Method for intelligent report management

Abstract

The application provides a kind of intelligent report management method, it is related to enterprise data management field, and the core is " identification, merging, distribution, fill report, remind, integration " whole process closed loop. Template containing field definition and reporting cycle is stored by fixed report library;Scan report field and calculate coincidence rate to determine whether to merge;According to coincidence rate, generate task and leave unique data source by merging repeated fields;Distribute tasks according to historical records and department responsibilities;Receive fill report data, check and integrate to generate summary report;According to preset deadline, alarm in stages. The method solves the problems of traditional report repeated filling, poor information, forgetting to report, etc., improves the standardization and efficiency of report management, promotes the intelligent upgrading of data management, and is suitable for various enterprise report management scenarios.

Description

Technical Field

[0001] This invention relates to the field of enterprise data management technology, and in particular to a method for intelligent report management. Background Technology

[0002] In the daily operation of an enterprise, report submission is a key link in conveying information to higher-level units. However, the traditional report submission model has many significant pain points, which have an adverse impact on the efficiency and standardization of enterprise data management.

[0003] First, there is a serious problem of duplicate reporting. The overlap rate of report information between different departments is high. For example, the sales data reports of the finance department and the marketing department often have the same indicators. This directly leads to a waste of manpower and increases unnecessary workload.

[0004] Secondly, information gaps are prominent, with a lack of unified information channels between departments. When the responsibility for filling out reports is unclear, it is easy for people to shirk their responsibilities. Furthermore, once data conflicts occur, it is difficult to trace the source of the data, which brings great difficulties to problem-solving.

[0005] Furthermore, reporting delays are frequent. The traditional model relies on manual memorization of report submission dates. For periodic reports such as monthly and quarterly reports, late submissions are often caused by staff forgetting, which in turn affects the data aggregation work of higher-level units and delays the overall decision-making process.

[0006] In addition, the report processing workflow is inefficient, errors are prone to occur when manually integrating reports from multiple departments, and there is a lack of comprehensive historical record support. When new employees take over the relevant work, it is difficult to quickly connect with the business and a lot of time is required to familiarize themselves with the process and data.

[0007] In the current technology, some enterprises use Excel or simple OA systems to manage reports. However, these tools lack core capabilities such as automatic report identification, automatic merging of duplicate content, and phased reminders for reporting. They cannot fundamentally solve the problems mentioned above in the traditional report submission mode and are unable to meet the needs of enterprises for intelligent and efficient report management. Summary of the Invention

[0008] The main objective of this invention is to provide an intelligent report management method that solves problems such as duplicate reporting, poor information flow, and forgotten reporting in traditional report submission.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for intelligent report management, the method comprising: S1. Obtain and store report templates through a fixed report library. The templates contain field definitions and reporting cycle information. S2. Calculate the content overlap rate by scanning the report fields. The overlap rate is used to determine whether the reports need to be merged. S3. Merge duplicate fields based on overlap rate to generate a merged report task, retaining a unique data source; S4. Distribute the merged reporting tasks to the responsible persons based on historical records and departmental responsibilities; S5. Receive the submitted data, verify and integrate it to generate a summary report; S6. Trigger alarm notifications to responsible persons in stages according to preset deadlines.

[0010] In the preferred embodiment, report templates are obtained and stored through a fixed report library, including: S11. Obtain report templates in various formats, including tabular documents and portable document formats; S12. Categorize and store report templates according to department and reporting cycle; S13. Define the associated fields for the report template and generate a standardized data dictionary; S14. Calculate the matching score between the report to be submitted and the stored template using a template matching algorithm. The matching score is based on field weights and a similarity function. The template matching algorithm formula is as follows: ,in To match scores, For fields The weight, For similarity function, For report fields, For template fields, when The match is considered successful at that time.

[0011] In the preferred solution, the content overlap rate is calculated by scanning report fields, including: S21. Scan the title, indicator name, and data item description of the report to be submitted; S22. Calculate the field duplication rate using a weighted formula. The formula is: duplication rate equals the number of duplicate fields divided by the total number of fields. The specific formula is as follows: ,in For repetition rate, For the number of repeated fields, This represents the total number of fields. S23. The repetition rate is calculated using a weighted average based on the weights of the title, indicators, and data items. The sum of the weights is a fixed value, and the weighted average calculation formula is as follows: ,in , For title weight, As the indicator weight, Weights for data items; S24. If the repetition rate reaches a first preset threshold, it is marked as a suspected repetition. The first preset threshold is... ; S25. If the repetition rate reaches a second preset threshold, automatic merging is triggered. The second preset threshold is... .

[0012] In the preferred solution, the task of merging duplicate fields and generating consolidated reports based on the overlap rate includes: S31. Determine the priority of duplicate fields according to the merging rules. The priority is that the latest data is higher than the historical high-frequency department data, and the historical high-frequency department data is higher than the system default data source. S32. Calculate the merged value using the conflict resolution formula. The formula is based on the weights of the latest reported value, historical data values, and default values. The conflict resolution formula is as follows: ,in This is the merged value. This is the latest reported value. For high-frequency department values, This is the default value. For the latest time, For historical time, The frequency of departmental reporting; S33. Generate a consolidated report task and record a consolidated log, which allows for the tracking of the consolidation process; S34. Supports manual adjustment of merge rules and updating of merge results.

[0013] In the preferred solution, the merged reporting tasks are distributed based on historical records and departmental responsibilities, including: S41. Determine the initial distribution department based on historical reporting records and field attribution; S42. If the receiving department determines that the task responsibilities do not match, a return reason code is generated. The rules for the return reason code are as follows: ,in For cause coding, As for the problem category, For specific subclasses; S43. New responsible departments are matched using a redistribution algorithm. The algorithm is based on the similarity between the department and the field, and historical distribution weights. The redistribution algorithm formula is as follows: ,in As the new responsible department, For the department With report fields similarity, Weighting is assigned to historical data. For rule base weights; S44. If rematching fails, submit to the superior for review and designate the responsible department. The review result is stored in the system rule base to optimize the subsequent distribution logic.

[0014] In the preferred embodiment, the process of receiving, verifying, integrating, and generating a summary report includes: S51. Obtain the data uploaded by each department. Data can be entered online or uploaded by file. S52. Verify data consistency using a consistency check formula. The formula outputs a pass or conflict result. The consistency check formula is: ,in For the verification result, 1 indicates success and 0 indicates conflict. For the department Enter the value, For reference only; S53. If data conflicts exist, mark the conflicting data and notify the relevant departments for review. Simultaneously, determine the conflict processing order using a conflict priority formula, which is as follows: ,in Prioritize conflict resolution. For the time to be filled in, As the base time, Deadline; S54. Update the data based on the review results and generate a summary report. The report supports export in multiple formats, including PDF and Excel, and an integration log is attached when the summary report is generated.

