Real estate intelligent registration method and system

Abstract

The invention discloses an intelligent real estate registration method and system, relates to the technical field of real estate registration, and solves the technical problems that real-time monitoring and effective management of key indexes of a real estate registration business process are lacked, and risks are difficult to effectively prevent. Key indexes such as business handling time limit, error rate and illegal behavior occurrence rate are monitored in real time, risk sources are analyzed from multiple dimensions based on a large amount of historical data, a risk identification model is constructed by adopting advanced modeling methods such as a neural network, potential risks in real estate registration business can be accurately identified, and the risk identification accuracy is improved. The risk level can be accurately evaluated by combining comprehensive risk score calculation and a risk level division standard, corresponding emergency plans are made for different risk levels, hierarchical management and control of risks are realized, timeliness and effectiveness of emergency response are improved, loss caused by the risks is reduced, and safe and stable development of real estate registration business is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of real estate registration, and in particular to a method and system for intelligent real estate registration. Background Art

[0002] In modern society, real estate registration is a vital legal and administrative procedure, which involves the confirmation, transfer, mortgage and change of real estate ownership.

[0003] According to the patent application with publication number CN118644361 B, a cross-network collaborative and shared real estate registration method and system are disclosed, which relates to the field of real estate registration technology. In this real estate registration method, the central server obtains the real estate registration request submitted by the user through the front-end application, and distributes the request to the nearest edge computing node for processing through load balancing; according to the cross-network request processing mechanism, the real estate registration request is subjected to two-factor authentication verification of identity and authority. After the verification is passed, fine-grained access control is implemented based on the zero-trust architecture, and the real estate registration data in the cross-network distributed database is synchronously shared using the smart contract of the alliance chain network.

[0004] However, while some real estate registration systems have introduced information technology to assist with business processing, deficiencies remain in areas such as application information acceptance and review, business process monitoring, and risk identification and resolution. For example, during the application acceptance phase, identity and document verification often relies on manual review, which is inefficient and prone to omissions. In terms of business process monitoring, there is a lack of real-time monitoring and early warning mechanisms for key indicators, making it difficult to effectively prevent risks. In the risk identification phase, historical data is insufficiently utilized, making it impossible to accurately identify potential risks, resulting in delayed emergency responses and impacting the security and efficiency of real estate registration services. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for intelligent real estate registration, which solves the problem of lack of real-time monitoring and effective management of key indicators of the real estate registration business process and difficulty in effectively preventing risks.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent registration of real estate, which specifically includes the following steps:

[0007] Obtaining real estate application information, making judgments based on the identity information and submitted materials in the real estate application information, and generating an acceptance signal or a rejection signal;

[0008] Analyze the acceptance signals, evaluate the number of audit processes, error impact indicators, and audit frequency of the verification items separately to obtain corresponding evaluation indicators, and then perform weighted summation to obtain comprehensive indicators. At the same time, compare the indicators with the preset values to classify the verification items into simple items and important items, and conduct audit processing at the same time to generate business audit information;

[0009] Analyze business audit information, compare business indicators of verification items with process parameters, and generate abnormal warning signals or normal monitoring signals;

[0010] Analyze normal monitoring signals, establish a risk identification model based on historical data, and substitute verification items into the model to identify existing abnormal situations. At the same time, determine the risk level based on the abnormal situation, determine the corresponding emergency plan, and generate emergency information.

[0011] As a further solution of the present invention, the specific method of generating the acceptance signal or the rejection signal is:

[0012] Obtain the real estate application information corresponding to the applicant, and verify and preliminarily review the applicant's identity and submitted materials. If the applicant's identity and submitted materials meet the requirements, an acceptance signal is generated. Otherwise, if any group does not meet the requirements, a rejection signal is generated.

