House resource allocation method and device, equipment, storage medium and program product

Abstract

The invention discloses a housing resource distribution method, device and equipment, a storage medium and a program product. The invention relates to the technical field of computers. Acquiring a housing resource application request sent by a target object; acquiring information of a target object and initial housing resource information based on the housing resource application request; screening the initial housing resource information based on the weights of a plurality of preset matching indexes to obtain candidate housing resource information; determining scores of a plurality of preset matching indexes between the target object and each piece of candidate housing resource information based on the information of the target object and the candidate housing resource information; and determining target housing resource information from the candidate housing resource information based on the scores and weights of the plurality of preset matching indexes. According to the housing resource distribution method provided by the embodiment of the invention, the calculation amount can be greatly reduced, and the housing resource distribution efficiency and accuracy can be improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a housing allocation method, device, equipment, storage medium and program product. Background Art

[0002] At present, there are still some difficulties in the allocation of housing, especially affordable housing, which mainly come from policy implementation, data management, demand differences and supervision mechanisms. Among them, the difficulties at the policy implementation level mainly lie in the complexity of setting access standards and review work; the difficulties in data management are mainly reflected in the opacity and poor publicity of information; the difficulties of demand differences lie in the differences in housing needs, economic conditions and living habits of different families, which makes the matching of people and houses quite complicated; the imperfection of the supervision mechanism is also an important factor affecting the effect of housing allocation. Therefore, it is particularly important to be able to accurately allocate housing and achieve human defense matching. Summary of the invention

[0003] The embodiments of the present invention provide a housing allocation method, device, equipment, storage medium and program product, which can improve the efficiency and accuracy of housing allocation.

[0004] In a first aspect, an embodiment of the present invention provides a housing allocation method, comprising:

[0005] Obtaining a housing application request sent by a target object; wherein the housing application request includes weights of a plurality of preset matching indicators, and the weights of the preset matching indicators are set by the target object;

[0006] Acquiring the target object information and initial housing source information based on the housing source application request; wherein the target object information includes object sub-information of multiple dimensions, and the housing source information includes housing source sub-information of multiple dimensions;

[0007] Filtering the initial housing source information based on the weights of the plurality of preset matching indicators to obtain candidate housing source information;

[0008] Determining scores of a plurality of preset matching indicators between the target object and each candidate housing source information based on the information of the target object and the candidate housing source information; wherein the scores of the preset matching indicators reflect the matching degree between the object sub-information and the housing source sub-information corresponding to the preset matching indicators;

[0009] Target housing information is determined from the candidate housing information based on the scores of the multiple preset matching indicators and the weights.

[0010] In a second aspect, an embodiment of the present invention further provides a housing allocation device, comprising:

[0011] A housing application request acquisition module, used to acquire a housing application request sent by a target object; wherein the housing application request includes weights of a plurality of preset matching indicators, and the weights of the preset matching indicators are set by the target object;

[0012] An information acquisition module, configured to acquire information of the target object and initial housing source information based on the housing source application request; wherein the information of the target object includes object sub-information of multiple dimensions, and the housing source information includes housing source sub-information of multiple dimensions;

[0013] A candidate housing source information acquisition module, configured to filter the initial housing source information based on the weights of the plurality of preset matching indicators to obtain candidate housing source information;

[0014] A matching index score determination module, used to determine the scores of a plurality of preset matching indexes between the target object and each candidate housing source information based on the information of the target object and the candidate housing source information; wherein the scores of the preset matching indexes reflect the matching degree between the object sub-information and the housing source sub-information corresponding to the preset matching index;

[0015] The target housing information determination module is used to determine the target housing information from the candidate housing information based on the scores of the multiple preset matching indicators and the weights.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the housing allocation method as described in the embodiment of the present invention is implemented.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the housing allocation method as described in the embodiment of the present invention.

[0018] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the housing allocation method as described in the embodiment of the present invention.

