Intelligent report generation method, device and system and electronic medium

Abstract

The invention provides an intelligent report generation method, device and system and an electronic medium, a front-end and rear-end separated architecture is adopted, a front end obtains a user instruction through an AI window module and sends the user instruction to a server, the server preprocesses the instruction and analyzes the instruction by means of an AI large model, and a report is generated. And after keyword extraction and weight determination, matching with semantic description information of functions in the comprehensive function library. If matching succeeds, report business logics such as data retrieval, report generation and chart drawing are executed, and a result is returned to the front end; if matching fails, error detection and correction are carried out, and information needing to be supplemented / modified is fed back. According to the method, the defects of traditional report data processing are overcome, efficient and accurate data arrangement and report generation are achieved through the automatic process and the AI technology, and the intelligent report analysis and statistics service efficiency and quality are improved.

Description

Technical Field

[0001] The present application belongs to the technical field of data processing and report generation, and specifically relates to an intelligent report generation method, device, system and electronic medium. Background Art

[0002] In the process of digitalization and intelligentization in the industrial field, the data analysis and statistical methods used in reports and charts are important means to present the real-time production status, operation status and production data of various production scenarios (such as factory workshops, production bases, etc.) to operators and decision makers in an effective and intuitive way. Industrial production often involves multiple complex subsystems, each of which continuously generates a large amount of real-time data. These data are generally transmitted to the report component in a collection form. The report component expands the data and completes the page rendering based on the preset configuration report template. In this process, how to efficiently realize data expansion and ensure that the report can be smoothly rendered according to the configuration template has become the core point to improve operational efficiency and decision-making quality.

[0003] As the industrial sector moves towards a higher level of digitalization and automation, the demand for statistical analysis reports that can provide accurate information in real time is increasing. However, traditional reporting components face many difficulties when processing data from industrial production subsystems. Especially in the report filling stage, it is often necessary to carry out complicated manual data sorting work. The staff must not only unify the format of data in different formats and from different sources to make it fit the various formats required by the report, such as standardized list formats, diversified chart formats, and complex calculation formulas, but also perform logical verification and summary calculations on the data. This process is not only time-consuming and labor-intensive, but also extremely inefficient, and manual operations will inevitably introduce errors, resulting in poor data accuracy. More importantly, due to the large amount of data and cumbersome processing procedures, potential errors are difficult to be quickly detected and corrected in a short period of time. In industrial production, erroneous data is very likely to lead to wrong decisions, which in turn brings potential production risks.

[0004] In response to the above problems, the present invention proposes an intelligent report generation system and method. The system uses advanced artificial intelligence technology to automatically complete the data collection, organization and expansion process, greatly speed up data processing, improve the accuracy of reports, and can detect and correct potential errors in data in real time. Summary of the invention

[0005] In view of the above shortcomings and deficiencies of the prior art, this application aims to overcome the drawbacks of low efficiency, insufficient accuracy, high operational complexity, lack of flexibility and adaptability of traditional report data processing, and provide an intelligent report generation method, device, system and electronic medium, which uses artificial intelligence algorithms to automatically detect and correct errors in data, reduce manual intervention, and thus achieve efficient and accurate data collation, and automatically match and execute the most suitable function. At the same time, through interactive operations, users can more intuitively adjust data and generate reports, improve work efficiency, and enhance user experience.

[0006] To achieve the above objectives, this application adopts the following technical solution, a method for generating intelligent reports, which adopts a front-end and back-end separation architecture, and the specific method includes: Obtain user instructions through the front end, and send the user instructions to the server through the AI ​​window module; The server receives user instructions and performs preprocessing; The server calls the AI ​​big model to parse the pre-processed user instructions and matches the parsed results with the description information pre-stored in the server. If the match is successful, the report business logic is executed; If the match fails, provide feedback on the information that needs to be supplemented / modified.

[0007] Furthermore, after the AI ​​window module is started, a communication connection is established with the server.

[0008] Furthermore, the server performs preprocessing on the user instructions including data cleaning, data format unification and data encoding conversion.

[0009] Furthermore, a comprehensive function library is provided in the server, which covers various functions required for creating reports and is equipped with semantic description information for each function.

