House rental evaluation processing method and apparatus

Abstract

The embodiments of the present specification provide a housing rental evaluation processing method and device, wherein a housing rental evaluation processing method comprises: in the process of carrying out the housing rental evaluation processing, inputting the first credit investigation data and the rental data of the tenant into a rental evaluation model to perform rental evaluation processing, obtaining a rental recommendation strategy of the tenant, and inputting the second credit investigation data, the rental data and the housing data of the landlord into a rental evaluation model to perform rental evaluation processing, obtaining a rental strategy, then performing bidirectional matching adjustment on the rental parameters contained in the rental recommendation strategy and the rental parameters contained in the rental strategy to recommend the tenant and the landlord, and obtaining a target rental recommendation strategy and a target rental strategy after matching adjustment, so as to recommend the housing rental through the target rental recommendation strategy and the target rental strategy.

Description

[0001] This patent application is a divisional application of Chinese patent application No. 202511457961.X, filed on October 13, 2025, entitled "Method and Apparatus for Housing Rental Assessment". Technical Field

[0002] This document relates to the field of data processing technology, and in particular to a method and apparatus for processing housing rental assessments. Background Technology

[0003] With the continuous development of internet technology, various online service platforms have emerged, such as housing rental platforms that provide housing rental services to users. However, as users' service requirements in the housing rental process increase and the number of participants continues to grow, all parties are paying more attention to the safety and standardization of the housing rental process. Furthermore, as the number of service providers offering rental-related services increases, the competition among these service providers is becoming increasingly fierce. Exploring new service methods around housing rental has become a key focus for all service providers. Summary of the Invention

[0004] This specification provides one or more embodiments of a housing rental assessment method, comprising: inputting a tenant's first credit data and rental data into a rental assessment model for rental assessment processing to obtain a rental recommendation strategy for the tenant; inputting a landlord's second credit data, rental data, and housing data into the rental assessment model for rental assessment processing to obtain a rental strategy; and performing bidirectional matching and adjustment of the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy to match the tenant with the landlord, thereby obtaining a target rental recommendation strategy and a target rental strategy after matching and adjustment.

[0005] This specification provides one or more embodiments of a housing rental assessment and processing apparatus, comprising: a strategy acquisition module configured to input a tenant's first credit data and rental data into a rental assessment model for rental assessment processing to obtain a rental recommendation strategy for the tenant; a rental strategy acquisition module configured to input a landlord's second credit data, rental data, and housing data into a rental assessment model for rental assessment processing to obtain a rental strategy; and a strategy matching and adjustment module configured to perform bidirectional matching and adjustment between the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy to match the tenant with the landlord, and obtain a target rental recommendation strategy and a target rental strategy after matching and adjustment.

[0006] This specification provides one or more embodiments of a housing rental assessment and processing device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: input first credit data and rental data of a tenant into a rental assessment model for rental assessment processing to obtain a rental recommendation strategy for the tenant; input second credit data, rental data, and housing data of a landlord into the rental assessment model for rental assessment processing to obtain a rental strategy; and perform bidirectional matching and adjustment of the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy to match the tenant with the landlord, obtaining a target rental recommendation strategy and a target rental strategy after matching and adjustment.

[0007] This specification provides one or more embodiments of a computer-readable storage medium for storing computer-executable instructions, which, when executed, perform the following process: Inputting the lessee's first credit data and rental data into a rental assessment model for rental assessment processing to obtain a rental recommendation strategy for the lessee. Inputting the lessor's second credit data, rental data, and property data into the rental assessment model for rental assessment processing to obtain a rental strategy. Performing bidirectional matching and adjustment between the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy to match the lessee with the lessor, and obtaining a target rental recommendation strategy and a target rental strategy after matching and adjustment. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 A schematic diagram illustrating the implementation environment of a housing rental assessment processing method provided in one or more embodiments of this specification; Figure 2 A flowchart illustrating a housing rental assessment method provided in one or more embodiments of this specification; Figure 3 A flowchart illustrating a housing rental assessment method applied to a housing rental scenario, provided for one or more embodiments of this specification; Figure 4 A schematic diagram of an embodiment of a housing rental assessment and processing device provided in one or more embodiments of this specification; Figure 5This is a structural schematic diagram of a housing rental assessment and processing device provided for one or more embodiments of this specification. Detailed Implementation

[0009] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0010] The housing rental assessment and processing method provided in one or more embodiments of this specification is applicable to the housing rental implementation environment. Figure 1 The implementation environment includes at least: server 101 of the rental platform; Server 101 is used to call the rental assessment model based on the lessee's relevant data to obtain a rental recommendation strategy, call the rental assessment model based on the lessor's relevant data to obtain a rental strategy, and perform bidirectional matching and adjustment based on the rental recommendation strategy and the rental strategy, and obtain the target rental recommendation strategy and the target rental strategy after matching and adjustment; Server 101 may be deployed with rental assessment model 101-1 and rental assessment model 101-2; Server 101 may be a single server, a server cluster consisting of several servers, or one or more cloud servers in a cloud computing platform; Rental assessment model 101-1 can perform rental assessment processing based on the tenant's first credit data and / or rental data input by server 101, and output rental recommendation strategies for the tenant; Rental assessment model 101-2 can perform rental assessment processing based on the landlord's second credit data, rental data and / or housing data input by server 101, and output rental strategies. In addition, the implementation environment may also include the lessee's user terminal 102 and the lessor's user terminal 103, which are used to cooperate with the server 101 to perform housing rental assessment processing. Specifically, the lessee's user terminal 102 and the lessor's user terminal 103 may be mobile phones, personal computers, tablets, e-book readers, devices that interact with information based on VR (Virtual Reality) and AR (Augmented Reality), in-vehicle terminals, IoT devices, wearable smart devices, laptops, and desktop computers, etc.

[0011] In this implementation environment, server 101 inputs the lessee's first credit data and rental data into rental assessment model 101-1. Rental assessment model 101-1 performs rental assessment processing based on the lessee's data input by server 101 and outputs a rental recommendation strategy for the lessee. Similarly, server 101 inputs the lessor's second credit data, rental data, and housing data into rental assessment model 101-2. Rental assessment model 101-2 performs rental assessment processing based on the lessor's data input by server 101 and outputs a rental strategy. Based on the rental recommendation strategy output by rental assessment model 101-1 and the rental strategy output by rental assessment model 101-2, server 101 performs bidirectional matching and adjustment of the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy to match the lessee with the lessor. After bidirectional matching and adjustment, a target rental recommendation strategy and a target rental strategy are obtained. Rental recommendations are then made based on the target rental recommendation strategy and the target rental strategy, thereby realizing housing rental.

