Dynamic optimization method for intelligent content generation workshop platform

Abstract

The invention relates to a dynamic optimization method for an intelligent content generation workshop platform, and belongs to the technical field of artificial intelligence and content generation. The method comprises the following steps: acquiring a user demand, constructing a demand feature vector, matching the demand feature vector with a functional feature matrix of a pre-training resource cluster, forming a target generation resource combination, and distributing adaptive computing power and a material resource bundle; starting a polymorphic content collaborative generation mechanism based on the resource combination, performing content multi-dimensional construction, and generating an initial content product; constructing a user evaluation scene operation industry standard three-dimensional feedback acquisition system, obtaining feedback data, and extracting and generating effect evaluation features; and calculating and adjusting core configuration parameters of the generation system based on the feature weight, updating the core configuration parameters to a dynamic generation framework, and forming an iterated content generation system. Accurate matching of generated resources and demands is achieved, the generation process is dynamically optimized through multi-dimensional feedback, and the content generation quality and the platform self-adaptive capacity are improved.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence and content generation technology, specifically relating to a dynamic optimization method for an intelligent content generation workshop platform. Background Technology

[0002] In existing technologies, intelligent content generation platforms typically employ pre-defined, linear processes to respond to user requests. These platforms first parse the user's text input, then invoke one or more pre-defined content generation models to create content, ultimately outputting a single text or visual element. Resource allocation throughout the process is often static, meaning computational resources are pre-allocated based on model type, lacking dynamic adjustments based on real-time demand complexity and scenario characteristics. After content generation, its effectiveness evaluation largely relies on subjective user ratings or simple click-through rates—single metrics that are fragmented and one-dimensional, making it difficult to systematically guide the optimization and evolution of the generation process itself.

[0003] A more prominent problem lies in the relative isolation of each stage in the generation process. There is a lack of effective collaboration and feedback loops between requirements analysis, resource matching, content construction, and effect evaluation. This makes it difficult for the platform to accurately understand complex content needs (such as requiring copywriting, images, and specific layout styles simultaneously), and hinders the deep collaborative generation and integration of multiple content elements, including text and visuals. Furthermore, due to the lack of a multi-dimensional feedback system that integrates user subjective evaluations, actual content operation data, and objective industry standards, the optimization and adjustment of the generation model are often arbitrary, failing to accurately iterate on adaptability deviations or quality shortcomings specific to application scenarios. The algorithms and materials in the resource library are also often static, unable to dynamically update matching strategies based on feedback. This results in insufficient adaptability of the platform when facing emerging scenarios or specialized needs, leading to bottlenecks in the quality, adaptability, and efficiency of the generated products.

[0004] Therefore, a dynamic optimization method is needed to break the aforementioned deadlock. This method should achieve a systematic, self-iterable, closed-loop process, from demand understanding and intelligent resource matching to collaborative generation of multi-format content and multi-source feedback-driven processes. Its core objective is to enable content generation platforms to flexibly allocate resources like a "workshop" and continuously learn and evolve based on multi-dimensional feedback like an "organism," thereby significantly improving the accuracy, integration, quality reliability, and adaptability of content generation to complex and ever-changing demands. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a dynamic optimization method for an intelligent content generation workshop platform. The objective of this invention can be achieved through the following technical solution: A dynamic optimization method for an intelligent content generation workshop platform includes: S1: Obtain the user input content to generate requirements and construct a requirement feature vector; calculate the adaptability between the requirement feature vector and the functional feature matrix of the pre-trained resource cluster to form a target generation resource combination, and at the same time match the material resource package of the corresponding scene and allocate the adaptation computing power to achieve accurate matching between generation resources and requirements. S2: Based on the generated resource combination, initiate a multi-form content collaborative generation mechanism, input the demand feature vector and material resource package into the generation process, and construct the content in multiple dimensions; form an initial content product through preset multi-dimensional content integration rules; S3: Construct a three-dimensional feedback acquisition system of user evaluation, scenario operation and industry standard, obtain multi-dimensional feedback data corresponding to the initial content product, perform standardized processing on the feedback data, extract generation effect evaluation features, and construct a three-dimensional feedback-driven generation effect evaluation feature set. S4: Based on the aforementioned feature set for evaluating the generation effect, calculate the weight of each feature through feature weight calculation logic, adjust the core configuration parameters of the generation system, update the optimized generation resource configuration parameters and collaborative operation strategy to the platform's dynamic generation framework, and form an iterative content generation system.

[0006] Specifically, the method for constructing the demand feature vector is as follows: The system performs semantic parsing on the user-input requirement text, extracting core requirement elements through word segmentation and core element filtering; it calls a preset scene feature library to match the core requirement elements with scene information in the library to obtain scene-related elements for the corresponding application scenario; it performs quantization encoding on the core requirement elements and scene-related elements respectively, converting textual elements into vectors through word embedding and normalizing numerical elements; it assigns weights based on element importance, and then weights and fuses the encoded element vectors to generate a requirement feature vector.

[0007] Specifically, the method for forming the target generation resource combination is as follows: A functional feature matrix is ​​constructed for the pre-trained resource cluster. The functional feature matrix includes the type, scenario adaptation range, quality output capability, and computing power requirement characteristics of each pre-trained algorithm logic within the cluster. The core weight ratio of the requirement feature vector is obtained based on the content theme and application scenario in the core requirement dimension. The fit between the requirement feature vector and the feature vector of each pre-trained algorithm logic in the functional feature matrix is ​​calculated, and pre-trained algorithm logics with a fit higher than a preset threshold are selected. The selected pre-trained algorithm logics are subjected to collaborative compatibility verification to generate conflict-free target generation resource combinations.

[0008] Specifically, the method for achieving precise matching between generated resources and requirements is as follows: Based on the content themes, quality thresholds, and application scenarios of the core requirement dimensions, the selected target generated resource combinations undergo adaptation consistency verification, and the resource combination parameters are corrected. The application scenarios, output formats, and audience attributes of the core requirement dimensions are associated, and material resource packages containing scene-specific elements, format adaptation, and style matching are selected from the preset material database. Combining the real-time computing power consumption of the target generated resource combinations, the complexity of material processing, and the timeliness requirements of content generation, adaptation computing power is dynamically allocated and elastic redundancy is reserved. Cross-adaptation verification is performed on the target generated resource combinations, material resource packages, and adaptation computing power, and resource matching results that meet operational stability requirements are generated through resource collaborative simulation testing.

