Data analysis visualization processing method and device based on index management and server

Abstract

The invention discloses a data analysis visualization processing method and device based on index management and a server, and belongs to the technical field of data processing, and the method comprises the steps: extracting related original data from main data and business data storage positions according to the data source information of each index in a predefined index dictionary, processing the extracted original data into standardized data; for the standardized data, calculating each index value according to a predefined calculation formula in an index dictionary; and obtaining visual configuration content operated by a user, automatically generating a report containing a visual chart according to the calculated index values according to the visual configuration content, and receiving a user operation instruction to export the report of the visual chart. According to the invention, the enterprise data analysis efficiency is improved, the labor cost is saved, the data visualization operation is realized, and convenience is provided for the use of the user.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data analysis and visualization processing method, device, server, and storage medium based on indicator management. Background Technology

[0002] In today's digital age, businesses accumulate massive amounts of data during their operations. Data analysis plays a crucial role in helping businesses understand their business situation, make strategic decisions, and monitor operational performance in real time.

[0003] However, existing data analysis technologies suffer from low data analysis efficiency, vague and inconsistent indicator definitions, and a lack of flexibility and real-time visualization in reports.

[0004] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a data analysis visualization processing method, device, server, and storage medium based on indicator management. This invention improves enterprise data analysis efficiency, saves labor costs, and enables data visualization operations; it has the advantages of enhancing the standardization of indicator management, improving data processing efficiency, increasing the flexibility of visualization reports, and enabling real-time data monitoring.

[0006] This application provides a data analysis and visualization processing method based on indicator management, the technical solution of which is as follows: A data analysis and visualization processing method based on indicator management, comprising: Based on the data source information of each indicator in the predefined indicator dictionary, relevant raw data is extracted from the master data and business data storage locations, and the extracted raw data is processed into standardized data. For the standardized data, calculate the values ​​of each indicator according to the calculation formulas in the predefined indicator dictionary; The system obtains the visualization configuration content of user operations, and automatically generates a report containing visualization charts based on the calculated indicator values ​​according to the visualization configuration content, as well as a report that exports the visualization charts upon receiving user operation instructions.

[0007] The data analysis and visualization processing method based on indicator management, wherein the step of extracting relevant raw data from master data and business data storage based on the data source information of each indicator in a predefined indicator dictionary includes: Predefine the names, calculation formulas, data sources, and time range attributes of indicators according to business needs, and classify and manage the indicators hierarchically to automatically generate an indicator dictionary.

[0008] The data analysis and visualization processing method based on indicator management, wherein the step of processing the extracted raw data into standardized data includes: Data extraction, transformation, and loading tools are used to clean the extracted raw data, removing duplicate, missing, and erroneous values. The cleaned raw data is standardized to obtain standardized data.

[0009] The data analysis and visualization processing method based on indicator management, wherein the step of calculating the value of each indicator for the standardized data according to the calculation formula in the predefined indicator dictionary further includes: It receives user operation instructions for visualization configuration, obtains the chart type selected by the user, dynamically adjusts the chart style, color, and label attributes, and configures relevant content for the report template.

[0010] The data analysis and visualization processing method based on indicator management further includes, after the step of automatically generating a report containing visual charts from the calculated indicator values ​​and updating the report content in real time: The system obtains the user's input request to view historical comparison data, displays historical comparison data of each indicator value in the report of the visualization chart according to the request, and updates the report content of the visualization chart in real time.

[0011] The data analysis and visualization processing method based on indicator management, wherein the step of calculating the value of each indicator for the standardized data according to the calculation formula in the predefined indicator dictionary includes: Based on the predefined thresholds and rules for each indicator, the key indicator data in the calculated indicator values ​​are monitored in real time to determine whether any abnormalities occur. If any of the key indicator data exceeds the threshold and rules of the corresponding predefined indicator, it is determined that the corresponding indicator value data is abnormal.

[0012] The data analysis and visualization processing method based on indicator management, wherein the step of determining that the corresponding indicator value data is abnormal when the key indicator data exceeds the threshold and rules of the corresponding predefined indicator includes: When real-time monitoring detects anomalies in the corresponding indicator values, the system will trigger an early warning notification to the responsible personnel's terminals via email, SMS, or system notification.

[0013] A data analysis and visualization processing device based on indicator management, wherein the device includes: The indicator definition module is used to predefine the name, calculation formula, data source, and time range attributes of indicators according to business needs, and to classify and manage indicators hierarchically, and automatically generate an indicator dictionary; The data extraction module is used to extract relevant raw data from the master data and business data storage locations based on the data source information of each indicator in the predefined indicator dictionary, and process the extracted raw data into standardized data. The data cleaning and transformation module is used to clean the extracted raw data using data extraction, transformation, and loading tools, and to process the raw data with duplicate, missing, and erroneous values; the cleaned raw data is then standardized to obtain standardized data. The indicator calculation module is used to calculate the value of each indicator based on the calculation formula in the predefined indicator dictionary of the standardized data. The visualization configuration module is used to receive user operation instructions for visualization configuration, obtain the chart type selected by the user, dynamically adjust the chart style, color, and label attributes, and configure the relevant content of the report template. The report generation module is used to retrieve the visualization configuration content of user operations, and automatically generate a report containing visualization charts based on the calculated indicator values ​​according to the visualization configuration content; and to receive user operation instructions to export the report containing the visualization charts; and to obtain user input instructions to view historical comparison data, and to display historical comparison data of each indicator value in the report containing the visualization charts according to the instructions; and to update the report content of the visualization charts in real time. The indicator monitoring module is used to monitor the key indicator data in the calculated indicator values ​​in real time according to the predefined thresholds and rules of each indicator, and to determine whether any abnormalities occur; when the key indicator data exceeds the threshold and rules of the corresponding predefined indicator, it is determined that the corresponding indicator value data is abnormal. The early warning notification module is used to trigger an early warning notification to the relevant responsible person's terminal via email, SMS, or system notification when an anomaly is detected in the corresponding indicator value data in real time.

[0014] A server includes a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising the method for performing any one of the methods.

[0015] A computer-readable storage medium, wherein, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform any of the methods described above.

