Data analysis method and device, equipment and storage medium

Abstract

The invention discloses a data analysis method and device, equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that in response to a received target file, at least one set of worksheets is determined based on the target file; an interactive interface is displayed according to the at least one set of worksheets, and the interactive interface comprises a thumbnail area and an actual file area; in response to a received operation instruction of a user on the thumbnail area and the actual file area, determining a target analysis rule according to the operation instruction; analyzing the data content in the at least one group of worksheets based on a target analysis rule to obtain target structured data, and displaying an interactive interface through the at least one group of worksheets determined according to the target file, therefore, the user can define the target analysis rule by performing simple operation on the thumbnail region and the actual file region in the interactive interface, and then the target structured data is generated according to the target analysis rule, so that the efficiency and accuracy of data analysis are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a data analysis method, apparatus, device and storage medium. Background Art

[0002] In today's digital age, data processing and analysis are becoming increasingly important, especially in the financial industry. Excel files, the primary medium for data exchange and reporting, have a direct impact on the timeliness and accuracy of business decisions. As businesses become more complex, the formats and content of Excel files are becoming increasingly diverse and complex, placing higher demands on existing data analysis technologies.

[0003] Currently, Excel file parsing technology primarily relies on programming libraries and artificial intelligence solutions. However, existing technologies face numerous problems and drawbacks in practical applications. The high barrier to entry for programming libraries restricts use by non-technical personnel, resulting in inefficient data parsing. Furthermore, the instability of artificial intelligence solutions when parsing complex Excel files makes it difficult to meet data accuracy requirements.

[0004] Therefore, it is necessary to propose a solution to improve the efficiency and accuracy of data analysis.

[0005] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a data analysis method, device, equipment and storage medium, aiming to improve the efficiency and accuracy of data analysis.

[0007] To achieve the above objectives, the present application provides a data analysis method, which includes:

[0008] In response to receiving the target file, determining at least one set of worksheets based on the target file;

[0009] Displaying an interactive interface according to the at least one set of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area;

[0010] In response to receiving a user's operation instruction on the thumbnail area and the actual file area, determining a target parsing rule according to the operation instruction;

[0011] The data content in the at least one group of worksheets is parsed based on the target parsing rules to obtain target structured data.

[0012] In one embodiment, after the step of parsing the data content in the at least one group of worksheets based on the target parsing rule, the step further includes:

[0013] Configure data collection activity information;

[0014] Executing the data collection activity process based on the data collection activity information through a dynamic task management model to obtain reporting data;

[0015] parsing the reported data based on the target parsing rules to obtain target task data, and verifying the target task data to obtain a verification result; and / or providing the reported data to the interactive interface in response to receiving a data viewing instruction that satisfies the access rights;

[0016] If the verification result is passed, the target task data is updated to the external system and / or database;

[0017] If the verification result is failure, the process returns to executing the step of executing the data collection activity process based on the data collection activity information through the dynamic task management model and subsequent steps.

[0018] In one embodiment, the dynamic task management model includes a task generation unit, a dynamic allocation unit, and a feedback adjustment unit. The step of executing the data collection activity process based on the data collection activity information by the dynamic task management model to obtain the reporting data includes:

[0019] Creating a data collection task by the task generation unit based on historical data collection activities and business requirements in combination with the data collection activity information;

[0020] The dynamic allocation unit determines a task allocation method according to at least one of the workload, field classification, and historical performance of each receiving node, and sends the data collection task to at least one receiving node according to the task allocation method through the management node, so that the at least one receiving node collects data according to the data collection task, and the feedback adjustment unit performs a preliminary check on the collected data, and performs feedback adjustment based on the check result to determine current data, and sends the current data to the management node;

[0021] The management node receives the current data and determines the reporting data according to the current data.

[0022] In one embodiment, the step of the management node receiving the current data and determining the reporting data according to the current data includes:

[0023] Obtaining past data of the target file;

[0024] Calculating the similarity between the current data and the previous data by using a similarity algorithm;

[0025] generating and displaying similarity ranking results according to the similarities, so that the user can select the similarity ranking results;

[0026] In response to receiving a selection instruction from the user for the similarity ranking result, the reported data is determined according to the selection instruction.

[0027] In one embodiment, the step of parsing the data content in the at least one group of worksheets based on the target parsing rules to obtain target structured data includes:

[0028] Locating the row header area, the column header area, and the data area in the at least one group of worksheets based on the target parsing rule;

[0029] Determine the data area to be parsed according to the intersection of the row header area, the column header area and the data area;

[0030] Traversing each cell of the data area to be analyzed, and determining the standard name, indicator identifier and indicator value corresponding to each cell;

[0031] The standard name, indicator identifier and indicator value corresponding to each cell are used as the target structured data.

[0032] In one embodiment, the step of locating the row header area, the column header area, and the data area in the at least one group of worksheets based on the target parsing rule includes:

[0033] Performing conditional judgment on cells in the at least one group of worksheets according to the screening conditions in the target parsing rule to determine cells that meet the screening conditions;

[0034] Associating and integrating cells across rows and / or columns among the cells that meet the screening conditions to determine a target cell;

[0035] The row header area is determined according to the row header corresponding to the target cell, the column header area is determined according to the column header corresponding to the target cell, and the data area is determined according to the data corresponding to the target cell.

