Honor certificate generation method and device based on multi-person interaction

Abstract

The invention relates to an honor certificate generation method and device based on multi-user interaction. The method comprises the following steps: creating a basic template; the method comprises the following steps: receiving input certificate content, learning the certificate content through a deep learning model, and generating associated content related to the certificate content; fusing the associated content with the basic template to obtain a certificate template; and sharing the certificate template to collect personal information, and generating an honor certificate in combination with the certificate template and the personal information. The honor certificate generation automation, personalization and efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method and device for generating an honor certificate based on multi-person interaction. Background Art

[0002] Regarding the process of generating certificates of honor, with the rapid development of information technology, people have begun to explore ways to automatically generate certificates of honor using computer and Internet technology.

[0003] Existing methods require manual information collection, which is not only time-consuming and labor-intensive, but also prone to errors. Existing automatic generation methods mostly have problems such as insufficient flexibility, low degree of personalization, and inability to adapt well to multi-person interaction scenarios. Summary of the invention

[0004] In view of the above deficiencies in the prior art, the purpose of the invention is to provide a method and device for generating an honor certificate based on multi-person interaction.

[0005] A first aspect of the present invention provides a method for generating an honor certificate based on multi-person interaction, comprising:

[0006] S1: Create a basic template;

[0007] S2: receiving input certificate content, learning the certificate content through a deep learning model, and generating associated content related to the certificate content;

[0008] S3: Merging the associated content with the basic template to obtain a certificate template;

[0009] S4: Sharing the certificate template to collect personal information, and generating an honorary certificate by combining the certificate template and the personal information.

[0010] According to a method for generating an honor certificate based on multi-person interaction provided by the present invention, step S1 further comprises:

[0011] S11: Using HTML5 technology, the client creates a first template that supports dynamic adjustment;

[0012] S12: introducing a responsive design into the first template to obtain a second template;

[0013] S13: Embed the CSS style into the second template to obtain a third template, and output the third template as a basic template.

[0014] According to a method for generating an honorary certificate based on multi-person interaction provided by the present invention, the basic template in step S13 includes a certificate title position, a certificate comment position, a personnel photo position, and a personnel name position.

[0015] According to a method for generating an honor certificate based on multi-person interaction provided by the present invention, step S2 further comprises:

[0016] S21: Process the certificate content by using an NLP tool to generate a speech content;

[0017] S22: obtaining background content based on the speech content;

[0018] S23: combining the speech content and the background content to obtain related content.

[0019] According to a method for generating an honor certificate based on multi-person interaction provided by the present invention, step S21 further comprises:

[0020] S211: Preprocess the certificate content to obtain preprocessed certificate content;

[0021] S212: Perform word segmentation analysis and word tagging on the pre-processed certificate content through the NLP tool to obtain certificate keywords;

[0022] S213: Generate speech content based on the certificate keywords.

[0023] According to a method for generating an honor certificate based on multi-person interaction provided by the present invention, step S22 further comprises:

[0024] S221: pre-collecting background images, and performing image feature extraction on the background images to obtain image features;

[0025] S222: extracting text features from the content to obtain text features;

[0026] S223: Match the image features and the text features through TBIR technology to obtain background content.

[0027] According to a method for generating an honorary certificate based on multi-person interaction provided by the present invention, in step S4, the certificate template is shared through QR code technology.

[0028] A second aspect of the present invention provides a device for generating an honor certificate based on multi-person interaction, comprising:

[0029] Create module: used to create basic templates;

[0030] Association module: used for receiving input certificate content, learning the certificate content through a deep learning model, and generating associated content related to the certificate content;

[0031] Fusion module: used to fuse the associated content with the basic template to obtain a certificate template;

[0032] Generation module: used to share the certificate template to collect personal information, and generate an honorary certificate by combining the certificate template and the personal information.

