House renting system and renting method based on block chain

Abstract

The invention relates to the technical field of intelligent house leasing, in particular to a house leasing system and leasing method based on a block chain. According to the method, tenants needing to rent houses currently serve as target tenants, initial house sources are screened out based on the requirements of the target tenants, a reference house source of each initial house source is obtained, and house information of each house source rented by each renting tenant in historical renting of each reference house source and the historical renting times of the renting tenants are calculated according to the house information of each house source rented by each renting tenant in historical renting of each reference house source. Acquiring the universal attention of each kind of house information of the reference house resource; and according to the universal attention degree, the difference of each kind of house information between each reference house source and other reference house sources, and the difference of the universal attention degree, obtaining the recommendation degree of each initial house source, and carrying out house source recommendation on the target tenant. According to the method, the recommendation degree of each initial house resource is accurately obtained, so that the interference of personal intentions of the tenants is effectively avoided, the accuracy and rationality of house resource recommendation for the target tenants are improved, and the leasing efficiency of the target tenants is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart house rental, and in particular to a house rental system and a rental method based on blockchain. Background Art

[0002] With the increase in urban population mobility, the housing rental market has gradually become an important part of modern urban life. In recent years, blockchain technology, as a decentralized and tamper-proof distributed ledger technology, can effectively solve the trust problem in the traditional rental model and improve the transparency and security of the rental process.

[0003] The existing house rental system usually displays all the house information to tenants. Tenants need to spend a lot of time in the process of finding suitable houses. At the same time, it is easy for the houses to fail to meet the needs of tenants, resulting in poor house rental efficiency and adverse effects on both tenants and landlords. Summary of the invention

[0004] In order to solve the technical problem that the existing house rental system recommends inaccurate house information to tenants, resulting in poor house rental efficiency, the purpose of the present invention is to provide a house rental system and rental method based on blockchain. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a blockchain-based housing rental method, the method comprising the following steps:

[0006] Any tenant who currently needs to rent a house is taken as a target tenant, and initial housing sources are screened out based on the rental needs of the target tenant, and the housing sources that appear in the housing rental system in the preset local area of ​​each initial housing source are taken as reference housing sources; according to each type of housing information of each reference housing source rented by each tenant in the historical rental, and the historical rental times of each tenant, the general attention of each type of housing information of each reference housing source is obtained;

[0007] Obtain the recommendation degree of each initial housing source according to the general attention of each housing information of each reference housing source in the preset local area of ​​each initial housing source, the difference of each housing information between each reference housing source and other reference housing sources, and the difference of general attention;

[0008] Recommend properties to target tenants based on the degree of recommendation.

[0009] Furthermore, the method for obtaining the general attention is:

[0010] For any reference property, each tenant who rents the reference property shall be regarded as the reference tenant;

[0011] For any reference tenant and any type of housing information, obtain the variance of the corresponding data of this type of housing information for all the rented housing sources of the reference tenant in historical rentals, as the degree of non - attention of the reference tenant to this type of housing information;

[0012] According to the historical rental times of the reference tenant and the degree of non - attention, obtain the degree of attention of the reference tenant to this type of housing information; among them, the historical rental times and the degree of attention are in a positive correlation, and the degree of non - attention and the degree of attention are in a negative correlation;

[0013] Obtain the mean value of the degrees of attention of all reference tenants to this type of housing information, as the general attention degree of this type of housing information of this reference housing source.

[0014] Furthermore, the method for obtaining the recommendation degree is as follows:

[0015] For any initial housing source, regard all the reference housing sources within the preset local area of the initial housing source as the analyzed housing sources;

[0016] For any two analyzed housing sources and any type of housing information, obtain the individual attention deviation degree of this type of housing information of the two analyzed housing sources according to the difference in the corresponding data of this type of housing information of the two analyzed housing sources and the difference in the general attention degree of this type of housing information of the two analyzed housing sources;

[0017] Based on the general attention degree of each type of housing information of each analyzed housing source, divide all types of housing information of each analyzed housing source to obtain the housing information category of each analyzed housing source;

[0018] Arrange each type of housing information of each analyzed housing source in descending order according to its general attention degree to obtain the housing information sequence of each analyzed housing source;

[0019] According to the similarity of the housing information categories where this type of housing information of the two analyzed housing sources is located and the difference in the positions where this type of housing information is located in the housing information sequences of the two analyzed housing sources, obtain the individual attention correction weight of this type of housing information of the two analyzed housing sources;

[0020] Take the product of the individual attention deviation degree and the individual attention correction weight as the corrected individual attention deviation degree of this type of housing information of the two analyzed housing sources;

[0021] According to the general attention degree of each type of housing information of each analyzed housing source and the corrected individual attention deviation degree of each type of housing information of each analyzed housing source with every other analyzed housing source, obtain the recommendation degree of the initial housing source.

[0022] Furthermore, the calculation formula for the individual attention deviation degree is: In the formula, is the degree of personal attention deviation of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source within the preset local area of the i-th initial housing source; is the general attention degree of the j-th housing information of the a-th analyzed housing source within the preset local area of the i-th initial housing source; is the general attention degree of the j-th housing information of the b-th analyzed housing source within the preset local area of the j-th initial housing source; is the data corresponding to the j-th housing information of the a-th analyzed housing source within the preset local area of the i-th initial housing source; is the data corresponding to the j-th housing information of the b-th analyzed housing source within the preset local area of the i-th initial housing source; is the first preset constant, greater than 0; | | is the absolute value function; norm is the normalization function.

