Intelligent real estate mobile terminal drop house system and method

Abstract

The invention discloses an intelligent real estate mobile terminal drop house system and method, and relates to the technical field of real estate registration, and the system comprises a data collection module which is used for collecting preference information of a user; the intelligent recommendation module is used for recommending related real estate information according to the preference information of the user acquired by the data acquisition module; the evaluation module is used for surveying and mapping the real estate according to the information of the data acquisition module and evaluating the value and potential risk of the real estate; information of real estate can be recommended in combination with requirements and characteristics of users, and personalized recommendation can improve user satisfaction and application stickiness, increase possibility of real estate transaction, optimize user experience and reduce search time; accurate real estate data can also be provided at the mobile terminal, the user is helped to make a more intelligent decision, and the user is assisted to quickly understand various information of logging in real estate at the mobile terminal.

Description

Technical Field

[0001] The present invention relates to the field of real estate registration technology, and more specifically, to an intelligent real estate mobile terminal settlement system and method. Background Art

[0002] The intelligent real estate mobile terminal settlement system is mainly used in the real estate field, especially in real estate registration, management and transactions. The role of the intelligent real estate mobile terminal settlement system is to improve the efficiency and transparency of real estate management, reduce the time and cost of users to handle related affairs, and at the same time ensure the security and privacy of user data. Through intelligent services, the system can provide users with a more convenient and personalized real estate management experience;

[0003] The Chinese patent application number 202211013899.1 proposes a settlement consultation service system, including a policy popularization terminal, an information collection terminal, an information analysis terminal, an intercommunication service terminal, and a tracking service terminal. Through the cooperation between the terminals, the best settlement plan is formulated for the customer, and real-time tracking and supervision services are provided to ensure the smooth implementation of the settlement plan and avoid giving up halfway and causing the settlement plan matched for the customer to fail;

[0004] However, in actual use, due to the interface limitations of the mobile terminal, it is difficult for users to browse the data they want in a short time. The sensor accuracy of mobile devices is insufficient, and the real estate surveying and evaluation required in the process of mobile terminal settlement will result in insufficient accuracy, making it impossible for users to understand the information of the real estate they log in to in a short time. Summary of the invention

[0005] In order to solve the problem, the present invention provides an intelligent real estate mobile terminal settlement system and method.

[0006] In order to achieve the above-mentioned purpose, on the one hand, an intelligent real estate mobile terminal settlement system is proposed, including a data collection module, wherein the data collection module is used to collect user preference information;

[0007] An intelligent recommendation module, which is used to recommend relevant real estate information based on the user's preference information collected by the data collection module, specifically:

[0008] Define preferred characteristics;

[0009] Extract preference features from the user's preference information collected by the data collection module; according to the formula Calculate and obtain the user's preference value Q, where W i is the actual value of the i-th preference feature, ω i is the weight coefficient of the i-th preference feature, and n is the number of preference features;

[0010] The above scheme is used to calculate and obtain the preference value Q of each real estate information;

[0011] A range threshold of a preference value is set in advance, and real estate information within the positive and negative range threshold of the user preference value Q is transmitted to the user's mobile terminal;

[0012] An evaluation module is used to survey and map real estate based on the information from the data acquisition module, and to evaluate the value and potential risks of the real estate.

[0013] Preferably, the working process of the intelligent recommendation module also includes:

[0014] Get the number of login times of the user, and mark the user who has logged in less than three times as a new user;

[0015] For new users, according to the formula Q n =Q*β1, obtain the user preference value Q of the new user n , where β1 is the preset adjustment coefficient;

[0016] In the new user preference value Q n Real estate information within the positive and negative range thresholds is transmitted to the user's mobile terminal.

[0017] Preferably, the intelligent recommendation module is also used to avoid recommending too much real estate information:

[0018] Divide a day into three time periods, set an adjustment coefficient for each time period, and obtain adjustment coefficients β2, β3 and β4;

[0019] For each time period, multiply the user preference value Q by the corresponding adjustment coefficient to obtain a new user preference value Q r .

[0020] Preferably, the specific manner in which the assessment module performs real estate surveying and mapping is as follows:

[0021] Data collection, which collects basic information about real estate, including location, size, and structure;

[0022] Site Survey: Conduct an on-site survey of the property and record detailed dimensions and features;

[0023] Data integration, the collected data is integrated and transmitted to the user's mobile database through signals for storage.

