Self-media marketing industrial intelligent creation system and method

Abstract

The invention relates to an industrial intelligent creation system and method for self-media marketing, and aims to solve the problems of empirical operation, tool function superficial layer, high learning threshold and the like in the current field of self-media creation and marketing. According to the system, sales, movie and psychology theories are integrated through a multidisciplinary fusion module, and a quantifiable creation parameter set is generated; a creation process is disassembled into 45 steps of standardized operation by adopting an SOP process module, and full-process automatic execution is realized through an AI agent; the AI generation module generates creative content according to user input based on the dynamic rule engine and the knowledge graph. Through the standardized process and the intelligent tool, the creation difficulty is reduced, the content differentiation rate and the user layering accuracy are improved, the creation efficiency and effect of self-media marketing are effectively improved, and an efficient and accurate creation solution is provided for a self-media creator.

Description

Technical Field

[0001] The present invention relates to the technical field of industrialized intelligent creation of self-media marketing, and specifically to a system and method for industrialized intelligent creation of self-media marketing. Background Art

[0002] The current field of self-media creation and marketing mainly relies on the following technical solutions: Empirical operation mode: Creators rely on personal experience to select topics, position accounts and operate traffic, lacking quantifiable underlying logic and standardized process support.

[0003] Generalized tool chain: With AI copywriting tools as the core, content is produced through templated processes, resulting in serious homogeneity and failure to achieve full process automation. For example, existing tools only support single-link assistance (such as copywriting generation) and cannot complete end-to-end creation from topic selection to manuscript completion.

[0004] Course-driven learning: Creators rely on theoretical courses to acquire knowledge, but the courses are separated from the tools and lack a systematic practical path, resulting in uneven learning results.

[0005] Existing technology defects Courses and tools are separated: There is no synergy between theoretical courses and tool functions. Creators need to complete creative thinking manually, which is inefficient (for example, a single creative brainstorming session takes more than 3 days).

[0006] Shallow tool functions: Existing AI tools only support template-based copy generation and lack a deep understanding of the creative process (e.g., it is impossible to “input a topic to generate a creative packaging effect”).

[0007] High learning threshold: The traditional SOP process is extensive (e.g. only 10-20 steps), which cannot cover the full-link details of account planning, content creation, and operation management, making it difficult to get started.

[0008] The existing technology is limited by experience-driven and tool generalization, resulting in a low degree of standardization in the creation process, reliance on manual experience, high content homogeneity, and a differentiation rate of ≤30%. At the same time, novices need 3-6 months to master the entire process, resulting in high learning costs. Therefore, technical personnel in this field provide a self-media marketing industrial intelligent creation system and method to solve the problems raised in the above background technology. Summary of the invention 1. Technical issues to be resolved

[0009] In view of the deficiencies in the prior art, the present invention provides an industrialized intelligent creation system and method for self-media marketing to solve the problems raised in the above-mentioned background technology. (II) Technical solution

[0010] To achieve the above object, the present invention provides the following technical solutions: The present invention proposes a self-media marketing industrial intelligent creation system, including a multidisciplinary integration module, an SOP process module, and an AI generation module.

[0011] Multidisciplinary integration module: used to integrate marketing positioning theory, film narrative structure, and psychological demand model to generate a quantifiable set of creation parameters. This parameter set includes packaging form parameters, nature dislocation parameters, value hierarchy parameters, content structure parameters, and demand topic selection parameters.

[0012] SOP process module: disassembles the self-media creation process into 45 standardized operation processes, including account planning SOP, single-content creation SOP, and creation management SOP, and realizes full-process automatic execution through an AI intelligent agent.

[0013] AI generation module: based on a dynamic rule engine and a knowledge graph, according to the topic or outline input by the user, combined with the parameter set of the multidisciplinary integration module, generates content effects with creative packaging, structured scripts, and supporting design suggestions.

[0014] Multidisciplinary integration module Packaging form parameter unit: defines more than 80 traffic password means, including suspense attraction subcategories (task challenge, reasoning truth, unknown collapse) and crisis suspense subcategories (time task crisis, secret exposure crisis, relationship conflict crisis), to capture user attention.

[0015] Nature dislocation parameter unit: defines more than 30 cross-attribute grafting methods, including age stage dislocation (appearance dislocation, stage task dislocation), species dislocation (form dislocation, habit behavior dislocation), and effect types (humorous deconstruction, cognitive subversion), to generate absurd contrast effects.