[0015] In the preferred scheme, alarm notifications are triggered in stages according to preset deadlines, including: S61. Configure a deadline for the report task. The deadline can be a natural period or a custom period. S62. Trigger the initial alarm based on the first reminder time, which is the deadline minus the first advance day. The formula for the first reminder time is: ,in The deadline is [date / time]. To provide advance warning by several days, the initial alert method is SMS. S63. Trigger subsequent alarms through a secondary reminder time, the time being the deadline minus the second advance day. The formula for the secondary reminder time is: Subsequent alerts will be sent via pop-up window and SMS. S64. Trigger an emergency alarm via a strong reminder time, which is one hour before the deadline. The formula for the strong reminder time is: Emergency alerts are sent via SMS and a system red dot. S65. Record all alarm information and support the person in charge to forward it to the person in charge.

[0016] The preferred solution also includes a report template version iteration management step, specifically including: S71. Establish template version identification rules, using version number encoding formula. ,in For version number, Create a year for the template. To create the month, For the creation date, The sub-version number for the current day; S72. When template field definitions or reporting cycles change, a version iteration is triggered. The difference between the new version and historical versions is calculated using a template difference algorithm. The formula for the difference algorithm is as follows: ,in For template difference, For the new version field The weight, For historical version fields The weight, For the new version field , For historical version fields ; S73. If If so, it is determined to be a major version iteration, and the system automatically sends a version update notification to all relevant departments, along with a document explaining the differences; if If so, it is judged as a minor version iteration, and only the template library version record is updated, without actively pushing notifications; S74. Retain all historical version templates, support backtracking queries by version number, and automatically match the currently valid version template when filling in reports to avoid using expired templates.

[0017] The preferred solution also includes a report data anomaly detection step, specifically including: S81. Collect historical data to construct a normal distribution model, and calculate the mean of each field using the model. and standard deviation The formula for the mean is: The standard deviation formula is: , The first in the historical data entry One data point; S82. After receiving the currently entered data, determine whether the data is abnormal using an anomaly detection formula. The anomaly detection formula is: ,in The result indicates an anomaly; 1 represents an anomaly, and 0 represents a normal result. This is the data currently being entered; S83. If data is determined to be abnormal, the system automatically marks the abnormal data and calculates the degree of abnormality. The formula for the degree of abnormality is: At the same time, an anomaly alert is sent to the reporting department, requesting an explanation of the reason for the data anomaly; S84. After receiving the explanation of the reasons for the anomalies submitted by the reporting department, manually review the abnormal data. If the data is confirmed to be correct, add the data to the historical data sample database and update the mean of the normal distribution model. and standard deviation If the data is confirmed to be incorrect, the department that submitted the data will be required to submit it again.

[0018] The preferred solution also includes steps for managing report data access permissions, specifically: S91. Establish a user permission level system, using permission values ​​to quantify user permissions. The formula for calculating permission values ​​is as follows: ,in For permission values, For role permission levels, For field access permissions, Access permissions; S92. When a user accesses report data, a permission matching algorithm is used to determine whether the user has the corresponding permissions. The permission matching algorithm formula is as follows: ,in For permission matching results, This represents the user's actual permission value. The minimum permission value required to access report data; S93. If the permissions match, grant the corresponding operation based on the user's permission level, as shown in the permission value. Users can only export reports. Users can review reports; if permission matching fails, the system will automatically intercept the access request and record the permission interception log, which includes user information, access time, accessed report name and required permission value. S94. Periodically evaluate the rationality of permission allocation using a permission auditing algorithm. The permission auditing algorithm formula is as follows: ,in For permission redundancy rate, For users The portion of the permission value that exceeds the required permission value. This is the highest level of privilege. For the number of users, if If so, a permission optimization suggestion will be sent to the administrator, prompting them to adjust redundant permissions.

[0019] This invention provides an intelligent report management method that effectively addresses the pain points of traditional report submission, bringing numerous benefits. In terms of efficiency improvement, by automatically identifying duplicate reports and merging duplicate fields, and by automating data verification and aggregation to replace manual operations, report integration time is shortened, significantly reducing labor costs and improving report processing efficiency. Regarding data accuracy, by utilizing weighted calculation of overlap rates, conflict resolution formulas, and consistency verification mechanisms, combined with manual review, the data conflict rate is reduced, ensuring accurate and reliable report data and providing high-quality data support for enterprise decision-making. In terms of timeliness enhancement, multi-level, phased reminders cover the entire report submission cycle, avoiding overdue reports due to human error, reducing the submission delay rate from 30% in the traditional model to below 5%, ensuring timely report aggregation. Regarding management standardization, a unified report library, standardized data dictionary, and complete task distribution, merging, and reminder records form a standardized process. New employees can quickly access historical records to familiarize themselves with the business, reducing training costs and driving the enterprise's data management towards intelligent and standardized upgrades. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a diagram illustrating the intelligent report management method of the present invention. Detailed Implementation

[0021] Example 1 like Figure 1 As shown, a method for intelligent report management includes: S1. Obtain and store report templates through a fixed report library. The templates contain field definitions and reporting cycle information. S2. Calculate the content overlap rate by scanning the report fields. The overlap rate is used to determine whether the reports need to be merged. S3. Merge duplicate fields based on overlap rate to generate a merged report task, retaining a unique data source; S4. Distribute the merged reporting tasks to the responsible persons based on historical records and departmental responsibilities; S5. Receive the submitted data, verify and integrate it to generate a summary report; S6. Trigger alarm notifications to responsible persons in stages according to preset deadlines.

[0022] When using this intelligent report management method, first execute step S1 to obtain and store report templates from a fixed report library. Templates in formats such as Excel and PDF are supported for import. Templates are categorized and stored by department and reporting cycle (monthly / quarterly / yearly). Each template is associated with fields such as "sales revenue" and "number of customers" to form a standardized data dictionary. Subsequently, a template matching algorithm can be used to determine whether the report to be submitted matches a template in the library. The algorithm formula is as follows: ; Where Score is the matching score. Here, k represents the weight of field k, and Sim is the similarity function. For report fields, For template fields, when Successful matching is determined in time to ensure standardized management of report templates.

