A data consistency management method for a cross-regional real estate registration information system

Abstract

This invention discloses a data consistency management method for a cross-regional real estate registration information system, specifically relating to the field of data management technology, including: data monitoring and collection, information feasibility assessment, algorithm model construction, algorithm implementation evaluation model construction, and data consistency verification and feedback. This invention provides a data consistency management method for a cross-regional real estate registration information system that combines machine learning algorithm models. Based on the monitoring and analysis of parameters such as information data generated by the target region for the cross-regional real estate registration information system, it assesses the feasibility of constructing a cross-regional real estate registration information system in the target region. For feasible cross-regional real estate registration information systems, it constructs a data verification and monitoring-machine learning algorithm model, then constructs an algorithm implementation evaluation model. Based on the algorithm model's data processing, it generates geographic reference information, model performance information, and rule effectiveness information, and judges the algorithm model's effectiveness in meeting user needs.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and more specifically, to a data consistency management method for a cross-regional real estate registration information system. Background Technology

[0002] Traditionally, real estate data is divided by region or province, with different regions possessing their own independent databases and information management systems. This model leads to information silos, making it difficult to effectively integrate cross-regional data. For example, an individual may own real estate in different cities, but the information is difficult to query and manage on a unified platform, causing inconvenience for unified real estate scheduling, mortgages, and transactions. Data consistency management methods for cross-regional real estate registration information systems involve the integration and collaborative management of real estate data from multiple regions. With the acceleration of economic development and urbanization, the demand for cross-regional real estate transactions, management, and evaluation is increasing, thus requiring an efficient, stable, and transparent cross-regional real estate registration information system. Such systems aim to integrate real estate data from different regions, providing consistent and accurate data support for users, governments, financial institutions, and others to meet diverse business needs.

[0003] However, the data consistency management methods for cross-regional real estate registration information systems still have many problems, such as inconsistent data formats and standards, data fragmentation, data consistency issues, and privacy and security issues. Therefore, a data consistency management method for cross-regional real estate registration information systems is needed to provide data quality assurance, decision support, and risk management for these systems. This would enable the standardization of real estate registration information in different regions, facilitate data sharing and integration, enhance cross-regional collaborative capabilities, ensure the rational allocation and effective utilization of resources, and improve overall operational efficiency. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a data consistency management method for a cross-regional real estate registration information system. Based on the monitoring and analysis of parameters such as information data generated by the cross-regional real estate registration information system in the target area, the feasibility of constructing a cross-regional real estate registration information system in the target area is evaluated. A data verification and monitoring-machine learning algorithm model is constructed for the feasible cross-regional real estate registration information system, and then an algorithm implementation evaluation model is constructed. Based on the data processing of the algorithm model, geographic reference information, model performance information, and rule effectiveness information are generated to determine the effectiveness of the algorithm model in meeting user needs.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for data consistency management in a cross-regional real estate registration information system includes the following steps:

[0007] Step S1: Monitor and collect cross-regional real estate registration information system data generated in the target area, including registration quantity information, personnel flow information and real estate standard information, and obtain geographic reference information, model performance information and rule effectiveness information after the algorithm model is constructed;

[0008] Step S2: Construct a feasibility assessment model for real estate registration information, obtain a feasibility assessment index for real estate registration information, and confirm the feasibility of constructing a cross-regional real estate registration information system in the target area;

[0009] Step S3: Construct a data verification and monitoring-machine learning algorithm model for feasible cross-regional real estate registration information systems, and output the acquired model;

[0010] Step S4: Construct an algorithm implementation evaluation model. Based on the algorithm model's processing of data, generate geographic reference information, model performance information, and rule effectiveness information to determine the algorithm model's effectiveness in meeting user needs.

[0011] Step S5: When fulfilling user requirements, perform consistency verification on real estate registration data from different ports and platforms, and feed back the verification results to step S3 as a factor in the construction of the data verification and monitoring-machine learning algorithm model.

[0012] Specifically, in step S1, the data consistency management method for the cross-regional real estate registration information system first requires an assessment of the feasibility of constructing the cross-regional real estate registration information system, selecting regions with high feasibility for its construction, processing the data in the computer software Apache Spark, and constructing, evaluating, and optimizing machine learning models in the Scikit-learn module of Python. In actual production, due to factors such as equipment costs and the necessity of real-time monitoring, this invention is based on the premise of continuously monitoring regional real estate registration information under the condition that the cross-regional real estate registration information system operates for a long time in a relatively stable environment. This may lead to anomalies in privacy data protection at a certain monitoring moment.

