Artificial intelligence content generation system based on large model

Abstract

The invention provides an artificial intelligence content generation system based on a large model, and the system comprises a data collection module which is used for obtaining multi-source data; the data preprocessing module is used for cleaning, formatting and denoising the data and extracting key features; the large model processing module generates content feature vectors based on the preprocessed data, and generates initial content output in combination with a feedback driving mechanism; the content optimization module performs multi-level optimization according to user requirements and feedback, and outputs final content; the output and feedback module publishes the final content to the platform and performs closed-loop optimization on the content generation strategy through user feedback data; and the control coordination module coordinates the operation of each module through a self-adaptive multi-level framework and a dynamic adjustment mechanism so as to realize efficient content generation. According to the method, the correlation, accuracy and user satisfaction of content generation can be effectively improved, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a large-scale model-based artificial intelligence content generation system. The system combines user needs, feedback information, and large-scale model technology to achieve efficient and accurate content creation through a multi-level optimization strategy. Background Art

[0002] With the rapid development of artificial intelligence (AI), particularly the application of large-scale pre-trained models (such as Deepseek and BERT), AI has made significant progress in natural language processing, image generation, and multimedia creation. Traditional content generation methods rely on manual editing and templates, resulting in low efficiency and difficulty ensuring the quality of the content created. Existing automated content generation systems often lack precise response to user needs, and the optimization process relies on manual intervention, resulting in insufficient flexibility and real-time performance in content creation.

[0003] To address these issues, AI-powered content generation systems based on large models have emerged. However, existing technologies often focus on generating a single output, failing to effectively incorporate user feedback for dynamic adjustment and optimization. Furthermore, they lack a multi-layered generation strategy framework. Therefore, how to adjust content in real time during the generation process and automatically optimize it based on user personalized needs and feedback remains a major challenge in current technology.

[0004] The present invention provides a new artificial intelligence content generation system. Through data preprocessing, large model processing, content optimization and feedback closed-loop mechanism, it can dynamically adjust the generation strategy according to user needs and feedback information, significantly improve the relevance, accuracy and user satisfaction of the content, and solve the shortcomings of the existing technology. Summary of the Invention

[0005] The purpose of this invention is to provide an artificial intelligence content generation system based on a large model, which improves the efficiency, accuracy, and personalization of content generation through automated and intelligent means. Combining large model processing with a feedback-driven dynamic adjustment mechanism, the system can optimize content in real time based on user feedback, generating high-quality content that meets user needs. Through an adaptive generation framework, the system flexibly adjusts content generation strategies in different application scenarios, ensuring the relevance, consistency, and real-time nature of generated content, thereby improving the overall user experience.

[0006] According to the present invention, there is provided an artificial intelligence content generation system based on a large model, comprising:

[0007] A data acquisition module, wherein the data acquisition module is used to collect original content data from various data sources, wherein the original content data includes text data, image data, and user interaction data;

[0008] A data preprocessing module, configured to clean, format, and denoise the original content data to generate a preprocessed data set;

[0009] A large model processing module, which generates content feature vectors based on the pre-processed data set through a pre-trained large model, and generates preliminary content output in combination with a feedback-driven dynamic adjustment mechanism;

[0010] A content optimization module, which performs multi-level optimization on the preliminary content output according to user needs and feedback information to generate final content;

[0011] An output and feedback module, which is used to output the final content to the target platform and collect user feedback data through a real-time feedback closed loop to form structured information, wherein the structured information is a processing result based on the user feedback data;

[0012] A control and coordination module determines and optimizes the content generation strategy through an adaptive multi-level content generation framework based on the feedback information, and coordinates the operation of the large model processing module and the content optimization module.

[0013] Furthermore, the data preprocessing module includes:

[0014] a cleaning unit, configured to remove invalid data and duplicate data from the original content data;

[0015] a formatting unit, configured to convert the original content data into a unified data format;

[0016] A denoising unit is configured to eliminate noise in the original content data through statistical analysis or a machine learning algorithm, and output the preprocessed data set.

[0017] Furthermore, the data preprocessing module further includes a feature extraction unit, which is used to extract key content features from the preprocessed data set, including keywords, sentiment tendencies and topic distribution, and calculate feature weights using the following formula:

[0018]

[0019] Among them, W i is the weight of the i-th feature, TF i is the word frequency of the feature in the document, DF i is the number of documents containing this feature, N is the total number of documents, S i is the sentiment tendency score, and α and β are adjustment coefficients to improve the generation efficiency of the large model processing module.

[0020] Furthermore, the large model processing module includes:

[0021] A feature generation unit, configured to extract features from the preprocessed data set using a pretrained large model to generate the content feature vector;

[0022] a content generating unit, wherein the content generating unit generates the preliminary content output through a decoder based on the content feature vector;

[0023] A quality assessment unit is used to perform a quality assessment on the preliminary content output according to a preset assessment standard, and feed the assessment result back to the content optimization module.

[0024] Furthermore, the content optimization module includes:

[0025] A demand analysis unit, configured to analyze user needs and extract user preferences and content constraints;

[0026] an optimization and adjustment unit, which performs text polishing or structural adjustment based on the user preferences and the preliminary content output using natural language processing technology;

[0027] A feedback integration unit, configured to dynamically adjust the optimization strategy based on the feedback information;

[0028] A style adaptation unit is used to adjust the form and tone of the final content according to the style requirements of the target platform or user group.

