information; after the repair information is filled in, the monitoring unit constructs and displays a window to remind the property personnel to process, and the property personnel grades the repair emergency degree of the repair information and determines the repair emergency degree to be one of light, medium and heavy; then assigning to specific maintenance personnel, and the assigned maintenance personnel receive the reminding information sent by the system and remind the maintenance personnel to process; and meanwhile, the system also monitors the unregistered repair information periodically, and if the repair time interval of the unregistered repair information and the current detection time interval are greater than a preset reminding threshold, a reminding unit and an information receiving and sending unit are started to send short messages to maintenance personnel to remind the corresponding maintenance personnel to process in time. The reminding threshold can be divided into an early warning threshold, a warning threshold and an overdue threshold, and different thresholds are triggered to send reminding information to corresponding maintenance personnel and also send information to corresponding managers so as to supervise and prompt maintenance; after the maintenance is finished, the property personnel or the tenants fill in maintenance evaluation information, and the maintenance information is finalized after the evaluation is finished; through setting up management module after hiring, can realize tracking and supervise relevant personnel and in time handle good tenant appeal, promote tenant satisfaction.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. An intelligent housing renting system based on a block chain is characterized by comprising a data entry module, a data acquisition module, an access statistic module, a data analysis module, a transaction evaluation module, a survey module, a database, a controller and a display module;
the data entry module is used for entering basic information of each house and transmitting the basic information of each house to the database, and the database generates an acquisition instruction after receiving the basic information of each house and transmits the acquisition instruction to the data acquisition module; the data acquisition module acquires a basic image corresponding to the house after receiving the acquisition instruction and transmits the acquired house basic image to the database for storage;
the access counting module is used for counting the number of times of being referred and the time of being referred of the house within 10 days before the current time of the system and transmitting the number of times of being referred and the time of being referred to the data analysis module;
the transaction module is used for counting transaction information of the system within 10 days before the current time and transmitting the transaction information to the data analysis module;
the data analysis module is used for processing the referred times, referred time and transaction information, and comprises the following specific processing steps:
the method comprises the following steps: and calculating the concerned value of the referred times and the referred time, wherein the specific calculation process is as follows:
s1: marking the number of times of being referred to of the house within 10 days before the current time of the system as A, and marking the time of being referred to of the house within 10 days before the current time of the system as B;
s2: using formulas
Calculating a value E of interest of the house, wherein a1, a2, a3, b1, b2 and b3 are coefficient factors;
step two: acquiring the area and the bargaining price corresponding to the trading house in the trading information within 10 days before the current time of the system; calculating a house renting attraction value Q of the area corresponding to the house;
step three: acquiring the house price of the house, marking the house price of the house as G, and marking the house contract price of the same area as Gi according to the area; n ═ 1.. n;
step four: using formulas
Calculating to obtain a selling price bias value H of the house;
step five: the data analysis module transmits the concerned value E of the house, the rent room attraction value Q of the corresponding region and the selling price bias value H of the house to the transaction evaluation module;
the investigation module is used for monitoring the communication condition between the lessor and the user of the house at preset time intervals and transmitting the communication condition to the transaction evaluation module;
the transaction evaluation module is used for analyzing the concerned value E of the house, the rent attraction value Q of the corresponding area, the selling price bias value H of the house and the communication condition transmitted by the investigation module, and the specific analysis process is as follows:
x1: marking the service evaluation coefficient as Kx; averaging the service evaluation coefficients Kx to obtain a service evaluation mean value K;
x2: marking the time of a user for proposing a question as T1x, marking the time of a lessor for answering the question as T2x, marking the number of words of the lessor for answering the question as Fx, marking the reaction time of the lessor as T3x, and marking T3x as T2x-T1 x; averaging the reaction time T3x to obtain an average reaction time T; averaging the answer word number Fx to obtain an average answer word number F; m 1.. m;
x3: marking the total number of rented houses under the name of the house renter as P1; the number of houses that have been committed under the house lessor name is marked as P2;
x4: using formulas
Calculating to obtain an active reputation value R of the house lessor, wherein c1, c2, c3, c4 and c5 are all coefficient factors;
x5: carrying out weight distribution on the concerned value E, the rented house attraction value Q of the corresponding region, the selling price bias value H and the active credit value R of the house lessor, wherein the weight of the concerned value E is marked as d1, and the weight of the rented house attraction value Q of the corresponding region is marked as d 2; the weight of the selling price bias value H is marked as d 3; the weight of the house lessor's active reputation value R is marked as d4, and d1+ d2+ d3+ d4 is 1;
x6: and calculating a leasing reference value Y of the house by using the formula Y-E × d1+ Q × d2-H × d3+ R × d 4.