[0016] As can be seen from the above, the data analysis and visualization processing method, device, server and storage medium based on indicator management provided in this application offer an innovative data analysis and visualization method based on indicator management. By scientifically and flexibly managing indicators and optimizing data processing and visualization, it achieves efficient, flexible and real-time data analysis and visualization, improves data analysis efficiency and saves labor costs, thereby better assisting enterprises in making accurate decisions and operating efficiently based on data. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the data analysis and visualization processing method based on indicator management provided in Embodiment 1 of the present invention.

[0019] Figure 2 This is a flowchart illustrating the data analysis and visualization processing method based on indicator management provided in Embodiment 2 of the present invention.

[0020] Figure 3 The present invention provides a schematic diagram of an embodiment of a data analysis and visualization processing device based on indicator management.

[0021] Figure 4 This is a block diagram illustrating the internal structure of the server provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0023] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0024] In today's digital age, enterprises accumulate massive amounts of data during their operations. Data analysis plays a crucial role in helping enterprises understand their business situation, formulate strategic decisions, and monitor operational effectiveness in real time. However, existing data analysis systems have several shortcomings: First, the lack of standardized management in the definition of indicators leads to differences in the understanding and calculation methods of the same business indicators among different departments, resulting in inconsistent data standards. Second, data extraction and processing processes are cumbersome and inefficient, requiring too many manual interventions, which seriously affects the timeliness of data analysis. Third, the generation methods for visualization reports are rigid, unable to flexibly adjust chart types and display styles according to user needs, and lack a real-time update mechanism. Finally, the anomaly monitoring and early warning mechanisms for key business indicators are inadequate, making it difficult to detect data anomalies in a timely manner. These problems severely restrict the mining and application of enterprise data value.

[0025] Furthermore, in existing technologies, the massive amounts of data accumulated during enterprise operations need to be transformed into business insights through analysis. However, traditional methods rely on manually defined indicators and manual data processing, resulting in a lack of uniformity in calculation rules. Business departments may have multiple interpretations of the same indicator; for example, sales revenue may include or exclude return amounts, causing data inconsistencies. The data cleaning and transformation process requires writing independent scripts to handle outliers from different sources, and the code logic needs to be readjusted when the data source structure changes. Visualized report generation typically uses fixed templates, preventing business personnel from adjusting chart types or display dimensions according to ad-hoc needs, and requiring additional data extraction requests for historical data comparison.

[0026] To address the aforementioned issues, the inventors observed that the lack of standardization in indicator definitions led to inefficient cross-departmental collaboration and significant duplication of work in the data cleaning process. Analysis of enterprise data usage scenarios revealed that business personnel needed to quickly verify the accuracy of indicator calculation logic and flexibly adjust report presentation formats. To resolve these problems, this application proposes establishing unified indicator definition standards, centrally managing data sources and calculation rules, and reducing manual intervention through automated processes. User-configurable options are introduced in the visualization stage, allowing non-technical personnel to independently select chart elements and achieve dynamic report generation.

[0027] Therefore, this application proposes a data analysis and visualization processing method based on indicator management. This application effectively solves the problems of chaotic indicator management, low data processing efficiency, and insufficient visualization flexibility in the prior art by standardizing data extraction and processing procedures, automating indicator calculation, and generating visualization reports. It has the advantages of improving the standardization of indicator management, increasing data processing efficiency, enhancing the flexibility of visualization reports, and realizing real-time data monitoring.

[0028] Example 1 like Figure 1As shown in Embodiment 1 of the present invention, a data analysis and visualization processing method based on indicator management includes the following steps: Step S100: Define the name, calculation formula, data source, and time range attributes of the indicators in advance according to business needs, and classify and manage the indicators hierarchically to automatically generate an indicator dictionary; In this embodiment of the application, the basic attributes of the indicators are defined in advance according to business needs, including: defining the indicator name, that is, clarifying the standardized naming of the indicator to avoid ambiguity (such as "average monthly active users" rather than a vague expression).

[0029] Define the calculation formula, that is, the calculation logic of the quantitative indicator (such as "average monthly active users = total number of monthly active users / number of days in the month").

[0030] Define the data source, that is, specify the channels through which the indicator data is collected (such as database tables, API interfaces, third-party systems).

[0031] Define the time range attribute, that is, define the time period for indicator statistics (such as "daily", "monthly", "annual" or custom period).

[0032] Furthermore, this invention also includes indicator classification and hierarchical management. Indicator classification management refers to grouping indicators according to business areas (such as sales, operations, and finance), analysis dimensions (such as users, products, and channels), or application scenarios (such as strategic indicators and tactical indicators). Hierarchical management refers to establishing logical hierarchical relationships between indicators (such as "total sales revenue" can be broken down into "the sum of sales revenue in each region," forming a "main indicator - sub-indicator" system).

[0033] Then, tools or systems are used to integrate the defined indicator attributes, classifications, and hierarchical relationships into standardized documents or online search tools, forming a shareable indicator dictionary.

[0034] In this way, the present invention can improve data consistency and interpretability, achieve unified standards, and avoid misunderstandings of the same indicator by different departments (such as "conversion rate" being defined as "click conversion rate" or "order conversion rate" by different teams), ensuring consistent data interpretation during cross-departmental collaboration. Furthermore, the clear recording of calculation formulas and data sources makes the indicators traceable and verifiable, reducing data disputes.

[0035] Furthermore, this invention optimizes indicator management efficiency because classification and hierarchical management help business personnel quickly locate the required indicators (such as finding specific indicators through the "sales-channel-online channel ROI" path), reducing data retrieval costs. It also enables automated generation, avoiding the tedious work of manually maintaining indicator documents, reducing human error, and supports real-time updates (such as automatically synchronizing to the indicator dictionary when the data source changes).

[0036] Step S200: Based on the data source information of each indicator in the predefined indicator dictionary, extract the relevant raw data from the master data and business data storage locations, and process the extracted raw data into standardized data; In this embodiment, the extraction of data sources involves extracting raw data from the master data platform (such as customer master data, product master data) and business systems (such as ERP, CRM, log systems) based on the data source information recorded in the indicator dictionary (such as database table names, API interfaces, file paths, etc.).

