Real estate data non-corresponding auditing method, system and terminal based on big data

Abstract

The invention belongs to the technical field of data management, and particularly relates to a real estate data non-corresponding auditing method and system based on big data and a terminal. According to the method, on the basis of a comparison data set constructed by data to be detected and a preset database, after real estate data information is matched with initial comparison items in the data set one by one, according to a comparison result of a matching value and a matching threshold value, all the initial comparison items with the high matching degree are screened and collected, and therefore standard data is formed. Through construction of standard data, systematic deviation possibly introduced by taking a single fixed data source as a comparison standard is avoided, the accuracy of subsequent non-corresponding item determination is improved, and a solid foundation is laid for implementation of the whole auditing process.

Description

Technical Field

[0001] This invention belongs to the field of data management technology, specifically relating to a method, system, and terminal for real estate data mismatch verification based on big data. Background Technology

[0002] With the deepening of digital transformation in the real estate sector, real estate data management systems have become an infrastructure for ensuring property rights security and optimizing the allocation of real estate resources. However, real estate data encompasses multi-source, heterogeneous information, including unified land and building registration, cadastral surveying, planning approvals, and status of rights restrictions. This data is not only structurally complex but also frequently updated and highly dynamic. When the accuracy, completeness, and consistency of system data cannot be effectively guaranteed, it will directly lead to distortion of real estate registration information, ultimately affecting the efficiency and credibility of related services such as mortgage loans, property transactions, and tax assessments.

[0003] Current technical audits are mostly limited to superficial rules such as data format verification and field integrity checks, lacking the ability to accurately judge deep logical contradictions in data across business operations and time periods. Faced with historical data that has undergone multiple transfers, changes, and corrections, current systems generally lack explainable evolutionary tracing capabilities. They mostly rely on simple snapshot overwriting or binary comparison methods, failing to intelligently identify whether data changes were based on legitimate correction procedures or administrative permits. This deficiency easily leads to the misjudgment and rejection of legitimate corrections as data errors, and also makes it easy to illegally accept new data without legitimate rights, overwriting historical valid registration records. This not only continuously lowers the overall quality of real estate data but also creates potential legal and business risks in the real estate rights transfer chain.

[0004] In view of this, the present invention proposes a method, system and terminal for real estate data mismatch verification based on big data. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and terminal for verifying discrepancies in real estate data based on big data, in order to solve the problems of incomplete verification of real estate data information and easy data errors in the prior art.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: The method for verifying discrepancies in real estate data based on big data includes the following steps: The process involves comparing real estate data with standard data to identify discrepancies; identifying and classifying erroneous items in the associated datasets to generate processing items; confirming discrepancies based on processing items to generate confirmed items; and verifying confirmed items based on historical data to output the final confirmation result. The standard data is determined in the following way: Based on real estate data and records in a pre-set database, a comparison dataset is constructed; Set the real estate data information as the data to be inspected, and set the data items in the comparison dataset as the initial comparison items; The data to be tested is matched against each item in the initial comparison list to output the matching value; The matching value is compared with a preset matching threshold. If the matching value is greater than the matching threshold, the corresponding initial comparison item is retained. All retained initial comparison items are aggregated to form standard data.

[0007] Preferably, before comparing the real estate data information with standard data, the real estate data information to be verified and the dataset associated with the real estate data information are obtained.

[0008] Preferably, the real estate data information includes at least one of the following: ownership certificate number, owner's name, owner's ID number, property location, property type, property area, and real estate unit number.

[0009] Preferably, the identification and classification of erroneous items in the dataset that do not correspond to each other includes: From the associated dataset, obtain real estate registration data and property registration data, and define the value of real estate registration data as the first value and the value of property registration data as the second value; Calculate the ratio of the first value to the second value, and determine whether the ratio is within a preset range. If the ratio is within the preset ratio range, the real estate registration data will be identified as the first verification data, and the remaining data items in the associated dataset will be marked as suspected errors. If the ratio exceeds the preset ratio range, the real estate registration data will be set as the reference benchmark, and the remaining data items in the associated dataset will be compared with the reference benchmark to determine the deviation value. The data item with the smallest deviation value will be determined as the second verification data, and the remaining data items other than the first and second verification data will be marked as suspected errors.