[0013] As a further solution of the present invention, the specific method of analyzing the acceptance signal is:

[0014] Get all the verification items and mark them as i, and i = 1, 2, ..., j, where j represents the number of verification items. At the same time, get the number of audit processes corresponding to verification item i and mark them as Ci, error impact index Bi and audit frequency Pi, and perform normalization. Then, according to the formula Qi = C i ×a1+B i ×a2+P i ×a3 calculates the comprehensive indicator Qi corresponding to the verification item i, where a1, a2 and a3 are the corresponding weight coefficients respectively.

[0015] As a further solution of the present invention, the specific method of generating business review information is:

[0016] Compare the comprehensive indicator Qi with the preset value Qy set by the operator. If Qi>Qy, the verification project is marked as a simple project and computer-assisted review is performed; if Qi≤Qy, it is marked as an important project and transferred to manual review. The review also generates business review information.

[0017] As a further solution of the present invention, the specific method of analyzing the business audit information is:

[0018] Obtain the business indicators corresponding to the verification project and the process parameters of the real-time business process, and compare the two. If the process parameters are different from the business indicators, it means that there is an abnormality in the real-time business process and an abnormal warning signal is generated. Conversely, if the process parameters are the same as the business indicators, it means that there is no abnormality in the real-time business process and a normal monitoring signal is generated.

[0019] As a further solution of the present invention, the specific method of analyzing the normal monitoring signal is:

[0020] Collect historical risks and normal business data of real estate registration covering multiple aspects such as applicants and business types, analyze risk sources from dimensions such as applicants and registration items, select a neural network model, extract characteristic variables such as applicant age and business type that can reflect risk status, and after encoding and standardization, divide the preprocessed data into training and test sets. Use the training set to train the model and the test set to evaluate it, and establish a risk identification model.

[0021] As a further solution of the present invention, the specific method of generating emergency information is:

[0022] Substitute the verification item i into the risk identification model, identify the abnormal situation through the analysis of the risk identification model, and then calculate the risk according to the formula Calculate the comprehensive risk score R corresponding to the verification item i, where ω i is the weight of each risk factor, f i (x) is the risk factor quantification function, and the comprehensive risk score Ri of all verification items i is obtained in this way;

[0023] The obtained comprehensive risk Ri is compared with the corresponding matching level index to determine the corresponding risk level, and the emergency plan is determined according to the risk level to generate emergency information.

[0024] The real estate intelligent registration system includes:

[0025] An information collection unit, which is used to collect real estate application information and transmit it to the acceptance analysis unit;

[0026] An acceptance analysis unit, which is used to make judgments based on the identity information and submitted materials in the real estate application information, generate an acceptance signal or a rejection signal, and transmit the acceptance signal to the review project comprehensive analysis unit;

[0027] The audit project comprehensive analysis unit is used to analyze the acquired acceptance signals, separately evaluate the number of audit processes, error impact indicators, and audit frequency of the verification items to obtain corresponding evaluation indicators, and perform weighted summation to obtain a comprehensive indicator. At the same time, it compares the comprehensive indicator with the preset value to classify the verification items into simple items and important items, and conducts audit processing at the same time to generate business audit information and transmit it to the control information output unit;

[0028] At the same time, the business audit information is analyzed, the business indicators of the verification items are compared with the process parameters, and abnormal warning signals or normal monitoring signals are generated. The abnormal warning signals are transmitted to the management and control information output unit, and the normal monitoring signals are transmitted to the risk management and control analysis unit;

[0029] The risk management and analysis unit is used to analyze normal monitoring signals, establish a risk identification model based on historical data, and substitute verification items into the model to identify existing abnormal situations. At the same time, it determines the risk level based on the abnormal situation, determines the corresponding emergency plan, generates emergency information, and then transmits it to the management and control information output unit;

[0030] The management and control information output unit is used to display the acquired business audit information, abnormal warning signals and emergency information to the corresponding operators.

[0031] The present invention provides a method and system for intelligent real estate registration. Compared with the existing technology, it has the following advantages:

[0032] This invention establishes a comprehensive business indicator system, monitoring key indicators such as transaction processing time limits, error rates, and violation rates in real time, and setting scientific and reasonable thresholds. Once an indicator exceeds the threshold, the system automatically triggers an early warning mechanism. Simultaneously, by capturing and analyzing business process parameters in real time, it can quickly identify anomalies, allowing managers to take timely measures to ensure the normal operation of business processes.

