A distributed relational database visual report intelligent generation method, device and system

Abstract

This application discloses a method, apparatus, and system for intelligently generating visual reports for distributed relational databases. It connects to the target database and parses its stored procedures, publishing them as callable data interfaces. It receives natural language indicator descriptions input by the user and, based on the published data interfaces, maps them to corresponding calling parameters using natural language processing technology. Reports are generated based on the calling parameters, and predictive dataset caching and updates are performed based on the user's historical access patterns and database source data change information. This application achieves deep integration with distributed databases, intelligent report configuration with zero code, and efficient data caching and updates, significantly improving report development efficiency, system response speed, and operation and maintenance management capabilities.

Description

Technical Field

[0001] This application relates to the fields of database management and computer technology, and in particular to a method, apparatus and system for intelligent generation of visual reports for distributed relational databases. Background Technology

[0002] With the advent of the big data era, enterprises' demand for data processing and analysis is growing rapidly. High-performance, highly available distributed relational databases are widely used in finance, e-commerce, logistics, and other fields. Especially against the backdrop of the replacement and upgrading of domestic databases, the popularization and application of distributed relational databases have been accelerated. However, despite the superior performance of distributed relational databases in data processing, traditional report generation tools fall short in the areas of report generation and data analysis. These tools are often tied to specific database systems and lack optimization and customization for distributed databases like OceanBase. Furthermore, they suffer from compatibility issues with existing company technologies and presentation effects. This often leads to complex configuration and debugging during report generation, presenting enterprises with challenges such as cumbersome operating procedures, inefficient data processing speeds, and difficulty adapting to rapidly changing business needs, further increasing operational and time costs. Therefore, developing a visual report generation method and system based on distributed relational databases is essential.

[0003] Traditional report generation tools, due to their binding to specific database systems, lack the necessary optimization and customization for distributed databases like OceanBase. Furthermore, these tools suffer from compatibility issues with existing reporting systems in areas such as data interfaces, front-end rendering engines, and permission models. This often leads to complex configuration and debugging during report generation, resulting in cumbersome processes, inefficient data processing, and difficulty adapting to rapidly changing business needs. This chain reaction ultimately increases the company's operational and time costs significantly, enhances the difficulty of domestic IT innovation, and severely impacts operational efficiency and market competitiveness.

[0004] More specifically, traditional methods face challenges in developing mobile reports, including long development cycles and inconsistent front-end styles, particularly when integrating with large distributed databases like OceanBase. The core technical issues are: traditional reporting tools lack sufficient support for distributed databases like OceanBase, leading to complex configurations, low processing efficiency, and difficulty adapting to rapidly changing business needs; long report development cycles, inconsistent front-end styles, reliance on manual coding, and high maintenance costs; and a lack of intelligent data caching and update mechanisms, resulting in slow report response and untimely data updates. Summary of the Invention

[0005] This application aims to address the problems of traditional report generation tools, such as configuration complexity, low efficiency, difficulty in adapting to rapid business needs, and high operation and maintenance costs, and provides a method, device, and system for generating intelligent visual reports using a distributed relational database.

[0006] In a first aspect, embodiments of this application provide a method for intelligently generating visual reports for distributed relational databases, comprising the following steps:

[0007] Connect to the target database, parse and register its stored procedures, and publish the stored procedures as a callable data interface with parameter definitions;

[0008] Receive natural language indicator descriptions input by users, and map the natural language indicator descriptions into call parameters for a specific data interface using natural language processing technology;

[0009] Reports are generated based on the call parameters, and predictive dataset caching and updates are performed based on the user's historical access patterns and source data change information in the target database.

[0010] In one embodiment of this method, parsing and registering the stored procedure specifically includes: obtaining the stored procedure name specified by the user; automatically reading the parameter definition information of the stored procedure from the target database; and generating and recording the registration information of the stored procedure in the system based on the parameter definition information.

[0011] In a more specific embodiment of this method, the distributed relational database is the OceanBase database.

[0012] Optionally, in the method, the steps of connecting to the target database, parsing and registering its stored procedures, and publishing the stored procedures as callable data interfaces with parameter definitions further include: ranking and recommending the output indicators corresponding to the data interface based on an indicator priority recommendation model; wherein, the indicator priority recommendation model calculates a priority score based on the number of times the indicator is used by users and the initial weight.

[0013] In a detailed embodiment, the priority score of the indicator consists of two parts: a base score and a dynamically weighted score; wherein, the base score is the initial weight of the indicator, and the dynamically weighted score is the product of the number of times the user uses the indicator, the adjustment coefficient, and the initial weight; the priority score is the sum of the dynamically weighted score and the base score.