[0010] Preferably, after determining the second proofreading data, the first proofreading data and the second proofreading data are compared numerically, and replacement data is determined based on the comparison results, so as to replace the suspected erroneous items with the replacement data; The determination of replacement data includes: If the value of the first verification data is the same as the value of the second verification data, then that value is determined as the replacement data; If they are different, one of the first and second calibration data is selected as the reference item, and the other is selected as the target item. The value of the target item is adjusted based on the modification offset value associated with the time node until the adjusted value is the same as the value of the reference item, and the adjusted value is determined as the replacement data.

[0011] Preferably, the process involves confirming non-corresponding items based on the processing details to generate confirmation items, including: Generate a number of collation items equal to the number of non-corresponding items; perform a confirmation operation on all collation items; and after the confirmation operation on all collation items is completed, generate a unified confirmation item from the confirmed collation items.

[0012] Preferably, the confirmation items are validated based on historical data to output the final confirmation result, including: Obtain the time range of the generation time of the covered confirmation items, and extract historical valid data from the time range; Compare historical valid data with the confirmed items to assess consistency; Based on the consistency assessment results, the effective usage period of the verification items is determined, and the effective usage period is output as the final verification result.

[0013] This invention also discloses a real estate data mismatch verification system based on big data, comprising the following modules: The data auditing module is used to compare real estate data with standard data to identify discrepancies. The mismatch processing module is used to identify and classify erroneous items in the dataset associated with mismatched items, and output the processing items. The confirmation item generation module is used to perform confirmation processing on the non-corresponding items according to the processing items, so as to generate confirmation items; And a result verification module, which is used to verify the confirmation items based on historical data in order to output the final confirmation result.

[0014] Preferably, the non-corresponding item processing module is used for: Obtain real estate registration data and property rights registration data from the associated dataset; Calculate the ratio of the value of real estate registration data to the value of property registration data; Based on whether the ratio is within a preset range, the data items in the associated dataset are marked as first proofreading data, second proofreading data, or suspected error items, respectively.

[0015] The present invention also discloses a real estate data mismatch verification terminal based on big data, including a processor and a memory; The memory stores a computer program, which, when executed by a processor, implements any of the big data-based real estate data mismatch verification methods described above. Beneficial effects

[0016] This invention utilizes a comparison dataset constructed from the data to be inspected and a pre-set database. Real estate data is matched one by one with initial comparison items in this dataset. Based on the comparison results of matching values ​​and matching thresholds, all initial comparison items with high matching degrees are selected and aggregated to form standard data. The construction of standard data avoids the systematic bias that may be introduced by using a single fixed data source as the comparison standard, improves the accuracy of subsequent identification of mismatched items, and lays a solid foundation for the implementation of the entire review process.

[0017] This invention objectively identifies suspected errors in related datasets by calculating the ratio of real estate registration data to property registration data and comparing it with a preset ratio range, or by calculating the deviation value. It then determines the first and second verification data and generates replacement data to complete the correction. Through quantitative analysis-based identification and classification, this invention improves the accuracy of error identification and the efficiency of processing. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely for explaining the invention and are not intended to limit the scope of protection of the invention. Example 1

[0020] See Figure 1 This invention discloses a method for reviewing discrepancies in real estate data based on big data, comprising the following steps: S1. Obtain the information to be verified; obtain the single or batch of real estate data information that needs to be reviewed, and simultaneously obtain the datasets directly related to the real estate data information from one or more external data sources; among them, the real estate data information is the core object of review, while the associated datasets come from historical archive databases, land administration systems, tax systems or civil affairs systems, etc., to provide a basis for cross-validation.

[0021] The real estate data information and datasets are structured and combined to form a unified information unit to be verified, which includes at least one of the following: ownership certificate number, owner's name, owner's ID number, property location, property type, property area, and real estate unit number.

[0022] The key fields such as the property certificate number, the name of the property owner, the property owner's ID number, the location of the property, the type of property, the area of ​​the property, and the real estate unit number together constitute the core identity and physical attribute description of the real estate. Any potential difference in any of these fields may indicate a registration error or outdated information.

[0023] By combining the aforementioned key fields into standardized information units, a multi-dimensional benchmark is established for subsequent automated comparison and analysis.

