Report online management method based on dynamic frequency adjustment

Abstract

The invention relates to a report online management method based on dynamic frequency adjustment, and aims to dynamically adjust report generation frequency and optimize a flow path according to real-time business data and a historical trend. The method comprises the following steps: configuring flow nodes and dynamically adjusting report generation frequency; registering an external or internal data source; creating a report template and binding a data source field; setting condition screening data according to business requirements; customizing report summarizing logic to generate a summarizing result; the accuracy and consistency of report data are automatically checked; monitoring the report flow progress, and optimizing the flow path. By means of the method, automation, flexibility and high efficiency of report management are achieved, and the accuracy and timeliness of enterprise management decision making are improved.

Description

Technical Field

[0001] The present invention relates to the field of report management, and in particular to an online report management method based on dynamic frequency adjustment, belonging to the technical field of enterprise management systems. Background Art

[0002] In modern enterprise management systems, report management is a key component of enterprise decision-making and business processes. Daily operations of an enterprise involve the generation, aggregation, analysis and reporting of a large amount of data, which is often distributed across multiple departments and multiple systems. Therefore, how to efficiently manage, process and circulate this data has become one of the core issues in improving the overall operational efficiency of the enterprise. Traditional report management methods usually rely on manual operations and fixed process configurations, which cannot flexibly respond to changes in business needs, nor can they monitor problems in the report generation and circulation process in real time.

[0003] In the daily operation of an enterprise, different departments and business units generate a wide variety of reports, including financial reports, sales reports, production reports, human resources reports, etc. These reports usually involve different data sources and business logics, and the generation frequency, format requirements and data processing rules of these reports may vary greatly according to different business needs. As the scale of enterprises expands and the complexity of business increases, traditional manual report management methods face the following challenges:

[0004] The business data of modern enterprises is usually stored in multiple different types of databases, including relational databases (such as SQL Server, MySQL, etc.) and non-relational databases (such as MongoDB, Cassandra, etc.), which are scattered in different systems and departments. To generate complete enterprise reports, it is often necessary to integrate data from multiple data sources, which requires operations such as data source registration, access permission management, data table selection, and format conversion. Traditional report management systems often lack sufficient automation and flexibility in this regard, making the registration and management of data sources a time-consuming and labor-intensive process.

[0005] In traditional report management systems, the report generation process is usually configured according to predefined fixed rules, and the report generation frequency, circulation path and approval process cannot be flexibly adjusted according to the actual business volume. With the growth of business, the fixed report generation and circulation process may lead to process blockage, especially during business peak periods, when a large number of reports need to be generated, reviewed and submitted at the same time. Manual adjustment of these processes is often inefficient and easily leads to delayed processing of key reports, thereby affecting the decision-making efficiency of corporate management.

[0006] Traditional report management systems often provide limited report templates. Users can only choose appropriate templates from fixed report formats and cannot freely create or customize report templates according to actual business needs. Even if custom templates are supported, users need to manually enter the structure, fields, and format requirements of the template, which not only increases the complexity of the operation, but also greatly increases the probability of errors. For some complex business scenarios, such as when it is necessary to filter and summarize data from different sources based on multiple conditions, the fixed templates of traditional report systems cannot meet user needs.

[0007] In the report generation process, it is often necessary to process and summarize the original data to generate analysis results that meet business needs. For example, in financial reports, it may be necessary to summarize and analyze the expenditure, income, and profit data of each department, while in sales reports, it may be necessary to group and count the data by time period, region, or product type. The summary logic of traditional reporting systems is often pre-defined, and data processing and summary logic cannot be customized according to different business scenarios. Such a fixed processing method is not conducive to adapting to complex and changing business needs, and cannot support the processing of real-time dynamic data.

[0008] The accuracy and consistency of report data are crucial factors in the corporate decision-making process. Traditional report management systems usually rely solely on manual inspection of reports, which is not only inefficient but also prone to human errors. For example, when companies generate financial reports, data omissions, data duplication, or logical errors (such as inconsistent time sequence, certain important fields not filled in, etc.) may occur. Traditional systems often lack sufficient automatic inspection and verification functions for such issues, resulting in a large amount of manual review after report generation, which seriously affects the efficiency and accuracy of report generation.

[0009] In enterprise management, after a report is generated, it needs to go through multiple stages of review and approval, usually involving the collaboration of multiple departments and personnel. However, the process of traditional report management systems is usually fixed, and the flow path and approval nodes of each report are set in advance, lacking the ability to automatically adjust according to business changes. Such a rigid process is prone to delays in report processing in actual operations, especially in the case of a surge in business volume or a shortage of human resources, the review of the report may be delayed, which in turn affects the decision-making efficiency of the entire enterprise. In addition, the real-time monitoring capabilities of traditional systems during the report circulation process are also relatively limited. The enterprise management cannot immediately understand the processing progress and circulation status of each report, which may result in missing key business nodes or failing to take corrective measures in a timely manner.