[0012] It should be noted that, considering that the primary credit data, rental data, and other related data involved in this specification may to some extent constitute the privacy of the lessee, and considering that the secondary credit data, rental data, and housing data involved in this specification may to some extent constitute the privacy of the lessor, authorization from the lessee or lessor should be obtained before collecting such data to ensure that the data collection operation complies with relevant data management regulations. For example, the lessee or lessor can authorize the data when the target application is launched. The specific method of data authorization can be to send a data authorization reminder to the lessee or lessor, who can obtain data authorization after confirming the reminder with an instruction. Alternatively, data authorization can also be obtained by signing a data authorization agreement to obtain authorization for data collection or data transmission. This embodiment does not limit this.

[0013] This specification provides one or more embodiments of a housing rental assessment method, as follows: Reference Figure 2 The housing rental assessment and processing method provided in this embodiment specifically includes steps S202 to S206.

[0014] Step S202: Input the lessee's first credit data and rental data into the rental assessment model for rental assessment processing to obtain the lessee's rental recommendation strategy.

[0015] In this embodiment, the lease assessment model refers to a model that evaluates and processes lease recommendation strategies for lessees based on relevant data of the lessee. The input of the lease assessment model is the lessee's credit data and / or lease data, and the output is the lessee's lease recommendation strategy. In addition, the input of the lease assessment model can also be other relevant data of the lessee, such as asset status data and employment status data; or, the input of the lease assessment model can also be at least one of the lessee's credit data and lease data, along with other relevant data of the lessee. First-level credit data refers to data used to characterize the lessee's credit information; for example, first-level credit data may include the lessee's performance record data, debt and repayment data, credit inquiry frequency and / or default record data; in addition, first-level credit data may also include the lessee's credit data; leasing data refers to data used to characterize the lessee's leasing behavior and / or leasing intention; for example, leasing data may include leasing behavior data, which may be data used to characterize the lessee's actual behavior records and performance data in historical leasing behavior, such as historical leasing duration, frequency of property changes, rent payment records and / or reasons and methods of termination; leasing data may also include leasing intention data, which may be data used to characterize the lessee's housing preferences, such as housing selection preference data; The aforementioned rental recommendation strategy refers to a strategy obtained after processing relevant data of the lessee for rental assessment, used to guide the lessee in setting rental transaction conditions. For example, the rental recommendation strategy may include rental parameters, which are parameters used to characterize the lessee's rental transaction conditions. Specifically, rental parameters may include rental risk level, lessee's credibility rating, rental deposit amount, rental payment method and / or rental period. In addition, rental parameters may also include other parameters related to rental transaction conditions, such as rent adjustment ratio. The rental risk level refers to the likelihood of a tenant defaulting on rent, breaching the lease, or damaging the property during the rental process, based on a comprehensive assessment of the tenant's credit data, debt level, repayment behavior, and / or multi-dimensional rental history data. For example, rental risk levels can be divided into three levels: Level 1, Level 2, and Level 3. Level 1 indicates the tenant has no overdue payment records, good credit data, and a low probability of risk events; Level 2 indicates the tenant has a low frequency of overdue payments and a moderate probability of risk events; Level 3 indicates... This indicates a high frequency of tenant defaults and a high probability of risk events. The rental deposit amount can be a suggested deposit amount based on the tenant's monthly rent, such as 0 months' deposit (no deposit), 1 month's deposit, or 2 months' deposit. The credibility rating refers to the compliance level of the tenant's performance and / or risk behavior during the rental process. For example, the credibility rating can be divided into credibility level, general level, and high-risk level based on the tenant's credit data, rental default records, and / or rental breach records. The rental payment method refers to the recommended rent payment cycle and settlement method for the tenant, such as monthly, quarterly, or annual payment.

[0016] In practice, during the housing rental assessment process, the credit data and / or rental data of the lessee are obtained, and the obtained credit data and / or rental data of the lessee are input into the rental assessment model. The rental assessment model then performs rental assessment based on the credit data and / or rental data of the lessee and outputs a rental recommendation strategy. Based on this, the rental recommendation strategy for the lessee is obtained.

[0017] Step S204: Input the lessor's second credit data, rental data, and housing data into the rental assessment model for rental assessment processing to obtain a rental strategy.

[0018] The rental assessment model refers to a model that evaluates and processes the rental strategy of a landlord based on the landlord's relevant data. The input of the rental assessment model is the landlord's credit data, rental data, and / or housing data, and the output is the landlord's rental strategy. In addition, the input of the rental assessment model can also be other relevant data of the landlord, such as identity and qualification information, and cash flow data; or, the input of the rental assessment model can also be at least one of the landlord's credit data, rental data, and housing data, along with other relevant data of the landlord. Secondary credit data refers to data used to characterize the lessor's credit information; for example, secondary credit data may include the lessor's historical rental dispute records, account change frequency, and / or overdue default records; in addition, secondary credit data may also include the lessor's credit data; rental data refers to data used to characterize the lessor's rental behavior and / or rental preference; for example, rental data may include rental behavior data, which may be data used to characterize the lessor's actual behavior records and performance data in historical rental behavior, such as historical rental frequency, tenant change frequency, deposit refund records, and / or tenant complaint records; rental data may also include rental preference data, which may be data used to characterize the lessor's preference for tenants who rent out the property, such as tenant screening preference data; The housing data refers to the relevant data of the houses that the lessor intends to rent or is currently renting out; specifically, the housing data may include the property ownership data, such as the property certificate number, house address, house area, and planned use; it may also include the rental status data, such as historical rental records and maintenance records; it may also include the housing display data, such as house photos, house videos, and floor plan; in addition, it may include other housing-related data, such as housing supporting facilities data.

[0019] The rental strategy refers to a strategy obtained by processing relevant data of the lessor to guide the lessor in setting rental transaction conditions. For example, the rental strategy may include rental parameters, which are parameters used to characterize the lessor's rental transaction conditions. Specifically, rental parameters may include the lessor's credibility rating, rental posting type, credit label, rental deposit amount, rental payment method, and / or risk parameters. In addition, rental parameters may also include other parameters related to rental transaction conditions, such as the rental period. Among them, the credibility rating refers to the compliance level of the lessor's performance and / or risk behavior in the housing rental process. For example, the credibility rating can be divided into credibility level, general level and high-risk level according to the lessor's credit data, housing dispute records and / or the authenticity of housing information. Rental listing type refers to the display and review status of a landlord's listed property on the rental platform. For example, rental listing types can be divided into normal display, enhanced review, and restricted listing based on the suggested regulatory intensity. Normal display applies to landlords with a trustworthy rating, in which case the landlord's property can be automatically listed without manual intervention. Enhanced review applies to landlords with a general trustworthy rating, in which case the landlord's property must be manually reviewed or supplemented with materials before it can be listed on the rental platform. Restricted listing applies to landlords with a high-risk trustworthy rating, in which case the landlord is prohibited from listing property or is only allowed to display property within a specific restricted scope. Credit tags are visual credit identifiers assigned to landlords by rental platforms. For example, landlords with a trust rating of "trustworthy" can be awarded the "trustworthy landlord" label to increase the exposure of their properties by a certain percentage.