[0029] Furthermore, the output and feedback module includes:

[0030] A content publishing unit, configured to publish the final content to the target platform in the form of text, image, or multimedia;

[0031] A feedback collection unit is used to collect user feedback data such as likes, comments and usage time of the final content in real time, and transmit the structured information to the control and coordination module.

[0032] Furthermore, the control coordination module includes:

[0033] An instruction evolution chain construction unit, the instruction evolution chain construction unit being configured to generate an instruction evolution chain based on the user feedback data, the instruction evolution chain being composed of a plurality of generation path nodes, each node recording context parameters and user satisfaction indicators corresponding to a specific content generation strategy;

[0034] The strategy tracing and dynamic selection unit is used to perform path tracing, comparison and optimization selection based on the instruction evolution chain, and calculate the strategy satisfaction through the following formula:

[0035] S=w1·U s +w2·C c +w3·T d

[0036] Among them, S is the strategy satisfaction, U s is the user satisfaction index, C c is the content consistency score, T d To achieve time efficiency, w1, w2, and w3 are weight factors w1+w2+w3=1. When the strategy satisfaction is lower than the set threshold, it automatically falls back to the previous path node and switches the content generation strategy to achieve adaptive evolution and context consistency maintenance in multiple rounds of content generation.

[0037] Furthermore, the system includes a security and communication module, which is used to realize data transmission between modules, ensure data security through encryption algorithms and identity authentication mechanisms, and has a data backup mechanism to prevent data loss.

[0038] Furthermore, the system includes a user interaction module, and the user interaction module includes:

[0039] A visualization display unit, which is used to display in real time the content generation process, the preliminary content output, and the quality assessment results of the final content. The quality assessment results are determined based on the weighted results of system evaluation indicators and user feedback indicators, and the weights of each indicator can be dynamically adjusted according to the application scenario to reflect the content quality in different scenarios;

[0040] An instruction input unit is used to receive customized requirements or adjustment instructions input by a user, and transmit the instructions to the control coordination module or the content optimization module.

[0041] This invention addresses existing problems such as low content generation efficiency, insufficient personalization, and poor accuracy in content generation through a large-scale model-based artificial intelligence content generation system. Traditional content creation relies on manual input and processing, which is inefficient. However, this invention significantly improves content generation speed by utilizing automated data acquisition, preprocessing, feature generation, and optimization modules, meeting the needs of large-scale content creation. Furthermore, the content generated in existing technologies lacks flexibility and personalization, often failing to accurately meet user needs. This invention, by introducing a feedback-driven dynamic adjustment mechanism and adaptive optimization strategy, can adjust generated content in real time based on user needs and feedback, thereby generating more personalized and accurate content. The accuracy and relevance of content generated in existing technologies are often insufficient. However, this invention, through a closed-loop feedback loop and an adaptive multi-level content generation framework, ensures improved relevance and accuracy of generated content, avoiding content bias. Furthermore, existing content generation often suffers from inconsistent styles and excessive time delays. This invention, through its multi-round adaptive generation mechanism and real-time feedback mechanism, ensures the consistency of generated content and improves the real-time performance of the generation process. Finally, traditional systems fail to fully consider users' real-time feedback, which may result in the generated content failing to meet users' expectations. However, the present invention significantly improves user experience by collecting and analyzing user feedback data in real time and dynamically adjusting content generation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of the framework of an artificial intelligence content generation system based on a large model provided by the present invention.

[0043] Figure 2 This is a schematic diagram of the data preprocessing and large model processing module structure provided by the present invention.

[0044] Figure 3 This is a structural diagram of the content optimization module provided by the present invention.

[0045] Figure 4 Schematic diagram of the output and feedback module structure provided by the present invention

[0046] Figure 5 This is a structural diagram of the control coordination module provided by the invention.

[0047] Reference numerals:

[0048] An artificial intelligence content generation system based on a large model 100, a data acquisition module 101, a data preprocessing module 102, a large model processing module 103, a content optimization module 104, an output and feedback module 105, a control and coordination module 106, a security and communication module 107, and a user interaction module 108;

[0049] Cleaning unit 1021, formatting unit 1022, denoising unit 1023, feature extraction unit 1024, feature generation unit 1031, content generation unit 1032, quality assessment unit 1033, model updating unit 1034, demand analysis unit 1041, optimization and adjustment unit 1042, feedback integration unit 1043, style adaptation unit 1044, content publishing unit 1051, feedback collection unit 1052, instruction evolution chain construction unit 1061, strategy tracing and dynamic selection unit 1062.

[0050] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0052] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features indicated. Thus, features specified as "first" or "second" may explicitly or implicitly include one or more of such features; and in the description of this application, unless otherwise specified, "plurality" means two or more.

[0053] In order to more clearly illustrate the technical solution of the present invention, the present invention is described in detail below in conjunction with specific embodiments, but this should not be construed as limiting the scope of protection of the present invention.

[0054] In this embodiment, if Figure 1 As shown, a large model-based artificial intelligence content generation system 100 is provided, which mainly includes a data acquisition module 101, a data preprocessing module 102, a large model processing module 103, a content optimization module 104, an output and feedback module 105, and a control and coordination module 106. During the specific implementation, each module works together to complete the entire content generation process.

[0055] Specifically, the present invention provides a large-scale model-based AI content generation system that achieves efficient and intelligent content generation through the collaborative work of multiple modules. The core of the system is to generate high-quality content that meets user needs by processing, analyzing, and optimizing massive amounts of data. To enhance the intelligence and accuracy of content generation, the system utilizes a pre-trained large-scale model and dynamically adjusts it based on real-time feedback, ensuring that the generated content is both efficient and highly relevant.