[0009] Specifically, the multi-form content collaborative generation mechanism includes text generation logic, visual construction logic, logic verification logic, and preset multi-dimensional content integration rules; each logic module works together through a data interaction interface to perform operations related to the generation and integration of multi-form content.

[0010] Specifically, the method for constructing content in multiple dimensions is as follows: Based on the demand feature vector analysis, the content is constructed with the theme orientation, style standards, and scene adaptation requirements; the material resource package is classified and decomposed into basic text materials and basic visual materials; the text generation logic and visual construction logic are started to operate in parallel. The text generation logic constructs the core copy according to the theme orientation and style standards, while the visual construction logic generates supporting visual elements by combining scene adaptation requirements and core copy information; the core information of the two types of generation logics is shared simultaneously through the data interaction interface.

[0011] Specifically, the method for forming the initial content product is as follows: It invokes preset multi-dimensional content integration rules, integrates text and visual elements based on application scenario format requirements and style standards, performs logical conflict verification and compliance verification on the integrated content, and generates an initial content product containing text, visual elements and scenario-adapted formats.

[0012] Specifically, the method for constructing the three-dimensional feedback collection system of user evaluation, scenario operation, and industry standards is as follows: Configure the user review collection link, set the trigger rules of the platform interaction interface, and trigger the collection program after the user submits a review to collect satisfaction scores and text suggestions; deploy the scenario running program in the content delivery channel, preset the timed collection cycle of dissemination data and interaction data, and build an industry standard collection link, connect to the industry compliance verification interface and quality evaluation system; unify the data field format of the three types of links, and establish a data aggregation channel.

[0013] Specifically, the method for extracting and generating evaluation features is as follows: The system acquires multi-dimensional feedback data collected by a three-dimensional feedback acquisition system, performs anomaly removal and standardization processing, and generates a standardized dataset. A feature extraction algorithm is used to initially extract features from the standardized dataset to obtain an original feature set. Based on three preset feedback feature dimensions—demand matching degree, quality compliance rate, and scene adaptation deviation—the original feature set is filtered to generate a subset of related features. The feature subset is then normalized and integrated to output the generated effect evaluation features.

[0014] Specifically, the method for calculating the weights of each feature through feature weight calculation logic is as follows: Obtain a set of features for evaluating the generation effect driven by 3D feedback; assign weights to each feature using the analytic hierarchy process (AHP) to generate initial weights; construct an optimization objective function based on the initial weights, using the negative of the sum of the products of each feature and its corresponding initial weight as the core expression of the objective function; iteratively solve the minimum value of the objective function using the gradient descent algorithm, and output the dynamic optimal weights of each feature.

[0015] Specifically, the method for adjusting the core configuration parameters of the generation system is as follows: Obtain the dynamic optimal weights of each feature and the features for evaluating the generation effect, and adjust the configuration dimension corresponding to the feature with the highest weight; optimize the semantic extraction dictionary and feature vector construction parameters and update the matching threshold based on the demand matching degree dimension; adjust the iteration steps and loss function parameters of the pre-training algorithm and supplement the special training data based on the quality compliance rate dimension; update the material classification labels and matching algorithm and adjust the collaborative generation scene adaptation parameters based on the scene adaptation deviation dimension.

[0016] Specifically, the method for forming the iterative content generation system is as follows: The core configuration parameters of the adjusted generation system are summarized, and a collaborative compatibility check is performed to generate an optimized parameter set. The optimized parameter set is then imported into the platform's dynamic generation framework based on a preset format for updating and overwriting. A parameter update report is generated to record the adjustment dimensions, changes, and corresponding feedback characteristics. The updated dynamic generation framework is then deployed to the platform's content generation process as the iterative content generation system.

[0017] The beneficial effects of this invention are as follows: (1) This invention constructs user needs as multi-dimensional feature vectors and performs intelligent adaptation calculations with the functional matrix of a pre-trained resource cluster, which can transcend the traditional keyword matching mode and deeply understand the connotation of the needs. On this basis, the system can synchronously associate scenario-based materials and dynamically allocate the most suitable computing resources, thereby ensuring the correctness of the direction of content generation and the efficiency of resource utilization at the source, improving the accuracy of demand response and the overall resource scheduling efficiency.

[0018] (2) A multi-dimensional feedback-driven closed-loop optimization mechanism was constructed. Unlike the traditional approach that relies on a single user rating, this invention innovatively establishes a three-dimensional feedback system that integrates subjective user evaluations, objective data on content scene operation, and industry standards. Through standardized processing and feature extraction of multi-source feedback data, the system can quantitatively evaluate the generation effect and dynamically adjust core configuration parameters based on feature weights. This mechanism makes the optimization process based on evidence and targeted, realizing a fundamental transformation of the content generation system from static and fixed to dynamic and adaptive, and continuously driving the improvement of generation quality and scene fit.

[0019] (3) An iterative and evolving content generation workshop ecosystem has been formed. This invention updates the optimized resource allocation and collaboration strategies to the platform's dynamic generation framework in real time, enabling the entire system to continuously learn and evolve. Each cycle of content generation and feedback provides an optimization basis for the next iteration of the system, allowing the platform to continuously adapt to changes in user preferences, the needs of emerging scenarios, and updates to industry standards. This not only ensures the stability and cutting-edge nature of long-term content production but also enhances the platform's vitality and market competitiveness. Attached Figure Description

[0020] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 This is a flowchart illustrating a dynamic optimization method for an intelligent content generation workshop platform according to the present invention. Figure 2 This is an architecture diagram of a dynamic optimization method for an intelligent content generation workshop platform according to the present invention. Detailed Implementation

[0022] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0023] Please see Figure 1-2 A dynamic optimization method for an intelligent content generation workshop platform, comprising: S1: Obtain the user input content to generate requirements and construct a requirement feature vector; calculate the adaptability between the requirement feature vector and the functional feature matrix of the pre-trained resource cluster to form a target generation resource combination, and at the same time match the material resource package of the corresponding scene and allocate the adaptation computing power to achieve accurate matching between generation resources and requirements. S2: Based on the generated resource combination, initiate a multi-form content collaborative generation mechanism, input the demand feature vector and material resource package into the generation process, and construct the content in multiple dimensions; form an initial content product through preset multi-dimensional content integration rules; S3: Construct a three-dimensional feedback acquisition system of user evaluation, scenario operation and industry standard, obtain multi-dimensional feedback data corresponding to the initial content product, perform standardized processing on the feedback data, extract generation effect evaluation features, and construct a three-dimensional feedback-driven generation effect evaluation feature set. S4: Based on the aforementioned feature set for evaluating the generation effect, calculate the weight of each feature through feature weight calculation logic, adjust the core configuration parameters of the generation system, update the optimized generation resource configuration parameters and collaborative operation strategy to the platform's dynamic generation framework, and form an iterative content generation system.