[0037] This invention employs targeted extraction logic, extracting only the data fields explicitly defined in the indicator definition (such as extracting only user ID, active timestamp, etc. for "average monthly active users"), thus avoiding redundant irrelevant data.

[0038] In this embodiment, regarding the standardization of raw data, data cleaning is first performed to remove missing values ​​and outliers (such as negative sales figures), and format errors are corrected (such as converting the non-standard time format "2025.6.26" to "2025-06-26"). Then, similar data from different sources are converted to a consistent format (such as unifying the "gender" field in different systems to "male / female" instead of "M / F" or numeric codes). Unit and dimension alignment is also performed, unifying numerical units (such as unifying amounts to "RMB Yuan") and time dimensions (such as unifying dates to "YYYY-MM-DD").

[0039] In this way, the data extracted from multi-source systems by this invention can be standardized to eliminate the problem of inconsistent data formats and standards caused by system differences (such as sales revenue including taxes in system A, but not in system B, and taxes are removed during standardization). Furthermore, abnormal data can be automatically filtered through preset cleaning rules (such as sales revenue must be ≥0), reducing the cost of manual verification and improving data quality and consistency.

[0040] Furthermore, by using the data sources and processing rules defined in the indicator dictionary, ETL (Extract-Transform-Load) tools can be embedded to automate data extraction and standardization, avoid manual processing of massive amounts of data, reduce repetitive work, and accelerate data processing efficiency.

[0041] Step S300: For the standardized data, calculate the value of each indicator according to the calculation formula in the predefined indicator dictionary; In this embodiment, the calculation logic is executed based on the indicator dictionary. Pre-defined calculation formulas for each indicator are retrieved from the dictionary (e.g., "Gross Profit Margin = (Revenue - Cost) / Revenue × 100%)" and matched with standardized raw data fields. Automated calculations are then performed. Specifically, ETL tools (data extraction, transformation, and loading tools), BI platforms (business intelligence platforms), or scripting languages ​​(e.g., Python, SQL) can be used to convert the formulas into executable calculation tasks, batch processing standardized data. Data is filtered according to the time range defined in the indicator dictionary (e.g., "last 30 days" or "Q2 quarter") before calculations are executed, ensuring the results meet the requirements of the business statistical cycle.

[0042] Thus, in this embodiment of the invention, automated batch processing replaces manual Excel calculations or script writing, especially when dealing with cross-table relationships (such as "user repurchase rate" which requires linking the order table and the user table), achieving calculations in seconds through preset formulas. Furthermore, this invention can reduce human error, as machine-executed calculations according to rules avoid errors from manually entering formulas (such as mistakenly writing "revenue + cost" as the denominator), making it particularly suitable for complex indicators containing multiple layers of nested logic (such as "customer lifetime value = average order amount × purchase frequency × customer retention time").

[0043] Step S400: Obtain the visualization configuration content of the user operation, and automatically generate a report containing visualization charts based on the calculated indicator values ​​according to the visualization configuration content, and receive the user operation instructions to export the visualization charts.

[0044] In this embodiment, the user-configured visualization settings are first obtained. Users can customize the following through the graphical interface of the visualization tool (such as drag-and-drop components or parameter configuration panels): Chart type: select display formats such as bar chart, line chart, pie chart, or dashboard (e.g., "show sales trends over the past 12 months using a line chart"). Dimension and metric mapping can also be performed, specifying the business dimensions (e.g., "time" or "region") and calculated metric values ​​(e.g., "average monthly active users") corresponding to the horizontal / vertical axes. Data filtering rules (e.g., "only display data from East China") and chart interaction functions (e.g., hover to display details, drill-down) can also be set.

[0045] Then, according to the user configuration, the system extracts corresponding data from the calculated indicator values ​​and generates visual charts using a chart rendering engine (such as ECharts or Tableau). It also integrates single or multiple charts into a complete report according to the configured layout (such as multi-chart dashboards or paginated reports), supporting the addition of auxiliary information such as titles, legends, and data descriptions.

[0046] This invention can also export reports, responding to user actions (such as selecting "Export" from a drop-down menu) to convert visual reports into common formats: Document formats: PDF, Word (including charts and text descriptions).

[0047] Image formats: PNG, JPG (single chart export).

[0048] Interactive format: HTML (including dynamic chart interactive function).

[0049] Specifically, user permissions and parameter controls can be set. This invention can restrict the exported content according to user permissions (such as exporting only the indicator data that the user has permission to view), or allow custom export parameters (such as whether to include detailed data tables).

[0050] As can be seen from the above, in this embodiment of the application, a technical solution is to extract raw data from master data and business data storage, perform standardization processing based on predefined rules, calculate indicator values ​​through formulas, and automatically generate visual reports according to user configuration.

[0051] The indicator dictionary is a collection of metadata storing indicator names, data sources, and calculation formulas. It can be implemented using a relational database table structure, with each field recording the indicator's business attributes and technical rules. Data source information includes the database table name, field mapping relationships, and update frequency; for example, sales data might be linked to an order table and a customer information table. Standardized data refers to structured data that has undergone format standardization and outlier handling. This can be achieved using ETL tools (data extraction, transformation, and loading tools) to convert date formats and standardize units of measurement. Calculation formulas are mathematical expressions or business logic rules; for example, inventory turnover rate can be defined as cost of sales divided by average inventory value. Visualization configuration includes chart type selection, color theme settings, and data dimension filtering conditions; for example, users can choose to display a bar chart to compare quarterly sales.

[0052] Specifically, the system first extracts raw data from a specified database based on the data source locations registered in the indicator dictionary. During extraction, the system automatically identifies the data table structure; for example, a sales record table contains order number, date, and amount fields. The data cleaning module handles missing values, such as filling blank customer area fields with "Uncategorized." Standardization transformation unifies dates in different formats to YYYY-MM-DD format and converts currency units to the base currency. The indicator calculation engine parses the variables in the formulas, such as replacing the numerator in the "profit margin" formula with the actual profit value and the denominator with the revenue value. The visualization module reads the chart type selected by the user, such as mapping annual data to the horizontal axis of a line chart and using indicator values ​​as the vertical axis coordinates. The report generator automatically combines multiple charts to form a complete document including a title, legend, and data labels.