[0024] S2. Determine standard data; Based on the acquired information units to be verified, the most valuable standard data are selected from massive historical data to provide a reliable and highly relevant comparison benchmark for subsequent difference analysis. The specific steps include: Based on the key fields in the information units to be checked, relevant records are retrieved from the preset database to construct a preliminary comparison dataset; the preset database is an authoritative data set that has been maintained, cleaned and verified over a long period of time. The real estate data information in the information unit to be checked is set as the data to be checked, and each data item in the comparison dataset is set as the initial comparison item; the data to be checked is matched with each initial comparison item separately to output a matching value that quantifies the similarity of multiple key fields between the data to be checked and the initial comparison item. The specific calculation process of the matching value includes: calculating the similarity of text type fields such as the owner's name according to the preset character matching rules; calculating the reciprocal of the numerical difference of numerical type fields such as house area; and weighting and summing the similarity of each field according to the preset weight of each field to obtain the final matching value. The calculated matching value is compared with the preset matching threshold; if the matching value is greater than or equal to the matching threshold, the corresponding initial comparison item is retained. After traversing and completing the matching process of all initial comparison items, all retained initial comparison items are gathered into a set, which is then determined as the standard data. This ensures that the final generated standard data is rigorously screened and highly correlated with the data to be tested in terms of preset matching requirements.

[0025] The matching threshold is dynamically set based on experience from historical data verification or determined by business rules. It is used to filter out matching items that do not meet the preset similarity requirements due to outdated information, input errors, or weak relevance.

[0026] S3. Generate verification results; Perform a precise comparison, item by item and field by field, between the real estate data information in the information unit to be verified and the standard data determined in the previous step to determine whether there are any differences between them; If the real estate data information and all records in the standard data are completely consistent in all key fields, it is determined that there are no differences, and a verification result is generated and marked as a corresponding item; Conversely, if any difference is found in the value or content of any field, it is determined that there are differences, and a verification result is generated and marked as a non-corresponding item. All records marked as non-corresponding items will automatically enter the subsequent more in-depth identification and classification processing flow.

[0027] S4, Output Processing Items; When the verification result is a mismatch, the preset diagnosis and correction logic is activated. This involves in-depth analysis of the data in the associated dataset to identify and classify specific errors and output clear processing instructions. Through cross-validation and logical reasoning, the root cause of the error is accurately located and diagnosed. The specific processing procedure is as follows: From the associated dataset, real estate registration data and property registration data from different authoritative sources are extracted, and the former value is defined as the first value and the latter value as the second value; the real estate registration data is preferably the land area recorded by the land administration system, and the property registration data is preferably the building area registered on the property certificate. Calculate the ratio of the first value to the second value to objectively reflect the degree of consistency between the two core data sources on a certain key value. Determine whether the ratio of the first value to the second value falls within a preset ratio range. If the ratio is within the preset ratio range, it indicates that the records of the two data sources are consistent within the preset tolerance. Based on this, it can be inferred that the error may originate from other data items in the associated dataset. At this time, the real estate registration data is identified as the first verification data, and the remaining data items in the associated dataset are uniformly marked as suspected error items. If the ratio exceeds the preset ratio range, it indicates that there is a significant difference between the real estate registration data and the property registration data that exceeds the preset tolerance. It is necessary to further determine which data source is more reliable. At this time, the data with the latest update timestamp is selected as the reference benchmark, and the remaining data items in the associated dataset are compared with the reference benchmark to determine the deviation value of each data item. The preset ratio range is a permissible fluctuation range derived from industry standards, local regulations, or historical data statistical analysis, used to tolerate reasonable differences caused by different measurement standards, recording accuracy, or statistical caliber. The data item with the smallest deviation value is identified as the second verification data, and the remaining data items other than the first and second verification data are marked as suspected errors.

[0028] Based on the principle of considering the data item with the smallest deviation as the second-best reliable source, in order to maximize the accuracy of data correction, after identifying suspected erroneous items, replacement data for correction is generated, thereby realizing the automated replacement of erroneous items. Specifically: After determining the second proofreading data, the first proofreading data and the second proofreading data are compared numerically. If the two values ​​are the same, the value is directly determined as the replacement data. If the two values ​​are different, one of them, such as the one with a higher confidence score or an earlier historical record, is selected as the reference item, and the other is selected as the target item. Obtain the modification offset value associated with the current business processing time node. Extract this modification offset value from the historical data change log. It reflects the amount of systematic batch adjustment to the data at a specific time node due to policy adjustments, changes in measurement units, or regional census corrections. Based on the modified offset value, a preset arithmetic operation is performed on the value of the target item until the adjusted value is the same as the value of the reference item. The adjusted value is then determined as the replacement data. Finally, the replacement data is used to replace all marked suspected error items to complete the initial data repair. The entire diagnosis and repair process is recorded as a processing item.