[0016] Value hierarchy parameter unit: divides user stratification (precision users, potential users, general interest users) and value carriers (basic layer information practicality, intermediate layer social recognition, high-level cognitive upgrade) based on the Maslow demand model to achieve precise content push.

[0017] Content structure parameter unit: provides more than 40 narrative structure formulas, including basic narrative structures (three-act structure, conflict closed structure), advanced narrative structures (goal - repeated action trial and error - result), and advanced narrative structures (circular structure, multi-line narrative), to support creative content creation.

[0018] Requirement Topic Selection Parameter Unit: Determine the user's industry based on the topic input by the user, generate a topic mapping through the demand weights of this industry (for example, the content of the mother and baby category is defaultly assigned a 'Safety Requirement' weight of 40% and dynamically adjusted according to holidays). And real-time associate with the Douyin / Weibo hot list data to adjust the hot topic relevance, ensuring the novelty and attractiveness of the topic selection.

[0019] SOP Process Module Account Planning SOP: Include steps such as clarifying the creation purpose, selecting the creation platform, user stratification analysis (precision / potential / general interest users), content type matching, monetization direction planning, and account identity positioning, providing comprehensive planning support for self-media accounts.

[0020] Single Content Creation SOP: Include steps such as topic optimization (differentiation rate verification), structure selection (narrative / discursive structure matching), packaging form generation (traffic password combination), property dislocation application, and demand level adaptation, ensuring the high quality and differentiation of content creation.

[0021] Creation Management SOP: Include steps such as process progress monitoring, content quality evaluation (differentiation rate, emotion index, timeliness index), and dynamic optimization feedback, providing strong support for the continuous optimization of self-media content.

[0022] AI Generation Module Packaging Form Generation Unit: According to the topic input by the user, call more than 80 traffic password parameters to generate creative packaging effects, screen the top 30 effects through a wonderfulness prediction model (factors such as information density, emotional fluctuation, etc.), and screen recommended solutions with a matching degree ≥ 80% through a knowledge graph, providing a packaging form that satisfies the user.

[0023] Property Dislocation Generation Unit: Based on the cross-category grafting rule of the attribute source and the target attribute, generate absurd contrast scenarios, and output humorous deconstruction or cognitive subversion effects through other 3 factors such as the proportion of contradictory elements (≥ 35%), increasing the interestingness and attractiveness of the content.

[0024] Requirement Topic Optimization Unit: Generate a topic mapping in combination with Maslow's hierarchy of needs (efficiency needs, growth needs, self-actualization), and real-time associate with the Douyin / Weibo hot list data to adjust the hot topic relevance, ensuring the novelty and timeliness of the topic selection.

[0025] Content Structure Generation Unit: According to the outline input by the user, call more than 40 structure formulas to generate a creative script, and provide formulaic expressions such as "problem + hypothesis iteration + solution" or "inciting incident + action confrontation". According to the structure effect prediction model (including factors such as suspense density, emotional curve slope, etc.), select a structure generation suggestion with an expected completion rate > 50% to support efficient content creation.

[0026] The present invention also provides an intelligent creation method for self-media marketing industrialization, which includes the following steps: Multidisciplinary parameter construction step: Integrate the marketing STP model, the three-act framework of film studies, and the Maslow's hierarchy of needs model in psychology to generate a quantitative parameter set for packaging form, nature dislocation, value hierarchy, content structure, and demand topic selection, providing theoretical support for creation.

[0027] SOP process execution step: Decompose the creation process into 45 standardized operations, and sequentially execute account planning (12 steps), single-content creation (28 steps), and creation management (5 steps) through an AI agent to achieve full-process automated execution.

