![Generate a landscape-oriented, academic-style flowchart illustrating the "Automatic Copyright Revenue Distribution Process (Based on Blockchain Smart Contracts)." The process should flow from left to right, employing a clean, vector style suitable for academic paper illustrations. Step 1: On the left, "User (Licensee / Buyer)," represented by a person or terminal icon, labeled "Payment (Purchase / Lease)." Step 2: An arrow points to "Select Copyright Category + Units + Duration." Step 3: Entering the "Smart Contract (Copyright Token Contract)," display a contract box, internally labeled "Price Verification (Sale / Lease Price per Unit)." Step 4: Fees enter the contract pool, labeled "Payment Received." Step 5: The contract queries "Category Share Table ownershipValues[tokenId][category]," using branching arrows to represent multiple rights holders. Step 6: The contract automatically distributes revenue according to the share ratio, with each rights holder receiving a corresponding proportion of income, labeled "Automatic Revenue Distribution." Step 7: The process endpoint displays "Record License (Usage Right) or Transfer Shares (Ownership Transfer)." Add a small annotation in the lower right corner: "Revenue distribution is based on the copyright share ratio of the same category." The overall color scheme should be primarily black, white, and gray, with a small amount of blue or green to emphasize "Automatic Distribution." Use a simple font, clear logic, and a serious academic style.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FjCoJNRkBELSfVC6rdUMC1o25GaZyLLZz%2F8ad3fd3f-1d13-4d84-8ccd-240e1364b1bb%2Fa301b08e-23a3-476a-ab44-a97c0f2edf58.png&w=3840&q=75)
Prompt Description
Generate a landscape-oriented, academic-style flowchart illustrating the "Automatic Copyright Revenue Distribution Process (Based on Blockchain Smart Contracts)." The process should flow from left to right, employing a clean, vector style suitable for academic paper illustrations. Step 1: On the left, "User (Licensee / Buyer)," represented by a person or terminal icon, labeled "Payment (Purchase / Lease)." Step 2: An arrow points to "Select Copyright Category + Units + Duration." Step 3: Entering the "Smart Contract (Copyright Token Contract)," display a contract box, internally labeled "Price Verification (Sale / Lease Price per Unit)." Step 4: Fees enter the contract pool, labeled "Payment Received." Step 5: The contract queries "Category Share Table ownershipValues[tokenId][category]," using branching arrows to represent multiple rights holders. Step 6: The contract automatically distributes revenue according to the share ratio, with each rights holder receiving a corresponding proportion of income, labeled "Automatic Revenue Distribution." Step 7: The process endpoint displays "Record License (Usage Right) or Transfer Shares (Ownership Transfer)." Add a small annotation in the lower right corner: "Revenue distribution is based on the copyright share ratio of the same category." The overall color scheme should be primarily black, white, and gray, with a small amount of blue or green to emphasize "Automatic Distribution." Use a simple font, clear logic, and a serious academic style.
![Method 2: Dynamic Coefficient Learning Based on Attention Mechanism
- **Core Idea**: Introduce an attention layer to allow the model to automatically learn the weights of different tokens in emotion regulation. Implementation steps:
(1) Construct Token Feature Representation: Extract the Token state H=[h1, h2, ....hn] (where n is the number of Tokens) from a certain layer of the model, and use the function vector (A - B) as the 'emotion query vector'.
(2) Calculate Token-Emotion Attention Weights: Calculate the matching degree between each Token and the emotion query vector through scaled dot-product attention:
ai = Softmax (huaye)
where d is the dimension of hi.
(3) Adaptive Injection of Function Vectors: Apply hi+ai.(A-B) to each Token state. This is the idea for my part.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2F21yePdfXfrXMwKl3x4fhPD0TH1d7yghe%2F4a7680a8-334b-44a9-bf77-6adf2b1dd5e7%2Fcd765bfe-d81d-4f08-b6e5-510889b6cb6c.png&w=3840&q=75)
![APPROVED
This section focuses on experimental verification and model evolution. The core aspects include: 1. Evolutionary Strategy Fusion: Integrating the Analytic Hierarchy Process (AHP) with adversarial distillation heterogeneous techniques to achieve self-evolution of the anti-interference model. 2. Model Library Generation: Applying graph-driven pruning and multi-metric channel compression to generate various intelligent anti-interference decision model variants. 3. Layered Testing: Conducting dual verification of basic performance and adversarial robustness on embedded devices and GPU server platforms.
The final content includes the following: 5. [Bottom Illustration/Formula]: Depicting a **"model evolution funnel"**: Original large model $\xrightarrow{Pruning/Distillation}$ Evolved Models V1/V2/V3 $\xrightarrow{Testing}$ Optimal Strategy.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2Fd4HJgWhDq5YPdBs9p3hto7zsqac9CQfA%2Ffe88eb94-ac9e-440f-8ba6-70151a779b37%2F53b4a50e-b721-4fe0-8cbb-60ab4bb29cd7.png&w=3840&q=75)