[0023] Next, proceed to step S2, scanning the fields of the report to be submitted, such as title, indicator name, and data item description, and first calculate the field duplication rate using a formula: ; Where R is the repetition rate. For the number of repeated fields, This represents the total number of fields; then, a weighted formula is used to calculate the repetition rate: ; in, , Assign weight to the title (default 0.3). The indicator weight (default 0.5). This is the weight of the data item (default 0.2). When The time stamp is marked as a suspected duplicate. The decision to merge the reports at that time provides a basis for subsequent report consolidation.

[0024] Then, in step S3, duplicate fields are merged based on the overlap rate results to generate a merged report task. This follows the priority rule of "latest submitted data > historically frequently submitted department data > system default data source," and the merged value is determined using a conflict resolution formula. ; in, This is the merged value. This is the latest reported value. For high-frequency department values, This is the default value. For the latest time, For historical time, Set the reporting frequency for each department. Simultaneously, record merge logs, support manual adjustment of merge rules, and ensure that the merged report data is unique and accurate.

[0025] After the merge is complete, step S4 is executed. The merged reporting tasks are automatically distributed based on the template's historical reporting departments and field affiliations (e.g., "Financial Indicators" corresponds to the Finance Department). If the receiving department determines a mismatch in responsibilities, a return reason is generated according to the return reason coding rules: ; Where C represents the cause code. Let S be the problem category and S be the specific subclass; then, a redistribution algorithm is used to match the new responsible department: ; in, As the new responsible department, The similarity between department d and report field f. Historical distribution weight (default 0.6). This is the weight of the rule base (default 0.4). If rematching fails, submit the task to a higher-level leader for approval and assign a responsible department to ensure accurate task distribution.

[0026] Next, the S5 step is carried out, receiving data submitted by various departments through online submissions or file uploads. First, the data consistency is verified using a consistency check formula: ; Here, Check represents the verification result (1 for pass, 0 for conflict). Enter a value for department i. These are reference values; if data conflicts exist, the processing order will be determined according to the conflict priority formula: ; in, Prioritize conflict resolution. For the time to be filled in, As the base time, The deadline is specified. Conflicting data is flagged and relevant departments are notified for review. Data is then combined based on the review results to generate a summary report, which can be exported in PDF and Excel formats and includes an integration log.

[0027] Finally, proceed to step S6 to set a natural cycle or a custom deadline for the report task, and trigger alarms in stages according to the following formula: First reminder time: ; Second reminder time: ; Strong reminder time: ; in, N represents the deadline, and N represents the number of days in advance. The first reminder is sent via SMS, the second via pop-up + SMS, and the final reminder via SMS + system red dot notification. The responsible person can forward reminders to the designated personnel with one click. All reminder records are archived to ensure reports are submitted on time.

[0028] This intelligent report management method effectively addresses the pain points of traditional report submission, bringing numerous benefits. In terms of efficiency, by automatically identifying and merging duplicate reports and fields, and by automating data validation and aggregation to replace manual operations, report integration time is shortened, significantly reducing labor costs and improving report processing efficiency. Regarding data accuracy, by utilizing weighted calculation of overlap rates, conflict resolution formulas, and consistency verification mechanisms, combined with manual review, the data conflict rate is reduced, ensuring accurate and reliable report data and providing high-quality data support for enterprise decision-making. In terms of timeliness, multi-level, phased reminders cover the entire report submission cycle, avoiding delays caused by human error. The submission delay rate has been reduced from 30% in the traditional model to below 5%, ensuring timely report aggregation. In terms of management standardization, a unified report library, standardized data dictionary, and complete task distribution, merging, and reminder records form a standardized process. New employees can quickly access historical records to familiarize themselves with the business, reducing training costs and driving the enterprise's data management towards intelligent and standardized upgrades.

[0029] Example 2 Report templates are obtained and stored through a fixed report library, including: S11. Obtain report templates in various formats, including tabular documents and portable document formats; S12. Categorize and store report templates according to department and reporting cycle; S13. Define the associated fields for the report template and generate a standardized data dictionary; S14. Calculate the matching score between the report to be submitted and the stored template using a template matching algorithm. The matching score is based on field weights and a similarity function. The template matching algorithm formula is as follows: ,in To match scores, For fields The weight, For similarity function, For report fields, For template fields, when The match is considered successful at that time.

[0030] When using the "obtain and store report templates through a fixed report library" solution, first execute step S11 to obtain the report templates that each department of the enterprise needs to submit. It supports a variety of common formats such as tabular documents and portable document formats, ensuring compatibility with different types of existing report files of the enterprise. The reports can be imported into the system without making significant adjustments to the original report formats.

[0031] Next, proceed to step S12, where the imported report templates are categorized and stored according to the department to which the report belongs and the reporting cycle. For example, the monthly revenue report of the finance department and the monthly sales report of the marketing department are respectively classified under the categories of "Finance Department - Monthly" and "Marketing Department - Monthly" to facilitate quick location and retrieval of the corresponding templates later.

[0032] Then, proceed with step S13 to associate specific field definitions with each report template. For example, in the "Monthly Revenue Report" template, specify the data type, unit, and statistical scope for fields such as "Main Business Revenue," "Other Business Revenue," and "Operating Costs." Based on these field definitions, generate a standardized data dictionary to ensure that different departments and personnel have a consistent understanding and filling standard for the fields when using the template, thus avoiding data entry deviations caused by ambiguous field definitions.

[0033] Finally, step S14 is executed. When a new report to be submitted enters the system, its matching score with the templates already stored in the fixed report library is calculated using a template matching algorithm. The algorithm formula is as follows: ,in Represents the matching score. It is the weight of the k-th field in the report to be matched. This is a similarity function used to calculate the similarity of fields in the report to be submitted. With template fields The degree of similarity, This represents the total number of fields involved in the matching; when calculated... When the system determines that the report to be submitted matches the template, it automatically calls the field definitions and filling requirements corresponding to the template. If the error occurs, staff will be prompted to confirm the report type or add a new template to the report library.