[0028] AI generation step: Receive the topic or outline input by the user, generate the content effect after creative packaging, the structured script, and the supporting design suggestions based on the parameter set, and output the optimization results of the differentiation rate, emotion index, and hot topic relevance to ensure the high quality and differentiation of the content. (III) Beneficial effects

[0029] Compared with the prior art, the present invention provides an intelligent creation system and method for self-media marketing industrialization. Through multidisciplinary integration, intelligent process reconstruction, and parameterized creation system, it realizes a double breakthrough in the efficiency and effect of self-media marketing creation, and has the following beneficial effects: 1. The creation difficulty is greatly reduced. By integrating multidisciplinary knowledge into self-media, the 45-step SOP process, and AI automatic tools, the traditional creation process that relies on course learning is transformed into standardized operations. 2. The content differentiation rate is greatly improved. Through the combination of multidisciplinary parameters (marketing positioning + film narrative + psychology demand model) and personalization in each process, the content homogenization rate is reduced, and the content differentiation is improved. 3. The accuracy of user stratification is greatly improved. Based on the value hierarchy parameters (precision / potential / general interest user stratification), combined with Maslow's hierarchy of needs, user stratification is realized, customized content value supply is provided, and the learning cost is reduced. Specific implementation manners

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] The present invention provides a technical solution, namely, a self-media marketing industrial intelligent creation system and method. This system includes a user interaction module, a multidisciplinary rule engine, an AI generation module, a process management module, and a database cluster. Each module conducts data interaction through API interfaces, and the specific functions are as follows: User interaction module Receives the topic selection parameters, creation goals, and style preferences input by the user; Supports structured form input (such as target user portrait tags, competitor analysis dimensions); Configures a natural language processing interface to parse unstructured text and extract semantic tags and sentiment tendencies.

[0032] Multidisciplinary rule engine Integrates the marketing positioning model (STP theory), the film narrative model (three-act structure), and the psychological needs model (Maslow's hierarchy of needs); Achieves parameter coupling through a weighted decision tree and outputs a combination of creation parameters (such as the differentiation rate, value level index).

[0033] AI generation module Based on the output of the multidisciplinary rule engine, it performs the following: Creative packaging: Integrates more than 80 traffic code strategies (such as suspense attraction, crisis suspense) to generate titles and opening hooks; Structural arrangement: Constructs the content framework according to 40 narrative structure formulas (such as the goal-repeated trial and error type); Mismatch optimization: Generates attribute mismatch scenarios through cross-attribute mapping (such as anthropomorphizing technology products as characters).

[0034] Process management module Builds a 45-step SOP workflow controller, supporting: Dynamically scheduling creation tasks (such as account planning, content production, release optimization); Real-time monitoring of creation quality (such as originality detection, sentiment tendency analysis).

[0035] Database cluster Stores structured data: Packaging form library (more than 80 strategies and applicable scenario tags); Content structure template (40 narrative structures and text formulas); Industry case knowledge graph (cross-domain content association network).

[0036] This method includes the following processes: The implementation steps include: Step S201: Requirement analysis Input: User-selected topic (such as "Using AI Painting Tools"), target users (such as the designer group); Processing: NLP parsing to extract keywords (AI, painting, tutorial); The marketing model calculates the competition index C = ∑(Ci / Si) to screen for differentiated topic directions.

[0037] Step S202: Parameter coupling Multi-disciplinary model collaborative output: The film studies model generates a candidate set of 40 narrative structures; The psychology model maps the value hierarchy (such as tool-based value: tutorial topics).

[0038] Step S203: Creativity generation AI module parallel execution: Packaging integration: Combine suspense attraction and crisis suspense to generate a title (such as "Learn AI Painting in 3 Steps? Reveal the Core to 50% Improvement in Designers' Abilities"); Structure arrangement: Generate a content framework according to the "goal - trial and error" structure; Dislocation optimization: Convert technical parameters into life analogies (such as "GPU computing power ≈ painter's palette accuracy").

[0039] Step S204: Process control Execute according to the 45-step SOP: Account planning stage: Generate a target user portrait through K-means clustering; Content creation stage: Dynamic quality inspection point Q check =Δ(originality, sentiment polarity, structural integrity).

[0040] Example Taking the topic of "AI Painting Tool Usage Tutorial" as an example: User input: Topic "AI Painting for Beginners with No Foundation", target users are "New Media Operators"; Rule engine calculation: Competition index C = 0.75 (triggering the differentiation strategy); Recommended structure s12 (advanced narrative structure: goal - full of problems - solution); Determine the value hierarchy V = tool-based (focusing on solution comparison).

[0041] AI generated output: Packaging form P15 (suspense attraction: "Why is your AI painting always a failure? These 3 parameter settings are the key"); Dislocation plan F07 (cross-attribute mapping: comparing the AI algorithm to a "digital colorist").

