An artificial intelligence content generation system based on large models

Abstract

The application provides an artificial intelligence content generation system based on a large model, which comprises a data acquisition module for acquiring multi-source data; a data preprocessing module for cleaning, formatting and denoising the data and extracting key features; a large model processing module for generating a content feature vector based on the preprocessed data and combining a feedback driving mechanism to generate a preliminary content output; a content optimization module for multi-level optimization according to user requirements and feedback to output a final content; an output and feedback module for publishing the final content to a platform and optimizing the content generation strategy through user feedback data; and a control coordination module for coordinating the operation of each module through an adaptive multi-level framework and a dynamic adjustment mechanism to achieve efficient content generation. The application can effectively improve the relevance, accuracy and user satisfaction of content generation, and has a wide application prospect.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and in particular to an AI content generation system based on a large model. This system combines user needs, feedback information, and large model technology to achieve efficient and accurate content creation through multi-level optimization strategies. Background Technology

[0002] With the rapid development of artificial intelligence (AI) technology, especially 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, which are inefficient and make it difficult to guarantee the quality of the created content. Existing automated content generation systems often lack precise responses 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-scale models have emerged. However, existing technologies largely focus on the single output of generated content, failing to effectively incorporate user feedback for dynamic adjustment and optimization, and lacking a multi-layered generation strategy framework. Therefore, how to adjust content in real-time during the generation process and automatically optimize it based on users' personalized needs and feedback has become a major challenge in current technology.

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

[0005] The purpose of this invention is to provide an AI-powered content generation system based on a large-scale model, which improves the efficiency, accuracy, and personalization of content generation through automation and intelligence. The system combines large-scale model processing with a feedback-driven dynamic adjustment mechanism, enabling it to optimize content in real time based on user feedback and generate high-quality content that meets user needs. Through an adaptive generation framework, the system flexibly adjusts its content generation strategy in different application scenarios, ensuring the relevance, consistency, and real-time nature of the generated content, thereby enhancing the overall user experience.

[0006] The present invention provides an artificial intelligence content generation system based on a large model, comprising:

[0007] A data acquisition module is used to acquire raw content data from multiple data sources, including text data, image data, and user interaction data.

[0008] The data preprocessing module is used to clean, format, and denoise the original content data to generate a preprocessed dataset.

[0009] The large model processing module generates content feature vectors based on the pre-processed dataset by using a pre-trained large model, and generates preliminary content output by combining a feedback-driven dynamic adjustment mechanism.

[0010] The content optimization module performs multi-level optimization on the initial content output based on user needs and feedback information to generate the final content.

[0011] The output and feedback module 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, which is the processing result based on user feedback data.

[0012] The control and coordination module determines and optimizes the content generation strategy based on the feedback information through an adaptive multi-level content generation framework, 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 is used to remove invalid and duplicate data from the original content data.

[0015] A formatting unit, wherein the formatting unit is used to convert the original content data into a unified data format;

[0016] A denoising unit is used to eliminate noise in the original content data through statistical analysis or machine learning algorithms, and output the preprocessed dataset.

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

[0018]

[0019] Among them, W i TF is the weight of the i-th feature. i DF is a feature of word frequencies in a document. i S represents the number of documents containing this feature, where N is the total number of documents, and S represents the number of documents containing this feature. i The sentiment score is represented by α and β, which 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 is used to extract features from the preprocessed dataset using a pre-trained large model to generate the content feature vector.

[0022] A content generation unit, which generates the preliminary content output based on the content feature vector through a decoder;

[0023] A quality assessment unit is used to assess the quality of the initial content output according to preset assessment standards and to feed the assessment results back to the content optimization module.

[0024] Furthermore, the content optimization module includes:

[0025] A requirements analysis unit, which is used to parse the user requirements and extract user preferences and content constraints;

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

[0027] A feedback integration unit is used 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, which is used to publish the final content to the target platform in the form of text, images or multimedia.