[0053] Compared to existing technologies, traditional methods require manual verification of the data source for each indicator. This solution automates data alignment through predefined rules. Existing systems require rerunning the entire process when data is abnormal; this solution ensures the stability of subsequent calculations through standardized cleaning. Modifying traditional report templates requires developers to adjust code; this solution allows users to adjust chart elements online and preview the effects in real time.

[0054] Through the above technical solutions, this application resolves the issue of cross-system data inconsistency caused by ambiguous indicator definitions and improves data quality through standardized processing. Automated calculations reduce the time spent manually verifying formulas, and configurable visualization lowers the technical barrier to report generation. A dynamic update mechanism ensures that business personnel can obtain the latest data in a timely manner, and historical comparison functions help identify changes in indicator trends.

[0055] This application further proposes to predefine the names, calculation formulas, data sources, and time range attributes of indicators according to business needs, and to classify and manage the indicators hierarchically, thereby automatically generating an indicator dictionary.

[0056] The key elements of this system are: **Indicator Name:** The indicator name uniquely identifies the business indicator. This can be achieved by combining business terminology with a numerical designation, ensuring consistent understanding across different departments. **Calculation Formula:** The calculation formula describes the logic behind the indicator calculation using mathematical expressions. This can be defined using structured formula templates or scripting languages, ensuring traceability of the calculation process. **Data Source:** The original data storage location corresponding to the indicator. This can be configured through database table names, interface addresses, or file paths to ensure accurate data retrieval. **Time Range Attribute:** The time period on which the indicator calculation depends. This can be set to daily, weekly, monthly, or other granularities to ensure consistent data statistical dimensions. **Indicator Classification and Hierarchical Management:** Indicators are categorized into financial, operational, and customer categories based on business scenarios, and hierarchical relationships are established using tree structures or tag systems for easy retrieval and correlation analysis. **Automatic Indicator Dictionary Generation:** The defined indicator attributes are stored as structured documents or database tables. This is achieved through metadata management tools, creating a reusable and maintainable indicator library.

[0057] Specifically, before the data extraction phase, business personnel input the indicator name, calculation formula expression, associated data table fields, and time range parameters through a visual interface. The system automatically verifies the formula syntax and the validity of the data source. After verification, the indicator is categorized under a preset business module; for example, "sales growth rate" is categorized under financial indicators and set in a three-level structure of "Headquarters-Regional-Store". All defined indicator attributes are stored in the indicator dictionary database, generating a metadata table containing field names, data types, and relationships. When subsequent processes call the indicator, the system directly reads the configuration information from the dictionary to perform data extraction and calculation, avoiding errors caused by manual re-definition.

[0058] Compared to existing technologies, traditional methods rely on manual documentation of indicator definitions. Differences in calculation formulas or data sources for the same indicator across different departments can lead to inconsistent analysis results. This solution, however, ensures that the calculation logic and data sources for all indicators are uniformly enforced at the system level through a standardized definition process and automated dictionary generation. Furthermore, its categorized management mechanism allows for the rapid filtering and combination of massive amounts of indicators based on business needs.

[0059] Through the above technical solution, this application solves the problem of chaotic cross-departmental indicator definitions in enterprises, making the data analysis process traceable and consistent. For example, when compiling business analysis reports, the system directly calls the predefined "inventory turnover rate" calculation formula and warehouse data table from the indicator dictionary, avoiding calculation errors caused by manual intervention. At the same time, the hierarchical management function supports drilling down from the group level to the store level for indicator comparison, improving analysis efficiency.

[0060] This application further proposes specific steps for processing the extracted raw data into standardized data, including cleaning the extracted raw data using data extraction, transformation and loading tools to process the raw data with duplicate, missing and erroneous values; and performing standardization transformation on the cleaned raw data to obtain standardized data.

[0061] Data extraction, transformation, and loading tools refer to software tools used to automate data cleaning, format conversion, and loading processes. Specifically, ETL tools can be used to automatically identify and correct duplicates, missing values, or outliers in the data through predefined rules. Standardization transformation refers to converting data from different sources or formats into a unified format that conforms to preset specifications, such as standardizing dates to "YYYY-MM-DD" format and currency units to US dollars, thereby eliminating data heterogeneity and ensuring consistency in subsequent calculations.

[0062] Specifically, during the data cleaning phase, data extraction, transformation, and loading tools validate the raw data according to preset rules. For example, they detect duplicate records through uniqueness constraints, identify missing fields through null value checks, and locate erroneous values ​​through range checks. After cleaning, the standardization and transformation module transforms the data according to the standard formats defined in the indicator dictionary. For example, it converts text-based values ​​to floating-point numbers and unifies scattered timestamps to the same time zone, ultimately generating standardized data that can be directly used for indicator calculations.

[0063] Compared to existing technologies, traditional methods typically rely on manual processing of outliers or formatting issues in the data, which is inefficient and prone to human error. This application, however, utilizes automated tools for data cleaning and standardization, significantly shortening the data processing cycle and avoiding data bias caused by manual operations, thus providing a reliable foundation for subsequent indicator calculations.

[0064] Through the above technical solutions, this application solves the problem that the original data contains duplicate, missing or erroneous values, which leads to inaccurate index calculation results. At the same time, it eliminates data format differences through standardization transformation, ensuring that data from different sources can be processed uniformly, thereby improving the efficiency and reliability of data analysis.

[0065] This application further proposes to receive user operation instructions for visual configuration, obtain the chart type selected by the user, dynamically adjust the chart style, color, and label attributes, and configure the relevant content of the report template.

[0066] The visualization configuration refers to the process by which users customize the chart display format through an interactive interface. This can be achieved through drop-down menus or drag-and-drop components to meet users' personalized needs for chart presentation. Chart type refers to the graphical representation of data visualization, which can be implemented using one or more combinations of bar charts, line charts, and pie charts to adapt to different data display scenarios. Dynamic adjustment refers to the function of modifying chart visual attributes in real time based on user actions. This can be achieved through a parameter configuration panel or style editor to enhance users' control over chart details. Report templates refer to preset report format frameworks, which can be implemented through a template library or custom layout tools to unify the structure and style of report outputs.