[0034] In the preferred solution, the content overlap rate is calculated by scanning report fields, including: S21. Scan the title, indicator name, and data item description of the report to be submitted; S22. Calculate the field duplication rate using a weighted formula. The formula is: duplication rate equals the number of duplicate fields divided by the total number of fields. The specific formula is as follows: ,in For repetition rate, For the number of repeated fields, This represents the total number of fields. S23. The repetition rate is calculated using a weighted average based on the weights of the title, indicators, and data items. The sum of the weights is a fixed value, and the weighted average calculation formula is as follows: ,in , For title weight, As the indicator weight, Weights for data items; S24. If the repetition rate reaches a first preset threshold, it is marked as a suspected repetition. The first preset threshold is... ; S25. If the repetition rate reaches a second preset threshold, automatic merging is triggered. The second preset threshold is... .

[0035] First, execute step S21. The system automatically scans the core content of the report to be submitted, specifically covering the report title, indicator name, and data item description. This ensures comprehensive coverage of key information dimensions that may be duplicated in the report, providing a complete data foundation for subsequent overlap rate calculations.

[0036] Next, in step S22, based on the field information obtained from the scan, the field repetition rate is calculated using a specified formula. The formula is: ,in Represents the repetition rate. Count the number of fields that overlap between the report to be submitted and existing reports in the fixed report library. Count the total number of fields in the report to be submitted. For example, if a report to be submitted has 10 fields, and 4 of them overlap with a report in the database, then the overlap rate is calculated. .

[0037] Then, step S23 is performed, which involves a weighted calculation based on the different importance of the title, indicators, and data items, using a formula. ,and By default (Title weight) is 0.3 (Indicator weight) is 0.5. The weight of each data item is 0.2. This formula is used to adjust the basic repetition rate, highlighting the role of core information such as indicators in the repetition determination. For example, if the repetition contribution of the title is 30%, the repetition contribution of the indicator is 60%, and the repetition contribution of the data item is 40%, then the weighted comprehensive repetition rate will be calculated accordingly. .

[0038] Then, step S24 is executed, and the calculated repetition rate is compared with the first preset threshold ( If the duplication rate reaches or exceeds 30%, the system marks the report to be submitted as "suspected duplication," reminding staff to pay further attention to the overlap of report content; finally, step S25 is executed, and if the duplication rate reaches or exceeds the second preset threshold ( If the system automatically triggers the subsequent report merging process, no manual intervention is required, ensuring timely processing of duplicate reports.

[0039] In the preferred solution, the task of merging duplicate fields and generating consolidated reports based on the overlap rate includes: S31. Determine the priority of duplicate fields according to the merging rules. The priority is that the latest data is higher than the historical high-frequency department data, and the historical high-frequency department data is higher than the system default data source. S32. Calculate the merged value using the conflict resolution formula. The formula is based on the weights of the latest reported value, historical data values, and default values. The conflict resolution formula is as follows: ,in This is the merged value. This is the latest reported value. For high-frequency department values, This is the default value. For the latest time, For historical time, The frequency of departmental reporting; S33. Generate a consolidated report task and record a consolidated log, which allows for the tracking of the consolidation process; S34. Supports manual adjustment of merge rules and updating of merge results.

[0040] When using the "Merge Duplicate Fields to Generate Consolidated Reports Task Based on Overlap Rate" solution, first execute step S31 to determine the priority order of duplicate fields according to the preset merging rules. That is, the latest data has a higher priority than historical high-frequency department data, and the historical high-frequency department data has a higher priority than the system default data source. For example, when the "Monthly Revenue" data filled in by the Finance Department 3 days ago is duplicated with the same field data filled in by the Marketing Department on the same day, the latest data filled in by the Marketing Department is selected first. If there is no latest data, the historical data of high-frequency departments such as the Finance Department is selected first. The system default data source is only used when there is no data of the first two types.

[0041] Next, proceed to step S32, where the merged value of duplicate fields is calculated using the conflict resolution formula. The formula is as follows: ,in This represents the final value after merging. These are the latest field values. The latest submission time. For historical reporting time, when Later At that time, take directly As the merged value; if and If they are the same, then determine the frequency of the reporting department. ,when At that time, take the department's (High-frequency sector value) is used as the merged value; if there is neither the latest data nor high-frequency sector data that meets the frequency requirements, then take... (System default value) is used as the merged value.

[0042] Then, step S33 is performed. After the system completes the merging of duplicate fields based on the above rules, it automatically generates a unified merge report task and records a detailed merge log. The log content includes the original data source of the duplicate fields (such as which department and when the data was submitted), the basis for selecting data during the merging process (such as selecting data from department A because it is the latest time, or selecting data from department B because it meets the frequency requirement), and a comparison of field values ​​before and after the merging. This allows for a complete traceability of the merging process during subsequent queries.

[0043] Finally, if staff find that the automatically merged results do not meet actual business needs (such as needing to prioritize data from specific departments rather than the latest data in special scenarios), they can manually adjust the merge rules (such as temporarily modifying the priority order or adjusting the frequency threshold of high-frequency departments). The system will then recalculate the merged values ​​based on the adjusted rules and update the merged report task results to ensure that the merged results match the actual business scenario.

[0044] The solution offers significant benefits. Firstly, it ensures the accuracy and rationality of merged data. Through clear priority ranking and quantified conflict resolution formulas, it avoids subjective arbitrariness in merging duplicate fields. For example, by using both chronological order and departmental reporting frequency, it ensures that the merged data is either the most up-to-date or comes from reliable, high-frequency departments, reducing report errors caused by improper data selection. Secondly, it enhances the traceability and flexibility of the merging process. Detailed merging logs record each step of the merging operation, allowing for quick identification of the merging basis when data issues arise, facilitating troubleshooting. It also supports manual adjustment of merging rules, addressing merging needs in specific business scenarios and avoiding the limitations of automated system rules. Furthermore, by automatically merging duplicate fields and generating unified reports, the solution reduces the workload of manually comparing and filtering duplicate data, improving report merging efficiency and laying an efficient and accurate data foundation for subsequent report distribution and submission processes.

[0045] In the preferred solution, the merged reporting tasks are distributed based on historical records and departmental responsibilities, including: S41. Determine the initial distribution department based on historical reporting records and field attribution; S42. If the receiving department determines that the task responsibilities do not match, a return reason code is generated. The rules for the return reason code are as follows: ,in For cause coding, As for the problem category, For specific subclasses; S43. New responsible departments are matched using a redistribution algorithm. The algorithm is based on the similarity between the department and the field, and historical distribution weights. The redistribution algorithm formula is as follows: ,in As the new responsible department, For the department With report fields similarity, Weighting is assigned to historical data. For rule base weights; S44. If rematching fails, submit to the superior for review and designate the responsible department. The review result is stored in the system rule base to optimize the subsequent distribution logic.

