A data association method, electronic device and computer readable storage medium

Abstract

The present application discloses a data association method, an electronic device, and a computer-readable storage medium, the data association method comprising: associating elements belonging to the same subject in an original database to obtain an element association library, the element association library containing multiple element association tables of different subjects; selecting a first element to be associated and a second element to be associated from all elements in the element association library to form an element group to be associated; based on the element group to be associated, selecting a first element association table and a second element association table from the element association library; based on the first element association table and the second element association table, calculating the association confidence of the first element to be associated and the second element to be associated. In the above manner, the present application can establish the association of the element group to be associated and obtain the corresponding association confidence.

Description

Technical Field

[0001] The present application relates to the field of data governance technology, and in particular to a data association method, an electronic device, and a computer-readable storage medium. Background Art

[0002] The ID Mapping algorithm can establish associations between elements of the same subject, connect various data domains, establish a complete information view of a subject, and realize the connection of various businesses. Through association fusion and spatiotemporal collision, various elements related to the entity are associated with specific entities, providing relationship management support for various applications such as early warning centers, label factories or holographic portraits. However, the current ID Mapping algorithm cannot directly establish associations between weakly correlated elements based on the data in the original database. Summary of the Invention

[0003] The present application provides a data association method, an electronic device, and a computer-readable storage medium, which can establish an association between groups of elements to be associated and obtain corresponding association confidences.

[0004] In order to solve the above technical problems, the technical solution adopted in this application is: to provide a data association method, which includes: associating elements belonging to the same subject in the original database to obtain an element association library, which contains multiple element association tables of different subjects; selecting a first element to be associated and a second element to be associated from all elements in the element association library to form a group of elements to be associated; based on the group of elements to be associated, selecting a first element association table and a second element association table from the element association library; and calculating the association confidence of the first element to be associated and the second element to be associated based on the first element association table and the second element association table.

[0005] To solve the above technical problems, another technical solution adopted in this application is: to provide an electronic device, which includes a memory and a processor connected to each other, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, it is used to implement the data association method in the above technical solution.

[0006] To solve the above technical problems, another technical solution adopted in this application is: providing a computer-readable storage medium for storing a computer program, which, when executed by a processor, is used to implement the data association method in the above technical solution.

[0007] Through the above scheme, the beneficial effect of the present application is: the association and association information between the elements corresponding to the same subject are established through the data in the original database, and the element association library containing multiple element association tables of different subjects is obtained. At the same time, the element association table can be used to establish the association of other element groups to be associated, and the association confidence between the element groups to be associated is calculated, thereby realizing the establishment of associations for weakly associated elements, so as to facilitate the analysis of the association between the element groups to be associated, and to explore potential element associations, providing relationship management support for various applications such as early warning centers, label factories, holographic portraits, and various businesses such as public security investigations. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. Among them:

[0009] Figure 1 This is a flow chart of an embodiment of a data association method provided by this application;

[0010] Figure 2 This is a flow chart of another embodiment of the data association method provided by the present application;

[0011] Figure 3 This is a flowchart of an embodiment of step 23 provided by this application;

[0012] Figure 4 This is a structural diagram of an embodiment of an electronic device provided by the present application;

[0013] Figure 5 It is a structural diagram of an embodiment of a computer-readable storage medium provided by this application. DETAILED DESCRIPTION

[0014] The present application will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only intended to illustrate the present application and are not intended to limit the scope of the present application. Similarly, the following examples are only some examples of the present application and not all examples. All other examples obtained by those of ordinary skill in the art without creative work are intended to fall within the scope of protection of this application.

[0015] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0016] It should be noted that the terms "first", "second" and "third" in this application are only used for descriptive purposes and should not be understood as indicating or suggesting relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units that are inherent to these processes, methods, products or devices.

[0017] See also Figure 1 , Figure 1 : is a flow chart of an embodiment of a data association method provided by the present application, the method comprising:

[0018] Step 11: Associate the elements belonging to the same subject in the original database to obtain an element association library.

[0019] The raw database can contain a variety of data from different data domains, such as registration data, perception data, and trajectory data. Registration data can be registration record data, such as the registration data of a subject in a hotel registration system or an internet cafe registration system. Perception data can be detection data of a subject captured by detection equipment, such as surveillance data obtained by a camera or detection data obtained by an infrared device. Trajectory data can be data that can express the trajectory of a subject's activities, such as the location information of a subject obtained by a positioning device (such as a mobile terminal such as a mobile phone) or the location information of a subject captured by a camera. Specifically, the data in the raw database can come from various data generated by public security business lines, and management personnel can also add record information to the raw database based on actual business conditions (such as associated backfill information, label information, or backtracking information).