[0010] Furthermore, the method in which the server calls the AI ​​big model to parse and match the user instructions includes: Call the AI ​​big model to parse the natural language keywords in the user's instructions; Determine the weight of keywords based on their position and part of speech in natural language; The parsed keywords and weights are matched with the semantic description information of each function in the comprehensive function library to obtain the function function that best meets the user's instructions.

[0011] Furthermore, after the instruction and the description information are successfully matched, the report business logic executed includes data retrieval, report generation, chart drawing, and the result is sent to the front end.

[0012] Furthermore, when the instruction fails to match the description information, the AI ​​big model is used to perform error detection on the user instruction, correct the semantic description information of the internal function of the comprehensive function library, and provide feedback on the information that needs to be supplemented / modified to the front end.

[0013] The present invention also provides another embodiment, an intelligent report generation device, which adopts a front-end and back-end separation architecture, including: Front-end acquisition module: used to obtain user instructions, receive and display the report business logic execution results or information feedback that needs to be supplemented / modified; AI window module: used for interactive dialogue and sending user instructions to the server module; Server module: used to call the AI ​​big model, parse user instructions to identify user needs, and match the function functions of the built-in comprehensive function library to configure the report configuration; Communication module: used to establish a communication connection between the AI ​​window module and the server module at startup, monitor user instructions and report business logic execution results or information feedback in real time.

[0014] The present application also discloses an intelligent report generation system, comprising: one or more processors; A computer readable storage medium for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the method described in any one of claims 1 to 7.

[0015] The present application also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

[0016] The beneficial effects of this application are: 1. Efficient data processing: Through automated data processing and AI technology, manual operations can be greatly reduced, report generation time can be greatly shortened, and business needs can be responded to quickly.

[0017] 2. Accurate data transmission: Use AI technology to reduce human errors, ensure the accuracy of data processing and report generation, and use AI technology to perform real-time error detection, identify potential data anomalies or operational errors, and provide a solid and reliable data foundation.

[0018] 3. Flexible and simple interactive operation: It has the characteristics of visual interface and interactive operation, allowing users to flexibly adjust data and generate reports through natural language. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present application is described with the aid of the following drawings: Figure 1 The flowchart of the intelligent report generation method of the present application is shown; Figure 2 The communication flow chart of the intelligent report generation method of the present application is shown; Figure 3 A flow chart of the user instruction parsing and matching method of the present application is shown. DETAILED DESCRIPTION

[0020] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below in conjunction with the accompanying drawings through specific implementation methods. It is understood that the specific embodiments described below are only used to explain the relevant inventions, rather than to limit the invention. It should also be noted that the embodiments and features in the embodiments of this application can be combined with each other in the absence of conflict; for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0021] The present invention aims to overcome the drawbacks of traditional report data processing in multiple industries, such as low efficiency, insufficient accuracy, high operational complexity, lack of flexibility and adaptability, and provide a universal, automated data processing and interactive process to reduce the time and labor intensity required for manual data processing, and use artificial intelligence algorithms to automatically detect and correct errors in data, thereby achieving efficient and accurate data sorting. It can automatically identify and apply appropriate formats, arrangements and functions to meet the data statistics and expansion methods required by different reports, and has the characteristics of a visual interface and interactive operation, so that users can more intuitively adjust data and generate reports, improve the work efficiency of various industries, enhance user experience, and make the report analysis and statistical business in various industry applications more efficient, accurate and convenient.

[0022] like Figure 1 As shown, the present invention provides an intelligent report generation method. The intelligent report generation component of the present invention adopts a front-end and back-end separation architecture to provide dynamic and flexible functions. It supports access to multiple data sources, including but not limited to relational databases, report component standard data interfaces, third-party HTTP interfaces, and data format interfaces for specific industries, providing a wide range of ways to obtain data for different industries. The specific method includes: S1: Get user instructions through the front end, and send the user instructions to the server through the AI ​​window module. Through the separation of the front and back ends, a simple and intuitive interactive interface is generated on the user front end through the AI ​​window module. The user inputs the requirements for generating reports in the form of natural language through the front end, and the AI ​​window module receives the user's requirements and forwards them to the server, which analyzes the user's actual needs.