[0056] First, the data acquisition module 101 is used to collect raw content data from a variety of data sources, including text, images, and user interaction data. This module collects data in real time and automatically, ensuring that the system can obtain diverse and comprehensive information. The rich variety of collected data contributes to the multidimensionality and accuracy of subsequent content generation.

[0057] Next, the data preprocessing module 102 cleans, formats, and denoises the raw content data. Specifically, the cleaning unit 1021 removes invalid and duplicate data, while the formatting unit 1022 unifies the data format, making subsequent processing more standardized and concise. The denoising unit 1023 effectively eliminates noise in the data through statistical analysis or machine learning algorithms, preserving valuable feature information. These processes significantly improve data quality, ensuring more accurate and efficient subsequent model training and content generation.

[0058] On this basis, the large model processing module 103 uses a pre-trained large model to perform the core work of content generation. By training on large-scale data sets, the large model can learn and master different knowledge in multiple fields and possess powerful content generation capabilities. The feature generation unit 1031 in this module uses the large model to extract features from the processed data set and generate content feature vectors; these feature vectors can highly summarize the key feature information in the data and provide the necessary input for subsequent content generation. Based on these feature vectors, the content generation unit 1032 generates preliminary content output through the large model's decoder and optimizes the generation process by combining a dynamic adjustment mechanism.

[0059] To ensure the quality of generated content, the quality assessment unit 1033 performs a quality assessment on the initially output content. Evaluation criteria include content relevance, logic, and language quality. The assessment results are fed back to the content optimization module 104 for further content improvement and optimization. This process not only ensures that content generation meets user needs but also gradually improves content quality through continuous evaluation and feedback.

[0060] Next, the content optimization module 104 optimizes the preliminary content based on user needs and real-time feedback. Within this module, the demand analysis unit 1041 first analyzes the user's specific needs, extracting their preferences and content constraints. Based on user needs and the preliminary content, the optimization and adjustment unit 1042 uses natural language processing technology to polish and restructure the text, optimizing the content's logic and presentation. The feedback integration unit 1043 dynamically adjusts the optimization strategy based on user feedback, ensuring that the generated content closely aligns with user expectations. The content optimization module 104 not only improves the quality of the content but also ensures its precise alignment with user needs.

[0061] The output and feedback module 105 is responsible for publishing the optimized final content to the target platform. The content publishing unit 1051 is responsible for uploading the final content to the target platform in the form of text, images, or multimedia, ensuring that the content is effectively communicated to end users. Users interact with the platform through likes, comments, and shares. The feedback collection unit 1052 collects user feedback data in real time, including the number of likes, comments, and usage time. This collected feedback data is converted into structured information and transmitted to the control and coordination module 106 for subsequent strategy optimization.

[0062] Finally, the control and coordination module 106 determines and optimizes the content generation strategy based on user feedback information through an adaptive multi-level content generation framework. In this module, the instruction evolution chain construction unit 1061 generates an instruction evolution chain based on feedback data, dynamically backtracks the generated path, optimizes and selects the optimal strategy; the strategy tracing and dynamic selection unit 1062 performs path backtracking and strategy selection based on the generated instruction evolution chain to ensure adaptive evolution and context consistency during the content generation process. In addition, to improve the accuracy and relevance of content generation, the system continuously updates the large model based on user feedback information, continuously optimizes the model parameters through incremental learning, and improves the relevance, accuracy and efficiency of content generation.

[0063] This embodiment, through a large-scale model-based artificial intelligence content generation system 100, addresses existing issues such as low content generation efficiency, insufficient personalization, and poor accuracy. Traditional content creation relies on manual input and processing, which is inefficient. However, the present invention significantly improves content generation speed through automated data acquisition, preprocessing, feature generation, and optimization modules, meeting the needs of large-scale content creation. At the same time, the content generated in the existing technology lacks flexibility and personalization, often failing to accurately meet user needs. By introducing a feedback-driven dynamic adjustment mechanism and adaptive optimization strategy, the present invention can adjust the generated content in real time based on user needs and feedback, thereby generating more personalized and accurate content. The accuracy and relevance of the content generated in the existing technology are often insufficient. However, through a closed-loop feedback loop and an adaptive multi-level content generation framework, the present invention ensures improved relevance and accuracy of the generated content, avoiding content bias. Furthermore, content generation in the existing technology often suffers from inconsistent styles and excessive time delays. The present invention, through a multi-round adaptive mechanism and real-time feedback mechanism, ensures the consistency of the generated content and improves the real-time performance of the generation process. Finally, traditional systems fail to fully consider users' real-time feedback, which may result in the generated content failing to meet users' expectations. However, the present invention significantly improves user experience by collecting and analyzing user feedback data in real time and dynamically adjusting content generation strategies.

[0064] In some embodiments, as Figure 2 As shown, in some embodiments, the data preprocessing module 102 further includes a cleaning unit 1021, a formatting unit 1022 and a denoising unit 1023. The specific implementation of each unit will be described in detail below.

[0065] Specifically, cleaning unit 1021 is responsible for removing invalid and duplicate data from the original content data. Invalid data includes data with incorrect formatting, missing information, or incomplete data. Duplicate data refers to redundant data generated by collecting the same content multiple times during the data collection process. Cleaning unit 1021 filters data using set rules and removes data items that do not meet requirements, ensuring that the content data entering the system is accurate and valid.