[0031] The 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 and coordination module includes:

[0033] An instruction evolution chain construction unit is used to generate an instruction evolution chain based on the user feedback data. The instruction evolution chain consists of multiple generation path nodes, and each node records 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 backtracking, comparison, and optimization selection based on the instruction evolution chain, and calculates the strategy satisfaction using the following formula:

[0035] S = w1·U s +w2·C c +w3·T d

[0036] Where S represents strategy satisfaction, U s C is a user satisfaction metric. c For content consistency score, T d To improve time efficiency, w1, w2, and w3 are weight factors, where w1 + w2 + w3 = 1. When the satisfaction level of the strategy falls below a set threshold, the system automatically reverts 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 authentication mechanisms, and has a data backup mechanism to prevent data loss.

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

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

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

[0041] This invention addresses the problems of low efficiency, insufficient personalization, and poor accuracy in content generation in existing technologies through a large-model-based AI content generation system. Traditional content creation relies on manual input and processing, resulting in low efficiency. This invention, however, significantly improves content generation speed by automating data acquisition, preprocessing, feature generation, and optimization modules, meeting the needs of large-scale content creation. Furthermore, existing technologies often lack flexibility and personalization in generated content, failing to accurately meet user needs. This invention introduces a feedback-driven dynamic adjustment mechanism and adaptive optimization strategy, enabling real-time adjustments to generated content based on user needs and feedback, resulting in more personalized and accurate content. Existing technologies often suffer from insufficient accuracy and relevance in generated content. This invention, through a feedback loop and an adaptive multi-level content generation framework, ensures improved relevance and accuracy, avoiding content deviation. Moreover, existing technologies frequently suffer from inconsistent styles and excessive time delays in content generation. This invention, through a multi-round adaptive generation mechanism and real-time feedback mechanism, guarantees consistency in generated content and improves the real-time performance of the generation process. Finally, traditional systems fail to fully consider real-time user feedback, which may result in generated content that does not meet user expectations. In contrast, this invention significantly improves the user experience by collecting and analyzing user feedback data in real time and dynamically adjusting the content generation strategy. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of an artificial intelligence content generation system framework 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 provided by the present invention.

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

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

[0046] Figure 5 A schematic diagram of the control and coordination module structure provided for the invention.

[0047] Figure label:

[0048] A large-model-based artificial intelligence content generation system 100, comprising 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] The unit comprises: a cleaning unit 1021, a formatting unit 1022, a noise reduction unit 1023, a feature extraction unit 1024, a feature generation unit 1031, a content generation unit 1032, a quality assessment unit 1033, a model update unit 1034, a requirements analysis unit 1041, an optimization and adjustment unit 1042, a feedback integration unit 1043, a style adaptation unit 1044, a content publishing unit 1051, a feedback collection unit 1052, an instruction evolution chain construction unit 1061, and a strategy tracing and dynamic selection unit 1062.

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

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort 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 number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of this application, unless otherwise stated, "multiple" means two or more.

[0053] To more clearly illustrate the technical solution of the present invention, the present invention will be described in detail below with reference to specific embodiments, but it should not be construed as a limitation on the scope of protection of the present invention.

[0054] In this embodiment, as Figure 1 As shown, an AI content generation system 100 based on a large model 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. In specific implementation, the various modules work together to complete the entire content generation process.

[0055] Specifically, this invention provides an AI content generation system based on a large-scale model. This system 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 improve the intelligence and accuracy of content generation, the system employs a pre-trained large-scale model and dynamically adjusts it based on real-time feedback, ensuring that the generated content is not only efficient but also highly relevant.

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

[0057] Next, the data preprocessing module 102 performs cleaning, formatting, and denoising operations on the raw content data. Specifically, the cleaning unit 1021 removes invalid and duplicate data, the formatting unit 1022 standardizes the data format, making subsequent processing more standardized and concise, and the denoising unit 1023 effectively eliminates noise in the data through statistical analysis or machine learning algorithms, retaining valuable feature information. Through these processes, the data quality is greatly improved, ensuring that subsequent model training and content generation are more accurate and efficient.

[0058] Building upon this, the large-scale model processing module 103 employs a pre-trained large-scale model for core content generation. Through training on a large-scale dataset, the large-scale model can learn and master different knowledge across multiple domains and possesses powerful content generation capabilities. The feature generation unit 1031 within this module utilizes the large-scale model to extract features from the processed dataset, generating content feature vectors. These feature vectors highly summarize key feature information in the data and provide necessary input for subsequent content generation. Based on these feature vectors, the content generation unit 1032 generates preliminary content output through the large-scale model's decoder and optimizes the generation process using a dynamic adjustment mechanism.