[0036] In one embodiment, the step of determining the standard name and indicator identifier corresponding to each cell includes:

[0037] Obtaining row headers from the at least one set of worksheets according to the row number corresponding to each cell, and obtaining column headers from the at least one set of worksheets according to the column number corresponding to each cell;

[0038] Loading a row dictionary to determine the standard name and alias corresponding to the row header, and loading a column dictionary to determine the standard name and alias corresponding to the column header;

[0039] Determine a standard row title according to the standard name and alias corresponding to the row title, and determine a standard column title according to the standard name and alias corresponding to the column title, and use the standard row title and the standard column title as the standard name;

[0040] The indicator identifier is generated according to the standard row header and the standard column header.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a data analysis device, which includes:

[0042] a grouping module for determining at least one group of worksheets based on the target file in response to receiving the target file;

[0043] A display module, configured to display an interactive interface according to the at least one group of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area;

[0044] a determination module, configured to, in response to receiving a user's operation instruction on the thumbnail area and the actual file area, determine a target parsing rule according to the operation instruction;

[0045] The parsing module is used to parse the data content in the at least one group of worksheets based on the target parsing rules to obtain target structured data.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a data analysis device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data analysis method described above.

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the data analysis method described above are implemented.

[0048] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the data parsing method described above.

[0049] One or more technical solutions proposed in this application have at least the following technical effects:

[0050] In response to receiving a target file, at least one group of worksheets is determined based on the target file; an interactive interface is displayed based on the at least one group of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area; in response to receiving a user's operation instructions on the thumbnail area and the actual file area, a target parsing rule is determined based on the operation instruction; based on the target parsing rule, the data content in the at least one group of worksheets is parsed to obtain target structured data, and an interactive interface is displayed by at least one group of worksheets determined based on the target file, so that the user can define the target parsing rule by performing simple operations on the thumbnail area and the actual file area in the interactive interface, and then generate the target structured data according to the target parsing rule, thereby improving the efficiency and accuracy of data parsing. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 A flowchart of the first embodiment of the data analysis method of this application is provided;

[0054] Figure 2 A flowchart of the second embodiment of the data analysis method of this application is provided;

[0055] Figure 3 Schematic diagram of data collection process according to an embodiment of the present application;

[0056] Figure 4 A flowchart of the third embodiment of the data analysis method of this application is provided;

[0057] Figure 5 This is a schematic diagram of the module structure of the data analysis device according to an embodiment of the present application;

[0058] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the data analysis method in the embodiment of the present application.

[0059] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0061] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0062] The main solution of the embodiment of the present application is: in response to receiving a target file, determining at least one group of worksheets based on the target file; displaying an interactive interface based on the at least one group of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area; in response to receiving a user's operation instructions on the thumbnail area and the actual file area, determining a target parsing rule based on the operation instruction; parsing the data content in the at least one group of worksheets based on the target parsing rule to obtain target structured data, and displaying an interactive interface by at least one group of worksheets determined according to the target file, so that the user can define the target parsing rules by performing simple operations on the thumbnail area and the actual file area in the interactive interface, and then generate target structured data according to the target parsing rules, thereby improving the efficiency and accuracy of data parsing.

[0063] In this embodiment, for ease of description, the following description is made with the data analysis device as the execution subject.

[0064] Currently, reports produced by various departments within banks, as well as interbank exchange data, are widely formatted in Excel. Due to the complexity of report formats, it's difficult to use a consistent parsing method to retrieve cell values across multiple files. When creating analytical reports or conducting business intelligence analysis, cross-references may be required to certain cells in another Excel report (which often contains unstructured file data, such as interbank exchange data). This requires staff to manually open each file to extract data, or use programming libraries to write file parsing logic, organize the data, and then integrate it into the required downstream formats, which consumes considerable time.

[0065] Currently, there are the following methods for parsing Excel files:

[0066] (1) Excel file programming library. Currently, existing programming libraries can operate Excel files, such as Python's Pandas and OpenPyXL, and Java's POI. Although they can be handled flexibly, they require writing code to complete, which has a certain threshold for use;

[0067] (2) OCR / AI / NLP / computer vision artificial intelligence solution: Although this solution uses new technologies and attempts to reduce user intervention through automatic system recognition, it is prone to failure and has problems with the accuracy of extracted data due to the high complexity of Excel cell data. This is an unacceptable shortcoming in the financial industry.

[0068] (3) Commercial tools and services, such as coupler.io, which provides automated data extraction from Excel to other platforms, and Supermetrics, which focuses on extracting and analyzing marketing data. These tools are user-friendly, but they involve subscription fees and have limited functionality and cannot be adapted to specific business rules.

[0069] The present application provides a solution that allows users to define complex parsing rules and logic to extract specific cell values through a user-friendly visual interface without the need for coding. The system can generate structured data based on user rules for seamless integration with other BI (Business Intelligence) applications, data analysis or large model analysis tools, making it easy to regularly integrate parsed data into a wider workflow. Through a user-friendly interactive interface, users can conveniently complete the mapping of the relationship between new and old data when faced with changes in table samples, ensuring that the parsed cell data (indicator data) can maintain data continuity and shielding the logic changes of downstream users. This solution solves the usability and flexibility problems of existing technical solutions through a user-friendly visual interface and flexible parsing logic, improves the accuracy and efficiency of data processing, and provides strong support for business analysis within the bank and among peers.