[0023] Furthermore, the method for obtaining the personal attention correction weight is as follows:

[0024] For the a-th analyzed housing source and the b-th analyzed housing source, and the j-th housing information within the preset local area of the i-th initial housing source; wherein, the a-th analyzed housing source and the b-th analyzed housing source are any two analyzed housing sources within the preset local area of the i-th initial housing source, the i-th initial housing source is any one initial housing source, and the j-th housing information is any kind of housing information of the housing source;

[0025] Take the housing information category where the j-th housing information is located in the housing information category of the a-th analyzed housing source as the first housing information category;

[0026] Take the housing information category where the j-th housing information is located in the housing information category of the b-th analyzed housing source as the second housing information category;

[0027] Obtain the number of the same housing information between the first housing information category and the second housing information category as the first quantity;

[0028] Take the ratio of the first quantity to the total number of elements in the first housing information category as the first reference value;

[0029] Take the ratio of the first quantity to the total number of elements in the second housing information category as the second reference value;

[0030] Take the result of the negative correlation of the product of the first reference value and the second reference value as the first eigenvalue of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source;

[0031] Take the position serial number of the j-th housing information from front to back in the housing information sequence of the a-th analyzed housing source as the first serial number;

[0032] Take the position serial number of the j-th housing information from front to back in the housing information sequence of the b-th analyzed housing source as the second serial number;

[0033] Take the result of normalizing the difference between the first serial number and the second serial number as the second eigenvalue of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source;

[0034] Take the result of normalizing the sum of the first eigenvalue and the second eigenvalue as the personal attention correction weight of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source.

[0035] Further, the method for obtaining the recommendation degree is as follows:

[0036] For any analyzed housing source and any type of housing information, obtain the local speculation reference degree of the housing information of this analyzed housing source according to the corrected personal attention deviation degree of the housing information of this analyzed housing source and each other analyzed housing source, and the general attention degree of the housing information of this analyzed housing source;

[0037] Take the sum result of the local speculation reference degrees of all types of housing information of this analyzed housing source as the overall speculation reference degree of this analyzed housing source;

[0038] Obtain the sum result of the overall speculation reference degrees of all analyzed housing sources as the recommendation degree of this initial housing source.

[0039] Further, the method for obtaining the local speculation reference degree is as follows:

[0040] Obtain the average value of the corrected personal attention deviation degrees of the housing information of this analyzed housing source and each other analyzed housing source as the personal attention degree of the housing information of this analyzed housing source;

[0041] Obtain the local speculation reference degree of the housing information of this analyzed housing source according to the general attention degree and personal attention degree of the housing information of this analyzed housing source; among them, the general attention degree and the local speculation reference degree are in a positive correlation relationship, and the personal attention degree and the local speculation reference degree are in a negative correlation relationship.

[0042] Further, the method for recommending housing sources to the target tenant based on the recommendation degree is as follows:

[0043] Arrange all the initial housing sources in descending order according to their recommendation degrees to obtain an initial housing source sequence;

[0044] Perform a preset number of grouping divisions on the initial housing source sequence from front to back to obtain housing source recommendation groups;

[0045] Recommend the housing source recommendation groups to the target tenant in the order of division.

[0046] Furthermore, the method for obtaining the data corresponding to the housing information is as follows:

[0047] For any housing information, when the housing information can be represented by available data, the data that the housing information can represent is the data corresponding to the housing information;

[0048] When the housing information can only be represented by text, various situations corresponding to the housing information are manually marked with data, and the marked data is used as the data corresponding to the housing information.

[0049] In a second aspect, another embodiment of the present invention provides a housing rental system based on a blockchain. The system includes: a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0050] The present invention has the following beneficial effects:

[0051] The present invention first screens out initial housing sources based on the rental needs of the target tenant, and analyzes on the basis of the initial housing sources to improve the efficiency of reasonably recommending housing sources to the target tenant; in order to accurately recommend housing sources to the target tenant, furthermore, all housing sources that appear in the housing rental system within the preset local area of each initial housing source are used as reference housing sources to improve the accuracy of analyzing each initial housing source; further, according to each reference housing source, for each type of housing information of the housing source rented by the tenant each time in the historical rental, and the historical rental times of each rental tenant, the general attention degree of each type of housing information of each reference housing source is obtained, which preliminarily reflects the attention degree of each type of housing information of each reference housing source by the tenant, and indirectly reflects the importance of each type of housing information of each reference housing source; in order to avoid the situation that the housing source recommendation for the target tenant is inaccurate due to the personal intention of the tenant, furthermore, according to the general attention degree of each type of housing information of each reference housing source within the preset local area of each initial housing source, the difference in each type of housing information between each reference housing source and other reference housing sources, and the difference in the general attention degree, the recommendation degree of each initial housing source is obtained, which accurately reflects the rationality of each initial housing source, and then based on the recommendation degree, an accurate and reasonable housing source recommendation is made to the target tenant, effectively improving the efficiency of the target tenant's rental. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1 Schematic flowchart of a blockchain-based house rental method provided by an embodiment of the present invention;

[0054] Figure 2 Flowchart of a method for obtaining a recommendation degree provided by an embodiment of the present invention;

[0055] Figure 3 Structural diagram of a blockchain-based house rental system provided by an embodiment of the present invention;

[0056] Figure 4 Schematic diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0057] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a blockchain-based house rental system and rental method proposed according to the present invention. In the following description, different "an embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0059] The following specifically describes the specific solutions of a blockchain-based house rental system and rental method provided by the present invention with reference to the drawings.