[0020] The subject exists in the objective world and is responsible for practical or cognitive activities. The subject includes but is not limited to an individual, and can also be a company or an organization, etc., which is not limited here. The element is the smallest unit that constitutes the subject and maintains its activities. It is unique and can identify the subject in a certain scenario. This embodiment takes an individual as an example. The elements corresponding to the individual may include citizen identification number, license plate number, mobile phone number or Media Access Control (MAC) address, etc.

[0021] In a specific embodiment, the original database may include multiple data tables, and the above-mentioned registration data, perception data, and trajectory data may be stored in different data tables. The data tables may include element information of elements corresponding to different subjects. For example, the hotel registration data table may include element information of elements such as the name, citizen ID number, mobile phone number, or address of different subjects.

[0022] Furthermore, "association" means that element A and element B belong to the same subject, and element A and element B appear at the same time and place. In this case, it means that element A and element B are associated. The element association library can store the spatiotemporal distribution (i.e. time and place) of the association between different elements of the same subject, and record element association information such as the association time, association place, and number of associations established, so as to support business tasks such as personnel identity management, personnel identity expansion, and network identity on-site investigation through the element association library.

[0023] The feature association library can contain multiple feature association tables of different subjects, and can associate features belonging to the same subject in the original database. For example, the citizen ID number and mobile phone number belonging to the same person in the hotel registration data table can be associated to obtain the feature association table of the citizen ID number and mobile phone number corresponding to the subject; then the citizen ID number and address belonging to the same person in the hotel registration data table can be associated to obtain the feature association table of the citizen ID number and address corresponding to the subject. By analogy, all features corresponding to all subjects can be associated, and any two features in each data table in the original database can be associated to obtain multiple feature association tables containing different subjects.

[0024] It can be understood that elements in the same data table are strongly associated, meaning that the elements are highly associated, and the association between the elements can be directly obtained from the same data table. For example, in the hotel registration data table, the association between the citizen ID number, mobile phone number, or address of the same subject can be directly obtained. The citizen ID number, mobile phone number, and address are strongly associated elements, and the strongly associated elements can be associated together through the above step 11. Elements between different data tables are weakly associated elements, meaning that the elements are not strongly associated, and it is impossible to directly establish an association between the weakly associated elements between different data tables. For example, the mobile phone number in the hotel registration data table and the license plate number in the vehicle registration data table, or the citizen ID number in the hotel registration data table and the MAC address in the network audit data table, etc. In this case, the weakly associated elements between different data tables can be associated through the following steps 12-14, and the association confidence between the weakly associated elements can be calculated. Therefore, when the subject changes their mobile phone number, the association between the mobile phone number and other elements of the same subject can be re-established to update the subject's element association information.

[0025] Step 12: Select a first to-be-associated element and a second to-be-associated element from all elements in the element association library to form a to-be-associated element group.

[0026] A first element to be associated and a second element to be associated are selected from all elements in the element association library. The first element to be associated and the second element to be associated may be weakly associated elements.

[0027] Step 13: Based on the group of elements to be associated, a first element association table and a second element association table are selected from the element association library.

[0028] According to the group of elements to be associated, the element association table containing the first element to be associated can be used as the first element association table, and the element association table containing the second element association table can be used as the second element association table, so as to establish an association between the first element to be associated and the second element to be associated according to the first element association table and the second element association table.

[0029] In a specific embodiment, an intermediate element can be used to establish an association between the first element to be associated and the second element to be associated, and the element association table belonging to the same subject and containing the first element to be associated and the intermediate element is determined as the first element association table, and the element association table belonging to the same subject and containing the second element to be associated and the intermediate element is determined as the second element association table, that is, the first element association table and the second element association table contain the same intermediate elements. For example: the first element to be associated is the citizen's ID number, and the second element to be associated is the MAC address. At this time, the element association table containing the citizen's ID number and the hotel can be determined as the first element association table, and the element association table containing the MAC address and the hotel can be determined as the second element association table, thereby establishing an association between the first element to be associated and the second element to be associated based on the above-mentioned first element association table and the second element association table.

