A natural semantic analysis method and system based on the integration of three archive tables

Abstract

The present invention relates to the field related to archive data management, and discloses a natural semantic analysis method and system based on the integration of three archive tables, which is used for standardized content management and storage of archive data. By integrating the tabular text data of multiple archive data of an object, duplicate data in the archive library can be effectively reduced, and the normalized archive data is more convenient to search and view, avoiding the tedious process of finding the archive containing the required content among numerous archive data.

Description

Technical Field

[0001] The present invention relates to the field related to archive data management, and in particular to a natural semantic analysis method and system based on the integration of three archive tables. Background Art

[0002] Archival data is a form of data recording used to record personnel information, event data, and activity conditions. Through the systematic management of archival data, important data can be stored in a centralized manner, facilitating subsequent management and query, and when there is a need, rapid data verification can be performed through historical archives.

[0003] However, the archive storage management method in the prior art will generate a large number of fragmented archive records in the continuous archive data records, that is, an object may have a large amount of archive data, which makes the subsequent query of object-related event records and information more cumbersome. The inquirer needs to search for similar items in a large number of archives and perform the final search by repeatedly and continuously opening the archives to determine the content. The process is very cumbersome. Summary of the invention

[0004] The purpose of the present invention is to provide a natural semantic analysis method and system based on the integration of three archive tables to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A natural semantic analysis method based on the integration of three archive tables, including:

[0007] Acquiring initial archive data, digitizing and unifying the initial archive data, and storing the digitized and unified archive data in a database;

[0008] Performing digital content recognition on the archive data to obtain a plurality of independent information data segments, performing feature judgment on the data content through natural semantic analysis, and marking the information data segments with data labels, wherein each data segment represents an independent information paragraph in the archive data;

[0009] Determine the content category based on the data tag. If multiple information data segments are of the same content category, integrate the data content of the information data segments. The content category is used to characterize personnel, events, node information, and text record information. The data content integration includes deduplication and merging of different data.

[0010] The event subject relevance of the merged information data segments is judged. If multiple information data segments share the same event subject, the time axis sequence of the multiple information data segments is judged, and the content is integrated and updated based on the time axis sequence.

[0011] As a further solution of the present invention: the information data segment includes a project data segment and an object data segment;

[0012] The project data segment and the object data segment exist in a group corresponding to each other, the project data segment is used to represent the content category of the object data segment, and the project data segment has fixed data content;

[0013] The object data segment is used to represent the information record data of the archive object under different content categories, and the object data segments of different content categories have their corresponding format content characteristics.

[0014] As a further solution of the present invention, the step of matching and associating the project data segment with the information data segment is also included, specifically including:

[0015] Based on the initial archive data, the coordinate occupation intervals of the plurality of information data segments in the coordinate space where the archive page is located are obtained. When the initial archive data includes a plurality of archive pages, the plurality of archive pages are connected in the reading direction based on the sequence of the archive pages to establish a complete archive coordinate space.

[0016] Based on the coordinate occupation interval, a horizontal and vertical cross judgment is performed to obtain information data segments corresponding to spaces adjacent to the current coordinate occupation interval in the horizontal and vertical directions, and the information data segments are respectively combined with the current information data segments to obtain multiple association evaluation groups;

[0017] Matching the format and content characteristics of each of the associated evaluation groups through a preset project data segment comparison library, and determining the information data segment corresponding to the project data segment in the associated evaluation group based on the matching result;

[0018] Performing invalidation determination on the association evaluation group, if a pair of information data segments in the association evaluation group are both project data segments or both object data segments, the association evaluation group is an invalid group;

[0019] The format and content features of the object data segment corresponding to the project data segment are obtained, and a match is made to the object data segment in the associated evaluation group based on the format and content features. If they match, the current associated evaluation group is a valid group, that is, the current project data segment and the object data segment are actually associated object data segments.

[0020] As a further solution of the present invention: if a plurality of the information data segments share a common event subject, the step of determining the time axis sequence of the plurality of the information data segments specifically includes:

[0021] Acquire the corresponding characteristic keyword of the common event subject, and perform a search request of the characteristic keyword to the archive based on the characteristic keyword to acquire the record archive file of the characteristic keyword, wherein the types of the record archive file include document data, video record data and voice record data;

[0022] Acquire multiple groups of characteristic keywords of multiple information data segments, and perform difference set calculation on the multiple groups of characteristic keywords to acquire differential keywords of multiple information data segments, wherein the differential keywords are used to represent non-shared characteristic keywords of two information data segments;

[0023] The record archive file is matched and searched based on the differential keyword to obtain the event time axis sequence of the differential keyword in the record archive file, and the event time axis sequence corresponds to the multiple information data segments.

