Multi-source data fusion method based on AI model technology

Abstract

The invention belongs to the technical field of data fusion, and particularly relates to a multi-source data fusion method based on an AI model technology, and the method comprises the steps: carrying out the data expression feature extraction of data in various identity information databases before data fusion, selecting an adaptive preprocessing mode, achieving the targeted and flexible preprocessing of data in different data sources, and improving the data fusion efficiency. The difficulty and the cost of the processing process are reduced while the processing efficiency is improved, data are classified into common information and unique information in the data fusion process, so that inconsistent information item identification and correction are performed on the common information, the unique information and the common information are associated and integrated to form a unique information association link, and the processing efficiency is improved. Therefore, classified integration of information in different data sources is realized, consistency of common information in the different data sources can be guaranteed, relevance of the information in the different data sources can be enhanced, and the fused data is higher in application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of data fusion, and specifically relates to a multi-source data fusion method based on AI model technology. Background Art

[0002] With the advent of the big data era, data plays an increasingly important role in decision making and execution. However, data from a single field often cannot provide enough information to support decision making. Therefore, integrating data from multiple fields can provide more comprehensive and accurate decisions.

[0003] There are also solutions for dealing with data fusion in the prior art, such as a cross-domain data fusion method disclosed in the Chinese invention patent application with publication number CN116662371A, which integrates and manages data from different data sources after parsing and standardization, generates a new data set, and then processes and analyzes the new data set and presents it to the user. The invention aims to solve the problems of data consistency and conflict when integrating and managing data from different data sources. This integration is mainly aimed at common information in different data sources. Since different data sources often come from different data collection sources, there may be data inconsistencies on the same information, which requires solving the inconsistency during integration. However, the invention ignores that there is not only common information but also unique information between data from different data sources. The lack of targeted integration of unique information in each data source may lead to the lack of certain data source-specific information in the integrated data, thereby affecting the integrity of the overall data. At the same time, it is also easy to cause the failure to fully utilize available data resources and affect the effectiveness of data management.

[0004] In addition, preprocessing the data from different data sources before data fusion is the primary task, the purpose of which is to improve data quality by cleaning data and processing outliers. However, in order to improve processing efficiency, existing data fusion usually adopts a unified incremental update processing method. This fixed processing method does not take into account the differences in data characteristics of different data sources. For some data sources with small data volume or low update frequency, the use of incremental updates may not be practical. Not only will it not improve processing efficiency, but it will increase processing complexity and overhead. Therefore, the unified incremental update processing method has limitations in applicability and cannot be applied to the data characteristics of different data sources. It may also cause data consistency risks. Summary of the invention

[0005] To this end, the present invention provides a multi-source data fusion method based on AI model technology for multi-source identity information data fusion for various identity information databases generated by social operations, which effectively solves the problems mentioned in the background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A multi-source data fusion method based on AI model technology, comprising the following steps: S1, respectively call up a type one identity information database, a type two identity information database and a type three identity information database, and extract data performance characteristics in the database, wherein the data performance characteristics include data occupied space, data storage format and data type, thereby selecting an adaptive preprocessing method for preprocessing.

[0007] S2. Determine the identity information as an identifier, and then extract the identity data corresponding to the identifier and the personnel information related to the identity data from each database, thereby matching the identity data extracted from the first-category identity information database, the second-category identity information database, and the third-category identity information database, and then retrieve the related information of the same person in different databases, and classify the related information into common information and unique information.

[0008] S3. Compare and map the common information of the same person in different databases to identify whether there is inconsistency. If there is inconsistency, output the inconsistent information items.

[0009] S4. Analyze the data updating timeliness and data quality of the first-category identity information database, the second-category identity information database, and the third-category identity information database, respectively, and thereby determine the use priority of the first-category identity information database, the second-category identity information database, and the third-category identity information database.

[0010] S5. Correct the information items with inconsistent output based on the use priorities of the first-category identity information database, the second-category identity information database, and the third-category identity information database.

[0011] S6. Standardize the presentation format of common information revised by the same person.

[0012] S7. Associate and integrate the unique information of the same person in different databases to form a unique information association link.