[0060] Embodiment 1:

[0061] The present invention proposes a blockchain-based house rental method. Please refer to Figure 1 , which shows a schematic flowchart of a blockchain-based house rental method provided by an embodiment of the present invention. The method includes the following steps:

[0062] Step S1: Take any tenant currently in need of renting a house as the target tenant, screen out the initial housing sources based on the rental needs of the target tenant, and regard all the housing sources that appear in the preset local area of each initial housing source in the house rental system as reference housing sources; obtain the general attention degree of each type of housing information of each reference housing source according to each type of housing information of the housing sources rented by each rental tenant in each historical rental and the historical rental times of each rental tenant.

[0063] Specifically, the house rental system serves as a medium for tenants to search for housing sources through terminals. The house rental system stores the relevant information of each housing source entered, and the information is transparent, enabling all tenants using the house rental system to cross-verify all housing source information data, helping tenants compare different housing sources and thus select the most desirable one. At the same time, the house rental system also stores the information of the using tenants but it is not transparent. Among them, the housing information of the housing source includes address, housing type, area, floor, orientation, housing type, rent, and real scene photos, etc.; the information of the tenant includes: name, ID number, contact information, occupation, work unit, income certificate (for evaluating payment ability), credit record (which can be obtained through a third-party credit agency), and historical rental record, etc. Among them, through the tenant's historical rental record, the housing information of the housing sources rented by the tenant in the past can be obtained. It should be noted that the housing information of the housing source and the information of the tenant are both classified and stored based on the blockchain, which is convenient for subsequent retrieval and operation.

[0064] To analyze this embodiment more clearly, this embodiment takes any tenant who currently needs to rent a house as the target tenant and analyzes it with this target tenant as an example. It should be noted that all subsequent target tenants refer to this target tenant. The target tenant inputs his / her housing rental needs (such as the price, area, location, and orientation of the housing source expected by the target tenant) into the house rental system, and the house rental system will conduct a preliminary screening of the housing sources to obtain the initial housing sources. At this time, there are relatively many initial housing sources screened by the house rental system, and due to the influence of the tenant's personal intention, the recommended initial housing sources are not very accurate, thus affecting the efficiency of the target tenant in finding a suitable housing source.

[0065] To improve the efficiency of the target tenant in finding a suitable housing source, this embodiment regards all housing sources that appear in the preset local area of each initial housing source in the house rental system as reference housing sources (including rented housing sources). Among them, this embodiment sets the preset local area as the community where each initial housing source is located, because tenants in the same community consider the housing information of the housing source more similarly, and the attention of tenants in the same community to each housing information has more specific reference significance, which is conducive to accurately analyzing the suitability of each initial housing source for the target tenant in the future. The implementer can set the range of the preset local area according to the actual situation, and no limitation is made here.

[0066] To improve the analysis efficiency, in this embodiment, the data corresponding to each type of housing information is selected. Among them, for any type of housing information, when the housing information can be represented by data, the data that the housing information can represent is the data corresponding to the housing information. For example, the data corresponding to the housing area is the size of the area, and the data corresponding to the housing floor is the number of floors. When the housing information can only be represented by text, various situations corresponding to the housing information are manually marked with data, and the marked data is used as the data corresponding to the housing information. For example, the housing orientations are east, south, west, and north. In this embodiment, east, south, west, and north are marked as 1, 2, 3, and 4 in sequence. Implementers can perform data marking according to the actual situation, which is not limited here. Therefore, the data corresponding to the housing orientations are 1, 2, 3, and 4 respectively.

[0067] By analyzing the reference housing sources within the preset local area of each initial housing source, the situations that tenants are concerned about for each type of housing information of each initial housing source during the rental process can be indirectly predicted, enabling more accurate housing source recommendations for target tenants. Therefore, in this embodiment, each reference housing source is first analyzed. By analyzing the fluctuation situations of each type of housing information of each rented housing source in the history of each rental tenant of each reference housing source, the general concern situations of each type of housing information of each reference housing source are determined. In actual situations, the more times each rental tenant of each reference housing source has rented in the history, the more accurate the results reflected by the data of the corresponding tenant, effectively avoiding contingency. To ensure the authenticity of the analysis results, furthermore, in this embodiment, according to each type of housing information of each rented housing source in the history of each rental tenant of each reference housing source, and the historical rental times of each rental tenant, the general attention degree of each type of housing information of each reference housing source is obtained.