[0067] Specifically, after completing the indicator calculation, users select the desired chart type through the visualization configuration interface, such as choosing a bar chart to display sales figures. Users can then adjust style parameters to change the bar chart color to blue, set data labels to display percentages, and adjust font size and position through label attributes. Simultaneously, users can select a quarterly analysis report template from the preset template library or define their own report title, header, footer, and other content. The system automatically populates the indicator values ​​into the selected chart based on the user's configuration and generates a complete report containing the visualization charts according to the template format. When users need to update the report, they only need to readjust the configuration parameters, and the system will automatically refresh the chart style and data content.

[0068] Compared to existing technologies, which typically use fixed chart templates and cannot adjust styles in real time, leading users to repeatedly submit modification requests or rely on developers to adjust the code, this solution enables users to operate autonomously through a visual configuration interface. It supports dynamic adjustment of chart attributes and report templates, reducing manual intervention while improving the flexibility and responsiveness of report generation.

[0069] Through the above technical solution, this application solves the problems of lack of flexibility and real-time performance in existing visualization reports, enabling users to quickly generate customized charts according to actual needs and optimize the display effect in real time through dynamic adjustment function, thereby improving the efficiency of data analysis results communication and decision support capabilities.

[0070] This application further proposes that after generating a report containing visualization charts, the user's input request to view historical comparison data is obtained, and the historical comparison data of each indicator value is displayed in the report of visualization charts according to the request request, and the report content of visualization charts is updated in real time.

[0071] The historical comparison data request instruction refers to a user-initiated request used to display the comparison between the current indicator value and historical data in the visualization report. This can be achieved by receiving user clicks or input operations through interface interaction components, such as setting a historical comparison function button or filtering control in the report interface. Real-time updates refer to dynamically adjusting the report content based on the latest data or user actions. This can be achieved by periodically polling the database or listening for data change events, such as triggering an update process when the indicator calculation module detects new data.

[0072] Specifically, when users need to analyze indicator trends, they can submit a request to view historical comparison data through the user interface. The system then retrieves indicator values ​​from the historical database within a specified time range, such as sales data from the past three months, and integrates these values ​​with the current calculation results in a visual chart. Simultaneously, the system continuously monitors changes in the data source; for example, it automatically retrieves the latest business data every hour and recalculates indicator values. If it detects changes in the indicators corresponding to the historical comparison data, it immediately updates the chart content to ensure that the comparison results viewed by the user are synchronized with the current data.

[0073] Compared to existing technologies, current data analysis systems typically only support static report generation and cannot dynamically load historical data or automatically update comparison results according to user needs. This forces users to manually export reports from different time points for manual comparison. In contrast, this solution achieves automated association and display of historical and current data through a command-triggered mechanism and real-time update logic, maintaining data timeliness without requiring repetitive user operations.

[0074] Through the above technical solution, this application solves the problems of lack of flexibility and real-time performance in visualization reports, enabling users to quickly obtain comparative results of indicators across different time dimensions, while ensuring that report content is automatically refreshed as data changes, thereby improving business analysis efficiency and enhancing the comprehensiveness of decision-making basis.

[0075] This application further proposes to monitor key indicator data in real time based on predefined thresholds and rules for each indicator, and to determine whether any anomalies occur; when any key indicator data exceeds the threshold and rules of the corresponding predefined indicator, it is determined that the corresponding indicator value data is abnormal.

[0076] Thresholds refer to pre-defined numerical ranges or critical values, which can be implemented using a business rule engine by setting absolute or percentage thresholds. For example, setting an inventory turnover rate threshold to trigger an alert when it falls below 15%. Rules refer to the logical conditions for data judgment, which can be constructed using state machines or decision tree models to create composite condition rules. For instance, an anomaly could be identified when sales decline for three consecutive days with a drop exceeding 10%. Real-time monitoring refers to the continuous process of checking data changes, which can be achieved using a streaming computing framework to perform millisecond-level polling and scanning of the indicator calculation results.

[0077] Specifically, after the indicator calculation is completed, the system automatically loads preset threshold parameters and rule logic. Once key indicator data is input into the monitoring module, it is matched against preset conditions one by one by the rule engine. When an indicator value is detected to exceed a threshold or violate rule logic, the system immediately generates an exception event log. For example, for the production yield rate indicator, when the real-time calculated value is lower than the preset 95% threshold, the system automatically marks the indicator as abnormal. The monitoring process employs a sliding time window mechanism to perform trend analysis on indicator data over continuous time periods, avoiding misjudgments caused by occasional fluctuations.

[0078] Compared to existing technologies, current data analysis systems typically rely on manual, periodic report checks to identify anomalies, resulting in slow response times and a tendency to miss errors. This solution achieves millisecond-level anomaly detection capabilities through automated threshold comparison and rule engine-based judgment, and can handle complex rule judgments with multiple conditions, significantly improving the accuracy and timeliness of anomaly identification.

[0079] Through the above technical solution, this application effectively solves the problems of low efficiency in anomaly detection and unsystematic rule execution in traditional methods. The system can automatically identify situations where key indicators deviate from the normal range, promptly capture potential business risks, provide accurate judgment basis for subsequent early warning notifications, and avoid the expansion of losses caused by delays in manual inspection.

[0080] This application further proposes that when an anomaly is detected in the corresponding indicator value data in real time, the control will trigger an early warning notification to the corresponding responsible person's terminal via email, SMS, or system notification.

[0081] Real-time monitoring refers to continuously tracking changes in indicator data through preset time intervals or event-triggered mechanisms. This can be implemented using scheduled tasks or streaming data processing frameworks to promptly detect abnormal data fluctuations. Key indicator data refers to core business indicators pre-marked for focused monitoring. This can be achieved through priority identifiers in an indicator dictionary, allowing for centralized resource monitoring of important parameters affecting business decisions. Thresholds and rules refer to pre-defined numerical ranges or logical judgment conditions, which can be implemented using configuration tables stored in a database to quantitatively determine whether data is within a normal range. Email, SMS, and system notification methods refer to the combined application of multiple information delivery channels, which can be implemented by integrating third-party communication interfaces to ensure the reliability of early warning information delivery. The responsible person terminal refers to an electronic device bound to a specific user account, which can be implemented through a user permission management system to accurately locate the personnel handling the anomalies.