[0070] It should be noted that the execution subject of this embodiment can be a computing service device with data parsing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device or data parsing device capable of performing the above functions. The following uses a data parsing device as an example to illustrate this embodiment and the following embodiments.

[0071] Based on this, the present application embodiment provides a data analysis method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the data analysis method of this application.

[0072] In this embodiment, the data analysis method includes steps S10 to S40:

[0073] Step S10, in response to receiving the target file, determining at least one set of worksheets based on the target file;

[0074] Specifically, the target file in the embodiment of the present application includes an Excel file uploaded by the user, which is the data source of the entire parsing process. A worksheet is a single table in an Excel file that contains the data that the user needs to parse. An Excel file can contain multiple worksheets.

[0075] For example, after a user uploads a target file, the system needs to identify and read all worksheets within the file. This step relies on file parsing technology, extracting the file's structural information from the file format specification (such as .xlsx or .xls) and identifying the individual worksheets within the file. The system parses the file's directory structure to extract the name and location of each worksheet.

[0076] For example, when a user uploads a local Excel file, the system will display a list of sheets in the file. For similarities in Excel reports, for example, if each institution has a separate sheet for the same table but the sheet styles are similar, these sheets can be grouped and a unified parsing rule can be configured to avoid the need to configure each sheet.

[0077] Step S20: displaying an interactive interface according to the at least one set of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area;

[0078] Furthermore, after determining at least one group of worksheets based on the target file, an interactive interface may be displayed according to the at least one group of worksheets.

[0079] Exemplarily, the interactive interface is the interface through which users interact with the system and is used to configure parsing rules. The interactive interface includes a thumbnail area and an actual file area. The thumbnail area is used to display a simplified view of the worksheet structure, helping users quickly locate and select areas. Exemplarily, the thumbnail area includes a row header area, a column header area, a data area, and a table label. The actual file area is used to display the contents of the actual worksheet, where users can perform detailed operations. Through an intuitive interactive interface, users can quickly locate and select the area to be parsed, simplifying the configuration process.

[0080] Exemplarily, the user identifies the structure of the worksheet through the target parsing rules configured in the interactive interface. The target parsing rules contain information about the row headers, column headers, and data areas selected by the user. The system reads the worksheet contents through the file parsing library and extracts the location information of the corresponding areas based on these rules. Specifically, the row header area usually identifies the row category of the data, the column header area identifies the column category of the data, and the data area contains the actual data content. By analyzing the coordinates or identifiers of these areas, the system can determine the specific location of each area in the worksheet.

[0081] For example, the user manually selects row headers, column headers, and data areas through an interactive interface. This method is highly flexible and suitable for complex or non-standard worksheets, ensuring that users can accurately specify the areas that need to be parsed. In addition, the system can automatically detect the worksheet structure and identify headers and data areas through a worksheet detection algorithm. This method is efficient and suitable for standardized worksheets. It can quickly locate key areas and reduce user operations. The system can also use predefined templates to match the worksheet structure and quickly locate key areas. This method is fast and accurate, suitable for worksheets with common formats, and can accelerate the positioning process and improve the user experience.

[0082] Exemplarily, based on the row headers, column headers and data areas in the worksheet manually selected by the user in the historical parsed data, the worksheet detection algorithm can be trained and / or a predefined template can be formed to improve the subsequent selection efficiency. In addition, the user can adjust the key areas for positioning of the worksheet detection algorithm and / or predefined template. The system can also further optimize and update the worksheet detection algorithm and / or predefined template based on the results of the user adjustment to further improve the accuracy and efficiency of positioning.

[0083] Step S30, in response to receiving a user's operation instruction on the thumbnail area and the actual file area, determining a target parsing rule according to the operation instruction;

[0084] Furthermore, after the interactive interface is displayed according to at least one set of worksheets, in response to receiving an operation instruction from the user on the thumbnail area and the actual file area, a target parsing rule may be determined according to the operation instruction.

[0085] For example, user actions on the interactive interface (such as selecting an area or marking a title) are captured and parsed by the system. Based on the user's actions, the system generates corresponding target parsing rules. These target parsing rules define the data extraction method, processing logic, and output format. This allows the user's actions to be converted into executable parsing rules, ensuring that the system extracts and processes data according to the user's intent.

[0086] Step S40: Parse the data content in the at least one group of worksheets based on the target parsing rules to obtain target structured data.

[0087] Furthermore, after the target parsing rules are determined according to the operation instructions, the data content in at least one group of worksheets can be parsed based on the target parsing rules to obtain target structured data.

[0088] For example, a parsing script is generated based on the user-configured target parsing rules to extract the data content in the worksheet. The script converts the data into a structured format (such as JSON or CSV). The resulting structured data can be directly used by other BI (Business Intelligence) tools or data analysis platforms.

[0089] This embodiment adopts the above scheme, specifically by responding to receiving a target file, determining at least one group of worksheets based on the target file; displaying an interactive interface based on the at least one group of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area; responding to receiving a user's operation instructions on the thumbnail area and the actual file area, determining a target parsing rule based on the operation instruction; parsing the data content in the at least one group of worksheets based on the target parsing rule to obtain target structured data, and displaying an interactive interface by at least one group of worksheets determined according to the target file, so that the user can define the target parsing rule by performing simple operations on the thumbnail area and the actual file area in the interactive interface, and then generate target structured data according to the target parsing rule, thereby improving the efficiency and accuracy of data parsing.