[0020] In practice, during the housing rental assessment process, the landlord's credit data, rental data, and / or housing data are obtained. The obtained landlord's credit data, rental data, and / or housing data are then input into the rental assessment model, so that the rental assessment model can perform rental assessment based on the landlord's credit data, rental data, and / or housing data, and output a rental strategy. Based on this, the landlord's rental strategy is obtained.

[0021] Step S206: Perform bidirectional matching and adjustment on the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy to match the lessee with the lessor. After matching and adjustment, obtain the target rental recommendation strategy and the target rental strategy.

[0022] As mentioned above, based on the obtained rental recommendation strategy and rental strategy, the rental recommendation strategy and rental strategy can be adjusted by two-way matching to obtain the target rental recommendation strategy and target rental strategy, and then housing rental recommendations can be made based on the target rental recommendation strategy and target rental strategy.

[0023] In practice, during the housing rental assessment process, based on the rental recommendation strategy and the rental strategy obtained, the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are extracted, and the rental parameters and rental parameters are adjusted through two-way matching to obtain the target rental recommendation strategy and the target rental strategy. Here, two-way matching adjustment can also be replaced with matchmaking matching or two-way matchmaking. Correspondingly, the relevant descriptions of two-way matching adjustment in the following sections can also be replaced with matchmaking matching or two-way matchmaking.

[0024] As mentioned above, rental parameters refer to parameters used to characterize the rental transaction conditions of the lessee during the housing rental process. Optionally, the rental parameters included in the rental recommendation strategy may include at least one of the following: rental risk level obtained from rental risk assessment, credibility rating obtained from calculating the lessee's credibility rating, rental deposit amount obtained from matching rental deposits, rental payment method obtained from recommending rental payment methods, and rental period obtained from predicting rental period. In addition, rental parameters may also be other parameters related to rental transaction conditions, such as electronic contract signing requirements (whether to use an electronic contract system to sign the agreement) and property verification methods (whether to recommend property inspection methods). Rental parameters refer to parameters used to characterize the rental transaction conditions of the lessor during the housing rental process. Optionally, the rental parameters included in the rental recommendation strategy may include at least one of the following: a credibility rating obtained by calculating the lessor's credibility rating, a rental posting type obtained by conducting compliance checks on rental postings, a credit tag obtained by conducting credit assessments of the lessor, a rental deposit amount obtained by matching rental deposits, a rental payment method obtained by recommending rental payment methods, and a risk parameter obtained by conducting rental risk assessments. In addition, rental parameters may also be other parameters related to rental transaction conditions, such as housing maintenance requirements obtained by recommending housing maintenance.

[0025] The target lease recommendation strategy refers to the final lease execution strategy applicable to the lessee, which may include the lessee's lease risk level, credibility rating, lease deposit amount, lease payment method and / or lease period; the target rental strategy refers to the final lease execution strategy applicable to the lessor, which may include the lessor's credibility rating, lease posting type, credit tag, lease deposit amount, lease payment method and / or risk parameters.

[0026] In practical applications, during the process of two-way matching and adjustment of the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy, in order to improve the efficiency of two-way matching and adjustment, the subsequent successful conversion rate of housing rental, and reduce the risks in the housing rental process, two-way matching and adjustment can also be performed based on the trust rating of the lessee and the lessor; or, based on the degree of rental intention of the lessee and the degree of rental intention of the lessor; or, a pre-screening mechanism can be introduced for two-way matching and adjustment; or, a matching and adjustment model can be introduced for two-way matching and adjustment. Based on this, the following will explain in detail the implementation methods of two-way matching and adjustment based on rental parameters and rental parameters.

[0027] (1) Two-way matching adjustment based on credibility rating In the specific implementation process, in order to reduce the default rate of tenants and landlords in the subsequent housing rental process and improve the security of housing rental transactions, the rental credit parameters included in the rental recommendation strategy and the rental credit parameters included in the rental strategy of the candidate landlord can be matched to obtain the credit matching result. Based on the credit matching result, the rental parameters and the rental adjustment parameters are determined and adjusted by parameter matching to obtain the target rental recommendation strategy and the target rental strategy. In one optional implementation of this embodiment, the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are adjusted through bidirectional matching, including: Credit matching results are obtained by matching the credibility ratings included in the leasing recommendation strategy with the credibility ratings included in the leasing strategies of candidate lessors. Based on the credit matching results, the leasing parameters and rental parameters are adjusted, and then parameter matching adjustments are performed on the adjusted leasing parameters and rental parameters to obtain the target leasing recommendation strategy and the target rental strategy. Specifically, during the two-way matching adjustment process, credit matching is performed based on the trust rating of the lessee and the trust rating of the candidate lessor to obtain the credit matching result. Then, based on the credit matching result, the original leasing parameters and rental parameters can be adjusted through the parameter adjustment rule base to obtain the adjusted leasing parameters and adjusted rental parameters. Further parameter matching adjustment is performed based on the adjusted leasing parameters and adjusted rental parameters to obtain the target leasing recommendation strategy and the target rental strategy.

[0028] Among them, the credit matching results can be: a low-risk type of a high-reliability-rated lessee and a high-reliability-rated candidate lessor; a high-tenant-low-landlord type of a high-reliability-rated lessee and a medium or low-reliability-rated candidate lessor; a low-tenant-high-landlord type of a medium or low-reliability-rated lessee and a high-reliability-rated candidate lessor; or a high-risk type of a medium or low-reliability-rated lessee and a medium or low-reliability-rated candidate lessor. Taking the credit matching results of low-risk and low-demand types as an example, in the process of determining the adjustment of rental parameters, the suggested deposit amount can be adjusted from 1 month to 0 months (deposit waiver), and the recommended payment method can be adjusted from quarterly payment to monthly payment; in the process of determining the adjustment of rental parameters, the rental posting type can be adjusted to normal display, and the rental deposit amount can be adjusted to accept deposit waiver or 1 month deposit. In addition, a mechanism of deposit waiver, priority recommendation and / or green channel can be provided to low-risk and low-demand tenants and / or lessors. For example, taking the credit matching results of a high-tenant-low-landlord type between a high-trust-rating tenant and a medium or low-trust-rating candidate landlord as an example, in the process of determining the adjustment of rental parameters, the suggested deposit amount can be adjusted from 1 month to 0 months (deposit waiver), and the recommended payment method can be adjusted from quarterly payment to monthly payment; in the process of determining the adjustment of rental parameters, the rental deposit amount can be adjusted from 3 months to 1 month or 0 months, and the rental posting type can be adjusted from normal display to enhanced review.