[0068] Preferably, in a realizable manner of this embodiment, the method for obtaining the general attention degree is as follows: for any reference housing source, each rental tenant of the reference housing source is used as a reference tenant; for any reference tenant and any type of housing information, the variance of the data corresponding to this type of housing information of all the rented housing sources of the reference tenant in the history of renting is obtained as the degree of non - concern of the reference tenant for this type of housing information. The greater the degree of non - concern, the more inconsistent the housing information of this type in the rented housing sources of the reference tenant in the history of renting, indirectly reflecting that the reference tenant is less concerned about this type of housing information. It should be noted that reference tenants with only one rental information are not analyzed because they have no reference significance. It is known that the more rented housing sources the reference tenant has in the history of renting, the more accurate the degree of non - concern. Furthermore, in this embodiment, according to the historical rental times and the degree of non - concern of the reference tenant, the attention degree of the reference tenant for this type of housing information is obtained. Among them, the historical rental times and the attention degree have a positive correlation, and the degree of non - concern and the attention degree have a negative correlation. Among them, the calculation formula for the attention degree is: where p u,j is the degree of attention of the u-th reference tenant to the j-th housing information; n u is the historical rental times of the u-th reference tenant; σ u,j is the degree of inattention of the u-th reference tenant to the j-th housing information; β is a second preset constant, greater than 0; in this embodiment, β is set to 1 to avoid the denominator being 0, and the implementer can set the size of β according to the actual situation, which is not limited here;

[0069] In order to accurately represent the attention of the target tenant to this type of housing information of the reference housing source, and then obtain the average value of the attention degrees of all reference tenants to this type of housing information as the general attention degree of this type of housing information of the reference housing source. The greater the general attention degree, the greater the possibility that this type of housing information of the reference housing source is generally considered by the target tenant, and the more important this type of housing information of the reference housing source is.

[0070] So far, the general attention degree of each type of housing information of each reference housing source is obtained.

[0071] Step S2: Obtain the recommendation degree of each initial housing source according to the general attention degree of each type of housing information of each reference housing source within the preset local area of each initial housing source, the difference of each type of housing information between each reference housing source and other reference housing sources, and the difference of the general attention degree.

[0072] In actual situations, different tenants have personal intentions during the leasing process. Due to the influence of personal intentions, different tenants have obvious differences in their attention to the same type of housing information. Therefore, it is impossible to directly obtain the attention situation of each type of housing information of each initial housing source based on the general attention degree of each type of housing information of each reference housing source within the preset local area of each initial housing source, and thus it is impossible to accurately provide reasonable housing sources for the target tenant. To avoid the situation where the housing sources recommended for the target tenant are chaotic due to personal intentions, this embodiment analyzes the reference housing sources within the preset local area of each initial housing source respectively. By analyzing the difference in each type of housing information between each reference housing source and other reference housing sources within the preset local area of a certain initial housing source, the interference of each type of housing information of each initial housing source by personal intentions is indirectly reflected, and then the true attention situation of each type of housing information of each initial housing source is accurately analyzed, and the recommendation degree of each initial housing source is accurately obtained. Therefore, this embodiment obtains the recommendation degree of each initial housing source according to the general attention degree of each type of housing information of each reference housing source within the preset local area of each initial housing source, the difference of each type of housing information between each reference housing source and other reference housing sources, and the difference of the general attention degree. Among them, the greater the recommendation degree, the earlier the corresponding initial housing source should be recommended to the target user.

[0073] Preferably, in a realizable manner of this embodiment, for the method of obtaining the recommendation degree, please refer to Figure 2 , which shows a flowchart of a method for obtaining the recommendation degree provided in this embodiment. The method includes the following steps:

[0074] Step S201: Obtain the degree of personal attention deviation.

[0075] For clearer analysis, for any initial housing unit, in this embodiment, the reference housing units within the preset local area of the initial housing unit are all regarded as analysis housing units; for any two analysis housing units and any type of housing information, when the difference in the corresponding data of this type of housing information of the two analysis housing units is smaller, it indicates that the two analysis housing units are more similar in this type of housing information. In the case where the difference in the corresponding data of this type of housing information of the two analysis housing units is smaller, when the difference in the general attention of this type of housing information of the two analysis housing units is larger, it indicates that the degree of influence of personal subjective awareness on this type of housing information of the two analysis housing units is greater, indirectly indicating that the degree of personal attention deviation of this type of housing information of the two analysis housing units is greater. Furthermore, in this embodiment, according to the difference in the corresponding data of this type of housing information of the two analysis housing units and the difference in the general attention of this type of housing information of the two analysis housing units, the degree of personal attention deviation of this type of housing information of the two analysis housing units is obtained.

[0076] Among them, the calculation formula for the degree of personal attention deviation is: In the formula, is the degree of personal attention deviation of the j-th type of housing information of the a-th analysis housing unit and the b-th analysis housing unit within the preset local area of the i-th initial housing unit; is the general attention of the j-th type of housing information of the a-th analysis housing unit within the preset local area of the i-th initial housing unit; is the general attention of the j-th type of housing information of the b-th analysis housing unit within the preset local area of the i-th initial housing unit; is the data corresponding to the j-th type of housing information of the a-th analysis housing unit within the preset local area of the i-th initial housing unit; is the data corresponding to the j-th type of housing information of the b-th analysis housing unit within the preset local area of the i-th initial housing unit; is the first preset constant, greater than 0; | | is the absolute value function; norm is the normalization function.

[0077] In this embodiment, is set to 1 to avoid the denominator being 0. The implementer can set the size of according to the actual situation, and no limitation is made here.