[0024] As a further solution of the present invention: it also includes an association management step:

[0025] Based on the characteristic keywords of the object currently entered into the archive, the historical archive data is recorded and traversed. If there is a corresponding data record in the historical archive data, the historical data record is semantically analyzed and a content summary is generated;

[0026] An associated mark is generated at a position corresponding to a characteristic keyword of the currently entered archive, and mark annotation information of the associated mark is established through the content summary, and an access guide link of the historical data record is established at the characteristic keyword.

[0027] The embodiment of the present invention aims to provide a natural semantic analysis system based on the integration of three archive tables, comprising:

[0028] The archive digitization module is used to obtain the initial archive data, digitize and unify the initial archive data, and store the digitized and unified archive data in a database;

[0029] An information segmentation module is used to perform digital content recognition on the archive data, obtain a plurality of independent information data segments, perform feature judgment on the data content through natural semantic analysis, and mark the information data segments with data labels, wherein each data segment represents an independent information segment in the archive data;

[0030] A content determination and integration module is used to determine the content category based on the data tag. If multiple information data segments are of the same content category, data content integration is performed on the information data segments. The content category is used to characterize personnel, events, node information, and text record information. The data content integration includes deduplication and merging of different data.

[0031] The timeline proofreading module is used to determine the event subject relevance of the merged information data segments. If multiple information data segments share a common event subject, the timeline sequence of the multiple information data segments is determined, and content integration and update is performed based on the timeline sequence.

[0032] As a further solution of the present invention: the information data segment includes a project data segment and an object data segment:

[0033] The project data segment and the object data segment exist in a group corresponding to each other, the project data segment is used to represent the content category of the object data segment, and the project data segment has fixed data content;

[0034] The object data segment is used to represent the information record data of the archive object under different content categories, and the object data segments of different content categories have their corresponding format content characteristics.

[0035] As a further solution of the present invention: it also includes a data segment association module, specifically including:

[0036] A space positioning unit, used for obtaining the coordinate occupation intervals of the plurality of information data segments in the coordinate space where the archive page is located based on the initial archive data, and when the initial archive data includes a plurality of archive pages, the plurality of archive pages are connected in the reading direction based on the sequence of the archive pages to establish a complete archive coordinate space;

[0037] An association preliminary selection unit, used to perform horizontal and vertical cross judgment based on the coordinate occupation interval, obtain information data segments corresponding to spaces adjacent to the current coordinate occupation interval in the horizontal and vertical directions, and respectively combine them with the current information data segments to obtain multiple association evaluation groups;

[0038] A matching and screening unit, used to match the format and content characteristics of each of the associated evaluation groups through a preset project data segment comparison library, and determine the information data segment corresponding to the project data segment in the associated evaluation group based on the matching result;

[0039] an invalidity determination unit, configured to perform invalidity determination on the associated evaluation group; if a pair of information data segments in the associated evaluation group are both project data segments or both object data segments, the associated evaluation group is an invalid group;

[0040] The association selection unit is used to obtain the format content characteristics of the object data segment corresponding to the project data segment, and to match and determine the object data segment in the association evaluation group based on the format content characteristics. If they match, the current association evaluation group is a valid group, that is, the current project data segment and the object data segment are actually associated object data segments.

[0041] As a further solution of the present invention: the time axis proofreading module includes:

[0042] A record matching unit, used to obtain the corresponding feature keyword of the common event subject, and perform a search request for the feature keyword to the archive based on the feature keyword to obtain a record archive file of the feature keyword, wherein the types of the record archive file include document data, video record data and voice record data;

[0043] a feature screening unit, used to obtain multiple groups of feature keywords of the multiple information data segments, and perform difference set calculation on the multiple groups of feature keywords to obtain differential keywords of the multiple information data segments, wherein the differential keywords are used to represent non-shared feature keywords of two information data segments;

[0044] The time proofreading unit is used to match and retrieve the record archive file based on the differential keyword to obtain the event timeline sequence of the differential keyword in the record archive file, and the event timeline sequence corresponds to the multiple information data segments.