[0019] The embodiment of the present invention discloses a housing allocation method, device, equipment, storage medium and program product, which obtain a housing application request sent by a target object; wherein the housing application request includes weights of multiple preset matching indicators, and the weights of the preset matching indicators are set by the target object; based on the housing application request, information of the target object and initial housing information are obtained; wherein the information of the target object includes object sub-information of multiple dimensions, and the housing information includes housing sub-information of multiple dimensions; based on the weights of multiple preset matching indicators, the initial housing information is screened to obtain candidate housing information; based on the information of the target object and the candidate housing information, scores of multiple preset matching indicators between the target object and each candidate housing information are determined; wherein the scores of the preset matching indicators reflect the matching degree between the object sub-information and the housing sub-information corresponding to the preset matching indicators; and target housing information is determined from the candidate housing information based on the scores and weights of the multiple preset matching indicators. The housing allocation method provided in the embodiment of the present invention first screens the initial housing information based on the weights of multiple preset matching indicators, then determines the scores of multiple preset matching indicators between the target object and the screened candidate housing information, and finally determines the target housing information based on the scores. This can not only greatly reduce the amount of calculation, but also improve the efficiency and accuracy of housing allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a housing allocation method provided by an embodiment of the present invention;

[0022] Figure 2 A flow chart of a housing allocation method provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of the structure of a housing allocation device provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0026] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations. It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility of the implementation of the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0027] In terms of policy implementation, the setting of entry thresholds has become quite complicated due to the large differences in economic development, residents' income and actual demand for affordable housing in various regions. At the same time, different forms of affordable housing, such as public rental housing, low-rent housing, economically affordable housing and price-limited housing, cover different groups of disadvantaged people, which further increases the difficulty of the review work. In addition, with the large-scale construction of affordable housing, the number of applicants has also increased significantly, making the review work more arduous and prone to unfair distribution problems.

[0028] In terms of data management, the lack of transparency in applicant information on public housing applications, the single disclosure method and means, and the small scope of coverage make it difficult for the public to effectively supervise. At the same time, due to the diversification of residents' income and the imperfection of the family income information system, the review of family economic income has become extremely difficult, which is prone to the problem of rent-seeking in public housing.

[0029] In terms of demand differences, the matching of people and housing has become quite complicated due to the differences in housing needs, economic conditions and living habits of different families. Especially under the current household registration system, the supply of affordable housing in some cities is mainly limited to families with local household registration, which further exacerbates the difficulty of matching people and housing.

[0030] In terms of supervision mechanisms, there is a lack of effective supervision and punishment mechanisms for violations in the allocation of affordable housing, which enables some people to exploit system loopholes to obtain undeserved benefits, seriously undermining fairness and justice.

[0031] In order to solve the above problems, this embodiment realizes accurate allocation of housing resources based on the information of the target object and the housing resource information.

[0032] Figure 1The present invention provides a flowchart of a housing allocation method, which is applicable to the case of accurately allocating housing resources. The method can be executed by a housing allocation device, which can be implemented in the form of software and / or hardware, and optionally, by an electronic device, which can be a mobile terminal, a PC or a server. Figure 1 As shown, the method comprises the following steps:

[0033] S110, obtaining a housing application request sent by a target object.

[0034] Among them, the housing application request includes the weights of multiple preset matching indicators, and the weights of the preset matching indicators are set by the target object. The target object can be understood as the housing applicant, and the weight of each preset matching indicator can be a value between 0 and 1. The preset matching indicator is a pre-set indicator that reflects the matching degree between the target object's information and the housing information. Exemplarily, the preset matching indicators may include: matching indicators of demand and supply, matching indicators of economic ability, and matching indicators of social background and community information. Among them, the matching indicator of demand and supply can be understood as: the matching between the housing demand of the housing applicant (such as: apartment type, area, location preference, etc.) and the supply of housing; the matching indicator of economic ability can be understood as: the matching between the economic situation of the housing applicant and the rental or selling price of the housing; the matching indicator of social background and community information can be understood as: the matching between the social background of the housing applicant (such as: occupation, etc.) and the cultural atmosphere and resident structure of the community where the housing is located.