[0029] S5. Generate Confirmation Items; Based on the processing items output in the previous step, perform a centralized transactional confirmation process on all data marked as non-corresponding items to generate final confirmation items, preventing intermediate states or inconsistencies in data during the correction process. The specific steps are as follows: Based on the number of non-corresponding items, corresponding verification items are generated. Each verification item is in the form of structured data, which fully contains the original value, diagnostic logic, correction process and finally adopted replacement data, forming a complete correction record and audit traceability chain. Perform batch confirmation operations on all verification items; before the confirmation operations of all verification items are successfully completed, no modifications will be permanently written to the main database. After the confirmation operations of all verification items are completed, all confirmed verification items are packaged together to generate a unique confirmation item with a timestamp, thereby effectively preventing data inconsistency caused by partial correction failure or system interruption. Accurately record the verification completion time of the confirmation item. This verification completion time is a key anchor point for data version control and historical traceability, providing an accurate timestamp for future data audits, rollbacks, or analyses.

[0030] S6. Output the final confirmation result; Based on massive historical data, perform a longitudinal time-dimensional comparison and verification of the confirmation items formed in the previous step to output the final confirmation result. This involves a final verification of the corrected data to assess whether the changes maintain logical consistency with the historical data evolution trend of the real estate. The specific process is as follows: Obtain a preset time interval covering the time point of confirmation item generation, preferably the past five years; extract all historical valid data related to the real estate from the time interval, and compare the historical valid data with the corrected data in the confirmation item to assess their consistency; The consistency assessment is used to analyze whether the data in the confirmation item conforms to the historical data change patterns and statistical distribution. For example, if the historical area data of a certain real estate has been stable at around 100 square meters in the past few years, and the statistical distribution of its historical data shows a shape with 100 as the center and a small variance, and the area in this confirmation item is corrected to a value that deviates significantly from the distribution range, such as 150 square meters, then its consistency assessment result is judged to be low. Based on the consistency assessment results, a dynamic effective usage period is determined for the confirmed item. If the consistency assessment result is greater than or equal to the preset consistency threshold, it indicates that the correction is reasonable and in line with historical trends, and a longer first preset effective usage period is set for it. If the consistency assessment result is less than the preset consistency threshold, it indicates that the correction may pose a risk or constitute a major change. In this case, a shorter second preset effective usage period is set for it, and a manual review process is triggered.

[0031] It should be noted that the first preset duration is longer than the second preset duration.

[0032] The effective usage period, along with the confirmation items themselves, is output and archived as the final confirmation result. The final confirmation result confirms the current state of the data while providing risk control guidance for its future management and use; among which, historical valid data is the set of all data related to a specific real estate extracted from historical records within a preset time interval, which is confirmed to be valid and can be used for consistency assessment; By comparing the revised data in the confirmation items with the changing patterns and statistical distribution of historical valid data, a quantitative assessment score is obtained to measure whether the data revision conforms to the historical trend. This quantitative assessment score serves as the consistency assessment result. Example 2

[0033] See Figure 2 This embodiment provides a real estate data mismatch verification system based on big data, including the following modules: The data verification module is used to compare real estate data with standard data to identify discrepancies, specifically including: Perform data acquisition operations, namely, acquire real estate data information and the dataset associated with the real estate data information. The real estate data information may include one or more of the following: ownership certificate number, owner's name, owner's ID number, house location, house type, house area, and real estate unit number. Construct standard data for comparison, that is, based on the acquired real estate data information, construct a comparison dataset from the records of one or more preset databases; wherein the preset database is preferably an authoritative property registration history database; Set the current real estate data as the data to be inspected, and set each relevant record in the comparison dataset as the initial comparison item; Using a pre-defined matching algorithm, such as fuzzy matching or semantic similarity calculation, the data to be tested is matched with each initial comparison item, and a quantified matching value is output. This matching value is compared with a pre-defined matching threshold. Only when the matching value is greater than the threshold is the corresponding initial comparison item considered highly relevant and reliable, and it is retained. All retained initial comparison items are aggregated to form the final standard data. After the standard data is constructed, the original real estate data information is compared with this standard data item by item, and any items with differences are identified as non-corresponding items and passed to the next module for processing.