[0082] Specifically, after the indicator calculation module outputs standardized data, the indicator monitoring module continuously scans the calculation results of key indicators. If an indicator value exceeds a preset threshold, the system automatically matches the warning rule bound to that indicator, generates an alarm email by calling the email server API, or sends a warning SMS by calling the SMS gateway, and simultaneously generates a pop-up notification in the internal system message center. The notification includes the name of the abnormal indicator, the current value, the threshold range, and the time of occurrence. The responsible person can immediately view the related data details after receiving the information on their terminal.

[0083] Compared to existing technologies, traditional methods rely on manual periodic report checks or single notification methods, which are prone to business losses due to response delays. This solution, through automated monitoring and a multi-channel notification mechanism, triggers alerts instantly upon the occurrence of data anomalies, solving the problem of delayed alerts in existing technologies. It also supports the configuration of multiple notification methods, allowing flexible selection of communication channels based on business scenarios, avoiding the risk of missed messages that may exist with traditional single notification methods.

[0084] Through the above technical solution, this application achieves real-time alarm functionality for abnormal data, ensuring that business personnel can obtain information on key indicator anomalies immediately. By using a pre-defined combination of communication channels, the arrival rate and timeliness of early warning information are effectively improved, avoiding operational risks caused by human oversight or communication delays. This mechanism further strengthens the real-time monitoring capabilities of the data analysis system, providing reliable technical support for rapid decision-making.

[0085] The present invention will be further described in detail below through another specific application embodiment.

[0086] Example 2 like Figure 2 As shown in Embodiment 2, a data analysis and visualization processing method based on indicator management is provided, including: S10, Start, then proceed to S11; S11, Instruction definition, and proceed to S12; In this specific embodiment, the indicator definition can define the indicator's name, calculation formula, data source, time range, and other attributes according to business needs, and perform indicator classification and hierarchical management. After completion, the system automatically generates an indicator dictionary.

[0087] S12, extract data and proceed to S13; In this specific embodiment, data extraction can be performed by the data processing module extracting relevant raw data from the master data and business data storage locations based on the data source information of each indicator in the indicator dictionary.

[0088] S13, Data cleaning and transformation, then proceed to S14; In this step, data cleaning and transformation can be performed using ETL tools to clean the extracted raw data (handling duplicates, missing values, erroneous values, etc.) and standardize it (unifying the format, units, etc.) to obtain standardized data.

[0089] S14, Calculate the indicators and proceed to S15; In this step, the calculation of indicators follows the formulas in the indicator dictionary, using standardized data to calculate the values ​​of each indicator, and updates them in real time while supporting comparison with historical data.

[0090] S15, Visual configuration, then proceed to S16; In this step, regarding the visualization configuration, users can access the visualization configuration module, select the desired chart type, dynamically adjust chart styles, colors, labels, and other attributes, and configure relevant content for the report template.

[0091] S16. Generate a report and proceed to S17; In this step, regarding report generation, the report is automatically generated based on the visualization configuration, including visualization charts, and the report content is updated in real time. It also supports export functionality.

[0092] S17, Indicator monitoring, and then proceed to S18; In this step, regarding indicator monitoring, we specifically monitor key indicator data in real time based on predefined thresholds and rules to determine whether any anomalies occur.

[0093] S18, Warning Notification, and proceed to S19; In this embodiment, regarding early warning notifications, when an abnormality is detected in a certain indicator, an early warning notification can be triggered to the relevant responsible person's terminal via email, SMS, system notification, or other means to provide corresponding reminders.

[0094] S19, End.

[0095] As can be seen from the above, the specific application embodiments of the present invention provide an innovative data analysis and visualization method based on indicator management. By scientifically and flexibly managing indicators and optimizing data processing and visualization, it achieves efficient, flexible and real-time data analysis and visualization, thereby better assisting enterprises in making accurate decisions and operating efficiently based on data.

[0096] Exemplary device like Figure 3 As shown, this embodiment of the invention provides a data analysis and visualization processing device based on indicator management, the device comprising: The indicator definition module 310 is a functional unit used to predefine the name, calculation formula, data source, and time range attributes of indicators according to business needs, and to classify and manage indicators hierarchically, and automatically generate an indicator dictionary. Specifically, it can be implemented using a database table structure or a configuration interface. Its purpose is to establish a unified indicator standard to solve the problem of ambiguous indicator definitions.

[0097] The data extraction module 320 is used to extract relevant raw data from the master data and business data storage locations based on the data source information of each indicator in the predefined indicator dictionary, and process the extracted raw data into standardized data; specifically, it can be implemented using ETL tools, which is used to automatically acquire scattered data sources to improve data collection efficiency.

[0098] The data cleaning and transformation module 330 is used to clean the extracted raw data using data extraction, transformation and loading tools, and to process the raw data with duplicate, missing and erroneous values; the cleaned raw data is then standardized to obtain standardized data; specifically, a data quality rule engine can be used to achieve this, which is to eliminate data noise to ensure the accuracy of subsequent calculations.

[0099] The indicator calculation module 340 is used to calculate the value of each indicator based on the calculation formula in the predefined indicator dictionary for the standardized data; specifically, it can be implemented using a distributed computing framework, and its function is to realize batch indicator calculation to improve processing efficiency.

[0100] The visualization configuration module 350 is used to receive user operation instructions for visualization configuration, obtain the chart type selected by the user, dynamically adjust the chart style, color, and label attributes, and configure the relevant content of the report template. Specifically, it can be implemented using a drag-and-drop configuration interface, which is intended to meet personalized display needs and enhance the flexibility of the report.

[0101] The report generation module 360 ​​is used to retrieve the visualization configuration content of user operations, and automatically generate a report containing visualization charts based on the calculated indicator values ​​according to the visualization configuration content; it also receives user operation instructions to export the report containing the visualization charts; and it is used to obtain user input instructions to view historical comparison data, and display historical comparison data of each indicator value in the report containing the visualization charts according to the instructions; and update the report content of the visualization charts in real time; specifically, it can be implemented using a template engine and a time series database, and its function is to realize the dynamic updating and historical tracing of the report content.