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

[0060] The subsequent content optimization module 104 optimizes the initial content based on user needs and real-time feedback. In this module, the needs analysis unit 1041 first analyzes the user's specific needs, extracting user preferences and content constraints; the optimization and adjustment unit 1042, based on user needs and the initial content, uses natural language processing technology to polish the text and adjust the structure, optimizing the content's logic and expression; the feedback integration unit 1043 dynamically adjusts the optimization strategy based on user feedback, ensuring that the generated content highly matches user expectations. The content optimization module 104 not only improves the quality of the content but also ensures its precise match with user needs.

[0061] The output and feedback module 105 is used to publish 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 can be effectively conveyed to end users. Users interact through the platform by liking, commenting, and sharing. The feedback collection unit 1052 collects user feedback data in real time, including the number of likes, comment content, and usage time. The 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, based on user feedback, determines and optimizes the content generation strategy using an adaptive multi-level content generation framework. Within this module, the instruction evolution chain construction unit 1061 generates an instruction evolution chain based on feedback data, dynamically backtracks the generation 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, ensuring adaptive evolution and contextual consistency during content generation. Furthermore, to improve the accuracy and relevance of content generation, the system continuously updates the large model based on user feedback, constantly optimizing the model's parameters through incremental learning to improve the relevance, accuracy, and efficiency of content generation.

[0063] This embodiment addresses the problems of low content generation efficiency, insufficient personalization, and poor accuracy in existing technologies through a large-model-based AI content generation system 100. Traditional content creation relies on manual input and processing, resulting in low efficiency. This invention, however, significantly improves content generation speed by automating data acquisition, preprocessing, feature generation, and optimization modules, meeting the needs of large-scale content creation. Furthermore, existing technologies often lack flexibility and personalization in generated content, frequently failing to accurately meet user needs. This invention, by introducing a feedback-driven dynamic adjustment mechanism and adaptive optimization strategy, can adjust the generated content in real time based on user needs and feedback, thereby generating more personalized and accurate content. Existing technologies often suffer from insufficient accuracy and relevance in generated content. This invention, through a feedback loop and an adaptive multi-level content generation framework, ensures improved relevance and accuracy, avoiding content deviation. Moreover, existing technologies often suffer from inconsistent styles and excessive time delays in content generation. This invention, through a multi-round adaptive generation mechanism and a real-time feedback mechanism, ensures consistency in generated content and improves the real-time performance of the generation process. Finally, traditional systems fail to fully consider real-time user feedback, which may result in generated content that does not meet user expectations. In contrast, this invention significantly improves the user experience by collecting and analyzing user feedback data in real time and dynamically adjusting the content generation strategy.

[0064] In some embodiments, such 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 noise reduction unit 1023. The specific implementation of each unit will be described in detail below.

[0065] Specifically, the 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, while duplicate data refers to redundant data generated during the data acquisition process due to multiple collections of the same content. The cleaning unit 1021 filters the data by setting rules and removes data items that do not meet the requirements, ensuring that the content data entering the system is accurate and valid.

[0066] Furthermore, the formatting unit 1022's task is to convert the acquired raw content data into a unified data format. Since the raw data may come from different data sources, its format and structure may differ. The formatting unit 1022 uses set data conversion rules to unify the data format, ensuring it conforms to the system's processing standards. For example, for text data, the formatting unit 1022 will unify character encoding formats, paragraph marks, and text labels; for image data, it may perform size adjustments and color normalization.

[0067] Furthermore, the denoising unit 1023 plays a crucial role in the data preprocessing process. The denoising unit 1023 eliminates noise from the original 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. The denoising unit 1023 uses algorithms to identify and remove this interfering information, generating a clear and accurate preprocessed dataset. These algorithms can be rule-based traditional methods or deep learning-based adaptive methods, depending on the data type and application scenario.