[0029] It should be noted that, in the process of two-way matching adjustment, in addition to the two-way matching adjustment based on the trust ratings of the lessee and lessor provided above, the trust ratings included in the lease recommendation strategy can also be replaced with other data, such as lease risk level, lease payment method, lease deposit amount and / or lease period, or can also be replaced with other lease parameters; for example, two-way matching adjustment of the lease parameters included in the lease recommendation strategy and the lease parameters included in the rental strategy includes: evaluating the lease risk level included in the lease recommendation strategy and the trust ratings included in the rental strategies of candidate lessors to obtain evaluation results; determining the adjusted lease parameters and adjusted rental parameters based on the evaluation results, and performing parameter matching adjustment on the adjusted lease parameters and adjusted rental parameters to obtain the target lease recommendation strategy and the target rental strategy; here, after the trust ratings included in the lease recommendation strategy are replaced with other data, the process of two-way matching adjustment based on the replaced data is similar to the process of two-way matching adjustment based on the trust ratings included in the lease recommendation strategy described above. You can refer to the process of two-way matching adjustment based on the trust ratings included in the lease recommendation strategy provided above and make adaptive modifications, changes or deletions as needed in the actual implementation process; Similarly, during the two-way matching adjustment process, the credibility rating included in the rental strategy can be replaced with other data, such as risk parameters, rental posting type, rental deposit amount and / or rental payment method, or it can be replaced with other rental parameters. After the credibility rating included in the rental strategy is replaced with other data, the process of two-way matching adjustment based on the replaced data is similar to the process of two-way matching adjustment based on the credibility rating included in the rental recommendation strategy described above. You can refer to the process of two-way matching adjustment based on the credibility rating included in the rental recommendation strategy provided above and make adaptive modifications, changes or deletions as needed in the actual implementation process. Alternatively, other data can be introduced during the two-way matching adjustment process based on the credibility rating included in the leasing recommendation strategy and the credibility rating included in the rental strategy. This involves performing two-way matching adjustment based on the credibility rating included in the leasing recommendation strategy, the credibility rating included in the candidate lessor's rental strategy, and other data. Alternatively, two-way matching adjustment can be performed based on multiple other data. The process of introducing other data for two-way matching adjustment, or the process of performing two-way matching adjustment based on multiple other data, can refer to the above-described process of two-way matching adjustment based on the credibility rating included in the leasing recommendation strategy and the credibility rating included in the candidate lessor's rental strategy, and can be adapted, modified, or deleted as needed during actual implementation. It will not be elaborated further here. It should also be noted that the credibility rating included in the leasing recommendation strategy and the credibility rating included in the rental strategy can be the same or different; this embodiment does not limit this.

[0030] (2) Two-way matching adjustment based on the degree of leasing intention and the degree of renting intention. In practice, to improve the success rate of matching lessees and lessors and the subsequent transaction conversion rate, a two-way matching adjustment can be made based on the lessee's and lessor's rental intentions. In one optional implementation of this embodiment, the two-way matching adjustment of the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy includes: The parameter adjustment weights for the lessee and lessor are determined based on their respective levels of interest in leasing and renting. The leasing and renting parameters are then adjusted based on these weights to obtain the target leasing recommendation strategy and the target renting strategy.

[0031] Specifically, in the process of two-way matching and adjustment based on the lessee's and lessor's rental intentions, the parameter adjustment weights for the degree of rental intention and the degree of rental intention can be determined based on the lessee's and lessor's rental intentions. Then, the rental parameters and rental parameters are adjusted based on the parameter adjustment weights to obtain the target rental recommendation strategy and target rental strategy after parameter adjustment. Here, the parameter adjustment weights corresponding to the degree of rental intention and the degree of rental intention can be the same weight or different weights.

[0032] For example, if the percentage of the lessee's willingness to lease is high and the percentage of the lessor's willingness to lease is low, the parameter adjustment weight for the lessee can be determined as a% based on the degree of the lessee's willingness to lease and the parameter adjustment weight for the lessor can be determined as b% (a>b) based on the degree of the lessor's willingness to lease. In the process of adjusting the lease parameters and rental parameters based on the parameter adjustment weights to obtain the corresponding target lease recommendation strategy and target rental strategy, a target lease recommendation strategy and target rental strategy with the lessor taking the lead and the lessee making concessions can be generated.

[0033] In addition, properties can be screened based on the tenants' willingness to rent, such as by property type, price range, and / or location. Based on this screening, the adjustment weights of parameters for both properties and landlords can be determined, and then a two-way matching adjustment can be performed based on these adjustment weights. Alternatively, tenants can be screened based on landlords' willingness to rent, and then the adjustment weights of parameters for both properties can be determined, and then a two-way matching adjustment can be performed based on these adjustment weights.

[0034] Similarly, in the process of two-way matching adjustment, in addition to the two-way matching adjustment based on the degree of leasing intention and the degree of renting intention provided above, the degree of leasing intention and / or renting intention can also be replaced with other data. For example, the degree of leasing intention can be replaced with the leasing risk level, the amount of the lease deposit, the lease payment method, the lease period and / or the lease demand information, or it can also be replaced with other leasing parameters; the degree of renting intention can be replaced with risk parameters, the amount of the lease deposit, the lease payment method, the lease period and / or the lease demand information, or it can also be replaced with other renting parameters; or, it can also be replaced with the degree of leasing intention and / or renting intention. Based on the level of rental intention, other data is introduced and combined with the level of rental intention and / or leasing intention to conduct two-way matching adjustments. Here, the process of two-way matching adjustment after the level of rental intention and / or leasing intention is replaced with other data, or after other data is introduced based on the level of rental intention and / or leasing intention, is similar to the process of two-way matching adjustment based on the level of rental intention and / or leasing intention described above. You can refer to the process of two-way matching adjustment based on the level of rental intention and / or leasing intention provided above and make adaptive modifications, changes or deletions according to the needs of actual implementation.

[0035] (3) Adjust the rental needs information of the lessee by performing two-way matching. In practice, to improve the success rate of matching tenants and landlords and the subsequent transaction conversion rate, bidirectional matching adjustments can be made based on the tenant's rental demand information and rental data and / or housing data. In one optional implementation of this embodiment, bidirectional matching adjustments are made between the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy, including: The rental demand information of the lessee is matched with rental data and housing data to obtain the matching degree. The rental parameters and rental parameters are adjusted according to the matching adjustment range corresponding to the matching degree to obtain the target rental recommendation strategy and the target rental strategy.

[0036] Specifically, in the process of two-way matching and adjustment based on the lessee's rental demand information, multiple matching adjustment intervals can be preset. Each matching adjustment interval corresponds to a different parameter adjustment strategy. The lessee's rental demand information can be matched with rental data and / or housing data to obtain the matching degree. The parameter adjustment strategy is determined according to the matching adjustment interval corresponding to the matching degree. Based on the parameter adjustment strategy, the rental parameters and rental parameters are adjusted to obtain the target rental recommendation strategy and the target rental strategy.