[0010] As the scale of enterprises continues to expand, business processes and data volumes are also increasing. In this case, traditional report management systems lack the ability to dynamically monitor and automatically optimize business data, and the system cannot adjust the frequency, flow path, and approval process of report generation based on real-time business data. For example, when the business volume of a department suddenly increases, the report management system cannot flexibly adjust the generation frequency or prioritize key reports, and manual intervention is required. This approach not only affects the automation level of the system, but also increases the complexity of management and operation.

[0011] In summary, traditional report management systems have shown many problems when facing the increasingly complex business needs of enterprises. How to design a system that dynamically adjusts the report generation frequency, automatically configures process nodes, monitors the progress of report circulation in real time, and ensures data accuracy and consistency has become a technical problem that needs to be solved urgently. Summary of the invention

[0012] In order to solve the above problems in the prior art, the present invention proposes an online report management method based on dynamic frequency adjustment, and the online management method comprises the following steps:

[0013] Step 1: Configure process nodes and dynamically adjust the report generation frequency to customize the organization; based on real-time business data and historical trend analysis, dynamically adjust process node configuration and report generation frequency to enable the system to automatically adapt to changes in business needs and prioritize key reports;

[0014] Step 2: Register the report data source and register the external database or internal data table into the system;

[0015] Step 3: Create a report template, set the report title, column titles, and data display area, and bind the fields in the template to the registered data source fields; set multiple conditions to filter the data in the report according to business needs;

[0016] Step 4: Customize report summary logic, summarize data and generate results according to business needs;

[0017] Step 5: Automatically check the accuracy and consistency of the report data to ensure that the report data meets the predetermined standards;

[0018] Step 6: Monitor the progress and quality of the report flow, and automatically determine the completion status of the process node to transfer the report to the next node.

[0019] The step 1 further comprises the following steps:

[0020] a) Configure process nodes and support customizing the order and priority of process nodes according to the enterprise organizational structure and departmental needs;

[0021] b) Monitor and collect the company's real-time business data, including sales, order quantity and inventory, and calculate the current business volume increment, short-term historical data change and long-term historical data change;

[0022] c) further calculating the frequency of generating new reports;

[0023] d) When the business volume surges, the system automatically increases the frequency of report generation, prioritizes key reports and transfers them to the corresponding nodes;

[0024] e) During peak periods, the report demand is identified through predictive algorithms, the report generation frequency is adjusted in advance and the circulation path is optimized.

[0025] The formula for calculating the new report generation frequency is:

[0026]

[0027] F new : The adjusted report generation frequency.

[0028] F base : The frequency of generating benchmark reports, usually a fixed frequency such as monthly or weekly.

[0029] ΔV current : Changes in business volume during the current business cycle (such as the day or week).

[0030] ΔV short : Changes in short-term historical data (such as changes in business volume in the past week).

[0031] ΔV long :The change in long-term historical data (for example, the change in business volume in the past quarter).

[0032] W current ,W short ,W long : Weight coefficients, representing the impact of current, short-term and long-term business volumes on the frequency of report generation.

[0033] V threshold : The threshold of business volume change. When the business volume increment exceeds this threshold, the system will trigger frequency adjustment.

[0034] The step 2 further comprises the following steps:

[0035] a) The user selects and enters relevant information of the external database or internal data table through the system interface, including but not limited to database connection string, database type, table name, field name, access rights and authentication credentials;

[0036] b) The system automatically performs a data source connection test to verify the accessibility of the data source and the legitimacy of the connection information. If the test passes, the data source registration is completed. If it fails, an error prompt is provided;

[0037] c) The system automatically manages the registered data sources and regularly extracts data from the data sources according to preset time intervals to ensure that the data used in the report is up to date;

[0038] The system supports multiple types of data sources, including relational databases, non-relational databases, and local files, and reads and processes these heterogeneous data sources through a unified interface; it supports setting data screening and filtering rules, allowing users to filter data according to specific time periods or fields, so that report generation only uses data that meets business needs.

[0039] The step 3 further includes: the user creates a report template through the system interface and defines the report title, column titles and data display area; the system binds the fields of the report template to the registered data source fields, and automatically extracts the corresponding data in the data source when the report is generated; the user uses the conditional filtering function to choose to filter data by multiple dimensions, including filtering data by year, department or business indicator; the system automatically generates a report based on the bound fields and filtering conditions, and the report content matches the latest business data.

[0040] The step 4 further comprises:

[0041] The user selects summary dimensions according to business needs, and the dimensions include department, time period and business type; the system performs data aggregation calculations according to preset conditions; the system automatically performs summary operations and generates summary results without manual intervention by the user; wherein, the summary operation includes a variety of summary methods, and statistical operations such as sum, average, maximum or minimum are selected; the summary results are displayed in a visual form, and the user can choose to present the summary results in the form of data tables, pie charts or bar charts.

[0042] The step 5 further comprises:

[0043] After the report is generated, the system verifies the data in the report based on preset checking rules; the system checks the total, consistency and format requirements of the sub-item data; the system performs dynamic checks based on the logical relationship between the data to determine whether the values ​​comply with the business logic; when data anomalies are detected, the system automatically generates error feedback and marks the specific error items; the system feedbacks error information through the notification function, and the system performs automated checks again after the user corrects the error.