[0046] First, execute step S41. The system will retrieve historical reporting records and, together with the report field attribution, determine the initial distribution department based on these two aspects. For example, if the merged report contains "monthly revenue data" and "sales cost" fields, and similar reports have historically been submitted by the finance department, then the report task will be initially distributed to the finance department.

[0047] Next, proceed to step S42. If, after reviewing the task, the receiving department finds that the responsibility for the "Equipment Procurement Progress" field in the report belongs to the Engineering Department, determines that its own responsibilities are inconsistent, and applies for a return, the system will generate a code according to the return reason coding rules. The rules are as follows: ,in For cause coding, The problem categories are: 1 = Mismatched responsibilities, 2 = Data conflict, 3 = Formatting error. For specific subclasses (e.g., under mismatched responsibilities, S=01 represents "incorrect field attribution," and S=02 represents "no corresponding business permission"), if the current situation involves both mismatched responsibilities and incorrect field attribution, then a code will be generated. Then, step S43 is performed, where the system matches the new responsible department using a redistribution algorithm. The algorithm formula is as follows: ,in As the new responsible department, Sim(d,f) represents the department. With report fields similarity, Historical distribution weight (default 0.6). Assuming the weights in the rule base are from the engineering department... Then the calculation yields Finance Department Calculated The system selects the engineering department corresponding to the maximum value of the calculation result as the new responsible department.

[0048] Finally, in step S44, if the redistribution algorithm fails to match a suitable department, the task is submitted to the vice president in charge or other superiors for review. After the leaders designate the planning department as the responsible department based on the actual business situation, the system stores the review result "the cross-departmental project budget field belongs to the planning department" in the rule base. When encountering report tasks with similar fields in the future, the task can be directly distributed according to this rule, thus optimizing the subsequent distribution logic.

[0049] In the preferred embodiment, the process of receiving, verifying, integrating, and generating a summary report includes: S51. Obtain the data uploaded by each department. Data can be entered online or uploaded by file. S52. Verify data consistency using a consistency check formula. The formula outputs a pass or conflict result. The consistency check formula is: ,in For the verification result, 1 indicates success and 0 indicates conflict. For the department Enter the value, For reference only; S53. If data conflicts exist, mark the conflicting data and notify the relevant departments for review. Simultaneously, determine the conflict processing order using a conflict priority formula, which is as follows: ,in Prioritize conflict resolution. For the time to be filled in, As the base time, Deadline; S54. Update the data based on the review results and generate a summary report. The report supports export in multiple formats, including PDF and Excel, and an integration log is attached when the summary report is generated.

[0050] First, execute step S51. Each department submits data according to the consolidated report task distributed by the system, either through online input or file upload. Online input allows direct entry of values, text, and other content into the system's preset report fields. File upload supports uploading locally edited reports in formats such as Excel and PDF to the system. The system automatically extracts the data from the file and maps it to the report fields, ensuring that different departments can efficiently complete data entry according to their own operating habits.

[0051] Next, step S52 is performed. The system verifies the data submitted by each department using a consistency check formula, which is: ,in This represents the verification result (1 for pass, 0 for conflict). It is a department The value of a certain field that is filled in, This is a reference value for the field (the reference value can be taken from historical accurate data, system preset standard values, or values ​​reported by other authoritative departments); for example, three departments report the "monthly total sales" field. The amount is 1 million yuan. Department 1 reports 1 million yuan, Department 2 reports 1 million yuan, and Department 3 reports 950,000 yuan. The system determines that there is a data conflict in this field.

[0052] Then, step S53 is performed. If the verification result is 0 (a conflict exists), the system will mark the conflicting data with a prominent identifier (such as red highlighting) and notify the relevant department that submitted the data for review via system message, SMS, etc.; at the same time, the conflict priority formula is used. Determine the order of conflict resolution, where The priority for conflict handling (the larger the value, the higher the priority). It is the time for the department to fill in the form. This is the base time for starting report submission. This is the deadline for submission; assuming For the 1st, The deadline is 5 days. Department A filled out the form on the 3rd. ), priority Department B filled out the form on the 4th ( ), priority If so, conflicting data from department B will be processed first to avoid reports being overdue due to unresolved conflicts near the deadline.

[0053] Finally, step S54 is executed. After the relevant departments complete the review and correction of the conflicting data, the system automatically splices and integrates the corrected data from all departments to generate a summary report. The summary report supports export in both PDF and Excel formats to meet the viewing and archiving needs in different scenarios. At the same time, an integration log is generated, which records information such as the data entry time, verification results, conflict handling process, and data correction records of each department, making it easy to trace the data source and integration details later.

[0054] In the preferred scheme, alarm notifications are triggered in stages according to preset deadlines, including: S61. Configure a deadline for the report task. The deadline can be a natural period or a custom period. S62. Trigger the initial alarm based on the first reminder time, which is the deadline minus the first advance day. The formula for the first reminder time is: ,in The deadline is [date / time]. To provide advance warning by several days, the initial alert method is SMS. S63. Trigger subsequent alarms through a secondary reminder time, the time being the deadline minus the second advance day. The formula for the secondary reminder time is: Subsequent alerts will be sent via pop-up window and SMS. S64. Trigger an emergency alarm via a strong reminder time, which is one hour before the deadline. The formula for the strong reminder time is: Emergency alerts are sent via SMS and a system red dot. S65. Record all alarm information and support the person in charge to forward it to the person in charge.

[0055] First, execute step S61 to configure the corresponding deadline for each report task. The configuration method supports two types: natural period and custom period. The natural period is suitable for reports with a fixed frequency, such as the "Monthly Financial Statement" submitted on the 25th of each month and the "Quarterly Sales Report" submitted on the last day of each quarter. The custom period is for reports with a non-fixed frequency, such as the "Project Cost Accounting Report" submitted within 7 days after the project is completed. The specific deadline can be flexibly set according to the actual business needs to ensure that the deadline is in line with the company's report submission schedule.

[0056] Next, in step S62, the system uses the configured deadline and the initial reminder time formula. Calculate the initial alarm time, where For the configured deadline, The advance notice period is specified in days (default 3 days). For example, if the deadline for the "Monthly Financial Statements" is May 25th, then the first reminder time will be [date to be specified]. Once the specified time is reached, the system will send an initial alert to the report manager via SMS, which includes the report name, deadline, and task viewing link, reminding the manager to start the report preparation process in advance.