[0045] As a further solution of the present invention: it also includes an association management module, specifically including:

[0046] An associated traversal unit, used to traverse the records of the historical archive data based on the characteristic keywords of the currently entered archive of the object, and if there is a corresponding data record in the historical archive data, perform semantic analysis on the historical data record and generate a content summary;

[0047] The association establishment unit is used to generate an association mark at the corresponding position of the characteristic keyword of the currently entered file, and establish the mark annotation information of the association mark through the content summary, and at the same time establish an access guidance link for the historical data record at the characteristic keyword.

[0048] Compared with the prior art, the beneficial effects of the present invention are: it is used for standardized content management and storage of archival data, and by integrating the tabular text data of multiple archival data of an object, it can effectively reduce the duplicate data in the archive, and the normalized archival data is easier to search and view, avoiding the tedious process of finding the archive containing the required content among a large number of archival data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flowchart of a natural semantic analysis method based on the integration of three archive tables.

[0050] Figure 2 This is a flowchart of the matching association steps in a natural semantic analysis method based on the integration of three archive tables.

[0051] Figure 3This is a block diagram of the composition of a natural semantic analysis system based on the integration of three archive tables. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0053] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0054] like Figure 1 The present invention provides a method for natural semantic analysis based on the integration of three archive tables, which includes the following steps:

[0055] S10, acquiring initial archive data, digitizing and unifying the initial archive data, and storing the digitized and unified archive data in a database;

[0056] S20, performing digital content recognition on the archive data to obtain a plurality of independent information data segments, performing feature judgment on the data content through natural semantic analysis, and marking the information data segments with data labels, wherein each data segment represents an independent information paragraph in the archive data;

[0057] S30, determining the content category based on the data tag, if multiple information data segments are of the same content category, integrating the data content of the information data segments, the content category is used to characterize personnel, events, node information and text record information, and the data content integration includes deduplication and merging of different data;

[0058] S40, judging the event subject relevance of the merged information data segments, if multiple information data segments share the same event subject, judging the time axis sequence of the multiple information data segments, and integrating and updating the content based on the time axis sequence.

[0059] In this embodiment, a natural semantic analysis method based on the integration of three archive tables is provided, which is used for the standardized management and storage of the content of archive data. By integrating the tabular text data of multiple archive data of an object, the duplicate data in the archive library can be effectively reduced, and the normalized archive data is more convenient to search and view, avoiding the tedious process of finding the archive where the required content is located in a large number of archive data; in the prior art, data associated with personal objects or events are usually recorded in the form of archive records, and the relevant matters and information of the objects are stored and preserved through the archives, but this will result in a large number of fragments in the continuous archive data records. Fragmented archive records, that is, an object may have a large amount of archive data, which makes it more cumbersome to query the event records and information related to the object in the future. The inquirer needs to search for similar items in a large number of archives, and perform the final search by repeatedly and continuously opening the archives to determine the content. The process is very cumbersome, and is subject to the limited computing resources that the system can provide. The frequent content determination process may be accompanied by a certain delay time during execution. Therefore, based on many existing problems, this embodiment proposes relevant technical solutions to normalize and integrate the archive data. Specifically, it includes: after obtaining the archive data, in order to be able to perform the final search, the archive data is processed. To organize based on content, the archives must first be digitized and unified, that is, converted into text data content that can be directly processed by the program. The initial archive data obtained can be a text archive, an image archive, or various data carrier types. Therefore, it is necessary to identify or convert it through the corresponding data processing program to unify the data type; the information data segment here refers to the paragraph content that is continuously recorded in the archive data. For example, in an information table, the information in a cell is continuous (such as a name). This continuous feature can be judged by the spacing characteristics between the text on the image. It can be a shorter word information or it can be Continuously presenting the record information of longer paragraphs; after determining the information data segment, in order to integrate multiple archive data, it is necessary to judge whether the information data segment is the same or similar content based on the content, and merge it according to the judgment result. However, after the merger, because it is a combination of multiple archive data contents, there may be interspersion on the timeline of the recorded content. Therefore, in order to facilitate the search of content in subsequent references, it is also necessary to manage it sequentially according to the corresponding timeline (or the record timeline in the initial archive data) (only for the content of the common event subject, because during the integration and merger, it is classified and merged based on the event subject).

