Digital content processing system based on big data

Abstract

The invention discloses a digital content processing method based on big data, and the method comprises the steps: receiving to-be-created PPT information inputted by a user, obtaining user PPT historical creation data and PPT template data, understanding a logic relation and a hierarchical relation between contents based on the to-be-created PPT information, reconstructing a logic architecture of the PPT contents, and processing the to-be-created PPT information. The method comprises the following steps: extracting a user personality feature tag based on user PPT historical creation data, recommending a corresponding theme PPT template in PPT template information for a user to select according to a logic architecture, selecting a consistent illustration picture by combining a selection result of the PPT template and the logic architecture, editing a to-be-created PPT first draft, and displaying the to-be-created PPT first draft to the user. And providing a secondary creation or feedback port on the basis of the first draft for the user, receiving modification and feedback of the user in real time, analyzing the requirement of the user for the PPT, and iteratively optimizing content processing logic, thereby solving the problems of inaccurate production of an automatic generation type PPT and low user satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of digital content processing, and in particular to a digital content processing system based on big data. Background Art

[0002] In today's digital age, the development speed of digital content processing is remarkable. With the continuous advancement of big data technology, we can process and analyze data at an unprecedented speed and scale, thereby creating rich and diverse digital content, including not only text, images, audio and video, but also more complex data visualization and interactive multimedia products. However, it is impossible to fully understand the deep meaning and logical relationship of PPT content, so it may not be accurate enough in content optimization and structural adjustment. In addition, there are obvious deficiencies in the automatic generation of presentations (PPT). The illustrations are often not highly relevant to the text content. Users usually need to manually replace the images, which reduces the user experience. Therefore, it is necessary to design a digital content processing system based on big data to improve the accuracy of PPT production and enhance the user experience. Summary of the invention

[0003] The purpose of the present invention is to provide a digital content processing system based on big data to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a digital content processing system based on big data, which is executed in a digital content processing method based on big data, and the operation steps of the method include: Step S1: receiving the PPT information to be created input by the user, and obtaining the user's PPT history creation data and PPT template data; Step S2: Based on the PPT information to be created, the logical relationship and hierarchical relationship between the contents are understood, and the logical structure of the PPT content is reconstructed; Step S3: extracting user personality feature tags based on the user's PPT history creation data, and recommending corresponding theme PPT templates for the user to choose from the PPT template information according to the logical architecture; Step S4: selecting a suitable illustration based on the selection result of the PPT template and the logical structure, and editing the draft of the PPT to be created to display to the user; Step S5: Provide users with a secondary creation or feedback port based on the first draft, receive user modifications and feedback in real time, analyze user requirements for PPT, and iteratively optimize content processing logic.

[0005] According to the above technical solution, step S1 further includes the following steps: Step S11: The PPT information to be created can be input through a variety of methods such as text input, language recognition, image scanning, and file upload. The time decay technology is used to count the user's input habits, predict the user's subsequent input method, and automatically select an input method that suits the user; Step S12: The PPT information to be created includes: subject information, key information or outline, target audience, page number requirements, and design preferences; the user PPT historical creation data includes: historical subject data, historical PPT final product data, and historical feedback or modification data; Step S13: The PPT template data refers to classifying different PPT templates, matching the creation PPT information received by the system to provide different users with templates that match the creation PPT information for recommendation, and adding keyword tags of the creation PPT information to the selected PPT templates according to the choices of different users, counting the number of times the PPT template is selected and the frequency of the keyword tags being similar, scoring the PPT templates, setting a threshold, and screening the scores of the PPT templates in each category. When the PPT template is less than the threshold, the PPT template is deleted, and the final PPT cases created by different users are optimized and the corresponding creation PPT information is optimized, a new PPT template is created and a tag is added, and elimination optimization iterations are performed.

[0006] According to the above technical solution, step S2 further includes the following steps: Step S21: Analyze the PPT information to be created by natural language processing technology, identify the internal connections in the PPT information to be created, the internal connections include causal relationships, parallel relationships, and inclusion relationships, and construct a preliminary semantic network; Step S22: Introduce the industry knowledge base to infer the logical structure and hierarchical relationship of the PPT content according to the semantic network, automatically adjust the presentation method and key distribution of the logical structure according to the characteristics and preferences of the target audience, extend or abbreviate the content of each layer according to the page number requirement, optimize the coherence and logic of the logical structure, and automatically detect and correct logical breaks or redundant information; Step S23: Based on the preliminary generation of the logic architecture, a preview of the logic architecture is displayed to the user through a graphical interface, and an interactive editing tool is provided to allow the user to fine-tune or reorganize the content as needed, collect user interaction behaviors in real time, establish a user feedback library, use user interaction behaviors and success cases as a learning library for the system, and continuously iterate and optimize the generation logic of the logic architecture.

