Execution guidance system for artistic culture project disassembly and management based on multi-agent collaboration

Abstract

The invention provides an execution guidance system for artistic culture project disassembly and management based on multi-agent collaboration, and belongs to the field of artificial intelligence and culture management. The system comprises an intelligent input layer, a multi-agent collaborative decision engine, a professional knowledge graph library, an execution guidance generation engine, an intelligent asset management engine and an intelligent output layer. The core features are as follows: multi-modal input recognition is realized through deep learning; constructing a professional knowledge graph fusing art history, creation techniques and the like; a multi-agent collaborative decision-making mechanism is adopted, and intelligent agents such as creative guidance, research analysis and the like are combined to realize full-period management; introducing an optimization mechanism based on practice feedback; intelligent digital asset management is established, and intelligent classification, naming and retrieval functions of multimedia data are provided. According to the invention, the management efficiency and specialization level of artistic culture projects are significantly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence, and particularly relates to an execution guidance system for disassembling and managing art and cultural projects based on multi-agent collaboration, and more particularly to a technical solution for applying artificial intelligence technology to art and cultural project management. Background Art

[0002] With the booming development of the cultural and creative industries, the management and execution of art and cultural projects are facing increasingly complex challenges. The following problems exist in the prior art: 1. Traditional project management methods are difficult to meet the special needs of the art and cultural field and lack effective support for the creative process; 2. The cost of obtaining professional knowledge and experience is high, and it is difficult for small and medium-sized teams to obtain high-quality professional guidance; 3. The management efficiency of project resources is low, and digital assets are difficult to organize and utilize effectively; 4. The cross-professional collaboration barriers are obvious, and the team collaboration efficiency is not high.

[0003] The existing solutions in the market mainly include: 1. General project management software systems, such as Microsoft Project, Trello, etc.; 2. Professional design collaboration tools, such as Adobe Creative Cloud, Figma, etc.; 3. Digital asset management systems, such as Digital Asset Management (DAM) systems.

[0004] These existing technologies have the following deficiencies: 1. Lack of in-depth understanding and support for the particularity of the art and cultural field; 2. Insufficient intelligence and unable to provide professional decision-making support; 3. The systems are fragmented and it is difficult to achieve seamless collaboration throughout the process; 4. Lack of an effective mechanism for knowledge accumulation and experience inheritance. Summary of the Invention

[0005] The purpose of the present invention is to provide an execution guidance system for disassembling and managing art and cultural projects based on multi-agent collaboration, aiming to solve the problems existing in the prior art. The system provides professional management support and execution guidance for art and cultural projects through multi-agent technology and deep learning algorithms.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A complete system architecture including an intelligent input layer, a multi-agent collaborative decision-making engine, a professional knowledge graph library, an execution guidance generation engine, an intelligent asset management engine, and an intelligent output layer is constructed. Among them: 1. The intelligent input layer realizes the intelligent recognition and processing of multi-modal information, including: (1) The handwriting recognition module uses a deep learning model for handwriting recognition; (2) The speech recognition module supports real-time transcription in multiple languages; (3) The image recognition module has functions of image classification and object detection; (4) The text processing module has the ability of natural language understanding.

[0007] 2. The multi-agent collaborative decision-making engine includes five types of professional agents: (1) The creative guidance agent is responsible for creative-related analysis and suggestions; (2) The research and analysis agent conducts project feasibility evaluation; (3) The execution management agent optimizes the execution plan; (4) The collaboration docking agent coordinates resource allocation; (5) The asset management agent manages digital assets.

[0008] 3. The professional knowledge graph library integrates: (1) The art history theory knowledge base; (2) The creative technique theory library; (3) The project management method library; (4) The digital asset management specification library. Beneficial effects

[0009] The present invention has the following beneficial effects: 1. Significantly improve project management efficiency: (1) Through the collaborative decision-making of agents, improve the project execution efficiency; (2) Improve the resource utilization rate and shorten the project delivery cycle; (3) Improve the team collaboration efficiency and reduce the communication cost.

[0010] 2. Lower the professional threshold: (1) Systematize the expert experience so that small and medium-sized teams and individuals can also obtain professional guidance; (2) Through the continuous accumulation of the knowledge graph, realize the effective inheritance of experience; (3) Intelligent decision support reduces the dependence on manual experience.

[0011] 3. Optimize asset management: (1) Improve the accuracy of intelligent classification of digital assets; (2) Improve the efficiency of asset retrieval; (3) Improve the utilization rate of storage space.

[0012] 4. Ensure project quality: (1) Reduce execution deviation; (2) Improve the success rate of the project; (3) Improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is the overall system architecture diagram of the present invention.

[0014] Figure 2 It is the structural schematic diagram of the multi-agent collaborative decision-making engine of the present invention.

[0015] Figure 3 It is the flow chart of knowledge graph construction of the present invention.

[0016] Figure 4 It is the flow chart of intelligent asset management of the present invention.

[0017] Figure 5 It is the flow chart of execution guidance generation of the present invention.

[0018] Figure 6 It is the schematic diagram of the user interface of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0020] As Figure 1 shown, the overall system architecture of the present invention includes six main modules: an intelligent input layer, a multi-agent collaborative decision-making engine, a professional knowledge graph library, an execution guidance generation engine, an intelligent asset management engine, and an intelligent output layer. Each module is deployed through a distributed architecture to ensure the high availability and scalability of the system.

[0021] A: Specific implementation of the intelligent input layer: 1. Handwriting recognition module: (1) Adopt an improved CNN-LSTM hybrid network architecture; (2) Support real-time recognition and batch processing; (3) Improve the recognition accuracy.