[0034] The mismatch processing module receives mismatches identified by the data audit module, identifies and classifies erroneous items in the associated dataset, and outputs processing details, including: From the associated dataset, accurately obtain real estate registration data and property registration data, and define their values ​​as the first value and the second value, respectively; Calculate the ratio of the first value and the second value, and determine whether the ratio is within a preset range. The preset range is usually derived based on business rules or historical data statistics and is used to determine the consistency between the two core data sources. If the result is within the preset range, it indicates that the real estate registration data and the property registration data have a high degree of consistency and reliability. At this time, the real estate registration data is identified as reliable first verification data, and the remaining data items in the associated dataset are uniformly marked as suspected error items, awaiting subsequent correction. If the judgment result is not within the preset ratio range, it indicates that there is a significant difference between the two core data sources. At this time, the real estate registration data is set as a temporary reference benchmark, and the remaining data items in the associated dataset are compared with the reference benchmark one by one to calculate their respective deviation values. The data item with the smallest deviation value is determined as the second verification data. Except for the real estate registration data that has been confirmed as the reference benchmark and the newly determined second verification data, all other data items are marked as suspected errors.

[0035] After identifying suspected errors, further corrective actions are performed, specifically: The first and second proofreading data are compared numerically. If the values ​​are the same, the value is directly designated as the replacement data; if the values ​​are different, a time-based adjustment mechanism is initiated. Select one as the reference item and the other as the target item, and modify the offset value based on the data recording time node, such as the adjustment coefficient calculated according to the historical data change pattern; The value of the target item is iteratively adjusted until its adjusted value is the same as the value of the reference item. The final adjusted value is then determined as the replacement data. Using this replacement data, all data marked as suspected errors are automatically replaced, generating a processing item containing correction suggestions or corrected data, and then outputting it.

[0036] The confirmation item generation module is used to perform final confirmation processing on the non-corresponding items based on the processing items output by the non-corresponding item processing module, in order to generate confirmation items, specifically including: Upon receiving a processing item, an equal number of check items are generated based on the number of non-corresponding items involved, each check item representing a data unit that has completed preliminary error correction. Perform one or more confirmation operations on all verification items. This confirmation operation is an automated logical consistency check. Specifically, it checks whether the corrected data meets business rule constraints, such as the area cannot be negative. Alternatively, it can be placed in a queue for manual review and final confirmation by the operator. In this embodiment, the confirmation operation is configured as an automated logical verification. Once this module confirms that all verification items have been successfully verified, it will package and format all verified items to generate one or more structured verification items. These verification items are data results that have been reviewed and corrected by the system and have been finally confirmed, and can be directly used by downstream systems.

[0037] The result verification module is used to periodically verify the confirmation items generated by the confirmation item generation module based on historical data, in order to evaluate their timeliness and output the final confirmation result. Once a confirmation item is generated, obtain the time interval covering the time point of its generation, for example, trace back one year from the time of generation; extract historically valid data related to the confirmation item that has been proven to be valid in history from the historical database within this time interval; The current confirmed items are compared with these historical valid data to assess their consistency. For example, it is analyzed whether the values ​​in the confirmed items are within the normal fluctuation range of historical data. Based on the consistency assessment results, the effective usage period of the current confirmed items is determined, i.e., the estimated time period. Within this time period, the confirmed items are considered reliable and valid. The calculated effective usage period is output as the final confirmation result, providing data users with an important reference on the timeliness of data. Example 3

[0038] This embodiment discloses a real estate data mismatch verification terminal based on big data, including a processor and a memory; The memory stores a computer program that, when executed by a processor, implements the aforementioned method for reviewing discrepancies in real estate data based on big data.