[0007] According to the above technical solution, step S3 further includes the following steps: Step S31: extracting user personality feature labels based on the user's PPT history creation data, the personality feature model includes but is not limited to: identifying the user's emotional tendency label through the text content in the user's PPT history creation data, extracting the user's visual preference label and design style label through the color matching, font selection, and layout style in the user's PPT history creation data, and identifying the user's work habit label, efficiency preference label, and key node labels in the creation process in combination with the user's PPT creation time, frequency, number of modifications, and content changes, and constructing a multi-dimensional user portrait based on the user's personality feature labels; Step S32: extracting features from each template in the PPT template data, wherein the feature extraction includes: theme, color matching, layout style, and element type, constructing a feature vector of the PPT template data, and calculating the similarity between the multi-dimensional user portrait and the feature vector of the PPT template data by cosine similarity, wherein the weights of each dimension are dynamically adjusted according to the PPT information to be created, and the similarities are arranged in descending order, and the PPT templates in the front are preferentially displayed to the user for selection, and the PPT templates that are not displayed are stored in a pre-display area, and the user's interactive feedback actions on the currently displayed PPT template are obtained in real time. When a signal of dissatisfaction from the user is received, the weights of each dimension are dynamically adjusted to re-sort the PPT templates in the pre-display area until the PPT template selected by the user is received; Step S33: For a user who does not have the user's PPT history creation data, the system preferentially displays PPT templates with high themes that match the PPT information to be created according to the PPT information to be created input by the user, obtains the user's interactive feedback on the currently displayed PPT template in real time, and dynamically adjusts the PPT templates with high scores in other styles when receiving a signal of dissatisfaction from the user; Step S34: Provide the user with an option to skip the PPT template. When the user's choice to skip the template is received, for users with the user's PPT history creation data, based on the calculation results of the similarity between the multi-dimensional user portrait and the PPT template data feature vector in steps S31 and S32, select the PPT template with the highest similarity as the PPT template to be created. For users without the user's PPT history creation data, based on step S33, select the PPT template with the highest theme score that matches the PPT information to be created as the PPT template to be created.

[0008] According to the above technical solution, step S4 further includes the following steps: Step S41: Based on the selection result of the PPT template to be created, the user is provided with an option of whether to retain the PPT template to be created. The user can choose to retain the PPT template to be created, or can choose to create a new one based on the PPT template to be created. When the user chooses to retain, the form of the PPT template to be created remains unchanged. When the user chooses to innovate, a new creation is performed based on the PPT template to be created, combined with the PPT information to be created and the multi-dimensional user portrait; Step S42: Based on the analysis result of the logical architecture, construct an alternative picture library, perform feature annotation for the alternative picture library, perform content feature annotation on the PPT information description to be created through natural language processing, match the feature annotation of the alternative picture library with the content feature standard, and regard the matched pictures as the pictures to be used; Step S43: Based on the logical structure and the page requirements, the layout of the illustrations and text content is automatically adjusted; during the PPT production process, a visual guidance path is designed through visual elements; innovative elements that are consistent with the PPT to be created are incorporated into the layout design; the innovative elements include: dynamic charts, interactive components, creative animations; and the edited draft of the PPT to be created is displayed to the user.

[0009] According to the above technical solution, step S5 further includes the following steps: Step S51: Based on the display of the PPT draft to be created, the intelligent feedback terminal allows the user to manually adjust the PPT draft to be created, and also allows the user to provide feedback through text or voice to express modification opinions or innovative ideas. The intelligent feedback terminal receives the user's feedback, and the feedback for manual adjustment is used to optimize the multi-dimensional user portrait. For text or voice feedback, the system modifies the PPT draft to be created and optimizes the multi-dimensional user portrait according to the expressed opinions or the innovative ideas. Step S52: Based on the user's selection results for the skippable PPT template option and the option of whether to retain the PPT template to be created, the time decay technology is used to count the user's selection habits, predict the user's subsequent selection, and automatically select the option that meets the user's selection habits.

[0010] According to the above technical solution, the system includes a data acquisition module, a content logic architecture construction module, a personalized template recommendation module, and a user feedback optimization module: The data acquisition module is used to receive the PPT information to be created input by the user, and obtain the user's PPT historical creation data and PPT template data; The content logic architecture building module is used to understand the logical relationship and hierarchical relationship between the contents based on the PPT information to be created, and reconstruct the logical architecture of the PPT content; The personalized template recommendation module is used to extract the user's personality feature tag based on the user's PPT history creation data, and recommend the corresponding theme PPT template to the user in the PPT template information for the user to choose according to the logical architecture; The user feedback optimization module is used to provide users with a secondary creation or feedback port based on the first draft, receive user modifications and feedback in real time, analyze user requirements for PPT, and iteratively optimize content processing logic.