[0013] In step S2, the real estate registration information of the target area is evaluated to confirm the feasibility of a cross-regional real estate registration information system in the target area. Specifically, this involves collecting registration quantity information, population flow information, and real estate standard information from the target area. The registration quantity information includes a registration intention coefficient, denoted as Y. dj Personnel mobility information includes a personnel mobility coefficient, denoted as L. ry Real estate standard information includes real estate standard coefficients, denoted as B. bd ;

[0014] Registration willingness coefficient Y dj A questionnaire survey was conducted among residents in the target area to obtain the number of residents willing to register their real estate information across regions, which was denoted as S. j The total resident population of the target area is denoted as S. z Then the registration willingness coefficient Y dj =S j / S z ;

[0015] Personnel mobility coefficient L ry By collecting data on the inflow and outflow of people in the target area over a year, and labeling them as R... r and C r The total resident population of the target area is denoted as S. z Then the personnel mobility coefficient L ry =(R r +C r ) / S z ;

[0016] Real Estate Standard Coefficient B bd To ensure consistency of core fields across regions, the real estate cross-regional data format is divided into n parts. Parts with consistent core fields receive one point, while those without receive zero points. Each individual part is denoted as F. i Let i be the part number of the core field, i = 1, 2, 3, ..., n, where n is a positive integer. Then, the standard coefficient of real estate...

[0017] The feasibility assessment model for real estate registration information is constructed by weighting three aspects: registration quantity information, personnel flow information, and real estate standard information, generating a feasibility assessment coefficient BK for real estate registration information. pg The corresponding coefficients are the registration intention coefficient Y. dj Personnel mobility coefficient L ry and real estate standard coefficient B bd The formula formed is BK pg =a1×Y dj +a2×L ry +a3×B bd ;

[0018] Meanwhile, a1, a2, and a3 are set according to the actual situation. For example, the expert weighting method can be used, which involves inviting experts in relevant fields to determine the weights of each indicator through professional opinion surveys and comprehensive evaluations, to ensure that the weight coefficients accurately reflect the importance of each indicator in the feasibility assessment of real estate registration information. In addition, methods such as the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation can also be considered to determine the weight coefficients to ensure their objectivity and scientific validity. These will not be elaborated upon here.

[0019] In step S2, the feasibility assessment coefficient of real estate registration information obtained from the real estate registration information feasibility assessment model is used to reflect the feasibility of the cross-regional real estate registration information system in the target area. The larger the value, the more people in the target area are willing to register real estate registration information, the greater the flow of people and the greater the volume of real estate transactions, that is, the greater the demand for the cross-regional real estate registration information system. At the same time, the more similar the real estate registration information standards between regions, the less difficult the integration is. Therefore, the greater the feasibility of the cross-regional real estate registration information system in the target area.

[0020] In step S2, when the feasibility assessment coefficient BK of the real estate registration information is... pg When the threshold is greater than or equal to the set threshold, it indicates that the cross-regional real estate registration information system is highly feasible in the target area, and an input data signal is sent to build an optimized management system.

[0021] When the feasibility assessment coefficient of real estate registration information is BK pg If the input threshold is less than the set threshold, it indicates that the cross-regional real estate registration information system is not feasible in the target area. Therefore, no input data signal is issued, the target area is temporarily not included in the cross-regional real estate registration information system, and the evaluation result is directly output.

[0022] In step S3, a data verification and monitoring-machine learning algorithm model is constructed. The data verification and monitoring automatically verifies the consistency and integrity of the data by building a rule engine to ensure that the input data meets the preset standards. At the same time, the machine learning algorithm analyzes and processes the optimization process and results, and automatically identifies and classifies real estate registration information based on historical data to improve the consistency and accuracy of the data.