[0024] Specifically, the method for constructing the demand feature vector is as follows: The system performs semantic parsing on the user-input request text, extracting core request elements through word segmentation and core element filtering. It then calls a pre-defined scenario feature library, matching the core request elements with scenario information in the library to obtain scenario-related elements for the corresponding application scenario. The system performs quantization encoding on both the core request elements and the scenario-related elements, converting textual elements into vectors through word embedding and normalizing numerical elements. Weights are assigned based on element importance, and the encoded element vectors are weighted and fused to generate a request feature vector. The core request elements include content theme, audience attributes, quality threshold, and output format; the scenario-related elements include the application scenario's adaptation format, dissemination characteristics, and scenario compliance requirements.

[0025] Specifically, the method for forming the target generation resource combination is as follows: A functional feature matrix is ​​constructed for the pre-trained resource cluster. The functional feature matrix includes the type, scenario adaptation range, quality output capability, and computing power requirement characteristics of each pre-trained algorithm logic within the cluster. The core weight ratio of the requirement feature vector is obtained based on the content theme and application scenario in the core requirement dimension. The fit between the requirement feature vector and the feature vector of each pre-trained algorithm logic in the functional feature matrix is ​​calculated, and pre-trained algorithm logics with a fit higher than a preset threshold are selected. The selected pre-trained algorithm logics are subjected to collaborative compatibility verification to generate conflict-free target generation resource combinations.

[0026] Specifically, the method for achieving precise matching between generated resources and requirements is as follows: Based on the content theme, quality threshold, and application scenario of the core requirement dimensions, the selected target generated resource combination is subjected to adaptation consistency verification, and the resource combination parameters are corrected until the adaptation deviation value is lower than the preset threshold. The application scenario, output format, and audience attributes of the core requirement dimensions are associated, and material resource packages containing scene-specific elements, format adaptation, and style matching are selected from the preset material database. Combining the real-time computing power consumption of the target generated resource combination, the complexity of material processing, and the timeliness requirements of content generation, the adaptation computing power is dynamically allocated and elastic redundancy is reserved. Cross-adaptation verification is performed on the target generated resource combination, material resource package, and adaptation computing power. Resource matching results that meet operational stability requirements are generated through resource collaborative simulation testing. The material database is pre-built by the platform, and its data sources include publicly authorized materials, industry-specific authorized materials, and user-uploaded authorized materials. The platform classifies, archives, and dynamically updates various materials according to application scenario, content type, and style attributes.

[0027] This embodiment implements the steps for generating marketing content on a certain platform for a beauty brand. A beauty brand plans to launch a new moisturizing face cream and needs to generate marketing content (including copy and product images) adapted to the platform. The target audience is women aged 20-30, and the style should be fresh and natural, highlighting the core selling points of "gentle moisturizing and suitable for sensitive skin." Combining historical practice data on beauty content generation on the platform with the "Beauty Industry Digital Marketing Content Adaptation Standards," the entire process of requirement analysis and resource matching is completed. Constructing a Demand Feature Vector: 1. Demand Text Acquisition: Users fill out a structured demand form through the platform's visual input interface. The form has built-in validation logic for required fields: "Topic-Audience-Style-Output Format." After successful validation, standardized demand text is generated: "Generate marketing copy and product display images for a new moisturizing face cream on Xiaohongshu, targeting women aged 20-30, with a fresh and natural style, emphasizing gentle moisturizing and suitability for sensitive skin." The platform automatically assigns a unique traceability ID to this demand and stores the text in a distributed demand database, along with metadata such as user account and submission time.

[0028] 2. Semantic Parsing and Core Element Extraction: ① Tool Selection and Configuration: The BERT-base-chinese pre-trained language model (adapted to deep semantic understanding scenarios of short Chinese texts) is adopted, and batch_size=8 is set [Setting Basis]: Based on the computing power of the platform server's single GPU (NVIDIA A10), batch_size=8 can achieve a parsing speed of 10 sentences / second, while ensuring a semantic understanding accuracy of ≥92%, which is the optimal configuration value for historical beauty-related demand parsing projects; ② Word Segmentation and Filtering: The jieba word segmentation tool (with "precise mode" enabled) is used to split the demand text into words such as "generate, new moisturizing face cream, platform, marketing copy, product display image, target audience 20-30-year-old women, fresh and natural style, gentle moisturizing, suitable for sensitive skin"; the part-of-speech tagging tool is used to filter core noun and adjective words, and redundant action words such as "generate" are removed, finally extracting the core elements of the demand: content theme (new moisturizing face cream marketing), target audience attributes (20-30-year-old women), quality threshold (fresh and natural style), and output form (copy + product display image).

[0029] 3. Scene-related element matching: ① Matching logic: Call the preset scene feature library (containing feature data of 120+ mainstream content generation scenes, regularly synchronized with the latest rules of industry platforms), and adopt a dual mechanism of "precise matching + fuzzy matching": precisely match the application scenario of the platform keyword positioning as "platform marketing scenario", and fuzzily match the extended information of the scene related to the theme of "beauty marketing"; ② Result output: Obtain scene-related elements: adapted format (vertical text and images, size 1080×1440px, title contains 1-2 emojis); dissemination characteristics (highly interactive, must include guiding words such as "planting grass" and "review"); scene compliance requirements (prohibit medical terms such as "medical grade" and "cure", efficacy descriptions must be supported by ingredients) based on the "Cosmetics Supervision and Management Regulations" and platform compliance guidelines.