[0011] According to the above technical solution, the content logic architecture construction module includes a natural language processing module, a knowledge base reasoning module, and a logic architecture preview module: The natural language processing module is used to analyze the PPT information to be created through a natural language processing terminal, identify the internal connections in the PPT information to be created, the internal connections include causal relationships, parallel relationships, and inclusion relationships, build a preliminary semantic network, annotate the content features of the PPT information description to be created through the natural language processing terminal, match the feature annotations of the candidate image library with the content feature standards, and regard the matched pictures as the images to be used; The knowledge base reasoning module is used to introduce the industry knowledge base to reason about the logical structure and hierarchical relationship of the PPT content according to the semantic network, automatically adjust the presentation mode and key distribution of the logical structure according to the characteristics and preferences of the target audience, extend or abbreviate the content of each layer according to the page number requirements, optimize the coherence and logic of the logical structure, and automatically detect and correct logical breaks or redundant information; The logic architecture preview module is used to display a preview of the logic architecture to the user through a graphical interface based on the preliminary generation of the logic architecture, and provide interactive editing tools to allow the user to fine-tune or reorganize the content as needed, collect user interaction behaviors in real time, establish a user feedback library, use user interaction behaviors and success cases as a learning library for the system, and continuously iterate and optimize the generation logic of the logic architecture.

[0012] According to the above technical solution, the personalized template recommendation module includes a user portrait construction module, a template feature extraction module, a new user template recommendation submodule, and a template selection optimization module: The user portrait construction module is used to extract the user's personality characteristic label based on the user's PPT historical creation data. The personality characteristic model is not limited to: identifying the user's emotional tendency label through the text content in the user's PPT historical creation data, extracting the user's visual preference label and design style label through the color matching, font selection, and layout style in the user's PPT historical creation data, and identifying the user's work habit label, efficiency preference label, and key node label in the creation process in combination with the user's PPT creation time, frequency, number of modifications, and content changes, and constructing a multi-dimensional user portrait based on the user's personality characteristic label; The template feature extraction module is used to extract features from each template in the PPT template data, wherein the feature extraction includes: theme, color matching, layout style, element type, construct the feature vector of the PPT template data, and calculate the similarity between the multi-dimensional user portrait and the feature vector of the PPT template data by cosine similarity, wherein the weight of each dimension is dynamically adjusted according to the PPT information to be created, the similarities are arranged in descending order, and the PPT templates ranked in front are preferentially displayed to the user for selection; The new user template recommendation submodule is used for users who do not have the user's PPT history creation data. The system uses the PPT information to be created input by the user to preferentially display PPT templates with high themes that match the PPT information to be created for the user, obtain the user's interactive feedback on the currently displayed PPT template in real time, and dynamically adjust the PPT templates with high scores in other styles when receiving a signal of dissatisfaction from the user; The template selection optimization module is used to provide users with an option to skip PPT templates. When receiving a user's skip template selection, for users with the user's PPT history creation data, according to the calculation results of the similarity between the multi-dimensional user portrait and the PPT template data feature vector in steps S31 and S32, the PPT template with the highest similarity is selected as the PPT template to be created. For users without the user's PPT history creation data, according to step S33, the PPT template with the highest theme score that meets the PPT information to be created is selected as the PPT template to be created.

[0013] According to the above technical solution, the user feedback optimization module includes an intelligent feedback module and a user habit prediction module: The intelligent feedback module is used for: based on the display of the PPT draft to be created, the intelligent feedback terminal allows the user to manually adjust the PPT draft to be created, and also allows the user to provide feedback through text or voice to express modification opinions or innovative ideas. The intelligent feedback terminal receives the user's feedback, and the feedback for manual adjustment is used to optimize the multi-dimensional user portrait. For text or voice feedback, the system modifies the PPT draft to be created and optimizes the multi-dimensional user portrait according to the expressed opinions or the innovative ideas; The user habit prediction module is used for the PPT information to be created. The PPT information can be input through multiple input methods such as text input, language recognition, image scanning, and file upload. The time decay technology is used to count the user's input habits, predict the user's subsequent input method, and automatically select an input method that suits the user for the user. Based on the user's selection results for the skippable PPT template option and the whether to retain the PPT template option to be created, the time decay technology is used to count the user's selection habits, predict the user's subsequent selection, and automatically select a selection method that suits the user's selection habits for the user.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention reduces the time investment of users in content organization, template selection and design through an automated PPT creation process, and utilizes user historical data and multi-dimensional user portraits to provide users with personalized template recommendations, so that the final PPT is more in line with the user's personal style and preferences. Through natural language processing and industry knowledge base, the system can automatically build a logical architecture and optimize the coherence and logic of the content. The system can dynamically optimize and continuously iterate and update according to user feedback and selection habits, making the system more intelligent and adaptable to user needs. By analyzing user behavior and preferences, the system can provide users with more accurate decision support in template selection and content creation, provide secondary creation and intelligent feedback modules, promote innovative interaction between users and systems, and help to generate more novel PPT designs that are more in line with users' personal styles and preferences, thereby improving the accuracy of PPT production and enhancing user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A flowchart diagram of a digital content processing method for automatically generating a PPT by a digital content processing system based on big data provided in the first embodiment of the present invention; Figure 2 A schematic diagram of the module composition of a digital content processing system based on big data provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: This embodiment can apply a scenario in which PPT is automatically generated. This method can be executed by a digital content processing system based on big data provided by this embodiment. Figure 1 A flowchart diagram of a digital content processing method for automatically generating a PPT by a digital content processing system based on big data provided in the first embodiment of the present invention is provided. The method specifically includes the following steps: Step S1: receiving the PPT information to be created input by the user, and obtaining the user's PPT history creation data and PPT template data; Step S2: Based on the PPT information to be created, the logical relationship and hierarchical relationship between the contents are understood, and the logical structure of the PPT content is reconstructed; Step S3: extracting user personality feature tags based on the user's PPT history creation data, and recommending corresponding theme PPT templates for the user to choose from the PPT template information according to the logical architecture; Step S4: selecting a suitable illustration based on the selection result of the PPT template and the logical structure, and editing the draft of the PPT to be created to display to the user; Step S5: Provide users with a secondary creation or feedback port based on the first draft, receive user modifications and feedback in real time, analyze user requirements for PPT, and iteratively optimize content processing logic.