[0023] Furthermore, the method for constructing the data verification and monitoring-machine learning algorithm model of this invention is as follows:

[0024] Step S3.1: Data preprocessing and partitioning, removing duplicates, missing values ​​and outliers, identifying key features related to the consistency of real estate registration information, such as address, right holder, area, etc., and performing standardization processing, dividing the dataset into training set and test set, usually in a ratio of 70%-80%;

[0025] Step S3.2, Construct and train the logistic regression model. Construct the logistic regression model in the machine learning algorithm, defining data consistency as a binary classification problem, i.e., Y=1 indicates consistency, Y=0 indicates inconsistency. Establish the model formula: Where P(Y=1|X) is the probability that the target variable is 1, X is the feature variable, and β is the model parameter; after construction, the parameter β is estimated using the maximum likelihood estimation method with the training set, and the loss function L(β) is: Where m is the number of samples, y i These are real labels;

[0026] Step S3.3, Prediction and Validation: Use the trained model to make predictions on the test set. Monitor the consistency of new data and process the data through cross-validation: Divide the dataset into K subsets. In each cross-validation, select one subset as the validation set and the remaining K-1 subsets as the training set. Repeat this process K times, using a different subset as the validation set each time, using the formula... Calculate the average error of all K validations, where Error(i) is the error of the i-th validation. Select the model with the lowest average error as the final model and output the obtained model.

[0027] In step S4, the algorithm implementation evaluation model is constructed. Specifically, this involves collecting data to verify and monitor the georeferenced information, model performance information, and rule effectiveness information within the machine learning algorithm model. The georeferenced information includes georeferenced coefficients, denoted as D. cz Model performance information includes model performance coefficients, calibrated as M. xn Rule performance information includes rule performance coefficients, denoted as G. xn ;

[0028] Geographic reference factor D cz Using the address information obtained in the algorithm model in step S3, geocoding technology is used to convert the address information into geographic coordinates, and the geographic coordinates in the x and y directions are respectively labeled as X. z and Y z z is the geographic coordinate number, z = 1, 2, 3, ..., m, where m is a positive integer. Obtain the standard geographic coordinates and label the standard geographic coordinates in the x and y directions as X. b and Y b Then the geographical reference coefficient

[0029] Model performance coefficients M xn By obtaining the results of the algorithm model in step S3, the proportions of correct predictions and incorrect predictions to all predictions are respectively denoted as Z. w and C w And change the prediction threshold, where w is the prediction number for different thresholds, w = 1, 2, 3, ..., h, where h is a positive integer, then the model performance coefficients are...

[0030] Rule effectiveness coefficient G xnBy obtaining the results of the algorithm model in step S3, the rules defined during the model construction process are removed. If the results obtained by the algorithm model remain unchanged after removal, the removed rules are considered invalid rules; if the results obtained by the algorithm model change after removal, the removed rules are considered valid rules. The number of valid rules and the number of invalid rules are respectively labeled as Y. g and W g Then the rule effectiveness coefficient G xn =Y g ÷(Y g +W g );

[0031] The algorithm implementation evaluation model is constructed by weighting three aspects: geographic reference information, model performance information, and rule effectiveness information in the algorithm model, and generates the algorithm implementation evaluation index SS. pg The corresponding coefficients are the geographical reference coefficient D. cz Model performance coefficients M xn and rule effectiveness coefficient G xn The formula formed is SS pg =γ1×D cz -γ2×M xn -γ3×G xn ;

[0032] In step S4, the algorithm implementation evaluation index obtained in the algorithm implementation evaluation model is used to reflect the implementation effect of the data verification and monitoring-machine learning algorithm model. The larger the algorithm implementation evaluation index, the larger the geographical location offset, the smaller the model evaluation performance, the lower the efficiency of the rules defined by the model, and the worse the implementation effect of the data verification and monitoring-machine learning algorithm model.

[0033] In step S4, when the algorithm achieves the evaluation index SS pg If the value exceeds the set threshold, it indicates that the data verification and monitoring-machine learning algorithm model cannot achieve the data consistency management required by the user. Return to step S3, redefine the model building factors and build the model.

[0034] When the algorithm achieves the evaluation index SS pg When the result is less than or equal to the set threshold, it indicates that the data verification and monitoring-machine learning algorithm model can achieve the data consistency management required by the user, directly output the evaluation result, and continue to use the current algorithm model.