[0022] 2. Speech recognition module: (1) Adopt a speech recognition model that optimizes the Transformer architecture; (2) Support real-time transcription in multiple languages; (3) Support the recognition of domain-specific terms.

[0023] 3. Image Recognition Module: (1) Adopt the optimized YOLOv5 object detection algorithm; (2) Support the recognition of multiple types of image content; (3) Support batch processing and real-time recognition.

[0024] B: Implementation Method of Multi-Agent Cooperative Decision-Making Engine: 1. Creative Guidance Agent: (1) Language model based on the GPT architecture; (2) Integrate the knowledge base of art history theory; (3) Have the ability of creative evaluation and suggestion generation.

[0025] 2. Research and Analysis Agent: (1) Adopt a multi-dimensional evaluation model; (2) Support quantitative and qualitative analysis; (3) Have the function of risk warning.

[0026] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in the present invention can be implemented by the prior art.

[0027] The present invention is not limited to the above specific embodiments. As long as various non-substantive improvements are made by adopting the technical solutions of the present invention, or the above concepts and technical solutions of the present invention are directly applied to other occasions without improvement, they should all be included in the protection scope of the present invention.

Claims

1. An execution guidance system for the disassembly and management of art and cultural projects based on multi-agent collaboration, characterized in that, The system includes: an intelligent input layer, equipped with multimodal information collection for receiving and processing information input by users through an intelligent whiteboard; a multi-agent collaborative decision-making engine, informationally connected to the intelligent input layer for analyzing and making decisions on the input information; a professional knowledge graph library, informationally connected to the multi-agent collaborative decision-making engine for providing professional knowledge support; an execution guidance generation engine, informationally connected to the multi-agent collaborative decision-making engine and the professional knowledge graph library for generating execution plans and guidance suggestions; an intelligent asset management engine, informationally connected to the execution guidance generation engine for managing project-related digital assets; and an intelligent output layer, informationally connected to the execution guidance generation engine and the intelligent asset management engine for presenting analysis results and execution suggestions to users.

2. The execution guidance system according to claim 1, wherein: The intelligent input layer includes: a handwriting recognition module for recognizing the handwriting content of users on the intelligent whiteboard, and the handwriting recognition module uses a deep learning model for handwriting text recognition; a speech recognition module for converting user speech into text information, and the speech recognition module supports multilingual real-time transcription; an image recognition module for recognizing and processing image information, and the image recognition module has functions of image classification and object detection; and a text processing module for processing directly input text information, and the text processing module has the ability of natural language understanding.

3. The execution guidance system according to claim 1, wherein: The multi-agent collaborative decision-making engine includes: a creative guidance agent for providing guidance suggestions related to creativity, and the creative guidance agent conducts creative analysis based on a deep learning model; a research analysis agent for conducting project feasibility and requirement analysis, and the research analysis agent adopts a multi-dimensional evaluation method; an execution management agent for formulating and optimizing execution plans, and the execution management agent has an adaptive optimization ability; a collaboration docking agent for coordinating various resources and task allocation, and the collaboration docking agent adopts an intelligent scheduling algorithm; and an asset management agent for managing and optimizing digital assets, and the asset management agent has functions of intelligent classification and retrieval.

4. The execution guidance system according to claim 1, characterized in that: The professional knowledge graph library includes: an art history theory knowledge library containing an art history theory system and art creation theory; a creation technique theory library containing various art creation methods and technical specifications; a project management method library containing project management best practices and industry standards; an industry standard specification library containing relevant industry technical standards and specification documents; and a digital asset management specification library containing digital asset management standards and best practice guidelines.

5. The execution guidance system according to claim 1, wherein: The intelligent asset management engine includes: a multimedia material intelligent classification module that automatically classifies videos, documents, pictures, etc. using a deep learning algorithm; an intelligent naming module that standardizes the naming of digital assets based on preset rules and machine learning algorithms; a tag generation module that automatically generates asset description tags using natural language processing technology; and an intelligent retrieval module that realizes an efficient asset retrieval service based on semantic understanding technology.

6. The execution guidance system according to claim 3, characterized in that: The functions of the asset management agent include: a file classification function based on deep learning, which uses a convolutional neural network for multi-modal content recognition; a label generation function based on a knowledge graph, which automatically generates content labels through semantic analysis; an intelligent archiving strategy formulation function, which automatically determines an archiving scheme according to content features; and a personalized retrieval optimization function, which optimizes retrieval results based on user behavior analysis.

7. The execution guidance system according to claim 1, wherein: The execution guidance generation engine includes: a project breakdown module, which decomposes a project into executable task units using the analytic hierarchy process; a resource allocation module, which generates a resource configuration scheme using an intelligent optimization algorithm; a progress management module, which formulates and adjusts the project progress based on the critical path method; and a quality control module, which conducts quality monitoring through a preset index system.

8. The execution guidance system according to claim 1, wherein: The intelligent output layer includes: a visualization display module, which intuitively displays analysis results using data visualization technology; a solution generation module, which outputs an execution solution based on templates and intelligent algorithms; a suggestion feedback module, which provides optimization suggestions by analyzing historical data; and a report generation module, which automatically integrates analysis results to generate a project-related report.

9. The execution guidance system according to claim 1, wherein: It also includes a practice feedback optimization module, which: collects execution process data through a data collection interface; analyzes execution effects using machine learning algorithms; optimizes the decision-making model based on the analysis results; and periodically updates the content of the knowledge graph.

10. The execution guidance system according to claim 1, wherein: The system adopts a distributed architecture, including: a cloud server, which is configured with a high-performance computing unit for running core algorithms and storing data; a local client, which provides an interaction interface for realizing user interaction; a data synchronization module, which uses an incremental synchronization mechanism to ensure data consistency; and a security encryption module, which uses a high-strength encryption algorithm to protect data security.

Images