[0042] SOP Execution Results: Generate a 1200-word step-by-step tutorial, including 2 conflict settings (expected effects vs. common problems) and 1 product principle animation demonstration; Distribution Strategy: Focus on technical details on Bilibili and case comparisons on Zhihu.

[0043] Verification of Technical Effects Verified by a test dataset (n = 500 cases): Improved creation efficiency: The average time consumption is reduced from 56 hours in the traditional process to 7.2 hours (the significance level of the experimental results is below 5%); The generation cost per piece of content is reduced by 62%.

[0044] Optimized content quality: The differentiation rate is increased to 79.8% (benchmark value 21.3%); The user interaction rate is increased by 2.8 times (95% confidence interval [2.4, 3.2]).

[0045] Verification of Commercial Value The conversion rate of ad clicks is increased by 38%; The user retention rate is increased by 24%.

[0046] The present invention achieves a technological breakthrough through a multi-disciplinary principle integration algorithm, an AI creative thinking architecture design, and a process automation industrial tool chain. The specific steps are as follows: 1. Self-media application of underlying logic deconstruction and multi-disciplinary integration Technical implementation: Step 1 - Integrate marketing positioning theories (such as the STP model), film narrative structures (such as the three-act framework), and psychological demand models (such as Maslow's hierarchy of needs) to construct creative technical points in the process of "positioning → topic selection → structure → packaging → copywriting → operation", and define core creative index parameters such as "packaging form", "nature dislocation", "value hierarchy", "content structure", and "demand topic selection".

[0047] (1) Packaging form parameters Definition: Combining marketing, literature, film studies, communication, etc., propose the concept of "packaging form" for self-media, a set of strategies for capturing user attention through means of subdividing traffic codes, including more than 80 specific methods, and more will be added continuously.

[0048] Attraction means in packaging form: Suspense attraction (further divided into task challenges, reasoning about the truth, unknown collapse, conflict outbreak, surprise, mystery and novelty, dilemmas, etc.), crisis suspense (further divided into time task crisis, secret exposure crisis, life and survival crisis, relationship conflict crisis, development and growth crisis, financial crisis, reputation crisis, etc.), a total of more than 80 specific packaging forms.

[0049] If the user comes up with a topic by themselves, they can combine the above more than eighty packaging forms to obtain the effect after "topic + traffic password".

[0050] (2)Nature dislocation parameter Definition: Combining comedy creation techniques and literary creation techniques, refining the concept of "nature dislocation" in the context of self-media, a creative method that creates absurd contrast effects through cross-attribute grafting and dislocation.

[0051] Attribute source: The original attributes of things (such as the "court drama" attribute of "Empresses in the Palace"); Target attribute: The target attribute to be dislocated (such as the objectivity of "news reporting"); Effect types: Humorous deconstruction (such as absurd conversations), cognitive subversion (such as changing a historical drama into a workplace drama); Nature dislocation: Summarize more than thirty natures that can be dislocated between different things: such as age stage dislocation (further divided into appearance dislocation, stage task dislocation, commonly used prop item dislocation, speech and behavior dislocation, frequently visited scene dislocation), species dislocation (further divided into form dislocation, habit and behavior dislocation, self-cognition dislocation, problem and contradiction dislocation, emotional dislocation, demand relationship dislocation, service object dislocation), etc., a total of more than thirty, and will continue to increase in the future.

[0052] (3)Value hierarchy parameter Definition: Based on psychology and marketing, refining the concept of "value hierarchy" in the context of self-media, a differential value supply strategy designed in combination with Maslow's needs model.

[0053] Parameters: a. User stratification: Precise users (with clear needs): Provide product function descriptions and usage tutorials (knowledge-based value); Potential users (with vague needs): Provide solution comparisons and case demonstrations (tool-based value); General interest users (without clear needs): Provide emotionally resonant content and entertainment presentations (experience-based value); b. Value carriers: Basic layer: Information creativity (such as dry goods collections); Middle layer: Social recognition (such as UGC interactions); High layer: Cognitive upgrade (such as industry trend analysis).

[0054] (4)Content structure parameters Definition: A content creation framework that integrates Chinese language theory and film theory, refining the concept of "content structure" for self-media, dividing the content structure into narrative structure and argumentative structure, and giving different creative structures for each category, and decomposing each structure into literal formulas.