[0035] The technical effects and advantages of this invention are as follows:

[0036] This invention assesses the feasibility of constructing a cross-regional real estate registration information system in the target area by monitoring and analyzing parameters such as information data generated by the cross-regional real estate registration information system in the target area. It then constructs a data verification and monitoring-machine learning algorithm model for the feasible cross-regional real estate registration information system, and further constructs an algorithm implementation evaluation model. Based on the data processing of the algorithm model, it generates geographic reference information, model performance information, and rule effectiveness information to determine the effectiveness of the algorithm model in meeting user needs. Attached Figure Description

[0037] Figure 1 This is a flowchart of a data consistency management method for a cross-regional real estate registration information system according to the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0039] This invention is a data consistency management method for a cross-regional real estate registration information system. It is based on monitoring and analyzing parameters such as information data generated by the cross-regional real estate registration information system in the target area, assessing the feasibility of building a cross-regional real estate registration information system in the target area, and constructing a data verification and monitoring-machine learning algorithm model for feasible cross-regional real estate registration information systems. Then, it constructs an algorithm implementation evaluation model, and generates geographic reference information, model performance information, and rule effectiveness information based on the data processing of the algorithm model, and judges the effectiveness of the algorithm model in meeting user needs.

[0040] Example 1

[0041] like Figure 1 As shown, the steps of a data consistency management method for a cross-regional real estate registration information system are as follows:

[0042] Step S1: Monitor and collect cross-regional real estate registration information system data generated in the target area, including registration quantity information, personnel flow information and real estate standard information, and obtain geographic reference information, model performance information and rule effectiveness information after the algorithm model is constructed;

[0043] Step S2: Construct a feasibility assessment model for real estate registration information, obtain a feasibility assessment index for real estate registration information, and confirm the feasibility of constructing a cross-regional real estate registration information system in the target area;

[0044] Step S3: Construct a data verification and monitoring-machine learning algorithm model for feasible cross-regional real estate registration information systems, and output the acquired model;

[0045] Step S4: Construct an algorithm implementation evaluation model. Based on the algorithm model's processing of data, generate geographic reference information, model performance information, and rule effectiveness information to determine the algorithm model's effectiveness in meeting user needs.

[0046] Step S5, Data Consistency Verification and Feedback: When fulfilling user requirements, the consistency of real estate registration data from different ports and platforms is verified, and the verification results are fed back to step S3 as a factor in the construction of the data verification and monitoring-machine learning algorithm model.

[0047] Specifically, in step S1, the data consistency management method for the cross-regional real estate registration information system first requires an assessment of the feasibility of constructing the cross-regional real estate registration information system, selecting regions with high feasibility for its construction, processing the data in the computer software Apache Spark, and constructing, evaluating, and optimizing machine learning models in the Scikit-learn module of Python. In actual production, due to factors such as equipment costs and the necessity of real-time monitoring, this invention is based on the premise of continuously monitoring regional real estate registration information under the condition that the cross-regional real estate registration information system operates for a long time in a relatively stable environment. This may lead to anomalies in privacy data protection at a certain monitoring moment.

[0048] In step S2, the real estate registration information of the target area is evaluated to confirm the feasibility of a cross-regional real estate registration information system in the target area. Specifically, this involves collecting registration quantity information, population flow information, and real estate standard information from the target area. The registration quantity information includes a registration intention coefficient, denoted as Y. dj Personnel mobility information includes a personnel mobility coefficient, denoted as L. ry Real estate standard information includes real estate standard coefficients, denoted as B. bd ;

[0049] Registration willingness coefficient Y dj A questionnaire survey was conducted among residents in the target area to obtain the number of residents willing to register their real estate information across regions, which was denoted as S. j The total resident population of the target area is denoted as S. z Then the registration willingness coefficient Y dj =S j / S z ;

[0050] Personnel mobility coefficient L ryBy collecting data on the inflow and outflow of people in the target area over a year, and labeling them as R... r and C r The total resident population of the target area is denoted as S. z Then the personnel mobility coefficient L ry =(R r +C r ) / S z ;

[0051] Real Estate Standard Coefficient B bd To ensure consistency of core fields across regions, the real estate cross-regional data format is divided into n parts. Parts with consistent core fields receive one point, while those without receive zero points. Each individual part is denoted as F. i Let i be the part number of the core field, i = 1, 2, 3, ..., n, where n is a positive integer. Then, the standard coefficient of real estate...

