## [Diagram/Workflow]: Black Myth: Wukong Video Analysis & Web Showcase Workflow ### Overview This image is a detailed workflow diagram illustrating a parallel processing strategy for analyzing 32 large gameplay video files (~40GB total) of the game "Black Myth: Wukong" and compiling the results into an interactive HTML web page. The diagram shows a hierarchical agent-based system where a main agent orchestrates multiple sub-agents to perform tasks in parallel. The visual style is a flowchart with text boxes, arrows, and embedded screenshots. ### Components/Axes The diagram is organized into three main spatial regions: 1. **Header (Top):** Contains the project title, instructions, and a row of video thumbnails. 2. **Main Chart (Center):** A large flowchart split into two columns. * **Left Column:** Represents the "MAIN AGENT's" high-level task sequence (THINK, SUMMARY, TOOL). * **Right Column:** Represents the detailed actions of spawned "SUB AGENT" instances (SUB AGENT 1, 2, 3... 32). 3. **Footer (Bottom):** Contains the final summary and output instructions. **Key Textual Elements & Labels:** * **Title/Instruction (Top Center):** "As a video web master, please analyze all these files (in ./video/wukong/) and create a cool HTML page: 1. Clarify the chronological timeline of video events 2. Identify the timestamps when player levels are upgraded 3. demonstrate your #2.5 agent's powerful long-video understanding and web-page design capability. Output results to ./video/wukong-result-video-web-master/" * **Project Stats (Top Right, Red Text):** "32Parts, 24Hours, 40G" * **Agent Labels:** "MAIN AGENT", "SUB AGENT 1", "SUB AGENT 2", "SUB AGENT 3", "SUB AGENT 32". * **Process Step Labels:** "THINK", "SUMMARY", "TOOL". * **Tool/Command Labels:** `ls -la ./video/wukong/`, `<Create Subagent x 32>`, `<Read File x 32>`, `<Create Subagent x 9>`, `<Build Frontend>`, `<Analyze Video> & <Extract Frames with FFMPEG> & <Read Media File> x N`, `<Extract Video Clip with FFMPEG>`. * **File Paths & Data:** * `./video/wukong/01-25805508462_Proleague.mp4` * `./video/wukong-result-video-web-master/analysis_01.json` * `./video/wukong-result-video-web-master/frames_01/` * `./video/wukong-result-video-web-master/clips/01_prologue_battle.mp4` * **Embedded Chinese Text (with English Translation):** * **Location:** Within the "THINK" box in the right column's "SUB AGENT 1" section. * **Transcription:** "用户想要我分析一个《黑神话：悟空》的游戏视频。" * **Translation:** "The user wants me to analyze a Black Myth: Wukong gameplay video." * **Data Points & Values:** * Number of videos: 32 * Total size: ~40GB * Individual file size range: 700MB - 2GB * Sub-agent threshold: Files >25MB * Extracted frames for Video #01: 82 frames * Clip extraction time range: 00:05:40 to 00:05:50 (10-second clip) * Clip encoding settings: CRF 23, preset fast ### Detailed Analysis The workflow proceeds as follows: 1. **Initialization (Main Agent - Left Column):** * **THINK:** Acknowledges the task: analyze 32 video files to create an HTML page with a timeline, level-up timestamps, and demonstrate capability. * **TOOL:** Lists the directory `./video/wukong/` to see the files. * **THINK:** Discovers 32 large video files (~40GB total). Decides on a parallel strategy using sub-agents because each file is >25MB. The plan is for each sub-agent to analyze one video for timeline, level-ups, and boss fights, then aggregate results. * **TOOL:** Spawns 32 sub-agents (`<Create Subagent x 32>`). 2. **Parallel Analysis (Sub Agents - Right Column):** * The diagram zooms into the process for "SUB AGENT 1" as an example. * **MAIN AGENT (to Sub Agent):** Assigns the task to analyze `./video/wukong/01-25805508462_Proleague.mp4` (Video #01 - Prologue, 320MB, ~5-8 minutes). * **SUB AGENT 1 - THINK:** Plans to explore the file and extract frames. * **SUB AGENT 1 - TOOL:** Uses tools to analyze the video, extract frames with FFMPEG, and read the media file. Shows a screenshot of extracted frames. * **SUB AGENT 1 - SUMMARY:** Reports completion, creating `analysis_01.json` and saving 82 frames to `./video/wukong-result-video-web-master/frames_01/`. 3. **Clip Extraction (Main Agent & Sub Agent):** * **Main Agent (Left Column):** After initial analysis, it reads the 32 analysis files to identify key moments (e.g., Prologue battle 00:05:40-00:06:10, Chapter 1 boss 02:27:00-02:27:30). It then spawns 9 more sub-agents to extract these clips in parallel. * **Sub Agent (Right Column - Lower Section):** Receives a command to extract a 10-second clip from the Prologue video (00:05:40 to 00:05:50) showing the battle with Erlang Shen. It uses `ffmpeg` with specific parameters (`-ss 00:05:40 -i ... -t 10 -c:v libx264 -crf 23 -preset fast`) and saves the output to `./video/wukong-result-video-web-master/clips/01_prologue_battle.mp4`. 4. **Compilation & Output (Main Agent - Left Column):** * **TOOL:** Gathers key analysis files and builds the frontend (`<Build Frontend>`). * **SUMMARY (Footer):** Declares "Analysis Complete! Black Myth: Wukong Journey Showcase". Instructs the user to open `./video/wukong-result-video-web-master/index.html` in a browser to view the interactive showcase with embedded clips, animated charts, and a chronological timeline. ### Key Observations * **Hierarchical Parallelism:** The core strategy is using a main agent to manage and spawn numerous sub-agents (up to 32 initially, then 9 more) to handle the large workload concurrently. * **Tool Integration:** The workflow heavily relies on external tools like `ls`, `ffmpeg` (for frame extraction and clip cutting), and file reading/writing operations. * **Structured Output:** The process generates structured data (`analysis_01.json`), media assets (frames, clips), and a final compiled HTML page. * **Visual Documentation:** The diagram itself uses embedded screenshots (video thumbnails, extracted frames) to provide concrete examples of the data being processed. * **Scale Indication:** The red text "32Parts, 24Hours, 40G" at the top right emphasizes the significant scale of the input data. ### Interpretation This diagram is a **Peircean investigative map** of a complex, automated data processing pipeline. It demonstrates a scalable solution to a big data problem in multimedia analysis. * **What it Suggests:** The system is designed for efficiency and scalability. By breaking down a monolithic task (analyzing 40GB of video) into independent, parallel sub-tasks (one per video file), it drastically reduces total processing time. The use of a main agent for orchestration and sub-agents for execution mimics a distributed computing model. * **How Elements Relate:** The left column (Main Agent) acts as the control plane, making strategic decisions and delegating work. The right column (Sub Agents) represents the data plane, performing the repetitive, intensive work. The arrows show the flow of commands and data between these layers. The final HTML page is the synthesized product, transforming raw video data into an interactive, human-readable format. * **Notable Anomalies/Patterns:** The workflow shows a two-phase parallel approach: first, broad analysis of all videos; second, targeted extraction of key clips based on the initial analysis. This is an intelligent, adaptive pattern. The explicit mention of file size thresholds (>25MB) for triggering sub-agent use indicates a built-in heuristic for resource management. The entire process is a practical example of "long-video understanding" as mentioned in the initial instructions, moving from raw pixels to structured metadata (timestamps, events) and finally to a curated presentation.