[0055] Content structure parameters: The narrative structure is divided into basic narrative structures (further divided into three-act, four-act, cause-effect-result, result-cause-effect, result-effect-cause, narrative parallelism, narrative progression, narrative contrast, conflict closure, multi-event parallelism...), advanced narrative structures (further divided into goal-repeated action and trial-and-error-result, goal-problem-ridden-solution...), advanced narrative structures (circular structure, multi-line narrative, etc.), a total of 40 structures.

[0056] Structure formula: Each structure is decomposed into literal formulas such as "problem + (hypothesis 1 + unsolved + thinking 1) + (hypothesis 2 + unsolved + thinking 2) +... + (hypothesis n + solution)" and "quick setup (goal appears) + inciting incident + confrontation between action and obstacle + confrontation with major crisis + success OR failure OR abandonment".

[0057] (5)Demand topic selection parameters Definition: Integrating psychology and marketing, refining the self-media concept of "demand topic selection", a content topic selection method that generates core topics of users and Maslow's hierarchy of needs.

[0058] Parameters: Core topic: The original topic of the user (such as "Using AI painting tools"); b. Demand mapping: Efficiency demand: Tutorial topic selection ("Learn AI painting in 3 steps"); Growth demand: Case comparison ("How AI painting can improve designers' abilities by 50%"); Self-actualization: Trend analysis ("New directions in AI art creation in 2025"); c. Topic selection optimization rules: Differentiation rate: The repetition rate with the existing content on the platform ≤ 25%; Emotion index: The recommended ratio of positive / neutral / negative is 7:2:1; Timeliness: The relevance to hot topics ≥ 60% (based on real-time tracking of the Douyin / Weibo hot lists).

[0059] 2.45-step SOP self-media marketing creation process breakdown Technical implementation: Step 2 - Full-process standardization breakdown The entire self-media marketing is disassembled into three SOP processes, including the account planning SOP (the steps include clarifying the creation purpose, selecting the creation platform, defining the account goals, defining the content type, obtaining content type suggestions using charts, finding three audiences, clarifying the content direction preferred by the target users, defining the monetization direction, defining the unique identity, temperament aura...), the single-piece content creation SOP, and the creation management SOP, with a total of 45 refined processes (which may be further refined and more steps added later). And an AI agent is used to build a 45-step workflow to reduce the creation difficulty, improve the efficiency and quality through 45 standardized operations.

[0060] 3. AI rule intelligent generation.

[0061] Technical implementation: Step 3 - Intelligent generation and optimization For concepts such as the "packaging form", "content structure", "demand topic selection", and "nature dislocation" mentioned above, users can input their own topics, and the AI can intelligently generate a large number of effects after application.

[0062] Packaging form generation matching (more than 80 traffic codes): Users input topics, the AI generates the effects after applying more than 80 traffic codes, and intelligently judges and screens the more wonderful effects among these more than 80 and gives suggestions to users.

[0063] Nature dislocation generation: Attribute source (Empresses in the Palace) → Target attribute (news report), effect type (humorous deconstruction / cognitive subversion). Users input their own things, and the AI generates a large number of scenarios after dislocation. There are currently more than thirty types of dislocatable natures.

[0064] Demand topic selection optimization: Topic core (input by users) → Demand mapping (physiological / respect / self-actualization), differentiation rate (≤25% of the platform's existing content). Users input topics, and the AI generates the topic selection effects after combining different Maslow's hierarchy of needs. Maslow's hierarchy of needs can be refined into small needs without an upper limit.

[0065] Content structure generation: Users input the topic outline, and the AI generates the effects after applying different creative structures. There are currently more than forty types of structures.

[0066] The technical effects of the present invention 1. The creation difficulty is greatly reduced Implementation method: Through the integration of multi-disciplinary knowledge into self-media, the 45-step SOP process, and AI automatic tools, the traditional creation process that relies on course learning is transformed into standardized operations.

[0067] Parameter support: (1) Cross - disciplinary integration, refining the creation parameters of self - media, solving the uneven effects caused by individual cognitive differences during the learning process, and shortening the learning time from 3 - 6 months to 3 - 6 weeks.