[0052] The feasibility assessment model for real estate registration information is constructed by weighting three aspects: registration quantity information, personnel flow information, and real estate standard information, generating a feasibility assessment coefficient BK for real estate registration information. pg The corresponding coefficients are the registration intention coefficient Y. dj Personnel mobility coefficient L ry and real estate standard coefficient B bd The formula formed is BK pg =a1×Y dj +a2×L ry +a3×B bd ;

[0053] Meanwhile, a1, a2, and a3 are set according to the actual situation. For example, the expert weighting method can be used, which involves inviting experts in relevant fields to determine the weights of each indicator through professional opinion surveys and comprehensive evaluations, to ensure that the weight coefficients accurately reflect the importance of each indicator in the feasibility assessment of real estate registration information. In addition, methods such as the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation can also be considered to determine the weight coefficients to ensure their objectivity and scientific validity. These will not be elaborated upon here.

[0054] In step S2, the feasibility assessment coefficient of real estate registration information obtained from the real estate registration information feasibility assessment model is used to reflect the feasibility of the cross-regional real estate registration information system in the target area. The larger the value, the more people in the target area are willing to register real estate registration information, the greater the flow of people and the greater the volume of real estate transactions, that is, the greater the demand for the cross-regional real estate registration information system. At the same time, the more similar the real estate registration information standards between regions, the less difficult the integration is. Therefore, the greater the feasibility of the cross-regional real estate registration information system in the target area.

[0055] In step S2, when the feasibility assessment coefficient BK of the real estate registration information is... pg When the threshold is greater than or equal to the set threshold, it indicates that the cross-regional real estate registration information system is highly feasible in the target area, and an input data signal is sent to build an optimized management system.

[0056] When the feasibility assessment coefficient of real estate registration information is BK pg If the input threshold is less than the set threshold, it indicates that the cross-regional real estate registration information system is not feasible in the target area. Therefore, no input data signal is issued, the target area is temporarily not included in the cross-regional real estate registration information system, and the evaluation result is directly output.

[0057] In step S3, a data verification and monitoring-machine learning algorithm model is constructed. The data verification and monitoring automatically verifies the consistency and integrity of the data by building a rule engine to ensure that the input data meets the preset standards. At the same time, the machine learning algorithm analyzes and processes the optimization process and results, and automatically identifies and classifies real estate registration information based on historical data to improve the consistency and accuracy of the data.

[0058] Furthermore, the method for constructing the data verification and monitoring-machine learning algorithm model of this invention is as follows:

[0059] Step S3.1: Data preprocessing and partitioning, removing duplicates, missing values ​​and outliers, identifying key features related to the consistency of real estate registration information, such as address, right holder, area, etc., and performing standardization processing, dividing the dataset into training set and test set, usually in a ratio of 70%-80%;

[0060] Step S3.2, Construct and train the logistic regression model. Construct the logistic regression model in the machine learning algorithm, defining data consistency as a binary classification problem, i.e., Y=1 indicates consistency, Y=0 indicates inconsistency. Establish the model formula: Where P(Y=1|X) is the probability that the target variable is 1, X is the feature variable, and β is the model parameter; after construction, the parameter β is estimated using the maximum likelihood estimation method with the training set, and the loss function L(β) is: Where m is the number of samples, y i These are real labels;

[0061] Step S3.3, Prediction and Validation: Use the trained model to make predictions on the test set. Monitor the consistency of new data and process the data through cross-validation: Divide the dataset into K subsets. In each cross-validation, select one subset as the validation set and the remaining K-1 subsets as the training set. Repeat this process K times, using a different subset as the validation set each time, using the formula... Calculate the average error of all K validations, where Error(i) is the error of the i-th validation. Select the model with the lowest average error as the final model and output the obtained model.

[0062] In step S4, the algorithm implementation evaluation model is constructed. Specifically, this involves collecting data to verify and monitor the georeferenced information, model performance information, and rule effectiveness information within the machine learning algorithm model. The georeferenced information includes georeferenced coefficients, denoted as D. cz Model performance information includes model performance coefficients, calibrated as M. xn Rule performance information includes rule performance coefficients, denoted as G. xn ;

[0063] Geographic reference factor D cz Using the address information obtained in the algorithm model in step S3, geocoding technology is used to convert the address information into geographic coordinates, and the geographic coordinates in the x and y directions are respectively labeled as X. z and Y z z is the geographic coordinate number, z = 1, 2, 3, ..., m, where m is a positive integer. Obtain the standard geographic coordinates and label the standard geographic coordinates in the x and y directions as X. b and Y b Then the geographical reference coefficient

[0064] Model performance coefficients M xn By obtaining the results of the algorithm model in step S3, the proportions of correct predictions and incorrect predictions to all predictions are respectively denoted as Z. w and C w And change the prediction threshold, where w is the prediction number for different thresholds, w = 1, 2, 3, ..., h, where h is a positive integer, then the model performance coefficients are...

