Nano Banana prompt: Create a BioRender-style, publication-ready vector...
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Model used
Nano Banana2Generation parameters
Image1376x768jpg
Prompt
Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas.
Layout
The figure has two main sections:
Section A (left/center): Research Content Framework (main flowchart)
A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4).
Each block is a rounded rectangle with a short title plus 2–4 bullet points.
Add clear arrows between steps.
Add a small triangle badge near Step 3 showing the trade-off.
Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles:
“闭环控制框架（流程图）”
“耦合误差示意（维恩图）”
“深度递归神经网络示意（时间展开）”
Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence.
Icons (flat, minimal)
Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy).
Keep icons minimal and consistent.
Chinese text to place in boxes (exact)
Title (top center)
“学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图”
Section A: Main research content framework (4 steps)
Step 1 (Block 1)
Title: “一致性误差机理刻画”
Bullets:
“建立统一闭环误差建模框架”
“刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用”
“解释收敛退化、触发频繁与性能下降的成因”
“覆盖无领导一致、领导跟随一致与协同跟踪场景”
Step 2 (Block 2)
Title: “低保守收敛性与有界性分析”
Bullets:
“显式利用触发区间信息构造分析工具”
“建立收敛性与有界性判据并降低保守性”
“推导误差上界、无有限时间无限触发条件与触发间隔下界”
“刻画触发间隔与拓扑、触发参数、辨识精度的定量关系”
Step 3 (Block 3)
Title: “协同设计与权衡机制”
Bullets:
“协同设计学习辨识器、动态事件触发与分布式控制协议”
“保证学习参数与内部递归状态有界”
“揭示学习率、触发参数、拓扑特征与一致性性能的定量关系”
“建立一致性性能—通信次数—能耗开销的可计算权衡”
Add-on icon near Step 3:
A small triangular trade-off badge with vertex labels (Chinese):
“一致性性能 / 通信次数 / 能耗开销”
Caption next to triangle: “可计算权衡”
Step 4 (Block 4)
Title: “仿真分析与实验验证”
Bullets:
“搭建含未知非线性、扰动与通信约束的仿真平台”
“对比不同触发规则、拓扑与学习精度下的性能与通信开销”
“在多无人机与多机器人平台开展验证”
“形成可推广的低通信、高可靠、可验证方法”
Section B: Three mini-schematics (right side)
Mini-panel 1: “闭环控制框架（流程图）”
Draw a left-to-right flowchart with rounded blocks and arrows:
Blocks (in order, Chinese text exact):
“非线性多智能体系统” →
“局部/邻域信息获取” →
“一致性误差计算” →
“学习辨识器（深度递归神经网络）” →
“分布式控制器” →
“动态事件触发器” →
“网络传输与采样保持” →
Back arrow to “非线性多智能体系统”
Add two dashed feedback arrows from “一致性误差计算” to:
“学习辨识器（深度递归神经网络）” (label: “误差驱动更新”)
“动态事件触发器” (label: “误差驱动更新”)
Add small notes:
Under “动态事件触发器”: “按需通信/按需更新”
Near “网络传输与采样保持”: “触发保持误差”
Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”.
Mini-panel 2: “耦合误差示意（维恩图）”
Draw a three-circle Venn diagram with semi-transparent circles:
Circle labels (Chinese):
“学习辨识误差” (with RNN icon)
“触发保持误差” (with clock + sample-and-hold icon)
“拓扑耦合误差” (with network graph icon)
Pairwise overlap labels:
“学习更新×非均匀更新”
“异步通信×拓扑传播”
“分布式辨识×邻域耦合”
Center overlap (bold):
“耦合项集合”
Under it:
“影响一致性误差演化”
Arrow from center to a right-side box titled “结果表征” with bullets:
“收敛退化（渐近→最终有界）”
“触发频繁/触发间隔变小”
“稳态误差界增大/性能下降”
Mini-panel 3: “深度递归神经网络示意（时间展开）”
Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese:
“上一时刻 → 当前时刻 → 下一时刻”
At each time slice, show stacked recurrent blocks:
Input label: “本体状态与邻域信息” →
Middle label: “递归记忆状态” →
Output label: “未知非线性与不确定项的在线辨识输出”
Connect time slices with arrows labeled:
“共享参数”
Add a side arrow from “一致性误差” into a small box:
“参数更新（投影/正则化/学习率调度）”
Then arrow into:
“学习参数更新”
Style constraints
BioRender clean scientific infographic, no photorealism, no clutter, high readability.
Strict rule: do not include any math symbols, letters, equations, or subscripts.
Negative prompt:
Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.
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