[0033] Based on extensive historical data, this invention analyzes risk sources from multiple dimensions and employs advanced modeling methods such as neural networks to construct a risk identification model, enabling precise identification of potential risks in real estate registration services. Combining comprehensive risk score calculations with risk grading standards, this method accurately assesses risk levels and develops corresponding emergency response plans for each risk level. This enables hierarchical risk management, improves the timeliness and effectiveness of emergency response, reduces losses caused by risks, and ensures the safe and stable operation of real estate registration services. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a diagram of the steps and methods of the present invention;

[0035] Figure 2 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Example 1

[0038] See also Figure 1 , this application provides a method for intelligent registration of real estate, which specifically includes the following steps:

[0039] Step 1: Obtain real estate application information and conduct acceptance judgment analysis to generate an acceptance signal or a rejection signal. The specific judgment and analysis methods are as follows:

[0040] After obtaining the real estate application information corresponding to the applicant, the system uses technical means such as identity authentication, electronic signature, and portrait collection to automatically verify and preliminarily review the applicant's identity and submitted materials to determine whether the application meets the acceptance conditions. If the applicant's identity and submitted materials meet the requirements, an acceptance signal is generated. Conversely, if any group does not meet the requirements, a rejection signal is generated.

[0041] Identity verification: Using multi-factor identity authentication technology, first perform basic identity verification through digital certificates, SMS verification codes, etc.; then call the public security system's population information database to compare the applicant's name, ID number, photo and other information to confirm the authenticity of the identity; at the same time, use high-precision facial recognition technology to compare the feature points of the portrait collected on-site with the ID card photo and the photo retained by the public security system to ensure the consistency of the person and the ID. After the verification is passed, an identity verification success mark is generated.

[0042] Material verification: Use optical character recognition (OCR) technology to convert paper materials into electronic text, compare the format and content with the system's standard template, and check whether the materials are complete and the format is standardized; for key materials, such as property ownership certificates, transaction contracts, etc., the system automatically cross-checks with the real estate registration database, tax department tax records, civil affairs department marriage registration information, etc. to verify the authenticity and validity of the materials; if the materials are vague, missing, logically contradictory, etc., a material problem list is generated.

[0043] Step 2: Analyze the generated acceptance signal, obtain all verification items and label them as i, where i = 1, 2, ..., j, where j represents the number of verification items. Then, by separately evaluating the number of review processes, error impact index, and review frequency of verification item i, obtain the corresponding evaluation index. Combine the three to calculate the comprehensive index Qi of verification item i. Based on the comprehensive index Qi, the verification items are classified into simple items and important items. The specific processing method is as follows:

[0044] The number of audit processes corresponding to verification item i is obtained and recorded as Ci. At the same time, the verification error results corresponding to verification item i are analyzed, the corresponding error impact is analyzed, and the corresponding score is recorded according to the error impact. The obtained score is recorded as the error impact index Bi, and the verification error results are divided into four levels: serious, serious, general, and minor. Corresponding scoring standards are formulated. Serious errors (such as falsification of applicant identity information, which may lead to major risks such as property rights disputes and economic fraud) are scored 8-10 points; serious errors (such as missing key materials, which affect the normal handling of business and registration accuracy) are scored 5-7 points; general errors (such as non-standard material formats, which require applicants to supplement and modify) are scored 2-4 points; minor errors (such as minor clerical errors in the materials that do not affect the core information) are scored 1 point. When an error occurs in verification item i, the system determines the error level based on the actual situation of the error and the evaluation system, and gives a corresponding score. For example, the verification item "Property Certificate Verification" found that the submitted certificate was a forged document, which is a serious error. Bi (Property Certificate Verification) = 9. Then calculate the audit frequency Pi corresponding to the verification item i, and the audit frequency = number of audits / total number of audits. The total number of audits here represents the total number of audits of the verification item during the audit of the real estate application information. For example, in the process of 100 verifications of the real estate application information, the number of verifications for the verification item is 100 times, then the corresponding frequency is 1. At the same time, substitute the obtained index into the formula Qi = C i ×a1+B i ×a2+P i ×a3 calculates the comprehensive indicator Qi corresponding to the verification item i, and the variables in the above formula are all normalized variables, among which a1, a2 and a3 are the corresponding weight coefficients respectively, and the specific values are set by the operator.