Claims

1. A method for verifying discrepancies in real estate data based on big data, characterized in that: Includes the following steps: The real estate data is compared with standard data to identify discrepancies; errors in the associated datasets are identified and categorized to output processing instructions. The process involves confirming non-corresponding items based on the processing items to generate confirmation items; and validating the confirmation items based on historical data to output the final confirmation result. The standard data is determined in the following way: A comparison dataset is constructed based on real estate data and records in a pre-set database; Set the real estate data information as the data to be inspected, and set the data items in the comparison dataset as the initial comparison items; The data to be tested is matched against each item in the initial comparison list to output the matching value; The matching value is compared with a preset matching threshold. If the matching value is greater than the matching threshold, the corresponding initial comparison item is retained. All retained initial comparison items are aggregated to form standard data.

2. The method for verifying discrepancies in real estate data based on big data as described in claim 1, characterized in that, Before comparing real estate data with standard data, obtain the real estate data to be verified and the dataset associated with the real estate data.

3. The method for verifying discrepancies in real estate data based on big data as described in claim 2, characterized in that, Real estate data information includes at least one of the following: ownership certificate number, owner's name, owner's ID number, property location, property type, property area, and real estate unit number.

4. The method for verifying discrepancies in real estate data based on big data as described in claim 1, characterized in that, Identifying and classifying erroneous items in a dataset with mismatched items includes: From the associated dataset, obtain real estate registration data and property registration data, and define the value of real estate registration data as the first value and the value of property registration data as the second value; Calculate the ratio of the first value to the second value, and determine whether the ratio is within a preset range. If the ratio is within the preset ratio range, the real estate registration data will be identified as the first verification data, and the remaining data items in the associated dataset will be marked as suspected errors. If the ratio exceeds the preset ratio range, the real estate registration data will be set as the reference benchmark, and the remaining data items in the associated dataset will be compared with the reference benchmark to determine the deviation value. The data item with the smallest deviation value will be determined as the second verification data, and the remaining data items other than the first and second verification data will be marked as suspected errors.

5. The method for verifying discrepancies in real estate data based on big data as described in claim 4, characterized in that, After determining the second proofreading data, the first proofreading data and the second proofreading data are compared numerically, and the replacement data is determined based on the comparison results, so as to replace the suspected error items with the replacement data; The determination of replacement data includes: If the value of the first verification data is the same as the value of the second verification data, then that value is determined as the replacement data; If they are different, one of the first and second calibration data is selected as the reference item, and the other is selected as the target item. The value of the target item is adjusted based on the modification offset value associated with the time node until the adjusted value is the same as the value of the reference item, and the adjusted value is determined as the replacement data.

6. The method for verifying discrepancies in real estate data based on big data as described in claim 1, characterized in that, Based on the processing items, the non-corresponding items are confirmed to generate confirmation items, including: Generate a number of collation items equal to the number of non-corresponding items; perform a confirmation operation on all collation items; and after the confirmation operation on all collation items is completed, generate a unified confirmation item from the confirmed collation items.

7. The method for verifying discrepancies in real estate data based on big data as described in claim 1, characterized in that, The confirmation items are validated based on historical data to output the final confirmation result, including: Obtain the time range of the generation time of the covered confirmation items, and extract historical valid data from the time range; Compare historical valid data with the confirmed items to assess consistency; Based on the consistency assessment results, the effective usage period of the verification items is determined, and the effective usage period is output as the final verification result.

8. The real estate data based on big data does not correspond to the auditing system, characterized in that, Includes the following modules: The data auditing module is used to compare real estate data with standard data to identify discrepancies. The mismatch processing module is used to identify and classify erroneous items in the dataset associated with mismatched items, and output the processing items. The confirmation item generation module is used to perform confirmation processing on the non-corresponding items according to the processing items, so as to generate confirmation items; And a result verification module, which is used to verify the confirmation items based on historical data in order to output the final confirmation result.

9. The real estate data mismatch verification system based on big data as described in claim 8, characterized in that, The non-corresponding item handling module is used for: Obtain real estate registration data and property rights registration data from the associated dataset; Calculate the ratio of the value of real estate registration data to the value of property registration data; Based on whether the ratio is within a preset range, the data items in the associated dataset are marked as first proofreading data, second proofreading data, or suspected error items, respectively.

10. Real estate data based on big data does not correspond to the verification terminal, characterized in that, Including the processor and memory; The memory stores a computer program that, when executed by a processor, implements the big data-based real estate data mismatch audit method as described in any one of claims 1 to 7.

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