[0060] As another preferred embodiment of the present invention, the information data segment includes a project data segment and an object data segment;

[0061] The project data segment and the object data segment exist in a group corresponding to each other, the project data segment is used to represent the content category of the object data segment, and the project data segment has fixed data content;

[0062] The object data segment is used to represent the information record data of the archive object under different content categories, and the object data segments of different content categories have their corresponding format content characteristics.

[0063] In this embodiment, the information data segments are divided into categories, wherein the project data segment specifically refers to the formatted text data segment used for content entry instructions originally existing in the archive data (such as information data segments such as "address" and "text"). Therefore, when merging, such information data segments usually need to be completely covered based on the consistency of their meanings; the object data segment is the part used to record the object information content, including the object's personal information, event information (location, time, method and purpose of the event, etc.) and event content records.

[0064] like Figure 2 As shown, as another preferred embodiment of the present invention, it also includes a step of matching and associating the project data segment with the information data segment, specifically including:

[0065] S51, based on the initial archive data, obtaining the coordinate occupation intervals of the plurality of information data segments in the coordinate space where the archive page is located. When the initial archive data includes a plurality of archive pages, the plurality of archive pages are connected in the reading direction based on the sequence of the archive pages to establish a complete archive coordinate space.

[0066] S52, performing a horizontal and vertical intersection judgment based on the coordinate occupation interval, obtaining information data segments corresponding to spaces adjacent to the current coordinate occupation interval in the horizontal and vertical directions, and combining them with the current information data segments to obtain multiple association evaluation groups;

[0067] S53, matching the format and content characteristics of each of the associated evaluation groups through a preset project data segment comparison library, and determining the information data segment corresponding to the project data segment in the associated evaluation group based on the matching result;

[0068] S54, performing invalidity determination on the associated evaluation group, if a pair of information data segments in the associated evaluation group are both project data segments or both object data segments, the associated evaluation group is an invalid group;

[0069] S55, obtaining the format content features of the object data segment corresponding to the project data segment, and performing matching judgment on the object data segment in the associated evaluation group based on the format content features. If they match, the current associated evaluation group is a valid group, that is, the current project data segment and the object data segment are actually associated object data segments.

[0070] In this embodiment, the association and matching steps of the project data segment and the object data segment are supplemented. Here, the judgment is made by auxiliary group division based on the spatial position relationship of the table in the initial file data. Because it has been learned in the previous embodiment that the project data segment is used to guide the filling record of the object data segment, the two are usually adjacent in spatial expression (generally the object data segment is on the right or below). Based on this spatial expression characteristic, multiple combinations can be determined first according to the spatial association, and then the characteristic judgment is made according to the two words in the combination to determine the association. When there are two project data segments or two object data segments in an association evaluation group, it is obviously an erroneous association combination method. If the characteristics of the project data segment and the object data segment do not match (for example, when the project data segment expresses address-related meanings, the object data segment is a data segment including name characteristics), it should also be an erroneous association combination method.

[0071] As another preferred embodiment of the present invention, if a plurality of the information data segments share a common event subject, the step of determining the time axis sequence of the plurality of the information data segments specifically includes:

[0072] Acquire the corresponding characteristic keyword of the common event subject, and perform a search request of the characteristic keyword to the archive based on the characteristic keyword to acquire the record archive file of the characteristic keyword, wherein the types of the record archive file include document data, video record data and voice record data;

[0073] Acquire multiple groups of characteristic keywords of multiple information data segments, and perform difference set calculation on the multiple groups of characteristic keywords to acquire differential keywords of multiple information data segments, wherein the differential keywords are used to represent non-shared characteristic keywords of two information data segments;

[0074] The record archive file is matched and searched based on the differential keyword to obtain the event time axis sequence of the differential keyword in the record archive file, and the event time axis sequence corresponds to the multiple information data segments.

[0075] In this embodiment, a method for judging the order of the timeline is supplemented. For the archive recorders, the related events they participated in usually have related archive records. Therefore, the corresponding content timeline can be verified in sequence by traversing the related event archive records; further, the non-shared feature keywords here can also be judged by the word frequency of the keywords. When the keyword overlap of multiple information data segments is high, the different keywords are judged whether they are differential keywords based on the frequency of occurrence of different keywords. For example, if the frequency of a certain keyword in a certain information data segment is significantly increased, it can be judged as a differential keyword compared to the previous information data segment.