[0078] So far, the degree of personal attention deviation of each type of housing information of any two analysis housing units is obtained.

[0079] Step S202: Obtain the personal attention correction weight.

[0080] Considering that in addition to housing information, tenants may have potential concerns. For example, the daylighting time in the living room is not recorded in detail. Instead, when historical tenants selected a certain housing unit and conducted on-site inspections, they excluded various housing information and chose the housing unit due to potential concerns. Therefore, it can be speculated that there is a certain error in determining the degree of personal intention influence on the corresponding housing information of the two analyzed housing units based only on the personal attention deviation degree obtained in step S201. It is known that potential concerns are often related to housing information. For example, the daylighting time in the living room is usually jointly determined by the floor, orientation of the housing unit, and the spacing between buildings in the property complex. The floor, orientation of the housing unit, and the spacing between buildings in the property complex are all actual housing information that can be obtained. For any potential concern, the housing information associated with it usually shows a phenomenon of similar general attention. Therefore, in this embodiment, first, the DBSCAN density clustering algorithm is used to divide all types of housing information of each analyzed housing unit based on the general attention of each type of housing information of each analyzed housing unit, and obtain the housing information categories of each analyzed housing unit; among them, the DBSCAN density clustering algorithm is a well-known technology and will not be elaborated here. The same housing information category may correspond to a potential concern.

[0081] In order to accurately analyze the reasonableness of the personal attention deviation degree of a certain type of housing information between any two analyzed housing units, and then analyze the similarity between the housing information categories of this type of housing information of the two analyzed housing units, as well as the difference in the corresponding importance degrees of this type of housing information of the two analyzed housing units, determine the personal attention correction weight of this type of housing information of the two analyzed housing units, which is beneficial to accurately correcting the personal attention deviation degree of this type of housing information of the two analyzed housing units in the subsequent process.

[0082] In order to show the importance degree of each type of housing information of each analyzed housing unit, in this embodiment, each type of housing information of each analyzed housing unit is arranged in descending order according to its general attention to obtain the housing information sequence of each analyzed housing unit; among them, the housing information that is more forward in the housing information sequence is more important. Then, according to the similarity between the housing information categories of this type of housing information of the two analyzed housing units, and the difference in the positions of this type of housing information in the housing information sequences of the two analyzed housing units, obtain the personal attention correction weight of this type of housing information of the two analyzed housing units;

[0083] Preferably, in an implementable manner of this embodiment, the method for obtaining the personal attention correction weight is as follows: for the a-th analyzed housing unit and the b-th analyzed housing unit, as well as the j-th type of housing information, within the preset local area of the i-th initial housing unit; wherein, the a-th analyzed housing unit and the b-th analyzed housing unit are any two analyzed housing units within the preset local area of the i-th initial housing unit, the i-th initial housing unit is any initial housing unit, and the j-th type of housing information is any type of housing information of the housing unit; taking the housing information category where the j-th type of housing information is located in the housing information category of the a-th analyzed housing unit as the first housing information category; taking the housing information category where the j-th type of housing information is located in the housing information category of the b-th analyzed housing unit as the second housing information category; obtaining the number of the same housing information between the first housing information category and the second housing information category, as the first quantity; the larger the first quantity, the less the personal attention deviation degree of the j-th type of housing information between the a-th analyzed housing unit and the b-th analyzed housing unit is of reference significance. In order to accurately analyze the reasonable degree of the personal attention deviation degree of the j-th type of housing information between the a-th analyzed housing unit and the b-th analyzed housing unit, furthermore, taking the ratio of the first quantity to the total number of elements in the first housing information category as the first reference value; taking the ratio of the first quantity to the total number of elements in the second housing information category as the second reference value; wherein, the larger the first reference value and the second reference value are, both indicate that the personal attention deviation degree of the j-th type of housing information between the a-th analyzed housing unit and the b-th analyzed housing unit has less reference significance. Furthermore, in this embodiment, taking the result of the negative correlation of the product of the first reference value and the second reference value as the first characteristic value of the j-th type of housing information between the a-th analyzed housing unit and the b-th analyzed housing unit; the larger the first characteristic value, the more reasonable the personal attention deviation degree of the j-th type of housing information between the a-th analyzed housing unit and the b-th analyzed housing unit, and the more reference significance it has. Wherein, the calculation formula of the first characteristic value is: In the formula, is the first characteristic value of the j-th type of housing information between the a-th analyzed housing unit and the b-th analyzed housing unit within the preset local area of the i-th initial housing unit; is the first quantity; is the total number of elements in the first housing information category; is the total number of elements in the second housing information category; is the first reference value; is the second reference value; exp is the exponential function with the natural constant as the base;

[0084] Take the position serial number of the j-th housing information from the front to the back in the housing information sequence of the a-th analyzed housing source as the first serial number; take the position serial number of the j-th housing information from the front to the back in the housing information sequence of the b-th analyzed housing source as the second serial number; take the normalized result of the absolute value of the difference between the first serial number and the second serial number as the second eigenvalue of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source; the larger the second eigenvalue, it also indicates that the personal attention deviation degree of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source is more reasonable and has more reference significance. Among them, in this embodiment, the norm normalization function is used to normalize the absolute value of the difference between the first serial number and the second serial number;

[0085] In order to accurately represent the reasonable situation of the personal attention deviation degree of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source, and then take the normalized result of the sum of the first eigenvalue and the second eigenvalue as the personal attention correction weight of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source. The larger the personal attention correction weight, it indicates that the personal attention deviation degree of the j-th housing information of the a-th analyzed housing source and the b-th analyzed housing source is more accurate. Among them, in this embodiment, the norm normalization function is used to normalize the sum result of the first eigenvalue and the second eigenvalue.