[0068] (2) 45 - step refined creation, standardizing the creation process and forming an AI workflow, shortening the creation practical research time from 1 - 3 months to 1 - 3 weeks.

[0069] (3) AI intelligent generation of creativity, changing the process of painstakingly seeking inspiration for creativity into simple screening, turning 3 - day creative brainstorming into 30 minutes.

[0070] 2. The content differentiation rate has been greatly improved. Implementation method: Through the combination of multi - disciplinary parameters (marketing positioning + film narrative + psychological demand model) and personalization in each process, reduce the content homogenization rate and improve content differentiation.

[0071] Parameter support: Differentiation rate formula: Different industries × Different topics × Different needs × Different packaging forms × Different content structures × Different presentation forms × Different nature misalignments × Different audio - visual styles 3. The accuracy of user stratification has been greatly improved. Implementation method: Based on value - level parameters (accurate / potential / general - interest user stratification), combined with Maslow's hierarchy of needs to achieve user stratification. Customize the content value supply.

[0072] Parameter support: Industry × Value level × Maslow's hierarchy of needs.

[0073] Key points of the present invention: 1. Precise construction of multi - disciplinary integration The present invention is not simply a patchwork of multiple disciplines. Instead, through careful design and practical verification, it deeply integrates multiple disciplines such as marketing, communication, aesthetics, film, psychology, and literature to construct a unique and effective underlying logic for self - media creation. This multi - disciplinary integration is based on a comprehensive understanding of self - media creation and an in - depth analysis of user needs. Each discipline plays a unique and irreplaceable role in this system, jointly providing support for content creation, marketing strategy formulation, and user experience optimization.

[0074] For example, communication helps construct the information dissemination path and the audience feedback mechanism, and aesthetics provides guidance for the visual presentation and aesthetic design of content. These disciplines are closely combined with marketing, film, and psychology to form an organic whole, acting synergistically in all aspects of self - media creation.

[0075] 2. Standardization and refinement of the 45 - step SOP process The 45-step SOP process breakdown is one of the core aspects of the present invention. It is not a simple listing of operation steps but rather, through extensive practice and optimization, comprehensively and meticulously standardizes the complex process of self-media creation and marketing. Each step has clear goals, specific operation methods, and precise parameter definitions. For example, in the account planning SOP, it clarifies how to position the account, find target users, and formulate a precise content strategy through the integration of multiple disciplines; in the content creation SOP, it details the specific creation requirements for different types of content at different stages, ensuring that every link from topic selection to the final product can be completed efficiently and with high quality.

[0076] This standardized and refined process design enables creators with different backgrounds and experiences to create according to unified norms, greatly reducing the learning cost and creation difficulty, and improving the quality and stability of the content.

[0077] 3. Personalization and Precision of AI Intelligent Generation The AI intelligent generation mechanism in the present invention is an important support for achieving efficient creation. It is not just based on simple algorithms and templates but fully considers the logic of multi-disciplinary integration and various information input by users. It can provide highly personalized creative solutions and implementation suggestions for creators based on various factors such as different topics, audience needs, and market environments.

[0078] For example, when a user inputs a topic, the AI system can analyze the dissemination potential of the topic on various platforms based on communication principles, find the most suitable topic packaging form for the topic by combining knowledge of marketing, literature, film studies, etc., use psychological models to predict the acceptance degree of users for different styles of content, then formulate the best promotion plan for the topic according to marketing strategies, and provide multiple innovative content packaging forms and nature dislocation schemes for the user to choose.

[0079] The protection points of the present invention: 1. Confidentiality of the overall technical solution Including the creative underlying logic based on multi-disciplinary integration, the 45-step SOP standardization process, and the collaborative working system of the AI intelligent generation mechanism. These three parts are closely linked and support each other, constituting a complete and unique self-media creation and marketing technical solution. Any act of unauthorized use, copying, or modification of this technical solution may constitute infringement.

[0080] For example, if a competitor attempts to imitate the overall architecture and operation mechanism of this application, it is difficult to effectively copy it without violating the basic principles due to the lack of in-depth research and practical experience on which the present invention is based. Even if modified, it is difficult to achieve the effect of the present invention. Such unauthorized use behavior is protected by this patent.