[0018] In an embodiment of the present invention, the step S1 further includes the following steps: Step S11: The PPT information to be created can be input through a variety of methods such as text input, language recognition, image scanning, and file upload. The time decay technology is used to count the user's input habits, predict the user's subsequent input method, and automatically select an input method that suits the user; Step S12: The PPT information to be created includes: subject information, key information or outline, target audience, page number requirements, and design preferences; the user PPT historical creation data includes: historical subject data, historical PPT final product data, and historical feedback or modification data; Step S13: The PPT template data refers to classifying different PPT templates, matching the creation PPT information received by the system to provide different users with templates that match the creation PPT information for recommendation, and adding keyword tags of the creation PPT information to the selected PPT templates according to the choices of different users, counting the number of times the PPT template is selected and the frequency of the keyword tags being similar, scoring the PPT templates, setting a threshold, and screening the scores of the PPT templates in each category. When the PPT template is less than the threshold, the PPT template is deleted, and the final PPT cases created by different users are optimized and the corresponding creation PPT information is optimized, a new PPT template is created and a tag is added, and elimination optimization iterations are performed.

[0019] In an embodiment of the present invention, the step S2 further includes the following steps: Step S21: Analyze the PPT information to be created by natural language processing technology, identify the internal connections in the PPT information to be created, the internal connections include causal relationships, parallel relationships, and inclusion relationships, and construct a preliminary semantic network; Step S22: Introduce the industry knowledge base to infer the logical structure and hierarchical relationship of the PPT content according to the semantic network, automatically adjust the presentation method and key distribution of the logical structure according to the characteristics and preferences of the target audience, extend or abbreviate the content of each layer according to the page number requirement, optimize the coherence and logic of the logical structure, and automatically detect and correct logical breaks or redundant information; Step S23: Based on the preliminary generation of the logic architecture, a preview of the logic architecture is displayed to the user through a graphical interface, and an interactive editing tool is provided to allow the user to fine-tune or reorganize the content as needed, collect user interaction behaviors in real time, establish a user feedback library, use user interaction behaviors and success cases as a learning library for the system, and continuously iterate and optimize the generation logic of the logic architecture.

[0020] In an embodiment of the present invention, step S3 further includes the following steps: Step S31: extracting user personality feature labels based on the user's PPT history creation data, the personality feature model includes but is not limited to: identifying the user's emotional tendency label through the text content in the user's PPT history creation data, extracting the user's visual preference label and design style label through the color matching, font selection, and layout style in the user's PPT history creation data, and identifying the user's work habit label, efficiency preference label, and key node labels in the creation process in combination with the user's PPT creation time, frequency, number of modifications, and content changes, and constructing a multi-dimensional user portrait based on the user's personality feature labels; Step S32: extracting features from each template in the PPT template data, wherein the feature extraction includes: theme, color matching, layout style, and element type, constructing a feature vector of the PPT template data, and calculating the similarity between the multi-dimensional user portrait and the feature vector of the PPT template data by cosine similarity, wherein the weights of each dimension are dynamically adjusted according to the PPT information to be created, and the similarities are arranged in descending order, and the PPT templates in the front are preferentially displayed to the user for selection, and the PPT templates that are not displayed are stored in a pre-display area, and the user's interactive feedback actions on the currently displayed PPT template are obtained in real time. When a signal of dissatisfaction from the user is received, the weights of each dimension are dynamically adjusted to re-sort the PPT templates in the pre-display area until the PPT template selected by the user is received; Step S33: For a user who does not have the user's PPT history creation data, the system preferentially displays PPT templates with high themes that match the PPT information to be created according to the PPT information to be created input by the user, obtains the user's interactive feedback on the currently displayed PPT template in real time, and dynamically adjusts the PPT templates with high scores in other styles when receiving a signal of dissatisfaction from the user; Step S34: Provide the user with an option to skip the PPT template. When the user's choice to skip the template is received, for users with the user's PPT history creation data, based on the calculation results of the similarity between the multi-dimensional user portrait and the PPT template data feature vector in steps S31 and S32, select the PPT template with the highest similarity as the PPT template to be created. For users without the user's PPT history creation data, based on step S33, select the PPT template with the highest theme score that matches the PPT information to be created as the PPT template to be created.