[0014] In an optional embodiment of this method, the step of mapping the natural language indicator description to a call parameter for a specific data interface using natural language processing technology further includes: matching the natural language indicator description with a pre-built business term-data field mapping model; if the match is successful, generating the call parameter based on the matching result; if the match fails, providing an interactive guidance interface, establishing a new mapping relationship based on user selection or input, and updating the mapping model.

[0015] In an optional embodiment of this method, the execution of predictive dataset caching and updating specifically includes: monitoring users' historical access patterns to reports and change information of source data in the target database; predicting the target dataset that will be accessed in the future and whose data has changed based on the historical access patterns and the change information; and proactively triggering data extraction and cache update operations for the target dataset during periods of low system load.

[0016] In an optional embodiment of this method, the method further includes: providing a cache monitoring dashboard for real-time display of one or more key indicators among cache hit rate, report opening efficiency, number of visitors, and data freshness.

[0017] In an optional embodiment of this method, generating the report specifically includes: in response to a user's drag-and-drop operation command on the visual configuration interface, obtaining the calling parameters; and generating the layout and style configuration of the report to form the front-end display page of the report.

[0018] Secondly, embodiments of this application also provide a distributed relational database visualization report intelligent generation device, used to implement the method described in the first aspect, including:

[0019] The interface publishing module is used to connect to the target database, parse and register its stored procedures, and publish the stored procedures as callable data interfaces with parameter definitions.

[0020] The indicator configuration module is used to receive natural language indicator descriptions input by users and to map the natural language indicator descriptions into call parameters for specific data interfaces through natural language processing technology.

[0021] The report and cache management module is used to generate reports based on the call parameters and perform predictive dataset caching and updates based on the user's historical access patterns and source data change information of the target database.

[0022] Optionally, the target database is a distributed relational database.

[0023] Thirdly, embodiments of this application also provide a distributed relational database visualization report intelligent generation system, including:

[0024] Database servers are used to store business data and provide stored procedures;

[0025] An application server is communicatively connected to the database server, and the application server is equipped with the apparatus described in the second aspect.

[0026] The user terminal is connected to the application server and is used to receive user instructions and display generated reports and configuration interfaces.

[0027] Optionally, the user terminal is a mobile terminal, and the application server further includes a mobile terminal publishing module for adapting and publishing the generated reports to the mobile terminal.

[0028] Fourthly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0029] Fifthly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor, and the program, when executed by the processor, implements the method as described in any one of the first aspects.

[0030] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0031] Through deep integration of the "zero-code" front-end development platform and database, the system greatly improves the efficiency of report development, shortens the development cycle, and reduces development costs.

[0032] The dataset caching and update mechanism ensures rapid report presentation and stable operation, enhancing the user experience. Especially in big data scenarios, the system can easily handle the processing and analysis needs of massive amounts of data.

[0033] The cache monitoring dashboard provides developers with tools for real-time monitoring and rapid response, improving operational efficiency. Simultaneously, the system supports automated operations and intelligent monitoring, further reducing operational costs.

[0034] The system not only supports specific databases, but also has good flexibility and scalability, enabling it to adapt to the business needs and data scale of different enterprises. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0036] Figure 1 A flowchart illustrating an embodiment of a distributed relational database visualization report intelligent generation method;

[0037] Figure 2 This is a flowchart of a specific step in the data interface publishing process in an embodiment of this application;

[0038] Figure 3 This is a flowchart of a specific step in the intelligent configuration of indicators in an embodiment of this application;

[0039] Figure 4 This is a flowchart illustrating the report generation and caching steps in an embodiment of this application.

[0040] Figure 5 This is a structural block diagram of an embodiment of the intelligent visualization report generation device of this application;

[0041] Figure 6 This is a structural block diagram of an embodiment of the intelligent visualization report generation system of this application;

[0042] Figure 7 This is a schematic diagram showing the display effect of the front-end report table style in one embodiment of this application;

[0043] Figure 8 This is a schematic diagram of the report management front-end search page involved in one embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0046] Example 1: Implementation of the core method based on claims 1-3

[0047] Figure 1 This is a flowchart illustrating an embodiment of the intelligent generation method for visual reports from a distributed relational database according to this application. The core process of this method aims to construct an "intelligent, efficient, and zero-code" closed loop from the data source to the final report. The method includes the following steps 110-130.