[0044] The step 6 further includes: the system monitors the flow process of the report in real time and records the status of each process node; the system automatically determines the completion status of each process node, and automatically transfers the report to the next node after the node operation is completed; the flow progress of the report is displayed through a visualization tool, including completed nodes and to-be-completed nodes; when the processing time of a certain node exceeds a predetermined time threshold, the system generates a reminder and notifies the relevant responsible person; the system dynamically adjusts the report flow path according to business volume and process changes, so that reports at key nodes are processed first.

[0045] The beneficial effect of the present invention is that by dynamically adjusting the report generation frequency, the system can automatically adapt to changes in business needs based on the real-time business data and historical trends of the enterprise, ensure that key reports are processed first, and thus improve the efficiency of report generation and circulation. In addition, the system can automatically check the accuracy and consistency of report data, reduce human errors, and ensure the reliability of report data. The system also provides automated report circulation and monitoring functions, real-time tracking of report progress, and dynamic adjustment of circulation paths according to business changes, optimizing overall process management, and improving the timeliness and accuracy of enterprise decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0047] Figure 1 It is the process node configuration and report flow path diagram of the comprehensive planning report online management system. DETAILED DESCRIPTION

[0048] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0049] Example 1: Online report management method based on dynamic frequency adjustment

[0050] like Figure 1 As shown, this embodiment provides a report online management method based on dynamic frequency adjustment, including the following steps:

[0051] Step 1: Configure process nodes and dynamically adjust report generation frequency

[0052] The system allows users to configure the levels and departments of each organization through the front-end interface. The system supports custom configuration and automated flow of process nodes. Based on real-time business data (such as sales volume, inventory changes, financial data, etc.) and historical trend analysis, the system dynamically adjusts the frequency of report generation and the configuration of process nodes.

[0053] For example, during peak business periods, the system will automatically identify key reports that need to be processed first and dynamically adjust the report generation frequency based on their business impact. For example, when the system detects a surge in orders for a unit, the report generation frequency will be adjusted from monthly to weekly or daily so that corporate management can obtain the latest data in a timely manner.

[0054] Dynamic frequency adjustment is implemented through embedded prediction algorithms, using historical data trend analysis and business volume monitoring. The system can automatically adjust the frequency of report generation and prioritize the flow of reports to key nodes for processing.

[0055] Step 2: Register the report data source

[0056] Users register external databases or internal data tables to the system through the system interface. The system supports multiple types of data sources, including SQL databases, NoSQL databases, and local files. Users can manually enter or select relevant information about the data source, such as database connection strings, table names, field names, etc. The system automatically establishes a connection with the data source and regularly extracts data for report generation.

[0057] The registered data source can be heterogeneous data from multiple systems, and the system manages and processes these data sources in a unified manner. Through the data source registration unit, the system automatically updates the data in the report and filters the data according to business needs.

[0058] Step 3: Create a report template and bind the data source

[0059] Users can create report templates through the system interface and customize the report title, column headers and data display area. The system provides a flexible template creation tool to support users to configure different types of reports according to actual business needs, such as sales reports, financial reports, etc.

[0060] After the template is created, users can use the system to bind the report template fields to the registered data source fields. This binding process automatically extracts the required data from the data source when the report is generated. The system provides a drag-and-drop field selection tool, and users only need to select the corresponding field from the data source field list, and the system will automatically complete the binding.

[0061] In addition, the system allows users to set multiple conditions to filter the data in the report. For example, users can filter out data that meets the conditions by selecting a specific year, department, or business indicator. The filtering conditions can be flexibly configured to meet the needs of different business scenarios.

[0062] Step 4: Customize report summary logic

[0063] In this step, users can customize the summary logic of the report according to different business needs. The system provides a variety of summary options, such as data summary by department, time period (annual, quarterly, monthly, etc.) and business type (sales, inventory, etc.).

[0064] For example, users can set conditions through the system interface to generate a quarterly sales report. The system will automatically calculate the total sales of each department and display it in the report. The summary results can be automatically generated and displayed according to the logic set by the user.

[0065] Step 5: Automatically check report data for accuracy and consistency

[0066] After the report is generated, the system automatically performs data checks to ensure the accuracy and consistency of the report data. The system uses preset check logic to verify the report data, for example, checking whether the data in the report meets the expected total, whether the data format meets the requirements, etc.

[0067] If data anomalies are found, the system will automatically provide feedback to the relevant users and mark the data errors. Users can correct the data according to the system's prompts to ensure the completeness and accuracy of the report data.

[0068] Step 6: Monitor report flow progress and quality

[0069] The system monitors the progress and quality of report transfer in real time. The transfer process of each report is recorded in detail in the system. The system automatically determines the completion status of each process node and transfers the report to the next node.

[0070] The system also provides a visual progress display tool, through which users can view the current status, completed nodes and pending nodes of each report through the system interface. When a processing delay occurs at a certain node, the system will automatically remind the responsible person to complete the report within the set time.