[0021] In an embodiment of the present invention, the step S4 further includes the following steps: Step S41: Based on the selection result of the PPT template to be created, the user is provided with an option of whether to retain the PPT template to be created. The user can choose to retain the PPT template to be created, or can choose to create a new one based on the PPT template to be created. When the user chooses to retain, the form of the PPT template to be created remains unchanged. When the user chooses to innovate, a new creation is performed based on the PPT template to be created, combined with the PPT information to be created and the multi-dimensional user portrait; Step S42: Based on the analysis result of the logical architecture, construct an alternative picture library, perform feature annotation for the alternative picture library, perform content feature annotation on the PPT information description to be created through natural language processing, match the feature annotation of the alternative picture library with the content feature standard, and regard the matched pictures as the pictures to be used; Step S43: Based on the logical structure and the page requirements, the layout of the illustrations and text content is automatically adjusted; during the PPT production process, a visual guidance path is designed through visual elements; innovative elements that are consistent with the PPT to be created are incorporated into the layout design; the innovative elements include: dynamic charts, interactive components, creative animations; and the edited draft of the PPT to be created is displayed to the user.

[0022] In an embodiment of the present invention, the step S5 further includes the following steps: Step S51: Based on the display of the PPT draft to be created, the intelligent feedback terminal allows the user to manually adjust the PPT draft to be created, and also allows the user to provide feedback through text or voice to express modification opinions or innovative ideas. The intelligent feedback terminal receives the user's feedback, and the feedback for manual adjustment is used to optimize the multi-dimensional user portrait. For text or voice feedback, the system modifies the PPT draft to be created and optimizes the multi-dimensional user portrait according to the expressed opinions or the innovative ideas. Step S52: Based on the user's selection results for the skippable PPT template option and the option of whether to retain the PPT template to be created, the time decay technology is used to count the user's selection habits, predict the user's subsequent selection, and automatically select the option that meets the user's selection habits.

[0023] Embodiment 2: Embodiment 2 of the present invention provides a digital content processing system based on big data. Figure 2 A schematic diagram of the module composition of a digital content processing system based on big data provided in the second embodiment of the present invention is shown in FIG. Figure 2 As shown, the system includes a data acquisition module, a content logic architecture building module, a personalized template recommendation module, and a user feedback optimization module: The data acquisition module is used to receive the PPT information to be created input by the user, and obtain the user's PPT historical creation data and PPT template data; The content logic architecture building module is used to understand the logical relationship and hierarchical relationship between the contents based on the PPT information to be created, and reconstruct the logical architecture of the PPT content; The personalized template recommendation module is used to extract the user's personality feature tag based on the user's PPT history creation data, and recommend the corresponding theme PPT template to the user in the PPT template information for the user to choose according to the logical architecture; The user feedback optimization module is used to provide users with a secondary creation or feedback port based on the first draft, receive user modifications and feedback in real time, analyze user requirements for PPT, and iteratively optimize content processing logic.

[0024] In some embodiments of the present invention, the content logic architecture construction module includes a natural language processing module, a knowledge base reasoning module, and a logic architecture preview module: The natural language processing module is used to analyze the PPT information to be created through a natural language processing terminal, identify the internal connections in the PPT information to be created, the internal connections include causal relationships, parallel relationships, and inclusion relationships, build a preliminary semantic network, annotate the content features of the PPT information description to be created through the natural language processing terminal, match the feature annotations of the candidate image library with the content feature standards, and regard the matched pictures as the images to be used; The knowledge base reasoning module is used to introduce the industry knowledge base to reason about the logical structure and hierarchical relationship of the PPT content according to the semantic network, automatically adjust the presentation mode and key distribution of the logical structure according to the characteristics and preferences of the target audience, extend or abbreviate the content of each layer according to the page number requirements, optimize the coherence and logic of the logical structure, and automatically detect and correct logical breaks or redundant information; The logic architecture preview module is used to display a preview of the logic architecture to the user through a graphical interface based on the preliminary generation of the logic architecture, and provide interactive editing tools to allow the user to fine-tune or reorganize the content as needed, collect user interaction behaviors in real time, establish a user feedback library, use user interaction behaviors and success cases as a learning library for the system, and continuously iterate and optimize the generation logic of the logic architecture.