[0086] Thus, the personal attention correction weight of each housing information of any two analyzed housing sources is obtained.

[0087] Step S203: Obtain the corrected personal attention deviation degree.

[0088] It is known that the larger the personal attention correction weight, the more accurate the personal attention deviation degree of the corresponding housing information of the corresponding two analyzed housing sources. Therefore, in this embodiment, the product of the personal attention deviation degree of a certain housing information of any two analyzed housing sources and the personal attention correction weight is taken as the corrected personal attention deviation degree of this housing information of these two analyzed housing sources.

[0089] Thus, the corrected personal attention deviation degree of each housing information of any two analyzed housing sources is obtained.

[0090] Step S204: Obtain the recommendation degree.

[0091] Specifically, when recommending housing sources to a target tenant, if the attention of a certain initial housing source to various housing information is greater than that of other initial housing sources, it indicates that the housing information of this initial housing source is more concerned by the tenant, indirectly reflecting that the housing information of this initial housing source is more important, and this initial housing source should be recommended to the target tenant. At the same time, in order to avoid the inaccurate general attention of each housing information of the reference housing sources within the preset local area of each initial housing source due to personal intentions, so as to more accurately analyze the attention of each housing information of each initial housing source and obtain the recommendation degree of each initial housing source. Therefore, in this embodiment, the recommendation degree of this initial housing source is obtained according to the general attention of each housing information of each analyzed housing source and the degree of corrected personal attention deviation of each housing information of each analyzed housing source from each other analyzed housing source.

[0092] Preferably, in an implementable manner of this embodiment, the method for obtaining the recommendation degree is as follows: for any analyzed housing source and any type of housing information, according to the degree of corrected personal attention deviation of this type of housing information of this analyzed housing source from each other analyzed housing source and the general attention of this type of housing information of this analyzed housing source, obtain the local speculation reference degree of this type of housing information of this analyzed housing source. The greater the local speculation reference degree, the more the housing information of this analyzed housing source is concerned by the tenant, indirectly indicating that the housing information of this analyzed housing source is more important. Among them, the method for obtaining the local speculation reference degree is: obtain the average value of the degree of corrected personal attention deviation of this type of housing information of this analyzed housing source from each other analyzed housing source, as the personal attention degree of this type of housing information of this analyzed housing source; the smaller the personal attention degree, the more accurate the general attention of this type of housing information of this analyzed housing source, and the less affected by personal intentions. Then, according to the general attention of this type of housing information of this analyzed housing source and the personal attention degree, obtain the local speculation reference degree of this type of housing information of this analyzed housing source; among them, the general attention and the local speculation reference degree are in a positive correlation, and the personal attention degree and the local speculation reference degree are in a negative correlation. Therefore, the calculation formula for the local speculation reference degree is: In the formula, is the local speculation reference degree of the j-th type of housing information of the a-th analyzed housing source within the preset local area of the i-th initial housing source; is the general attention of the j-th type of housing information of the a-th analyzed housing source within the preset local area of the i-th initial housing source; K is the number of analyzed housing sources within the preset local area of the i-th initial housing source; is the degree of corrected personal attention deviation of the j-th type of housing information of the a-th analyzed housing source from the k-th analyzed housing source within the preset local area of the i-th initial housing source; γ is a third preset constant, greater than 0; is the personal attention level of the j-th type of housing information of the a-th analyzed housing source; in this embodiment, γ is set to 1 to avoid the denominator being 0. Implementers can set the size of γ according to the actual situation, and no limitation is made here;

[0093] Then, the sum of the local speculation reference levels of all types of housing information of the analyzed housing source is used as the overall speculation reference level of the analyzed housing source; finally, the sum of the overall speculation reference levels of all analyzed housing sources is obtained as the recommendation level of the initial housing source.

[0094] Thus, the recommendation level of each initial housing source is obtained.

[0095] Step S3: Recommend housing sources to the target tenant based on the recommendation level.

[0096] It is known that the greater the recommendation level, the earlier the corresponding initial housing source should be recommended to the target user. In order to recommend housing sources to the target tenant more reasonably and efficiently, in this embodiment, all initial housing sources are sorted in descending order of their recommendation levels to obtain an initial housing source sequence; then, the preset quantity is set to 8. Implementers can set the size of the preset quantity according to the actual situation, and no limitation is made here. The initial housing source sequence is divided into groups of the preset quantity from front to back to obtain housing source recommendation groups, that is, one housing source recommendation group contains 8 initial housing sources. The housing source recommendation groups are recommended to the target tenant in the order of division, effectively avoiding the interference of personal preferences, making the recommendation of housing sources more reasonable and accurate, and effectively improving the efficiency of the target tenant's rental.

