Report similarity analysis method and device based on header field and medium
By structuring and vectorizing the report headers and automatically calculating the similarity of header fields, the efficiency and accuracy issues of grassroots units in processing Excel reports are solved, and efficient report merging is achieved.
Patent Information
- Application Number
- CN202511474631.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-15
- Publication Date
- 2026-02-06
AI Technical Summary
In existing technologies, when processing Excel reports, grassroots units need to manually and subjectively judge the similarity of report fields, resulting in low efficiency and poor accuracy, especially when processing a large number of reports, the merging efficiency is significantly reduced.
By identifying the header area of a report, parsing the header fields and performing text vectorization, and using a vector database to calculate the similarity of header vectors, the system automatically completes cross-report field similarity analysis and provides standardized field similarity analysis results.
It improves the efficiency and accuracy of report similarity analysis, reduces manual intervention, and enables efficient merging of batch reports.
Smart Images
Figure CN121478839A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a method, device and medium for report similarity analysis based on header fields. Background Technology
[0002] Grassroots units need to process various Excel reports issued by higher authorities, such as resident information forms and cadre recommendation forms, and distribute these reports to relevant personnel responsible for filling them out. However, when filling out reports from different departments, these personnel often need to repeatedly fill in the same type of core data. For example, resident name information is required in the Civil Affairs Department's "Application Form for Minimum Living Allowance," the Health and Family Planning Department's "Vaccination Form," and the Public Security Department's "Population Registration Form." However, the header field names for the same type of core data may differ slightly between different reports. For instance, resident name information is represented by header fields such as "Resident Name," "Person Name," and "Household Name" in different reports. This results in the need to repeatedly enter personnel names into multiple forms.
[0003] Therefore, grassroots units often merge reports before distributing them, for example, by consolidating report fields with similar meanings to reduce the number of reports distributed. However, the current report merging process mainly relies on manual judgment. Grassroots staff open each report to be distributed, subjectively determine which fields belong to the same category based on their personal experience, create a unified merge table, and copy the corresponding data row by row. But when the number of reports to be processed is large, the time required increases significantly, drastically reducing merging efficiency. Furthermore, relying entirely on manual subjective judgment makes it easy to misjudge similar fields, leading to low merging accuracy. Summary of the Invention
[0004] This application provides a method, device, and medium for analyzing report similarity based on header fields, which addresses the problems of low accuracy and efficiency in manually subjectively judging the similarity between reports.
[0005] The embodiments of this application adopt the following technical solutions: On the one hand, embodiments of this application provide a report similarity analysis method based on header fields, the method including: Identify the header areas of multiple reports based on the report header identifiers; By parsing the fields in the header area, the structured header fields of each report header area are obtained; The structured header fields of each report header area are processed into text vectors, and each structured header field is converted into a header vector. Iterate through the header vectors of each report, take the header vector of each report as the query vector in turn, and perform similarity queries with the header vectors of other reports as the comparison vectors to obtain the similarity between each query vector and each comparison vector. By comparing similarity scores with a similarity threshold, the similarity fields of each query vector are determined. By performing quantitative analysis on the similar fields corresponding to each query vector, the field similarity analysis results of multiple reports are obtained.
[0006] In one example, identifying the header areas of multiple reports based on the report header identifier specifically includes: Select any labeled cell in the report as the starting point, and expand cell by cell in the four directions of upward, downward, left, and right, retaining consecutive labeled cells during the expansion process to form the header area.
[0007] In one example, obtaining the structured header fields of each report header area by parsing the fields of the header area specifically includes: The hierarchy of the table header is determined by the total number of rows of cells contained vertically within the header area; Based on the hierarchy type of the table header, the fields of the table header area are parsed to obtain the structured header fields of each report header area.
[0008] In one example, determining the header hierarchy type by the total number of rows of cells vertically contained within the header area specifically includes: If the header area consists of only one row of consecutive cells vertically, then the header hierarchy is determined to be a single-level header. If the header area contains two or more consecutive vertical rows of cells, then the header is determined to be a multi-level header.