[0025] In some embodiments of the present invention, the personalized template recommendation module includes a user portrait construction module, a template feature extraction module, a new user template recommendation submodule, and a template selection optimization module: The user portrait construction module is used to extract the user's personality characteristic label based on the user's PPT historical creation data. The personality characteristic model is not limited to: identifying the user's emotional tendency label through the text content in the user's PPT historical creation data, extracting the user's visual preference label and design style label through the color matching, font selection, and layout style in the user's PPT historical creation data, and identifying the user's work habit label, efficiency preference label, and key node label in the creation process in combination with the user's PPT creation time, frequency, number of modifications, and content changes, and constructing a multi-dimensional user portrait based on the user's personality characteristic label; The template feature extraction module is used to extract features from each template in the PPT template data, wherein the feature extraction includes: theme, color matching, layout style, element type, construct the feature vector of the PPT template data, and calculate the similarity between the multi-dimensional user portrait and the feature vector of the PPT template data by cosine similarity, wherein the weight of each dimension is dynamically adjusted according to the PPT information to be created, the similarities are arranged in descending order, and the PPT templates ranked in front are preferentially displayed to the user for selection; The new user template recommendation submodule is used for users who do not have the user's PPT history creation data. The system uses the PPT information to be created input by the user to preferentially display PPT templates with high themes that match the PPT information to be created for the user, obtain the user's interactive feedback on the currently displayed PPT template in real time, and dynamically adjust the PPT templates with high scores in other styles when receiving a signal of dissatisfaction from the user; The template selection optimization module is used to provide users with an option to skip PPT templates. When receiving a user's skip template selection, for users with the user's PPT history creation data, according to the calculation results of the similarity between the multi-dimensional user portrait and the PPT template data feature vector in steps S31 and S32, the PPT template with the highest similarity is selected as the PPT template to be created. For users without the user's PPT history creation data, according to step S33, the PPT template with the highest theme score that meets the PPT information to be created is selected as the PPT template to be created.

[0026] In some embodiments of the present invention, the user feedback optimization module includes an intelligent feedback module and a user habit prediction module: The intelligent feedback module is used for: based on the display of the PPT draft to be created, the intelligent feedback terminal allows the user to manually adjust the PPT draft to be created, and also allows the user to provide feedback through text or voice to express modification opinions or innovative ideas. The intelligent feedback terminal receives the user's feedback, and the feedback for manual adjustment is used to optimize the multi-dimensional user portrait. For text or voice feedback, the system modifies the PPT draft to be created and optimizes the multi-dimensional user portrait according to the expressed opinions or the innovative ideas; The user habit prediction module is used for the PPT information to be created. The PPT information can be input through multiple input methods such as text input, language recognition, image scanning, and file upload. The time decay technology is used to count the user's input habits, predict the user's subsequent input method, and automatically select an input method that suits the user for the user. Based on the user's selection results for the skippable PPT template option and the whether to retain the PPT template option to be created, the time decay technology is used to count the user's selection habits, predict the user's subsequent selection, and automatically select a selection method that suits the user's selection habits for the user.

[0027] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0028] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital content processing method based on big data, The invention comprises: The operating steps of the method include: Step S1: receiving the PPT information to be created input by the user, and obtaining the user's PPT history creation data and PPT template data; Step S2: Based on the PPT information to be created, the logical relationship and hierarchical relationship between the contents are understood, and the logical structure of the PPT content is reconstructed; Step S3: extracting user personality feature tags based on the user's PPT history creation data, and recommending corresponding theme PPT templates for the user to choose from the PPT template information according to the logical architecture; Step S4: selecting a suitable illustration based on the selection result of the PPT template and the logical structure, and editing the draft of the PPT to be created to display to the user; Step S5: Provide users with a secondary creation or feedback port based on the first draft, receive user modifications and feedback in real time, analyze user requirements for PPT, and iteratively optimize content processing logic.

2. The method for processing digital content based on big data according to claim 1, characterized in that: The step S1 further comprises the following steps: Step S11: The PPT information to be created can be input through a variety of methods such as text input, language recognition, image scanning, and file upload. The time decay technology is used to count the user's input habits, predict the user's subsequent input method, and automatically select an input method that suits the user; Step S12: The PPT information to be created includes: subject information, key information or outline, target audience, page number requirements, and design preferences; the user PPT historical creation data includes: historical subject data, historical PPT final product data, and historical feedback or modification data; Step S13: The PPT template data refers to classifying different PPT templates, matching the creation PPT information received by the system to provide different users with templates that match the creation PPT information for recommendation, and adding keyword tags of the creation PPT information to the selected PPT templates according to the choices of different users, counting the number of times the PPT template is selected and the frequency of the keyword tags being similar, scoring the PPT templates, setting a threshold, and screening the scores of the PPT templates in each category. When the PPT template is less than the threshold, the PPT template is deleted, and the final PPT cases created by different users are optimized and the corresponding creation PPT information is optimized, a new PPT template is created and a tag is added, and elimination optimization iterations are performed.