[0024] Preferably, the evaluation module is also used to adjust the surveying and mapping results of the real estate, specifically:

[0025] According to the formula Calculate and obtain the comprehensive deviation calibration value T of real estate surveying and mapping, where k is the number of deviation factors, ∈ y is the actual value of the yth deviation factor, X y is the weight coefficient of the y-th deviation factor;

[0026] Add the comprehensive variation calibration value T to the real estate surveying result to obtain the corrected surveying value H y , where the mapping value H y It can be any one of H1, H2 and H3, which represent the surveying area, surveying length and surveying angle of the real estate respectively.

[0027] Preferably, the specific method of obtaining the deviation factor is as follows:

[0028] Defining deviation factors in advance, including positioning deviation correction value B1;

[0029] Scale deviation calibration value B2;

[0030] Angle deviation calibration value B3;

[0031] Environmental factor calibration value B4;

[0032] Correspondingly, ∈1 represents the positioning deviation correction value B1, ∈2 represents the scale deviation calibration value B2, and so on.

[0033] Preferably, the specific method of evaluating the value of the real estate by the evaluation module is as follows:

[0034] Obtain the market value of the real estate J;

[0035] Obtain rental income F from real estate;

[0036] According to the formula Z=(J*σ1)+(F*σ2), the comprehensive value Z of the user's registered real estate is obtained, where σ1 and σ2 are preset proportional coefficients.

[0037] Preferably, the specific manner in which the assessment module assesses the potential risk of real estate is as follows:

[0038] Obtain the risk probability N of the real estate logged in by the user;

[0039] Obtain the risk impact M of the real estate logged in by the user;

[0040] The potential risk L of real estate is obtained by multiplying the risk probability N by the risk impact M;

[0041] A potential risk threshold is set in advance. If the potential risk L of the real estate is greater than the potential risk threshold of the real estate, an alarm signal is generated and transmitted to the user's mobile terminal.

[0042] On the other hand, the present invention also proposes an intelligent real estate mobile terminal settlement method, which is applicable to the intelligent real estate mobile terminal settlement system described in claim 1, comprising the following steps:

[0043] Step 1: Collect user preference information;

[0044] Step 2: used to recommend relevant real estate information based on preference information;

[0045] Step 3: Survey the real estate based on the collected information and assess the value and potential risks of the real estate.

[0046] Beneficial effects: Real estate information can be recommended based on user needs and characteristics. Personalized recommendations can improve user satisfaction and application stickiness, increase the possibility of real estate transactions, optimize user experience, and reduce search time.

[0047] It can also provide accurate real estate data on mobile devices, help users make more informed decisions, reduce human errors in the evaluation process, and assist users to quickly understand various types of information about logged-in real estate on mobile devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0050] like Figure 1 As shown: an intelligent real estate mobile settlement system, including a data collection module, which is used to collect user preference information; it should be noted that, in this embodiment, collecting user preference information specifically includes user behavior tracking: collecting user browsing history, search history and interactive behavior in the application.

[0051] Preference identification: Identify user preferences and needs by analyzing user behavior.

[0052] Equipment information collection: Collect sensor data and location information of mobile devices for real estate surveying and evaluation, which can provide basic data for personalized services and provide accurate input information for subsequent evaluations;

[0053] It should also be noted that the intelligent real estate mobile terminal settlement system is mainly used in the real estate field, especially in real estate registration, management and transactions. The role of the intelligent real estate mobile terminal settlement system is to improve the efficiency and transparency of real estate management, reduce the time and cost of users to handle related affairs, and at the same time ensure the security and privacy of user data. Through intelligent services, the system can provide users with a more convenient and personalized real estate management experience;

[0054] However, in actual use, due to the interface limitations of the mobile terminal, it is difficult for users to browse the data they want in a short time. The sensor accuracy of mobile devices is insufficient. The real estate surveying and evaluation required in the process of mobile terminal settlement will produce insufficient accuracy, making it impossible for users to understand the information of the real estate they log in in a short time.