[0030] 4. Quantitative Coding: ① Text-based element coding: The Word2Vec word embedding model was used (training corpus consisted of 100,000+ marketing texts from the beauty industry). Text elements such as "new moisturizing cream," "platform," and "fresh and natural" were transformed into 128-dimensional vectors. Based on the 128-dimensional dimension, which is a commonly used dimension for embedding short Chinese texts, platform testing showed that this dimension can balance vector discriminability and computational efficiency, achieving a 93% accuracy rate in matching beauty-related text elements; ② Numerical element coding: The median value of the target audience age "20-30 years old" was taken as 25. The min-max normalization method was used for processing. The calculation method was: Normalized value = (original value - minimum value) / (maximum value - minimum value), where the minimum age was 18 and the maximum age was 50. The basis for this calculation was: Referencing the age distribution data of the target audience in the beauty industry, the 18-50 age group covers more than 95% of beauty consumers; The result of this calculation was: (25-18) / (50-18) = 0.21875.

[0031] 5. Weight Allocation and Fusion: ① Weight Calculation: A judgment matrix is ​​constructed using the Analytic Hierarchy Process (AHP). The influence of each element on the demand analysis is compared pairwise (e.g., "content theme" has a greater influence on the generation direction than "audience attributes"). After passing the consistency test (CR=0.06<0.1, the test is qualified), the weight ratio is determined as follows: content theme 35%, audience attributes 25%, quality threshold 20%, and scene-related elements 20%. This is based on the platform's experience in weight configuration of 500+ beauty-related demand analysis projects over the past 12 months. This ratio can reduce the demand matching deviation rate to below 3%. ② Vector Fusion: The encoded element vectors are weighted and summed according to their corresponding weights to generate a 128-dimensional demand feature vector (example fragment: [0.12,0.35,0.21,0.18,...]). The vector dimensions are uniformly stored in memory in float32 format.

[0032] Forming a target-generating resource combination 1. Constructing a Functional Feature Matrix: ① Data Collection: Quantify the features of six core algorithm logics (text generation, image generation, video editing, audio synthesis, layout design, and compliance verification) within the pre-trained resource cluster. The quantification dimensions refer to the "Intelligent Content Generation Algorithm Evaluation Specification." ② Explanation and Basis of Quantification Indicators: Taking the target algorithm as an example, the quantification features of the "Platform Style Text Generation Algorithm" are: Algorithm Type (Text Generation, Encoding 1), Scene Adaptability Range (Platform Marketing Scene Adaptability 0.9), Calculation Method: Adaptability = Number of Historical Output Contents that Meet Scene Requirements / Total Output Quantity × 100%, based on statistics from over 1000 platform copywriting generation data in the past 3 months), Quality Output Capability (Text Fluency 0.92), Definition and Calculation: Based on the N-gram language model, calculate the probability of sentence fluency. Fluency = Number of Sentences that Meet Grammar Rules / Total Sentence Quantity × 100%. The historical output fluency of this algorithm is... The value is 0.92, which is higher than the industry average (0.85) and the computing power requirement characteristics (0.5 GPU / h per task); the quantitative characteristics of the "Fresh Image Generation Algorithm" are: algorithm type (image generation, encoding 2), scene adaptation range (platform marketing scene adaptation degree 0.85), and quality output capability (image clarity 0.95). The definition and calculation are based on peak signal-to-noise ratio (PSNR), PSNR=10×log10((2^8-1)^2 / MSE) (n=8-bit image). A clarity of 0.95 corresponds to PSNR≥35dB, which meets the platform's image publishing clarity requirements and computing power requirement characteristics (1.2 GPU / h per task); ③ Matrix construction: according to the two-dimensional structure of "algorithm logic-feature dimension-feature value", an 8-row × 4-column functional feature matrix is ​​generated (rows: 6 types of algorithms + 2 types of backup algorithms; columns: algorithm type, scene adaptation range, quality output capability, computing power requirement).

[0033] 2. Determine the core weight percentage: ① Calculation method: The entropy weight method is used to calculate the information entropy of each requirement dimension. The smaller the information entropy, the higher the dimension differentiation and the larger the weight percentage; ② Calculation results: Among the core requirement dimensions, the information entropy of "Content Theme (Beauty Marketing)" is 0.32, with a weight of 30%; the information entropy of "Application Scenario" is 0.28, with a weight of 35%; the sum of the weight percentages of the two is 65%. The basis for setting this is: the general standard for content generation resource adaptation in the industry is that the weight percentage of the core theme and scenario is not less than 60%. This 65% configuration can further improve the accuracy of resource adaptation.

[0034] 3. Adaptability Calculation and Screening: ① Algorithm Selection: The cosine similarity algorithm is used to calculate the adaptability between the requirement feature vector and the feature vectors of each algorithm. The similarity calculation formula is: cosθ=(A·B) / (|A|×|B|), where A is the requirement feature vector and B is the algorithm feature vector; ② Threshold Setting: A preset similarity threshold of 0.7 is set based on historical data verification from the platform. When the similarity is ≥0.7, the requirement fit of the content generated by the resource combination is ≥85%, and the fit drops sharply when it is below 0.7; ③ Screening Results: The similarity of "Platform Style Text Generation Algorithm" is 0.82, and the similarity of "Fresh Style Image Generation Algorithm" is 0.78, both of which are higher than the threshold of 0.7; the similarity of "Video Editing Algorithm" is 0.45 (not matching the output format "text and image"), which is lower than the threshold and is therefore rejected.

[0035] 4. Collaborative Compatibility Verification: ① Verification Dimensions and Standards: Build a simulation test environment (simulating the actual computing power and network environment of the platform) and verify three core dimensions: interface protocol consistency (both must support HTTP / JSON interfaces), resource usage conflict (peak GPU utilization ≤80% during simultaneous operation to avoid insufficient computing power), and output result format compatibility (text algorithms output UTF-8 encoded text, image algorithms output JPG format images, which can be directly integrated). Based on: the platform's general compatibility standard for multi-algorithm collaborative operation; ② Test Results: Both algorithms support HTTP / JSON interfaces, and the peak GPU utilization during simultaneous operation is 72% (≤80%), and the output formats are compatible; ③ Result Output: Generate a conflict-free target generation resource combination: platform-style text generation algorithm + clean-style image generation algorithm.