[0035] In this embodiment, the housing application request may be initiated by the target object through the housing application interface. Optionally, the housing application request sent by the target object may be obtained by: displaying the housing application interface; obtaining the weight of each preset matching indicator in response to the setting operation of the target object in the weight setting item; and generating the housing application request based on the weight of each preset matching indicator.

[0036] Among them, the housing application interface includes multiple preset matching index weight setting items. The preset matching index weight setting item displays the preset matching index and its settable weight range, which is 0-1. When setting the weight of each preset matching index, the following rules must be followed: the sum of the weights of each preset matching index is 1.

[0037] Specifically, the target object logs in to the housing application system through the client, and the housing application interface is displayed in the client. In the interface, there are multiple weight setting items of preset matching indicators. The target object sets the weight of each preset matching indicator in the weight setting item according to its own demand for housing. After the user clicks to confirm, the housing application system generates a housing application request according to the weight of each preset matching indicator set by the user. Exemplarily, for the matching indicators of demand and supply, the matching indicators of economic ability, and the matching indicators of social background and community information, if the user attaches more importance to the matching of economic ability, the weight of the matching indicator of economic ability can be set higher, such as: the weight of the matching indicator of demand and supply is set to 0.2; the weight of the matching indicator of economic ability is set to 0.5, and the weight of the matching indicator of social background and community information is set to 0.3.

[0038] S120, obtaining target object information and initial housing information based on the housing application request.

[0039] The target object information includes object sub-information of multiple dimensions, and the housing source information includes housing source sub-information of multiple dimensions. If the target object information is pre-collected and stored in the database, the target object information can be obtained from the database according to the object identifier in the housing source application request; if the target object information is not pre-collected, the target object needs to be reminded to enter the corresponding information. The initial housing source information can be pre-collected and stored in the database.

[0040] In this embodiment, the object sub-information of multiple dimensions may include: basic information of the object, economic status information, housing demand and current situation information, social background information, application motivation and expectation information, personal credit and behavior information, etc. Among them, basic information of the target may include family structure (such as: single, couple, family with children, etc.), educational background, professional status and employment stability, etc.; economic status information may include family income (income source, income level and changing trend), property status (including movable property, immovable property, etc.), etc.; housing demand and status information may include current housing conditions (currently living house type, area and facilities, etc.), housing demand (target object expected housing type, location, rent and selling price, etc.) and specific needs (such as: barrier-free facilities, close to schools or medical facilities); social background information may include household registration status (whether it is a local household registration) and social relations (family members, relatives and friends, community relations), etc.; application motivation and expectation information may include the reason for applying for housing (such as: financial difficulties, improving living conditions), the understanding and evaluation of housing allocation policies and expectations and plans for future life; personal credit and behavior information may include personal credit status (whether there is a bad credit record, such as: overdue repayment, arrears, etc.) and compliance with rules and regulations.

[0041] In this embodiment, the sub-information of multiple dimensions of housing sources includes: basic housing source information, housing source facilities and supporting information, housing source cost information, housing source application condition information, housing source status information, and housing source community information. The basic housing source information may include the geographical location (including the area, street, community, etc. where the housing source is located), the type of house (such as an apartment, a detached house, a townhouse, etc.) and the area and type of house (including the building area, usable area and unit structure of the house source). The housing source facilities and supporting information may include indoor facilities (including the equipment of kitchens, toilets, air conditioners, heating and other equipment), public area facilities (such as elevators, greening, activity venues, etc. that affect the living experience and social scope) and surrounding facilities (schools, hospitals, supermarkets, transportation stations and other surrounding facilities to reflect the convenience of life). The housing source cost information may include the rent or selling price, other additional fees (such as: property management fees, water and electricity fees and network fees, etc.). The housing source application condition information can be understood as the applicant's qualification conditions, etc. (such as: income restrictions, family structure, etc.). The information on the housing status may include the current state of the house (including the age and decoration of the house), the repair and maintenance status (including the responsible party for house repair and maintenance, and the repair cycle). The information on the community where the house is located includes the community atmosphere (describing the cultural characteristics of the community, the composition of residents, etc.), and the organization of activities (introducing various activities organized by the community, such as festivals, celebrations, cultural and sports activities, etc.).