[0013] S8. Link the common information and unique information of the same person in different databases that have been corrected and presented in a unified format to form a data set.

[0014] In combination with all the above-mentioned technical solutions, the positive effects of the present invention are as follows: 1. The present invention extracts data representation features from various identity information databases, thereby selecting adaptive preprocessing methods for data in different databases, thereby achieving targeted and flexible preprocessing of data in different data sources, having better applicability, improving processing efficiency while reducing the difficulty and cost of the processing process, and at the same time minimizing the risk of data consistency caused by preprocessing, which helps to achieve more accurate, efficient and secure data processing and analysis.

[0015] 2. In the process of fusing data from various identity information databases, the present invention first classifies the data into common information and unique information, thereby identifying and correcting inconsistent information items in the common information, and associating and integrating the unique information with the common information to form a unique information association link, thereby realizing the classified integration of information in different data sources. It can not only ensure the consistency of common information in different data sources and ensure the stability of data quality, but also strengthen the correlation of information in different data sources based on the unique information association link, so that the application value of the fused data is higher, which helps to form a more comprehensive and integrated information perspective and provide more accurate information support for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention proposes a multi-source data fusion method based on AI model technology, comprising the following steps: S1, respectively retrieving a type one identity information database, a type two identity information database and a type three identity information database, wherein different databases correspond to one data source, and each identity information database is established for different data sources, and extracting data performance characteristics from the database, wherein the data performance characteristics include data occupied space, data storage format and data type, thereby selecting an adaptive preprocessing method for preprocessing.

[0020] Applied to the above embodiments, the space occupied by data refers to the physical space or storage capacity required to store data, and the data storage format refers to organizing data in a specific structure and specification to facilitate storage, retrieval and processing in a computer system, including but not limited to text files, Excel files, XML, binary formats, etc.

[0021] Further applied to the above embodiment, the data type includes dynamic data and static data. In one example, the identification method of the data type can be based on judging the nature of the data according to its source. If the data source is data from sources such as sensors and log records, the data is often dynamic data because they record real-time events and changes. If the data source is data from sources such as fixed reports and database snapshots, the data may be static data because they usually represent snapshots at a fixed point in time.

[0022] In another example, data tags can be used to identify whether the data is dynamic or static. For example, a data source may provide a timestamp to indicate when the data was generated. If the timestamp changes frequently, it means that the data is dynamic.

[0023] As another example, the data structure can be used to help determine the nature of the data. For example, if the data contains time series or real-time monitoring data, it may be dynamic data. If the data contains a fixed list, it may be static data.

[0024] In the specific implementation of data type identification, combined judgment can be performed based on the combination of the above examples to improve the accuracy of identification.

[0025] It should be noted that the reason for using data space, data storage format and data type as data representation characteristics in the data source is that these factors directly affect the data processing method, efficiency and demand for system resources. By analyzing and understanding these characteristics, we can better select and optimize data processing methods and improve data processing efficiency and quality.

[0026] As a preferred implementation of the above scheme, the adaptation preprocessing method is selected according to the following process: the data occupied space is extracted from the data performance characteristics of the first category identity information database, the second category identity information database and the third category identity information database, and the data occupied space is subtracted from the set occupied space threshold, and then the difference result is divided by the occupied space threshold to obtain the data scale corresponding to the first category identity information database, the second category identity information database and the third category identity information database.

[0027] The occupied space threshold mentioned above is an initial setting, and the purpose of setting it is to assist in the quantitative calculation of data scale.

[0028] The storage format and data type of each data are extracted from the data presentation characteristics of the first, second and third category identity information databases, where the data types include dynamic data and static data. The extracted storage formats and data types are then deduplicated, and the number of storage formats and data types after deduplication are summarized. At the same time, data with the same storage format are classified, and data with the same data type are classified, and the amount of data classified for each storage format and the amount of data classified for each data type are counted.

[0029] The data distribution diversity is calculated based on the number of storage formats, the number of data types, the amount of data classified by each storage format, and the amount of data classified by each data type after deduplication. The calculation formula is: Where M and Y represent the number of storage formats and data types after deduplication, respectively; X represents the total amount of data in the database; Dmax and Dmin represent the maximum and minimum amounts of data classified by each storage format, respectively; Fmax and Fmin represent the maximum and minimum amounts of data classified by each data type, respectively. The fewer repeated storage formats and data types in the data source, the more diverse the data distribution, which, to a certain extent, represents the complexity of the data.