[0045] In a specific example, suppose that in the past month, a total of 1,000 reviews of real estate application information were conducted, of which the number of reviews for the "Applicant's ID Card Information Verification" item was 1,000, with 5 errors, the error type being incorrect ID card number format (a general error, with a score of 3 points); the number of reviews for the "Property Certificate Verification" item was 800, with 2 errors, the error type being forged certificates (a serious error, with a score of 9 points).

[0046] Number of review processes: It is known that "Applicant's ID card information verification" is associated with 3 review processes, that is, C (Applicant's ID card information verification) = 3; assuming that "Property certificate verification" is associated with 2 review processes, that is, C (Property certificate verification) = 2.

[0047] Error impact indicators: Bi (verification of applicant’s ID card information) = 3, Bi (verification of property rights certificate) = 9.

[0048] Review frequency: Pi (verification of applicant’s ID card information) = 1000 / 1000 = 1; Pi (verification of property rights certificate) = 800 / 1000 = 0.8;

[0049] Determine the corresponding weight coefficients, for example, a1=0.3, a2=0.5, a3=0.2, and then substitute the obtained parameters into the formula. Taking the property rights certificate verification as an example, Qi=2×0.3+9×0.5+0.8×0.2=5.26.

[0050] The obtained comprehensive index Qi is compared with a preset value Qy, and the specific value of the preset value Qy is set by the operator. If the comprehensive index Qi is greater than the preset value Qy, the corresponding verification item is marked as a simple item. Conversely, if the comprehensive index is less than the preset value Qy, the corresponding verification item is marked as an important item, and the different items obtained by classification are reviewed and processed separately;

[0051] Simple items classified by category are subject to computer-assisted review, while important items classified by category are subject to manual review, and business review information is generated at the same time.

[0052] Step 3. Then analyze the obtained business audit information, obtain the business indicators corresponding to the verification items, and determine a series of key indicators to measure the operation of the real estate registration business, such as business processing time limit, error rate, violation rate, etc. Set reasonable thresholds for these indicators. When the indicators exceed the thresholds, the system automatically triggers the early warning mechanism. For example, when the business processing error rate within a certain time period exceeds the set standard, or the processing time of a certain business exceeds the prescribed time limit, the system will send an early warning message to the relevant management personnel so that timely measures can be taken to deal with it, and obtain the real-time business process of the verification project, and at the same time obtain the process parameters of the real-time business process, and compare the obtained process parameters with the business indicators. If the process parameters are different from the business indicators, it means that there is an abnormality in the real-time business process, and an abnormal early warning signal is generated. On the contrary, if the process parameters are the same as the business indicators, it means that there is no abnormality in the real-time business process, and a normal monitoring signal is generated.

[0053] The core business indicators are defined as follows:

[0054] Business processing time: the time from the applicant submitting complete documents to the completion of the business (registration or issuance of result documents), calculated separately by different business types (such as first registration, transfer registration, mortgage registration, etc.);

[0055] Error rate: The ratio of the number of businesses that failed the review or were found to have errors in subsequent stages to the total business volume, distinguishing between material errors, process errors, data entry errors, and other sub-categories;

[0056] Violation rate: the ratio of the number of violations of registration regulations, operating procedures or laws and regulations during business processing to the total business volume.

[0057] Audit pass rate: The ratio of the number of businesses that passed the audit in one go to the total audited business volume, reflecting the accuracy and efficiency of the audit process.