[0033] A third aspect of the present invention provides a device for generating an honor certificate based on multi-person interaction, comprising:

[0034] A memory and at least one processor, wherein instructions are stored in the memory;

[0035] At least one of the processors calls the instructions in the memory to enable a device for generating an honor certificate based on multi-person interaction to execute a method for generating an honor certificate based on multi-person interaction as described in any one of the above items.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, and when the instructions are executed by a processor, a method for generating an honorary certificate based on multi-person interaction as described in any one of the above items is implemented.

[0037] The present invention provides a method, device, equipment and storage medium for generating an honorary certificate based on multi-person interaction. The method analyzes the input certificate content through a deep learning model, and automatically generates related content (such as a speech, background, etc.) closely related to the certificate content, which not only significantly improves the automation level of honorary certificate generation, but also makes each honorary certificate unique, thereby enhancing the personalized expression of the certificate. The basic template of the present invention is made using HTML5 technology, supports dynamic adjustment, and can meet the changing needs in different scenarios. The responsive design enables the certificate template to maintain a good display effect on different devices, thereby enhancing the user experience. The certificate template is also shared through QR code technology, which facilitates users to quickly obtain and fill in personal information, simplifies the certificate generation process, and enhances interaction. At the same time, the basic template with CSS style is embedded, so that the certificate template has a unified appearance and style, which improves the aesthetics of the certificate; the text and image are matched through NLP tools and TBIR technology, so that the background picture that best matches the certificate content can be found, which enhances the professionalism and attractiveness of the certificate; in addition, the automated generation process of the present invention reduces manual intervention, shortens the certificate generation time, and improves work efficiency. The digital generation method reduces the dependence on traditional paper materials and saves resource costs; the sharing function of the certificate template of the present invention allows multiple people to participate in the customization and generation process of the certificate, promotes team collaboration, and shares certificates through digital means such as QR codes, which is convenient for display and dissemination on platforms such as social media, and expands the audience range of the certificate. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0039] Figure 1 A schematic diagram of a flow chart of a method for generating an honor certificate based on multi-person interaction provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a device for generating an honor certificate based on multi-person interaction provided in an embodiment of the present invention.

[0041] Reference numerals:

[0042] 100, creation module; 200, association module; 300, fusion module; 400, generation module. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0044] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0045] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0047] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0048] like Figure 1 As shown, the first aspect of the present invention provides a method for generating an honor certificate based on multi-person interaction, comprising:

[0049] S1: Create a basic template.

[0050] Wherein, step S1 further comprises:

[0051] S11: The first template supporting dynamic adjustment is created by the client through HTML5 technology.

[0052] Furthermore, the above-mentioned HTML5 technology is the latest version of Hypertext Markup Language (HTML), which provides more elements and attributes, so in step S11, a template is created in the web page editor through HTML5 to support subsequent multi-element insertion.

[0053] S12: Introduce responsive design into the first template to obtain a second template.

[0054] Responsive design is introduced in step S12, so that the web page can provide the best user experience on different devices and screen sizes. When it is used to support multi-person collaboration in the future, that is, when collecting the personal information of each colleague, it can ensure that the web page can be well displayed on mobile phones, tablets, desktop computers and other devices, thereby obtaining a more flexible and adaptable second template.

[0055] S13: Embed the CSS style into the second template to obtain a third template, and output the third template as a basic template.

[0056] Furthermore, in step S13, CSS styles are embedded in the template to control the appearance and format of the web page, that is, specific style rules are applied to the second template to define visual elements such as fonts, colors, and layouts. By embedding CSS styles, we obtain a third template with the final appearance and functionality.

[0057] The basic template in step S13 includes a certificate title location, a certificate comment location, a personnel photo location, and a personnel name location.

[0058] S2: receiving input certificate content, learning the certificate content through a deep learning model, and generating associated content related to the certificate content.

[0059] Wherein, step S2 further comprises:

[0060] S21: Process the certificate content through an NLP tool to generate a speech content.

[0061] Wherein, step S21 further includes:

[0062] S211: Preprocess the certificate content to obtain preprocessed certificate content.

[0063] S212: Perform word segmentation analysis and word tagging on the pre-processed certificate content through the NLP tool to obtain certificate keywords.