[0042] S130, screening the initial housing source information based on the weights of a plurality of preset matching indicators to obtain candidate housing source information.

[0043] The number of initial housing information is multiple, and the number of candidate housing information is one or more. In this embodiment, the number of initial housing information is large. In order to reduce the amount of calculation, the initial housing information can be screened first. The weights of multiple preset matching indicators can reflect the target object's demand ranking for various factors of the housing, so the initial housing information can be screened first based on the weights of multiple preset matching indicators.

[0044] Specifically, the initial housing information is screened based on the weights of multiple preset matching indicators, and the method for obtaining candidate housing information can be: determining the preset matching indicator with the largest weight as the target matching indicator; and screening the initial housing information based on the object sub-information and housing sub-information corresponding to the target matching indicator to obtain candidate housing information.

[0045] The larger the preset matching index is, the more the target object is concerned about the matching degree between the house source sub-information and the object sub-information in the preset matching index. In this embodiment, the initial house source information is screened based on the object sub-information and the house source sub-information corresponding to the target matching index, and the house source information that matches the target object to a certain extent can be preferentially screened out.

[0046] Optionally, the initial housing information is screened based on the object sub-information and housing sub-information corresponding to the target matching index, and the method for obtaining the candidate housing information may be: extracting the object sub-information corresponding to the target matching index from the information of the target object, and extracting the housing sub-information of the target matching index object from the initial housing information; comparing the extracted object sub-information with the extracted housing sub-information respectively; and screening out the initial housing information that matches the extracted housing sub-information with the extracted object sub-information to obtain the candidate housing information.

[0047] Among them, the matching index is used to reflect the matching relationship between a certain object sub-information in the target object information and a certain house source sub-information in the house source information, that is, the corresponding object sub-information and house source sub-information can be obtained from the matching index. Exemplarily, if the target matching index is a matching index of demand and supply, then the corresponding object sub-information is housing demand information, and the corresponding house source sub-information is the geographical location, house type and area and apartment type in the house source basic information. If the target matching index is a matching index of economic ability, then the corresponding object sub-information is economic status information, and the corresponding house source sub-information is house source cost information. If the target matching index is a matching index of social background and community information, then the corresponding object sub-information is social background information, and the corresponding house source sub-information is the community information where the house source is located.

[0048] Specifically, the extracted object sub-information is compared with the extracted property sub-information respectively; if the extracted property sub-information matches the extracted object sub-information, the initial property information is determined as candidate property information; if the extracted property sub-information matches the extracted object sub-information, the initial property information is eliminated.

[0049] S140: Determine scores of a plurality of preset matching indicators between the target object and each candidate housing source information based on the target object information and the candidate housing source information.

[0050] Among them, the score of the preset matching index reflects the matching degree between the object sub-information corresponding to the preset matching index and the property sub-information. The higher the score, the higher the matching degree between the object sub-information corresponding to the preset matching index and the property sub-information. The larger the score, the lower the matching degree between the object sub-information corresponding to the preset matching index and the property sub-information.

[0051] Optionally, the method for determining the scores of multiple preset matching indicators between the target object and each candidate housing information based on the information of the target object and the candidate housing information may be: for each preset matching indicator, extracting the object sub-information corresponding to the preset matching indicator from the information of the target object, and extracting the housing sub-information of the object of the preset matching indicator from the candidate housing information, and then determining the matching degree between the extracted object sub-information and the extracted housing sub-information, respectively, to obtain the score of the preset matching indicator.

[0052] Optionally, a method for determining the scores of multiple preset matching indicators between the target object and each candidate housing information based on the target object information and the candidate housing information may be: inputting the target object information and each candidate housing information into a pre-trained machine learning model, and outputting the scores of multiple preset matching indicators between the target object and each candidate housing information.

[0053] The pre-trained machine learning model is used to determine the scores of multiple preset matching indicators between the output target object information and the housing source information. In this embodiment, before the target object information and the candidate housing source information are input into the pre-trained machine learning model, the target object information and the candidate housing source information can be standardized, that is, the structural format of the target object information and the candidate housing source information can be unified.