[0030] Step 14: Calculate the association confidence between the first to-be-associated element and the second to-be-associated element based on the first element association table and the second element association table.

[0031] Based on the element association information in the first element association table and the second element association table, the association confidence of the first element to be associated and the second element to be associated can be calculated. The association confidence can be used to indicate the degree of association between the first element to be associated and the second element to be associated. The higher the confidence, the more relevant the first element to be associated and the second element to be associated. In one embodiment, when the association confidence between the first element to be associated and the second element to be associated exceeds a preset threshold, it is determined that the first element to be associated and the second element to be associated are associated. The preset threshold can be set according to actual conditions, and its value can be set to 0.6. In another embodiment, the association confidence can also be used to establish an association map of element information corresponding to the same subject to provide service support for subsequent public security investigations and other businesses.

[0032] This embodiment can establish associations and association information (i.e., spatiotemporal distribution) between strongly associated elements corresponding to the same subject through data in the original database to obtain an element association library containing multiple element association tables of different subjects. At the same time, it can use the element association table to establish associations between weakly associated element groups to be associated, calculate the association confidence between the element groups to be associated, and thus realize the establishment of associations for weakly associated elements, so as to facilitate the analysis of the associations between the element groups to be associated, and explore potential element associations, providing relationship management support for various applications such as early warning centers, label factories, holographic portraits, and various businesses such as public security investigations.

[0033] See also Figure 2 , Figure 2 : is a flow chart of another embodiment of the data association method provided by the present application, the method comprising:

[0034] Step 21: Match the elements belonging to the same subject in each data table to obtain the element association intermediate library.

[0035] The original database may contain multiple data tables. The elements in each data table that belong to the same subject may be matched first to obtain an element association intermediate library. The element association intermediate library contains multiple element association intermediate tables. The element association intermediate tables contain element information corresponding to the elements belonging to the same subject and the corresponding sub-element association information. For example: the elements of the same subject may include but are not limited to "citizen ID number and mobile phone number", "citizen ID number and biometrics (such as DNA, etc.)", "document number and network application account", "license plate number and audio and video data", etc. It can be understood that when the subject is an organization / company, the elements of the same subject can also be "organization / company name and bank card number", etc., and examples are not given one by one here; specifically, the specific structure of the element association intermediate table is shown in the following table:

[0036]

[0037]

[0038] It can be understood that the same two elements belonging to the same subject can be associated in one and / or multiple data domains (i.e., in the collection records of different data tables), and the two elements belonging to the same subject can correspond to multiple element association intermediate tables. For example, element A and element B belonging to the same subject in the above table can correspond to multiple element association intermediate tables. Each element association intermediate table can contain element association information corresponding to each association. The element association information in each element association intermediate table can be called sub-element association information; specifically, the sub-element association information may include the association time and association location of the elements belonging to the same subject, i.e., the collection time and collection location in the above table; further, the sub-element association information may also include the area where the element association occurs (i.e., the occurrence area in the above table), and the occurrence area can be specifically divided into municipal level and below (county / district / town, etc.).

[0039] Step 22: Summarize and count the sub-element association information contained in the element association intermediate table with the same element information to obtain an element association table.

[0040] The feature association library contains multiple feature association tables of different entities. The feature association table may include feature information of features belonging to the same entity and corresponding feature association information. The sub-feature association information contained in the feature association intermediate table with the same feature information may be summarized and counted to obtain the feature association table. Specifically, the feature association table may be shown in the following table:

[0041]

[0042]

[0043] Among them, the same element information refers to the same two elements of the same subject. For example, all sub-element association information contained in the intermediate table of all pixel associations corresponding to the citizen ID number and mobile phone number belonging to the same subject can be summarized and counted, and all association times and all association locations where the citizen ID number and mobile phone number are associated can be summarized and counted to obtain element association information.

[0044] Specifically, the element association information may include the total number of association days, the total number of associations, the time trajectory information and the location trajectory information of the element associations belonging to the same subject, among which the total number of associations is the number of times the association occurs, and the total number of association days indicates the time span of the association. For example, a subject checked in at a hotel using his citizen ID number and mobile phone number on November 12, 2020 and November 14, 2020, respectively. The total number of association days of the subject's citizen ID number and mobile phone number is 2 days. The time trajectory information may include the first association time and the final association time (that is, the first_collection time and the most recent_collection time in the above table), and the location trajectory information may include the location where the association occurred (that is, the collection location in the above table).