[0036] Generate a precise match between resources and demand. 1. Adaptation Consistency Verification: ① Verification Indicators and Weights: Theme matching degree (weight 40%), quality compliance rate (weight 30%), and scenario adaptability (weight 30%) are set, based on the historical weighting configuration of the platform's beauty category requirement adaptation verification. Theme matching is the core prerequisite, hence it is assigned the highest weight. ② Scoring Rules: A 100-point system is used. Each indicator is scored by converting "feature overlap degree / historical compliance rate" (e.g., if the theme keyword overlap degree is 100%, the theme matching degree will receive 100 points). ③ Passing Score and Deviation Threshold Setting: The overall passing score is 85 points, based on the recommended passing score of 80 points in the "Beauty Industry Digital Marketing Content Adaptation Standards" and combined with the platform's past beauty category project data, 85 points is the minimum passing score. The conversion rate of the above projects' content delivery improved by 23% compared to those below 80 points, so a 5-point improvement is guaranteed to ensure effectiveness; the adaptation deviation threshold is ≤5%, calculated as follows: Deviation value = |(Overall score - Passing score) / Passing score| × 100%. Based on engineering practice experience, deviation values ​​> 5% require fine-tuning of parameters, while deviation values ​​≤ 5% do not require fine-tuning, balancing adaptation accuracy and efficiency; ④ Testing and judgment: The overall score for this test = 90 (theme matching degree) × 40% + 88 (quality compliance rate) × 30% + 92 (scenario adaptation degree) × 30% = 90.0 points, which is higher than the passing score of 85 points; Deviation value = |(90-85) / 85| × 100% = 2.3% (≤ 5%), so no fine-tuning of resource combination parameters is required.

[0037] 2. Material Resource Package Selection: ① Selection Rules: Based on application scenarios, output format (text and images), and target audience (women aged 20-30), three levels of selection rules are set: scene element rules (including beauty product display, fresh green plant background, etc.), format rules (vertical 1080×1440px material), and style rules (light colors, fresh feel, color saturation 30%-50%). Basis: Visual feature statistics of highly interactive beauty content on the platform; ② Selection Process: The material database is searched through "keyword search (platform + beauty + fresh) + feature comparison (size + color tone)" to initially select 20 candidate material packages; then, through manual verification (platform operators randomly check 30% of the materials) materials with style deviations are eliminated, and finally the "Platform Beauty Fresh Style Material Package" (including 15 background images, 8 fonts, and 12 product border elements) is determined.

[0038] 3. Dynamic Computing Power Allocation: ① Basic Computing Power Calculation: Using an LSTM computing power demand prediction model, the basic computing power requirement is calculated based on the input algorithm type, number of materials, and generation timeliness requirements: text generation 0.5 GPU / h + image generation 1.2 GPU / h = 1.7 GPU / h; ② Redundancy Setting: The material processing complexity is set to "medium" (including 15 background images and 8 fonts), increasing computing power redundancy by 20%. This is based on the correspondence between the complexity of materials and redundancy configurations in the platform's beauty-related image and text generation projects. "Medium" complexity corresponds to 20% redundancy, which can reduce the generation failure rate to below 1%; ③ Computing Power Allocation: Actual allocated computing power = 1.7 GPU / h × (1 + 20%) = 2.04 GPU / h. Two computing power nodes with a specification of 1.02 GPUs are called from the platform's computing power resource pool. The target generation resource combination is bound through Kubernetes containerization technology to ensure exclusive computing power.

[0039] 4. Cross-fit verification and result output: ① Verification process: Simulate the complete generation process through resource collaboration simulation test (input requirement feature vector and material package, run target resource combination), test duration 10 minutes; ② Verification indicators: generation timeliness (≤30 minutes), output quality (copywriting conforms to Xiaohongshu style, image clarity ≥0.9), resource usage stability (GPU usage fluctuation ≤5%), based on: the core acceptance indicators of platform content generation resource matching; ③ Test results: generation timeliness 25 minutes (≤30 minutes), copywriting style adaptation 95%, image clarity 0.96 (≥0.9), GPU usage fluctuation 3% (≤5%), all indicators meet the standards; ④ Final output: Generate a resource matching result report that meets the operational stability requirements, including target generation resource combination, material resource package, computing power configuration information, and associated requirement ID stored in the matching result database for subsequent S2 steps.

[0040] Specifically, the multi-form content collaborative generation mechanism includes text generation logic, visual construction logic, logic verification logic, and preset multi-dimensional content integration rules; each logic module works together through a data interaction interface to perform operations related to the generation and integration of multi-form content.

[0041] In this embodiment, the social marketing content generation requirements of a new moisturizing face cream from a certain beauty brand have been adapted to the target resource combination of "social marketing style text generation algorithm + fresh style visual construction algorithm". The matched material resources include light-colored background images, fresh style fonts and product detail materials. It is necessary to complete the collaborative construction and integration of "copywriting + product display images" through a multi-form content collaborative generation mechanism to form an initial content product that meets the format requirements of the social marketing scenario.

[0042] Multi-format content collaborative generation mechanism activation and parameter configuration The system initiates text generation, visual construction, and logical validation logic, along with pre-defined multi-dimensional content integration rules. These three logics are collaboratively bound via a RESTful data interaction interface. The interface uses HTTP / JSON as the transmission protocol, with a data transfer rate set to 10MB / s to ensure seamless information synchronization. Specifically, the text generation logic loads training parameters specific to social marketing scenarios (sentence length limited to 15-25 characters / sentence, keyword density ≥3%), the visual construction logic configures fresh-style color parameters (hue value 30-60°, saturation 30%-50%), and the logical validation logic employs a triple validation dimension of "compliance + logic + style adaptability." The pre-defined multi-dimensional content integration rules clearly define the layout standards of "text pinned to the top + visual elements arranged in columns + keyword highlighting."

[0043] Multi-dimensional construction of content Demand Analysis and Material Decomposition: Based on the 128-dimensional demand feature vector generated by S1, the theme orientation (emphasizing gentle moisturizing and suitable for sensitive skin), style standards (fresh and natural, youthful), and scene adaptation requirements (vertical layout, visual emphasis on product texture) of the content construction are analyzed; the material resource package is decomposed into text basic materials (product core selling point description, scenario-based marketing script templates) and visual basic materials (product still life images, green plant background images, fresh style border elements), and stored in the corresponding logical material cache pool.