[0009] In one example, the process of parsing the fields of the header area based on the hierarchy type of the header to obtain the structured header fields of each report header area specifically includes: When the header hierarchy is single-level, extract the field content from the cell to obtain the structured header fields of the report header area; When the header hierarchy is multi-level, the hierarchy order of the header is determined, and the field content in the cell is extracted according to the hierarchy order to obtain the structured header fields of the report header area.
[0010] In one example, when the header hierarchy is multi-level, the hierarchy order of the headers is determined, and the field content of the cells is extracted according to the hierarchy order to obtain the structured header fields of the report header area. Specifically, this includes: The hierarchy of the table headers is determined based on the row number values of the cells in the header area; wherein, the table header corresponding to the row with the smallest row number value is the highest level table header in the multi-level table headers, and the table header hierarchy corresponding to the rows with progressively larger row number values is progressively lower. For cells in the same column of the header area, the fields of the cells below belong to the fields of the cells above, with the fields of the cells above being the parent fields and the fields of the cells below being the child fields; Traverse the first column to the last column of the header area, extract the valid field content of all rows in each column, and concatenate the valid field content into the column field content according to the hierarchical format to obtain a single-column multi-level structured field.
[0011] In one example, determining the similarity fields for each query vector by comparing similarity to a similarity threshold specifically includes: The structured header fields corresponding to the comparison vectors whose similarity to the query vector is greater than or equal to the similarity threshold are determined as the similarity fields of the query vector.
[0012] In one example, after performing text vectorization on the structured header fields of each report header area and converting each structured header field into a header vector, the method further includes: Treat any two reports as a comparison pair, and denote them as the first report and the second report. Calculate the similarity between each header vector in the first report and each header vector in the second report; The similarity is compared with the similarity threshold to determine the matching similar field pairs between the first report and the second report; The number of similar field pairs is counted and compared with the total number of fields in the first report and the total number of fields in the second report, respectively, to obtain the matching ratio of the fields in the first report in the second report and the matching ratio of the fields in the second report in the first report; The similarity analysis results between the comparison report pairs are determined based on the matching ratio.
[0013] On the other hand, embodiments of this application provide a report similarity analysis device based on a header field, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a report similarity analysis method based on a header field as described above.
[0014] On the other hand, embodiments of this application provide a non-volatile computer storage medium for report similarity analysis based on header fields, which stores computer-executable instructions capable of executing any of the above-described report similarity analysis methods based on header fields.
[0015] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: By pre-marking the table headers and extracting structured header fields based on their hierarchy, and then vectorizing these structured header fields into text, standardized fields are provided for subsequent header field similarity analysis. Through traversing and querying vectors and calling the vector database interface, the similarity calculation of fields between a single report and all other reports is completed. Batch comparisons across reports can be performed without manual intervention, significantly improving the efficiency and accuracy of report similarity analysis. Attached Figure Description
[0016] To more clearly illustrate the technical solution of this application, some embodiments of this application will be described in detail below with reference to the accompanying drawings, in which: Figure 1 A flowchart illustrating a report similarity analysis method based on header fields provided in this application embodiment; Figure 2 A schematic diagram of a report header identifier provided in an embodiment of this application; Figure 3 A schematic diagram illustrating the results of a visual analysis of field similarity among multiple reports, provided as an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a report similarity analysis device based on a header field, provided in an embodiment of this application. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0018] Some embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0019] Figure 1This is a flowchart illustrating a report similarity analysis method based on header fields, provided in an embodiment of this application. This method can be applied to different business domains. The process can be executed by computing devices specific to that domain, and certain input parameters or intermediate results can be manually adjusted to help improve accuracy.
[0020] The analysis method involved in the embodiments of this application can be implemented by a terminal device or a server, and this application does not impose any special limitations on it. For ease of understanding and description, the following embodiments are all described in detail using a server as an example.
[0021] It should be noted that the server can be a single device or a system composed of multiple devices, i.e., a distributed server. This application does not make any specific limitations on this.
[0022] S101. Identify the header areas of multiple reports based on the header identifiers of the reports.
[0023] To distinguish between the header and the data area, preset markers are set in the report in advance. For example, the header can be marked with color, with the header area cells being light gray and the data area cells being white. Alternatively, the header can be marked with format, such as bolding the header font and making the borders bold. This application does not limit this.