[0030] The data scale and data distribution diversity corresponding to the first-category identity information database, the second-category identity information database, and the third-category identity information database are weighted averaged. By way of example, the weight values ​​corresponding to the data scale and the data distribution diversity in the weighted average calculation are 0.4 and 0.6, and the special preprocessing requirement index corresponding to the database is obtained, and compared with the set limit value. If the special preprocessing requirement index corresponding to a database is less than the set limit value, ordinary processing is selected as the adaptive preprocessing method corresponding to the database, where ordinary preprocessing refers to a common and general data preprocessing method. Otherwise, incremental processing is selected as the adaptive preprocessing method corresponding to the database.

[0031] It should be understood that the larger the data scale and the more diverse the data distribution, the more special processing is needed. This is because large-scale data is usually accompanied by more data quality problems, such as missing values, outliers, duplicate data, etc. Diverse data distribution may lead to more complex data quality problems. Special preprocessing techniques can help identify and handle these problems to ensure that data quality meets the requirements of analysis and modeling. In addition, the processing of large-scale data requires more computing and storage resources, and ordinary processing may limit the processing efficiency. Special preprocessing techniques can help reduce the dimension of data, feature selection, noise reduction, etc., thereby reducing the complexity of data and improving processing efficiency. On the other hand, for data with a small scale and a relatively simple data distribution, ordinary preprocessing methods can often meet the needs, and have higher processing efficiency, avoiding complex special processing processes. In addition, special preprocessing methods may overfit to specific data distributions or problem characteristics, resulting in poor performance on other data sets. In the case of small data scale and relatively simple distribution, ordinary preprocessing methods are more likely to avoid overfitting problems and have better generalization capabilities.

[0032] It is further understood that when performing special preprocessing, selecting incremental processing can achieve the ability to process data in real time. Through incremental processing, newly arrived data can be processed in a timely manner, allowing the system to respond in a timely manner. At the same time, for large-scale data sets, loading all data at once for processing may take up a lot of computing resources and memory space. Incremental processing can process data in batches, reducing the demand for computing resources and memory usage, thereby using resources more efficiently. In addition, in response to dynamic data in the data source, which may be generated in real time, selecting incremental processing can reduce the latency of data processing. Incremental processing can process data immediately after it is generated, without waiting for all data to be collected. Finally, incremental processing can usually be designed as a fault-tolerant and recoverable process. Even if errors or interruptions occur during the processing process, the system can continue to process data from the last interruption by restarting the incremental processing process to ensure that data is not lost.

[0033] It should be added that data preprocessing includes operations such as data cleaning and data noise reduction. After selecting the adaptive data preprocessing method corresponding to each database, the data in each database is imported into the intelligent model of the corresponding preprocessing method for preprocessing to improve data processing efficiency and accuracy.

[0034] S2. Determine the identity information as an identifier, and then extract the identity data corresponding to the identifier and the personnel information related to the identity data from each database, thereby matching the identity data extracted from the first-category identity information database, the second-category identity information database, and the third-category identity information database, and then retrieve the related information of the same person in different databases, and classify the related information into common information and unique information.

[0035] It should be added that identity information is used as an identifier because identity information is usually unique and unchangeable for a person. By using identity information as an association identifier, it can ensure that data in different databases can be accurately matched and associated, and all relevant information of the same person in different data sources can be extracted based on identity information.

[0036] Preferably, the classification of the relevant information into common information and unique information is specifically implemented as follows: the relevant information of the same person in different databases is compared. If there is some relevant information that only exists in one database, the relevant information is classified as unique information, otherwise the relevant information is classified as common information.

[0037] It needs to be understood that although various types of identity information databases are established from different data sources, since they involve personal identity, there will be cross-overlapping situations, thereby generating common information. At the same time, various types of identity information databases will involve different fields due to differences in data sources, and therefore will have unique information that is different from other databases.