[0044] The system operates in parallel with two logics: the text generation logic and the visual construction logic run in parallel. The text generation logic, based on the theme and style standards, calls marketing script templates from the cache pool and generates three versions of core copy based on the product's core selling points (example: "A savior for those suffering from dry skin during seasonal changes! This moisturizing cream has a silky smooth texture like milk cream, absorbs instantly upon application, and is safe for sensitive skin~"). The visual construction logic simultaneously extracts keywords such as "milk cream texture" and "suitable for sensitive skin" from the copy, matches them with corresponding product still life images and green plant background images, and constructs three matching visual elements using layer overlay technology, with two highlighting the product texture and one showcasing the usage scenario.

[0045] Real-time sharing of core information: Information synchronization between the two types of generation logic is achieved through data interaction interfaces. The text generation logic pushes copy keywords and style tags to the visual construction logic in real time to ensure that visual elements echo the core information of the copy. The visual construction logic feeds back the size parameters and color values ​​of visual elements to the text generation logic to help adjust the adaptability of the copy layout and avoid conflicts between the text and image styles.

[0046] Initial content product formation Multi-dimensional content integration: The preset multi-dimensional content integration rules are invoked, and the optimal copy (determined after preliminary screening) and 3 visual elements are integrated in a column layout of "1 text + 3 images" according to the social marketing scenario format requirements (vertical 1080×1440px). The copy is placed at the top using a fresh font (font size 24pt, font weight 500), and the visual elements are arranged in the order of "product quality map + usage scenario image + detail display image". The keywords "gentle moisturizing" and "suitable for sensitive skin" are highlighted in yellow.

[0047] Dual verification execution: The logic verification logic performs verification on the integrated content. The compliance verification checks for prohibited terms such as "medical grade" and "radical cure" (no violations were detected this time). The logic verification checks the correlation between images and text (one visual element was found to have a darker tone, which is inconsistent with the fresh style). A verification report is generated and fed back to the visual construction logic through the interface.

[0048] Correction and Product Output: After receiving the verification feedback, the visual construction logic adjusts the brightness parameters of the darker visual elements (increases the brightness value by 15%) and re-uploads them to the integration process. After verifying again and confirming that there are no problems, it generates the initial content product containing text, visual elements, and scene-adapted formats. The product format is JPG (text and image integrated version) + TXT (pure text version). The associated requirement ID is stored in the product database for subsequent S3 steps to call.

[0049] Specifically, the method for constructing content in multiple dimensions is as follows: Based on the demand feature vector analysis, the content is constructed with the theme orientation, style standards, and scene adaptation requirements; the material resource package is classified and decomposed into basic text materials and basic visual materials; the text generation logic and visual construction logic are started to operate in parallel. The text generation logic constructs the core copy according to the theme orientation and style standards, while the visual construction logic generates supporting visual elements by combining scene adaptation requirements and core copy information; the core information of the two types of generation logics is shared simultaneously through the data interaction interface.

[0050] Specifically, the method for forming the initial content product is as follows: It invokes preset multi-dimensional content integration rules, integrates text and visual elements based on application scenario format requirements and style standards, performs logical conflict verification and compliance verification on the integrated content, and generates an initial content product containing text, visual elements and scenario-adapted formats.

[0051] Specifically, the method for constructing the three-dimensional feedback collection system of user evaluation, scenario operation, and industry standards is as follows: Configure the user review collection link, set the trigger rules of the platform interaction interface, and trigger the collection program after the user submits a review to collect satisfaction scores and text suggestions; deploy the scenario running program in the content delivery channel, preset the timed collection cycle of dissemination data and interaction data, and build an industry standard collection link, connect to the industry compliance verification interface and quality evaluation system; unify the data field format of the three types of links, and establish a data aggregation channel.

[0052] Specifically, the method for extracting and generating evaluation features is as follows: The system acquires multi-dimensional feedback data collected by a three-dimensional feedback acquisition system, performs anomaly removal and standardization processing, and generates a standardized dataset. A feature extraction algorithm is used to initially extract features from the standardized dataset to obtain an original feature set. Based on three preset feedback feature dimensions—demand matching degree, quality compliance rate, and scene adaptation deviation—the original feature set is filtered to generate a subset of related features. The feature subset is then normalized and integrated to output the generated effect evaluation features.

[0053] Specifically, the method for calculating the weights of each feature through feature weight calculation logic is as follows: Obtain a set of features for evaluating the generation effect driven by 3D feedback; assign weights to each feature using the analytic hierarchy process (AHP) to generate initial weights; construct an optimization objective function based on the initial weights, using the negative of the sum of the products of each feature and its corresponding initial weight as the core expression of the objective function; iteratively solve the minimum value of the objective function using the gradient descent algorithm, and output the dynamic optimal weights of each feature.

[0054] Specifically, the method for adjusting the core configuration parameters of the generation system is as follows: Obtain the dynamic optimal weights of each feature and the features for evaluating the generation effect, and adjust the configuration dimension corresponding to the feature with the highest weight; optimize the semantic extraction dictionary and feature vector construction parameters and update the matching threshold based on the demand matching degree dimension; adjust the iteration steps and loss function parameters of the pre-training algorithm and supplement the special training data based on the quality compliance rate dimension; update the material classification labels and matching algorithm and adjust the collaborative generation scene adaptation parameters based on the scene adaptation deviation dimension.

[0055] Specifically, the method for forming the iterative content generation system is as follows: The core configuration parameters of the adjusted generation system are summarized, and a collaborative compatibility check is performed to generate an optimized parameter set. The optimized parameter set is then imported into the platform's dynamic generation framework based on a preset format for updating and overwriting. A parameter update report is generated to record the adjustment dimensions, changes, and corresponding feedback characteristics. The updated dynamic generation framework is then deployed to the platform's content generation process as the iterative content generation system.

[0056] In this embodiment, the social media marketing content for a new moisturizing face cream from a beauty brand has completed three-dimensional feedback collection and feature extraction, generating an effect evaluation feature set containing three core features: demand matching degree (feature A), quality compliance rate (feature B), and scene adaptation deviation (feature C). Among them, the quality compliance rate feedback shows "insufficient copywriting fluency and inaccurate description of product efficacy," and the scene adaptation deviation feedback shows "the fit between visual elements and scene style needs to be improved." It is necessary to clarify the optimization priority through feature weight calculation and then adjust the core configuration parameters of the generation system accordingly.