[0090] Based on the first embodiment of the present application, the second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as those in the above-mentioned first embodiment can be referred to the above introduction and will not be described in detail later. Figure 2 After step S40, the data analysis method further includes steps S51 to S54:

[0091] Step S51: configuring data collection activity information;

[0092] For example, Excel reports or Excel shared data are generally routine work, and there may be new data every week / monthly / quarterly / yearly. In addition to the analysis configuration in the above embodiment, this solution also provides a configuration for collection activity process management, so that one-time configuration can be used for subsequent continuous analysis.

[0093] For example, data collection activity information refers to user-defined information about a data collection task, including the submitter, reviewer, collection activity name, frequency, delivery method, deadline, and notification content. The submitter is the user or role responsible for submitting data; the reviewer is the user or role responsible for reviewing the submitted data; the collection activity name is the identifier of the data collection task; the collection frequency is the data collection period (e.g., daily, weekly, monthly, etc.); the delivery method is the method of distributing the data collection task (e.g., via email, system notification, etc.); the deadline is the deadline for data submission; and the notification content is task-related notification information.

[0094] For example, users enter information about data collection activities through an interactive interface, and the system stores this information in a configuration file or database. The system uses this information to create and manage data collection tasks, including task assignment, progress tracking, and result aggregation. This clarifies the rules and participants for data collection activities, ensures that tasks are executed as planned, and promptly notifies relevant personnel.

[0095] For example, the system provides a form or configuration interface where users can fill in and submit details about the data collection activity. The system verifies the completeness and accuracy of the input data and saves it for future use. First, configure the submitter and reviewer. Next, configure the collection activity information, such as the collection activity name, activity administrator, collection frequency (daily, weekly, monthly, quarterly, and annually), distribution method, deadline, and notification content.

[0096] Step S52: executing the data collection activity process based on the data collection activity information through the dynamic task management model to obtain reporting data;

[0097] Exemplarily, the dynamic task management model includes a task generation unit, a dynamic allocation unit, and a feedback adjustment unit.

[0098] Exemplarily, the steps of executing the data collection activity process based on the data collection activity information through the dynamic task management model to obtain the reporting data include:

[0099] Creating a data collection task by the task generation unit based on historical data collection activities and business requirements in combination with the data collection activity information;

[0100] The dynamic allocation unit determines a task allocation method according to at least one of the workload, field classification, and historical performance of each receiving node, and sends the data collection task to at least one receiving node according to the task allocation method through the management node, so that the at least one receiving node collects data according to the data collection task, and the feedback adjustment unit performs a preliminary check on the collected data, and performs feedback adjustment based on the check result to determine current data, and sends the current data to the management node;

[0101] The management node receives the current data and determines the reporting data according to the current data.

[0102] Exemplarily, the dynamic task management model can intelligently generate data collection tasks based on historical data collection activities and business needs. For example, the system can analyze the frequency and content of past data collection, automatically create regular data collection tasks, and automatically adjust the task schedule according to business cycles (such as monthly, quarterly). The dynamic task management model can dynamically assign data collection tasks based on the historical performance, workload, and field classification of the receiving node (such as the reporter). For example, if a reporter has demonstrated high data reporting accuracy and efficiency in the past, the system can prioritize key tasks to the reporter, and / or prioritize tasks with larger workloads to reporters with lower workloads. In addition, task allocation can be performed by setting priorities or weights corresponding to historical performance, workload, and field classification, which can ensure the quality of task completion while taking into account the rationality of the allocation method.

[0103] For example, the feedback adjustment unit in the dynamic task management model can perform a preliminary check on the data collected by the submitter and provide real-time feedback on the verification results. If the data contains anomalies or errors, the system can provide specific correction suggestions and allow the submitter to make corrections and resubmit directly through the original submission channel. This can effectively avoid false positives during data collection and improve data accuracy from the data reporting stage.

[0104] For example, for data collection tasks that require collaboration, the system provides collaborative editing, allowing multiple people to edit and submit data simultaneously. The system can also record each person's modification history, ensuring data traceability and clear accountability.

[0105] For example, all data is encrypted during transmission to ensure data security. The system supports multiple encryption protocols to prevent data theft or tampering during transmission. The system provides fine-grained permission control to ensure that only authorized personnel can access and modify specific data collection tasks and data. For example, different access rights can be set based on department, role, and data sensitivity level to protect data privacy.

[0106] For example, the management node can monitor the execution of data collection tasks in real time, including task progress, data submission time and quality, etc. The system can generate detailed monitoring reports to help users promptly identify and resolve problems in task execution.

[0107] Exemplarily, the step of determining the reporting data based on the current data includes:

[0108] Obtaining past data of the target file;

[0109] Calculating the similarity between the current data and the previous data by using a similarity algorithm;

[0110] generating and displaying similarity ranking results according to the similarities, so that the user can select the similarity ranking results;

[0111] In response to receiving a selection instruction from the user for the similarity ranking result, the reported data is determined according to the selection instruction.

[0112] For example, traditional task management processes usually rely on manual operations to complete task allocation, reporting, review and data processing. This approach has significant defects such as low efficiency and high error rate. With the development of information technology, automated task management systems have gradually become an effective way to solve the above problems. However, existing automation systems often lack a complete process reversal mechanism and are unable to fully integrate the tasks of issuing, reporting, reviewing, data viewing and data synchronization, resulting in overall efficiency and data consistency that are difficult to meet enterprise-level requirements. In the embodiments of the present invention, by introducing a system design based on process reversal, the shortcomings of the existing technology can be overcome.