[0038] S3. Compare and map the common information of the same person in different databases to identify whether there is inconsistency. If there is inconsistency, output the inconsistent information items.

[0039] Specifically, in the above scheme, the following process is used to identify whether there is inconsistency: each item of common information of the same person in different databases is compared. If there is a certain information that is the same in different databases, the information is identified as consistent, otherwise the information is identified as inconsistent.

[0040] S4. Analyze the data updating timeliness and data quality of the first-category identity information database, the second-category identity information database, and the third-category identity information database, respectively, and thereby determine the use priority of the first-category identity information database, the second-category identity information database, and the third-category identity information database.

[0041] Optimally, the data update timeliness analysis is as follows: the historical update records corresponding to the first-category identity information database, the second-category identity information database, and the third-category identity information database are retrieved respectively, and the updated data volume, update time, and the total data volume existing in the database at the time of update are extracted from the records.

[0042] The updated data volume in each historical update record is compared with the total data volume in the database at the time of update to obtain the data update ratio value corresponding to each historical update record, and the average is calculated to obtain the average data update ratio value.

[0043] The update time in each historical update record is compared with adjacent records to obtain the average update interval duration.

[0044] The average data update ratio value is combined with the average update interval duration using the formula The data update timeliness DT is obtained, where Δt represents the average update interval, T represents the time between the update time of the first record and the update time of the last record in the historical update record, and λ represents the average data update ratio value. The larger the data update ratio value, the shorter the average update interval, and the greater the data update timeliness.

[0045] It should be understood that the reason why the data update ratio and update interval duration are used as analysis indicators when conducting data update timeliness analysis of the data source is that the data update ratio can intuitively reflect the amount of data change brought about by each data update, thereby evaluating the freshness of the data, and the update interval duration can tell the frequency of data updates, further helping to evaluate the timeliness of the data. By combining the two, the timeliness of the data source can be comprehensively evaluated.

[0046] For further optimization, the data quality analysis refers to the following process: performing missing data detection on the data existing in the first category identity information database, the second category identity information database and the third category identity information database respectively, and calculating the proportion of missing data.

[0047] In specific optimization implementation, missing values ​​detection can use data visualization tools such as scatter plots, box plots, heat maps, etc. to visualize data and intuitively identify missing values ​​in the data. Missing values ​​are usually marked with specific symbols or colors to make them easy to identify in visual charts.

[0048] As another example, during the data preprocessing stage, missing values ​​may be marked as specific placeholders, thereby identifying missing values ​​by checking whether these specific placeholders exist in the data.

[0049] The issuing agencies corresponding to each data in the first category identity information database, the second category identity information database and the third category identity information database are obtained respectively, where the issuing agencies include government-related agencies and third-party agencies. The data released by the same issuing agency are classified, and the proportion of data release corresponding to government-related agencies is counted. The larger the proportion of data release corresponding to government-related agencies, the higher the authority and reliability of the data. This is because government-related agencies are usually the main producers and managers of data, and have authoritative data sources and data management mechanisms.

[0050] It is important to understand that the reason why there are third-party agencies in the first, second, and third category identity information databases is that government departments may lack some professional technology and resources to effectively manage and publish large-scale data. Therefore, the government may entrust a third-party agency to publish data.

[0051] The missing data proportion values ​​corresponding to the first-category identity information database, the second-category identity information database and the third-category identity information database and the data release proportion values ​​corresponding to government-related agencies are introduced into the data quality analysis formula DQ=(1-χ)*δ to obtain the data quality coefficient DQ corresponding to the first-category identity information database, the second-category identity information database and the third-category identity information database, where χ and δ represent the missing data proportion value and the data release proportion value corresponding to government-related agencies, respectively.

[0052] Furthermore, the use priority of the first category identity information database, the second category identity information database and the third category identity information database is determined by referring to the following process: the data update timeliness and data quality coefficient corresponding to the first category identity information database, the second category identity information database and the third category identity information database are weighted averaged to obtain the use priority. By way of example, the weight values ​​of the data update timeliness and the data quality coefficient in the weighted average can be 0.3 and 0.7.