[0023] When the AI ​​window module is started, it will establish a communication connection with the server, such as Figure 2 As shown, it is a communication flow chart of the intelligent report generation method of the present invention, in which MQTT is used as an example for the communication connection. Other communication connections, such as Websocket, rabbitMQ, Eclipse Mosquitto, etc., can also achieve the same effect. When the AI ​​assistant window is started, it will automatically establish an MQTT connection with the report server and subscribe to the command-response topic to realize real-time data transmission and communication functions. The user enters the operation or requirement he wants to perform in the AI ​​assistant window. After the front-end system captures these instructions, it sends them to the user-input topic through the MQTT protocol. This topic is the main entry point for user instructions to be delivered to the server.

[0024] S2: The server receives the user's instruction and performs preprocessing. After receiving the user-input topic message, the server performs preprocessing on the user's instruction, including data cleaning, data format unification, and data encoding conversion, to ensure the accuracy, consistency, and system compatibility of the input message.

[0025] S3: The server calls the AI ​​big model to parse the pre-processed user instructions and matches the parsing results with the pre-stored description information in the server. If the match is successful, the report business logic is executed; if the match fails, information feedback that needs to be supplemented / modified is provided.

[0026] Furthermore, a comprehensive function library is provided in the server, which covers various functions required for creating reports, including creating reports, inserting charts, configuring data sources, generating data sets, binding cells, generating formulas, and finally generating reports, and equips each function with semantic description information. The semantic description information of each function includes several keywords of the function, and different weights are configured according to different keywords, so that when users use natural language to generate user instructions in an interactive form, they can match the most appropriate report function through the semantic description information, thereby forming the most appropriate function function and generating the corresponding report.

[0027] like Figure 3 As shown, after receiving the user-input topic message, the server uses the built-in AI big model interface to call the AI ​​big model to parse and match the user instructions. The method includes: S301: Call the AI ​​big model to parse the keywords of the natural language in the user's instructions. Among them, the AI ​​big model can use large language models such as ChatGPT or BERT to realize deep analysis and processing of natural language. The server calls the AI ​​big model through the built-in big model interface to deeply analyze the user's instructions and extract the keyword information. For example, if the user enters "analyze the number of equipment failures on offshore drilling platforms last month, and generate a line chart to show the trend", the AI ​​big model will identify keywords such as "last month", "offshore drilling platform", "number of equipment failures", "line chart", and "show trend". These keywords are the basis for understanding user needs. They represent the time range, data subject, focus indicator, chart type, and the expected form of information presentation.

[0028] S302: Determine the weight of the keyword according to the position and part of speech of the keyword in the natural language. Based on the AI ​​big model, the position and part of speech of each keyword in the natural language in the user's instructions are identified, and the weight of each keyword is identified. The actual needs of the user are accurately identified by combining keywords and weights. For example, in the above instructions, keywords such as "number of equipment failures" and "display trends" that are directly related to the core content and purpose of the report will be given a higher weight; while "last month" as a time qualifier, although important, has a relatively lower weight. In terms of position, if the keyword appears at the beginning or end of the sentence, it often plays a more important role in expressing the core intent, and the weight will increase accordingly. In terms of part of speech, nouns and verbs usually reflect key information better than adjectives and adverbs, and the weight will also be tilted. In this way, the AI ​​big model can more accurately grasp the focus and key needs of user instructions.

[0029] S303: Match the parsed keywords and weights with the semantic description information of each function in the comprehensive function library to obtain the function function that best meets the user's instructions. In the matching process, the AI ​​big model is not just a simple vocabulary comparison, but is based on a deep understanding of keywords and semantic descriptions. It will consider the logical relationship between keywords and the overall fit with the semantic description. For example, for the instruction "analyze the number of equipment failures on offshore drilling platforms last month and generate a line chart to show the trend", the AI ​​big model will match the semantic descriptions of "last month" and "filter data by time range" to determine the specific function for time filtering; combine the semantic descriptions of "number of equipment failures", "generate statistical reports" and "draw charts" to find the corresponding functions for data statistics and chart drawing. By comprehensively considering keyword weights, semantic understanding and logical relationships, the function function combination that best meets user instructions is obtained.