[0066] Furthermore, the formatting unit 1022 is responsible for converting the collected raw content data into a unified data format. Because raw data may come from different sources and have different formats and structures, the formatting unit 1022 uses predefined data conversion rules to unify the data format, ensuring it meets the system's processing standards. For example, for text data, the formatting unit 1022 will unify the character encoding format, paragraph marks, and text tags; for image data, it may perform size adjustment and color normalization.

[0067] Furthermore, the denoising unit 1023 plays a crucial role in data preprocessing. It eliminates noise from the raw content data through statistical analysis or machine learning algorithms. Noise includes unnecessary data points that may interfere with subsequent processing, such as interfering pixels in an image or typos in text. Denoising unit 1023 uses algorithms to identify and remove this interfering information, generating a clear and accurate preprocessed data set. These algorithms can be traditional rule-based methods or adaptive methods based on deep learning, depending on the data type and application scenario.

[0068] It is understandable that after the preprocessed data set is generated, it will be transmitted to the next processing stage of the system, such as the large model processing module 103. Through the above steps of cleaning, formatting and denoising, the quality of the data input to the system is ensured, and the efficiency and accuracy of subsequent model processing are improved.

[0069] In some embodiments, the data preprocessing module 102 further includes a feature extraction unit 1024, which is used to extract key content features from the preprocessed data set, including keywords, sentiment tendency, and topic distribution, and calculate feature weights using the following formula:

[0070] Specifically, feature extraction unit 1024 extracts multiple features from the cleaned, formatted, and denoised dataset. The extracted features include keywords, sentiment, and topic distribution in the text. Keywords are identified using word frequency statistics and natural language processing techniques, sentiment is calculated using a sentiment analysis algorithm, and topic distribution is generated using a topic model (e.g., an LDA model).

[0071] Furthermore, the calculation formula of feature weight is as follows:

[0072]

[0073] The parameters are defined as follows:

[0074] W i : The weight of the i-th feature is the influence of this feature on the generated content.

[0075] TF i : The word frequency of feature i in the dataset, indicating how often the feature appears in the text. The higher the word frequency, the greater the influence of the feature on the generated results.

[0076] DF i : The number of documents containing feature i, indicating how widely this feature appears in all documents. The prevalence of this feature is measured by calculating its frequency of occurrence in all documents.

[0077] N: The total number of documents, used to normalize the frequency of occurrence of features in the entire dataset.

[0078] S i : Sentiment tendency score, which reflects the sentiment tendency represented by feature iii. It uses a sentiment analysis model (such as a sentiment classification model based on deep learning) to predict the sentiment in the text and assign a corresponding numerical score.

[0079] α and β: Adjustment coefficients, used to control the influence of word frequency and sentiment on feature weights, respectively. The values of adjustment coefficients α and β are obtained through experimental optimization and generally satisfy 0 ≤ α ≤ 10, 0 ≤ β ≤ 10, and α + β = 1, to ensure that the sum of the weights of the two factors is 1.

[0080] Specifically, the feature weight W calculated by the above formula is i This is passed as input to the subsequent large model processing module 103 to optimize the quality and accuracy of content generation. In particular, by dynamically adjusting the values of α and β, the priority of feature extraction can be flexibly adjusted according to different application scenarios and data characteristics, thereby improving the generation efficiency and relevance of the model.

[0081] Furthermore, by adjusting the weight coefficients for word frequency and sentiment, features can be customized according to different user needs and content generation tasks, ensuring that the generated content better meets user expectations. For example, in social media content generation that requires a high degree of emotional resonance, the value of β can be appropriately increased to pay more attention to sentiment.

[0082] In some embodiments, as Figure 2 As shown, the large model processing module 103 includes a feature generation unit 1031 , a content generation unit 1032 and a quality assessment unit 1033 .

[0083] Specifically, the feature generation unit 1031 is used to extract features from the preprocessed data set using a pretrained large model and generate a content feature vector. The pretrained large model can be a natural language processing model based on deep learning, such as GPT (Generative Pretrained Transformer) or BERT (Bidirectional Encoder Representation Model). The feature generation unit 1031 extracts high-dimensional semantic features, image features, and behavioral pattern features from the text, images, and user interaction data in the input data set. These features will serve as the basis for content generation. In particular, the feature generation unit 1031 converts the original data into a feature vector of fixed dimension through an encoder, and the specific number of dimensions is adjusted according to the training situation of the model and the task requirements.

[0084] Furthermore, content generation unit 1032 generates preliminary content outputs via a decoder based on the generated content feature vectors. The decoder can be a pre-trained decoder corresponding to feature generation unit 1031. Content generation unit 1032 generates not only text content but also image or video content. The specific type of content generated depends on user needs and the supported formats of the target platform. The preliminary content output generated can be natural language text, images, videos, or a mixture of multiple modalities.

[0085] Specifically, in order to ensure that the generated content meets the preset standards, the quality assessment unit 1033 is used to perform a quality assessment on the preliminary content output and generate an assessment result. The quality assessment unit 1033 evaluates the quality of the content based on preset quality standards, such as the readability of the text, grammatical accuracy, image clarity and structural rationality. For text content, quality assessment can be quantitatively evaluated using evaluation indicators such as BLEU (Bilingual Evaluation Understudy) scores and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores; for image content, it can be evaluated through image clarity and structural rationality scores; for video content, it can be comprehensively evaluated through indicators such as frame quality, content fluency and emotional resonance.