[0113] For example, during the reporting process, if the content of a common file in a certain period changes, in order to reduce the complexity of the configuration, an analysis of the differences will be provided when submitting the current data, allowing users to provide the relationship between the new and old mappings, and providing auxiliary support through text similarity algorithms.

[0114] For example, to solve the problem of matching differences between a new version of a file and an old version of a file, for example: the row title in the new file is "Bank A", and the column title during configuration contains a number of bank names. To find the mapping with the highest similarity, the semantic similarity between a question and multiple candidate questions can be calculated based on the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm and the vector space model, and a similarity ranking (for example, descending order) result can be given to assist users in making choices.

[0115] For example, TF-IDF (Term Frequency-Inverse Document Frequency) is an algorithm for evaluating text similarity, which is widely used in information retrieval. It measures the importance of a word by analyzing its frequency (TF) in a single document and its rarity (IDF) in the entire document set.

[0116] The calculation formula includes:

[0117] Term Frequency (TF):

[0118] Inverse Document Frequency (IDF):

[0119] Document similarity is calculated using cosine similarity:

[0120]

[0121] For example, suppose there are two documents:

[0122] Document 1: “the cat sat on the mat”

[0123] Document 2: "the dog sat on the rug"

[0124] The total number of documents is 3 (including another article titled "the bird flew over the sky"). After calculation, the similarity between Document 1 and Document 2 is approximately 0.120, indicating a small amount of similarity (due to the shared words "sat" and "on"). The TF-IDF contribution to shared common words (such as "the") is 0, which may cause documents that appear similar to be considered dissimilar.

[0125] For example, a ranked list is generated based on the similarity calculation results and displayed to the user through a user interface. The user can select the most similar past data, and the system determines the reported data based on the selection. Providing a user-friendly selection interface helps users quickly identify and select the most similar past data, improving data processing accuracy and user satisfaction.

[0126] Step S53: parsing the reported data based on the target parsing rules to obtain target task data, and verifying the target task data to obtain a verification result; and / or, in response to receiving a data viewing instruction that satisfies the access rights, providing the reported data to the interactive interface;

[0127] Reference Figure 3 , Figure 3 This is a schematic diagram of the data collection process according to an embodiment of the present application, as shown in FIG. Figure 3 As shown, the core process in the embodiment of the present application includes at least one of the following five main steps:

[0128] (1) Task distribution: This link is the starting point of the process. The system distributes tasks to relevant responsible persons through management nodes according to preset rules or manual operations of the administrator. In the diagram, "task distribution" is represented by a rectangular box, located at the beginning of the process, and connected to the "task filling" link through a one-way arrow. This link triggers task allocation notifications through the system interface or database to ensure that task information is accurately and efficiently delivered to the designated executor;

[0129] (2) Task reporting: In this phase, the task recipient completes the input of data or information according to the task requirements. In the diagram, "task reporting" is represented by a rectangular box "reporting", which is located after "task issuance" and points to the subsequent "task review" or "data viewing" phase through an arrow. The reporting process supports online forms (through the spreadjs control integrated with the online Excel experience) to complete data entry. The system automatically records the reporting time and content to ensure the integrity and traceability of the data;

[0130] (3) Task review: This step is an optional process, and whether it is enabled depends on the complexity or importance of the task. In the diagram, "task review" is represented by a diamond-shaped decision box, which contains two branches: "yes" and "no": the "yes" branch indicates that the review is passed, and the process enters the "data review" step; the "no" branch indicates that the review is not passed, and the task returns to the "task filling" step for modification. The review can be completed manually by designated personnel, and a multi-level review mechanism is supported to meet different needs;

[0131] (4) Data review: This step is used to display the results or conduct supervision and inspection after the task is completed. In the diagram, "Data review" is represented by a rectangular box. It is located after the "Task Review" is passed and is connected to the "Data Synchronization" step through an arrow. This step supports multi-user access rights management. Administrators or relevant personnel can view the task execution details through the web interface or mobile terminal, including information such as the filled content, review records, and timestamps;

[0132] (5) Data synchronization: This step is the end point of the process and is responsible for updating task-related data to the external system or database.

[0133] Step S54: if the verification result is passed, updating the target task data to an external system and / or database;

[0134] If the verification result is passed, refer to the above Figure 3 In the example, "Data Synchronization" is represented by the rectangular "Data Synchronization (DXP / DB / API)" at the end of the process. Data synchronization is implemented through a DXP (Data Exchange Platform), database, or API interface. It supports real-time or scheduled synchronization, ensuring data consistency between internal and external systems and providing logging for subsequent tracking.

[0135] Step S55: If the verification result is failure, return to the step of executing the data collection activity process based on the data collection activity information through the dynamic task management model and subsequent steps.

[0136] If the verification result is passed, refer to the above Figure 3, return to the "Task Reporting" link to make modifications, obtain new reporting data, and then parse and verify the new reporting data. After the verification passes, perform task review, data viewing or data synchronization to ensure the accuracy of the synchronized data.