[0030] After parsing the user instructions, the server will match the parsed user instructions with the semantic description information of various functions pre-stored in the comprehensive function library in the server, and match the corresponding function functions according to the parsed user instruction keywords and keyword weights. If the match is successful, the server will execute the report business logic, which includes: data retrieval, accurately extracting data that meets the report requirements from various data sources; report generation, integrating data according to user requirements and preset templates and building a complete report structure; chart drawing, using a customizable chart template library, users can choose chart templates of different types and styles according to their needs, and can personalize the color, label, coordinate axis and other elements of the chart to generate intuitive and vivid charts to assist in data display. And send it to the front end through the command-response theme for users to view. When the user needs to adjust the generated report again, he can enter the command again through the front end. The AI ​​window module monitors the changes in user commands in real time through the command-response topic and feeds back the results to the server. The server calls the AI ​​big model again to parse the changes in user commands, adjust the generated report, and continuously push the generated report to the front end until a report that meets user needs is generated, allowing users to adjust data and generate reports more intuitively, thereby improving work efficiency.

[0031] If the match fails, and the user input cannot be successfully matched with the description information in the comprehensive function library, for example, the user input command is vague, the AI ​​model will detect errors in the user command, and check whether the semantic description information of the function in the comprehensive function library is accurate. If deviations are found in the semantic description information, corrections will be made. According to the various report functions ultimately required in the user command, as well as the keywords and weights in the user command, the semantic descriptions of the various report functions in the comprehensive function library will be corrected. The accuracy of the semantic description information corresponding to each function in the comprehensive function library will be continuously adjusted and optimized, so that the intelligent report component can more accurately identify the user's actual needs and continuously optimize the user experience. At the same time, the server sends a prompt message through the command-response topic, provides the front end with information feedback that needs to be supplemented / modified, guides the user to further clarify or correct the command content, and ensures the smooth progress of the report generation process.

[0032] At this point, the intelligent report generation method provided by the present invention can realize interactive operations, so that users can adjust data and generate reports more intuitively, thereby improving work efficiency. In addition, during the entire report generation process, the AI ​​big model is used to perform real-time error detection on the report data. When data anomalies or operation errors are detected, the system automatically starts the correction mechanism. For problems caused by missing or errors in the comprehensive function library, corrections are automatically performed, including automatic updating of the comprehensive function library code, repairing function logic errors, and supplementing missing function modules to ensure the integrity and correctness of the comprehensive function library; for other types of errors, such as data semantic errors, data return anomalies, or deviations, timely reminders are given to users to inform them whether the semantic description is correct, whether the returned data is normal, and what deviations exist, so that users can discover and handle problems in a timely manner to ensure the accuracy and reliability of report data.

[0033] 1. Efficient data processing: Through automated data processing and AI technology, manual operations can be greatly reduced, report generation time can be greatly shortened, and business needs can be responded to quickly.

[0034] 2. Accurate data transmission: Use AI technology to reduce human errors, ensure the accuracy of data processing and report generation, and use AI technology to perform real-time error detection, identify potential data anomalies or operational errors, and provide a solid and reliable data foundation.

[0035] 3. Flexible and simple interactive operation: It has the characteristics of visual interface and interactive operation, allowing users to flexibly adjust data and generate reports through natural language.

[0036] Another embodiment of the present invention is an intelligent report generation device, which adopts a front-end and back-end separation architecture, including: Front-end acquisition module: used to obtain user instructions, receive and display the report business logic execution results or information feedback that needs to be supplemented / modified; AI Window Module: used for interactive dialogue and will; Server module: used to call the AI ​​big model, parse user instructions to identify user needs, and match the function functions of the built-in comprehensive function library to configure the report configuration; Communication module: used to establish a communication connection between the AI ​​window module and the server module at startup, monitor user instructions and report business logic execution results or information feedback in real time.

