Intelligent self-defined report generation method and device based on database diagnosis result

Abstract

The invention provides an intelligent custom report generation method and device based on a database diagnosis result, and particularly relates to a technology for automatically generating a custom report by utilizing artificial intelligence and machine learning technologies. According to one embodiment, the method for generating the report based on the database diagnosis result comprises the following steps: extracting diagnosis data from a database; analyzing the diagnosis data to obtain a diagnosis result; generating a report containing the diagnosis result; and providing the generated report.

Description

Technical Field

[0001] This invention relates to a method for generating intelligent customized reports based on database diagnostic and analysis results, and particularly to a technology for automatically generating customized reports using artificial intelligence and machine learning techniques. Background Technology

[0002] Database systems play a vital role in today's information society, essential for the data storage, management, and analysis of enterprises and organizations. These databases store various types of data and provide the ability to effectively search and modify them. However, as databases increase in size and complexity, continuous monitoring and maintenance of their performance, reliability, security, and standards compliance becomes increasingly important. In particular, database performance degradation or security vulnerabilities can have a significant impact on business operations, thus requiring proactive and rapid diagnosis and response.

[0003] Existing database diagnostics and reports are primarily created manually. This typically involves database administrators (DBAs) or experts collecting and analyzing data and creating reports based on the results. This process includes various diagnostic items such as performance analysis, security checks, and evaluations of backup and recovery plans, but it largely depends on the expert's subjective judgment, which may not guarantee the consistency and quality of the reports. Furthermore, it is often difficult to reflect the specific needs or preferences of users, which may limit the usefulness of the reports.

[0004] To address these challenges of traditional reporting methods, automated report generation technologies have emerged. These technologies automate the data collection and analysis process, automatically generating reports based on the results, thus reducing report creation time and maintaining consistent quality. However, this technology also has limitations. For example, most automated systems use static and fixed report templates, making it difficult to generate customized reports. Furthermore, it may not adequately respond to various variables or anomalies that may arise during data analysis.

[0005] In recent years, advancements in artificial intelligence (AI) and machine learning (ML) have offered new approaches to addressing these issues. AI-based data analytics engines can identify patterns in large datasets and build predictive models, leading to more accurate diagnostic results. In particular, customized recommendation systems using machine learning techniques can effectively deliver personalized content or services by learning from each user's past behavioral data. These technologies can also be applied to the generation of database diagnostic reports, which can learn from users' historical report generation patterns and automatically generate reports tailored to individual user needs.

[0006] Furthermore, advancements in Natural Language Processing (NLP) technology play a crucial role in report writing. NLP is a technique for understanding and analyzing large amounts of textual data, which can be used to summarize report content, extract key keywords, and generate text. This makes it easier for users to understand the results of complex data analysis and increases access to key information. Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] According to one embodiment, the company aims to provide a method for automatically generating customized reports based on database diagnostic data, and continuously improving report quality by incorporating user feedback. Specifically, it learns from users' past report generation patterns to recommend personalized report configurations and provides various customization options to optimize report components.

[0009] means for solving problems

[0010] According to one embodiment, a method for generating a report based on database diagnostic results is to extract diagnostic data from the database; analyze the diagnostic data to obtain diagnostic results; generate a report containing the diagnostic results; and provide the generated report.

[0011] The diagnostic data is multi-dimensional, including information such as database performance, security, stability, compliance, capacity, backup, redundancy, and archiving settings. The process involves using an AI-based analytics engine to analyze the diagnostic data, calculating a score for each diagnostic item, and assigning a rating based on both individual and total scores. The report generation step involves generating a dynamic report template with detailed analysis results for each diagnostic item. This template includes improvement suggestions, relevant SQL tuning guidelines, and score and rating information. Machine learning algorithms are used to learn the user's historical report generation patterns and recommend customized report configurations based on these patterns. The user interface provides feedback on custom settings and recommended configurations for the Dynamic Report template. The user-defined settings, feedback, and detailed analysis results are automatically applied to the dynamic report template to generate a customized diagnostic report.