[0058] Document supplement and correction rate: the ratio of the number of cases requiring applicants to supplement or revise documents to the total number of cases, reflecting the rigor of the initial document review;

[0059] Based on the business data of the past 6-12 months, calculate the average and standard deviation of each indicator, and use "average + n times the standard deviation" (n is determined by the degree of business risk, generally 1-3) as the warning threshold. For example, the average processing time for transfer registration business in a certain region over the past year was 3 working days, with a standard deviation of 0.5. The threshold is set at 4 working days (3 + 2 × 0.5). Refer to the service standards issued by national or provincial real estate registration agencies. If it is stipulated that the processing time for mortgage registration business shall not exceed 2 working days, this is used as the rigid threshold;

[0060] The system captures the operation time, operator, operation content, data status and other parameters of each business process node in real time. For example, in the audit phase, it collects data such as the audit start time, audit duration, and modification records. The real-time collected process parameters are compared with the preset business indicators. If the following situations occur, it is determined to be abnormal:

[0061] The operation time of key nodes exceeds the corresponding business processing time threshold;

[0062] The same operator handles a large number of similar businesses in a short period of time (possibly illegal batch operations);

[0063] When the frequency or content of data modification exceeds the normal range, the system will generate an abnormal warning signal, automatically record the abnormal details and push them to the monitoring platform.

[0064] Step 4: Analyze the generated normal monitoring signals, establish a risk identification model based on historical data, and identify and analyze the risks of the verification items. The specific identification and analysis methods are as follows:

[0065] Obtain historical data and analyze risk sources from multiple dimensions, including applicants, registration matters, registration materials, and business operations. For example, applicants may face risks such as identity fraud and providing false materials; registration matters may involve property rights disputes, rights restrictions, and other issues; registration materials may be incomplete or untrue; business operations may involve human errors and illegal operations, etc. Collect historical data related to real estate registration, including data on past risk events and normal business data. The data should cover applicant information, registration business type, application material content, business processing time, processing personnel, and other aspects. In order to ensure the accuracy and completeness of the data, the data should be cleaned and pre-processed to remove duplicate, erroneous, or incomplete data records, and fill or delete missing values;

[0066] Based on the characteristics of the data and the objectives of risk identification, an appropriate modeling method is selected. Common methods include decision trees, support vector machines, neural networks, logistic regression, etc. In this application, a neural network model is selected. Representative feature variables are extracted from the original data. These features should be able to reflect the risk status in the real estate registration business. For example, the applicant's age, occupation, application frequency, type of registration business, amount involved, completeness and authenticity of application materials, etc. can all be used as feature variables. At the same time, the obtained feature variables are encoded and standardized, non-numeric features are converted to numerical types, and numerical features are normalized or standardized;

[0067] At the same time, preprocessed data is obtained and divided into a training set and a test set, usually in a ratio of 7:3 or 8:2. The training set is used to train the selected model, and the test set is used to evaluate the trained model to establish the corresponding risk identification model;

[0068] Substitute verification item i into the risk identification model, identify the abnormal situation through analysis of the risk identification model, and determine the corresponding risk level based on the abnormal situation. At the same time, determine the corresponding emergency plan based on the obtained risk level and generate emergency information. The specific processing method is as follows:

[0069] According to the formula Calculate the comprehensive risk score R corresponding to the verification item i, where ω i For each risk factor, the weight is given. For example, if the probability of occurrence is considered to be more important in risk assessment, it can be assigned a relatively high weight, such as 0.3 mentioned in the figure; the weight of the impact range is 0.25, etc. The sum of the weights is usually 1 to ensure that the impact of each risk factor can be reasonably distributed. i (x) is the risk factor quantification function, and the comprehensive risk score Ri of all verification items i is obtained in this way;

[0070] The obtained comprehensive risk Ri is compared with the corresponding matching level index to determine the corresponding risk level. The emergency plan is determined according to the risk level and emergency information is generated. The matching level index is specifically divided into: R < 30, low risk, 30 ≤ R < 70, medium risk, R ≥ 70, high risk. For different risk levels, the corresponding emergency plans are as follows:

[0071] Low risk: Initiate automated verification processes, restrict abnormal account permissions, and notify applicants to provide additional documents;

[0072] Medium risk: Freeze related businesses, conduct manual review, and activate backup systems;

[0073] High risk: Notify the real estate registration security management department to intervene in the investigation.