[0065] Rule effectiveness coefficient G xn By obtaining the results of the algorithm model in step S3, the rules defined during the model construction process are removed. If the results obtained by the algorithm model remain unchanged after removal, the removed rules are considered invalid rules; if the results obtained by the algorithm model change after removal, the removed rules are considered valid rules. The number of valid rules and the number of invalid rules are respectively labeled as Y. g and W g Then the rule effectiveness coefficient G xn =Y g ÷(Y g +W g );

[0066] The algorithm implementation evaluation model is constructed by weighting three aspects: geographic reference information, model performance information, and rule effectiveness information in the algorithm model, and generates the algorithm implementation evaluation index SS. pg The corresponding coefficients are the geographical reference coefficient D. cz Model performance coefficients M xn and rule effectiveness coefficient G xn The formula formed is SS pg =γ1×D cz -γ2×M xn -γ3×G xn ;

[0067] In step S4, the algorithm implementation evaluation index obtained in the algorithm implementation evaluation model is used to reflect the implementation effect of the data verification and monitoring-machine learning algorithm model. The larger the algorithm implementation evaluation index, the larger the geographical location offset, the smaller the model evaluation performance, the lower the efficiency of the rules defined by the model, and the worse the implementation effect of the data verification and monitoring-machine learning algorithm model.

[0068] In step S4, when the algorithm achieves the evaluation index SS pg If the value exceeds the set threshold, it indicates that the data verification and monitoring-machine learning algorithm model cannot achieve the data consistency management required by the user. Return to step S3, redefine the model building factors and build the model.

[0069] When the algorithm achieves the evaluation index SS pg When the result is less than or equal to the set threshold, it indicates that the data verification and monitoring-machine learning algorithm model can achieve the data consistency management required by the user, directly output the evaluation result, and continue to use the current algorithm model.

[0070] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0071] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of this application are generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0072] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0076] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0077] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data consistency management method for a cross-regional real estate registration information system, characterized in that, Includes the following steps: Step S1, Data Monitoring and Collection: Monitor and collect cross-regional real estate information system data generated in the target area, including registration quantity information, personnel flow information and real estate standard information, and obtain geographic reference information, model performance information and rule effectiveness information after the algorithm model is constructed; Step S2, Information Feasibility Assessment: Construct a real estate information feasibility assessment model, obtain the real estate information feasibility assessment index, and confirm the feasibility of constructing a cross-regional real estate information system in the target area; Step S3, construct the algorithm model: construct a data verification and monitoring-machine learning algorithm model for feasible cross-regional real estate information systems, and output the acquired model; Step S4, constructing an algorithm implementation evaluation model: Constructing an algorithm implementation evaluation model, generating geographic reference information, model performance information, and rule effectiveness information based on the algorithm model's data processing, and judging the algorithm model's effectiveness in meeting user needs; Step S5, Data Consistency Verification and Feedback: When fulfilling user requirements, the consistency of real estate registration data from different ports and platforms is verified, and the verification results are fed back to step S3 as a factor in the construction of the data verification and monitoring-machine learning algorithm model; In step S2, the real estate information of the target area is evaluated to confirm the feasibility of a cross-regional real estate information system in the target area. Specifically, this involves collecting registration quantity information, population flow information, and real estate standard information from the target area. The registration quantity information includes a registration intention coefficient, which is calibrated as... Personnel mobility information includes a personnel mobility coefficient, calibrated as follows: Real estate standard information includes real estate standard coefficients, calibrated as follows: ; Registration willingness coefficient A questionnaire survey was conducted among residents in the target area to obtain the number of residents willing to register their real estate information across regions, which was then defined as [the target number]. The total resident population of the target area is defined as follows: Then the registration willingness coefficient ; Personnel mobility coefficient By collecting data on the inflow and outflow of people in the target area over a year, and labeling them as follows: and The total resident population of the target area is defined as follows: Then the personnel mobility coefficient ; Real Estate Standard Coefficient To ensure consistency of core fields across regions, the real estate cross-regional data format is divided into n parts. Parts with consistent core fields receive one point, otherwise zero points. The score for each individual part is assigned as follows: Let i be the part number of the core field, i = 1, 2, 3, ..., n, where n is a positive integer. Then, the standard coefficient of real estate... ; The real estate information feasibility assessment model is constructed by weighting three aspects: registration quantity information, personnel flow information, and real estate standard information, and generates a real estate information feasibility assessment coefficient. The corresponding coefficients are the registration intention coefficients. Personnel mobility coefficient and real estate standard coefficient The formula formed is In the formula, These are the weighting coefficients for the corresponding indicators.