[0012] It may include further steps to collect user reactions and feedback on the intelligent personalized diagnostic reports, reflect them in machine learning algorithms, and use them to generate future reports.

[0013] The steps for creating a dynamic report template include: visualizing the analysis results by diagnostic item and representing them in the form of interactive charts and graphs; summarizing the analysis results using natural language processing technology and extracting key keywords using text mining technology; constructing a dynamic report template, including the aforementioned interactive charts, graphs and extracted key keywords; and providing a custom configuration interface based on speech recognition in the dynamic report template.

[0014] The user-customized report configuration recommendation steps include: collecting data from the user's historical report generation patterns and preprocessing it through data normalization and outlier removal; training a recurrent neural network (RNN) model, which includes short-term and long-term memory (LSTM) networks based on the preprocessed data; using the trained RNN model to predict the current user's preferences, but only adopting the prediction results if the prediction accuracy is above a threshold; determining the optimal combination of report components based on the predicted preferences, but using a genetic algorithm to derive the optimal solution; and recommending the determined optimal combination to the user, collecting user feedback and incorporating it into the next learning cycle; the user-defined settings include the following seven categories: report title, logo, cover design, font, color, layout, importance of each diagnostic item, emphasis on improvement suggestions, report detail, type of graphics to be included, and table of contents configuration, and may provide three or more detailed options for each of the seven items set above.

[0015] The purpose of this invention is not limited to the stated purpose, and other purposes not mentioned may be clearly understood from the following description.

[0016] Invention Effects

[0017] According to the provisional example, it can efficiently process input signals based on motor load requirements and provide the necessary power to each motor.

[0018] According to the provisional example, the reliability and safety of the system can be improved by detecting faults in the inverter module and switching to fault mode. Attached Figure Description

[0019] Figure 1 This refers to a report generation system based on database diagnostic results, according to the daily implementation plan.

[0020] Figure 2 It is a flowchart showing the report operations of a computer device according to a typical implementation.

[0021] Figure 3 It is a flowchart illustrating the report generation operations of a computer device according to a single embodiment.

[0022] Figure 4 It is a flowchart illustrating the operations of generating reports using a computer device according to an embodiment.

[0023] Figure 5 This is a schematic diagram illustrating a block configuration of a computer device according to a single embodiment. Detailed Implementation

[0024] The embodiments are described in detail below with reference to the accompanying drawings. However, various modifications can be made to the embodiments, and therefore the scope of the patent application is not limited to or restricted by these embodiments. Any changes, equivalents, or substitutions to the embodiments should be understood to be included within the scope of the claims.

[0025] The specific structural or functional descriptions of the embodiments are provided for illustrative purposes only and may be modified and implemented in various forms. Therefore, the embodiments are not limited to a particular form of disclosure, and the scope of this specification includes changes, uniformities, or substitutions incorporated into the descriptive concepts.

[0026] Terms such as "first" or "second" can be used to describe various components, but the interpretation of these terms should only be used to distinguish one component from another. For example, the first component can be named the second component, and similarly, the second component can be named the first component.

[0027] When a component is said to be "connected" to another component, it should be understood that it may be directly connected to or connected to another component, but there may be another component between them.

[0028] The terminology used in the embodiments is for illustrative purposes only and should not be construed as intended to be limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as “comprising” or “having” are intended to indicate the presence of a feature, number, step, action, component, part, or combination thereof described in the specification, rather than one or more other features or numbers, steps, actions, or components, and should be understood to not exclude the possibility of the presence or addition of parts or combinations thereof.

[0029] Unless otherwise defined, all terms used herein, including technical or scientific terms, shall have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments pertain. Terms such as those defined in common dictionaries shall be interpreted as having the meaning consistent with their meaning in the relevant descriptive context and shall not be interpreted in an idealistic or overly formal sense unless expressly defined in this application.

[0030] Furthermore, when describing the accompanying drawings, regardless of the drawing code, the same reference numerals should be assigned to the same elements, and identical repetitive descriptions should be omitted. When describing embodiments, detailed descriptions should be omitted if it is determined that a specific description of the relevant technical notifications may unnecessarily obscure the essential points of the embodiment.