[0037] In addition to the aforementioned two-way matching and adjustment based on the lessee's rental demand information with rental data and housing data, two-way matching and adjustment can also be performed based on the lessor's rental demand information with the lessee's primary credit data and / or rental data. Based on this, the matching degree is calculated by matching the lessee's rental demand information with rental data and housing data, and the rental parameters and rental parameters are adjusted according to the matching adjustment range corresponding to the matching degree to obtain the target rental recommendation strategy and target rental strategy. This can be replaced by: matching the lessor's rental demand information with the lessee's primary credit data and / or rental data to calculate the matching degree, and adjusting the rental parameters and rental parameters according to the matching adjustment range corresponding to the matching degree to obtain the target rental recommendation strategy and target rental strategy. Here, in the process of two-way matching and adjustment based on the lessee's rental demand information with rental data and housing data, the rental demand information, rental data, and / or housing data can be replaced with other data, or other data can be introduced based on the rental demand information, rental data, and / or housing data. For example, two-way matching and adjustment can be performed based on the lessee's rental demand information with the lessor's second credit data and rental data, or based on the lessee's degree of rental intention with the lessor's second credit data, rental data, and housing data. Here, the two-way matching and adjustment process after the rental demand information, rental data, and / or housing data are replaced with other data, or after other data are introduced, is similar to the above-mentioned process of two-way matching and adjustment based on the lessee's rental demand information with rental data and housing data. You can refer to the above-mentioned process of two-way matching and adjustment based on the lessee's rental demand information with rental data and housing data and make adaptive modifications, changes, or deletions as needed in the actual implementation process. Similarly, in the process of two-way matching and adjustment based on the lessor's rental demand information and the lessee's primary credit data and / or lease data, the rental demand information, the lessee's primary credit data and / or lease data can also be replaced with other data or other data can be introduced on this basis. For example, two-way matching and adjustment can be performed based on the lessor's rental demand information and the lessee's primary credit data and lease risk level. Another example is two-way matching and adjustment based on the lessor's rental intention level and the lessee's primary credit data, lease data and lease period. Here, the two-way matching and adjustment process after the rental demand information, the lessee's primary credit data and / or lease data are replaced with other data or other data are introduced on this basis is similar to the above-mentioned two-way matching and adjustment process based on the lessor's rental demand information and the lessee's primary credit data and / or lease data.

[0038] (4) Perform bidirectional matching adjustment based on the matching adjustment model In specific implementation, to improve the efficiency and accuracy of bidirectional matching adjustment, a matching adjustment model can be introduced during the bidirectional matching adjustment process. This model enables bidirectional matching adjustment of the rental recommendation strategy and the leasing recommendation strategy. In one optional implementation of this embodiment, bidirectional matching adjustment of the rental parameters included in the rental recommendation strategy and the leasing parameters included in the leasing strategy includes: The rental recommendation strategy and the rental strategy are input into the matching and adjustment model for matching and adjustment to obtain the target rental recommendation strategy and the target rental strategy.

[0039] The matching adjustment model refers to a model used to match and adjust the rental recommendation strategy and the rental strategy. The input of the matching adjustment model is the rental recommendation strategy and the rental strategy, and the output is the target rental recommendation strategy and the target rental strategy. In addition, the input of the matching adjustment model can also be other data used to assist in the matching and adjustment of the strategy, such as the lessee's rental demand information, the lessor's rental demand information, the lessee's rental intention level and / or the lessor's rental intention level.

[0040] Specifically, in the process of bidirectional matching and adjustment of the rental recommendation strategy and the rental strategy, the rental recommendation strategy and the rental strategy can be input into the matching and adjustment model so that the matching and adjustment model can match and adjust based on the rental recommendation strategy and the rental strategy, and output the target rental recommendation strategy and the target rental strategy, thereby obtaining the target rental recommendation strategy and the target rental strategy.

[0041] Optionally, the matching adjustment model includes: a rental parameter adjustment module for adjusting rental parameters, and a rental parameter adjustment module for adjusting leasing parameters.

[0042] In this embodiment, during the process of adjusting the rental parameters in the matching adjustment model's rental parameter adjustment module, the parameter adjustment is implemented in an optional implementation method as follows: The adjustment parameters for the rental recommendation strategy are determined based on the input target rental strategy and rental recommendation strategy. Determine the adjustment factors for adjusting the rental parameters, and adjust the parameters according to the adjustment factors to obtain the target rental recommendation strategy.

[0043] Specifically, during the parameter adjustment process, the rental parameter adjustment module can determine the adjustment parameters of the rental recommendation strategy based on at least one of the following: the target rental strategy and rental recommendation strategy and / or the first credit data and rental data, the degree of rental intention, and rental demand information. It can also determine the adjustment factor of the rental parameters based on at least one of the following: the target rental strategy and rental recommendation strategy and / or the first credit data and rental data, the degree of rental intention, and rental demand information, as well as the lessor's relevant information. The parameters are then adjusted according to the adjustment factor to obtain the target rental recommendation strategy.

[0044] Similarly, the matching adjustment model also includes a rental parameter adjustment module, which can adjust the rental parameters. In one optional implementation of this embodiment, the parameter adjustment performed by the rental parameter adjustment module is achieved in the following manner: The rental parameters for adjusting the rental strategy are determined based on the input target rental recommendation strategy and rental strategy. Determine the adjustment factors for adjusting the rental parameters, and adjust the parameters according to the adjustment factors to obtain the target rental strategy.

[0045] Specifically, during the parameter adjustment process, the rental parameter adjustment module can determine the rental parameters for the rental strategy based on at least one of the following: the target rental recommendation strategy and rental strategy and / or the second credit data and rental data, housing data, rental intention level, and rental demand information. It can also determine the adjustment factors for the rental parameters based on at least one of the following: the target rental recommendation strategy and rental strategy and / or the second credit data and rental data, housing data, rental intention level, and rental demand information, as well as the tenant's relevant information. The parameters are then adjusted according to the adjustment factors to obtain the target rental strategy.

[0046] (5) Two-way matching adjustment based on pre-screening mechanism In the specific implementation process, during the two-way matching adjustment, a pre-screening mechanism can be introduced to screen lessors and / or lessees, and further two-way matching adjustments can be made based on the candidate lessors and / or candidate lessees obtained through screening; in an optional implementation of this embodiment, before performing two-way matching adjustments on the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy, the following steps are also included: The rental data of the lessee is matched with the rental data and housing data to obtain a matching value, and candidate lessors are selected from the lessor set according to the matching value.

[0047] For example, a matching value threshold can be preset. After matching the tenant's rental data with the rental data and / or housing data to obtain the matching value, the matching value is compared with the matching value threshold. In the set of landlords, the landlords corresponding to the housing with matching values ​​higher than the matching value threshold are identified as candidate landlords.