2. The data consistency management method for a cross-regional real estate registration information system according to claim 1, characterized in that: In step S2, when the feasibility assessment coefficient of real estate information is... When the input threshold is greater than or equal to the set input threshold, an input data signal is issued to build an optimized management system; When the feasibility assessment coefficient of real estate information When the input threshold is less than the set threshold, no input data signal is issued, the target area is temporarily not included in the cross-regional real estate information system, and the evaluation result is directly output.

3. The data consistency management method for a cross-regional real estate registration information system according to claim 1, characterized in that: In step S3, a data verification and monitoring-machine learning algorithm model is constructed. The data verification and monitoring automatically verifies the consistency and integrity of the data by building a rule engine to ensure that the input data meets the preset standards. At the same time, the machine learning algorithm analyzes and processes the optimization process and results, and automatically identifies and classifies real estate information based on historical data to improve the consistency and accuracy of the data. Furthermore, the data validation and monitoring—the construction method of the machine learning algorithm model is as follows: Step S3.1, data preprocessing and partitioning: remove duplicates, missing values ​​and outliers, identify key features related to the consistency of real estate information, and perform standardization processing to divide the dataset into training set and test set; Step S3.2: Construct and train the logistic regression model. Construct the logistic regression model in the machine learning algorithm. After the model is constructed, use the training set to estimate the parameters using the maximum likelihood estimation method. Step S3.3, Prediction and Validation: Use the trained logistic regression model to predict the test set, monitor the consistency of the new data, process the data through cross-validation, select the model with the lowest average error as the final model, and output the obtained model.

4. The data consistency management method for a cross-regional real estate registration information system according to claim 3, characterized in that: In step S4, the algorithm implementation evaluation model is constructed. Specifically, this involves collecting data to verify and monitor the geographic reference information, model performance information, and rule effectiveness information within the machine learning algorithm model. The geographic reference information includes geographic reference coefficients, calibrated as follows: Model performance information includes model performance coefficients, calibrated as follows: Rule performance information includes rule performance coefficients, calibrated as follows: ; Geographic reference coefficient Using the address information obtained in step S3 from the algorithm model, geocoding technology is used to convert the address information into geographic coordinates, and the geographic coordinates in the x and y directions are respectively labeled as follows: and z represents the geographic coordinate number, z = 1, 2, 3, ..., m, where m is a positive integer. Obtain the standard geographic coordinates and calibrate the standard geographic coordinates in the x and y directions as follows: and Then the geographical reference coefficient ; Model performance coefficients By obtaining the results of the algorithm model in step S3, the proportions of correct predictions and incorrect predictions to all predictions are respectively labeled as follows: and And change the prediction threshold, where w is the prediction number for different thresholds, w=1,2,3,...,h, where h is a positive integer, then the model performance coefficients are... ; Rule effectiveness coefficient By obtaining the results of the algorithm model in step S3, the rules defined during the model construction process are removed one by one. If the results obtained by the algorithm model remain unchanged after removal, the removed rules are considered invalid rules; if the results obtained by the algorithm model change after removal, the removed rules are considered valid rules. The number of valid rules and the number of invalid rules are respectively labeled as follows: and Then the rule effectiveness coefficient .

5. The data consistency management method for a cross-regional real estate registration information system according to claim 4, characterized in that: The algorithm implementation evaluation model is constructed by weighting three aspects: geographic reference information, model performance information, and rule effectiveness information in the algorithm model, and generates an algorithm implementation evaluation index. The corresponding coefficients are the geographical reference coefficients. Model performance coefficients and rule effectiveness coefficient The formula formed is .

6. The data consistency management method for a cross-regional real estate registration information system according to claim 5, characterized in that: In step S4, when the algorithm achieves the evaluation index If the value exceeds the set threshold, return to step S3, redefine the model building factors, and build the model. When the algorithm achieves the evaluation index If the result is less than or equal to the set threshold, the evaluation result is output directly, and the current algorithm model is continued to be used.

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