3. The method for processing digital content based on big data according to claim 2, characterized in that: The step S2 further comprises the following steps: Step S21: Analyze the PPT information to be created through a natural language processing terminal, identify the internal connections in the PPT information to be created, the internal connections include causal relationships, parallel relationships, and inclusion relationships, and construct a preliminary semantic network; Step S22: Introduce the industry knowledge base to infer the logical structure and hierarchical relationship of the PPT content according to the semantic network, automatically adjust the presentation method and key distribution of the logical structure according to the characteristics and preferences of the target audience, extend or abbreviate the content of each layer according to the page number requirement, optimize the coherence and logic of the logical structure, and automatically detect and correct logical breaks or redundant information; Step S23: Based on the preliminary generation of the logic architecture, a preview of the logic architecture is displayed to the user through a graphical interface, and an interactive editing tool is provided to allow the user to fine-tune or reorganize the content as needed, collect user interaction behaviors in real time, establish a user feedback library, use user interaction behaviors and success cases as a learning library for the system, and continuously iterate and optimize the generation logic of the logic architecture.

4. The method for processing digital content based on big data according to claim 3, characterized in that: The step S3 further comprises the following steps: Step S31: extracting user personality feature labels based on the user's PPT history creation data, the personality feature model includes but is not limited to: identifying the user's emotional tendency label through the text content in the user's PPT history creation data, extracting the user's visual preference label and design style label through the color matching, font selection, and layout style in the user's PPT history creation data, and identifying the user's work habit label, efficiency preference label, and key node labels in the creation process in combination with the user's PPT creation time, frequency, number of modifications, and content changes, and constructing a multi-dimensional user portrait based on the user's personality feature labels; Step S32: extracting features from each template in the PPT template data, wherein the feature extraction includes: theme, color matching, layout style, and element type, constructing a feature vector of the PPT template data, and calculating the similarity between the multi-dimensional user portrait and the feature vector of the PPT template data by cosine similarity, wherein the weights of each dimension are dynamically adjusted according to the PPT information to be created, and the similarities are arranged in descending order, and the PPT templates in the front are preferentially displayed to the user for selection, and the PPT templates that are not displayed are stored in a pre-display area, and the user's interactive feedback actions on the currently displayed PPT template are obtained in real time. When a signal of dissatisfaction from the user is received, the weights of each dimension are dynamically adjusted to re-sort the PPT templates in the pre-display area until the PPT template selected by the user is received; Step S33: For a user who does not have the user's PPT history creation data, the system preferentially displays PPT templates with high themes that match the PPT information to be created according to the PPT information to be created input by the user, obtains the user's interactive feedback on the currently displayed PPT template in real time, and dynamically adjusts the PPT templates with high scores in other styles when receiving a signal of dissatisfaction from the user; Step S34: Provide the user with an option to skip the PPT template. When the user's choice to skip the template is received, for users with the user's PPT history creation data, based on the calculation results of the similarity between the multi-dimensional user portrait and the PPT template data feature vector in steps S31 and S32, select the PPT template with the highest similarity as the PPT template to be created. For users without the user's PPT history creation data, based on step S33, select the PPT template with the highest theme score that matches the PPT information to be created as the PPT template to be created.

5. The method for processing digital content based on big data according to claim 4, characterized in that: The step S4 further comprises the following steps: Step S41: Based on the selection result of the PPT template to be created, the user is provided with an option of whether to retain the PPT template to be created. The user can choose to retain the PPT template to be created, or can choose to create a new one based on the PPT template to be created. When the user chooses to retain, the form of the PPT template to be created remains unchanged. When the user chooses to innovate, a new creation is performed based on the PPT template to be created, combined with the PPT information to be created and the multi-dimensional user portrait; Step S42: Based on the analysis result of the logical architecture, construct an alternative picture library, perform feature annotation for the alternative picture library, perform content feature annotation on the PPT information description to be created through a natural language processing terminal, match the feature annotation of the alternative picture library with the content feature standard, and regard the matched pictures as the pictures to be used; Step S43: Based on the logical structure and the page requirements, the layout of the illustrations and text content is automatically adjusted; during the PPT production process, a visual guidance path is designed through visual elements; innovative elements that are consistent with the PPT to be created are incorporated into the layout design; the innovative elements include: dynamic charts, interactive components, creative animations; and the edited draft of the PPT to be created is displayed to the user.

6. The method for processing digital content based on big data according to claim 5, characterized in that: The step S5 further comprises the following steps: Step S51: Based on the display of the PPT draft to be created, the intelligent feedback terminal allows the user to manually adjust the PPT draft to be created, and also allows the user to provide feedback through text or voice to express modification opinions or innovative ideas. The intelligent feedback terminal receives the user's feedback, and the feedback for manual adjustment is used to optimize the multi-dimensional user portrait. For text or voice feedback, the system modifies the PPT draft to be created and optimizes the multi-dimensional user portrait according to the expressed opinions or the innovative ideas. Step S52: Based on the user's selection results for the skippable PPT template option and the option of whether to retain the PPT template to be created, the time decay technology is used to count the user's selection habits, predict the user's subsequent selection, and automatically select the option that meets the user's selection habits.