[0064] S213: Generate speech content based on the certificate keywords.

[0065] In step S21 and the corresponding S211 to S213, the certificate content is first preprocessed. Specifically, the certificate content data is first collected, including the reason for the award, the award name, the winner information, etc., and then irrelevant characters and noise, such as punctuation marks and redundant spaces, are removed. After that, the NLP tool is used to segment the text and perform part-of-speech tagging.

[0066] Secondly, keyword extraction is performed on the preprocessed certificate content. The TF-IDF method is used to extract keywords in the certificate content. Then, the BERT pre-trained model is used to perform semantic analysis on the certificate content to understand its meaning.

[0067] Finally, design a template for the award speech, including the beginning, middle (reason for winning, significance of the award, etc.) and ending parts. Based on the extracted keywords and semantic analysis results, fill the key information in the certificate content into the award speech template, and then use GPT or other natural language generation models to optimize the language of the generated award speech to make it more fluent and in line with the context.

[0068] S22: Retrieve background content based on the speech content.

[0069] Wherein, step S22 further comprises:

[0070] S221: pre-collecting background images, and performing image feature extraction on the background images to obtain image features;

[0071] S222: extracting text features from the content to obtain text features;

[0072] S223: Match the image features and the text features through TBIR technology to obtain background content.

[0073] In step S22 and the corresponding S221 to S223, text feature extraction is first performed, that is, the text description is converted into a feature vector using the text processing technology word embedding, and then image feature extraction is performed, that is, feature extraction is performed on each picture in the theme library to generate image feature vectors. These feature vectors can be based on multiple attributes such as color, texture, and shape.

[0074] After obtaining the feature vector, it is necessary to match the text feature vector with the image feature vector, that is, convert the text query into an image feature vector through TBIR technology, and match it with the feature vector in the image database to find the most similar image. Finally, the matched image is displayed to the user as a background image option.

[0075] S23: combining the speech content and the background content to obtain related content.

[0076] S3: Merge the associated content with the basic template to obtain a certificate template.

[0077] The process of combining these two elements in step S3 usually involves placing the speech content, i.e., the background content, at an appropriate position in the background content, wherein, due to the aforementioned embedding technology, the template can adaptively adjust the size, color, font and other attributes of the text, as well as the layout and style of the background content to ensure clear communication of the visual information between them.

[0078] S4: Sharing the certificate template to collect personal information, and generating an honorary certificate by combining the certificate template and the personal information.

[0079] Wherein, in step S4, the certificate template is shared through QR code technology.

[0080] After obtaining the certificate template in steps S1 to S3, you can use a QR code generator. In this embodiment, QR Code is used to generate the certificate. Then the corresponding personnel can enter the system by scanning the QR code. The generated template is displayed on the page, and the items that need to be supplemented by the employee are listed in sequence. The name of the logged-in person will be automatically entered in the place where the employee's name is preset, and the location of the employee's photo is preset. After the production is completed, the certificate can be downloaded and forwarded.

[0081] like Figure 2 As shown, the present invention also provides a device for generating an honor certificate based on multi-person interaction, comprising:

[0082] Creation module 100: used to create a basic template;

[0083] The association module 200 is used to receive the input certificate content, learn the certificate content through a deep learning model, and generate associated content related to the certificate content;

[0084] Fusion module 300: used to fuse the associated content with the basic template to obtain a certificate template;

[0085] Generating module 400: used to share the certificate template to collect personal information, and generate an honorary certificate by combining the certificate template and the personal information.

[0086] The present invention also provides a device for generating an honor certificate based on multi-person interaction, comprising:

[0087] A memory and at least one processor, wherein instructions are stored in the memory;

[0088] At least one of the processors calls the instructions in the memory to enable a device for generating an honor certificate based on multi-person interaction to execute a method for generating an honor certificate based on multi-person interaction as described in any one of the above items.

[0089] The present invention also provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, a method for generating an honor certificate based on multi-person interaction as described in any one of the above items is implemented.