[0057] Then proceed with step S63, following the secondary reminder time formula. The subsequent alarm time is calculated, which is one day before the deadline. Taking the "monthly financial statement" mentioned above as an example, the second reminder time is May 24. At this time, the system triggers the alarm in two ways: "pop-up window + SMS". A prominent task reminder window pops up on the system interface where the person in charge logs in, and at the same time, an SMS is sent again to strengthen the reminder effect and avoid delays caused by the person in charge missing the SMS.

[0058] Then execute step S64, using the strong reminder time formula. The emergency alarm time is calculated as one hour before the deadline. For example, if the strong reminder time for the "Monthly Financial Statement" is 17:00 on May 25th (assuming the deadline is 18:00), the system will trigger an emergency alarm via SMS and a red notification dot in the system task icon. This visually alerts the person in charge that the task is about to end and requires immediate attention. Finally, step S65 is executed, and the system automatically records all alarm information, including alarm time, alarm method, recipient, and report name. It also supports the person in charge to forward alarm information to the specific person in charge with one click. For example, the CFO can forward the "Monthly Financial Statement" alarm to the accountant responsible for filling it out, ensuring that the person in charge is aware of the deadline and avoiding information gaps.

[0059] Example 3 To further illustrate with reference to Example 1, the method also includes a report template version iteration management step, specifically including: S71. Establish template version identification rules, using version number encoding formula. ,in For version number, Create a year for the template. To create the month, For the creation date, The sub-version number for the current day; S72. When template field definitions or reporting cycles change, a version iteration is triggered. The difference between the new version and historical versions is calculated using a template difference algorithm. The formula for the difference algorithm is as follows: ,in For template difference, For the new version field The weight, For historical version fields The weight, For the new version field , For historical version fields ; S73. If If so, it is determined to be a major version iteration, and the system automatically sends a version update notification to all relevant departments, along with a document explaining the differences; if If so, it is judged as a minor version iteration, and only the template library version record is updated, without actively pushing notifications; S74. Retain all historical version templates, support backtracking queries by version number, and automatically match the currently valid version template when filling in reports to avoid using expired templates.

[0060] The preferred solution also includes a report data anomaly detection step, specifically including: S81. Collect historical data to construct a normal distribution model, and calculate the mean of each field using the model. and standard deviation The formula for the mean is: The standard deviation formula is: , The first in the historical data entry One data point; S82. After receiving the currently entered data, determine whether the data is abnormal using an anomaly detection formula. The anomaly detection formula is: ,in The result indicates an anomaly; 1 represents an anomaly, and 0 represents a normal result. This is the data currently being entered; S83. If data is determined to be abnormal, the system automatically marks the abnormal data and calculates the degree of abnormality. The formula for the degree of abnormality is: At the same time, an anomaly alert is sent to the reporting department, requesting an explanation of the reason for the data anomaly; S84. After receiving the explanation of the reasons for the anomalies submitted by the reporting department, manually review the abnormal data. If the data is confirmed to be correct, add the data to the historical data sample database and update the mean of the normal distribution model. and standard deviation If the data is confirmed to be incorrect, the department that submitted the data will be required to submit it again.

[0061] The preferred solution also includes steps for managing report data access permissions, specifically: S91. Establish a user permission level system, using permission values ​​to quantify user permissions. The formula for calculating permission values ​​is as follows: ,in For permission values, For role permission levels, For field access permissions, Access permissions; S92. When a user accesses report data, a permission matching algorithm is used to determine whether the user has the corresponding permissions. The permission matching algorithm formula is as follows: ,in For permission matching results, This represents the user's actual permission value. The minimum permission value required to access report data; S93. If the permissions match, grant the corresponding operation based on the user's permission level, as shown in the permission value. Users can only export reports. Users can review reports; if permission matching fails, the system will automatically intercept the access request and record the permission interception log, which includes user information, access time, accessed report name and required permission value. S94. Periodically evaluate the rationality of permission allocation using a permission auditing algorithm. The permission auditing algorithm formula is as follows: ,in For permission redundancy rate, For users The portion of the permission value that exceeds the required permission value. This is the highest level of privilege. For the number of users, if If so, a permission optimization suggestion will be sent to the administrator, prompting them to adjust redundant permissions.

[0062] When using the above-mentioned report template version iteration management, report data anomaly detection, and report data access control solutions, first implement report template version iteration management: execute step S71, and encode the version number using the formula. ; Assign a unique version number to the template, where Retrieve template creation year Get creation month Get creation date, Taking the sub-version number within the day, the version number is 2024052001, clearly identifying the template creation time and iteration order; when the template's field definitions or reporting cycle are adjusted in step S72, version iteration is triggered, using the difference algorithm formula. To calculate the differences between the old and new versions, if the sum of the numerators after calculating the weight and similarity of a certain template field is 20 and the sum of the denominators is 100, then... ; Proceed to step S73, if (As mentioned above, 20%), if this is deemed a major iteration, the system will push an update notification containing a document explaining the differences to the relevant departments. Only the version record is updated; step S74 retains all historical versions and automatically matches the current valid version when filling in the form, avoiding the use of expired templates.

[0063] Next, anomaly detection is performed on the report data: Step S81 collects historical data to construct a normal distribution model, using the mean formula... (e.g., summing 5 historical data points and dividing by 5) Calculate the mean. Using the standard deviation formula Calculate the data dispersion; after receiving the currently entered data in step S82, use the anomaly detection formula: ; Detect anomalies; if the current data is different from... The difference exceeds ,but (Abnormal); In step S83, abnormal data will be marked using the abnormality degree formula. Calculate the degree of abnormality (e.g., a difference of 6, If it is 2, then At the same time, the department that submitted the data was notified to explain the reason; after manual review in step S84, if the data is correct, it will be included in the historical database for updating. and If there is an error, you will be required to fill in the form again.

[0064] Finally, implement report data access control: Step S91 uses the permission value formula. Build a permission system, in which Character level Field permissions Operation permissions, then the permission value is When a user accesses a service in step S92, the access control is matched using the permission matching formula. Determine permissions; if the user's permission value 322 is greater than or equal to the minimum required permission value 300, then... (pass); In step S93, authorized users grant permissions according to their assigned permissions (e.g., ...). Export only); if access fails, the access will be blocked and logged; the S94 steps are periodically audited using a permission auditing algorithm. To assess the reasonableness, if the total value of the permissions exceeding the limit for 10 users is 300, and the maximum permission value is 500, then... ,like Then, optimization suggestions will be pushed to the administrator.