[0055] An intelligent recommendation module, which is used to recommend relevant real estate information based on the user's preference information collected by the data collection module, specifically:

[0056] Define the preferred features; it should be noted that, in this embodiment, the preferred features include real estate type (residential, commercial, industrial, etc.), location, price range, area and number of rooms;

[0057] Extracting preference features from the user's preference information collected by the data collection module; It should be noted that, in this embodiment, preference features can be extracted from the user's browsing history, search history, favorites, likes, comments and other behaviors; using text analysis, sentiment analysis and other methods to extract preference information from the user's comments;

[0058] According to the formula Calculate and obtain the user's preference value Q, where W i is the actual value of the i-th preference feature, ω i is the weight coefficient of the i-th preference feature, and n is the number of preference features; it should be noted that in this embodiment, the number of preference features is five, namely, real estate type (residential, commercial, industrial, etc.), location, price range, area and number of rooms, where W1 represents the actual value of the real estate type, and ω1 represents the weight coefficient of the real estate type;

[0059] It should also be noted that the value of the real estate type can be 1 if it is residential, 2 if it is commercial, and 3 if it is industrial, and the location is the distance value of the real estate to the nearest business district;

[0060] The above scheme is used to calculate and obtain the preference value Q of each real estate information;

[0061] A range threshold of a preference value is set in advance, and real estate information within the positive and negative range threshold of the user preference value Q is transmitted to the user's mobile terminal; it should be noted that user satisfaction and application stickiness can be improved, the possibility of real estate transactions can be increased, user experience can be optimized, and search time can be reduced;

[0062] An evaluation module is used to map the real estate according to the information from the data acquisition module and evaluate the value and potential risks of the real estate. It should be noted that providing accurate real estate data helps users make more informed decisions and reduces human errors in the evaluation process.

[0063] As an optional embodiment: the working process of the intelligent recommendation module also includes: It should be noted that, however, in actual use, new users or new real estate lack sufficient historical data, which makes it difficult for the recommendation system to generate accurate recommendations; user behavior data may not be rich enough, which makes it difficult for the recommendation system to capture user preferences;

[0064] Get the number of login times of the user, and mark the user who has logged in less than three times as a new user;

[0065] For new users, according to the formula Q n =Q*β1, obtain the user preference value Q of the new user n , where β1 is a preset adjustment coefficient; it should be noted that, in this embodiment, the value of β1 is 0.982;

[0066] In the new user preference value Q n The real estate information within the positive and negative range thresholds is transmitted to the user's mobile terminal. It should be noted that new users or new real estate lack sufficient historical data, which makes it difficult for the recommendation system to generate accurate recommendations. In this technical solution, by reducing the user preference value, it can rely more on the similarity of item attributes in the absence of user data.

[0067] As an optional embodiment: the intelligent recommendation module is also used to avoid recommending too much real estate information: it should be noted that the recommendation system may over-recommend popular or common real estate, ignoring the diverse needs of users; this technical solution can avoid possible deviations in the intelligent recommendation module as much as possible, and improve the accuracy of the recommendation system and user satisfaction;

[0068] A day is divided into three time periods, and an adjustment coefficient is set for each time period to obtain adjustment coefficients β2, β3, and β4; it should be noted that, in this embodiment, the adjustment coefficients β2, β3, and β4 are used to adjust the user preference value, and β2, β3, and β4 are all 0.5 to 1.5. In this embodiment, the values ​​of β2, β3, and β4 can be 0.982, 1.023, and 1.173;

[0069] For each time period, multiply the user preference value Q by the corresponding adjustment coefficient to obtain a new user preference value Q r It should be noted that, in this embodiment, each time period is eight hours. In this implementation, for example, if it is the first time period, the user preference value Q is multiplied by β2 to obtain a new user preference value Q r , and then according to Q r To recommend real estate information, let the user's preference value automatically adjust slightly over time during the day to avoid recommending duplicate content to users.

[0070] As an optional embodiment: the specific manner in which the assessment module performs real estate surveying and mapping is as follows:

[0071] Data collection, which collects basic information about real estate, including location, size, and structure;

[0072] Site Survey: Conduct an on-site survey of the property and record detailed dimensions and features;

[0073] It should be noted that it also includes the use of mobile device sensors, using the GPS, laser scanner or other sensors of the mobile device for measurement;

[0074] Data integration, the collected data is integrated and transmitted to the user's mobile terminal database through signals for storage. It should be noted that the information of real estate can be surveyed and mapped through the mobile terminal, which increases convenience.

[0075] As an optional embodiment: the evaluation module is also used to adjust the surveying and mapping results of the real estate, specifically: it should be noted that when real estate is surveyed and mapped on a mobile device, some deviations may occur due to various reasons such as equipment limitations, environmental factors, and operating errors. This technical solution can reduce the deviation of surveying and mapping;

[0076] According to the formula Calculate and obtain the comprehensive deviation calibration value T of real estate surveying and mapping, where k is the number of deviation factors, ∈ y is the actual value of the yth deviation factor, X y is the weight coefficient of the y-th deviation factor;

[0077] Add the comprehensive variation calibration value T to the real estate surveying result to obtain the corrected surveying value H y , where the mapping value H y It can be any one of H1, H2 and H3, which represent the surveying area, surveying length and surveying angle of the real estate respectively.