[0086] Furthermore, the output evaluation results of the quality assessment unit 1033 are fed back to the content optimization module 104, which adjusts the quality of the generated content based on the evaluation results to ensure that the final content meets user needs. For example, if the evaluation results show that the emotional tendency of the content deviates from expectations, the content optimization module 104 will adjust the optimization strategy to increase or decrease emotional tone, or adjust the structure to better match user needs and the requirements of the target platform.

[0087] In this embodiment, if Figure 2 As shown, in some embodiments, the large model processing module 103 further includes a model updating unit 1034. The function of the model updating unit 1034 is to update the parameters of the pre-trained large model through incremental learning based on feedback information to improve the relevance and accuracy of content generation. The working principle and implementation of this module are described in detail below.

[0088] Specifically, the model update unit 1034 receives user feedback data, content generation quality assessment data, and other system operation data from the feedback and output module, and dynamically updates the model parameters based on this information. In this embodiment, the model update unit 1034 uses an incremental learning method to gradually update the parameters of the pre-trained large model without the need to retrain the entire model, thereby improving efficiency and reducing computing resource consumption. The key to incremental learning is that it can gradually adjust the parameters of the model according to new data without the need to fully train the model every time. In this way, the system can respond to user needs more quickly and optimize content generation.

[0089] Furthermore, during the update process, the model update unit 1034 first analyzes the feedback data to extract the key factors that affect the quality of the generated content. For example, if user feedback shows that the generated content does not meet expectations or is not relevant enough, the model update unit 1034 will focus on analyzing the features that affect the relevance or accuracy of the content and adjust the parameters related to these features in the large model. At the same time, the model update unit 1034 will also combine pre-set evaluation criteria (such as grammatical accuracy, emotional tendency, topic consistency, etc.) to make fine-grained adjustments to the model to ensure that the generated content better meets user needs.

[0090] Specifically, the parameter update in the incremental learning process can be based on the gradient descent algorithm to gradually reduce the error, making the model output more accurate. For example, when processing text data, the model update unit 1034 can calculate the loss function (such as cross entropy loss) and update the corresponding weights by comparing the difference between user feedback and generated content. In the image generation task, the model update unit 1034 may adjust the filter parameters in the convolutional neural network (CNN) based on the user's feedback on the image content, so that the image generation is more in line with the user's visual needs.

[0091] Furthermore, to ensure the stability and efficiency of the incremental learning process, the model updating unit 1034 also employs technical means to avoid the phenomenon of "catastrophic forgetting," that is, to ensure that previously learned knowledge is not lost when learning new data. To this end, the model updating unit 1034 can use regularization techniques (such as L2 regularization) or experience replay mechanisms to retain old data and combine it with new data, so that the model update can not only improve the generation capability but also ensure the validity of historical data.

[0092] Specifically, the large model updated through this incremental learning will be able to better reflect user preferences, content relevance, and other evaluation criteria during the next content generation, thereby generating more accurate and high-quality content. For example, after receiving user feedback, the system can automatically generate content that meets user needs based on the adjusted model parameters the next time it generates content, and the generation efficiency is significantly improved compared to the initial training.

[0093] In some embodiments, as Figure 3 As shown, the content optimization module 104 includes a demand analysis unit 1041 , an optimization adjustment unit 1042 , a feedback integration unit 1043 and a style adaptation unit 1044 .

[0094] Specifically, the demand analysis unit 1041 is used to parse user needs and extract user preferences and content constraints. This unit analyzes and identifies user needs by receiving input information and historical feedback data from the user. For example, the user may provide a specific content type (such as text, image, or multimedia), style requirements (such as formal, humorous, etc.), and some constraints (such as length, structure, etc.). In this embodiment, the demand analysis unit 1041 uses natural language processing (NLP) technology to semantically understand the text requirements input by the user and converts them into structured information that can be used by the system. This information will serve as the basis for subsequent content generation and optimization.

[0095] Furthermore, the optimization and adjustment unit 1042 adjusts the initially generated content through natural language processing technology or image processing technology. The task of this unit is to polish or structurally adjust the generated content based on the user preferences and constraints extracted by the demand analysis unit 1041. For example, if the user prefers a formal tone, the optimization and adjustment unit 1042 will ensure that the generated text meets this requirement through text modification, sentence adjustment, etc. If the user has specific composition or color requirements for the image content, the optimization and adjustment unit 1042 will adjust the style of the image based on these requirements. The unit can use the generative adversarial network (GAN) technology in deep learning to adjust the image style to ensure that the generated content is highly matched with the user's needs.

[0096] Specifically, the feedback integration unit 1043 is used to dynamically adjust the optimization strategy based on user feedback information. This unit analyzes the user's satisfaction with the generated content by receiving real-time feedback data from the output and feedback module 105, and adjusts the optimization strategy accordingly. For example, if the user's feedback on the initial content is "not attractive enough", the feedback integration unit 1043 will adjust the content generation direction in the optimization strategy based on the emotional tendency analysis results of the feedback to enhance the attractiveness of the generated content. Based on the specific feedback on the content, this unit will decide whether to add detailed descriptions of certain parts, adjust the tone, or make structural modifications. In this way, the content generation process can adapt to the needs and preferences of users and continuously optimize.

[0097] Furthermore, the style adaptation unit 1044 adjusts the form and tone of the generated content based on the style requirements of the target platform or user group. For example, if the target platform is social media and users prefer a relaxed and humorous style, the style adaptation unit 1044 will adjust the language style of the generated content to this requirement, adding more humorous elements or a lighthearted tone. If the content publishing platform is an academic paper website, the style adaptation unit 1044 will adjust the generated content to a more formal and academic language. This unit automatically adjusts the content's expression by analyzing the user group characteristics, historical content style, and platform specifications of the target platform to ensure that the content achieves the best effect across different platforms or user groups.