[0065] These solutions offer significant benefits. Report template version iteration management, through clear version identification and difference determination, avoids errors caused by template confusion. Major iteration notifications ensure relevant departments are promptly informed of changes, and historical version retention facilitates tracing template adjustment paths. Report data anomaly detection relies on a normal distribution model to accurately identify abnormal data. The combination of anomaly quantification and manual review reduces misjudgments while ensuring data accuracy. Updating the historical database allows for continuous model optimization, improving subsequent detection accuracy. Report data access control, through quantified permissions and dynamic matching, achieves refined management of data access, preventing data leaks or unauthorized operations. Permission auditing promptly cleans up redundant permissions, ensuring system data security while improving access management efficiency. These three elements collectively improve the intelligent report management system, further enhancing the standardization and security of enterprise data management.

[0066] Example 4 To further illustrate with reference to Example 1, when actually deploying the above-mentioned intelligent report management method, the preliminary preparations must first be completed, and the hardware and software infrastructure must be clearly defined: At the hardware level, existing enterprise servers can be selected (such as physical servers equipped with Intel Xeon series CPUs, 32GB or more of memory, and 1TB or more of storage), or cloud servers (such as Alibaba Cloud ECS, Tencent Cloud CVM) can be used to meet the elastic expansion requirements; at the software level, the operating system should be Linux (such as CentOS 7 / 8) or Windows Server (such as Windows Server 2019), and the database should be MySQL 8.0 or PostgreSQL 14 to store fixed report library templates, historical reporting records, user permission data, merged logs and other information. At the same time, a JavaEE or Python Django technology stack should be built as the system development framework to support the development and operation of each functional module.

[0067] The deployment steps should be implemented sequentially according to functional modules: The first step is to deploy the fixed report library module. This involves developing a web-based upload interface (supporting Excel and PDF file uploads), and using Apache POI (Java) or Pandas (Python) components to parse report templates, extract field definitions and reporting cycle information from the templates, and store them in the database by department and cycle. Simultaneously, a template matching algorithm module is integrated to match formulas... ; The code implementation automatically calculates the matching score when reports are uploaded, determining a successful match and associating it with the corresponding template if the score is ≥70. The second step involves deploying a module for calculating and automatically merging overlap rates, and developing a report field scanning component (supporting scanning titles, indicator names, and data item descriptions) to implement the overlap rate formula. ; Weighted formula: ; And the conflict resolution formula: ; The embedded code flags suspected duplicates when R≥30% and triggers automatic merging when R≥50%, simultaneously generating merge logs and storing them in the database. The third step involves deploying an intelligent distribution module, which, based on historical reporting records and a departmental responsibility mapping table in the database, develops initial distribution logic and encodes return reason rules.

[0068] Redistribution algorithm: ; The system serves as a backend service, automatically generating codes and re-matching responsible departments when tasks are returned by departments. If matching fails, it triggers the superior's review process. The fourth step involves deploying a data entry integration module, developing online data entry forms (supporting text, numerical, and other input types) and file upload components, and integrating data validation functions, including consistency verification formulas. ; Conflict Priority Formula: ; The code implementation marks data and notifies departments for review when conflicts occur. After review, it automatically concatenates data to generate a summary report, supporting export of PDF and Excel reports via iText (Java) or ReportLab (Python) components, while also generating integrated logs. The fifth step involves deploying a multi-level reminder module based on a scheduled task framework (such as Quartz or Celery), with the initial reminder time formula as follows: ; Secondary reminder time formula: ; Strong reminder time formula: ; The process is transformed into a scheduled task, and SMS sending is implemented through an integrated SMS interface. System pop-up and red dot alert components are developed and embedded in the web interface. Simultaneously, all alarm information is recorded in the database, allowing administrators to forward alerts to relevant personnel via the system interface. The sixth step involves deploying modules for report template version iteration, data anomaly detection, and data access control, encoding the version number using the following formula: ; Difference algorithm:

[0069] Mean of the normal distribution model: Standard deviation Calculation and anomaly detection formulas: ; Permission value formula: ; Permission matching algorithm: ; And access control auditing algorithm: ; Implement them separately and integrate them into the corresponding functional modules to ensure that version management, anomaly detection, and permission control functions operate normally.

[0070] The deployment process also requires system testing and go-live preparation: complete the joint debugging test of each module, verify the accuracy of formula calculations and the integrity of functional logic, and conduct stress tests to ensure the system runs stably when multiple departments fill in reports simultaneously; after the tests are passed, import the company's existing report templates into the fixed report library in batches, configure the responsibilities mapping relationship of each department and the user permission level, organize relevant personnel to conduct operation training, and finally officially go live. After the go-live, regularly maintain the database and monitor the system's operating status to ensure that the intelligent report management system stably and efficiently supports the company's report management work.

[0071] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be defined as the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for intelligent report management, characterized by: The method includes S1. Obtain and store report templates through a fixed report library. The templates contain field definitions and reporting cycle information. S2. Calculate the content overlap rate by scanning the report fields. The overlap rate is used to determine whether the reports need to be merged. S3. Merge duplicate fields based on overlap rate to generate a merged report task, retaining a unique data source; S4. Distribute the merged reporting tasks to the responsible persons based on historical records and departmental responsibilities; S5. Receive the submitted data, verify and integrate it to generate a summary report; S6. Trigger alarm notifications to responsible persons in stages according to preset deadlines; Report templates are obtained and stored through a fixed report library, including: S11. Obtain report templates in various formats, including tabular documents and portable document formats; S12. Categorize and store report templates according to department and reporting cycle; S13. Define the associated fields for the report template and generate a standardized data dictionary; S14. Calculate the matching score between the report to be submitted and the stored template using a template matching algorithm. The matching score is based on field weights and a similarity function. The template matching algorithm formula is as follows: ,in To match scores, For fields The weight, For similarity function, For report fields, For template fields, when The match is considered successful at that time. Calculate content overlap rate by scanning report fields, including: S21. Scan the title, indicator name, and data item description of the report to be submitted; S22. Calculate the field duplication rate using a weighted formula. The formula is: duplication rate equals the number of duplicate fields divided by the total number of fields. The specific formula is as follows: ,in For repetition rate, For the number of repeated fields, This represents the total number of fields. S23. The duplication rate is calculated using a weighted average based on the weights of the title, indicators, and data items. The sum of the weights is a fixed value, and the weighted average calculation formula is as follows: ,in , For title weight, As the indicator weight, Weights for data items; S24. If the repetition rate reaches a first preset threshold, it is marked as a suspected repetition. The first preset threshold is... ; S25. If the repetition rate reaches a second preset threshold, automatic merging is triggered. The second preset threshold is... .