[0048] Here, in the process of screening landlords based on the lessee's rental data, rental data, and / or property data, the rental data, rental data, and / or property data can be replaced with other data. For example, rental data can be replaced with the lessee's primary credit data, rental risk level, rental intention level, rental deposit amount, rental payment method, rental period, and / or rental demand information, or it can also be replaced with other rental parameters or other relevant data of the lessee; rental data and / or property data can be replaced with secondary credit data, risk parameters, rental intention level, rental deposit amount, rental payment method, rental period, and / or rental demand information, or it can also be replaced with other rental parameters or other relevant data of the lessor; for example, screening landlords based on the lessee's primary credit data, rental data, and / or property data; or other data can be introduced on the basis of rental data, rental data, and / or property data, for example, screening landlords based on the lessee's primary credit data, rental data, rental data, property data, and risk parameters; After the rental data, lease data, and / or housing data are replaced with other data, or after other data are introduced, the lessor screening process is similar to the lessor screening process based on the lessee's rental data, lease data, and / or housing data described above. You can refer to the lessor screening process based on the lessee's rental data, lease data, and / or housing data provided above and make adaptive modifications, changes, or deletions as needed in the actual implementation process.

[0049] Accordingly, based on the selection of candidate lessors, the risk leasing parameters of the leasing recommendation strategy and the risk leasing parameters of the leasing strategy can be determined according to the risk information contained in the leasing recommendation strategy and / or the risk information contained in the leasing strategy. The risk leasing parameters and the risk leasing parameters can be adjusted by two-way matching to obtain the target leasing recommendation strategy and the target leasing strategy. In one optional implementation of this embodiment, the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are adjusted through bidirectional matching, including: Based on the rental risk level included in the rental recommendation strategy and / or the risk parameters included in the rental strategies of candidate lessors, determine the risk rental parameters of the rental recommendation strategy and the adjustment parameters of the rental strategy. By performing bidirectional matching and adjustment of risk leasing parameters and adjustment parameters, a target leasing recommendation strategy and a target rental strategy are obtained.

[0050] Specifically, based on the rental risk level and / or risk parameters, determine the risk rental parameters of the rental recommendation strategy and the adjustment parameters of the rental strategy, and perform two-way matching and adjustment of the risk rental parameters and adjustment parameters.

[0051] For example, if the lease risk level is low and the candidate lessor's risk parameters show no risk record, the lease transaction conditions for both the lessee and the candidate lessor can be relaxed during the matching and adjustment of risk lease parameters and adjustment parameters; as another example, if the lease risk level is low and the candidate lessor's risk parameters are medium / high risk parameters, the lessee's lease transaction protection can be improved; as yet another example, if the lease risk level is medium / high and the candidate lessor's risk parameters show no risk record, the lessor's lease transaction protection can be improved.

[0052] Here, during the two-way matching adjustment based on the rental risk level and / or risk parameters, the rental risk level and / or risk parameters can be replaced with other data. For example, the rental risk level can be replaced with the degree of rental intention, the amount of rental deposit, the rental payment method, the rental period and / or rental demand information, or it can also be replaced with other rental parameters; the risk parameters can be replaced with the degree of rental intention, the amount of rental deposit, the rental payment method, the rental period and / or rental demand information, or it can also be replaced with other rental parameters. Alternatively, other data can be introduced based on the lease risk level and / or risk parameters to match and adjust the lease risk level and / or risk parameters with other data. Here, the process of two-way matching adjustment after the lease risk level and / or risk parameters are replaced with other data, or after other data are introduced, is similar to the process of two-way matching adjustment based on the lease risk level and / or risk parameters described above. You can refer to the process of two-way matching adjustment based on the lease risk level and / or risk parameters provided above and make adaptive modifications, changes or deletions as needed in the actual implementation process.

[0053] Similarly, before performing two-way matching adjustments, tenants can be screened, and further two-way matching adjustments can be made based on the candidate tenants obtained from the screening; in an optional implementation of this embodiment, before performing two-way matching adjustments on the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy, the following steps are also included: The lessor's rental data is matched with the lease data and / or the first credit data to obtain a matching value, and candidate lessees are selected from the set of lessees according to the matching value; Accordingly, the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are adjusted through bidirectional matching, including: Based on the risk parameters included in the rental strategy and / or the rental risk level included in the rental recommendation strategy of the candidate lessee, determine the risk rental parameters of the rental strategy and the adjustment parameters of the rental recommendation strategy. By performing bidirectional matching and adjustment of risk leasing parameters and adjustment parameters, a target leasing recommendation strategy and a target leasing strategy are obtained.

[0054] Here, in the process of screening tenants based on the lessor's rental data, lease data, and / or primary credit data, the rental data, lease data, and / or primary credit data can be replaced with other data, or other data can be introduced on this basis, such as screening tenants based on the lessor's rental data, housing data, and lease data. Correspondingly, after replacing or introducing other data, you can refer to the above-mentioned process of screening tenants based on the lessor's rental data, lease data, and primary credit data, and make adaptive modifications, changes, or deletions as needed in the actual implementation process.

[0055] It should be noted that the five implementation methods for bidirectional matching and adjustment based on leasing parameters and rental parameters provided above can be combined in any form as needed in the actual implementation process, or can be adapted, modified, changed, or deleted before being combined in any form. For example, bidirectional matching and adjustment of leasing parameters included in the leasing recommendation strategy and rental parameters included in the rental strategy includes: performing credit matching between the credibility rating included in the leasing recommendation strategy and the credibility rating included in the rental strategy of the candidate lessor to obtain a credit matching result; determining the risk leasing parameters of the leasing recommendation strategy and the risk rental parameters of the rental strategy based on the risk information included in the leasing recommendation strategy and the risk rental parameters of the rental strategy; determining the parameter adjustment weights of the lessee's leasing intention and the lessor's rental intention based on the credit matching result, risk leasing parameters, risk rental parameters, and the parameter adjustment weights of leasing intention and rental intention; and performing parameter matching and adjustment on the adjusted leasing parameters and the adjusted rental parameters to obtain the target leasing recommendation strategy and the target rental strategy.

[0056] Subsequently, after obtaining the target lease recommendation strategy and the target rental strategy, a lessor recommendation set for the lessee and / or a lessor's lessee recommendation set can be determined based on the target lease recommendation strategy and / or the target rental strategy. In an optional implementation of this embodiment, the lease parameters included in the lease recommendation strategy and the rental parameters included in the rental strategy are bidirectionally matched and adjusted to match lessees with lessors. After obtaining the target lease recommendation strategy and the target rental strategy after matching and adjustment, the method further includes: Based on the matching scores of the target rental recommendation strategy and the target leasing strategy, the recommendation combinations corresponding to the target rental recommendation strategy and the target leasing strategy are ranked. The lessee's lessor recommendation set and / or the lessor's lessee recommendation set are determined based on the ranking results.

[0057] Specifically, a matching score is determined according to the target leasing recommendation strategy and the target rental strategy. Based on the matching score, the recommended combinations corresponding to the target leasing recommendation strategy and / or the target rental strategy are ranked. Based on the ranking results, a lessor recommendation set can be determined for the lessee, or a lessee recommendation set can be determined for the lessor.