[0048] Step 110: Connect to the target database, parse and register its stored procedures, and publish the stored procedures as callable data interfaces with parameter definitions.

[0049] This step involves publishing the data interface. The system deeply integrates with the target database, such as OceanBase, leveraging its high performance, high availability, and distributed characteristics. Through the establishment of distributed indexes, it provides stable and efficient data support for report development. The aim is to address the shortcomings of traditional reporting tools, which are tied to specific database systems and lack optimization and customization for distributed databases. This application achieves efficient integration with the OceanBase distributed database. By optimizing query statements and data processing strategies, specifically including establishing global or local indexes aligned with partitioned tables for frequently queried fields in the OceanBase database, and reducing cross-node data access through a distributed indexing mechanism, the efficiency of data retrieval and report generation is significantly improved.

[0050] As a further optimization example, based on the distributed architecture characteristics of OceanBase, data retrieval performance is optimized through at least one of the following strategies: index alignment, execution plan solidification, parallel query splitting, predicate pushdown, or result set compression.

[0051] In this application, the stored procedure refers to a set of SQL statements pre-compiled and stored in the database, which can be executed by calling with parameters and returning a result set. Compared with traditional ad-hoc queries, stored procedures have advantages in distributed databases such as fixed execution plans, fewer network interactions, and finer-grained access control. This application encapsulates stored procedures as data interfaces to achieve standardized and reusable publishing of report data sources, while retaining the optimization capabilities of stored procedures on the database side.

[0052] The system supports publishing multiple data interfaces to meet the needs of different reports. This process realizes the core link in the "interface-oriented encapsulation and dynamic recommendation process for stored procedures": after connecting to the database, the system allows users to specify the stored procedure name and automatically reverse-parses the parameter definition of the stored procedure, encapsulating it into a callable interface with complete metadata description. Its technical effect lies in seamlessly upgrading the stored procedure at the database layer to a reusable data interface at the service layer, which is a core technical means to achieve efficient and stable integration with distributed databases.

[0053] In this application, the data interface specifically refers to a standardized data service unit that encapsulates stored procedures, views, or predefined SQL statements, and has clearly defined input parameters, output fields, and data types. Once published, this interface is decoupled from the underlying database logic. Upper-layer reporting applications only need to be concerned with the interface name and parameter format, without needing to be aware of the physical implementation details of OceanBase such as partitioning strategies and replica distribution, thereby achieving a separation of concerns between report development and database operation and maintenance.

[0054] Step 120: Receive the natural language indicator description input by the user, and map the natural language indicator description into call parameters for a specific data interface using natural language processing technology.

[0055] This step enables intelligent configuration of metrics. Its purpose is to automatically translate the business intent of users (especially business personnel) into computer-executable data query commands, thereby lowering the technical barrier and achieving rapid response. This application provides "zero-code" publishing and management functionality for mobile reports, enabling managers to view and track performance reports anytime, anywhere, improving development efficiency and management decision-making speed.

[0056] In this application, zero-code configuration refers to the process by which end users complete the entire process of data source binding, indicator filtering, chart type definition, layout design, and format adjustment for reports through visual interactive methods such as drag-and-drop, point-and-click, natural language input, and table style template selection, without writing any front-end code (HTML / CSS / JavaScript) or back-end query statements (SQL). The configuration process is previewed in real time, and the configuration results take effect immediately, enabling business personnel without programming skills to independently design and publish professional-grade reports.

[0057] This process implements a closed-loop mapping and correction process from natural language to query parameters. After the user inputs a natural language description, the system performs a search and matching within a pre-built mapping model. If successful, the conversion is automatic; if unsuccessful, interactive guidance is triggered to learn new relationships. Its technical effectiveness lies in achieving intelligent input of requirements and adaptive learning capabilities, making it one of the core engines for generating "zero-code" reports. This "closed-loop mapping method from natural language to stored procedure call parameters" establishes an intelligent bridge between business language and database execution logic. Its innovation lies in solving the problem of precise binding between semantic understanding and database objects (especially stored procedure parameters).

[0058] Step 130: Generate a report based on the call parameters, and perform predictive dataset caching and updates based on the user's historical access patterns and source data change information of the target database.

[0059] This step involves report generation and caching in this application. Its purpose is to automatically generate aesthetically pleasing and consistent front-end reports, and to ensure fast report access and real-time data transmission through intelligent caching technology. This step integrates intelligent report generation and optimization with report management and caching functions.