[0074] Example 2

[0075] See also Figure 2 This application provides a real estate intelligent registration system, which includes an information collection unit, an acceptance analysis unit, an audit project comprehensive analysis unit, a risk control analysis unit and a control information output unit, and combines Figure 2 It can be known that all the above functional units are electrically connected in one direction.

[0076] An information collection unit, which is used to collect real estate application information and transmit it to the acceptance analysis unit;

[0077] An acceptance analysis unit, which is used to make a judgment based on the identity information and submitted materials in the real estate application information, generate an acceptance signal or a rejection signal, and transmit the acceptance signal to the review project comprehensive analysis unit. The specific processing method is the same as the processing process of step 1 in embodiment 1;

[0078] The audit project comprehensive analysis unit is used to analyze the acquired acceptance signal, separately evaluate the number of audit processes, error impact indicators, and audit frequency of the verification project to obtain corresponding evaluation indicators, and perform weighted summation to obtain a comprehensive indicator. At the same time, it compares the comprehensive indicator with the preset value to classify the verification project into simple projects and important projects, and performs audit processing at the same time to generate business audit information and transmit it to the control information output unit. The processing method here is similar to the processing process of step 2 in embodiment 1;

[0079] At the same time, the business audit information is analyzed, the business indicators of the verification items are compared with the process parameters, and an abnormal warning signal or a normal monitoring signal is generated. The abnormal warning signal is transmitted to the control information output unit, and the normal monitoring signal is transmitted to the risk control analysis unit. The processing method here is similar to the processing process of step 3 in embodiment 1;

[0080] The risk management and analysis unit is used to analyze normal monitoring signals, establish a risk identification model based on historical data, and substitute verification items into the model to identify existing abnormal situations. At the same time, it determines the risk level according to the abnormal situation, and determines the corresponding emergency plan, generates emergency information, and then transmits it to the management and control information output unit. The specific processing method is the same as the processing process of step four in embodiment one.

[0081] Some of the data in the above formulas are calculated based on their numerical values and are not substituted into parameter units for calculation. At the same time, the contents not described in detail in this specification belong to the existing technology known to those skilled in the art.

[0082] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for intelligent registration of real estate, characterized in that: The method specifically comprises the following steps: Obtaining real estate application information, making judgments based on the identity information and submitted materials in the real estate application information, and generating an acceptance signal or a rejection signal; Analyze the acceptance signals, evaluate the number of audit processes, error impact indicators, and audit frequency of the verification items separately to obtain corresponding evaluation indicators, and then perform weighted summation to obtain comprehensive indicators. At the same time, compare the indicators with the preset values to classify the verification items into simple items and important items, and conduct audit processing at the same time to generate business audit information; Analyze business audit information, compare business indicators of verification items with process parameters, and generate abnormal warning signals or normal monitoring signals; Analyze normal monitoring signals, establish a risk identification model based on historical data, and substitute verification items into the model to identify existing abnormal situations. At the same time, determine the risk level based on the abnormal situation, determine the corresponding emergency plan, and generate emergency information.

2. The intelligent real estate registration method according to claim 1, characterized in that: The specific method of generating the acceptance signal or the rejection signal is: Obtain the real estate application information corresponding to the applicant, and verify and preliminarily review the applicant's identity and submitted materials. If the applicant's identity and submitted materials meet the requirements, an acceptance signal is generated. Otherwise, if any group does not meet the requirements, a rejection signal is generated.