[0058] As described above, in the process of housing rental assessment, rental assessment models, leasing assessment models, and / or matching adjustment models can be introduced to improve the processing efficiency of housing rental assessment. Furthermore, in order to improve the accuracy of rental assessment models, leasing assessment models, and / or matching adjustment models during the housing rental assessment process, parameter optimization can be performed on the rental assessment models, leasing assessment models, and / or matching adjustment models based on performance records. In one optional implementation of this embodiment, it further includes: Obtain the execution records of the executed target rental recommendation strategy and the executed strategies within the target rental strategy; The parameters of the lease assessment model, rental assessment model, and / or matching adjustment model are optimized based on the performance record.

[0059] Specifically, during the housing rental assessment process, the performance records of the executed target rental recommendation strategy and the executed strategies in the target rental strategy are obtained. The performance records are compared with the model outputs of the rental assessment model, the rental assessment model and / or the matching adjustment model to obtain the deviation. Based on the deviation, the model parameters of the rental assessment model, the rental assessment model and / or the matching adjustment model are adjusted to optimize the parameters of the rental assessment model, the rental assessment model and / or the matching adjustment model.

[0060] In summary, the housing rental assessment method provided in this embodiment, during the housing rental assessment process, inputs the tenant's first credit data and rental data into the rental assessment model. The rental assessment model performs rental assessment based on the input tenant data and outputs a rental recommendation strategy for the tenant. Similarly, it inputs the landlord's second credit data, rental data, and housing data into the rental assessment model. The rental assessment model performs rental assessment based on the input landlord data and outputs a rental strategy. Based on the rental recommendation strategy and rental strategy output by the rental assessment model, the rental parameters included in the rental recommendation strategy and the rental strategy included in the rental strategy are bidirectionally matched and adjusted to match tenants and landlords. After bidirectional matching and adjustment, a target rental recommendation strategy and a target rental strategy are obtained. Rental recommendations are then made based on the target rental recommendation strategy and the target rental strategy, thereby improving the accuracy of rental recommendations and thus increasing the success rate of housing rental transactions.

[0061] The following example uses a housing rental assessment method provided in this embodiment as an example in a housing rental scenario, combined with... Figure 3 The housing rental assessment and processing method provided in this embodiment will be further explained below. Figure 3 The housing rental assessment and processing method applied to housing rental scenarios includes the following steps.

[0062] Step S302: Input the lessee's first credit data and rental data into the rental assessment model for rental assessment processing to obtain the lessee's rental recommendation strategy.

[0063] Step S304: Input the lessor's second credit data, rental data, and housing data into the rental assessment model for rental assessment processing to obtain a rental strategy.

[0064] Step S306: Perform credit matching between the credibility rating included in the rental recommendation strategy and the credibility rating included in the rental strategy of the candidate lessor to obtain the credit matching result.

[0065] Step S308: Based on the risk information contained in the leasing recommendation strategy and the risk information contained in the rental strategy, determine the risk rental parameters of the leasing recommendation strategy and the risk rental parameters of the rental strategy.

[0066] Step S310: Determine the parameter adjustment weights for the lessee and the lessor based on the lessee's degree of leasing intention and the lessor's degree of leasing intention.

[0067] Step S312: Determine the adjustment parameters and the adjustment lease parameters based on the credit matching results, risk leasing parameters, risk rental parameters, leasing intention level, and rental intention level parameter adjustment weights.

[0068] Step S314: Perform parameter matching adjustments on the rental parameters and leasing parameters to obtain the target rental recommendation strategy and the target leasing strategy.

[0069] Step S316: Sort the recommended combinations corresponding to the target rental recommendation strategy and the target leasing strategy according to the recommendation matching scores of the target rental recommendation strategy and the target leasing strategy.

[0070] Step S318: Determine the lessor recommendation set for the lessee and the lessee recommendation set for the lessor based on the sorting results.

[0071] It should be noted that any one or more steps in steps S302 to S318 can be combined with any one or more steps in steps S202 to S206 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features in steps S302 to S318 can be selected and combined with any one or more technical features provided in steps S202 to S206 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features in steps S302 to S318 can be replaced with any one or more technical features provided in steps S202 to S206 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0072] This manual provides an embodiment of a housing rental assessment and processing device as follows: In the above embodiments, a housing rental assessment processing method is provided, and correspondingly, a housing rental assessment processing device is also provided, which will be described below with reference to the accompanying drawings.

[0073] Reference Figure 4 This illustration shows a schematic diagram of an embodiment of a housing rental assessment and processing device provided in this embodiment.

[0074] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0075] This embodiment provides a housing rental assessment and processing device, the device comprising: The strategy acquisition module 402 is configured to input the lessee's first credit data and rental data into the rental assessment model for rental assessment processing, and obtain the rental recommendation strategy for the lessee; The rental strategy acquisition module 404 is configured to input the lessor's second credit data, rental data and housing data into the rental assessment model for rental assessment processing to obtain the rental strategy. The strategy matching and adjustment module 406 is configured to perform bidirectional matching and adjustment of the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy in order to recommend and match the lessee and the lessor, and obtain the target rental recommendation strategy and the target rental strategy after matching and adjustment.

[0076] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0077] This manual provides an example of a housing rental assessment and processing device as follows: Corresponding to the housing rental assessment and processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a housing rental assessment and processing device, which is used to execute the housing rental assessment and processing method provided above. Figure 5 This is a structural schematic diagram of a housing rental assessment and processing device provided for one or more embodiments of this specification.

[0078] This embodiment provides a housing rental assessment and processing device, comprising: like Figure 5As shown, device 500 mainly consists of a communication interface 502, a user interface 504, a processor 506, and a data storage 508. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 510. The communication interface 502 enables device 500 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 502 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 502 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 502 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 502 may also include multiple physical communication interfaces, such as Wi-Fi, Bluetooth, and wide-area wireless interfaces. The user interface 504 includes receiving user input and providing output to the user. Therefore, user interface 504 may include input components such as a keypad, keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 504 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 504 may include software, circuitry, or other forms of logic capable of transmitting and receiving data to and from external user input / output devices. Additionally or alternatively, device 500 may support remote access from other devices via communication interface 502 or another physical interface (not shown). User interface 504 may be configured to receive user input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 504 may also be configured as a display device for rendering or displaying text fragments.

[0079] Processor 506 may include one or more general-purpose processors and / or special-purpose processors. Data storage 508 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 506. Data storage 508 may include removable and non-removable components.

[0080] Processor 506 is capable of executing program instructions 518 (e.g., compiled or uncompiled program logic and / or machine code) stored in data storage 508 to perform the various functions described herein. Data storage 508 may contain a non-transitory computer-readable medium on which program instructions are stored, which, when executed by device 500, enable device 500 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Execution of program instructions 518 by processor 506 may result in processor 506 using data 512. For example, program instructions 518 may include an operating system 522 (e.g., an operating system kernel, device drivers, and / or other modules) installed on device 500 and one or more application programs 520 (e.g., a browser, social application, or game application). Similarly, data 512 may include operating system data 516 and application data 514. Operating system data 516 is primarily accessible to operating system 522, while application data 514 is primarily accessible to one or more application programs 520. Application data 514 may reside in a file system visible or hidden from the user of device 500. Application 520 can communicate with operating system 522 through one or more application programming interfaces (APIs). These APIs facilitate application 520 in reading and / or writing application data 514, transmitting or receiving information via communication interface 502, and receiving or displaying information on user interface 504. In some terms, application 520 may be simply referred to as "app". Furthermore, application 520 can be downloaded to device 500 through one or more online app stores or app markets. However, applications can also be installed on device 500 in other ways, such as through a web browser or a physical interface on device 500 (e.g., a USB port).