[0024] Figure 2 This is a schematic diagram of a report header identifier provided in an embodiment of this application, such as... Figure 2 As shown, the table header can be marked in gray.
[0025] In some embodiments of this application, the header area of multiple reports is identified based on the header identifier of the report. Specifically, this includes: selecting any cell with an identifier in the report as the starting point, expanding cell by cell in the four directions of upward, downward, left, and right, and retaining the continuous cells with identifiers during the expansion process to form the header area.
[0026] S102. By parsing the fields in the header area, the structured header fields of each report header area are obtained.
[0027] In some embodiments of this application, the hierarchy type of the header can be determined, for example, by the total number of rows of cells vertically contained within the header area. Based on the hierarchy type of the header, the fields of the header area are parsed to obtain the structured header fields of each report header area.
[0028] For example, firstly, the hierarchy type of the header is determined by the total number of rows of cells contained vertically within the header area. Specifically, if the header area contains only one row of consecutive cells vertically, the header is determined to be a single-level header. If the header area contains two or more rows of consecutive cells vertically, the header is determined to be a multi-level header.
[0029] In this table, a single-level header occupies only one row of cells and has no nesting relationship, while a multi-level header occupies two or more rows of cells and involves nested management of parent and child fields. Row and column distribution refers to the number of rows (vertical distribution) and the number of columns (horizontal distribution) within the header area.
[0030] Subsequently, based on the hierarchy type of the table header, the fields in the table header area are parsed to obtain the structured header fields of each report header area. Specifically, when the hierarchy type of the table header is a single-level header, the field content in the cell is extracted to obtain the structured header fields of the report header area.
[0031] When the header hierarchy is multi-level, determine the header hierarchy order, extract the field content from the cells according to the header hierarchy order, and obtain the structured header fields of the report header area.
[0032] For example, the hierarchy of table headers can be determined based on the row number values of the cells in the header area. The row with the smallest row number corresponds to the highest-level header in a multi-level header hierarchy, and the header hierarchy decreases as the row number values increase.
[0033] For cells in the same column of the header area, fields in cells below are subordinate to fields in cells above them, with the fields in the cells above being the parent fields and the fields in the cells below being the child fields. The header area is traversed from the first column to the last, and the valid field content of all rows in each column is extracted. This valid field content is then concatenated with the column's field content according to a hierarchical format to obtain a single-column multi-level structured field.
[0034] Furthermore, when a row in a multi-level header contains merged cells, the method also includes ensuring that the fields of all the cells below the merged cell belong to the parent field of the merged cell.
[0035] S103. Perform text vectorization processing on the structured header fields of each report header area, and convert each structured header field into a header vector.
[0036] S104. Traverse the header vectors of each report, take the header vector of each report as the query vector in turn, and perform similarity query with the header vectors of other reports as the comparison vectors to obtain the similarity between each query vector and each comparison vector.
[0037] In some embodiments of this application, the header vectors of each report are traversed, and each report's header vector is used sequentially as a query vector. Similarity queries are performed using the header vectors of other reports as comparison vectors to obtain the similarity between each query vector and each comparison vector. Specifically, this includes: Iterate through the header vectors of each report, and determine each header vector as the current query vector. For the current query vector, use the header vectors of other reports as comparison vectors. Call the similarity query interface of the vector database, input the current query vector and all corresponding comparison vectors, perform similarity calculation, and obtain the similarity between the current query vector and each comparison vector.
[0038] For example, the initial similarity can be calculated using cosine similarity or Euclidean distance. After calculating the initial similarity using cosine similarity or Euclidean distance, the similarity between the current query vector and each comparison vector can be obtained by standardizing the initial similarity.
[0039] S105. By comparing the similarity with the similarity threshold, the similarity fields of each query vector are determined.
[0040] In some embodiments of this application, the similarity fields of the query vector are determined by comparing the similarity between the query vector and each comparison vector with a similarity threshold, specifically including: The structured header fields corresponding to the comparison vectors whose similarity to the query vector is greater than or equal to the similarity threshold are determined as the similarity fields of the query vector.
[0041] S106. By performing quantitative analysis on the similar fields corresponding to each query vector, the field similarity analysis results of multiple reports are obtained.