[0060] Regarding caching and updating, this step specifically includes: monitoring users' historical access patterns to reports and changes to source data in the target database; predicting target datasets that will be accessed in the future and whose data has changed based on the historical access patterns and the change information; and proactively triggering data extraction and cache update operations for the target datasets during periods of low system load. This achieves a closed-loop process for intelligent update and maintenance of dataset caching based on two-factor triggering. Through caching technology, the system improves the frequency of front-end report access and achieves efficient dataset caching functionality.

[0061] In this application, the dataset caching refers to pre-loading the result set returned by the report query into the application server's memory, SSD cache, or distributed caching middleware (such as Redis) in key-value pair or columnar storage format. Unlike traditional database query caching, the dataset caching in this application is a report presentation layer-oriented cache—the cache stores a snapshot of the processed report data that can be directly rendered. When a user opens a report, the system directly reads the data from the cache, without repeatedly calling the OceanBase stored procedure, thereby reducing the report opening time from seconds to milliseconds.

[0062] By intelligently analyzing historical data and user access patterns in the database, the system can predict and cache report datasets that users may need. This feature significantly reduces the waiting time for users to open reports, ensuring that reports are presented in a very short time. The system intelligently updates the report dataset cache based on data changes in the database and user behavior. This application aims to protect the technical implementation of its intelligent dataset caching and update mechanism, including the design of the caching strategy, data change monitoring technology, and user behavior analysis algorithms, to ensure the real-time performance and accuracy of report data.

[0063] In an optional embodiment, the method further includes providing a cache monitoring dashboard to display one or more key indicators among cache hit rate, report opening efficiency, number of visitors, and data freshness in real time. The system provides a cache monitoring dashboard that displays key indicators such as report opening efficiency, number of visitors, and cache hit count in real time.

[0064] The cache monitoring dashboard is a graphical real-time monitoring interface for developers and operations personnel. Through data collection and log aggregation technologies, the dashboard presents the following key metrics in real time: ① Cache hit rate: the percentage of requests directly responding from the cache; ② Report opening time (P95 / P99): reflecting the true distribution of user experience; ③ Cache data freshness: the time difference between the cached data and the last synchronization with the OceanBase source data; ④ Cache space utilization: the ratio of the current cache capacity to a preset threshold. The dashboard supports anomaly alerts and drill-down analysis, helping operations teams quickly locate performance issues such as "cache breakdown" and "cache avalanche." This feature helps developers promptly identify and fix problematic reports, improving development and operations efficiency. This transforms performance optimization from a "black box" to a "white box," representing a creative solution to the pain points of enterprise-level, high-concurrency reporting system operations.

[0065] In summary, the overall concept of this technical solution is to significantly improve the efficiency, flexibility, and user experience of mobile report development by deeply integrating large-scale databases (such as OceanBase) and innovatively designing a "zero-code" front-end development platform and dataset caching and update mechanism. This solution not only solves the problems existing in traditional mobile report development methods but also provides enterprises with more intelligent and convenient report publishing and management services, especially excelling when handling distributed databases such as OceanBase.

[0066] It should be noted that this application does not solely rely on OceanBase, but is applicable to the general technical scenario of distributed / large-scale databases that support stored procedures. The innovation of the stored procedure interface lies in shifting the data access object of reporting tools from "data tables" to "stored procedures"; the innovation of NLP-to-parameter mapping lies in establishing a bridge between business language and stored procedure parameters; and the innovation of predictive caching lies in combining both user behavior and data change factors. It should be clarified that these functions are not specifically targeted at OceanBase; this application can be applied to large-scale databases or target databases, but OceanBase is emphasized as a preferred embodiment.

[0067] like Figure 7 As shown, the report generated in this embodiment supports a pure table style display. The table header supports custom field names, alignment, column width, and data format (such as decimal places for numbers, percentage format, currency symbols, etc.). The table body supports advanced functions such as conditional formatting coloring, row / column freezing, and cross-row merging to meet the needs of refined data viewing.

[0068] Example 2: Specific Method Examples Including Index Recommendation Models

[0069] In this embodiment, step 110 (data interface publishing step) of embodiment one is further refined, and a smart indicator recommendation function is added.

[0070] Figure 2 The document demonstrates a specific implementation flow for step 110 (data interface publishing step), which includes two main sub-steps 111-112.

[0071] Step 111: Receive database connection information input by the user, and establish a connection with the target database based on the connection information.

[0072] In step 111, a data source is established. The user selects a database (such as OceanBase, or other databases such as Gaussian are also supported) as the data source and enters the database connection string, including the database address, port number, username, password, and other necessary information to establish a connection with the database.