[0054] Among them, the machine learning model is obtained by training based on the collected sample set, and the sample set includes multiple information pairs consisting of object information and housing information, and the information pair carries the real scores of multiple preset matching indicators. Specifically, the training method of the machine learning model can be: input the information pairs in the sample information set into the machine learning model, output the predicted scores of multiple preset matching indicators of the information pair, and then determine the loss function of the predicted score and its corresponding real score, and finally perform reverse gradient parameter adjustment on the machine learning model based on the loss function to obtain the trained machine learning model. In this embodiment, the scores of multiple preset matching indicators between the target object and each candidate housing source are determined by a pre-trained machine learning model, which can improve the calculation accuracy and efficiency.

[0055] S150, determining target housing information from candidate housing information based on scores and weights of multiple preset matching indicators.

[0056] The number of the determined target property information may be one or more, and the number of the target property information may be preset.

[0057] Specifically, the method for determining the target housing information from the candidate housing information based on the scores and weights of multiple preset matching indicators can be: weighted summing the scores of multiple preset matching indicators based on the weights to obtain the matching scores of the target object and each candidate housing information; and determining a preset number of candidate housing information with the highest matching scores as the target housing information.

[0058] Among them, in this embodiment, after obtaining the scores of multiple preset matching indicators between the information of the target object and each candidate housing information, it is necessary to perform weighted summation on the scores of the multiple preset matching indicators to obtain the matching scores of the target object and each candidate housing information, and finally determine the preset number of candidate housing information with the highest matching scores as the target housing information.

[0059] Specifically, the candidate housing information is sorted in descending order according to the matching score, and then a preset number of candidate housing information ranked at the top are determined as target housing information; or, the candidate housing information is sorted in descending order according to the matching score, and then a preset number of candidate housing information ranked at the bottom are determined as target housing information.

[0060] In this embodiment, in order to improve the accuracy of the determined target housing information, the target housing information may be further modified. Optionally, after the target housing information is determined from the candidate housing information based on the scores and weights of the plurality of preset matching indicators, the following step is further included: adjusting the target housing information based on the personalized demand sub-information in the target object information to obtain the adjusted target housing information.

[0061] Among them, the personalized demand sub-information can be understood as the specific demand in the above-mentioned housing source sub-information (such as barrier-free facilities, proximity to schools or medical facilities). Specifically, the process of adjusting the target housing source information based on the personalized demand sub-information in the target object information can be: extracting the surrounding facilities information in the target housing source information, and then comparing the surrounding facilities information with the personalized demand sub-information to determine whether the surrounding facilities information matches the personalized demand sub-information. If they match, the target housing source information is retained. If not, the target housing source information is deleted, and the remaining candidate housing source information (candidate housing source information other than the target housing source information) is traversed in order of matching scores from high to low, and the surrounding facilities information traversed to the candidate housing source information is extracted to determine whether the surrounding facilities information matches the personalized demand sub-information. If they match, the traversed candidate housing source information is determined as the new target housing source information, and the traversal is stopped. If not, the next candidate housing source information is traversed until the new target housing source information is determined. In this embodiment, the personalized demand sub-information adjusts the target housing source information so that the determined target housing source information meets the personalized demand of the user, thereby improving the success rate of housing source allocation.

[0062] In this embodiment, in order to further improve the accuracy of the target housing source information, the target housing source information may be manually reviewed or manually verified.

[0063] Optionally, after determining the target property information from the candidate property information based on the scores and weights of multiple preset matching indicators, the following steps are also included: obtaining a verification result in response to a user's verification operation on the target property information; and retaining the target property information that has passed the verification.

[0064] The verification result includes a verification pass and a verification fail. Specifically, when the user verifies the target house information, the verification can be performed based on a preset verification rule. In this embodiment, the content of the preset verification rule is not limited.

[0065] Optionally, after determining the target housing information from the candidate housing information based on the scores and weights of multiple preset matching indicators, the following step is also included: pushing the target housing information to the target object to receive confirmation information or rejection information of the target object for the target housing information.