[0031] Figure 1 This refers to a report generation system based on database diagnostic results, according to a daily implementation. The report generation system may include computer device 120 and database 110.

[0032] Database 110 comprises multiple tables and relational structures, designed to systematically store data. Database 110 can use indexes, keys, constraints, and other techniques to efficiently store and retrieve data.

[0033] Database 110 is an element within an organization or system that stores and manages important data. According to one embodiment of the invention, the database can be the primary object of analysis in a report generation system. Database 110 can be evaluated from multiple aspects, including performance, security, reliability, standards compliance, capacity, backup status, redundancy settings, and archiving settings. These criteria can be used to collect and analyze diagnostic data to generate reports in the report generation system. Database 110 can consist of a complex structure including various tables, indexes, views, triggers, etc. This structure enables efficient data storage and retrieval and can be designed to maintain data integrity and consistency.

[0034] The report generation system can extract data from database 110 and perform analysis. During this process, database 110 can be evaluated against various performance metrics and security settings, which can determine the current state of the system and the causes of problems. Database 110 provides various management functions for data preservation, integrity, and security. This may include data backup, recovery procedures, user access control, data encryption, etc. These functions ensure that database 110 can operate reliably.

[0035] Computer device 120 is a component of a report generation system that can extract data from database 110, analyze it to generate a report, and perform the function of providing the generated report to the user.

[0036] Computer device 120 can extract diagnostic data from database 110. You can use data access languages ​​(such as SQL queries) to retrieve the required data and prepare analysis data. Computer device 120 can analyze the extracted data to evaluate the database's performance, security, reliability, etc. Using artificial intelligence and machine learning technologies, a score can be calculated for each diagnostic item, and a total score and rating can be assigned. Computer device 120 can generate dynamic reports based on the analysis results. This report includes detailed analysis results, improvement suggestions, relevant SQL tuning guidelines, and score and rating information, and can be customized by the user.

[0037] Computer device 120 learns from the user's past report generation patterns and can recommend customized report configurations. To do this, recurrent neural networks (such as short-term and long-term memory networks) can be used to predict user preferences, and genetic algorithms can be used to determine the optimal report components. Computer device 120 can provide a user interface for user interaction. It allows users to define report templates, provide feedback, and use various input methods, including text input, clicking, dragging and dropping, and speech recognition. Computer device 120 can provide a user interface (UI) to the user terminal. This UI can be implemented in various forms, such as web applications and mobile applications, and can provide users with functions such as generating reports, changing settings, and viewing data. This allows users to control the system and examine data through computer device 120.

[0038] Computer device 120 can perform data preprocessing, such as data normalization, outlier removal, and data transformation. This preprocessing helps improve the accuracy and efficiency of machine learning algorithms.

[0039] Computer device 120 can provide generated reports in PDF, HTML, or other formats. Reports can be delivered to users via email, web portal, mobile application, etc., and various output formats are supported.

[0040] Computer device 120 can provide a variety of security mechanisms to ensure data security and integrity. These include data encryption, user authentication and access control, access control and audit log generation, and can perform management functions such as system performance monitoring, log management, and backup and recovery procedures.

[0041] Computer device 120 can act as a server, centrally undertaking data processing and management functions. As a server, computer device 120 connects to user terminals and database 110 via a network and is capable of handling data requests and responses. It can use various network protocols and security mechanisms to support stable data communication. Computer device 120 can send and receive data between user terminals (unknown) and database 110. For example, it can receive data requests from user terminals, retrieve required data from database 110, and transmit the required data to user terminals according to the requests. Conversely, data generated by user terminals can be stored in database 110 via computer device 120.

[0042] Figure 2 It is a flowchart showing the report operations of a computer device according to a typical implementation.

[0043] refer to Figure 2In step S210, the computer device can extract diagnostic data from the database. The computer device can extract multidimensional diagnostic data, including database performance, security, reliability, standards compliance, capacity, backup, redundancy, and archive configuration information. The computer device can use a combination of methods to extract diagnostic data, such as using APIs provided by the database management system, executing pre-configured SQL scripts, collecting real-time performance monitoring data, or analyzing database log files to detect anomalies.