[0045] In one embodiment, before matching the elements in the original database to obtain the element association library, the data in the original database can be standardized to obtain the processed original database; then the elements belonging to the same subject in the processed original database can be associated to obtain the element association library.

[0046] Specifically, the standardization processing may include data cleaning processing, code conversion processing and field unification processing. The data cleaning processing may include correcting the data in the original database. For example, if there is a typo in the name registered in the hotel registration data table, the typo in the registered name can be modified. If the registered name does not match the name on the citizen's ID card, the data can be deleted as invalid data; the field unification processing includes unifying the data format in the original database, for example: Chinese / English, uppercase / lowercase, whether there is a slash / underscore in the MAC address format, etc. After the data is fielded and unified, the processed data in the format of qualifier + underscore + data element (for example: Zhang San-name) can be obtained.

[0047] Step 23: Select a first to-be-associated element and a second to-be-associated element from all elements in the element association library to form a to-be-associated element group.

[0048] The first element to be associated and the second element to be associated are weakly associated elements, such as Figure 3 As shown, in a specific embodiment, the following steps can be adopted:

[0049] Step 31: Select any one of the elements in the element association library as the current element.

[0050] Step 32: Determine whether the current element is associated with other elements in the element association library.

[0051] Select any one element from all elements included in all element association tables as the current element. If the current element is not associated with any other elements in the element association library, return to the step of selecting any one element from all elements in the element association library as the current element. Specifically, the other elements are elements in the element association library other than the current element. When the current element is not associated with any other elements, it means that there is no strong association between the current element and any other elements, and it is currently impossible to establish an association between the current element and other elements. Then, another element is selected from the element association library as the current element, and the process continues until a current element associated with other elements is selected.

[0052] It can be understood that in one embodiment, in the process of establishing the element association library, elements that are not associated with other elements can be directly filtered out. In this case, any element in the element association library has other elements associated with it. At this time, there is no situation where the current element mentioned above is not associated with other elements in the element association library.

[0053] Step 33: If the current element is associated with other elements in the element association library, a first element to be associated and a second element to be associated are determined based on the other elements and the remaining elements in the element association library.

[0054] When the current element is associated with other elements in the element association library, it can be determined whether the other elements are associated with the remaining elements and the remaining elements are not associated with the current element. The remaining elements are elements other than the current element and other elements associated with the current element. If the other elements are associated with the remaining elements and the remaining elements are not associated with the current element, then the current element is determined to be the first element to be associated, the remaining elements are the second elements to be associated, and the other elements are intermediate elements. Specifically, taking the current element as element A as an example, the current element A is associated with other element B in the element association library. At this time, it is determined whether the other element B is associated with the remaining elements and the remaining elements are not associated with the current element A, that is, find element C in the remaining elements that has a strong association relationship with other element B and a weak association relationship with the current element A, then take the current element A as the first element to be associated, the remaining element C as the second element to be associated, and the other element B as the intermediate element, so that the other element B is used as a bridge to establish the association between the current element A and the remaining elements C, so as to establish the association between the weakly associated current element A and the remaining elements C.

[0055] If the above judgment conditions are not met, that is, other elements are associated with the remaining elements and the remaining elements are associated with the current element, or other elements are not associated with the remaining elements and the remaining elements are associated with the current element, or other elements are not associated with the remaining elements and the remaining elements are not associated with the current element, then return to the step of selecting any element from all elements in the element association library as the current element until other elements are associated with the remaining elements and the remaining elements are not associated with the current element.

[0056] It can be understood that all elements in the element association library can be searched to select all groups of elements to be associated that meet the above-mentioned judgment conditions, and then the association confidence corresponding to the group of elements to be associated is calculated; in another embodiment, the user can also independently select the group of elements to be associated, and then call the corresponding first element association table and second element association table to calculate the association confidence of the first element to be associated and the second element to be associated based on the first element association table and the second element association table. The selection method of the group of elements to be associated is not limited here.

[0057] Step 24: Based on the group of elements to be associated, a first element association table and a second element association table are selected from the element association library.

[0058] The element association table that belongs to the same subject and includes the first element to be associated and the intermediate element can be determined as the first element association table; the element association table that belongs to the same subject and includes the second element to be associated and the intermediate element can be determined as the second element association table. Then, based on the first element association table and the second element association table, the association confidence between the first element to be associated and the second element to be associated is calculated. The specific description is as follows.