2. The system for intelligent housing rental based on block chain as claimed in claim 1, wherein the specific calculation process of the housing rental attraction value Q of the corresponding area in the step two is as follows:
SS 1: marking the total transaction number of 10 days before the current time of the system as C; marking the transaction assembly price within 10 days before the current time of the system as D;
SS 2: accumulating the bargaining prices of houses in the same region according to the region to form a regional total bargaining price, and marking the regional total bargaining price as D1; accumulating the number of trades of the houses in the same region according to the region to form the total number of regional trades, and marking the total number of regional trades as C1; the regional total transaction price D1 corresponds to the regional total transaction number C1;
SS 3: using formulas
And calculating a rental housing attraction value Q of the corresponding region, wherein β 1, β 2, β 3, η 1 and η 2 are all coefficient factors.
3. The system of claim 1, wherein the data analysis module is further configured to transmit basic information of a house and a basic image of the house to the controller, the transaction evaluation module is further configured to transmit a rental reference value Y of the house to the controller, and the controller arranges and transmits the basic information of the corresponding house and the basic image of the house to the display module for real-time display according to the order of the rental reference value Y from high to low.
4. The system of claim 1, further comprising a demand input module, wherein the demand input module is used for a user to transmit selected information to the controller, and the selected information is basic information of a certain house; and the controller transmits the selected information to the display module for real-time display when receiving the selected information transmitted by the requirement input module.
5. The system according to claim 1, further comprising a post-rental management module, wherein the post-rental management module comprises a repair reporting unit, a maintenance unit, a monitoring unit and an evaluation unit, and the repair reporting unit is used for tenants to report house repair reporting information, repair reporting emergency degree and responsible parties; the maintenance unit is used for recording maintenance progress; the monitoring unit is used for detecting and tracking maintenance progress; the evaluation unit is used for recording the evaluation of the tenant on the repair processing;
the monitoring unit comprises a detection unit, a judgment unit, a reminding unit and an information receiving and transmitting unit; the detection unit is used for periodically detecting the reporting time of the uncounted reported and repaired information in the management module, and the judgment unit is used for judging whether the reporting time of the uncounted reported and repaired information and the current detection time interval are larger than a preset reminding threshold value or not; when the time interval is larger than a reminding threshold value, triggering a reminding unit; the reminding unit constructs and displays a reminding window body, reminds the current property personnel to process the non-settlement repair information displayed in the window body and larger than a reminding threshold value, and triggers the information receiving and sending unit; the information receiving and transmitting unit starts to send short messages to inform maintenance personnel and managers;
the emergency degree of the repair is divided into mild degree, moderate degree and severe degree, and different emergency degrees respectively correspond to different reminding threshold values;
the detection unit is further used for detecting newly-increased repair information, the reminding unit is triggered when the detection unit detects the newly-increased repair information, the reminding unit constructs and displays the reminding window body to remind current property personnel to display the newly-increased repair information in the window body to be distributed to maintenance personnel, the information receiving and sending unit is triggered after the newly-increased repair information is distributed to the maintenance personnel, and the information receiving and sending unit is started to send corresponding repair information to the maintenance personnel.
6. The intelligent housing renting system based on the block chain as claimed in claim 1, wherein the data acquisition module comprises a scene modeling device, the scene modeling device comprises an image acquisition unit, an image analysis unit, a 3DMAX modeling unit, a URL texture mapping unit and a model publishing unit; the data acquisition module distributes corresponding staff to carry out image acquisition after receiving an acquisition instruction, and the specific distribution steps are as follows:
s61: marking the house corresponding to the acquisition instruction as a house to be collected; sending a position acquisition instruction to a mobile phone terminal of a worker to acquire the position of the worker, and calculating the distance difference between the position of the worker and the position of a house to be mined to obtain a worker distance RG;
s62: setting the working age of a worker as RF; setting the collection frequency of a worker as RC;
s63: using formulas
Acquiring a value RZ to be acquired of a worker;
s64: selecting the worker with the largest value to be mined as the collector of the house to be mined, and sending the position of the house to be mined to the mobile phone terminal of the collector; meanwhile, the collection times of the collection personnel are increased once;
s65, collecting the house to be collected after the collector receives the position of the house to be collected, and uploading the image to a database, wherein the method comprises the following steps:
s651: collecting personnel enters a house to be collected by carrying a scene modeling device, and an image collecting unit obtains a house collecting image in the visual field range of a user;
s652: the image analysis unit analyzes the light and shadow characteristics in the house collected image to obtain a house basic image;
s653: the method comprises the following steps that an acquisition worker generates a furniture three-dimensional image through a 3D MAX modeling unit and a URL texture mapping unit;
and S654, the acquisition personnel synthesizes the furniture three-dimensional image and the house basic image to form a house basic image and uploads the house basic image to the database through the model issuing unit.
7. The system of claim 1, wherein the house basic information data comprises house type, area, orientation, decoration, building type, floor, address and price, and each house basic information is associated with a house basic image; the transaction information comprises regions corresponding to transaction houses and bargaining prices; the communication condition comprises the time for the user to ask a question, the time for the lessor to answer the question, the number of words for the lessor to answer the question, a service evaluation coefficient, the total number of rented houses under the house lessor name and the number of houses already paid under the house lessor name; the service evaluation coefficient rule is as follows: the lessor service is scored as 100 points full.
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