[0057] I. Feature weight calculation (corresponding to claim 10) Feature set acquisition: Extract the three-dimensional feedback-driven feature set generated by S3, and obtain the feature values ​​after quantization: Feature A (demand matching degree) = 0.82, Feature B (quality compliance rate) = 0.65, Feature C (scene adaptation deviation) = 0.73. All feature values ​​are normalized to the [0,1] interval to ensure calculation consistency.

[0058] Analytic Hierarchy Process (AHP) weighting to generate initial weights: Constructing a judgment matrix: Using "content generation effect optimization priority" as the target layer and three major features as the criterion layer, a 1-9 scale is used to compare feature importance pairwise (1 = equally important, 9 = extremely important). Based on the platform's experience in generating beauty-related content, the judgment matrix is ​​as follows: feature Feature A Feature B Feature C Feature A 1 1 / 3 1 / 2 Feature B 3 1 2 Feature C 2 1 / 2 1 Weight calculation: The sum of the elements in each row of the judgment matrix is ​​calculated by summation (feature A = 1 + 1 / 3 + 1 / 2 ≈ 1.833, feature B = 3 + 1 + 2 = 6, feature C = 2 + 1 / 2 + 1 = 3.5). After normalization, the initial weights are obtained: feature A ≈ 0.18, feature B ≈ 0.59, feature C ≈ 0.23.

[0059] Consistency check: The largest eigenvalue λmax≈3.015 is calculated, the consistency index CI=(λmax-n) / (n-1)≈0.0075 (n=3), the random consistency index RI=0.58, and the consistency ratio CR=CI / RI≈0.013<0.1. The check is qualified, and the initial weights are valid.

[0060] Construct the optimization objective function: With the goal of "minimizing the negative of the sum of the products of features and weights", the core expression is: minf(x) = -Σ(wi × xi), where wi is the initial weight and xi is the feature value, i.e., f(x) = -(0.18 × 0.82 + 0.59 × 0.65 + 0.23 × 0.73). This expression strengthens the guidance of high-weight features on the optimization direction, ensuring that resources are tilted towards the core problem.

[0061] Gradient descent algorithm for finding dynamically optimal weights: Parameter configuration: Set the learning rate η=0.01 (based on platform algorithm optimization experience, this value can balance convergence speed and stability), the maximum number of iterations is 1000, and the convergence threshold ε=1e-6.

[0062] Iterative solution: Calculate the gradient of the objective function ∇f(x) = -wi, and update the weights along the negative gradient direction: wi n ₊1=wi n -η×∇f(x), repeat the iteration until the weight change is less than the convergence threshold.

[0063] Results: After 823 iterations, the results converged. The dynamic optimal weights were: Feature A ≈ 0.16, Feature B ≈ 0.63, and Feature C ≈ 0.21. The quality compliance rate (Feature B) had the highest weight and was determined as the core optimization dimension.

[0064] Adjustment of core configuration parameters of the generation system Priority optimization is determined by prioritizing the configuration dimension corresponding to the quality compliance rate (weight 0.63) based on dynamic optimal weights, while simultaneously fine-tuning the parameters of the scenario adaptation deviation and the degree of matching with requirements.

[0065] Adjustments to core parameters of the quality compliance rate dimension: Pre-training algorithm iteration steps adjustment: The original iteration steps were 500. In response to feedback issues such as "insufficient fluency of copywriting and low accuracy of efficacy descriptions", the text generation algorithm iteration steps were increased to 700. The rationale for this setting is that platform tests show that when the iteration steps of the beauty copywriting generation algorithm were increased from 500 to 700, fluency improved by 18%, keyword accuracy improved by 22%, and there was no significant increase in computing power consumption.

[0066] Loss function parameter optimization: A combined loss function of "cross-entropy loss + mean squared error loss" is adopted. The original weight coefficient of cross-entropy loss was 0.6, and the weight coefficient of mean squared error loss was 0.4. After adjustment, the weight coefficient of cross-entropy loss is increased to 0.8, and the weight coefficient of mean squared error loss is reduced to 0.2. The logic is that cross-entropy loss focuses more on text semantic matching and classification accuracy. Increasing its weight can strengthen the copywriting's accurate expression of the core selling point of "gentle moisturizing and suitable for sensitive skin".

[0067] Supplementary data on specialized training: Data sources: Public beauty ingredient database (containing 5000+ entries for moisturizing ingredients for sensitive skin), product formula documents provided by brands, and a collection of industry-compliant efficacy descriptions.

[0068] Data preprocessing: Remove duplicate data (duplicate rate ≤ 5%) and data with non-compliant expressions, and encode features using the Word2Vec word embedding model to unify them into 128-dimensional vectors, consistent with the original training data format.

[0069] Expanding the scale: Add 8,000 new specialized training data entries (including 3,000 ingredient efficacy descriptions, 3,000 copywriting examples for sensitive skin application scenarios, and 2,000 compliant marketing phrases), increasing the size of the text generation algorithm's training dataset from the original 20,000 entries to 28,000 entries; Expected results: The specialized data can improve the algorithm's learning of beauty industry-specific terminology and compliant expressions, reducing the problem of ambiguous efficacy descriptions.

[0070] Fine-tuning of other dimension parameters: Scene adaptation deviation dimension: Update material category tags (add "Ingredient display" and "Sensitive skin friendly" secondary tags), and increase the style adaptation weight in the material matching algorithm from 0.3 to 0.4.

[0071] Demand matching dimension: The semantic extraction dictionary was optimized (50 new ingredient keywords such as "ceramide" and "sodium hyaluronate" were added), and the feature vector matching threshold was adjusted from 0.7 to 0.72 to improve the accuracy of demand parsing.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dynamic optimization method for an intelligent content generation workshop platform, characterized in that, include: S1: Obtain user input to generate requirements and construct a requirement feature vector; The adaptation degree is calculated by comparing the demand feature vector with the functional feature matrix of the pre-trained resource cluster to form a target generation resource combination. At the same time, the material resource package corresponding to the scene is matched and the adaptation computing power is allocated to achieve accurate matching between generation resources and demand. S2: Based on the generated resource combination, initiate a multi-form content collaborative generation mechanism, input the demand feature vector and material resource package into the generation process, and construct the content in multiple dimensions; form an initial content product through preset multi-dimensional content integration rules; S3: Construct a three-dimensional feedback acquisition system of user evaluation, scenario operation and industry standard, obtain multi-dimensional feedback data corresponding to the initial content product, perform standardized processing on the feedback data, extract generation effect evaluation features, and construct a three-dimensional feedback-driven generation effect evaluation feature set. S4: Based on the aforementioned feature set for evaluating the generation effect, calculate the weight of each feature through feature weight calculation logic, adjust the core configuration parameters of the generation system, update the optimized generation resource configuration parameters and collaborative operation strategy to the platform's dynamic generation framework, and form an iterative content generation system.