[0044] For example, multidimensional diagnostic data extracted by computer devices encompasses various aspects of the database. This includes performance data, security information, reliability metrics, standard compliance, capacity information, backup settings, redundancy configurations, and archiving settings. Performance data may include CPU utilization, memory usage, disk I / O speed, and query execution time. Security information may include user permission settings, encryption status, and access logs. Reliability metrics include system uptime, frequency of potential failures, and recovery time. To extract this data, computer devices use a combination of methods. You can directly extract system-level information using the official API provided by the DBMS, and you can collect the database's internal state and statistics by executing predefined SQL queries. Furthermore, real-time monitoring allows you to periodically sample and collect performance metrics from the database, parse and analyze database log files, and detect error patterns, performance issues, and security threats.

[0045] According to one embodiment of the present invention, a computer device can derive a diagnostic result by analyzing diagnostic data from stage S220. The computer device can use an AI-based analytics engine to analyze the diagnostic data. The computer device calculates a score for each diagnostic subset and assigns a rating based on individual scores and a composite score. Furthermore, the computer device can train a recurrent neural network (RNN) model, which includes short-term and long-term memory (LSTM) networks, to predict the current user's preferences.

[0046] For example, computer equipment preprocesses the collected data. During this process, data normalization, outlier removal, and missing value handling can be performed. The computer equipment calculates a score for each diagnostic item. To do this, it uses predefined evaluation criteria and weights, and can calculate a separate score for each domain of the database (e.g., performance, security, and reliability). After calculating the individual scores, the computer equipment aggregates them to calculate a composite score representing the overall health of the database. The importance of each diagnostic item is considered when calculating the composite score, and its application may vary depending on the database's purpose and operating environment. The computer equipment assigns a rating to the database based on the calculated scores. The rating criteria use a predefined range, typically from A to F.

[0047] According to an embodiment of the present invention, a computer device can generate a report containing diagnostic results in step S230. The computer device can generate a dynamic report template, including detailed analysis results, improvement suggestions, relevant SQL tuning guidelines, and scores and ratings for each diagnostic item. The computer device can visualize the analysis results for each diagnostic item in interactive charts and graphs, and use natural language processing and text mining techniques to summarize the analysis results and extract key keywords. Furthermore, the computer device can provide a user-defined configuration interface based on speech recognition.

[0048] According to an embodiment of the present invention, in step S240, a computer device can provide a generated report. The computer device can recommend customized report configurations, and the user interface can be used to input user-defined settings for a dynamic report template and receive feedback on the recommended configuration. The computer device can automatically apply user-defined settings, feedback, and detailed analysis results to the dynamic report template to generate and provide intelligently customized diagnostic reports. Furthermore, the computer device can collect user reactions and feedback on the provided reports and incorporate them into a machine learning algorithm for generating future reports.

[0049] Figure 3 It is a flowchart illustrating the report generation operations of a computer device according to a single embodiment. Figure 3 The operation of the computer equipment corresponds to the operation of step S230 in section 2.

[0050] Please see Figure 3 The computer device can generate a dynamic report template in step S310. This template includes detailed analysis results, improvement suggestions, relevant SQL tuning guidelines, and score and rating information for each diagnostic item. The computer device can visualize the analysis results by diagnostic item in interactive charts and graphs. Furthermore, the computer device utilizes natural language processing technology to summarize the analysis results. Key keywords can be extracted using text mining techniques. The extracted keywords, interactive charts, and graphs can be included to construct the dynamic report template. Additionally, the computer device can provide a customizable configuration interface based on speech recognition within this template.

[0051] In one embodiment, the computer device can recommend a customized report configuration in phase S320. The computer device first collects data from the user's historical report generation patterns, then preprocesses it through data normalization and outlier elimination. Based on the preprocessed data, a recurrent neural network (RNN) model incorporating short-term and long-term memory (LSTM) networks can be trained. The computer device can use the trained RNN model to predict the current user's preferences, and only adopts the prediction if the prediction accuracy is higher than a set threshold. When determining the optimal combination of report components based on the predicted preferences, the computer device can utilize a genetic algorithm to derive the optimal solution.