[0059] Step 25: Obtain first element association information corresponding to the first element to be associated and the intermediate element in the first element association table.

[0060] Step 26: Obtain the second element association information corresponding to the second element to be associated and the intermediate element in the second element association table.

[0061] The first element association information contained in the first element association table and the second element association information contained in the second element association table are extracted respectively, the first element association information including the association time and association location of the first element to be associated with the intermediate element, and the second element association information including the association time and association location of the second element to be associated with the intermediate element.

[0062] Step 27: Based on the first element association information and the second element association information, generate third element association information corresponding to the first element to be associated and the second element to be associated.

[0063] The third element association information may include the association time, association location, association times and number of association locations of the first element to be associated with the second element to be associated; the association times is the number of times two elements belonging to the same subject appear at the same time and place, and the association locations is the number of locations where two elements belonging to the same subject appear at the same time and place. The number of times the association time corresponding to the first element association information is the same as the association time corresponding to the second element association information can be calculated to obtain the association times; the number of locations where the association locations corresponding to the first element association information and the association locations corresponding to the second element association information are the same at the same time can be calculated to obtain the number of association locations, and then the association confidence of the first element to be associated with the second element to be associated can be calculated based on the association times and the number of association locations.

[0064] Step 28: Calculate the association confidence based on the third factor association information.

[0065] Taking the example where the first and second elements to be associated are the citizen's ID number and MAC address respectively, the association confidence can be calculated based on the number of associations between the ID number and the MAC address and the number of associated locations. The higher the number of associations, the more associated locations, and the closer the association between the citizen's ID number and the MAC address, the higher the calculated association confidence.

[0066] In a specific embodiment, a logistic regression function may be used to calculate the number of associations to obtain a first value; then, the logistic regression function may be used to calculate the number of associated locations to obtain a second value; and then, the product of the first value and the second value may be calculated to obtain an association confidence. Specifically, the association confidence may be calculated using the following formula:

[0067]

[0068] In the above formula, Score represents the association confidence, gx_cnt represents the number of associations, and gx_dnt represents the number of associated locations.

[0069] In a specific implementation, after calculating the association confidence, a knowledge graph can be constructed based on the association confidence, the first element association table, and the second element association table. The knowledge graph can include the associations between the elements corresponding to each subject and the element association information. The knowledge graph can be used to provide relationship management support for various applications such as early warning centers, label factories, or holographic portraits, and provide services for public security and other businesses.

[0070] This embodiment can establish associations between elements belonging to the same subject from data tables in multiple different data domains, and simultaneously obtain spatiotemporal information (including association time and association location) for each pair of elements belonging to the same subject. A first element association table containing the first element to be associated and the intermediate element, and a second element association table containing the second element to be associated and the intermediate element, are then used to establish associations between the weakly associated first and second elements to be associated using the association information of the strongly associated elements. Furthermore, the spatiotemporal information of the associations between the first and second elements to be associated can be used to calculate the association confidence between the two elements using a logistic regression function to facilitate analysis of the association between the first and second elements to be associated. This embodiment can fuse strongly associated data with weakly associated data, and analyze spatiotemporal trajectory information to uncover potential weakly associated element associations and corresponding association confidences for different subjects. Furthermore, a knowledge graph can be constructed based on the association confidence, the first element association table, and the second element association table, forming a complete personal data graph. This provides relationship management support for various applications such as early warning centers, label factories, or holographic profiling, and provides services for public security and other businesses.

[0071] See also Figure 4 , Figure 4 This is a structural diagram of an embodiment of an electronic device provided in the present application. The electronic device 40 includes a memory 41 and a processor 42 connected to each other. The memory 41 is used to store computer programs. When the computer program is executed by the processor 42, it is used to implement the data association method in the above embodiment.

[0072] See also Figure 5 , Figure 5 This is a structural diagram of an embodiment of a computer-readable storage medium provided in the present application. The computer-readable storage medium 50 is used to store a computer program 51. When the computer program 51 is executed by a processor, it is used to implement the data association method in the above embodiment.

[0073] The computer-readable storage medium 50 can be a server, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented.

[0075] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0076] In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in the form of hardware or software functional units.