[0071] In addition, the system optimizes the report flow path according to changes in business volume and processes to avoid bottlenecks in the process. For example, when there is a delay in the approval node, the system will automatically transfer the report to other available nodes to ensure the smooth operation of the overall process.

[0072] Through the coordinated work of the above steps, this embodiment implements report management based on dynamic frequency adjustment, effectively improving the efficiency of report generation, data processing and circulation.

[0073] Example 2 Dynamically Adjusting Report Generation Frequency

[0074] This embodiment provides a report generation frequency management mechanism based on dynamic adjustment, which aims to flexibly adjust the report generation frequency according to the changes in the real-time business data of the enterprise, so as to give priority to key reports when business needs increase, while improving the efficiency of overall management. The system automatically adjusts the report generation frequency through complex algorithms and custom weights, combined with current, short-term and long-term business data changes, so that enterprises can obtain accurate business data support in a timely manner.

[0075] 1. Process node configuration

[0076] The system allows users to configure the levels and departments of each organization through the front-end interface, and supports custom configuration and automated flow of process nodes. The automatic configuration of process nodes is based on the business needs of different departments. Users can flexibly set the order, priority and executor of multiple nodes such as approval, review, and feedback. For example, the sales department's report can be submitted to the finance department for further approval after the initial review.

[0077] When the business volume increases, the system automatically detects changes in report flow nodes and prioritizes key nodes so that high-priority reports can be quickly transferred to the corresponding departments or personnel for review. The automation of process node configuration allows the system to dynamically adapt to business changes and improves the flexibility and efficiency of report management.

[0078] To more precisely adjust the report generation frequency, the system uses the following formula:

[0079]

[0080] F new : The adjusted report generation frequency.

[0081] F base : The frequency of generating benchmark reports, usually a fixed frequency such as monthly or weekly.

[0082] ΔV current : Changes in business volume during the current business cycle (such as the day or week).

[0083] ΔV short : Changes in short-term historical data (such as changes in business volume in the past week).

[0084] ΔV long : Changes in long-term historical data (such as changes in business volume in the past quarter).

[0085] W current ,W short ,W long : Weight coefficients, representing the impact of current, short-term and long-term business volumes on the frequency of report generation.

[0086] Vthreshold : The threshold of business volume change. When the business volume increment exceeds this threshold, the system will trigger frequency adjustment.

[0087] 2. Algorithm execution process

[0088] Data collection and monitoring:

[0089] The system obtains the increments in the current business cycle by real-time monitoring of key business data (such as sales, order quantity, inventory, etc.), and analyzes business change trends through short-term and long-term data. The system integrates these data to obtain the current business status increment as the basis for subsequent frequency adjustments.

[0090] Data trend and forecast analysis:

[0091] The system uses time series analysis and historical data models, combined with actual business changes, to calculate the current business volume change ΔV current , short-term change ΔV short and long-term variation ΔV long For example, when sales have increased rapidly in the past week, the system will calculate the increase in short-term business volume and perform trend analysis based on the current business increase.

[0092] Frequency Adjustment:

[0093] Through the above formula, the system calculates the new value F of the report generation frequency new When the business volume surges within a specific period of time, the system automatically increases the frequency of report generation. For example, when the system detects that the order volume has increased rapidly in a short period of time, the report generation frequency will be adjusted from monthly or weekly to daily or hourly, so that the report data is updated in time and transferred to key nodes.

[0094] For example, suppose the base report generation frequency F base Once a month, the current business volume increment ΔV current =150, short-term business volume increment ΔV short =250, long-term business volume increment ΔV long =100. The weights are W current =0.5, W short =0.3, W long =0.2, traffic volume threshold V threshold =400.

[0095]

[0096] F new =F base ×(1+0.425)=F base ×1.425

[0097] At this point, the report generation frequency will increase by 42.5%, which means that the report generation frequency will be adjusted from once a month to approximately once every 21 days.

[0098] Prioritize key reports:

[0099] In the case of a surge in business volume, the system not only adjusts the frequency of report generation, but also identifies and processes key reports through a priority algorithm. The system allows you to set a weight for each report and prioritize key reports based on the weight. For example, financial reports or sales reports required for management decisions will be processed first, allowing these reports to be generated quickly and transferred to the next approval node.

[0100] 3. Peak Optimization

[0101] During business peak periods (such as quarterly settlement or promotional activities), the system will use prediction algorithms to identify possible report demand peaks in advance and increase the report generation frequency accordingly. Combined with historical business trends, the system can predict future peak business volume growth and adjust the report generation plan in advance to enable timely processing of key data. In this way, the system effectively avoids the flow bottleneck problem that occurs during peak periods.

[0102] Through complex formulas that dynamically adjust the frequency of report generation and multi-level business data analysis, the system intelligently adjusts the frequency of report generation, prioritizes key reports, and effectively responds to business fluctuations and peak periods. This mechanism ensures the flexibility and efficiency of the report management system and provides reliable data support for timely decision-making by enterprises.