[0078] As an optional embodiment: the specific method of obtaining the deviation factor is as follows:

[0079] The deviation factor is defined in advance, specifically including the positioning deviation correction value B1; it should be noted that, in this embodiment, the positioning deviation correction value B1 is obtained by calculating the deviation through calibration through known fixed control points;

[0080] Scale deviation calibration value B2; It should be noted that, in this embodiment, the scale deviation is determined by comparing the measurement results of the standard scale tool and the mobile device to obtain the scale deviation calibration value B2;

[0081] Angle deviation calibration value B3; It should be noted that, in this embodiment, an accurate angle measurement tool (such as a total station) is used as a reference to calculate the deviation to obtain the angle deviation calibration value B2;

[0082] Environmental factor calibration value B4; It should be noted that, in this embodiment, environmental parameters (such as temperature, humidity, etc.) are monitored, and the environmental factor calibration value B4 is obtained by calculating the calibration value according to the model of the impact of the environment on the performance of the measuring device;

[0083] Correspondingly, ∈1 represents the positioning deviation correction value B1, ∈2 represents the scale deviation calibration value B2, and so on. It should also be noted that there are four corresponding weight coefficients of the deviation factors, specifically X1, X2, X3 and X4, corresponding to each deviation factor respectively. In this embodiment, the values ​​of X1, X2, X3 and X4 can be 0.192, 0.283, 0.172 and 0.353.

[0084] As an optional embodiment: the specific manner in which the evaluation module evaluates the value of the real estate is as follows:

[0085] The market value J of the real estate is obtained; it should be noted that the value of the real estate to be appraised is estimated by comparing the prices of similar real estates sold recently, which can be obtained by multiplying the adjusted area H1 by the average price per square meter;

[0086] Obtain the rental income F of the real estate; it should be noted that if the real estate is used for rental, the income method estimates its value by calculating the net operating income of the real estate and dividing it by the capitalization rate. The net operating income is based on the rental area and rent, while the capitalization rate reflects the return on investment;

[0087] According to the formula Z = (J*σ1) + (F*σ2), the comprehensive value Z of the real estate registered by the user is obtained, where σ1 and σ2 are preset proportional coefficients. It should be noted that in this embodiment, the values ​​of σ1 and σ2 can be 0.493 and 0.507. It should also be noted that in order to obtain a more accurate real estate value, this technical solution combines the market value and the income value of the real estate value to obtain the comprehensive value Z of the real estate.

[0088] As an optional embodiment: the specific manner in which the assessment module assesses the potential risk of real estate is as follows:

[0089] The risk probability N of the real estate logged in by the user is obtained; it should be noted that, in this embodiment, the acquisition method is to divide the total number of historical risk times by the total number of observations;

[0090] The risk impact M of the real estate registered by the user is obtained; it should be noted that in this embodiment, the risk impact M is obtained by multiplying the comprehensive value Z of the real estate registered by the user by the potential loss. In this embodiment, the potential loss may be 0.2;

[0091] The potential risk L of real estate is obtained by multiplying the risk probability N by the risk impact M;

[0092] A potential risk threshold is set in advance. If the potential risk L of the real estate is greater than the potential risk threshold of the real estate, an alarm signal is generated and transmitted to the user's mobile terminal. Accurate real estate data can also be provided on the mobile terminal to help users make more informed decisions, reduce human errors in the assessment process, and assist users in quickly understanding various types of information logged in to the real estate on the mobile terminal.

[0093] like Figure 2 As shown, on the other hand, the present invention also proposes an intelligent real estate mobile terminal settlement method, comprising the following steps:

[0094] Step 1: Collect user preference information;

[0095] Step 2: used to recommend relevant real estate information based on preference information;

[0096] Step 3: Survey the real estate based on the collected information and assess the value and potential risks of the real estate.

[0097] Working principle:

[0098] Real estate information can be recommended based on user needs and characteristics. Personalized recommendations can improve user satisfaction and application stickiness, increase the possibility of real estate transactions, optimize user experience, and reduce search time.

[0099] It can also provide accurate real estate data on mobile devices, help users make more informed decisions, reduce human errors in the evaluation process, and assist users to quickly understand various types of information about logged-in real estate on mobile devices.