[0081] 3. Originality of Core Algorithms and Parameters The specific algorithms, models adopted in the process of multi-disciplinary integration, and the key parameters set in each link are important features that distinguish the present invention from the prior art. These algorithms and parameters are the optimal combinations summarized based on a large amount of data statistics, experimental verification, and practical application experience, and can achieve the efficiency, differentiation, and accuracy of content creation.

[0082] For example, the specific processing methods, parameter ranges, and logical relationships between each step in the 45-step SOP are all carefully designed and optimized. These unique algorithm and parameter settings constitute the technical secrets of the present invention, and unauthorized use by others will constitute infringement.

[0083] Application Possibilities for Expanding the Protection Scope of the Present Invention: 1. Applicability to Different Self-Media Platforms The technical solution of the present invention is not limited to specific self-media platforms and has strong generality and portability. Whether it is mainstream platforms such as Weibo, WeChat, Douyin, or emerging vertical self-media platforms, the multi-disciplinary integration creation solution and SOP process of the present invention can be applied to improve the content quality and creation efficiency.

[0084] For example, on different platforms, although the presentation forms and dissemination rules of content vary, the underlying logic and algorithms of the present invention can be flexibly adjusted and optimized according to the platform characteristics to provide consistent creation guidance and support for creators on each platform.

[0085] 2. Creation of Different Types of Content In addition to the creation of content in common forms such as text, pictures, and videos, the technical solution of the present invention can also be applied to the creation of various new content forms such as audio, virtual reality, and augmented reality. As long as it involves links such as content planning, creation, marketing, and user experience optimization, the present invention can be used to achieve differentiated and precise creation.

[0086] For example, for audio content creation, more attractive audio expression methods can be designed according to aesthetic and psychological principles, and effective promotion strategies can be formulated using communication knowledge; for virtual reality content creation, realistic scenes and immersive experiences can be constructed based on film studies and aesthetics.

[0087] Avoidable or Replaceable Technical Means of the Present Invention: 1. Difficulty of Substituting Disciplinary Combinations Although theoretically, other combinations of disciplines can be attempted, there are great difficulties in realizing the underlying creative logic that achieves the same effect as the present invention. A large amount of market research, data analysis, and experimental verification need to be carried out again to determine the adaptation relationship, weight parameters, and cooperation mechanism between different disciplines.

[0088] For example, integrating sociology with communication studies and culturology as the new underlying creative logic requires redefining the interaction methods between disciplines, determining key parameters, and adjusting the entire creative process in actual operation. This involves complex theoretical research and practical exploration, and the results may not necessarily achieve the effect of the present invention.

[0089] 2. Complexity of SOP Process Adjustment In terms of process decomposition and optimization, the 45-step SOP of the present invention is the result of a large amount of practical optimization, with a unique logic and rhythm. Other possible adjustments or simplifications may lead to chaos in the creative process or the omission of key links, affecting the content quality and creative efficiency.

[0090] For example, simplifying the SOP to 20 steps may ignore some important creative details and fail to fully consider the audience needs and market environment; expanding it to 60 steps may make the process too cumbersome, increasing the burden on creators and reducing the creative efficiency.

[0091] 3. Challenges of Replacing the AI Generation Mechanism To achieve a personalized and precise creative solution generation with the same effect as the AI intelligent generation mechanism in the present invention, new algorithm models and data training systems need to be developed, and the stability and effectiveness of the new system in different environments and data need to be ensured.

[0092] For example, attempting to use a reinforcement learning model to replace the existing AI rule-based intelligent generation method requires re-collecting and organizing a large amount of data, designing new reward mechanisms and training strategies, and conducting a large number of tests and optimizations on the new model. This requires a large amount of resource and time investment and is difficult to reach the technical level of the present invention in the short term.

[0093] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article, or device.

[0094] In this text, unless otherwise clearly defined and limited, terms such as "installation", "setting", "connection", "fixation", "swivel connection" and the like shall be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. Unless otherwise clearly defined, for those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0095] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A self-media marketing industrial intelligent creation system, characterized in that, Including: A multidisciplinary integration module for integrating the positioning theory of marketing, the narrative structure of film studies, and the psychological demand model to generate a quantifiable set of creative parameters, including packaging form parameters, property dislocation parameters, value hierarchy parameters, content structure parameters, and demand topic selection parameters; An SOP process module that disassembles the self-media creation process into 45 standardized operation processes, including account planning SOP, single-piece content creation SOP, and creation management SOP, and realizes full-process automated execution through an AI agent; An AI generation module that, based on a dynamic rule engine and a knowledge graph, generates a content effect with creative packaging, a structured script, and supporting design suggestions according to the topic or outline input by the user, in combination with the parameter set of the multidisciplinary integration module.