[0103] Example 3 Registering a report data source

[0104] Through a user-friendly interface, the system allows users to easily register external databases or internal data tables into the system for unified data management and processing. The system supports multiple types of data sources, including relational databases (such as SQL databases), non-relational databases (such as NoSQL databases), and local files (such as CSV, Excel, etc.). This diverse support enables the system to adapt to the needs of various heterogeneous data sources in the enterprise, allowing data from different sources to be aggregated into a unified management platform.

[0105] In specific operations, users can manually enter or select relevant information of the data source through the system front-end interface. This information includes but is not limited to: database connection string, database type (such as MySQL, PostgreSQL, MongoDB, etc.), table name, field name, access permission, authentication credentials, etc. The system will automatically establish a connection with the data source based on the configuration information provided by the user, and extract data regularly according to the synchronization rules set by the user.

[0106] Detailed description of the registration process:

[0107] 1. Data source selection and input

[0108] The user first selects the type of data source to be registered. The system provides a variety of options, including SQL database, NoSQL database, cloud storage data source, API interface data source, etc. Each data source has corresponding configuration requirements. The user can enter the basic information required for the connection (such as database address, port, user name and password, etc.) according to the system prompts so that the system can successfully establish a connection.

[0109] 2. Verification and connection test

[0110] After the user enters the data source information, the system will automatically perform a connection test to ensure the accessibility of the data source. The connection test includes verification of the database address, legitimacy check of access credentials, and monitoring of the connection response time. If the test passes, the system will return a success message to the user; if the connection fails, the system will provide a detailed error message to help the user correct the configuration in time.

[0111] 3. Automatic update and management of data sources

[0112] Once a data source is successfully registered, the system will automatically manage and regularly update it. The system has a built-in scheduled task mechanism that automatically extracts the latest data from the data source at preset time intervals (such as daily, hourly, etc.) to ensure that the data used in the report is always up to date. This automated update not only greatly reduces the workload of manual maintenance, but also improves the accuracy and real-time nature of the data.

[0113] 4. Unified management of heterogeneous data

[0114] The system supports integrating multiple different types of data sources into a unified management platform. Whether it is structured data in SQL databases, unstructured data in NoSQL databases, or table data in local files, the system reads and processes them through a unified interface. This function not only improves the flexibility of data management, but also provides a solid data foundation for subsequent report generation, data analysis and other operations.

[0115] 5. Data screening and filtering

[0116] After the data source registration is completed, users can also set data screening and filtering rules through the system. For example, in some scenarios, users only need data from certain time periods or certain fields. The system allows users to set filtering conditions when extracting data so that only data that meets business needs is used for report generation. In this way, the system can effectively reduce the transmission and storage burden of redundant data and further improve data processing efficiency.

[0117] Through the above process, the system realizes the unified management and processing of multiple heterogeneous data sources, allowing different types of business data to flow smoothly in the system and provide accurate and real-time basic data for report generation. This highly automated data source management model greatly improves the efficiency of enterprise report generation while reducing the complexity and error rate of manual operations.

[0118] Example 4: Create a report template and bind a data source

[0119] Users can create report templates through the system interface and flexibly customize various components of the report according to actual business needs, including the report title, column titles, data display area, etc. The system provides a powerful and easy-to-use template creation tool that supports users to configure different types of reports in different business scenarios, such as sales reports, financial reports, production management reports, etc., greatly meeting the diverse management needs of enterprises.

[0120] 1. Creation of report template

[0121] Through the system front-end interface, users can flexibly design report templates according to the needs of different business scenarios. The system allows users to specify titles for reports to facilitate management and differentiation of different types of reports (such as monthly sales reports, quarterly financial reports, etc.). At the same time, users can freely define column headers and set the name, format, and display order of column headers. For specific data areas that need to be displayed, the system provides flexible configuration options. Users can define the layout and format of data display to make the report content intuitive and easy to understand.

[0122] For example, users can set column titles for sales reports such as "Product Name", "Sales Quantity", "Sales Amount", "Sales Date", etc., and set the report display area to a sales data list of the time dimension. For financial reports, users can set column titles including "Income", "Expenses", "Net Profit", etc., and display relevant financial data in the template by quarter or year. The flexibility provided by the system enables users to quickly respond to different business needs and generate reports that meet management needs.

[0123] 2. Binding of report template and data source

[0124] After the template is created, the system allows users to bind the fields of the report template to the registered data source fields. This process allows the system to automatically extract the corresponding data from the registered data source when the report is generated, avoiding the tedious operation of users manually entering data. The system provides an intuitive drag-and-drop field selection tool, allowing users to easily select the corresponding fields from the data source field list and bind them one by one to the fields in the report template.

[0125] Specifically, users only need to bind the "sales" field in the data source to the corresponding column title in the report template by dragging and dropping, and the system will automatically complete the mapping of the field. In this way, when the system generates a report, it will automatically extract sales data from the data source and fill it into the report based on the bound field. This binding method not only improves the automation of report generation, but also reduces the possibility of errors.