[0137] For example, the process reversal mechanism in the embodiments of the present application realizes fully automated management of tasks from issuance to synchronization, which has the following advantages compared with traditional manual processes:

[0138] Improved efficiency: Automated task allocation and reporting significantly reduces manual intervention time;

[0139] Reduced error rate: Systematic data recording and review mechanism reduces human errors;

[0140] Enhanced quality control: Optional audit process ensures that task results meet expected standards;

[0141] Data consistency assurance: The data synchronization function enables efficient integration among multiple systems.

[0142] This embodiment adopts the above scheme, specifically by configuring data collection activity information; executing the data collection activity process based on the data collection activity information through the dynamic task management model to obtain the reporting data; parsing the reporting data based on the target parsing rules to obtain the target task data, and verifying the target task data to obtain the verification result; and / or, in response to receiving a data viewing instruction that meets the access rights, providing the reporting data to the interactive interface; if the verification result is passed, updating the target task data to the external system and / or database, realizing fully automated management of tasks from issuance to synchronization, automated task allocation and reporting significantly reduces manual intervention time, systematic data recording and auditing mechanism reduces human errors, the audit link ensures that the task results meet the expected standards, and the data synchronization function realizes efficient integration between multiple systems.

[0143] Based on any of the above embodiments of this application, a third embodiment of this application is proposed. In the third embodiment of this application, the same or similar contents as any of the above embodiments can be referred to the above introduction and will not be described in detail later. Figure 4 , step S40 further includes steps S401 to S404:

[0144] Step S401: locating the row header area, column header area, and data area in the at least one group of worksheets based on the target parsing rule;

[0145] Step S402: determining the data area to be parsed according to the intersection of the row header area, the column header area, and the data area;

[0146] Step S403: traverse each cell of the data area to be analyzed, and determine the standard name, indicator identifier, and indicator value corresponding to each cell;

[0147] Step S404: taking the standard name, indicator identifier and indicator value corresponding to each cell as the target structured data.

[0148] Exemplarily, the step of locating the row header area, column header area, and data area in the at least one group of worksheets based on the target parsing rule includes:

[0149] Performing conditional judgment on cells in the at least one group of worksheets according to the screening conditions in the target parsing rule to determine cells that meet the screening conditions;

[0150] Associating and integrating cells across rows and / or columns among the cells that meet the screening conditions to determine a target cell;

[0151] The row header area is determined according to the row header corresponding to the target cell, the column header area is determined according to the column header corresponding to the target cell, and the data area is determined according to the data corresponding to the target cell.

[0152] Exemplarily, embodiments of the present application allow users to define the specific logic of data parsing based on specific data structures and business rules. For example, users can define how to judge and extract data based on specific conditions. For example, users can set rules to identify and extract data from specific cells that meet conditions such as specific text patterns or numeric ranges; users can specify data conversion rules, such as converting text strings to numeric values or unifying date formats. This ensures that the extracted data conforms to the expected format and type, facilitating subsequent analysis; users can also define how to handle errors or exceptions encountered during the parsing process. For example, when missing data is encountered, the system can be configured to fill it with a default value or mark it as an exception for subsequent review; for worksheets containing merged cells or data across columns / rows, users can define special parsing rules to correctly extract and reorganize data; when data is distributed across multiple worksheets or files, users can define how to associate and integrate this data, such as merging data from different worksheets using a common identifier to determine the target cell, determine the row header area based on the row header corresponding to the target cell, determine the column header area based on the column header corresponding to the target cell, and determine the data area based on the data corresponding to the target cell. The system provides a visual interface or template to guide users to complete the customization of parsing logic. Users select and set rules through the interface, and the system provides instant feedback on the configuration effect, so that the parsing configuration can adapt to various complex data formats and business logic, ensuring the accuracy and efficiency of data parsing.

[0153] Exemplarily, the step of determining the standard name and indicator identifier corresponding to each cell includes:

[0154] Obtaining row headers from the at least one set of worksheets according to the row number corresponding to each cell, and obtaining column headers from the at least one set of worksheets according to the column number corresponding to each cell;

[0155] Loading a row dictionary to determine the standard name and alias corresponding to the row header, and loading a column dictionary to determine the standard name and alias corresponding to the column header;

[0156] Determine a standard row title according to the standard name and alias corresponding to the row title, and determine a standard column title according to the standard name and alias corresponding to the column title, and use the standard row title and the standard column title as the standard name;

[0157] The indicator identifier is generated according to the standard row header and the standard column header.

[0158] For example, an embodiment of the present application provides a widely adaptable Excel data parsing solution that can parse any Excel file into structured data, and at the same time ensure that downstream data systems can stably reference data by generating a continuous "indicator ID" for cell data.

[0159] Exemplarily, the system parses the structure of the worksheet according to the area selection information in the target parsing rule, and extracts the location information of the row header area, the column header area, and the data area.

[0160] For example, the system quickly locates three key parts in the sheet based on preset rules:

[0161] Row header area: such as B4:L5, records the row names (such as products).

[0162] Column header area: such as A6:A23, record the column name (such as organization).

[0163] Data area: such as B6:L25, stores specific data (such as sales).

[0164] The system loads row and column dictionaries that identify standard names and their aliases:

[0165] Row dictionary example: ˋPosition[6]:Standard name[Product A]:Alias name[Product a];

[0166] Column dictionary example: ˋPosition[B]:Standard Name[A Bank]:Alias Name[A1, A2];

[0167] The data area to be parsed (such as B6:L23) is determined by taking the intersection of the configuration area (such as B6:L25) and the intersection area of the row header and column header (such as B6:L23).