2. The system according to claim 1, wherein The multidisciplinary integration module includes: A packaging form parameter unit that defines more than 80 traffic password means, including a suspense attraction subclass and a crisis suspense subclass; A property dislocation parameter unit that defines more than 30 cross-attribute grafting methods, including age stage dislocation, species dislocation, and effect type; A value hierarchy parameter unit that divides user stratification and value carriers based on the Maslow demand model; A content structure parameter unit that provides more than 40 narrative structure formulas, including basic narrative structures, advanced narrative structures, and advanced narrative structures; A demand topic selection parameter unit that generates differentiated topic selections based on the core topic input by the user and sets optimization rules with a differentiation rate ≤ 25%, an emotional index ratio of 7:2:1, and a hot topic relevance ≥ 60%.

3. The system according to claim 1, wherein The SOP process module includes: Account planning SOP, which includes steps: clarifying the creation purpose, selecting the creation platform, analyzing user stratification, matching the content type, planning the monetization direction, and positioning the account identity; Single-piece content creation SOP, which includes steps: topic optimization, structure selection, generation of packaging forms, application of property dislocation, and adaptation of demand levels; Creation management SOP, which includes steps: monitoring the process progress, evaluating the content quality (differentiation rate, emotional index, timeliness indicators), and providing dynamic optimization feedback.

4. The system according to claim 1, characterized in that The AI generation module includes: A packaging form generation unit that, according to the topic input by the user, calls more than 80 traffic password parameters to generate a creative packaging effect and screens recommended solutions with a matching degree ≥ 80% through a knowledge graph; A property dislocation generation unit that generates absurd contrast scenarios based on the cross-category grafting rules of the attribute source and the target attribute, and outputs humorous deconstruction or cognitive subversion effect types; A demand topic selection optimization unit that generates topic mappings in combination with the Maslow demand hierarchy (efficiency demand, growth demand, self-actualization) and adjusts the hot topic relevance in real time by associating with the Douyin / Weibo hot list data; A content structure generation unit that, according to the outline input by the user, calls more than 40 structure formulas to generate a creative script and provides formulaic expressions such as "problem + hypothesis iteration + solution" or "inciting incident + action confrontation".

5. A self-media marketing industrial intelligent creation method, characterized in that, Including the following steps: Multidisciplinary parameter construction step: integrating the marketing STP model, the three-act framework of film studies, and the Maslow demand model of psychology to generate a quantifiable parameter set for packaging form, property dislocation, value hierarchy, content structure, and demand topic selection; SOP process execution steps: The creation process is broken down into 45 standardized operations, and an AI agent sequentially executes account planning, single-content creation, and creation management; AI generation steps: Receive the topic or outline input by the user, generate the content effect after creative packaging, the structured script, and the supporting design suggestions based on the parameter set, and output the optimization results of the differentiation rate, emotion index, and hotspot relevance.

6. The method according to claim 5, wherein In the multi-disciplinary parameter construction step: The packaging form parameters capture the user's attention through more than 80 traffic password means; The nature dislocation parameters generate more than 30 absurd contrast effects through cross-attribute grafting; The content structure parameters break down the narrative structure into text formulas, including logical chains of "quick setup + inciting incident + action confrontation" or "problem + hypothesis iteration + solution".

7. The method according to claim 5, characterized in that, In the SOP process execution step: In the account planning stage, match the content strategy through user stratification and value level; In the single-content creation stage, the AI rule engine automatically calls the parameter set to generate a topic optimization, structure matching, and packaging form combination plan; In the creation management stage, dynamically adjust the content generation strategy by real-time monitoring of the differentiation rate, emotion index, and hotspot relevance.

8. The method according to claim 5, characterized in that, In the AI generation step: After the user inputs a topic, the system generates more than 80 traffic password packaging effects and recommends the top 10% of the matching solutions based on the knowledge graph; After the user inputs an outline, the system calls more than 40 content structure formulas to generate a creative script and provides supporting visual design suggestions.