[0097] In summary, in this embodiment, the tenant who currently needs to rent a house is used as the target tenant, the initial housing sources are screened based on the needs of the target tenant, the reference housing sources of each initial housing source are obtained, and according to the housing information of each rented housing source by the renting tenant in the historical rental and the historical rental times of the renting tenant, the general attention level of each type of housing information of the reference housing source is obtained; according to the general attention level, the difference between each type of housing information between each reference housing source and other reference housing sources, and the difference in the general attention level, the recommendation level of each initial housing source is obtained, and then housing sources are recommended to the target tenant. The present invention effectively avoids the interference of the tenant's personal intention by accurately obtaining the recommendation level of each initial housing source, improves the accuracy and rationality of recommending housing sources to the target tenant, and effectively improves the efficiency of the target tenant's rental.

[0098] Embodiment 2:

[0099] The present invention also proposes a blockchain-based housing rental system. Please refer to Figure 3 , which shows a structural diagram of a blockchain-based housing rental system provided by an embodiment of the present invention. The system includes: a general attention level acquisition module 10, a recommendation level acquisition module 20, and a housing source recommendation module 30.

[0100] The general attention acquisition module 10 is configured to use any tenant who currently needs to rent a house as a target tenant, screen out initial housing sources based on the housing rental needs of the target tenant, and use all the housing sources that appear in the housing rental system within the preset local area of each initial housing source as reference housing sources; according to each rental tenant's each type of housing information for each rented housing source in past housing rentals and the historical rental times of each rental tenant, obtain the general attention of each type of housing information of each reference housing source.

[0101] The recommendation degree acquisition module 20 is configured to obtain the recommendation degree of each initial housing source according to the general attention of each type of housing information of each reference housing source within the preset local area of each initial housing source, the difference of each type of housing information between each reference housing source and other reference housing sources, and the difference of the general attention.

[0102] The housing source recommendation module 30 is configured to recommend housing sources to the target tenant based on the recommendation degree.

[0103] It should be noted that: for the system provided in the above embodiment, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a blockchain-based housing rental system and a blockchain-based housing rental method embodiment provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0104] Embodiment 3:

[0105] The present invention also proposes a blockchain-based housing rental device, which includes a memory and a processor. Among them, an executable program code is stored in the memory, and the processor is configured to call and execute the executable program code to execute a blockchain-based housing rental method provided in an embodiment of the present application. The device may specifically be a chip, a component or a module. The chip may include a connected processor and a memory; among them, the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a blockchain-based housing rental method provided in the above embodiment.

[0106] In addition, an embodiment of the present application also protects a computer device. Please refer to Figure 4 ., the computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. Among them, when the processor 402 executes the computer program 403, the computer device can execute any one of the blockchain-based housing rental methods introduced above.

[0107] Example 4:

[0108] This embodiment also provides a computer-readable storage medium, in which computer program code is stored. When the computer program code runs on a computer, the computer is caused to execute the above-related method steps to implement a blockchain-based house rental method provided in the above embodiment.

[0109] Example 5:

[0110] This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer is caused to execute the above-related steps to implement a blockchain-based house rental method provided in the above embodiment.

[0111] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0112] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A house rental method based on blockchain, characterized in that: The method comprises the following steps: Any tenant who currently needs to rent a house is taken as a target tenant, and initial housing sources are screened out based on the rental needs of the target tenant, and the housing sources that appear in the housing rental system in the preset local area of ​​each initial housing source are taken as reference housing sources; according to each type of housing information of each reference housing source rented by each tenant in the historical rental, and the historical rental times of each tenant, the general attention of each type of housing information of each reference housing source is obtained; Obtain the recommendation degree of each initial housing source according to the general attention of each housing information of each reference housing source in the preset local area of ​​each initial housing source, the difference of each housing information between each reference housing source and other reference housing sources, and the difference of general attention; Make property recommendations to target tenants based on the degree of recommendation.

2. A blockchain-based house rental method as claimed in claim 1, characterized in that: The method for obtaining the general attention is: For any reference property, each tenant who rents the reference property shall be regarded as the reference tenant; For any reference tenant and any type of housing information, obtain the variance of the corresponding data of the housing information of all rental sources of the reference tenant in the historical rental, as the degree of the reference tenant's indifference to the housing information; According to the historical rental times of the reference tenant and the degree of indifference, the degree of attention of the reference tenant to the housing information is obtained; wherein the historical rental times and the degree of attention are positively correlated, and the degree of indifference and the degree of attention are negatively correlated; The average of the attention levels of all reference tenants to this type of housing information is obtained as the general attention level of this type of housing information of the reference housing source.

3. A blockchain-based house rental method as claimed in claim 1, characterized in that: The method for obtaining the recommendation degree is: For any initial listing, all reference listings within the preset local area of ​​the initial listing are used as analysis listings; For any two analyzed housing sources and any type of housing information, according to the difference in corresponding data of the type of housing information of the two analyzed housing sources and the difference in the general attention of the type of housing information of the two analyzed housing sources, the degree of personal attention deviation of the type of housing information of the two analyzed housing sources is obtained; Divide all types of housing information of each analyzed housing source based on the general popularity of each type of housing information of each analyzed housing source, and obtain the housing information category of each analyzed housing source; Arrange each type of house information of each analyzed house source in descending order according to its general attention degree, and obtain a house information sequence of each analyzed house source; According to the similarity of the housing information categories of the housing information of the two analyzed housing sources and the difference in the positions of the housing information of the two analyzed housing sources in the housing information sequences of the two analyzed housing sources, obtaining the personal attention correction weights of the housing information of the two analyzed housing sources; The product of the personal attention deviation degree and the personal attention correction weight is used as the correction personal attention deviation degree of the housing information of the two analyzed housing sources; The recommendation degree of the initial housing source is obtained according to the general attention degree of each housing information of each analyzed housing source and the corrected personal attention deviation degree between each analyzed housing source and each housing information of other analyzed housing sources.