2. The method for intelligent report management according to claim 1, characterized in that: The task of generating a consolidated report by merging duplicate fields based on overlap rate includes: S31. Determine the priority of duplicate fields according to the merging rules. The priority is that the latest data is higher than the historical high-frequency department data, and the historical high-frequency department data is higher than the system default data source. S32. Calculate the merged value using the conflict resolution formula. The formula is based on the weights of the latest reported value, historical data values, and default values. The conflict resolution formula is as follows: ,in This is the merged value. This is the latest reported value. For high-frequency department values, This is the default value. For the latest time, For historical time, The frequency of departmental reporting; S33. Generate a consolidated report task and record a consolidated log, which allows for the tracking of the consolidation process; S34. Supports manual adjustment of merge rules and updating of merge results.

3. The method for intelligent report management according to claim 1, characterized in that: Distribute the consolidated reporting tasks based on historical records and departmental responsibilities, including: S41. Determine the initial distribution department based on historical reporting records and field attribution; S42. If the receiving department determines that the task responsibilities do not match, a return reason code is generated. The rules for the return reason code are as follows: ,in For cause coding, As for the problem category, For specific subclasses; S43. New responsible departments are matched using a redistribution algorithm. The algorithm is based on the similarity between the department and the field, and historical distribution weights. The redistribution algorithm formula is as follows: ,in As the new responsible department, For the department With report fields similarity, Weighting is assigned to historical data. For rule base weights; S44. If rematching fails, submit to the superior for review and designate the responsible department. The review result is stored in the system rule base to optimize the subsequent distribution logic.

4. The method for intelligent report management according to claim 1, characterized in that: Receive, validate, and integrate the submitted data to generate a summary report, including: S51. Obtain the data uploaded by each department. Data can be entered online or uploaded by file. S52. Verify data consistency using a consistency check formula. The formula outputs a pass or conflict result. The consistency check formula is: ,in The verification result is 1 for pass and 0 for conflict. For the department Enter the value, For reference only; S53. If data conflicts exist, mark the conflicting data and notify the relevant departments for review. Simultaneously, determine the conflict processing order using a conflict priority formula, which is as follows: ,in Prioritize conflict resolution. For the time to be filled in, As the base time, Deadline; S54. Update the data based on the review results and generate a summary report. The report supports export in multiple formats, including PDF and Excel, and an integration log is attached when the summary report is generated.

5. The method for intelligent report management according to claim 1, characterized in that: Alarms are triggered in stages according to preset deadlines, including: S61. Configure a deadline for the report task. The deadline can be a natural period or a custom period. S62. Trigger the initial alarm based on the first reminder time, which is the deadline minus the first advance day. The formula for the first reminder time is: ,in The deadline is [date / time]. To provide advance warning by several days, the initial alert method is SMS. S63. Trigger subsequent alarms through a secondary reminder time, the time being the deadline minus the second advance day. The formula for the secondary reminder time is: Subsequent alerts will be sent via pop-up window and SMS. S64. Trigger an emergency alarm via a strong reminder time, which is one hour before the deadline. The formula for the strong reminder time is: Emergency alerts are sent via SMS and a system red dot. S65. Record all alarm information and support the person in charge to forward it to the person in charge.

6. The method for intelligent report management according to claim 1, characterized in that: It also includes report template version iteration management steps, specifically including: S71. Establish template version identification rules, using version number encoding formula. ,in For version number, Create a year for the template. To create the month, For the creation date, The sub-version number for the current day; S72. When template field definitions or reporting cycles change, a version iteration is triggered. The difference between the new version and historical versions is calculated using a template difference algorithm. The formula for the difference algorithm is as follows: ,in For template difference, For the new version field The weight, For historical version fields The weight, For the new version field , For historical version fields ; S73. If If so, it is determined to be a major version iteration, and the system automatically sends a version update notification to all relevant departments, along with a document explaining the differences; if If so, it is judged as a minor version iteration, and only the template library version record is updated, without actively pushing notifications; S74. Retain all historical version templates, support backtracking queries by version number, and automatically match the currently valid version template when filling in reports to avoid using expired templates.

7. The method for intelligent report management according to claim 1, characterized in that: It also includes a report data anomaly detection step, specifically including: S81. Collect historical data to construct a normal distribution model, and calculate the mean of each field using the model. and standard deviation The formula for the mean is: The standard deviation formula is: , The first in the historical data entry One data point; S82. After receiving the currently entered data, determine whether the data is abnormal using an anomaly detection formula. The anomaly detection formula is: ,in The result indicates an anomaly; 1 represents an anomaly, and 0 represents a normal result. This is the data currently being entered; S83. If data is determined to be abnormal, the system automatically marks the abnormal data and calculates the degree of abnormality. The formula for the degree of abnormality is: At the same time, an anomaly alert is sent to the reporting department, requesting an explanation of the reason for the data anomaly; S84. After receiving the explanation of the reasons for the anomalies submitted by the reporting department, manually review the abnormal data. If the data is confirmed to be correct, add the data to the historical data sample database and update the mean of the normal distribution model. and standard deviation If the data is confirmed to be incorrect, the department that submitted the data will be required to submit it again.

8. The method for intelligent report management according to claim 1, characterized in that: Also includes The steps for managing report data access permissions specifically include: S91. Establish a user permission level system, using permission values ​​to quantify user permissions. The permission value calculation formula is as follows: ,in For permission values, For role permission levels, For field access permissions, Access permissions; S92. When a user accesses report data, a permission matching algorithm is used to determine whether the user has the corresponding permissions. The permission matching algorithm formula is as follows: ,in For permission matching results, This represents the user's actual permission value. The minimum permission value required to access report data; S93. If the permissions match, grant the corresponding operation based on the user's permission level, and specify the permission value. Users can only export reports. Users can review reports; if permission matching fails, the system will automatically intercept the access request and record the permission interception log, which includes user information, access time, accessed report name and required permission value. S94. Periodically evaluate the rationality of permission allocation using a permission auditing algorithm. The permission auditing algorithm formula is as follows: ,in For permission redundancy rate, For users The portion of the permission value that exceeds the required permission value. This is the highest level of privilege. For the number of users, if If so, a permission optimization suggestion will be sent to the administrator, prompting them to adjust redundant permissions.

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