[0066] In this embodiment, if the target object confirms the target house source information, it is preliminarily determined that the house source allocation is successful, and the target object can proceed with the subsequent application process. If the target object rejects the target house source information, the house source allocation fails.

[0067] The technical solution of this embodiment is to obtain a housing application request sent by a target object; wherein the housing application request includes weights of multiple preset matching indicators, and the weights of the preset matching indicators are set by the target object; based on the housing application request, the information of the target object and the initial housing information are obtained; wherein the information of the target object includes object sub-information of multiple dimensions, and the housing information includes housing sub-information of multiple dimensions; the initial housing information is screened based on the weights of multiple preset matching indicators to obtain candidate housing information; based on the information of the target object and the candidate housing information, the scores of multiple preset matching indicators between the target object and each candidate housing information are determined; wherein the scores of the preset matching indicators reflect the matching degree between the object sub-information and the housing sub-information corresponding to the preset matching indicators; and the target housing information is determined from the candidate housing information based on the scores and weights of the multiple preset matching indicators. The housing allocation method provided by the embodiment of the present invention first screens the initial housing information based on the weights of multiple preset matching indicators, then determines the scores of multiple preset matching indicators between the target object and each screened candidate housing information, and finally determines the target housing information based on the scores, which can not only greatly reduce the amount of calculation, but also improve the efficiency and accuracy of housing allocation.

[0068] Based on the above embodiments, Figure 2 is a flow chart of a housing allocation method provided by an embodiment of the present invention, such as Figure 2 As shown, the method comprises the following steps:

[0069] S210, displaying a housing application interface, obtaining weights of various preset matching indicators in response to the target object's setting operation in the weight setting item, and generating a housing application request based on the weights of various preset matching indicators.

[0070] S220, obtaining target object information and initial housing information based on the housing application request.

[0071] S230: Determine the preset matching index with the largest weight as the target matching index.

[0072] S240, extracting the object sub-information corresponding to the target matching index from the information of the target object, and extracting the property sub-information of the target matching index object from the initial property information, and comparing the extracted object sub-information with the extracted property sub-information respectively; screening out the initial property information that matches the extracted property sub-information with the extracted object sub-information to obtain candidate property information.

[0073] S250, input the target object information and each candidate housing source information into a pre-trained machine learning model, and output the scores of multiple preset matching indicators between the target object and each candidate housing source.

[0074] S260, performing weighted summation on scores of multiple preset matching indicators based on weights to obtain a matching score between the target object and each candidate housing source information; and determining a preset number of candidate housing source information with the highest matching scores as target housing source information.

[0075] S270, adjusting the target housing source information based on the personalized demand sub-information in the target object information to obtain adjusted target housing source information.

[0076] S280, pushing the target housing source information to the target object to receive confirmation information or rejection information of the target object for the target housing source information.

[0077] Figure 3 is a schematic diagram of the structure of a housing allocation device provided by an embodiment of the present invention, such as Figure 3 As shown, the device comprises:

[0078] The housing application request acquisition module 310 is used to acquire the housing application request sent by the target object; wherein the housing application request includes weights of multiple preset matching indicators, and the weights of the preset matching indicators are set by the target object;

[0079] The information acquisition module 320 is used to acquire the target object information and the initial housing source information based on the housing source application request; wherein the target object information includes object sub-information of multiple dimensions, and the housing source information includes housing source sub-information of multiple dimensions;

[0080] A candidate housing source information acquisition module 330 is used to filter the initial housing source information based on the weights of multiple preset matching indicators to obtain candidate housing source information;

[0081] The matching index score determination module 340 is used to determine the scores of multiple preset matching indexes between the target object and each candidate housing source information based on the target object information and the candidate housing source information; wherein the scores of the preset matching indexes reflect the matching degree between the object sub-information and the housing source sub-information corresponding to the preset matching index;

[0082] The target housing information determination module 350 is used to determine the target housing information from the candidate housing information based on the scores and weights of multiple preset matching indicators.