[0068] Understandably, after the preprocessed dataset is generated, it will be sent to the next processing stage of the system, such as the large model processing module 103. Through the above-mentioned cleaning, formatting, and denoising steps, 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 dataset, including keywords, sentiment, and topic distribution, and calculate feature weights using the following formula:

[0070] Specifically, the feature extraction unit 1024 extracts multiple features from the cleaned, formatted, and denoised dataset. The extracted features include keywords, sentiment, and topic distribution within the text. Keywords are identified using word frequency statistics and natural language processing techniques, sentiment is calculated using sentiment analysis algorithms, and topic distribution is generated using topic models (such as LDA models).

[0071] Furthermore, the formula for calculating the feature weights is as follows:

[0072]

[0073] The parameters are defined as follows:

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

[0075] TF i The term frequency of feature i in the dataset represents the frequency with which the feature appears in the text. The higher the term frequency, the greater the influence of the feature on the generated result.

[0076] DF i : The number of documents containing feature i, representing the prevalence of that feature across all documents. Its prevalence is measured by calculating the frequency of its occurrence across all documents.

[0077] N: Total number of documents, used to standardize the frequency of features across the entire dataset.

[0078] S i Sentiment score: Reflects the sentiment tendency represented by feature iii. Sentiment analysis models (such as deep learning-based sentiment classification models) are used to predict the sentiment in the text and assign a corresponding numerical score.

[0079] α and β: Adjustment coefficients, used to control the degree of influence of word frequency and sentiment on feature weights, respectively. The values ​​of adjustment coefficients α and β are obtained through experimental optimization, and typically 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 using the above formula i The input is passed 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 weighting coefficients of 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 the generation of social media content that requires a high degree of emotional resonance, the value of β can be appropriately increased to focus more on sentiment.

[0082] In some embodiments, such 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 evaluation unit 1033.

[0083] Specifically, the feature generation unit 1031 is used to extract features from the preprocessed dataset using a pre-trained large model and generate content feature vectors. This pre-trained large model can be a deep learning-based natural language processing model, such as GPT (Generative Pre-trained 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 dataset. These features will serve as the basis for content generation. In particular, the feature generation unit 1031 converts the raw data into fixed-dimensional feature vectors using an encoder; the specific number of dimensions is adjusted according to the model's training and task requirements.

[0084] Furthermore, the content generation unit 1032 generates preliminary content output based on the generated content feature vector through a decoder. The decoder can be a pre-trained decoder corresponding to the feature generation unit 1031. This content generation unit 1032 can generate not only text content but also image or video content, depending on user needs and the supported formats of the target platform. The generated preliminary content output can be natural language text, images, videos, or multimodal mixed content.

[0085] Specifically, to ensure that the generated content meets preset standards, the quality assessment unit 1033 performs a quality assessment on the initial content output and generates assessment results. The quality assessment unit 1033 evaluates the content quality based on preset quality standards, such as text readability, grammatical accuracy, image clarity, and structural rationality. For text content, quality assessment can use evaluation indicators such as BLEU (Bilingual Evaluation Understudy) score and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score for quantitative evaluation; 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 evaluation results output by the quality assessment unit 1033 are fed back to the content optimization module 104. This module adjusts the quality of the generated content based on the evaluation results to ensure that the final generated content meets the user's needs. For example, if the evaluation results show that the sentiment of the content deviates from expectations, the content optimization module 104 will adjust the optimization strategy, increase or decrease the emotional tone, or adjust the structure to better match user needs and the requirements of the target platform.

[0087] In this embodiment, as Figure 2 As shown, in some embodiments, the large model processing module 103 further includes a model update unit 1034. The function of the model update unit 1034 is to update the parameters of the pre-trained large model through incremental learning based on feedback information, so as to improve the relevance and accuracy of content generation. The working principle and implementation of this module will be 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 incremental learning to gradually update the parameters of the pre-trained large model without retraining the entire model, thereby improving efficiency and reducing computational resource consumption. The key to incremental learning is its ability to gradually adjust the model parameters based on new data without requiring full training of the model each 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 key factors affecting the quality of the generated content. For example, if user feedback indicates 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 the grammatical accuracy, sentiment, and thematic consistency of the content) to make fine-grained adjustments to the model to ensure that the generated content better meets user needs.

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

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

[0092] Specifically, the large model updated through this incremental learning will be better able to reflect user preferences, content relevance, and other evaluation criteria in the next content generation, thus 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 in the next content generation, and the generation efficiency is significantly improved compared to the initial training.