4. A blockchain-based housing rental method as claimed in claim 3, characterized in that: The calculation formula for the degree of personal attention bias is: In the formula, is the degree of personal attention deviation of the j-th type of housing information of the a-th analysis housing source and the b-th analysis housing source in the preset local area of ​​the i-th initial housing source; is the general attention degree of the j-th type of housing information of the a-th analyzed housing source in the preset local area of ​​the i-th initial housing source; is the general attention degree of the j-th type of housing information of the b-th analyzed housing source in the preset local area of ​​the i-th initial housing source; The data corresponding to the j-th type of housing information of the a-th analyzed housing source in the preset local area of ​​the i-th initial housing source; The data corresponding to the j-th type of housing information of the b-th analyzed housing source in the preset local area of ​​the i-th initial housing source; is the first preset constant, which is greater than 0; || is the absolute value function; norm is the normalization function.

5. A blockchain-based house rental method as claimed in claim 3, characterized in that: The method for obtaining the personal attention correction weight is: For the a-th analyzed house source and the b-th analyzed house source, and the j-th type of housing information in the preset local area of ​​the i-th initial house source; wherein the a-th analyzed house source and the b-th analyzed house source are any two analyzed houses in the preset local area of ​​the i-th initial house source, the i-th initial house source is any initial house source, and the j-th type of housing information is any type of housing information of the house source; The housing information category of the j-th housing information in the a-th analyzed housing source is taken as the first housing information category; The housing information category of the j-th housing information in the b-th analyzed housing source is used as the second housing information category; Obtaining the number of identical housing information between the first housing information category and the second housing information category as a first number; taking a ratio of the first quantity to the total number of elements in the first housing information category as a first reference value; The ratio of the first quantity to the total quantity of elements in the second housing information category is used as a second reference value; The result of negatively correlating the product of the first reference value and the second reference value is used as the first characteristic value of the j-th type of housing information of the a-th analysis housing source and the b-th analysis housing source; The position number of the j-th housing information in the housing information sequence of the a-th analyzed housing source from the front to the back is used as the first serial number; The position number of the j-th housing information in the housing information sequence of the b-th analyzed housing source from the front to the back is used as the second serial number; The result of normalizing the difference between the first serial number and the second serial number is used as the second eigenvalue of the j-th type of housing information of the a-th analyzed housing source and the b-th analyzed housing source; The result of normalizing the sum of the first eigenvalue and the second eigenvalue is used as the personal attention correction weight of the j-th type of housing information of the a-th analyzed housing source and the b-th analyzed housing source.

6. A blockchain-based house rental method as claimed in claim 3, characterized in that: The method for obtaining the recommendation degree is: For any analyzed house source and any type of housing information, according to the corrected personal attention deviation degree between the analyzed house source and the type of housing information of each other analyzed house source, and the general attention degree of the type of housing information of the analyzed house source, the local speculation reference degree of the type of housing information of the analyzed house source is obtained; The sum of the local inference reference levels of all types of house information of the analyzed house source is used as the overall inference reference level of the analyzed house source; The sum of the overall estimated reference levels of all analyzed properties is obtained as the recommendation level of the initial property.

7. A blockchain-based housing rental method as claimed in claim 6, characterized in that: The method for obtaining the local inference reference degree is: Obtain the average of the corrected personal attention deviations of the analyzed house source and the house information of each other analyzed house source as the personal attention degree of the house information of the analyzed house source; According to the general attention degree and personal attention degree of the housing information of the analyzed housing source, the local speculation reference degree of the housing information of the analyzed housing source is obtained; wherein, the general attention degree and the local speculation reference degree are positively correlated, and the personal attention degree and the local speculation reference degree are negatively correlated.

8. A blockchain-based house rental method as claimed in claim 1, characterized in that: The method for recommending housing sources to target tenants based on the recommendation degree is as follows: Arrange all initial listings in descending order according to their recommendation levels to obtain an initial listing sequence; Divide the initial housing sequence into a preset number of groups from front to back to obtain recommended housing groups; Recommend the property recommendation groups to the target tenants in the order of classification.

9. A blockchain-based house rental method as claimed in claim 2, characterized in that: The method for obtaining the data corresponding to the housing information is as follows: For any type of housing information, when the housing information can be represented by data, the data that can be represented by the housing information is the data corresponding to the housing information; When the house information can only be expressed in words, data marking is performed manually on various situations corresponding to the house information, and the marked data is used as the corresponding data of the house information.

10. A house rental system based on blockchain, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When executing the computer program, the processor implements the steps of a blockchain-based house rental method as described in any one of claims 1 to 9.

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