[0083] Optionally, the housing application request acquisition module 310 is further used to:

[0084] Displaying a housing application interface; wherein the housing application interface includes a plurality of weight setting items of preset matching indicators;

[0085] In response to the setting operation of the target object in the weight setting item, the weight of each preset matching indicator is obtained;

[0086] Generate a property application request based on the weights of each preset matching indicator.

[0087] Optionally, the candidate housing source information acquisition module 330 is further used to:

[0088] Determine the preset matching index with the largest weight as the target matching index;

[0089] The initial housing source information is screened based on the object sub-information and housing source sub-information corresponding to the target matching index to obtain candidate housing source information.

[0090] Optionally, the candidate housing source information acquisition module 330 is further used to:

[0091] Extracting object sub-information corresponding to the target matching index from the target object information, and extracting property sub-information of the target matching index object from the initial property information;

[0092] Compare the extracted object sub-information with the extracted property sub-information respectively;

[0093] The initial housing source information that matches the extracted housing source sub-information and the extracted object sub-information is screened out to obtain candidate housing source information.

[0094] Optionally, the matching index score determination module 340 is further configured to:

[0095] The target object information and the candidate property information are input into a pre-trained machine learning model, and the scores of multiple preset matching indicators between the target object and the candidate property are output.

[0096] Optionally, the target housing source information determination module 350 is further configured to:

[0097] Based on the weights, the scores of multiple preset matching indicators are weighted and summed to obtain the matching scores between the target object and each candidate housing source information;

[0098] A preset number of candidate housing information with the highest matching scores are determined as target housing information.

[0099] Optionally, it also includes: a target property information adjustment module, which is used to:

[0100] The target housing source information is adjusted based on the personalized demand sub-information in the target object information to obtain the adjusted target housing source information.

[0101] Optionally, it also includes: a verification module, used to:

[0102] In response to the user's verification operation on the target property information, a verification result is obtained; wherein the verification result includes a verification pass and a verification fail;

[0103] Keep the target property information that has passed the verification.

[0104] Optionally, it also includes: a target property information push module, which is used to:

[0105] The target property information is pushed to the target object to receive the target object's confirmation or rejection information on the target property information.

[0106] Optionally, the object sub-information of multiple dimensions includes: basic information of the object, economic status information, housing demand and current situation information, social background information, application motivation and expectation information, personal credit and behavior information.

[0107] Optionally, multiple dimensions of property sub-information include: basic property information, property settings and supporting information, property cost information, property application conditions information, property status information, and property community information.

[0108] The above device can execute the methods provided by all the above embodiments of the present invention, and has the corresponding functional modules and beneficial effects of executing the above methods. For technical details not described in detail in this embodiment, please refer to the methods provided by all the above embodiments of the present invention.

[0109] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0110] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a housing allocation method.

[0113] In some embodiments, the housing allocation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the housing allocation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the housing allocation method in any other appropriate manner (e.g., by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0116] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0120] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the housing allocation method provided in any embodiment of the present application.

[0121] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0122] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A housing allocation method, characterized in that: include: Obtaining a housing application request sent by a target object; wherein the housing application request includes weights of a plurality of preset matching indicators, and the weights of the preset matching indicators are set by the target object; Acquiring the target object information and initial housing source information based on the housing source application request; wherein the target object information includes object sub-information of multiple dimensions, and the housing source information includes housing source sub-information of multiple dimensions; Filtering the initial housing source information based on the weights of the plurality of preset matching indicators to obtain candidate housing source information; Determining scores of a plurality of preset matching indicators between the target object and each candidate housing source information based on the information of the target object and the candidate housing source information; wherein the scores of the preset matching indicators reflect the matching degree between the object sub-information and the housing source sub-information corresponding to the preset matching indicators; Target housing information is determined from the candidate housing information based on the scores of the multiple preset matching indicators and the weights.

2. The method according to claim 1, characterized in that Get the housing application request sent by the target object, including: Displaying a housing application interface; wherein the housing application interface includes a plurality of weight setting items of preset matching indicators; In response to the setting operation of the target object in the weight setting item, obtaining the weight of each preset matching indicator; A housing application request is generated based on the weights of the preset matching indicators.