[0168] For example, the system receives the Excel file uploaded each period and performs the following operations:

[0169] The system traverses each cell in the data area to be parsed (such as B6:L23) and:

[0170] Get the "column header" (such as "Bank A") from the file based on the column number (such as B).

[0171] Get the "row header" (such as "Product A") from the file based on the row number (such as 10).

[0172] If the title does not match the standard name (e.g. "Product A" instead of "Product A"):

[0173] The system calculates the similarity and prompts the user to confirm. The user can mark the new one, match existing data, or ignore it. After the user confirms, the standardized name will be used uniformly.

[0174] For example, in the embodiment of the present application, a unique indicator identifier (i.e., indicator ID) is generated for each cell. The indicator identifier is determined based on one or more of the following factors:

[0175] Template ID (unique within the collection activity, for example, "Monthly Main Indicators by Institution Table" is collected monthly);

[0176] Sheet name (e.g., “【1】Monthly main indicators breakdown table”);

[0177] Table name (such as "Indicator Table");

[0178] Standard row headers (e.g., "Product A");

[0179] Standard column headers (such as "Bank A").

[0180] Exemplary output data formats include:

[0181] Indicator ID: such as: H19LDD;

[0182] Activity name (monthly main indicators by institution table, collected monthly);

[0183] Issue (202501);

[0184] Position (B6);

[0185] indicator value (6000);

[0186] Submitter (Zhang San);

[0187] Submission time (2023-10-01 10:10:10).

[0188] For example, in the embodiment of the present application, by mapping aliases to standard names, the system ensures that the indicator ID remains consistent in different files, which is convenient for downstream use.

[0189] For example, the following is a simplified Excel spreadsheet example to illustrate the parsing process:

[0190]

[0191] Exemplarily, the parsing step includes:

[0192] 1. The system recognizes row headers (e.g., A2 = "Product A") and column headers (e.g., B1 = "2023").

[0193] 2. For cell B2, combine A2 ("Product A") and B1 ("2023");

[0194] 3. If A2 is written as "Product a", the system prompts the user to confirm and then standardizes it to "Product A";

[0195] 4. Generate indicator ID: `H19LDD unique within the system`, and record the indicator value as 100.

[0196] This embodiment adopts the above scheme, specifically by locating the row header area, column header area and data area in the at least one group of worksheets based on the target parsing rules; determining the data area to be parsed based on the intersection of the row header area, column header area and data area; traversing each cell of the data area to be parsed, and determining the standard name, indicator identifier and indicator value corresponding to each cell; using the standard name, indicator identifier and indicator value corresponding to each cell as the target structured data, through a user-friendly visual interface, allowing users to define complex parsing rules and logic to extract specific cell values without coding. The system can generate structured data according to user rules for seamless integration with other BI applications, data analysis or large model analysis tools, and can flexibly integrate the parsed data into a wider range of workflows on a regular basis. Through the interactive interface, users can quickly complete the mapping of the relationship between new and old data when facing changes in table samples, ensuring that the parsed cell data (indicator data) can maintain data continuity and shield the logical changes of downstream users.

[0197] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data parsing method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0198] This application also provides a data analysis device, please refer to Figure 5 , the data parsing device includes:

[0199] a grouping module 10 for determining at least one group of worksheets based on the target file in response to receiving the target file;

[0200] A display module 20, configured to display an interactive interface according to the at least one set of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area;

[0201] a determination module 30 for determining a target parsing rule according to a user's operation instruction on the thumbnail area and the actual file area in response to receiving the operation instruction;

[0202] The parsing module 40 is configured to parse the data content in the at least one group of worksheets based on the target parsing rules to obtain target structured data.

[0203] The data parsing device provided in this application utilizes the data parsing method described in the aforementioned embodiments to solve the technical problems of data parsing. Compared to the prior art, the data parsing device provided in this application has the same beneficial effects as the data parsing method described in the aforementioned embodiments. Other technical features of the data parsing device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0204] The present application provides a data analysis device, which 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute the data analysis method in the above-mentioned embodiment one.

[0205] Reference below Figure 6 , which shows a schematic diagram of the structure of a data parsing device suitable for implementing the embodiments of the present application. The data parsing device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The data analysis device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0206] like Figure 6 As shown, the data analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the data analysis device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the data parsing device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a data parsing device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or provided instead.

[0207] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0208] The data parsing device provided in this application utilizes the data parsing method described in the aforementioned embodiments to solve the technical problems of data parsing. Compared to the prior art, the beneficial effects of the data parsing device provided in this application are the same as those of the data parsing method described in the aforementioned embodiments. Other technical features of the data parsing device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0209] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0210] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0211] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, wherein the computer-readable program instructions are used to execute the data parsing method in the above-mentioned embodiment.

[0212] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0213] The computer-readable storage medium may be included in the data analysis device, or may exist independently without being incorporated into the data analysis device.