[0126] 3. Condition screening and data extraction

[0127] In addition to field binding, the system also provides a rich set of conditional filtering functions, allowing users to flexibly configure filtering conditions according to business needs when generating reports. During the template configuration process, users can select multiple dimensions to filter data, such as filtering by year, department, business type, or specific business indicators (such as sales, inventory, etc.). Users can set conditions to filter data that meets specific conditions and automatically generate accurate reports.

[0128] For example, users can select the filter conditions of "Year = 2024" and "Department = Sales Department" in the sales report, and the system will automatically filter out the relevant data of the sales department in 2024 from the data source for report display. This flexible filtering function allows users to quickly generate reports that meet their needs based on actual business scenarios, improving the accuracy of data analysis and decision-making.

[0129] 4. Automation and flexibility

[0130] The system greatly reduces the manual operation of users in the report generation process through automatic template binding and conditional filtering, and improves the automation level of report generation. At the same time, users can also flexibly adjust the template structure and filtering conditions according to different scenarios, so that the report content reflects business changes in real time. For example, in the sales report template, users can adjust the filtering conditions at any time to generate quarterly or annual sales data, and in the financial report, group and count financial data by department or project category. This high flexibility makes enterprises more efficient in data management and analysis.

[0131] Through flexible report template creation tools and automatic binding with data source fields, the system greatly simplifies the report generation process, allowing the report content to automatically extract the latest data from the data source. In addition, the conditional filtering function provides users with more freedom in data filtering, quickly and accurately generating reports that meet business needs. The automation and flexibility of the system make it suitable for various complex business scenarios, thereby improving the efficiency of enterprise management and decision-making support capabilities.

[0132] Example 5 Customized report summary logic

[0133] In this step, users can flexibly customize the summary logic of the report according to specific business needs to meet the analysis requirements of different management levels and business scenarios. The system provides a variety of flexible summary options, covering the needs of data aggregation by department, time period (such as annual, quarterly, monthly) and business type (such as sales, inventory level, production quantity, etc.). Through these options, users can aggregate and analyze business data from multiple perspectives to support the company's decision-making process.

[0134] 1. Flexible configuration of summary dimensions

[0135] The system allows users to select multiple dimensions for data aggregation when setting aggregation logic. These dimensions usually include department, time period, business type, etc. Users can freely combine them according to specific management needs. Each aggregation dimension corresponds to a different business scenario. For example, aggregation by department can be used to analyze the performance of each business unit; aggregation by time period helps to observe the cyclical changes and long-term trends of the business; aggregation by business type can help companies extract key data from different business lines for horizontal comparison.

[0136] In actual operation, users can easily set the summary dimension through the system's visual interface. For example, users can choose to summarize "by department", and the system will automatically calculate the business indicators of each department in a given time period, such as sales, costs, inventory changes, etc. The system not only supports the summary of a single dimension, but also allows users to perform multi-dimensional combined summary, such as double aggregation of data "by quarter and department", thereby generating more complex and detailed analysis results.

[0137] 2. Time period summary

[0138] Time period is one of the most common dimensions in data aggregation. The system supports aggregation by different time periods such as year, quarter, month, etc., allowing users to flexibly view business performance in different time periods. Time period aggregation is very important for enterprises to carry out strategic planning and performance evaluation. For example, enterprises can set conditions through the system to generate sales reports summarized by quarter. The system will automatically summarize the sales data of each department by quarter and display it in the report. In this way, management can quickly grasp the business performance in each time period, identify sales peak and off-season, and optimize market and sales strategies.

[0139] For the accumulation and analysis of long-term data, the system also supports year-on-year or quarter-on-quarter analysis of data from different time periods. Users can automatically generate quarterly year-on-year reports through the system to quickly view the sales data comparison between this year and the same quarter of last year, helping companies to conduct longitudinal analysis of historical data.

[0140] 3. Business Type Summary

[0141] In addition to time periods, the system also supports data aggregation by business type, which is particularly suitable for companies with multiple business lines and multiple product lines. By aggregating by business type, companies can analyze the performance of different business modules in detail and find the business units with the fastest growth or the highest profit contribution, thereby optimizing resource allocation and business development strategies.

[0142] For example, companies can generate sales summary reports by product type. The system will automatically calculate the sales volume and sales amount of each product in a specified time period and display its share of total sales. Through this summary logic, companies can easily determine which product lines perform well and which products need further optimization, and then formulate more targeted production and sales plans.

[0143] 4. Implementation of automated aggregation logic

[0144] The system not only supports flexible aggregation dimension selection, but also realizes the automatic processing of aggregation logic. After the user sets the aggregation conditions, the system will automatically perform the aggregation operation without manual intervention by the user. Specifically, the system will automatically read the associated data source, aggregate the data according to the preset dimensions and conditions, generate the aggregation results, and present them to the user in the form of tables or charts.