[0100] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. An intelligent real estate mobile settlement system, It is characterized in that A data collection module is included, wherein the data collection module is used to collect user preference information; An intelligent recommendation module, which is used to recommend relevant real estate information based on the user's preference information collected by the data collection module, specifically: Define preferred characteristics; Extract preference features from the user's preference information collected by the data collection module; according to the formula Calculate and obtain the user's preference value Q, where W i is the actual value of the i-th preference feature, ω i is the weight coefficient of the i-th preference feature, and n is the number of preference features; The above scheme is used to calculate and obtain the preference value Q of each real estate information; A range threshold of a preference value is set in advance, and real estate information within the positive and negative range threshold of the user preference value Q is transmitted to the user's mobile terminal; An evaluation module is used to survey and map real estate based on the information from the data acquisition module, and to evaluate the value and potential risks of the real estate.

2. According to claim 1, an intelligent real estate mobile terminal settlement system, It is characterized in that The working process of the intelligent recommendation module also includes: Get the number of login times of the user, and mark the user who has logged in less than three times as a new user; For new users, according to the formula Q n =Q*β 1 , get the user preference value Q of the new user n , where β 1 is the preset adjustment factor; In the new user preference value Q n Real estate information within the positive and negative range thresholds is transmitted to the user's mobile terminal.

3. According to claim 2, an intelligent real estate mobile terminal settlement system, It is characterized in that The intelligent recommendation module is also used to avoid recommending too much real estate information: Divide a day into three time periods, set an adjustment coefficient for each time period, and obtain the adjustment coefficient β 2 , β 3 and β 4 ; For each time period, multiply the user preference value Q by the corresponding adjustment coefficient to obtain a new user preference value Q r .

4. According to claim 1, an intelligent real estate mobile terminal settlement system, It is characterized in that The specific method of the assessment module for real estate surveying and mapping is as follows: Data collection, which collects basic information about real estate, including location, size, and structure; Site Survey: Conduct an on-site survey of the property and record detailed dimensions and features; Data integration, the collected data is integrated and transmitted to the user's mobile database through signals for storage.

5. According to claim 4, an intelligent real estate mobile terminal settlement system, It is characterized in that The evaluation module is also used to adjust the surveying and mapping results of the real estate, specifically: According to the formula Calculate and obtain the comprehensive deviation calibration value T of real estate surveying and mapping, where k is the number of deviation factors, ∈ y is the actual value of the yth deviation factor, X y is the weight coefficient of the y-th deviation factor; Add the comprehensive variation calibration value T to the real estate surveying result to obtain the corrected surveying value H y , where the mapping value H y Can be H 1 , H 2 and H 3 Any one of them represents the surveying area, surveying length and surveying angle of the real estate.

6. According to claim 5, an intelligent real estate mobile terminal settlement system, It is characterized in that The specific method of obtaining the deviation factor is as follows: Define the deviation factors in advance, including the positioning deviation correction value B 1 ; Scale deviation calibration value B 2 ; Angle deviation calibration value B 3 ; Environmental factor calibration value B 4 ; The corresponding ∈ 1 Represents the positioning deviation correction value B 1 ,∈ 2 Represents the scale deviation calibration value B 2 , and so on.

7. The intelligent real estate mobile terminal settlement system according to claim 1, It is characterized in that The specific method of evaluating the value of real estate by the evaluation module is as follows: Obtain the market value of the real estate J; Obtain rental income F from real estate; According to the formula Z=(J*σ 1 )+(F*σ 2 ), and obtain the comprehensive value Z of the user's registered real estate, where σ 1 and σ 2 is the preset scaling factor.

8. The intelligent real estate mobile terminal settlement system according to claim 7, It is characterized in that The specific method of evaluating the potential risk of real estate by the evaluation module is as follows: Obtain the risk probability N of the real estate logged in by the user; Obtain the risk impact M of the real estate logged in by the user; The potential risk L of real estate is obtained by multiplying the risk probability N by the risk impact M; A potential risk threshold is set in advance. If the potential risk L of the real estate is greater than the potential risk threshold of the real estate, an alarm signal is generated and transmitted to the user's mobile terminal.

9. An intelligent real estate mobile terminal settlement method, applicable to the intelligent real estate mobile terminal settlement system according to claim 1, It is characterized in that The following steps are involved: Step 1: Collect user preference information; Step 2: used to recommend relevant real estate information based on preference information; Step 3: Survey the real estate based on the collected information and assess the value and potential risks of the real estate.

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