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

[0094] Specifically, the requirements analysis unit 1041 is used to parse user requirements and extract user preferences and content constraints. This unit analyzes and identifies user requirements by receiving input information from the user and historical feedback data. For example, the user may provide specific content types (such as text, images, or multimedia), style requirements (such as formal, humorous, etc.), and some constraints (such as length, structure, etc.). In this embodiment, the requirements analysis unit 1041 uses Natural Language Processing (NLP) technology to semantically understand the user's input text requirements, converting them into structured information usable 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 using natural language processing or image processing techniques. This unit's task is to refine or structurally adjust the generated content based on user preferences and constraints extracted by the requirements analysis unit 1041. For example, if the user prefers a formal tone, the optimization and adjustment unit 1042 will ensure the generated text meets this requirement through text modification and sentence structure adjustment. If the user specifies composition or color tone requirements for the image content, the optimization and adjustment unit 1042 will adjust the image style based on these requirements. This unit can use generative adversarial networks (GANs) from deep learning to adjust the image style, ensuring the generated content highly matches the user's needs.

[0096] Specifically, the feedback integration unit 1043 dynamically adjusts the optimization strategy based on user feedback. This unit receives real-time feedback data from the output and feedback module 105, analyzes user satisfaction with the generated content, and adjusts the optimization strategy accordingly. For example, if a user's initial feedback on the content is "not attractive enough," the feedback integration unit 1043 will adjust the content generation direction in the optimization strategy based on the sentiment analysis results of the feedback, enhancing the attractiveness of the generated content. The unit will decide whether to add detailed descriptions, adjust the tone, or make structural modifications based on the specific feedback regarding the content. In this way, the content generation process can adapt to user needs and preferences, continuously optimizing.

[0097] Furthermore, the style adaptation unit 1044 adjusts the form and tone of the generated content according to the style requirements of the target platform or user group. For example, if the target platform is social media and users prefer a light and humorous style, the style adaptation unit 1044 will adjust the language style of the generated content accordingly, 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 more formal and academic language. This unit automatically adjusts the expression of the content by analyzing the user group characteristics, historical content style, and platform specifications of the target platform to ensure that the content achieves the best results on different platforms or user groups.

[0098] Specifically, to achieve this goal, the style adaptation unit 1044 can combine deep learning technology, utilizing existing corpora and style tags, to perform style transfer 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 color tone, composition, and other elements of the images through image style transfer technology, so that the style of the images and text is 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 cooperate with each other to complete the optimization process of the generated content. The output of each unit is passed to the next unit, forming a closed-loop optimization process to ensure that the final content meets the user's needs, the platform's requirements, and can be improved in quality.

[0100] In some embodiments, such 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 final generated content to the target platform in text, image, or multimedia format. According to the requirements of the target platform, the content publishing unit 1051 automatically converts the generated content into a format suitable for the platform and sends it to the target platform via an interface. These platforms can be social media, news websites, online stores, or user-customized dedicated platforms, etc. If the content is in text format, the content publishing unit 1051 converts the text data into a format supported by the platform (such as HTML, Markdown, etc.). If the content is in image or video format, the publishing unit compresses the image or video and converts it into a file format supported by the platform to ensure successful upload. The key technologies of this unit lie in its compatibility with the target platform and data transmission efficiency.

[0102] Furthermore, the feedback acquisition unit 1052 is used to collect user feedback data in real time, such as likes, comments, shares, and usage time of the final content, and convert this feedback data into structured information, which is then transmitted to the control and coordination module 106. The feedback acquisition unit 1052 interacts with the target platform through an interface to obtain user interaction information in real time. For example, on social media platforms, user likes, comments, and reposts can be collected as feedback data; on video platforms, user viewing time and frequency are also important feedback indicators. The feedback acquisition unit 1052 can accurately capture this information and analyze and process it to form structured feedback data, ensuring the effective transmission and use of information.

[0103] Specifically, to improve the accuracy of feedback collection, the feedback collection unit 1052 can also utilize machine learning technology to predict which types of feedback data will 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 collecting feedback information closely related to user satisfaction and content quality, thereby improving the quality and value of the feedback data.