[0145] In summary calculation, the system has built-in multiple summary methods, including sum calculation, average value, maximum value, minimum value and other commonly used statistical calculation functions. Users can choose the appropriate calculation method according to actual needs, so that the generated summary results accurately reflect the business status. For example, an enterprise can choose "sum" calculation to generate a quarterly sales summary report, or choose "average value" calculation to analyze the fluctuation trend of monthly sales.

[0146] 5. Visualization of summary results

[0147] Once the summary calculation is completed, the system will automatically generate a visual summary result. Users can choose to display the summary results in a variety of charts such as data tables, pie charts, and bar charts. The system's visualization tools help users understand data more intuitively and make business decisions quickly. For example, when viewing the department sales summary, users can clearly see the sales performance comparison of each department through the bar chart generated by the system, providing support for resource optimization and department performance evaluation.

[0148] Through this automated and visual summary logic, users can not only generate summary reports that meet their needs in the shortest time, but also intuitively view and analyze the changing trends of key data, greatly improving the efficiency and accuracy of data processing.

[0149] Through flexible dimension selection, automated aggregation logic and visual display, the system provides users with powerful custom aggregation functions. This multi-dimensional aggregation logic not only meets the data analysis needs of enterprises in different business scenarios, but also greatly simplifies the complexity of data aggregation and presentation, and improves the scientificity and timeliness of enterprise decision-making.

[0150] Example 6 Automatically checking the accuracy and consistency of report data

[0151] In this step, after the report is generated, the system automatically executes the preset data check logic to ensure that the data in the report is accurate and consistent. When checking the data, the system verifies the report in detail based on multiple dimensions, including the logical relationship of the data, format requirements, and the total consistency between the data items.

[0152] Specifically, the system first checks each item of data in the report one by one according to the checking rules set by the user. For example, the system will automatically check whether the data of each sub-item in the report meets the expected total, so that the summarized data accurately reflects the actual situation of each sub-item. In addition, the system will also check the format of the data, for example, whether the data items such as date, currency, and number meet the predefined format requirements, to avoid confusion or errors in data interpretation caused by format errors.

[0153] During the inspection process, the system not only performs static verification of the data, but also performs dynamic analysis based on the business logic in the report. For example, the system can make a reasonable judgment on the relationship between the sales quantity and sales amount in the sales report, so that there is a corresponding relationship between the values ​​that conforms to the business logic. If the value of a data item does not match the expected logic, the system will detect the anomaly in time and record the relevant error information.

[0154] Once the system finds data anomalies or inconsistencies, it will automatically generate error feedback and mark the specific error items. The system will also automatically feedback the error information to the relevant users through the notification function. Users can correct the erroneous data according to the system prompts. The corrected data will go through the system's automated inspection process again to ensure that the problem has been properly resolved. Through this iterative inspection and correction mechanism, the system effectively ensures the integrity and accuracy of report data and avoids business decision-making errors caused by data errors.

[0155] The automated checking function of this step greatly reduces the workload of manual proofreading of report data, and improves the efficiency and reliability of data processing. At the same time, the system's instant feedback and correction prompt mechanism enables users to quickly locate and fix errors in the data, ensuring the credibility of the data used in the corporate decision-making process.

[0156] Example 7: Monitoring report circulation progress and quality

[0157] like Figure 1 As shown in the figure, the system has the function of real-time monitoring of the progress and quality of report circulation, tracking the circulation process of each report, and recording the status of each node of the report in detail. The system automatically determines the completion status of each process node. Once the operation of a node is completed, the system will immediately transfer the report to the next node, so that the circulation of the report complies with the predetermined process without lag.

[0158] During the circulation process, the system provides users with a visual progress display tool, and users can intuitively view the current status of each report through the system interface. The system will clearly identify the completed nodes, the nodes to be completed, and the overall circulation progress of the report. If a node is delayed or the processing time exceeds the predetermined time threshold, the system will automatically generate a reminder and promptly notify the person responsible for the node to speed up the processing process so that the report can be circulated within the specified time.

[0159] In addition, the system also has the function of intelligently optimizing the report flow path. The system can not only monitor the node status of the current process, but also dynamically adjust according to the changes in business volume and actual process. When the system detects that a certain node may have processing delays or bottlenecks, such as a node in the approval process that takes too long to process, the system will automatically transfer the report to other available nodes with corresponding approval authority to avoid bottlenecks in the process, thereby improving the overall efficiency of report flow.

[0160] In order to further enhance the flexibility of process management, the system supports dynamic optimization of multi-path flows. For example, the system prioritizes key node reports based on the importance of the report and the urgency of business needs. In specific business scenarios, such as when a department encounters a business peak, the system will automatically adjust the report flow path to prioritize reports with higher business importance, avoiding delays at non-critical nodes that affect the smooth operation of the overall process.

[0161] Through the above functions, the system realizes all-round real-time monitoring and intelligent optimization of the report circulation process, making the circulation of reports not only efficient and smooth, but also flexible to adjust according to actual business needs, thereby improving the overall management efficiency and decision-making support capabilities of the enterprise.