[0042] In some embodiments of this application, based on a list of similar fields corresponding to a query vector, report field similarity analysis results are obtained. Specifically, this includes: associating each similar field in the list with the structured header field and report identifier of the corresponding query vector to construct an associated data set. Based on the associated dataset, the similarity and distribution of a single target query field are statistically analyzed to generate field-level detailed information. This field-level detailed information includes basic information about the target query field, similarity statistics, the similarity score between each similar field and the target query field, the total number of similar fields that can be matched after merging, and the number of Excel worksheets containing the similar fields. Based on the associated data set, the proportion of similar fields between any two reports is calculated to obtain the report similarity calculation results. These results include the number of overlapping similar fields, the proportion of similar fields, and a recommended report merging identifier.
[0043] The data categories in the associated dataset are aggregated according to data type to generate data category statistics. These statistics include the number of similar fields and the number of reports covered by each data category.
[0044] By integrating field-level detailed information, report-level similarity results, and business-level analysis results, field similarity analysis results from multiple reports are obtained.
[0045] Figure 3 This is a schematic diagram illustrating the results of a visual analysis of field similarity across multiple reports, provided as an embodiment of this application. For example... Figure 3 As shown, this is a display interface for report analysis results, used to present the core information of field standardization and similar report identification after multi-report analysis. The report analysis conclusion section summarizes the analysis scope and core results. The report field analysis results section more clearly and intuitively displays the similarity status of the reports. The serial number is the field's sequential number, and the field name is the original field name in the report, such as name or ID number. The field description explains the meaning of the field, such as employee's name or employee's ID number. The field example provides examples of the field content, such as "Sun Wu Wu" or "310125201506151333". The number of merged similar fields is the number of similar fields that can be merged, etc., which will not be elaborated here.
[0046] Furthermore, to quantify the similarity matching between two reports and achieve accurate analysis of the similarity relationship between reports, after converting the structured header fields in the header area of each report into text vectors, the method also includes: Any two reports can be used as a comparison pair, denoted as the first report and the second report.
[0047] Calculate the similarity between each header vector in the first report and each header vector in the second report.
[0048] By comparing the similarity scores with a similarity threshold, the matching pairs of similar fields between the first and second reports are determined.
[0049] The number of similar field pairs is counted and compared with the total number of fields in the first report and the total number of fields in the second report, respectively, to obtain the matching ratio of the fields in the first report in the second report and the matching ratio of the fields in the second report in the first report.
[0050] The similarity analysis results between the comparison report pairs are determined based on the matching ratio.
[0051] The method provided in this application improves the accuracy of subsequent report similarity analysis by pre-marking the table headers to ensure that all subsequent analyses are based on the actual table header fields. Structured table header fields are extracted based on the table header hierarchy type, and these structured table header fields are vectorized into text, providing standardized fields for subsequent table header field similarity analysis. By traversing and querying vectors and calling the vector database interface, the method completes the field similarity calculation between a single report and all other reports, enabling batch comparisons across reports without manual intervention, significantly improving the efficiency and accuracy of report similarity analysis.
[0052] By quantitatively analyzing similar fields, the similarity relationships between fields are transformed into specific data indicators, providing data support for subsequent report merging processes, thereby improving the accuracy and efficiency of report merging.
[0053] It should be noted that, although the embodiments in this application are based on... Figure 1 Steps S101 to S106 will be described sequentially, but this does not mean that steps S101 to S106 must be performed in a strict order. The reason this embodiment follows this order is... Figure 1 The order in which steps S101 to S106 are described is provided to facilitate understanding of the technical solutions of the embodiments of this application by those skilled in the art. In other words, in the embodiments of this application, the order of steps S101 to S106 can be appropriately adjusted according to actual needs.
[0054] Based on the same idea, some embodiments of this application also provide devices and non-volatile computer storage media corresponding to the above methods.
[0055] Figure 4 A schematic diagram of the structure of a report similarity analysis device based on a header field, provided for an embodiment of this application, includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a report similarity analysis method based on a header field as described above.
[0056] Some embodiments of this application provide a non-volatile computer storage medium for report similarity analysis based on header fields, which stores computer-executable instructions capable of executing any of the above-described report similarity analysis methods based on header fields.
[0057] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.
[0058] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.