3. The intelligent real estate registration method according to claim 1, characterized in that: The specific method of analyzing the acceptance signal is as follows: Get all the verification items and mark them as i, and i = 1, 2, ..., j, where j represents the number of verification items. At the same time, get the number of audit processes corresponding to verification item i and mark them as Ci, error impact index Bi and audit frequency Pi, and perform normalization. Then, according to the formula Qi = C i ×a1+B i ×a2+P i ×a3 calculates the comprehensive indicator Qi corresponding to the verification item i, where a1, a2 and a3 are the corresponding weight coefficients respectively.

4. The intelligent real estate registration method according to claim 1, characterized in that: The specific method of generating the business review information is: Compare the comprehensive indicator Qi with the preset value Qy set by the operator. If Qi>Qy, the verification project is marked as a simple project and computer-assisted review is performed; if Qi≤Qy, it is marked as an important project and transferred to manual review. The review also generates business review information.

5. The method for intelligent real estate registration according to claim 1, characterized in that: The specific method of analyzing the business audit information is as follows: Obtain the business indicators corresponding to the verification project and the process parameters of the real-time business process, and compare the two. If the process parameters are different from the business indicators, it means that there is an abnormality in the real-time business process and an abnormal warning signal is generated. Conversely, if the process parameters are the same as the business indicators, it means that there is no abnormality in the real-time business process and a normal monitoring signal is generated.

6. The intelligent real estate registration method according to claim 1, characterized in that: The specific method of analyzing the normal monitoring signal is as follows: Collect historical risks and normal business data of real estate registration covering multiple aspects such as applicants and business types, analyze risk sources from dimensions such as applicants and registration items, select a neural network model, extract characteristic variables such as applicant age and business type that can reflect risk status, and after encoding and standardization, divide the preprocessed data into training and test sets. Use the training set to train the model and the test set to evaluate it, and establish a risk identification model.

7. The intelligent real estate registration method according to claim 1, characterized in that: The specific method of generating emergency information is: Substitute the verification item i into the risk identification model, identify the abnormal situation through the analysis of the risk identification model, and then calculate the risk according to the formula Calculate the comprehensive risk score R corresponding to the verification item i, where ω i is the weight of each risk factor, f i (x) is the risk factor quantification function, and the comprehensive risk score Ri of all verification items i is obtained in this way; The obtained comprehensive risk Ri is compared with the corresponding matching level index to determine the corresponding risk level, and the emergency plan is determined according to the risk level to generate emergency information.

8. A real estate intelligent registration system, implemented by the real estate intelligent registration method according to any one of claims 1 to 7, characterized in that: The system includes: An information collection unit, which is used to collect real estate application information and transmit it to the acceptance analysis unit; An acceptance analysis unit, which is used to make judgments based on the identity information and submitted materials in the real estate application information, generate an acceptance signal or a rejection signal, and transmit the acceptance signal to the review project comprehensive analysis unit; The audit project comprehensive analysis unit is used to analyze the acquired acceptance signals, separately evaluate the number of audit processes, error impact indicators, and audit frequency of the verification items to obtain corresponding evaluation indicators, and perform weighted summation to obtain a comprehensive indicator. At the same time, it compares the comprehensive indicator with the preset value to classify the verification items into simple items and important items, and conducts audit processing at the same time to generate business audit information and transmit it to the control information output unit; At the same time, the business audit information is analyzed, the business indicators of the verification items are compared with the process parameters, and abnormal warning signals or normal monitoring signals are generated. The abnormal warning signals are transmitted to the management and control information output unit, and the normal monitoring signals are transmitted to the risk management and control analysis unit; The risk management and analysis unit is used to analyze normal monitoring signals, establish a risk identification model based on historical data, and substitute verification items into the model to identify existing abnormal situations. At the same time, it determines the risk level based on the abnormal situation, determines the corresponding emergency plan, generates emergency information, and then transmits it to the management and control information output unit; The management and control information output unit is used to display the acquired business audit information, abnormal warning signals and emergency information to the corresponding operators.

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