[0053] The first-category identity information database, the second-category identity information database, and the third-category identity information database are arranged in descending order of usage priority to obtain the usage priorities of the first-category identity information database, the second-category identity information database, and the third-category identity information database.

[0054] S5. Based on the usage priority of the first-category identity information database, the second-category identity information database, and the third-category identity information database, the inconsistent output information items are corrected. The specific correction process is: (1) extracting the values ​​of the inconsistent information items in the first-category identity information database, the second-category identity information database, and the third-category identity information database. If the values ​​of the inconsistent information items in the three databases are all different, execute (2); otherwise execute (3).

[0055] (2) The information item is modified using the value in the database corresponding to the highest usage priority, where the database corresponding to the highest usage priority is the database ranked first in the descending order of usage priority.

[0056] (3) Obtain the usage priority of the database with the same value of the inconsistent information item and compare it with the highest usage priority. If there is a highest usage priority, the information item is corrected with the value; if there is no highest usage priority, the information item is corrected with the value in the database corresponding to the highest usage priority.

[0057] When correcting inconsistent information items in a type I identity information database, a type II identity information database, and a type III identity information database, the present invention analyzes the use priorities of different databases and makes corrections accordingly. On the one hand, it can ensure that information from high-quality data sources has priority processing and correction, thereby improving the credibility of information correction. On the other hand, it can improve the efficiency of information correction, avoid the waste of time and resources caused by blind correction, and at the same time prevent the spread of erroneous information, ensure that erroneous information will not further affect data quality, and protect the overall data quality level.

[0058] S6. The common information of the same person after correction is presented in a unified format. The specific implementation is as follows: the common information of the same person in different databases is classified into corrected common information and uncorrected common information according to whether it is corrected or not.

[0059] For the modified common information, the presentation format of the information in each database and the database adopted for the modification are obtained, and then the presentation information of the information is unified by taking the presentation format in the modified adopted database as the target presentation information.

[0060] For the uncorrected common information, the presentation format of the information in each database is obtained, and then the presentation information of the information is unified by using the presentation format in the database corresponding to the maximum usage priority as the target presentation information.

[0061] It is important to understand that when performing multi-source data fusion of the first, second, and third identity information databases, the common information has different presentation formats because different data sources may use different data collection methods, resulting in different representations of the same information. For example, some data sources may collect data by manually filling out forms or questionnaires, while other data sources may collect data through automated systems or sensors. These different collection methods may result in differences in data formats, and it is very important to unify the format of this information to ensure data consistency and accuracy.

[0062] S7. Integrate and associate the unique information of the same person in different databases to form a unique information association link. For details, see the following process: formulate association principles, and associate and sort the first-class identity information database, the second-class identity information database, and the third-class identity information database, and then select the unique information in the first-ranked database and the unique information in other databases according to the association principle based on the sorting results to obtain the unique information in other databases associated with each unique information in each database.

[0063] It should be understood that the principles for associating the unique data in the above-mentioned various identity information databases should be formulated in combination with the characteristics of the fields in which the various identity information databases are located.

[0064] The unique information in each database is associated with the unique information in other databases to form a unique information association link.

[0065] Exemplarily, the association order of the first-category identity information database, the second-category identity information database, and the third-category identity information database is set to first associate the unique information in the first-category identity information database with the unique information corresponding to the second-category identity information database and the third-category identity information database, respectively; secondly, associate the unique information in the second-category identity information database with the unique information corresponding to the first-category identity information database and the third-category identity information database, respectively; and finally, associate the unique information in the third-category identity information database with the unique information corresponding to the first-category identity information database and the second-category identity information database, respectively.

[0066] In the above example, assume that the first category of identity information database is A, the unique information existing in the first category of identity information database is a1, a2, and a3 respectively, the first category of identity information database is B, the unique information existing in the second category of identity information database is b1 and b2 respectively, the third category of identity information database is C, and the unique information existing in the first category of identity information database is c1 and c2 respectively. According to the association principle, the unique information in each database is associated with the unique information in other databases, which are a1→b1, a2→c1, a3→c2, b1→c2, b2→a2, c1→b1, c2→a2 respectively. At this time, the unique information association link is a1→b1→c2, a2→c1→b1, b1→c2→a2.