[0076] As another preferred embodiment of the present invention, it also includes an association management step:

[0077] Based on the characteristic keywords of the object currently entered into the archive, the historical archive data is recorded and traversed. If there is a corresponding data record in the historical archive data, the historical data record is semantically analyzed and a content summary is generated;

[0078] An associated mark is generated at a position corresponding to a characteristic keyword of the currently entered archive, and mark annotation information of the associated mark is established through the content summary, and an access guide link of the historical data record is established at the characteristic keyword.

[0079] In this embodiment, an association management step is added, and its purpose is to discover related archival data content during the process of archive sorting and storage, and to summarize the content and guide viewing through annotations and the generation of guide links. During use, when the corresponding keywords are seen, the corresponding data storage interval can be quickly accessed to facilitate the acquisition of required information content.

[0080] like Figure 3 As shown, the present invention also provides a natural semantic analysis system based on the integration of three archive tables, which includes:

[0081] The archive digitization module 100 is used to obtain initial archive data, digitize and unify the initial archive data, and store the digitized and unified archive data in a database;

[0082] The information segmentation module 200 is used to perform digital content recognition on the archive data, obtain a plurality of independent information data segments, perform feature judgment on the data content through natural semantic analysis, and mark the information data segments with data tags, each of which represents an independent information segment in the archive data;

[0083] The content determination and integration module 300 is used to determine the content category based on the data tag. If multiple information data segments are of the same content category, data content integration is performed on the information data segments. The content category is used to characterize personnel, events, node information, and text record information. The data content integration includes deduplication and merging of different data.

[0084] The timeline checking module 400 is used to determine the event subject relevance of the merged information data segments. If multiple information data segments share a common event subject, the timeline sequence of the multiple information data segments is determined, and content integration and update is performed based on the timeline sequence.

[0085] As another preferred embodiment of the present invention, the information data segment includes a project data segment and an object data segment;

[0086] The project data segment and the object data segment exist in a group corresponding to each other, the project data segment is used to represent the content category of the object data segment, and the project data segment has fixed data content;

[0087] The object data segment is used to represent the information record data of the archive object under different content categories, and the object data segments of different content categories have their corresponding format content characteristics.

[0088] As another preferred embodiment of the present invention, it also includes a data segment association module, specifically including:

[0089] A space positioning unit, used for obtaining the coordinate occupation intervals of the plurality of information data segments in the coordinate space where the archive page is located based on the initial archive data, and when the initial archive data includes a plurality of archive pages, the plurality of archive pages are connected in the reading direction based on the sequence of the archive pages to establish a complete archive coordinate space;

[0090] An association preliminary selection unit, used to perform horizontal and vertical cross judgment based on the coordinate occupation interval, obtain information data segments corresponding to spaces adjacent to the current coordinate occupation interval in the horizontal and vertical directions, and respectively combine them with the current information data segments to obtain multiple association evaluation groups;

[0091] A matching and screening unit, used to match the format and content characteristics of each of the associated evaluation groups through a preset project data segment comparison library, and determine the information data segment corresponding to the project data segment in the associated evaluation group based on the matching result;

[0092] an invalidity determination unit, configured to perform invalidity determination on the associated evaluation group; if a pair of information data segments in the associated evaluation group are both project data segments or both object data segments, the associated evaluation group is an invalid group;

[0093] The association selection unit is used to obtain the format content characteristics of the object data segment corresponding to the project data segment, and to match and determine the object data segment in the association evaluation group based on the format content characteristics. If they match, the current association evaluation group is a valid group, that is, the current project data segment and the object data segment are actually associated object data segments.

[0094] As another preferred embodiment of the present invention, the time axis correction module includes:

[0095] A record matching unit, used to obtain the corresponding feature keyword of the common event subject, and perform a search request for the feature keyword to the archive based on the feature keyword to obtain a record archive file of the feature keyword, wherein the types of the record archive file include document data, video record data and voice record data;

[0096] a feature screening unit, used to obtain multiple groups of feature keywords of the multiple information data segments, and perform difference set calculation on the multiple groups of feature keywords to obtain differential keywords of the multiple information data segments, wherein the differential keywords are used to represent non-shared feature keywords of two information data segments;

[0097] The time proofreading unit is used to match and retrieve the record archive file based on the differential keyword to obtain the event timeline sequence of the differential keyword in the record archive file, and the event timeline sequence corresponds to the multiple information data segments.