[0104] Furthermore, the feedback acquisition unit 1052 can also utilize sentiment analysis technology to analyze the sentiment tendencies of user comments, converting user comment information into sentiment scores, and transmitting this data to the control and coordination module 106. Through sentiment analysis, the system can identify users' positive or negative emotional responses to content, thereby helping the content optimization module 104 better understand users' true needs and make targeted optimization adjustments.

[0105] Specifically, the content publishing unit 1051 and the feedback collection unit 1052 work together to form a complete content publishing and feedback collection process. The content publishing unit 1051 pushes the final content to the target platform, while the feedback collection unit 1052 continuously collects user feedback to ensure that the dissemination effect of the content 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, meaning it can acquire 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 basis for content optimization, helping to achieve more precise content adjustments and optimizations.

[0107] In some embodiments, such as Figure 5 As shown, the control and 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 consists of multiple generation path nodes, each recording contextual parameters and user satisfaction metrics corresponding to a specific content generation strategy. Each path node represents the application of a content generation strategy, including input data related to that strategy, parameter settings during the generation process, and the relationship between the content generated by that strategy and user feedback. For example, a node can record user comments, likes, and dwell time received by content generated through a specific sentiment analysis model, as well as contextual information directly related to the effectiveness of the strategy (such as time, region, user group, etc.). This evolution chain clearly demonstrates the evolution process and history of the generation strategy, helping the system make more accurate decisions in subsequent content generation processes.

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

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

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

[0112] 3. User satisfaction metrics: such as number of likes, sentiment analysis of comments, sharing rate, and dwell time.

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

[0114] S = w1·U s +w2·C c +w3·T d

[0115] Where S represents strategy satisfaction, U s C is a user satisfaction metric. c For content consistency score, T d To ensure generation time efficiency, w1, w2, and w3 are weighting factors, and w1 + w2 + w3 = 1.

[0116] Furthermore, the specific definitions and calculation methods of each parameter in this formula are as follows:

[0117] U s User satisfaction metrics: A comprehensive user satisfaction score is calculated based on factors such as the number of likes, sentiment analysis of comments, and number of shares.

[0118] C c Content Consistency Score: This score assesses the consistency between content generation and user needs, platform requirements, and expected goals. For example, does the content align with user preferences and fit 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 better for strategy optimization.

[0120] Specifically, when the strategy satisfaction level falls below a set threshold, the strategy tracing and dynamic selection unit 1062 automatically reverts to the previous path node and switches the content generation strategy to achieve adaptive evolution and contextual consistency maintenance in multiple rounds of content generation. This process ensures that the system can continuously adjust and optimize the generation strategy, repeatedly learn during content generation, and make more appropriate strategy choices, thereby improving content quality and user satisfaction.

[0121] Furthermore, the strategy tracing and dynamic selection unit 1062 can dynamically adjust the values ​​of weight factors w1, w2, and w3 based on user feedback. For example, as a user's preference for a certain type of content gradually increases, the system can correspondingly increase the content consistency score C. c Weighting reduces generation time efficiency T d The weighting of content will be adjusted to give greater emphasis to its refinement and relevance.

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

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

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

[0125] In addition, the security and communication module 107 also features a data backup mechanism. During system operation, all important data will be backed up regularly and stored in the cloud or other secure storage locations. Backup data can be automatically generated at preset time intervals and stored in an encrypted manner to ensure that the system can quickly recover in the event of an accident, preventing data loss or damage.

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

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

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

[0129] Furthermore, the visualization unit can dynamically adjust the displayed content based on user feedback. If a user reports that the quality of a certain aspect of the generated content is unsatisfactory, the system will use a visual interface to highlight the weaknesses in the current quality assessment (such as grammatical issues in a piece of text or color distortion in an image) and guide the user to perform further operations or adjustments. For example, if a user wants to enhance the emotional tone in text generation, they can specify the emotional tone using the adjustment button on the interface. The system will then adjust the generation strategy according to this instruction and display the effect in real time.