[0067] S8. Link the common information and unique information of the same person in different databases that have been corrected and presented in a unified format to form a data set.

[0068] In the process of fusing data in various identity information databases, the present invention first classifies the data into common information and unique information, thereby identifying and correcting inconsistent information items in the common information, and associating and integrating the unique information with the common information to form a unique information association link, thereby realizing the classified integration of information in different data sources, which can not only ensure the consistency of common information in different data sources and ensure the stability of data quality, but also strengthen the correlation of information in different data sources based on the unique information association link, so that the application value of the fused data is higher, which helps to form a more comprehensive and integrated information perspective and provide more accurate information support for decision-making.

[0069] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A multi-source data fusion method based on AI model technology, characterized in that: The following steps are involved: S1. Retrieve the first-category identity information database, the second-category identity information database, and the third-category identity information database respectively, and extract the data performance characteristics in the database, wherein the data performance characteristics include the data occupied space, data storage format, and data type, and select an adaptive preprocessing method for preprocessing; S2. Determine the identity information as an identifier, and then extract the identity data corresponding to the identifier and the person information related to the identity data from each database, thereby matching the identity data extracted from the first-category identity information database, the second-category identity information database, and the third-category identity information database, and then retrieve the related information of the same person in different databases, and classify the related information into common information and unique information; S3, compare and map the common information of the same person in different databases to identify whether there is inconsistency, and if there is inconsistency, output the inconsistent information items; S4. Analyze the data updating timeliness and data quality of the first-category identity information database, the second-category identity information database, and the third-category identity information database, respectively, and thereby determine the use priority of the first-category identity information database, the second-category identity information database, and the third-category identity information database; S5, correcting the information items with inconsistent output based on the use priority of the first-category identity information database, the second-category identity information database, and the third-category identity information database; S6. Unify the presentation format of the common information revised by the same person; S7. Associate and integrate the unique information of the same person in different databases to form a unique information association link; S8. Link the common information and unique information of the same person in different databases that have been corrected and presented in a unified format to form a data set.

2. The multi-source data fusion method based on AI model technology according to claim 1, characterized in that: The selection of the adaptation preprocessing method refers to the following process: Extract the data occupied space from the data performance characteristics of the first-category identity information database, the second-category identity information database, and the third-category identity information database, and subtract it from the set occupied space threshold, and then divide the subtraction result by the occupied space threshold to obtain the data scale corresponding to the first-category identity information database, the second-category identity information database, and the third-category identity information database; Extracting the storage format and data type of each piece of data from the data performance characteristics of the first-category identity information database, the second-category identity information database, and the third-category identity information database, wherein the data types include dynamic data and static data, and then deduplicating the extracted storage formats and data types, summarizing the number of storage formats and data types after deduplication, and classifying data of the same storage format and the same data type, and counting the amount of data classified by each storage format and the amount of data classified by each data type; The data distribution diversity is calculated based on the number of storage formats, the number of data types, the amount of data classified by each storage format, and the amount of data classified by each data type after deduplication. The calculation formula is: Where M and Y represent the number of storage formats and data types after deduplication, respectively; X represents the total amount of data in the database; Dmax and Dmin represent the maximum and minimum amounts of data in each storage format; Fmax and Fmin represent the maximum and minimum amounts of data in each data type; respectively; The data scale and data distribution diversity corresponding to the first-category identity information database, the second-category identity information database, and the third-category identity information database are weighted averaged to obtain the special preprocessing requirement index corresponding to the database, and compared with the set limit value. If the special preprocessing requirement index corresponding to a database is less than the set limit value, ordinary processing is selected as the adaptation preprocessing method corresponding to the database; otherwise, incremental processing is selected as the adaptation preprocessing method corresponding to the database.

3. The multi-source data fusion method based on AI model technology according to claim 1, characterized in that: The classification of the information involved into common information and unique information is specifically implemented as follows: Compare the relevant information of the same person in different databases. If there is certain relevant information that only exists in one database, then the relevant information will be classified as unique information; otherwise, the relevant information will be classified as common information.