[0098] As another preferred embodiment of the present invention, it also includes an association management module, specifically including:

[0099] An associated traversal unit, used to traverse the records of the historical archive data based on the characteristic keywords of the currently entered archive of the object, and if there is a corresponding data record in the historical archive data, perform semantic analysis on the historical data record and generate a content summary;

[0100] The association establishment unit is used to generate an association mark at the corresponding position of the characteristic keyword of the currently entered file, and establish the mark annotation information of the association mark through the content summary, and at the same time establish an access guidance link for the historical data record at the characteristic keyword.

[0101] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0102] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0103] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A natural semantic analysis method based on the integration of three archive tables, characterized in that: Include: Acquiring initial archive data, digitizing and unifying the initial archive data, and storing the digitized and unified archive data in a database; Performing digital content recognition on the archive data to obtain a plurality of independent information data segments, performing feature judgment on the data content through natural semantic analysis, and marking the information data segments with data labels, wherein each data segment represents an independent information paragraph in the archive data; Determine the content category based on the data tag. If multiple information data segments are of the same content category, integrate the data content of the information data segments. The content category is used to characterize personnel, events, node information, and text record information. The data content integration includes deduplication and merging of different data. The event subject relevance of the merged information data segments is judged. If multiple information data segments share the same event subject, the time axis sequence of the multiple information data segments is judged, and the content is integrated and updated based on the time axis sequence; The information data segment includes a project data segment and an object data segment; The project data segment and the object data segment exist in a group corresponding to each other, the project data segment is used to represent the content category of the object data segment, and the project data segment has fixed data content; The object data segment is used to represent the information record data of the archive object under different content categories, and the object data segments of different content categories have their corresponding formats and content characteristics; The method further includes a step of matching and associating the project data segment with the information data segment, specifically including: Based on the initial archive data, the coordinate occupation intervals of the plurality of information data segments in the coordinate space where the archive page is located are obtained. When the initial archive data includes a plurality of archive pages, the plurality of archive pages are connected in the reading direction based on the sequence of the archive pages to establish a complete archive coordinate space. Based on the coordinate occupation interval, a horizontal and vertical cross judgment is performed to obtain information data segments corresponding to spaces adjacent to the current coordinate occupation interval in the horizontal and vertical directions, and the information data segments are respectively combined with the current information data segments to obtain multiple association evaluation groups; Matching the format and content characteristics of each of the associated evaluation groups through a preset project data segment comparison library, and determining the information data segment corresponding to the project data segment in the associated evaluation group based on the matching result; Performing invalidation determination on the association evaluation group, if a pair of information data segments in the association evaluation group are both project data segments or both object data segments, the association evaluation group is an invalid group; The format and content characteristics of the object data segment corresponding to the project data segment are obtained, and the object data segment in the associated evaluation group is matched and determined based on the format and content characteristics. If they match, the current associated evaluation group is a valid group, that is, the current project data segment and the object data segment are actually associated object data segments.

2. According to claim 1, a natural semantic analysis method based on the integration of three archive tables is characterized in that: If the multiple information data segments share the same event subject, the step of determining the time axis sequence of the multiple information data segments specifically includes: Acquire the corresponding characteristic keyword of the common event subject, and perform a search request of the characteristic keyword to the archive based on the characteristic keyword to acquire the record archive file of the characteristic keyword, wherein the types of the record archive file include document data, video record data and voice record data; Acquire multiple groups of characteristic keywords of multiple information data segments, and perform difference set calculation on the multiple groups of characteristic keywords to acquire differential keywords of multiple information data segments, wherein the differential keywords are used to represent non-shared characteristic keywords of two information data segments; The record archive file is matched and searched based on the differential keyword to obtain the event time axis sequence of the differential keyword in the record archive file, and the event time axis sequence corresponds to the multiple information data segments.