[0052] In one embodiment, the computer device may receive input from stage S330 for user-defined template settings and feedback on the suggested configuration. The computer device can receive user-defined dynamic report template settings via a user interface. These settings may include the following seven categories: report title, logo, cover design, font, color, layout, importance of each diagnostic item, focus of improvement suggestions, report detail level, types of charts to include, and table of contents configuration, and may provide three or more detailed options for each item. Furthermore, the computer device may receive input from the user regarding the suggested report configuration from the previous step.

[0053] In one embodiment, the computer device can generate a customized diagnostic report in stage S340. The computer device can apply user-defined settings, feedback, and detailed analysis results to a dynamic report template to generate an intelligently customized diagnostic report. The generated report can be provided in an optimized format that reflects user preferences and requirements. Furthermore, the computer device can incorporate user feedback collected during this process into the next learning cycle, thereby continuously improving the report generation process.

[0054] Figure 4 It is a flowchart illustrating the operations of generating reports using a computer device according to an embodiment. Figure 4 The operation of computer equipment corresponds to Figure 2 The operation of step S240.

[0055] refer to Figure 4In step S410, the computer device can collect user responses and feedback. First, the computer device can automatically track and analyze user report usage patterns. The computer device may collect quantitative data, such as the total time a user spends viewing the report, the time spent reading each section, scrolling patterns, and the number of times a specific section is accessed repeatedly. Additionally, the computer device may record the number of interactions and the manner of interaction between the user and interactive elements in the report (e.g., drop-down menus, expandable sections, filter options, etc.). The computer device can collect user opinions through direct feedback mechanisms integrated into the report. For example, the computer device may provide a usefulness rating option of 1 to 5 at the end of each section, or it may display a star rating for satisfaction with the entire report. Furthermore, the computer device may collect detailed comments and improvement suggestions from users through feedback forms containing open-ended questions. The computer device can also obtain indirect feedback by monitoring subsequent user behavior after providing feedback. For example, the computer device may track whether users actually implement the suggestions presented in the report, request more information or help, or share the report with other stakeholders.

[0056] In one embodiment, the computer device can feed feedback into a machine learning algorithm in stage S420. The computer device can preprocess and analyze the user response and feedback data collected in step S410, transforming it into a form that can be fed into the machine learning algorithm. The computer device can use this data to retrain or update an existing machine learning model. For example, the computer device can adjust the weights of a recurrent neural network (RNN) model or add new feedback data to the model's training set. Furthermore, the computer device can re-evaluate the importance of the reporting component based on the feedback, which is reflected in the goodness-of-fit function of the genetic algorithm. Specifically, in the case of an RNN model, the computer device can retrain the model using new data and adjust the learning rate to control the impact of new feedback. In the case of a genetic algorithm, the computer device can modify the goodness-of-fit function based on the feedback and adjust the mutation probability to expand or narrow the exploration range of new report construction. This process allows the computer device to generate more accurate and user-friendly reports over time. Additionally, the computer device can analyze the feedback collected in this stage and use it to improve the database diagnostic process itself.

[0057] Figure 5 This is a schematic diagram illustrating a block configuration of a computer device according to a single embodiment.

[0058] The display computer device includes, but is not limited to, a communication unit 510, a memory 520, and a processor 530. The communication unit 510, the memory unit 520, and the processor 530 can each exist as physically independent component units.

[0059] Communication unit 510 performs the function of sending and receiving signals over a network. All or part of communication unit 420 may be referred to as a transmitting unit, a receiving unit, or a transmitting and receiving unit. Communication unit 510 can direct hardware and software components to enable computer devices to communicate with external networks. Communication unit 510 can support various communication protocols, such as Ethernet, Wi-Fi, and Bluetooth, or provide a physical connection via external connectors such as an RJ45 jack or antenna. The adapter internally contains a unique identifier called a MAC address, which uniquely identifies the computer device on the network.