[0162] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A report online management method based on dynamic frequency adjustment, characterized in that: The online management method comprises the following steps: Step 1: Configure process nodes and dynamically adjust the report generation frequency to customize the organization; based on real-time business data and historical trend analysis, dynamically adjust process node configuration and report generation frequency to enable the system to automatically adapt to changes in business needs and prioritize key reports; Step 2: Register the report data source and register the external database or internal data table into the system; Step 3: Create a report template, set the report title, column titles, and data display area, and bind the fields in the template to the registered data source fields; set multiple conditions to filter the data in the report according to business needs; Step 4: Customize report summary logic, summarize data and generate results according to business needs; Step 5: Automatically check the accuracy and consistency of the report data to ensure that the report data meets the predetermined standards; Step 6: Monitor the progress and quality of the report flow, and automatically determine the completion status of the process node to transfer the report to the next node.

2. The method for online management of reports based on dynamic frequency adjustment according to claim 1, characterized in that: The step 1 further comprises the following steps: a) Configure process nodes and support customizing the order and priority of process nodes according to the enterprise organizational structure and departmental needs; b) Monitor and collect the company's real-time business data, including sales, order quantity and inventory, and calculate the current business volume increment, short-term historical data change and long-term historical data change; c) further calculating the frequency of generating new reports; d) When the business volume surges, the system automatically increases the frequency of report generation, prioritizes key reports and transfers them to the corresponding nodes; e) During peak periods, the report demand is identified through predictive algorithms, the report generation frequency is adjusted in advance and the circulation path is optimized.

3. The method for online management of reports based on dynamic frequency adjustment according to claim 2, characterized in that: The formula for calculating the new report generation frequency is: F new : Adjusted report generation frequency; F base : The frequency of generating benchmark reports, usually a fixed frequency such as monthly or weekly; ΔV current : Changes in business volume during the current business cycle; ΔV short : Change in short-term historical data; ΔV long : Long-term historical data change; W current ,W short ,W long : Weight coefficients, representing the impact of current, short-term and long-term business volumes on the frequency of report generation; V threshold : The threshold of business volume change. When the business volume increment exceeds this threshold, the system will trigger frequency adjustment.

4. The method for online management of reports based on dynamic frequency adjustment according to claim 1, characterized in that: The step 2 further comprises the following steps: a) The user selects and enters relevant information of the external database or internal data table through the system interface, including but not limited to database connection string, database type, table name, field name, access rights and authentication credentials; b) The system automatically performs a data source connection test to verify the accessibility of the data source and the legitimacy of the connection information. If the test passes, the data source registration is completed. If it fails, an error prompt is provided; c) The system automatically manages the registered data sources and regularly extracts data from the data sources according to preset time intervals to ensure that the data used in the report is up to date; The system supports multiple types of data sources, including relational databases, non-relational databases, and local files, and reads and processes these heterogeneous data sources through a unified interface; it supports setting data screening and filtering rules, allowing users to filter data according to specific time periods or fields, so that report generation only uses data that meets business needs.

5. The method for online management of reports based on dynamic frequency adjustment according to claim 1, characterized in that: The step 3 further includes: the user creates a report template through the system interface and defines the report title, column titles and data display area; the system binds the fields of the report template to the registered data source fields, and automatically extracts the corresponding data in the data source when the report is generated; the user uses the conditional filtering function to choose to filter data by multiple dimensions, including filtering data by year, department or business indicator; the system automatically generates a report based on the bound fields and filtering conditions, and the report content matches the latest business data.

6. The method for online management of reports based on dynamic frequency adjustment according to claim 1, characterized in that: The step 4 further comprises: The user selects summary dimensions according to business needs, and the dimensions include department, time period and business type; the system performs data aggregation calculations according to preset conditions; the system automatically performs summary operations and generates summary results without manual intervention by the user; wherein, the summary operation includes a variety of summary methods, and statistical operations such as sum, average, maximum or minimum are selected; the summary results are displayed in a visual form, and the user can choose to present the summary results in the form of data tables, pie charts or bar charts.

7. The method for online management of reports based on dynamic frequency adjustment according to claim 1, characterized in that: The step 5 further comprises: After the report is generated, the system verifies the data in the report based on preset checking rules; the system checks the total, consistency and format requirements of the sub-item data; the system performs dynamic checks based on the logical relationship between the data to determine whether the values ​​comply with the business logic; when data anomalies are detected, the system automatically generates error feedback and marks the specific error items; the system feedbacks error information through the notification function, and the system performs automated checks again after the user corrects the error.

8. The method for online management of reports based on dynamic frequency adjustment according to claim 1, characterized in that: The step 6 further includes: the system monitors the flow process of the report in real time and records the status of each process node; the system automatically determines the completion status of each process node, and automatically transfers the report to the next node after the node operation is completed; the flow progress of the report is displayed through a visualization tool, including completed nodes and to-be-completed nodes; when the processing time of a certain node exceeds a predetermined time threshold, the system generates a reminder and notifies the relevant responsible person; the system dynamically adjusts the report flow path according to business volume and process changes, so that reports at key nodes are processed first.

Images