[0073] Step 112: Obtain the stored procedure name specified by the user, parse the parameter definition of the stored procedure, and publish the stored procedure as a callable data interface with the parameter definition.

[0074] In step 112, stored procedure registration and interface publishing are performed. The user enters the name of a stored procedure already established in the database. The system automatically retrieves the definition of the stored procedure through the database connection, including information such as input parameters, output parameters, and data types, and saves it in the system. This process completes the publishing of a data interface.

[0075] Specifically, the data interface publishing process implements the parsing and registration of stored procedures: obtaining the stored procedure name specified by the user; automatically reading the parameter definition information of the stored procedure from the target database; and generating and recording the registration information of the stored procedure in the system based on the parameter definition information. The system supports publishing multiple data interfaces to meet the needs of different reports.

[0076] After the API is released, there are many metrics, and similar metrics may have multiple options. Different scenarios may require different metrics. The system backend builds a metric priority recommendation model, which calculates the metric priority score based on the number of times the user uses the metrics and the initial weights, helping users quickly select the appropriate metrics.

[0077] In this application, the indicator priority recommendation model is a hybrid recommendation algorithm. This model abandons the traditional reporting tools' reliance on a single dimension for indicator ranking (such as alphabetical order or frequency of use only), and innovatively integrates the domain knowledge of business experts (with preset weights) with users' real behavioral feedback (usage frequency) in a weighted manner. The priority score output by the model maintains the strategic importance of core business indicators while dynamically responding to changes in user habits, ensuring that frequently used indicators receive higher rankings in the recommendation list and shortening user search time.

[0078] Specifically, the indicator-priority recommendation model is as follows:

[0079] set up: This indicates the dynamic frequency of user usage of indicator i. Let represent the preset business weight of indicator i, and k represent the adjustment coefficient of user usage frequency on the weight. Then, the priority score of indicator i is... It can be represented as: .

[0080] This model embodies the innovative feature of a "priority recommendation model oriented towards metrics." It's not simply a "popular ranking," but a hybrid intelligent recommendation system combining preset business weights and dynamic usage frequency. Intelligent processing begins at the data access layer, shortening the time users spend searching for metrics. The metric priority score consists of two parts: a base score and a dynamically weighted score. The base score is the initial weight of the metric, and the dynamically weighted score is the product of user usage frequency, adjustment coefficient, and the initial weight. The priority score is the sum of the dynamically weighted score and the base score.

[0081] Preferably, the output metrics corresponding to the data interface are ranked and recommended based on the metric priority recommendation model, which can be achieved through the following process:

[0082] When a user publishes a data interface, the system automatically parses the SELECT field list and output parameter list of the corresponding stored procedure or query statement, registers each field / parameter as an independent output metric, and assigns a unique metric ID and initial preset weight to each metric. and initial number of uses The system periodically (e.g., daily at midnight) or in real-time (when a user accesses the system) calls the above priority scoring formula to calculate the current priority score for all output metrics under the current data interface. .

[0083] After a user creates a new report and selects a data interface for it, the system enters the indicator selection panel. At this point, the system extracts all output indicators associated with the current interface and selects them by... Values ​​are sorted in descending order from highest to lowest, and only the Top N are displayed by default (N is a system-configurable parameter with a default value of 10; users can expand "View More" to load the full list). The sorted list of metrics is presented in a visually hierarchical manner in the metric selection panel. For example, the "Recommended for You" area displays the top 3-5 metrics by priority score, accompanied by a popularity icon or a "High Priority" label; the "All Metrics" area displays the remaining metrics in descending order of score, and supports secondary filtering by metric name (pinyin) and data type.

[0084] Each time a user selects and confirms adding a metric to a report from the metric selection panel, the system records this action as a metric. (Usage count +1), and trigger priority score in real time or asynchronously. Incremental updates ensure that subsequent ranking results reflect the latest user group preferences.

[0085] The above mechanism sorts and recommends output metrics corresponding to data interfaces. The recommended objects are sets of metrics bound to specific data interfaces; the recommendation method is a weighted sort based on priority scores; and the recommendations are presented as a visual, hierarchical recommendation list. This mechanism significantly improves metric search efficiency and report configuration experience compared to traditional reporting tools that simply list all fields alphabetically and require users to manually search.

[0086] Example 3: Specific method implementation examples including interactive mapping learning

[0087] In this embodiment, step 120 (intelligent indicator configuration step) of Embodiment 1 is further refined, and the handling mechanism when mapping fails is specifically explained. Figure 3 This demonstrates a specific implementation process in step 120 (intelligent configuration of indicators), which maps natural language to call parameters. This process specifically includes steps 121 to 125.