[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0063] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0064] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0065] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0066] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0067] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the technical principles of this application should fall within the protection scope of this application.
Claims
1. A report similarity analysis method based on header fields, characterized in that, The method includes: Identify the header areas of multiple reports based on the report header identifiers; By parsing the fields in the header area, the structured header fields of each report header area are obtained; The structured header fields of each report header area are processed into text vectors, and each structured header field is converted into a header vector. Iterate through the header vectors of each report, take the header vector of each report as the query vector in turn, and perform similarity queries with the header vectors of other reports as the comparison vectors to obtain the similarity between each query vector and each comparison vector. By comparing similarity scores with a similarity threshold, the similarity fields of each query vector are determined. By performing quantitative analysis on the similar fields corresponding to each query vector, the field similarity analysis results of multiple reports are obtained.
2. The method according to claim 1, characterized in that, The step of identifying the header areas of multiple reports based on the report header identifier specifically includes: Select any labeled cell in the report as the starting point, and expand cell by cell in the four directions of upward, downward, left, and right, retaining consecutive labeled cells during the expansion process to form the header area.
3. The method according to claim 1, characterized in that, The process of parsing the fields in the header area to obtain the structured header fields for each report header area specifically includes: The hierarchy of the table header is determined by the total number of rows of cells contained vertically within the header area; Based on the hierarchy type of the table header, the fields of the table header area are parsed to obtain the structured header fields of each report header area.
4. The method according to claim 3, characterized in that, The step of determining the header hierarchy by the total number of rows of cells vertically contained within the header area specifically includes: If the header area consists of only one row of consecutive cells vertically, then the header hierarchy is determined to be a single-level header. If the header area contains two or more consecutive vertical rows of cells, then the header is determined to be a multi-level header.
5. The method according to claim 4, characterized in that, Based on the hierarchy type of the table header, the fields of the table header area are parsed to obtain the structured header fields of each report header area, specifically including: When the header hierarchy is single-level, extract the field content from the cell to obtain the structured header fields of the report header area; When the header hierarchy is multi-level, the hierarchy order of the header is determined, and the field content in the cell is extracted according to the hierarchy order to obtain the structured header fields of the report header area.
6. The method according to claim 5, characterized in that, When the header hierarchy is multi-level, the hierarchy order of the headers is determined, and the field content of the cells is extracted according to the hierarchy order to obtain the structured header fields of the report header area, specifically including: The hierarchy of the table headers is determined based on the row number values of the cells in the header area; wherein, the table header corresponding to the row with the smallest row number value is the highest level table header in the multi-level table headers, and the table header hierarchy corresponding to the rows with progressively larger row number values is progressively lower. For cells in the same column of the header area, the fields of the cells below belong to the fields of the cells above, with the fields of the cells above being the parent fields and the fields of the cells below being the child fields; Traverse the first column to the last column of the header area, extract the valid field content of all rows in each column, and concatenate the valid field content into the column field content according to the hierarchical format to obtain a single-column multi-level structured field.
7. The method according to claim 1, characterized in that, The process of determining the similarity fields for each query vector by comparing similarity with a similarity threshold specifically includes: The structured header fields corresponding to the comparison vectors whose similarity to the query vector is greater than or equal to the similarity threshold are determined as the similarity fields of the query vector.
8. The method according to claim 1, characterized in that, After performing text vectorization processing on the structured header fields of each report header area and converting each structured header field into a header vector, the method further includes: Treat any two reports as a comparison pair, and denote them as the first report and the second report. Calculate the similarity between each header vector in the first report and each header vector in the second report; The similarity is compared with the similarity threshold to determine the matching similar field pairs between the first report and the second report; The number of similar field pairs is counted and compared with the total number of fields in the first report and the total number of fields in the second report, respectively, to obtain the matching ratio of the fields in the first report in the second report and the matching ratio of the fields in the second report in the first report; The similarity analysis results between the comparison report pairs are determined based on the matching ratio.
9. A report similarity analysis device based on header fields, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a report similarity analysis method based on a header field as described in any one of claims 1-8.
10. A non-volatile computer storage medium for report similarity analysis based on header fields, storing computer-executable instructions, characterized in that, The computer-executable instructions are capable of executing the report similarity analysis method based on the header field as described in any one of claims 1-8.