4. The multi-source data fusion method based on AI model technology according to claim 1, characterized in that: The process of identifying whether there is inconsistency is as follows: Each common information of the same person in different databases is compared. If a certain information is the same in different databases, the information is identified as consistent. Otherwise, the information is identified as inconsistent.

5. The multi-source data fusion method based on AI model technology according to claim 1, characterized in that: The data update timeliness analysis is as follows: Retrieve the historical update records corresponding to the first-category identity information database, the second-category identity information database, and the third-category identity information database, respectively, and extract the updated data volume, update time, and the total data volume in the database at the time of update from the records; Compare the updated data volume in each historical update record with the total data volume in the database at the time of update to obtain the data update ratio value corresponding to each historical update record, and perform mean calculation to obtain the average data update ratio value; Compare the update time in each historical update record with adjacent records to obtain the average update interval duration; The average data update ratio value is combined with the average update interval duration using the formula The data update timeliness DT is obtained, where Δt represents the average update interval, T represents the time between the update time of the first record and the update time of the last record in the historical update record, and λ represents the average data update ratio.

6. The multi-source data fusion method based on AI model technology as claimed in claim 5, characterized in that: The data quality analysis is described in the following process: Conduct missing data detection on the first-category identity information database, the second-category identity information database, and the third-category identity information database, and calculate the percentage of missing data; Obtain the issuing agencies corresponding to each piece of data in the first-category identity information database, the second-category identity information database, and the third-category identity information database, respectively, where the issuing agencies include government-related agencies and third-party agencies, thereby classifying the data issued by the same issuing agency and calculating the proportion of data issued by government-related agencies; The missing data proportion values ​​corresponding to the first-category identity information database, the second-category identity information database and the third-category identity information database and the data release proportion values ​​corresponding to the government-related agencies are imported into the data quality analysis formula to obtain the data quality coefficients corresponding to the first-category identity information database, the second-category identity information database and the third-category identity information database.

7. The multi-source data fusion method based on AI model technology according to claim 6, characterized in that: The determination of the use priority of the first-category identity information database, the second-category identity information database, and the third-category identity information database is described in the following process: The use priority is obtained by performing a weighted average calculation of the data update timeliness and data quality coefficient corresponding to the first-category identity information database, the second-category identity information database, and the third-category identity information database; The first-category identity information database, the second-category identity information database, and the third-category identity information database are arranged in descending order of usage priority to obtain the usage priorities of the first-category identity information database, the second-category identity information database, and the third-category identity information database.

8. The multi-source data fusion method based on AI model technology according to claim 1, characterized in that: The correction of the output inconsistent information items refers to the following process: (1) extracting the values ​​of the inconsistent information items in the first-category identity information database, the second-category identity information database, and the third-category identity information database. If the values ​​of the inconsistent information items in the three databases are all different, then execute (2); otherwise, execute (3); (2) modifying the information item according to the value in the database corresponding to the highest priority; (3) Obtain the usage priority of the database with the same value of the inconsistent information item and compare it with the highest usage priority. If there is a highest usage priority, the information item is corrected with the value; if there is no highest usage priority, the information item is corrected with the value in the database corresponding to the highest usage priority.

9. The multi-source data fusion method based on AI model technology according to claim 1, characterized in that: The unified presentation format of the common information corrected by the same person is implemented as follows: Classify the common information of the same person in different databases into revised common information and unrevised common information according to whether it has been revised; For the revised common information, the presentation format of the information in each database and the database adopted for the revision are obtained, and then the presentation information of the information is unified by taking the presentation format in the revised adopted database as the target presentation information; For the uncorrected common information, the presentation format of the information in each database is obtained, and then the presentation information of the information is unified by using the presentation format in the database corresponding to the maximum usage priority as the target presentation information.

10. The multi-source data fusion method based on AI model technology according to claim 1, characterized in that: The process of associating and integrating the unique information of the same person in different databases is as follows: Formulate association principles, and perform association sorting on the first-class identity information database, the second-class identity information database, and the third-class identity information database, and then select the unique information in the first-ranked database and the unique information in other databases according to the association principle based on the sorting result, and obtain the unique information in other databases associated with each unique information in each database; The unique information in each database is associated with the unique information in other databases to form a unique information association link.

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