[0088] Step 121: Receive natural language indicator descriptions input by the user, such as "monthly premium" or "monthly long-term insurance performance manpower".

[0089] Step 122: Match the natural language indicator description with the pre-built business term-data field mapping model.

[0090] Step 123: Determine if the match was successful.

[0091] Step 124: If the match is successful, generate the call parameters for the specific data interface based on the matching result.

[0092] Step 125: If the matching fails, an interactive guidance interface is provided to establish a new mapping relationship based on the user's selection or input, and the mapping model is updated.

[0093] In step 125, if the natural language description fails to match the pre-built model, the system provides an interactive guided interface. For example, the system can prompt the user to manually select or edit to complete the structured adjustment process. Based on the user's selection or input, the system establishes a new mapping relationship and updates the "business terminology-data field mapping model." This mechanism enables the system to have self-learning capabilities, continuously enriching its knowledge base and adapting to new business terms and indicators, reflecting the intelligent characteristics of "interactive mapping."

[0094] Regarding the report generation in step 130, Figure 4 A specific implementation process is shown, which may further include steps 131 to 133.

[0095] In step 130, for example, after a user creates a new report and selects a published interface, the system initiates a call to the backend to retrieve JSON-formatted return data from that interface. The frontend parses the JSON data structure and automatically maps it to report fields, generating a complete report containing all fields of that interface with a single click. Key names in the JSON data are automatically mapped to metric names, and key-value types are automatically matched to frontend format controls.

[0096] Step 131: In response to the user's drag-and-drop operation command on the visual configuration interface, obtain the calling parameters generated in step 120.

[0097] Step 132: The system generates the layout and style configuration of the report based on the obtained call parameters.

[0098] The system provides a drag-and-drop report configuration tool, allowing users to quickly design report structures and styles without writing code. This feature not only lowers the development threshold but also ensures a consistent and aesthetically pleasing front-end style. Users can access advanced front-end features to configure metric names, formats (numerical, percentage), colors, alignment, whether to merge, chart types, sorting, and more. Furthermore, to enhance report readability and usability, the system offers notes and comments. Users can add detailed explanations and interpretations to reports to help other users better understand the report's content and meaning.

[0099] Step 133: Render the generated configuration as the front-end display page of the report.

[0100] After successful saving, the required report will be generated. This application aims to protect the technical implementation of its "zero-code" front-end report design and configuration tool, including the user interface design, drag-and-drop logic for report elements, data binding mechanism, etc., to ensure that users can easily and quickly design performance reports.

[0101] Example 4: Application Example Based on Insurance Business

[0102] This embodiment further illustrates the implementation process and effects of this method in conjunction with a specific application scenario.

[0103] Application Scenario 1: Intelligent generation and publishing of mobile performance reports based on OceanBase.

[0104] Insurance companies need to produce numerous tracking reports based on the planning scheme at each stage to facilitate management's monitoring. These reports cover analyses of premiums, performance, teams, basic management, and customer operations.

[0105] In the traditional model, developing this reporting system is time-consuming, the front-end design is unattractive, and it's particularly slow when integrating with distributed databases like OceanBase, resulting in slow data processing and untimely responses to requests. By applying this method:

[0106] Step 110A: In step 110, the system deeply integrates with the OceanBase database, and the user establishes a data source and publishes stored procedures involving premium and manpower calculations as data interfaces.

[0107] Step 120A: In step 120, when business personnel create a new report, they input indicators such as "this month's term insurance premium" and "this month's long-term insurance performance manpower" using natural language. The system uses NLP technology to map these into the call parameters of the corresponding stored procedure interface.

[0108] Step 130A: In step 130, the system generates or configures a bar chart-style performance report (e.g., ...) with a single click through a drag-and-drop configuration interface. Figure 5 (As shown in the image), and utilizes a smart caching mechanism to ensure fast loading speed of the newsletter.

[0109] Implementation results: The report development cycle was significantly shortened, and the front-end interface was aesthetically pleasing and easy to read; the performance reports could reflect the execution effect of the planning scheme in real time, improving the decision-making efficiency of managers; the system ran stably, the data processing speed was fast, and the operation and maintenance costs of insurance companies were reduced.

[0110] Application Scenario 2: Intelligent generation and release of short-term risk benefit monitoring system based on OceanBase.