[0077] The above description is merely an embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data association method, characterized in that: include: Associating elements belonging to the same subject in the original database to obtain an element association library, wherein the element association library includes multiple element association tables of different subjects; Selecting a first to-be-associated element and a second to-be-associated element from all elements in the element association library to form an to-be-associated element group; Based on the to-be-associated element group, selecting a first element association table and a second element association table from the element association library; Calculating the association confidence between the first element to be associated and the second element to be associated based on the first element association table and the second element association table; The element association table includes element information corresponding to elements belonging to the same subject and corresponding element association information, and the step of selecting the first element association table and the second element association table from the element association library based on the element group to be associated includes: Determine an element association table that belongs to the same subject and includes the first element to be associated and the intermediate element as the first element association table; Determine an element association table that belongs to the same subject and includes the second element to be associated and the intermediate element as the second element association table; The step of calculating the association confidence between the first element to be associated and the second element to be associated based on the first element association table and the second element association table includes: Acquire first element association information corresponding to the first element to be associated and the intermediate element in the first element association table, wherein the first element association information includes an association time and an association location at which the first element to be associated and the intermediate element are associated; Acquire second element association information corresponding to the second element to be associated and the intermediate element in the second element association table, wherein the second element association information includes an association time and an association location at which the second element to be associated and the intermediate element are associated; Based on the first element association information and the second element association information, third element association information corresponding to the first element to be associated and the second element to be associated is generated, the third element association information including the number of associations and the number of associated locations where the first element to be associated and the second element to be associated occur; the number of associations is obtained by calculating the number of times the association time corresponding to the first element association information is the same as the association time corresponding to the second element association information, and the number of associated locations is obtained by calculating the number of locations where the associated locations corresponding to the first element association information are the same as the associated locations corresponding to the second element association information at the same time; The association confidence is calculated based on the number of associations and the number of associated locations.

2. The data association method according to claim 1, characterized in that: The step of calculating the association confidence based on the third factor association information further comprises: A knowledge graph is constructed based on the association confidence, the first element association table, and the second element association table.

3. The data association method according to claim 2, characterized in that: The third element association information includes association time and association location.

4. The data association method according to claim 3, characterized in that: The step of calculating the association confidence based on the number of associations and the number of associated locations comprises: Calculating the number of associations using a logistic regression function to obtain a first value; Calculating the number of associated locations using the logistic regression function to obtain a second value; The product of the first value and the second value is calculated to obtain the association confidence.

5. The data association method according to claim 1, characterized in that: The step of selecting the first element to be associated and the second element to be associated from all elements in the element association library includes: Selecting any one element from all elements in the element association library as the current element; Determining whether the current element is associated with other elements in the element association library; If not, return to the step of selecting any one element from all elements in the element association library as the current element; If so, the first to-be-associated element and the second to-be-associated element are determined based on the other elements and the remaining elements in the element association library.

6. The data association method according to claim 5, characterized in that: The step of determining the first element to be associated and the second element to be associated based on the other elements and the remaining elements in the element association library includes: When the current element is associated with other elements in the element association library, determining whether the other elements are associated with the remaining elements and the remaining elements are not associated with the current element; If so, the current element is the first element to be associated, the remaining elements are the second elements to be associated, and the other elements are intermediate elements; If not, the process returns to the step of selecting any one element from all elements in the element association library as the current element.

7. The data association method according to claim 1, characterized in that: The original database includes multiple data tables, and the step of associating elements belonging to the same subject in the original database to obtain an element association library includes: Matching the elements belonging to the same subject in each of the data tables to obtain an element association intermediate library; the element association intermediate library includes multiple element association intermediate tables, and the element association intermediate tables include element information corresponding to the elements belonging to the same subject and corresponding sub-element association information; The sub-element association information contained in the element association intermediate table with the same element information is aggregated and counted to obtain the element association table.

8. The data association method according to claim 7, characterized in that: The element association table includes the element information of the elements belonging to the same subject and the corresponding element association information, the element association information includes the total number of association days, the total number of association times, the time trajectory information and the location trajectory information of the element associations belonging to the same subject, and the sub-element association information includes the association time and association location of the element associations belonging to the same subject.

9. The data association method according to claim 1, characterized in that: The step of associating the elements belonging to the same subject in the original database to obtain the element association library includes: Performing standardization processing on the data in the original database to obtain a processed original database; The elements belonging to the same subject in the processed original database are associated to obtain the element association library.

10. An electronic device, characterized in that: The invention comprises a memory and a processor connected to each other, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, it is used to implement the data association method according to any one of claims 1 to 9.

11. A computer-readable storage medium for storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the data association method according to any one of claims 1 to 9.

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