[0214] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the data analysis device, the data analysis device: determines at least one group of worksheets based on the target file in response to receiving the target file; displays an interactive interface based on the at least one group of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area; determines a target parsing rule based on the operation instruction in response to receiving the user's operation instruction on the thumbnail area and the actual file area; parses the data content in the at least one group of worksheets based on the target parsing rule to obtain target structured data, and displays the interactive interface based on the at least one group of worksheets determined according to the target file, so that the user can define the target parsing rule by performing simple operations on the thumbnail area and the actual file area in the interactive interface, and then generates target structured data according to the target parsing rule, thereby improving the efficiency and accuracy of data analysis.

[0215] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0216] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0217] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0218] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned data parsing method, thereby resolving the technical issues of data parsing. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the data parsing method provided in the aforementioned embodiments, and are not further elaborated here.

[0219] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned data parsing method when executed by a processor.

[0220] The computer program product provided in this application can solve the technical problem of data analysis. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the data analysis method provided in the above embodiment, which will not be repeated here.

[0221] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data analysis method, characterized in that: The method comprises: In response to receiving the target file, determining at least one set of worksheets based on the target file; Displaying an interactive interface according to the at least one set of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area; In response to receiving a user's operation instruction on the thumbnail area and the actual file area, determining a target parsing rule according to the operation instruction; The data content in the at least one group of worksheets is parsed based on the target parsing rules to obtain target structured data.

2. The data analysis method according to claim 1, wherein: After the step of parsing the data content in the at least one group of worksheets based on the target parsing rule, the following step further comprises: Configure data collection activity information; Executing the data collection activity process based on the data collection activity information through a dynamic task management model to obtain reporting data; parsing the reported data based on the target parsing rules to obtain target task data, and verifying the target task data to obtain a verification result; and / or providing the reported data to the interactive interface in response to receiving a data viewing instruction that satisfies the access rights; If the verification result is passed, the target task data is updated to the external system and / or database; If the verification result is failure, the process returns to executing the step of executing the data collection activity process based on the data collection activity information through the dynamic task management model and subsequent steps.

3. The data analysis method according to claim 2, wherein: The dynamic task management model includes a task generation unit, a dynamic allocation unit, and a feedback adjustment unit. The step of executing the data collection activity process based on the data collection activity information by the dynamic task management model to obtain the reporting data includes: Creating a data collection task by the task generation unit based on historical data collection activities and business requirements in combination with the data collection activity information; The dynamic allocation unit determines a task allocation method according to at least one of the workload, field classification, and historical performance of each receiving node, and sends the data collection task to at least one receiving node according to the task allocation method through the management node, so that the at least one receiving node collects data according to the data collection task, and the feedback adjustment unit performs a preliminary check on the collected data, and performs feedback adjustment based on the check result to determine current data, and sends the current data to the management node; The management node receives the current data and determines the reporting data according to the current data.

4. The data analysis method according to claim 3, wherein: The step of determining the reporting data based on the current data includes: Obtaining past data of the target file; Calculating the similarity between the current data and the previous data by using a similarity algorithm; generating and displaying similarity ranking results according to the similarities, so that the user can select the similarity ranking results; In response to receiving a selection instruction from the user for the similarity ranking result, the reported data is determined according to the selection instruction.

5. The data analysis method according to claim 1, wherein: The step of parsing the data content in the at least one group of worksheets based on the target parsing rules to obtain target structured data includes: Locating the row header area, the column header area, and the data area in the at least one group of worksheets based on the target parsing rule; Determine the data area to be parsed according to the intersection of the row header area, the column header area and the data area; Traversing each cell of the data area to be analyzed, and determining the standard name, indicator identifier and indicator value corresponding to each cell; The standard name, indicator identifier and indicator value corresponding to each cell are used as the target structured data.

6. The data analysis method according to claim 5, wherein: The step of locating the row header area, column header area, and data area in the at least one group of worksheets based on the target parsing rule comprises: Performing conditional judgment on cells in the at least one group of worksheets according to the screening conditions in the target parsing rule to determine cells that meet the screening conditions; Associating and integrating cells across rows and / or columns among the cells that meet the screening conditions to determine a target cell; The row header area is determined according to the row header corresponding to the target cell, the column header area is determined according to the column header corresponding to the target cell, and the data area is determined according to the data corresponding to the target cell.

7. The data analysis method according to claim 5, wherein: The step of determining the standard name and indicator identifier corresponding to each cell includes: Obtaining row headers from the at least one set of worksheets according to the row number corresponding to each cell, and obtaining column headers from the at least one set of worksheets according to the column number corresponding to each cell; Loading a row dictionary to determine the standard name and alias corresponding to the row header, and loading a column dictionary to determine the standard name and alias corresponding to the column header; Determine a standard row title according to the standard name and alias corresponding to the row title, and determine a standard column title according to the standard name and alias corresponding to the column title, and use the standard row title and the standard column title as the standard name; The indicator identifier is generated according to the standard row header and the standard column header.

8. A data analysis device, characterized in that: The device comprises: a grouping module for determining at least one group of worksheets based on the target file in response to receiving the target file; A display module, configured to display an interactive interface according to the at least one group of worksheets, wherein the interactive interface includes a thumbnail area and an actual file area; a determination module, configured to, in response to receiving a user's operation instruction on the thumbnail area and the actual file area, determine a target parsing rule according to the operation instruction; The parsing module is used to parse the data content in the at least one group of worksheets based on the target parsing rules to obtain target structured data.

9. A data analysis device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data parsing method according to any one of claims 1 to 6.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data analysis method according to any one of claims 1 to 6 are implemented.

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