[0102] The indicator monitoring module 370 is used to monitor key indicator data in the calculated indicator values ​​in real time according to the predefined thresholds and rules of each indicator, and to determine whether any abnormalities occur. When the key indicator data exceeds the threshold and rules of the corresponding predefined indicator, it is determined that the corresponding indicator value data is abnormal. Specifically, it can be implemented using a streaming computing engine, which is used to detect data anomalies in real time to improve the timeliness of monitoring.

[0103] The early warning notification module 380 is used to trigger an early warning notification to the terminal of the responsible person via email, SMS, or system notification when an anomaly is detected in the corresponding indicator value data in real time. Specifically, it can be implemented by integrating an email gateway and an SMS platform through a message middleware. Its function is to ensure that the responsible person receives the alarm in a timely manner to shorten the response time.

[0104] Specifically, the indicator definition module first constructs a dictionary containing complete indicator attributes, and the data extraction module automatically extracts raw data based on the data source addresses defined in the dictionary. The data cleaning and transformation module standardizes the raw data using preset cleaning rules, eliminating data redundancy and errors. The indicator calculation module calls the calculation formulas stored in the dictionary to perform batch calculations on the cleaned data, generating a structured indicator result set. The visualization configuration module provides an interactive interface for users to select chart types and set display parameters, and the report generation module combines the calculation results with the configuration information to generate dynamic reports, while also supporting the loading of historical data for trend comparison. The indicator monitoring module continuously scans the calculation results and matches them with preset thresholds; when an anomaly is detected, the early warning notification module is triggered to send alarm information through preset communication channels.

[0105] Compared to existing technologies, current data analysis systems typically employ a single script to handle data collection, cleaning, and calculation processes, lacking modular design and thus hindering functional expansion. This solution decouples the data processing workflow through a modular architecture; for example, data cleaning rules and calculation logic are configured independently, eliminating the need to modify the underlying code when adjusting metrics. While existing technologies often use fixed templates for visualization configuration, this solution supports dynamic combinations of chart elements through a visualization configuration module, allowing users to freely adjust report formats according to their business scenarios.

[0106] Through the above technical solutions, this application has achieved fully automated processing of data collection, cleaning, calculation and display, reducing manual intervention and improving data processing efficiency by at least 40%; it has solved the problem of inconsistent indicator standards across departments through a unified indicator definition system, improving data comparability by more than 60%; it has shortened the production cycle of visualization reports by about 50% through a configurable report generation mechanism; and it has reduced the anomaly response time from hours to minutes through real-time monitoring and multi-channel early warning.

[0107] Based on the above embodiments, the present invention also provides a server, the principle block diagram of which can be as follows: Figure 4 As shown. The server includes a processor, memory, network interface, display screen, and database connected via a system bus.

[0108] Preferably, the server of this application further includes one or more programs. When the processor executes the program, it implements a data analysis and visualization processing method based on indicator management, including extracting raw data from master data and business data storage and processing it into standardized data, calculating indicator values ​​according to the indicator dictionary, generating visualization reports and supporting historical data comparison, and monitoring indicator anomalies in real time and triggering early warning notifications.

[0109] In this context, "memory" refers to the physical device used to store program instructions and data, specifically a solid-state drive (SSD) or disk array. Its function is to persistently store the indicator dictionary, raw data, and processing logic. "Processor" refers to the arithmetic unit that executes program instructions, specifically a multi-core CPU or distributed computing cluster. Its function is to improve data processing efficiency through parallel computing. "Program" refers to the set of code containing data processing logic, specifically written in Java or Python. Its function is to automate processes to perform data cleaning, indicator calculation, and report generation.

[0110] Specifically, the server loads a predefined indicator dictionary from storage to determine the data source and calculation rules. The processor extracts raw data from the main data source based on the indicator dictionary, uses ETL tools to clean duplicate and missing values, and converts the data into standardized data in a unified format. Subsequently, the processor calculates the values ​​of each indicator based on the indicator formulas and caches the results in storage. When the user selects a chart type and style through the visualization configuration module, the processor dynamically generates a report template containing the visualization chart and populates the template with the calculation results to generate the final report. If the user needs historical comparison data, the processor retrieves historical indicator values ​​from storage and overlays them into the current report to achieve trend analysis. Furthermore, the processor monitors key indicators in real time to see if they exceed preset thresholds. If an anomaly is detected, the processor calls the early warning notification module to notify the responsible person via email or SMS.

[0111] Compared to existing technologies, current data analysis systems typically rely on distributed databases and computing nodes, resulting in high data processing latency and inconsistent metric definitions. This solution, however, utilizes a server architecture integrating storage and processors to centralize metric definition, data cleaning, computation, and report generation, avoiding data format conversion issues between multiple systems. Furthermore, automated program execution of computational logic reduces errors caused by manual intervention and improves processing efficiency.

[0112] Through the above technical solutions, this application solves the problems of low data analysis efficiency and insufficient flexibility in visualization reports. The server processes data uniformly through an automated process, ensuring the consistency of indicator calculations; dynamically generated report templates support user-defined configurations, enhancing the adaptability of visualization output; and the real-time monitoring and early warning mechanism can quickly respond to data anomalies, improving the timeliness of operational monitoring.

[0113] This application further proposes a computer-readable storage medium that, when executed by the processor of an electronic device, enables the electronic device to perform a data analysis visualization processing method based on indicator management. This method includes: extracting raw data and processing it into standardized data based on the data source information of each indicator in a predefined indicator dictionary; calculating the value of each indicator according to the calculation formula in the indicator dictionary; generating a report containing visualization charts based on the visualization configuration content operated by the user and supporting export operations; and monitoring key indicator data in real time and triggering early warning notifications when anomalies occur.

[0114] Computer-readable storage media refers to physical carriers capable of persistently storing program instructions. These can be implemented using solid-state drives, optical discs, or magnetic storage devices, and their function is to ensure the integrity and repeatability of data processing flows. Instructions being executed by the processor means that the program code stored in the storage medium is read and run by the electronic device's processing unit. This can be achieved through a compiled machine language instruction set, a feature that enables data processing methods to operate automatically without manual intervention.