2. The method according to claim 1, characterized in that, The specific method for constructing the requirement feature vector is as follows: The system performs semantic parsing on the user-input requirement text, extracting core requirement elements through word segmentation and core element filtering; it calls a preset scene feature library to match the core requirement elements with scene information in the library to obtain scene-related elements for the corresponding application scenario; it performs quantization encoding on the core requirement elements and scene-related elements respectively, converting textual elements into vectors through word embedding and normalizing numerical elements; it assigns weights based on element importance, and then weights and fuses the encoded element vectors to generate a requirement feature vector.

3. The method according to claim 1, characterized in that, The specific method for forming the target resource combination is as follows: Construct a functional feature matrix for a pre-trained resource cluster. The functional feature matrix includes the type, scenario adaptation range, quality output capability, and computing power requirement characteristics of each pre-trained algorithm logic within the cluster. Based on the content theme and application scenario in the core requirement dimension, obtain the core weight ratio of the requirement feature vector; calculate the fit between the requirement feature vector and the feature vector of each pre-trained algorithm logic in the functional feature matrix, and select the pre-trained algorithm logic with a fit higher than the preset threshold; perform collaborative compatibility verification on the selected pre-trained algorithm logic to generate a conflict-free target generation resource combination.

4. The method according to claim 1, characterized in that, The specific method for achieving precise matching between generated resources and requirements is as follows: Based on the content themes, quality thresholds, and application scenarios of the core requirement dimensions, the selected target generated resource combinations undergo adaptation consistency verification, and the resource combination parameters are corrected. The application scenarios, output formats, and audience attributes of the core requirement dimensions are associated, and material resource packages containing scene-specific elements, format adaptation, and style matching are selected from the preset material database. Combining the real-time computing power consumption of the target generated resource combinations, the complexity of material processing, and the timeliness requirements of content generation, adaptation computing power is dynamically allocated and elastic redundancy is reserved. Cross-adaptation verification is performed on the target generated resource combinations, material resource packages, and adaptation computing power, and resource matching results that meet operational stability requirements are generated through resource collaborative simulation testing.

5. The method according to claim 1, characterized in that, The multi-format content collaborative generation mechanism includes text generation logic, visual construction logic, logic verification logic, and preset multi-dimensional content integration rules; each logic module works together through a data interaction interface to perform operations related to the generation and integration of multi-format content.

6. The method according to claim 1, characterized in that, The specific method for constructing content in multiple dimensions is as follows: Based on the demand feature vector analysis, the content is constructed with the theme orientation, style standards, and scene adaptation requirements; the material resource package is classified and decomposed into basic text materials and basic visual materials; the text generation logic and visual construction logic are started to operate in parallel. The text generation logic constructs the core copy according to the theme orientation and style standards, and the visual construction logic generates supporting visual elements in combination with scene adaptation requirements and core copy information. The two types of generation logic share core information synchronously through a data interaction interface.

7. The method according to claim 1, characterized in that, The specific method for forming the initial content product is as follows: It invokes preset multi-dimensional content integration rules, integrates text and visual elements based on application scenario format requirements and style standards, performs logical conflict verification and compliance verification on the integrated content, and generates an initial content product containing text, visual elements and scenario-adapted formats.

8. The method according to claim 1, characterized in that, The specific method for constructing the three-dimensional feedback collection system of user evaluation, scenario operation, and industry standards is as follows: Configure the user review collection link, set the platform interaction interface trigger rules, and trigger the collection program after the user submits a review to collect satisfaction scores and text suggestions; deploy the scenario running program in the content delivery channel, preset the timed collection cycle of dissemination data and interaction data, and build an industry standard collection link to connect to the industry compliance verification interface and quality evaluation system. Unify the data field format of the three types of links and establish a data aggregation channel.

9. The method according to claim 1, characterized in that, The specific method for extracting and generating evaluation features is as follows: The system acquires multi-dimensional feedback data collected by a three-dimensional feedback acquisition system, performs anomaly removal and standardization processing, and generates a standardized dataset. A feature extraction algorithm is used to initially extract features from the standardized dataset to obtain an original feature set. Based on three preset feedback feature dimensions—demand matching degree, quality compliance rate, and scene adaptation deviation—the original feature set is filtered to generate a subset of related features. The feature subset is then normalized and integrated to output the generated effect evaluation features.

10. The method according to claim 1, characterized in that, The specific method for calculating the feature weights through feature weight calculation logic is as follows: Obtain the feature set for evaluating the generation effect driven by 3D feedback; use the analytic hierarchy process (AHP) to assign weights to each feature and generate initial weights; An optimization objective function is constructed based on the initial weights, and the negative of the sum of the products of each feature and its corresponding initial weight is used as the core expression of the objective function. The minimum value of the objective function is solved iteratively by the gradient descent algorithm, and the dynamic optimal weights of each feature are output.

11. The method according to claim 1, characterized in that, The specific method for adjusting the core configuration parameters of the generation system is as follows: Obtain the dynamic optimal weights of each feature and the features for evaluating the generation effect, and adjust the configuration dimension corresponding to the feature with the highest weight; optimize the semantic extraction dictionary and feature vector construction parameters and update the matching threshold based on the demand matching degree dimension; adjust the iteration steps and loss function parameters of the pre-training algorithm and supplement the special training data based on the quality compliance rate dimension; update the material classification labels and matching algorithm and adjust the collaborative generation scene adaptation parameters based on the scene adaptation deviation dimension.

12. The method according to claim 1, characterized in that, The specific method for forming the iterative content generation system is as follows: Summarize the core configuration parameters of the adjusted generation system, perform collaborative compatibility verification to generate an optimized parameter set; import the optimized parameter set into the platform's dynamic generation framework based on a preset format for updating and overwriting, and generate a parameter update report to record the adjustment dimensions, changes, and corresponding feedback characteristics. The updated dynamic generation framework will be deployed as the iterative content generation system to the platform's content generation process.

Images