3. The method according to claim 1, characterized in that The initial housing source information is screened based on the weights of the plurality of preset matching indicators to obtain candidate housing source information, including: Determine the preset matching index with the largest weight as the target matching index; The initial housing source information is screened based on the object sub-information and housing source sub-information corresponding to the target matching index to obtain candidate housing source information.

4. The method according to claim 3, characterized in that The initial housing source information is screened based on the object sub-information and the housing source sub-information corresponding to the target matching index to obtain candidate housing source information, including: Extracting object sub-information corresponding to the target matching index from the target object information, and extracting property sub-information of the target matching index object from the initial property information; Compare the extracted object sub-information with the extracted property sub-information respectively; The initial housing source information that matches the extracted housing source sub-information and the extracted object sub-information is screened out to obtain candidate housing source information.

5. The method according to claim 1, characterized in that Determining scores of a plurality of preset matching indicators between the target object and each candidate housing source information based on the target object information and the candidate housing source information includes: The information of the target object and the information of each candidate property are input into a pre-trained machine learning model, and the scores of multiple preset matching indicators between the target object and each candidate property are output.

6. The method according to claim 1, characterized in that Determining target housing information from the candidate housing information based on the scores of the plurality of preset matching indicators and the weights includes: Based on the weights, weighted summation is performed on the scores of the plurality of preset matching indicators to obtain a matching score between the target object and each candidate housing source information; A preset number of candidate housing information with the highest matching scores are determined as target housing information.

7. The method according to claim 1 or 6, characterized in that: After determining the target housing source information from the candidate housing source information based on the scores of the plurality of preset matching indicators and the weights, the method further includes: The target housing source information is adjusted based on the personalized demand sub-information in the target object information to obtain adjusted target housing source information.

8. The method according to claim 1 or 6, characterized in that: After determining the target housing source information from the candidate housing source information based on the scores of the plurality of preset matching indicators and the weights, the method further includes: In response to the user's verification operation on the target property information, a verification result is obtained; wherein the verification result includes a verification pass and a verification fail; Keep the target property information that has passed the verification.

9. The method according to claim 1 or 6, characterized in that: After determining the target housing source information from the candidate housing source information based on the scores of the plurality of preset matching indicators and the weights, the method further includes: The target housing source information is pushed to the target object to receive confirmation information or rejection information of the target object for the target housing source information.

10. The method according to claim 1, characterized in that The object sub-information of the multiple dimensions includes: basic information of the object, economic status information, housing demand and current situation information, social background information, application motivation and expectation information, personal credit and behavior information.

11. The method according to claim 1, characterized in that: The multiple dimensions of housing sub-information include: basic housing information, housing settings and supporting information, housing cost information, housing application conditions information, housing status information, and housing community information.

12. A housing allocation device, characterized in that: include: A housing application request acquisition module, used to acquire a housing application request sent by a target object; wherein the housing application request includes weights of a plurality of preset matching indicators, and the weights of the preset matching indicators are set by the target object; An information acquisition module, configured to acquire information of the target object and initial housing source information based on the housing source application request; wherein the information of the target object includes object sub-information of multiple dimensions, and the housing source information includes housing source sub-information of multiple dimensions; A candidate housing source information acquisition module, configured to filter the initial housing source information based on the weights of the plurality of preset matching indicators to obtain candidate housing source information; A matching index score determination module, used to determine the scores of a plurality of preset matching indexes between the target object and each candidate housing source information based on the information of the target object and the candidate housing source information; wherein the scores of the preset matching indexes reflect the matching degree between the object sub-information and the housing source sub-information corresponding to the preset matching index; The target housing information determination module is used to determine the target housing information from the candidate housing information based on the scores of the multiple preset matching indicators and the weights.

13. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the housing allocation method as described in any one of claims 1 to 11 when executing the computer program.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the housing allocation method as described in any one of claims 1-11 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the housing allocation method according to any one of claims 1 to 11.

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