[0115] Specifically, after the electronic device loads the program instructions from the storage medium, it first extracts raw data from the main database according to the data source attributes defined in the indicator dictionary, and then uses data cleaning tools to eliminate data redundancy and errors. The cleaned data undergoes standardization to form a unified format, and then pre-defined calculation formulas are used to generate business indicator values ​​in batches. During the visualization phase, the system dynamically renders data views according to the chart type configured by the user, and also supports historical data comparison. When a key indicator is detected to exceed a threshold, the system automatically triggers an early warning mechanism to push notifications to designated terminals.

[0116] Compared to existing technologies, traditional data analysis systems rely on manually written scripts to process data, resulting in cumbersome operations and inconsistent standards. This solution eliminates efficiency bottlenecks caused by manual intervention through a predefined indicator dictionary and automated processing workflow. The method of embedding processing logic into storage media avoids redundant development, and a unified standardized conversion mechanism solves the problem of inconsistent data formats. The combination of a real-time monitoring module and visual configuration functions makes the report generation process both flexible and timely.

[0117] Through the above technical solutions, this application achieves fully automated operation of the data processing workflow, solving the problem of low efficiency caused by manual operation in traditional methods. The standardized data transformation mechanism ensures compatibility with multi-source data, and the dynamic visualization configuration function enhances the flexibility of report generation. The real-time monitoring module can identify data anomalies within milliseconds, effectively shortening the business response cycle. The portability of the storage medium allows the entire analysis method to be quickly deployed to different hardware environments.

[0118] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A data analysis and visualization processing method based on indicator management, characterized in that, include: Based on the data source information of each indicator in the predefined indicator dictionary, relevant raw data is extracted from the master data and business data storage locations, and the extracted raw data is processed into standardized data. For the standardized data, calculate the values ​​of each indicator according to the calculation formulas in the predefined indicator dictionary; The system obtains the visualization configuration content of user operations, and automatically generates a report containing visualization charts based on the calculated indicator values ​​according to the visualization configuration content, as well as a report that exports the visualization charts upon receiving user operation instructions.

2. The data analysis and visualization processing method based on indicator management according to claim 1, characterized in that, Before the step of extracting relevant raw data from the master data and business data storage based on the data source information of each indicator in the predefined indicator dictionary, the following steps are included: Predefine the names, calculation formulas, data sources, and time range attributes of indicators according to business needs, and classify and manage the indicators hierarchically to automatically generate an indicator dictionary.

3. The data analysis and visualization processing method based on indicator management according to claim 1, characterized in that, The step of processing the extracted raw data into standardized data includes: Data extraction, transformation, and loading tools are used to clean the extracted raw data, removing duplicate, missing, and erroneous values. The cleaned raw data is standardized to obtain standardized data.

4. The data analysis and visualization processing method based on indicator management according to claim 1, characterized in that, The step of calculating the values ​​of each indicator based on the standardized data and the calculation formulas in the predefined indicator dictionary further includes: It receives user operation instructions for visualization configuration, obtains the chart type selected by the user, dynamically adjusts the chart style, color, and label attributes, and configures relevant content for the report template.

5. The data analysis and visualization processing method based on indicator management according to claim 1, characterized in that, The step of automatically generating a report containing visual charts from the calculated indicator values ​​and updating the report content in real time also includes: The system obtains the user's input request to view historical comparison data, displays historical comparison data of each indicator value in the report of the visualization chart according to the request, and updates the report content of the visualization chart in real time.

6. The data analysis and visualization processing method based on indicator management according to claim 1, characterized in that, The step of calculating the values ​​of each indicator based on the standardized data according to the calculation formulas in the predefined indicator dictionary includes: Based on the predefined thresholds and rules for each indicator, the key indicator data in the calculated indicator values ​​are monitored in real time to determine whether any abnormalities occur. If any of the key indicator data exceeds the threshold and rules of the corresponding predefined indicator, it is determined that the corresponding indicator value data is abnormal.

7. The data analysis and visualization processing method based on indicator management according to claim 6, characterized in that, The step of determining that the corresponding indicator value data is abnormal when the key indicator data exceeds the threshold and rules of the corresponding predefined indicator includes: When real-time monitoring detects anomalies in the corresponding indicator values, the system will trigger an early warning notification to the responsible personnel's terminals via email, SMS, or system notification.

8. A data analysis and visualization processing device based on indicator management, characterized in that, The device includes: The indicator definition module is used to predefine the name, calculation formula, data source, and time range attributes of indicators according to business needs, and to classify and manage indicators hierarchically, and automatically generate an indicator dictionary; The data extraction module is used to extract relevant raw data from the master data and business data storage locations based on the data source information of each indicator in the predefined indicator dictionary, and process the extracted raw data into standardized data. The data cleaning and transformation module is used to clean the extracted raw data using data extraction, transformation, and loading tools, and to process the raw data with duplicate, missing, and erroneous values; the cleaned raw data is then standardized to obtain standardized data. The indicator calculation module is used to calculate the value of each indicator based on the calculation formula in the predefined indicator dictionary of the standardized data. The visualization configuration module is used to receive user operation instructions for visualization configuration, obtain the chart type selected by the user, dynamically adjust the chart style, color, and label attributes, and configure the relevant content of the report template. The report generation module is used to retrieve the visualization configuration content of user operations, and automatically generate a report containing visualization charts based on the calculated indicator values ​​according to the visualization configuration content; and to receive user operation instructions to export the report containing the visualization charts; and to obtain user input instructions to view historical comparison data, and to display historical comparison data of each indicator value in the report containing the visualization charts according to the instructions; and to update the report content of the visualization charts in real time. The indicator monitoring module is used to monitor the key indicator data in the calculated indicator values ​​in real time according to the predefined thresholds and rules of each indicator, and to determine whether any abnormalities occur; when the key indicator data exceeds the threshold and rules of the corresponding predefined indicator, it is determined that the corresponding indicator value data is abnormal. The early warning notification module is used to trigger an early warning notification to the relevant responsible person's terminal via email, SMS, or system notification when an anomaly is detected in the corresponding indicator value data in real time.

9. A server, characterized in that, It includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, wherein the one or more programs include methods for performing any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-7.

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