[0081] In one specific embodiment, the housing rental assessment processing device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the housing rental assessment processing device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following: The lessee's initial credit data and rental data are input into the rental assessment model for rental assessment processing to obtain the rental recommendation strategy for the lessee; The landlord's second credit data, rental data, and housing data are input into the rental assessment model for rental assessment processing to obtain rental strategies. The rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are bidirectionally matched and adjusted to recommend and match the lessee and the lessor. After matching and adjustment, the target rental recommendation strategy and the target rental strategy are obtained.

[0082] This specification provides an embodiment of a computer-readable storage medium as follows: Corresponding to the housing rental assessment and processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0083] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process: The lessee's initial credit data and rental data are input into the rental assessment model for rental assessment processing to obtain the rental recommendation strategy for the lessee; The landlord's second credit data, rental data, and housing data are input into the rental assessment model for rental assessment processing to obtain rental strategies. The rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are bidirectionally matched and adjusted to recommend and match the lessee and the lessor. After matching and adjustment, the target rental recommendation strategy and the target rental strategy are obtained.

[0084] It should be noted that the embodiments of a computer-readable storage medium described in this specification and the embodiments of a housing rental assessment processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0085] This specification provides an example of a computer program product as follows: Corresponding to the housing rental assessment and processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0086] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps: The lessee's initial credit data and rental data are input into the rental assessment model for rental assessment processing to obtain the rental recommendation strategy for the lessee; The landlord's second credit data, rental data, and housing data are input into the rental assessment model for rental assessment processing to obtain rental strategies. The rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are bidirectionally matched and adjusted to recommend and match the lessee and the lessor. After matching and adjustment, the target rental recommendation strategy and the target rental strategy are obtained.

[0087] It should be noted that the embodiments of a computer program product described in this specification and the embodiments of a housing rental assessment processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0088] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments. For example, the device embodiment, equipment embodiment and computer-readable storage medium embodiment are all similar to the method embodiment, so the description is relatively simple. When reading the relevant content of the device embodiment, equipment embodiment and computer-readable storage medium embodiment, please refer to the description of the method embodiment.

[0089] While one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is merely one possible execution order among many steps, and does not represent the only execution order. Therefore, when the claims involve method steps, any changes or adjustments to the order of such steps, or the parallelism between steps, are also within the scope of protection of the claims. This specification uses specific terms to describe embodiments of this specification. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0090] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0091] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0092] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 525D, Atmel AT91SAM, Microchip PIC18F25K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0093] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0094] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0095] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0099] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0100] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0101] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising at least one…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0103] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0104] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A method for assessing and processing housing rentals, comprising: The lessee's initial credit data and rental data are input into the rental assessment model for rental assessment processing to obtain the rental recommendation strategy for the lessee; The landlord's second credit data, rental data, and housing data are input into the rental assessment model for rental assessment processing to obtain rental strategies. The parameter adjustment weights of the lessee and the lessor are determined based on the lessee's degree of rental intention and the lessor's degree of rental intention. Based on the parameter adjustment weights of the two, the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are adjusted to obtain the target rental recommendation strategy and the target rental strategy.

2. The housing rental assessment and processing method according to claim 1, after the step of determining the parameter adjustment weights of the tenant's rental intention and the landlord's rental intention, and adjusting the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy based on the parameter adjustment weights of the two, to obtain the target rental recommendation strategy and the target rental strategy, further includes: Based on the matching scores of the target rental recommendation strategy and the target leasing strategy, the recommended combinations corresponding to the target rental recommendation strategy and the target leasing strategy are ranked. The lessor recommendation set and / or the lessor's lessee recommendation set are determined based on the ranking results.

3. The housing rental assessment and processing method according to claim 1, wherein the rental recommendation strategy includes rental parameters comprising at least one of the following: The system includes: a lease risk level obtained from a lease risk assessment; a trust rating obtained from a tenant trust rating calculation; a lease deposit amount obtained from a lease deposit matching process; a lease payment method obtained from a lease payment method recommendation process; and a lease period obtained from a lease period prediction process.

4. The housing rental assessment and processing method according to claim 1, wherein the rental strategy includes rental parameters comprising at least one of the following: The system includes: a trust rating calculated from the lessor's trust rating; a lease posting type obtained from compliance testing of lease postings; a credit tag obtained from the lessor's credit assessment; a lease deposit amount obtained from matching lease deposits; a lease payment method recommended from the lease payment method recommendations; and risk parameters obtained from the lease risk assessment.

5. The housing rental assessment and processing method according to claim 1, before the step of determining the parameter adjustment weights of the lessee's and lessor's rental intentions, further includes: The lessee's rental data is matched with the rental data and the housing data to obtain a matching value, and candidate lessors are filtered from the lessor set according to the matching value.

6. The housing rental assessment and processing method according to claim 1 further includes: Obtain the execution records of the executed target rental recommendation strategy and the executed strategies within the target rental strategy; The parameters of the lease assessment model and / or the rental assessment model are optimized based on the performance record.

7. A housing rental assessment and processing device, comprising: The strategy acquisition module is configured to input the lessee's initial credit data and rental data into the rental assessment model for rental assessment processing, and obtain the rental recommendation strategy for the lessee. The rental strategy acquisition module is configured to input the landlord's second credit data, rental data, and housing data into the rental assessment model for rental assessment processing to obtain the rental strategy. The strategy matching and adjustment module is configured to determine the parameter adjustment weights of the lessee and the lessor based on their rental intentions, and adjust the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy based on the parameter adjustment weights of the two parties to obtain the target rental recommendation strategy and the target rental strategy.

8. A housing rental assessment and processing device, comprising: processor; And, a memory configured to store computer-executable instructions, which, when executed, cause the processor to: The lessee's initial credit data and rental data are input into the rental assessment model for rental assessment processing to obtain the rental recommendation strategy for the lessee; The landlord's second credit data, rental data, and housing data are input into the rental assessment model for rental assessment processing to obtain rental strategies. The parameter adjustment weights of the lessee and the lessor are determined based on the lessee's degree of rental intention and the lessor's degree of rental intention. Based on the parameter adjustment weights of the two, the rental parameters included in the rental recommendation strategy and the rental parameters included in the rental strategy are adjusted to obtain the target rental recommendation strategy and the target rental strategy.

9. A computer-readable storage medium for storing computer-executable instructions that, when executed, implement the steps of the method of claim 1.

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