[0098] Specifically, to achieve this goal, the style adaptation unit 1044 can combine deep learning technology, utilize existing corpora and style tags, and perform style conversion on the generated content through transfer learning. For example, if the target platform requires content with both text and images, the style adaptation unit 1044 will not only adjust the style of the text content, but also adjust the image's tone, composition, and other elements through image style transfer technology to ensure that the style of the image and text are consistent, thereby improving the overall quality of the content.

[0099] Furthermore, the demand analysis unit 1041, optimization and adjustment unit 1042, feedback integration unit 1043, and style adaptation unit 1044 work together to optimize the generated content. The output of each unit is passed to the next unit, forming a closed-loop optimization process, ensuring that the final content meets user needs and platform requirements, and is improved in quality.

[0100] In some embodiments, as Figure 4 As shown, the output and feedback module 105 includes a content publishing unit 1051 and a feedback collection unit 1052 .

[0101] Specifically, the content publishing unit 1051 is used to publish the finally generated content in the form of text, image or multimedia to the target platform. The content publishing unit 1051 automatically converts the generated content into a form that adapts to the platform format according to the requirements of the target platform, and sends it to the target platform through an interface. These platforms can be social media, news websites, online shopping malls or user-customized dedicated platforms, etc. If the content is in the form of text, the content publishing unit 1051 will convert the text data into a format supported by the platform (such as HTML, Markdown, etc.). If the content is in the form of an image or video, the publishing unit will compress the image or video and convert it into a file format supported by the platform to ensure that the content can be uploaded smoothly. The key technology of this unit lies in compatibility with the target platform and data transmission efficiency.

[0102] Furthermore, the feedback collection unit 1052 is used to collect user feedback data such as likes, comments, shares, and usage time of the final content in real time, and convert these feedback data into structured information and transmit it to the control and coordination module 106. The feedback collection unit 1052 obtains user interaction information in real time by interacting with the interface of the target platform. For example, on social media platforms, user operations such as likes, comments, and forwarding can all be collected as feedback data; on video platforms, user viewing time, viewing frequency, and other information are also important feedback indicators. The feedback collection unit 1052 can accurately capture this information, analyze and process it to form structured feedback data, and ensure the effective transmission and use of information.

[0103] Specifically, to improve the accuracy of feedback collection, the feedback collection unit 1052 can also use machine learning technology to predict which types of feedback data have a greater impact on content optimization and generation based on historical data and user behavior patterns. For example, by analyzing the relationship between user viewing time, like frequency, and content quality, the feedback collection unit 1052 can prioritize feedback information closely related to user satisfaction and content quality, thereby improving the quality and value of feedback data.

[0104] Furthermore, the feedback collection unit 1052 can also use sentiment analysis technology to analyze the sentiment tendencies of user comments, convert the user comment information into sentiment scores, and transmit this data to the control and coordination module 106. Through sentiment analysis, the system can identify the user's positive or negative emotional response to the content, thereby helping the content optimization module 104 better understand the user's real needs and make targeted optimization adjustments.

[0105] Specifically, the work of content publishing unit 1051 and feedback collection unit 1052 complement each other, forming a complete content publishing and feedback collection process. Content publishing unit 1051 pushes the final content to the target platform, while feedback collection unit 1052 continuously collects user feedback, ensuring that the content dissemination effect and user satisfaction can be monitored and improved in real time.

[0106] Furthermore, in some embodiments, the feedback collection unit 1052 also supports cross-platform data collection, that is, it can obtain user feedback data from multiple platforms and aggregate and analyze this data. The integration of cross-platform feedback data provides a more comprehensive and diverse feedback basis for content optimization, which helps to achieve more accurate content adjustment and optimization.

[0107] In some embodiments, as Figure 5 As shown, the control coordination module 106 includes an instruction evolution chain construction unit 1061 and a strategy tracing and dynamic selection unit 1062. The specific implementation method and working principle of each unit will be described in detail below.

[0108] Specifically, the instruction evolution chain construction unit 1061 is used to generate an instruction evolution chain based on user feedback data. The instruction evolution chain is composed of multiple generation path nodes, and each node records the context parameters and user satisfaction indicators corresponding to a specific content generation strategy. Each path node represents the application of a content generation strategy. The node includes input data related to the strategy, parameter settings during the generation process, and the relationship between the content generated by the strategy and user feedback. For example, the node can record feedback information such as user comments, number of likes, and dwell time received for content generated by a certain specific sentiment analysis model, as well as context information directly related to the effect of the strategy (such as time, region, user group, etc.). The evolution chain can clearly display the evolution process and history of the generation strategy, helping the system make more accurate decisions in the subsequent content generation process.

[0109] Furthermore, the instruction evolution chain construction unit 1061 automatically generates an instruction evolution chain using an algorithm based on historical data and user feedback. Specifically, the system analyzes the effects of applying multiple generation strategies and converts them into a traceable path chain. Each node contains the following information:

[0110] 1. Content generation strategies: such as emotional adjustment and style adaptation;

[0111] 2. Context parameters: including time, user attributes, platform characteristics, etc.

[0112] 3. User satisfaction indicators: such as number of likes, comment sentiment analysis, sharing rate, length of stay, etc.