[0111] In today's complex and ever-changing market environment, cost reduction and efficiency improvement have become the key to the company's sustainable development and enhanced competitiveness. It is imperative to improve distribution efficiency and management level through digital technology.

[0112] By applying this method, a multi-dimensional, full-process benefit monitoring model can be quickly built to monitor the operation of short-term insurance business, accurately analyze cost-effectiveness, and provide scientific and accurate decision support for management.

[0113] Step 110B: In step 110, publish the stored procedure interface related to cost indicators such as short-term insurance claims, commissions, and handling fees.

[0114] Step 120B: In step 120, the analysis indicators are configured by segment, population, and insured unit through natural language.

[0115] Step 130B: In step 130, a report is generated to conduct a comprehensive analysis of the key cost indicators under the policy.

[0116] Implementation results: It provided comprehensive support for the accurate setting of front-end plans and the subsequent benefit analysis for insurance companies.

[0117] Example 5: Device Example

[0118] Figure 5 This is a structural block diagram of an embodiment of a distributed relational database visualization report intelligent generation device according to this application. The device 500 includes:

[0119] The interface publishing module 51 is used to connect to the target database, parse and register its stored procedures, and publish the stored procedures as a callable data interface with parameter definitions. This module is used to perform the functions described in step 110 of Embodiment 1 or 2.

[0120] The indicator configuration module 52 is used to receive natural language indicator descriptions input by the user, and to map the natural language indicator descriptions into call parameters for a specific data interface using natural language processing technology. This module is used to perform the functions described in step 120 of Embodiment 1 or 3.

[0121] The report and cache management module 53 is used to generate reports based on the calling parameters and to perform predictive dataset caching and updates based on the user's historical access patterns and source data change information in the target database. This module is used to perform the functions described in step 130 of Embodiment 1.

[0122] Optionally, the target database is a distributed relational database, such as OceanBase. In other embodiments of this application, the target database is not limited to OceanBase, but may also be other distributed relational databases that support stored procedures, such as TiDB, GaussDB, and CockroachDB. Those skilled in the art will understand that the technical solutions of this application are also applicable.

[0123] Example 6: System Example

[0124] Figure 6 This is a structural block diagram of an embodiment of a distributed relational database visualization report intelligent generation system according to this application. The system 600 includes:

[0125] Database server 61 is used to store business data and provide stored procedures. In a preferred embodiment of this application, database server 61 runs OceanBase distributed relational database.

[0126] Application server 62 is communicatively connected to database server 61. The device 500 as described in Embodiment 5 or Embodiment 7 is deployed on application server 62. Application server 62 is responsible for executing the core logic of the entire intelligent report generation process.

[0127] User terminal 63 is communicatively connected to application server 62 and is used to receive user commands and display generated reports and configuration interfaces. User terminal 63 can be a PC or a mobile device.

[0128] In one specific embodiment, the user terminal 63 is a mobile terminal, and the application server 62 further includes a mobile terminal publishing module. Figure 6 (Not shown in the image), used to adapt and publish the generated report to the mobile terminal. This allows the report to be easily viewed on mobile devices such as phones and tablets.

[0129] In this application, mobile deployment refers to automatically generating report versions adapted to the screen sizes and touch operation habits of mobile devices such as smartphones and tablets by using a series of technical means, including responsive layout adaptation, interactive component replacement, and network protocol optimization, based on reports designed using PC-based visual configuration tools. This process requires no reconfiguration by business personnel; the system's built-in mobile rendering engine dynamically loads adapted resources based on device type, screen resolution, and network environment, and supports mobile-native features such as offline caching, QR code sharing, and push notifications, achieving a seamless extension of reports from the "office" to the "fingertips."

[0130] In addition, this application innovatively constructs an integrated management system for the entire process of report "development-release-operation and maintenance" based on the OceanBase environment. This system can be regarded as a collection of functions of this system 600, including report management, user management, permission management, notification center and other functions. The front end supports report search and subscription, which improves the user experience.

[0131] It should be noted that the execution entities of each step in the method provided in the embodiments can be the same device, or they can be executed by different devices or system modules respectively. For example, the data interface publishing step (corresponding to step 110) can be executed by the interface publishing module deployed on the application server; the indicator intelligent configuration step (corresponding to step 120) can be executed by the indicator configuration module on the same server; and the report generation and caching step (corresponding to step 130) can be executed by the report and cache management module, which can also be deployed on a separate cache server to improve performance. As another example, in the system architecture, the database server is responsible for stored procedures and data provision, the application server carries out interface publishing, indicator configuration and report generation functions, and the user terminal is responsible for interaction and display, thereby realizing the distributed deployment and collaborative processing of functions.