[0130] Specifically, the instruction input unit receives customized requirements or adjustment instructions from the user and transmits them to the control coordination module 106 or the content optimization module 104. Users can input specific requirements for the generated content through this unit, such as text style, image theme, and video pacing. User-input instructions can be natural language instructions, which the system will parse using natural language processing (NLP) technology and convert into specific generation parameters. If the user selects an image style, adjusts text tone, or sets video length through a graphical interface, the instruction input unit will transmit this information to the relevant modules, guiding the system to adjust the content generation process in real time. For more complex requirements, the system also supports multi-turn interactions, allowing users to continuously adjust instructions until their needs are met.

[0131] Furthermore, the instruction input unit also features intelligent recommendation functionality. 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 hashtags, emotional expressions, or popular visual styles based on the user's past content generation, helping the user more easily generate content that matches the style of the target platform.

[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 to 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 transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A large-model-based artificial intelligence content generation system, characterized in that, include: A data acquisition module is used to acquire raw content data from multiple data sources, including text data, image data, and user interaction data. The data preprocessing module is used to clean, format, and denoise the original content data to generate a preprocessed dataset. The data preprocessing module includes a feature extraction unit, which is used to extract key content features from the preprocessed dataset, including keywords, sentiment, and topic distribution, and calculate feature weights using the following formula: in, Let i be the weight of the i-th feature. For features, word frequency in the document, N represents the number of documents containing this feature, and N is the total number of documents. Score for sentiment tendency and To adjust the coefficients and improve the generation efficiency of the large model processing module; The large model processing module generates content feature vectors based on the pre-processed dataset by using a pre-trained large model, and generates preliminary content output by combining a feedback-driven dynamic adjustment mechanism. The content optimization module performs multi-level optimization on the initial content output based on user needs and feedback information to generate the final content. The output and feedback module 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, which is the processing result based on user feedback data. A control and coordination module, which, based on the feedback information, uses an adaptive multi-level content generation framework to determine and optimize the content generation strategy, and coordinates the operation of the large model processing module and the content optimization module, includes: An instruction evolution chain construction unit is used to generate an instruction evolution chain based on the user feedback data. The instruction evolution chain consists of multiple generation path nodes, and each node records context parameters and user satisfaction indicators corresponding to a specific content generation strategy. The context parameters include time, user attributes, and platform characteristics. The user satisfaction indicators include the number of likes, comment sentiment analysis, sharing rate, and dwell time. Each path node represents the application of a content generation strategy. The node includes the input data related to the strategy, the parameter settings in the generation process, and the relationship between the content generated by the strategy and user feedback. The strategy tracing and dynamic selection unit is used to perform path backtracking, comparison, and optimization selection based on the instruction evolution chain, and calculates the strategy satisfaction using the following formula: Where S represents strategy satisfaction. As a user satisfaction indicator, To score for content consistency, To improve time efficiency, , , Weighting factors + + = 1, when the satisfaction of the strategy is lower than the set threshold, automatically fall back to the previous path node and switch the content generation strategy to achieve adaptive evolution and context consistency maintenance in multi-round content generation.

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

3. The system according to claim 1, characterized in that, The large model processing module includes: A feature generation unit is used to extract features from the preprocessed dataset using the pre-trained large model to generate the content feature vector. A content generation unit, which generates the preliminary content output based on the content feature vector through a decoder; A quality assessment unit is used to assess the quality of the initial content output according to preset assessment standards and to feed the assessment results back to the content optimization module.

4. The system according to claim 1, characterized in that, The large model processing module further includes a model update unit, which is used to update the parameters of the pre-trained large model through incremental learning based on the feedback information, so as to improve the relevance and accuracy of content generation.

5. The system according to claim 1, characterized in that, The content optimization module includes: A requirements analysis unit, which is used to parse the user requirements and extract user preferences and content constraints; An optimization and adjustment unit, which, based on the user preferences and the initial content output, performs text polishing or structural adjustment using natural language processing technology; A feedback integration unit is used 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.

6. The system according to claim 1, characterized in that, The output and feedback module includes: A content publishing unit, which is used to publish the final content to the target platform in the form of text, images or multimedia. The feedback collection unit is used to collect users' likes, comments, and usage time of the final content in real time, and transmit the structured information to the control and coordination module.

7. The system according to claim 1, characterized in that, It further includes a security and communication module, which is used to realize data transmission between modules, ensure data security through encryption algorithms and authentication mechanisms, and has a data backup mechanism to prevent data loss.

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

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