[0113] Specifically, the strategy tracing and dynamic selection unit 1062 is used to perform path tracing, comparison, and optimization selection based on the instruction evolution chain, and calculate the strategy satisfaction by the following formula:

[0114] S=w1·U s +w2·C c +w3·T d

[0115] Among them, S is the strategy satisfaction, U s is the user satisfaction index, C c is the content consistency score, T d To generate time efficiency, w1, w2, and w3 are weight factors, and they satisfy w1+w2+w3=1.

[0116] Furthermore, the specific definitions and calculation methods of the various parameters in the formula are as follows:

[0117] U s User satisfaction index: Based on the number of likes, comment sentiment analysis, sharing volume, etc. of users, a comprehensive user satisfaction score is calculated.

[0118] C c Content consistency score: Assess the consistency between content generation and user needs, platform requirements, and expected goals, such as whether the content meets user preferences and fits the style of the target platform.

[0119] T d Generation time efficiency: Evaluate the time cost of content generation. The lower the generation time, the more conducive it is to strategy optimization.

[0120] Specifically, when policy satisfaction falls below a set threshold, policy tracing and dynamic selection unit 1062 automatically reverts to the previous path node and switches content generation strategies, enabling adaptive evolution and maintaining contextual consistency across multiple rounds of content generation. This process ensures the system can continuously adjust and optimize generation strategies, repeatedly learning and making more appropriate strategy choices during content generation, thereby improving content quality and user satisfaction.

[0121] Furthermore, the policy tracing and dynamic selection unit 1062 can also dynamically adjust the values of the weight factors w1, w2, and w3 according to user feedback. For example, when the user's preference for a certain type of content gradually increases, the system can increase the content consistency score C accordingly. c The weight of the generated time is reduced to reduce the efficiency of T d The weight of the content is given to pay more attention to the refinement and fit of the content.

[0122] As can be understood, through the aforementioned optimization process of strategy tracing and dynamic selection, control and coordination module 106 is able to continuously optimize content generation strategies based on real-time feedback, ensuring the adaptability and consistency of content across multiple rounds of generation. This module's operating principle enables the system to adapt to changing user needs and platform characteristics, thereby ensuring that the final output content achieves high user satisfaction in a variety of scenarios.

[0123] In some embodiments, as Figure 1 As shown, the security and communication module 107 ensures secure data transmission between modules. This module includes an encryption unit that uses modern encryption algorithms, such as AES (Advanced Encryption Standard) or RSA (RSA Public Key Cryptography), to ensure confidentiality and integrity of data during transmission. Specifically, data exchanged between all modules is encrypted using the encryption algorithm, preventing potential man-in-the-middle attacks or data leaks.

[0124] Furthermore, the security and communication module 107 includes an authentication mechanism to ensure the identities of both parties involved in data exchange are confirmed. Through multi-factor authentication or biometric-based authentication, only authorized users and systems can access or transmit sensitive data. This mechanism effectively prevents unauthorized access and enhances the overall security of the system.

[0125] Furthermore, the security and communication module 107 also features a data backup mechanism. During system operation, all important data is regularly backed up and stored in the cloud or other secure storage location. This backup data is automatically generated at pre-set intervals and stored in an encrypted format, ensuring rapid system recovery in the event of an incident, preventing data loss or corruption.

[0126] The design of the security and communication module 107 effectively improves the security and reliability of the system, and solves the problems in the prior art caused by unsafe data transmission or data loss.

[0127] In some embodiments, as Figure 1 As shown, the user interaction module 108 includes a visualization display unit and an instruction input unit. The specific implementation and working principle of each unit will be described in detail below.

[0128] Specifically, the visual display unit is used to display the content generation process, preliminary content output, and the quality assessment results of the final content in real time. This unit uses a graphical interface to show the user the various steps of the generation process, including data collection, preprocessing, feature extraction, content generation, optimization and adjustment, etc. During the content generation process, users can view the progress of content generation in real time and understand the completion status of each stage through intuitive charts or progress bars. For text content, the system will display the step-by-step generation process of the content and the key features involved; for image or video content, the system will display the generated intermediate frames and the visual effects of each stage. The quality assessment results of the final generated content will also be presented in a visual form, including evaluation indicators (such as text fluency, image clarity, emotional tendency, etc.), and marked with colors, icons, etc., to help users more intuitively understand the quality of the generated content.

[0129] Furthermore, the visualization display unit can dynamically adjust the displayed content based on user feedback. If a user reports that the quality of a certain aspect of the content generation is unsatisfactory, the system will prompt the user through the visualization interface to identify weaknesses in the current quality assessment (such as grammatical issues in a certain text or color distortion in an image) and guide the user to make further adjustments. For example, if the user wishes to strengthen the emotional tendency in text generation, they can specify the emotional color through the adjustment button on the interface. The system will adjust the generation strategy based on this instruction and display the results in real time.

[0130] Specifically, the instruction input unit is used to receive customized requirements or adjustment instructions input by the user and pass them to the control coordination module 106 or the content optimization module 104. Users can use this unit to input specific requirements for generating content, such as text style, image theme, video rhythm, etc. The instructions input by the user can be natural language instructions. The system will parse these instructions through natural language processing (NLP) technology and convert them into specific generation parameters. If the user selects an image style, adjusts the text tone, or sets the video length through a graphical interface, the instruction input unit will pass this information to the relevant modules to guide the system to make real-time adjustments to the content generation process. For more complex requirements, the system also supports multiple rounds of interaction, allowing users to continuously adjust instructions until their needs are met.