[0132] like Figure 8 As shown, this application also provides a report lifecycle management interface, including:

[0133] Report Management Dashboard: Displays the name, status, creator, last access time, and access popularity of published reports in a list format, and supports batch delisting and relisting, permission copying, and metadata export;

[0134] Front-end search module: Provides a global search box, supports full-text search by report name, indicator field, and data interface name, and sorts search results by relevance and access popularity;

[0135] Subscribe to the notification center: Users can subscribe to reports they are interested in. The system monitors changes in the underlying data through the OceanBase Change Data Capture mechanism. When the source data on which the report depends is updated, it will automatically push a reminder to the subscribing users.

[0136] Example 7: Computer-readable storage media and electronic devices

[0137] This application also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the embodiments of the first aspect of this application.

[0138] This application also proposes an electronic device comprising: one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, wherein the programs, when executed by the processors, implement the method as described in any embodiment of the first aspect of this application.

[0139] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 A device for a process or multiple processes and / or a function specified in one or more blocks in a block diagram.

[0140] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0142] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0143] The above description is merely an embodiment of this application and is not intended to limit the scope 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 the claims of this application.

Claims

1. A method for intelligently generating visual reports from a distributed relational database, characterized in that, include: Connect to the target database, parse and register its stored procedures, and publish the stored procedures as a callable data interface with parameter definitions; Receive natural language indicator descriptions input by users, and map the natural language indicator descriptions into call parameters for a specific data interface using natural language processing technology; Reports are generated based on the call parameters, and predictive dataset caching and updates are performed based on the user's historical access patterns and source data change information in the target database.

2. The method according to claim 1, characterized in that, The process of parsing and registering the stored procedure specifically includes: obtaining the name of the stored procedure specified by the user; automatically reading the parameter definition information of the stored procedure from the target database; and generating and recording the registration information of the stored procedure in the system based on the parameter definition information.

3. The method according to claim 1, characterized in that, The data interface publishing step also includes: The output metrics corresponding to the data interface are sorted and recommended based on the metric priority recommendation model. The indicator priority recommendation model calculates the indicator priority score based on the number of times the indicator is used by users and the initial weight.

4. The method according to claim 3, characterized in that, The indicator priority score consists of two parts: a base score and a dynamically weighted score. The base score is the initial weight of the indicator, and the dynamically weighted score is the product of the number of times the user uses the indicator, the adjustment coefficient, and the initial weight. The indicator priority score is the sum of the dynamically weighted score and the base score.

5. The method according to claim 1, characterized in that, The step of mapping the natural language indicator description to call parameters for a specific data interface using natural language processing techniques further includes: The natural language metric descriptions are matched with a pre-built business terminology-data field mapping model; If a match is found, the calling parameters are generated based on the matching result; If a match fails, an interactive guidance interface is provided to establish a new mapping relationship based on the user's selection or input, and the mapping model is updated.

6. The method according to claim 1, characterized in that, Perform predictive dataset caching and updates, specifically including: Monitor users' historical access patterns to reports, as well as changes to source data in the target database; Based on the historical access patterns and the change information, predict the target dataset that will need to be accessed in the future and whose data has changed. During periods of low system load, the system actively triggers data extraction and cache update operations for the target dataset.

7. The method according to claim 1, characterized in that, The report generation specifically includes: In response to the user's drag-and-drop operation command on the visual configuration interface, the calling parameters are obtained; the layout and style configuration of the report are generated to form the front-end display page of the report.

8. A distributed relational database visualization report intelligent generation device, used to implement the method described in any one of claims 1 to 7, characterized in that, include: The interface publishing module is used to connect to the target database, parse and register its stored procedures, and publish the stored procedures as callable data interfaces with parameter definitions. The indicator configuration module is used to receive natural language indicator descriptions input by users and to map the natural language indicator descriptions into call parameters for specific data interfaces through natural language processing technology. The report and cache management module is used to generate reports based on the call parameters and perform predictive dataset caching and updates based on the user's historical access patterns and source data change information of the target database.

9. A distributed relational database visualization report intelligent generation system, characterized in that, include: Database servers are used to store business data and provide stored procedures; An application server, which is communicatively connected to the database server, wherein the application server is equipped with the apparatus as described in claim 8 or configured to perform the method as described in any one of claims 1 to 7; The user terminal is connected to the application server and is used to receive user instructions and display generated reports and configuration interfaces.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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