[0090] Furthermore, the device for generating a certificate of honor based on multi-person interaction provided by the present invention may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU), for example, one or more processors and memories, one or more storage media for storing applications or data, such as one or more mass storage devices, wherein the memories and storage media may be short-term storage or permanent storage, and the program stored in the storage medium may include one or more modules, each module may include a series of instruction operations in a device for generating a certificate of honor based on multi-person interaction, and further, the processor may be configured to communicate with the storage medium to execute a series of instruction operations in the storage medium on a device for generating a certificate of honor based on multi-person interaction.

[0091] It may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input and output interfaces, and one or more operating systems, such as WindowsServe, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that the structure of the device for generating an honor certificate based on multi-person interaction provided by the present invention does not constitute a limitation on the device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0092] The present invention provides a method and device for generating an honorary certificate based on multi-person interaction. The method creates a responsive basic template, uses a deep learning model and NLP tools to automatically generate related content closely related to the certificate content, and cleverly integrates the speech and background content, and finally generates a personalized honorary certificate template that is suitable for multi-device display. In addition, through the QR code sharing technology, it is convenient for multiple people to collaborate to supplement personal information, thereby realizing the convenient generation and sharing of honorary certificates, and greatly improving the efficiency and personalization level of certificate production.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the above embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for generating an honor certificate based on multi-person interaction, characterized in that: include: S1: Create a basic template; S2: receiving input certificate content, learning the certificate content through a deep learning model, and generating associated content related to the certificate content; S3: Merging the associated content with the basic template to obtain a certificate template; S4: Sharing the certificate template to collect personal information, and generating an honorary certificate by combining the certificate template and the personal information.

2. The method for generating a certificate of honor based on multi-person interaction according to claim 1, characterized in that: Step S1 further comprises: S11: Using HTML5 technology, the client creates a first template that supports dynamic adjustment; S12: introducing a responsive design into the first template to obtain a second template; S13: Embed the CSS style into the second template to obtain a third template, and output the third template as a basic template.

3. The method for generating a certificate of honor based on multi-person interaction according to claim 2, characterized in that: The basic template in step S13 includes a certificate title position, a certificate comment position, a personnel photo position, and a personnel name position.

4. The method for generating a certificate of honor based on multi-person interaction according to claim 1, characterized in that: Step S2 further comprises: S21: Process the certificate content by using an NLP tool to generate a speech content; S22: Retrieve background content based on the speech content; S23: combining the speech content and the background content to obtain related content.

5. The method for generating a certificate of honor based on multi-person interaction according to claim 4, characterized in that: Step S21 further includes: S211: Preprocess the certificate content to obtain preprocessed certificate content; S212: Perform word segmentation analysis and word tagging on the pre-processed certificate content through the NLP tool to obtain certificate keywords; S213: Generate speech content based on the certificate keywords.

6. The method for generating a certificate of honor based on multi-person interaction according to claim 4, characterized in that: Step S22 further includes: S221: pre-collecting background images, and extracting image features from the background images to obtain image features; S222: extracting text features from the content to obtain text features; S223: Match the image features and the text features through TBIR technology to obtain background content.

7. The method for generating a certificate of honor based on multi-person interaction according to claim 1, characterized in that: In step S4, the certificate template is shared through QR code technology.

8. A device for generating a certificate of honor based on multi-person interaction, characterized in that: include: Create module: used to create basic templates; Association module: used for receiving input certificate content, learning the certificate content through a deep learning model, and generating associated content related to the certificate content; Fusion module: used to fuse the associated content with the basic template to obtain a certificate template; Generation module: used to share the certificate template to collect personal information, and generate an honorary certificate by combining the certificate template and the personal information.

9. A device for generating a certificate of honor based on multi-person interaction, characterized in that: include: A memory and at least one processor, wherein instructions are stored in the memory; At least one of the processors calls the instructions in the memory to enable a device for generating an honor certificate based on multi-person interaction to execute a method for generating an honor certificate based on multi-person interaction as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by the processor, a method for generating an honor certificate based on multi-person interaction as described in any one of claims 1-7 is implemented.

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