[0131] Furthermore, the command input unit also features intelligent recommendation capabilities. After the user provides initial instructions, the system can automatically recommend relevant adjustment options or content generation styles based on historical data and user preferences. For example, when generating social media content, the system can recommend popular tags, emotional expressions, or popular visual styles based on the user's historical content, helping users more easily create content that suits the target platform's style.

[0132] Specifically, the collaborative work of the visualization unit and the command input unit allows users to participate in content creation more intuitively and conveniently during the generation process, and can continuously adjust the style and quality of the generated content based on real-time feedback. This interactive method not only improves the user experience, but also enhances the flexibility and personalization of content generation.

[0133] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformation made by utilizing the contents of the present invention's description and drawings under the technical concept of the present invention, or directly / indirectly applied in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. An artificial intelligence content generation system based on a large model, characterized in that: include: A data acquisition module, wherein the data acquisition module is used to collect original content data from various data sources, wherein the original content data includes text data, image data, and user interaction data; A data preprocessing module, configured to clean, format, and denoise the original content data to generate a preprocessed data set; A large model processing module, which generates content feature vectors based on the pre-processed data set through a pre-trained large model, and generates preliminary content output in combination with a feedback-driven dynamic adjustment mechanism; A content optimization module, which performs multi-level optimization on the preliminary content output according to user needs and feedback information to generate final content; An output and feedback module, which is used to output the final content to the target platform and collect user feedback data through a real-time feedback closed loop to form structured information, wherein the structured information is a processing result based on the user feedback data; A control and coordination module determines and optimizes the content generation strategy through an adaptive multi-level content generation framework based on the feedback information, and coordinates the operation of the large model processing module and the content optimization module.

2. The system according to claim 1, wherein: The data preprocessing module includes: a cleaning unit, configured to remove invalid data and duplicate data from the original content data; a formatting unit, configured to convert the original content data into a unified data format; A denoising unit is configured to eliminate noise in the original content data through statistical analysis or a machine learning algorithm, and output the preprocessed data set.

3. The system according to claim 2, characterized in that The data preprocessing module further includes a feature extraction unit, which is used to extract key content features from the preprocessed data set, including keywords, sentiment tendencies, and topic distributions, and calculate feature weights using the following formula: Among them, W i is the weight of the i-th feature, TF i is the word frequency of the feature in the document, DF i is the number of documents containing this feature, N is the total number of documents, S i is the sentiment tendency score, and α and β are adjustment coefficients to improve the generation efficiency of the large model processing module.

4. The system according to claim 1, wherein: The large model processing module includes: A feature generation unit, configured to extract features from the preprocessed data set using the pretrained large model to generate the content feature vector; a content generating unit, wherein the content generating unit generates the preliminary content output through a decoder based on the content feature vector; A quality assessment unit is used to perform a quality assessment on the preliminary content output according to a preset assessment standard, and feed the assessment result back to the content optimization module.

5. The system according to claim 4, characterized in that The large model processing module further includes a model updating unit, which is used to update the parameters of the pre-trained large model through incremental learning according to the feedback information to improve the relevance and accuracy of content generation.

6. The system according to claim 1, wherein: The content optimization module includes: A demand analysis unit, configured to analyze user needs and extract user preferences and content constraints; an optimization and adjustment unit, which performs text polishing or structural adjustment based on the user preferences and the preliminary content output using natural language processing technology; A feedback integration unit, configured to dynamically adjust the optimization strategy based on the feedback information; A style adaptation unit is used to adjust the form and tone of the final content according to the style requirements of the target platform or user group.

7. The system according to claim 1, wherein: The output and feedback module includes: A content publishing unit, configured to publish the final content to the target platform in the form of text, image, or multimedia; A feedback collection unit is used to collect user feedback data such as likes, comments and usage time of the final content in real time, and transmit the structured information to the control and coordination module.

8. The system according to claim 1, wherein: The control coordination module includes: An instruction evolution chain construction unit, the instruction evolution chain construction unit being configured to generate an instruction evolution chain based on the user feedback data, the instruction evolution chain being composed of a plurality of generation path nodes, each node recording context parameters and user satisfaction indicators corresponding to a specific content generation strategy; The strategy tracing and dynamic selection unit is used to perform path tracing, comparison and optimization selection based on the instruction evolution chain, and calculate the strategy satisfaction through the following formula: S=w1·U s +w2·C c +w3·T d Among them, S is the strategy satisfaction, U s is the user satisfaction index, C c is the content consistency score, T d To achieve time efficiency, w1, w2, and w3 are weight factors w1+w2+w3=1. When the strategy satisfaction is lower than the set threshold, it automatically falls back to the previous path node and switches the content generation strategy to achieve adaptive evolution and context consistency maintenance in multiple rounds of content generation.

9. The system according to claim 1, wherein: It further includes a security and communication module, which is used to realize data transmission between modules, ensure data security through encryption algorithm and identity authentication mechanism, and has a data backup mechanism to prevent data loss.

10. The system according to claim 1, wherein: Further comprising a user interaction module, the user interaction module comprising: A visualization display unit, which is used to display in real time the content generation process, the preliminary content output, and the quality assessment results of the final content. The quality assessment results are determined based on the weighted results of system evaluation indicators and user feedback indicators, and the weights of each indicator can be dynamically adjusted according to the application scenario to reflect the content quality in different scenarios; An instruction input unit is used to receive customized requirements or adjustment instructions input by a user, and transmit the instructions to the control coordination module or the content optimization module.

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