[0060] The memory 520 can store various data for the overall operation of the computer device, such as programs for processing or controlling the processor 530 in the computer device.

[0061] Memory 520 can store various running applications, data, and instructions for operating the computer device. Memory 520 can be implemented as internal memory, such as ROM and RAM included in processor 530, or it can be implemented as a different type of memory than processor 530. According to one embodiment, memory 520 can store neural networks and training data.

[0062] The processor 530 can be configured to perform general control of the computer device according to the present invention.

[0063] Processor 530 is a component responsible for the calculation and control of computer equipment. Processor 530 is capable of performing database diagnostics and report generation.

[0064] Processor 530 can process database diagnostic data received via communication unit 510. Processor 530 can analyze this data to extract information about performance, security, reliability, standards compliance, capacity, backup, redundancy, and archiving settings.

[0065] The processor 530 can run an AI-based analytics engine by executing program instructions stored in memory 520. This engine allows the processor 530 to calculate a score for each diagnostic item and assign a rating based on the individual scores and the total score.

[0066] Processor 530 is capable of executing machine learning algorithms. These algorithms learn from past report generation patterns of users and can recommend customized report configurations based on this. Processor 530 can train and run recurrent neural network (RNN) models that include short-term and long-term memory (LSTM) networks. This model allows processor 530 to predict the current user's preferences.

[0067] Processor 530 can generate and manage dynamic report templates. During this process, processor 530 can convert the analysis results into interactive charts and graphs using diagnostic items. Processor 530 can summarize the analysis results using natural language processing techniques. Furthermore, text mining techniques can be applied to extract key keywords.

[0068] The processor 530 can process user-defined settings input through a user interface. These settings may include report title, logo, cover design, font, color, layout, the importance of each diagnostic item, whether to highlight improvement suggestions, report detail level, types of charts to include, and table of contents. The processor 530 can apply these settings to dynamic report templates to generate intelligent, customized diagnostic reports.

[0069] Processor 530 can run a genetic algorithm to determine the optimal combination of reporter components. In this process, processor 530 is able to simulate several generations of evolution to derive the optimal solution.

[0070] Processor 530 can collect and analyze user reactions and feedback on generated reports. This data may include the time spent reading the report, the time spent in each section, and the elements clicked. Processor 530 can feed this information into machine learning algorithms to retrain or update the model. Through this process, processor 530 is able to continuously improve its report generation capabilities.

[0071] Specifically, processor 530 can control the operation of the computer device through various programs stored in computer device memory 520. Processor 530 may include a CPU, RAM, ROM, system bus, etc. Processor 530 can be implemented as a single CPU or multiple CPUs (or DSP, SoC). As an example, processor 530 is a digital signal processor (DSP) that processes digital signals, and it can be implemented as a microprocessor or time controller (TCON). However, it may include, but is not limited to, one or more central processing units (CPUs), microcontroller units (MCUs), microprocessors (MPUs), controllers, application processors (APs), communication processors (CPs), or ARM processors. Furthermore, processor 530 can be implemented as a system-on-a-chip (SoC) with built-in processing algorithms, a large-scale integrated circuit (LSI), or in the form of a field-programmable gate array (FPGA). Further, processor 530 may include a neural processing unit (NPU), a graphics processing unit (GPU), and a tensor processing unit (TPU).

[0072] Although the above embodiments have been described with limited drawings, those skilled in the art can make various technical modifications and alterations based on the above description. For example, appropriate results may be obtained if the described techniques are performed in a different order than the described methods, and / or if the components of the described systems, structures, devices, circuits, etc., are combined or combined in a different manner than the described methods, or are replaced or substituted by other components or equivalents.

[0073] Therefore, other embodiments, other embodiments, and those equivalent to the patent claims also fall within the scope of the claims described below.

Claims

1. A method for generating intelligent custom reports based on database diagnostic results, wherein, The intelligent custom report generation method based on database diagnostic results includes the following steps: extracting diagnostic data from the database; Analyze the diagnostic data to obtain a diagnostic result; Generate a report containing the diagnostic results; and Provide the generated report.

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