7. A digital content processing system based on big data, characterized by: The system includes data acquisition module, content logic architecture construction module, personalized template recommendation module, and user feedback optimization module: The data acquisition module is used to receive the PPT information to be created input by the user, and obtain the user's PPT historical creation data and PPT template data; The content logic architecture building module is used to understand the logical relationship and hierarchical relationship between the contents based on the PPT information to be created, and reconstruct the logical architecture of the PPT content; The personalized template recommendation module is used to extract the user's personality feature tag based on the user's PPT history creation data, and recommend the corresponding theme PPT template to the user in the PPT template information for the user to choose according to the logical architecture; The user feedback optimization module is used to provide users with a secondary creation or feedback port based on the first draft, receive user modifications and feedback in real time, analyze user requirements for PPT, and iteratively optimize content processing logic.

8. The digital content processing system based on big data according to claim 7, characterized in that: The content logic architecture building module includes a natural language processing module, a knowledge base reasoning module, and a logic architecture preview module: The natural language processing module is used to analyze the PPT information to be created through a natural language processing terminal, identify the internal connections in the PPT information to be created, the internal connections include causal relationships, parallel relationships, and inclusion relationships, build a preliminary semantic network, annotate the content features of the PPT information description to be created through the natural language processing terminal, match the feature annotations of the candidate image library with the content feature standards, and regard the matched pictures as the images to be used; The knowledge base reasoning module is used to introduce the industry knowledge base to reason about the logical structure and hierarchical relationship of the PPT content according to the semantic network, automatically adjust the presentation mode and key distribution of the logical structure according to the characteristics and preferences of the target audience, extend or abbreviate the content of each layer according to the page number requirements, optimize the coherence and logic of the logical structure, and automatically detect and correct logical breaks or redundant information; The logic architecture preview module is used to display a preview of the logic architecture to the user through a graphical interface based on the preliminary generation of the logic architecture, and provide interactive editing tools to allow the user to fine-tune or reorganize the content as needed, collect user interaction behaviors in real time, establish a user feedback library, use user interaction behaviors and success cases as a learning library for the system, and continuously iterate and optimize the generation logic of the logic architecture.

9. The digital content processing system based on big data according to claim 8, characterized in that: The personalized template recommendation module includes a user portrait construction module, a template feature extraction module, a new user template recommendation submodule, and a template selection optimization module: The user portrait construction module is used to extract the user's personality characteristic label based on the user's PPT historical creation data. The personality characteristic model is not limited to: identifying the user's emotional tendency label through the text content in the user's PPT historical creation data, extracting the user's visual preference label and design style label through the color matching, font selection, and layout style in the user's PPT historical creation data, and identifying the user's work habit label, efficiency preference label, and key node label in the creation process in combination with the user's PPT creation time, frequency, number of modifications, and content changes, and constructing a multi-dimensional user portrait based on the user's personality characteristic label; The template feature extraction module is used to extract features from each template in the PPT template data, wherein the feature extraction includes: theme, color matching, layout style, element type, construct the feature vector of the PPT template data, and calculate the similarity between the multi-dimensional user portrait and the feature vector of the PPT template data by cosine similarity, wherein the weight of each dimension is dynamically adjusted according to the PPT information to be created, the similarities are arranged in descending order, and the PPT templates ranked in front are preferentially displayed to the user for selection; The new user template recommendation submodule is used for users who do not have the user's PPT history creation data. The system uses the PPT information to be created input by the user to preferentially display PPT templates with high themes that match the PPT information to be created for the user, obtain the user's interactive feedback on the currently displayed PPT template in real time, and dynamically adjust the PPT templates with high scores in other styles when receiving a signal of dissatisfaction from the user; The template selection optimization module is used to provide users with an option to skip PPT templates. When receiving a user's skip template selection, for users with the user's PPT history creation data, according to the calculation results of the similarity between the multi-dimensional user portrait and the PPT template data feature vector in steps S31 and S32, the PPT template with the highest similarity is selected as the PPT template to be created. For users without the user's PPT history creation data, according to step S33, the PPT template with the highest theme score that meets the PPT information to be created is selected as the PPT template to be created.

10. The digital content processing system based on big data according to claim 9, characterized in that: The user feedback optimization module includes an intelligent feedback module and a user habit prediction module: The intelligent feedback module is used for: based on the display of the PPT draft to be created, the intelligent feedback terminal allows the user to manually adjust the PPT draft to be created, and also allows the user to provide feedback through text or voice to express modification opinions or innovative ideas. The intelligent feedback terminal receives the user's feedback, and the feedback for manual adjustment is used to optimize the multi-dimensional user portrait. For text or voice feedback, the system modifies the PPT draft to be created and optimizes the multi-dimensional user portrait according to the expressed opinions or the innovative ideas; The user habit prediction module is used for the PPT information to be created. The PPT information can be input through multiple input methods such as text input, language recognition, image scanning, and file upload. The time decay technology is used to count the user's input habits, predict the user's subsequent input method, and automatically select an input method that suits the user for the user. Based on the user's selection results for the skippable PPT template option and the whether to retain the PPT template option to be created, the time decay technology is used to count the user's selection habits, predict the user's subsequent selection, and automatically select a selection method that suits the user's selection habits for the user.

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