[0037] Through the AI ​​window module, an interactive window platform is established with the user, and the server module is used to call the AI ​​model to parse the user's natural language, identify the specific needs of the user's instructions, match the corresponding report function, and display it to the user through the front end. When user needs change, the communication module monitors the changes in user needs in real time, and adjusts the report function in time according to the latest user needs, so that users can adjust data and generate reports more intuitively, improving work efficiency.

[0038] In addition, during the entire report generation process, AI big models are used to perform real-time error detection on report data. When data anomalies or operation errors are detected, the system automatically starts the correction mechanism and automatically corrects the comprehensive function library in a timely manner; or reminds the user to inform the user whether the semantic description is correct, whether the returned data is normal, and what kind of deviations exist, so that the user can discover and deal with problems in a timely manner to ensure the accuracy and reliability of the report data.

[0039] In summary, the present invention uses the AI ​​big model to build an interactive platform with users, and matches the semantic description information of the functions in the comprehensive function library after keyword extraction and weight determination. If the match is successful, the report business logic such as data retrieval, report generation, and chart drawing will be executed and the results will be returned to the front end; if the match fails, error detection and correction will be performed, and the information that needs to be supplemented / modified will be fed back. The drawbacks of traditional report data processing can be eliminated through automated processes and AI technology to achieve efficient and accurate data collation and report generation, thereby improving the efficiency and quality of intelligent report analysis and statistical services.

[0040] A third embodiment of the present invention is an intelligent report generation system, comprising: one or more processors; A computer readable storage medium for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the method described in any one of claims 1 to 7.

[0041] A fourth embodiment of the present invention is a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

[0042] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several distinct components and by means of a suitably programmed computer. The use of the words first, second, third, etc., is for convenience of expression only and does not imply any order. These words may be understood as part of the component name.

[0043] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0044] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the claims should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0045] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include these modifications and variations.

Claims

1. An intelligent report generation method, characterized in that: Adopting a front-end and back-end separation architecture, the specific methods include: Obtain user instructions through the front end, and send the user instructions to the server through the AI ​​window module; The server receives user instructions and performs preprocessing; The server calls the AI ​​big model to parse the pre-processed user instructions and matches the parsed results with the description information pre-stored in the server. If the match is successful, the report business logic is executed; If the match fails, provide feedback on the information that needs to be supplemented / modified.

2. The intelligent report generation method according to claim 1, characterized in that: After the AI ​​window module is started, a communication connection is established with the server.

3. The intelligent report generation method according to claim 1, characterized in that: The server performs preprocessing on user instructions including data cleaning, data format unification and data encoding conversion.

4. The intelligent report generation method according to claim 1, characterized in that: The server is provided with a comprehensive function library, which covers various functions required for creating reports and is equipped with semantic description information for each function.

5. The intelligent report generation method according to claim 4, characterized in that: The method for the server to call the AI ​​big model to parse and match the user instruction includes: Call the AI ​​big model to parse the natural language keywords in the user's instructions; Determine the weight of keywords based on their position and part of speech in natural language; The parsed keywords and weights are matched with the semantic description information of each function in the comprehensive function library to obtain the function function that best meets the user's instructions.

6. The intelligent report generation method according to claim 1, characterized in that: After the instruction is successfully matched with the description information, the report business logic executed includes data retrieval, report generation, chart drawing, and the result is sent to the front end.

7. The intelligent report generation method according to claim 1, characterized in that: When the instruction fails to match the description information, the AI ​​big model is used to perform error detection on the user instruction, correct the semantic description information of the internal function of the comprehensive function library, and provide the front end with information feedback that needs to be supplemented / modified.

8. An intelligent report generation device, characterized in that: Adopts a front-end and back-end separation architecture, including: Front-end acquisition module: used to obtain user instructions, receive and display the report business logic execution results or information feedback that needs to be supplemented / modified; AI window module: used for interactive dialogue and sending user instructions to the server module; Server module: used to call the AI ​​big model, parse user instructions to identify user needs, and match the function functions of the built-in comprehensive function library to configure the report configuration; Communication module: used to establish a communication connection between the AI ​​window module and the server module at startup, monitor user instructions and report business logic execution results or information feedback in real time.

9. An intelligent report generation system, characterized in that: include: one or more processors; A computer readable storage medium for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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