3. According to claim 1, a natural semantic analysis method based on the integration of three tables of archives is characterized in that: Also includes association management steps: Based on the characteristic keywords of the archive object currently entered into the archive, the historical archive data is recorded and traversed. If there is a corresponding data record in the historical archive data, the historical data record is semantically analyzed and a content summary is generated; An associated mark is generated at a position corresponding to a characteristic keyword of the currently entered archive, and mark annotation information of the associated mark is established through the content summary, and an access guide link of the historical data record is established at the characteristic keyword.

4. A natural semantic analysis system based on the integration of three archive tables, characterized in that: Include: The archive digitization module is used to obtain the initial archive data, digitize and unify the initial archive data, and store the digitized and unified archive data in a database; An information segmentation module is used to perform digital content recognition on the archive data, obtain a plurality of independent information data segments, perform feature judgment on the data content through natural semantic analysis, and mark the information data segments with data labels, wherein each data segment represents an independent information segment in the archive data; A content determination and integration module is used to determine the content category based on the data tag. If multiple information data segments are of the same content category, data content integration is performed on the information data segments. The content category is used to characterize personnel, events, node information, and text record information. The data content integration includes deduplication and merging of different data. A timeline checking module is used to judge the event subject relevance of the merged information data segments. If multiple information data segments share the same event subject, the timeline sequence of the multiple information data segments is judged, and content integration and update is performed based on the timeline sequence. The information data segment includes a project data segment and an object data segment: The project data segment and the object data segment exist in a group corresponding to each other, the project data segment is used to represent the content category of the object data segment, and the project data segment has fixed data content; The object data segment is used to represent the information record data of the archive object under different content categories, and the object data segments of different content categories have their corresponding formats and content characteristics; It also includes a data segment association module, specifically including: A space positioning unit, used for obtaining the coordinate occupation intervals of the plurality of information data segments in the coordinate space where the archive page is located based on the initial archive data, and when the initial archive data includes a plurality of archive pages, the plurality of archive pages are connected in the reading direction based on the sequence of the archive pages to establish a complete archive coordinate space; An association preliminary selection unit, used to perform horizontal and vertical cross judgment based on the coordinate occupation interval, obtain information data segments corresponding to spaces adjacent to the current coordinate occupation interval in the horizontal and vertical directions, and respectively combine them with the current information data segments to obtain multiple association evaluation groups; A matching and screening unit, used to match the format and content characteristics of each of the associated evaluation groups through a preset project data segment comparison library, and determine the information data segment corresponding to the project data segment in the associated evaluation group based on the matching result; an invalidity determination unit, configured to perform invalidity determination on the associated evaluation group; if a pair of information data segments in the associated evaluation group are both project data segments or both object data segments, the associated evaluation group is an invalid group; The associated selection unit is used to obtain the format and content characteristics of the object data segment corresponding to the project data segment, and to match and determine the object data segment in the associated evaluation group based on the format and content characteristics. If they match, the current associated evaluation group is a valid group, that is, the current project data segment and the object data segment are actually associated object data segments.

5. A natural semantic analysis system based on the integration of three archive tables according to claim 4, characterized in that: The timeline proofreading module comprises: A record matching unit, used to obtain the corresponding feature keyword of the common event subject, and perform a search request for the feature keyword to the archive based on the feature keyword to obtain a record archive file of the feature keyword, wherein the types of the record archive file include document data, video record data and voice record data; a feature screening unit, used to obtain multiple groups of feature keywords of the multiple information data segments, and perform difference set calculation on the multiple groups of feature keywords to obtain differential keywords of the multiple information data segments, wherein the differential keywords are used to represent non-shared feature keywords of two information data segments; The time proofreading unit is used to match and retrieve the record archive file based on the differential keyword to obtain the event timeline sequence of the differential keyword in the record archive file, and the event timeline sequence corresponds to the multiple information data segments.

6. A natural semantic analysis system based on the integration of three archive tables according to claim 4, characterized in that: It also includes an association management module, including: An associated traversal unit, used to traverse the records of the historical archive data based on the characteristic keywords of the archive object currently entered into the archive, and if there is a corresponding data record in the historical archive data, perform semantic analysis on the historical data record and generate a content summary; The association establishment unit is used to generate an association mark at the corresponding position of the characteristic keyword of the currently entered file, and establish the mark annotation information of the association mark through the content summary, and at the same time establish an access guidance link for the historical data record at the characteristic keyword.

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