在这幅现代科技风格的图片中，一台高科技的服务器房展现在眼前。这个服务器房内部布置得井井有条，设备整齐地排列在机架上。灯光昏暗，透过柔和的光线，可以看到一些工程师正在专注地操作着控制台。在图片中心，一块巨大的显示屏上出现了一张精确的故障报修处理流程图，清晰地展示了从故障报修的接收到处理的整个流程。而在图像的右侧，一名年轻的工程师正佩戴着专业的耳麦，手持便携式设备，专注地巡视着服务器房。背景中的墙壁上挂满了各种监控摄像头，保障着整个服务器房的安全。整个画面以现代科技的冷色调为主，突出了智慧运维的主题。在一侧的显示屏旁边，预留了一个合适的区域，放置了一个简洁明亮的故障报修二维码，以黑色为背景，与整体画面形成鲜明对比。通过扫描二维码，观众可以快速进入故障报修系统，体验科技的便利性和智能化。这幅图片将现代科技与智慧运维完美结合，呈现出一幅充满活力和专业感的画面，让人感受到科技进步所带来的便利和效率。
030
DreamShaper
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.
138
Nano Banana
在这幅现代科技风格的图片中，一台高科技的服务器房展现在眼前。这个服务器房内部布置得井井有条，设备整齐地排列在机架上。灯光昏暗，透过柔和的光线，可以看到一些工程师正在专注地操作着控制台。在图片中心，一块巨大的显示屏上出现了一张精确的故障报修处理流程图，清晰地展示了从故障报修的接收到处理的整个流程。而在图像的右侧，一名年轻的工程师正佩戴着专业的耳麦，手持便携式设备，专注地巡视着服务器房。背景中的墙壁上挂满了各种监控摄像头，保障着整个服务器房的安全。整个画面以现代科技的冷色调为主，突出了智慧运维的主题。在一侧的显示屏旁边，预留了一个合适的区域，放置了一个简洁明亮的故障报修二维码，以黑色为背景，与整体画面形成鲜明对比。通过扫描二维码，观众可以快速进入故障报修系统，体验科技的便利性和智能化。这幅图片将现代科技与智慧运维完美结合，呈现出一幅充满活力和专业感的画面，让人感受到科技进步所带来的便利和效率。
030
DreamShaper
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.
138
Nano Banana
在这幅现代科技风格的图片中，一台高科技的服务器房展现在眼前。这个服务器房内部布置得井井有条，设备整齐地排列在机架上。灯光昏暗，透过柔和的光线，可以看到一些工程师正在专注地操作着控制台。在图片中心，一块巨大的显示屏上出现了一张精确的故障报修处理流程图，清晰地展示了从故障报修的接收到处理的整个流程。而在图像的右侧，一名年轻的工程师正佩戴着专业的耳麦，手持便携式设备，专注地巡视着服务器房。背景中的墙壁上挂满了各种监控摄像头，保障着整个服务器房的安全。整个画面以现代科技的冷色调为主，突出了智慧运维的主题。在一侧的显示屏旁边，预留了一个合适的区域，放置了一个简洁明亮的故障报修二维码，以黑色为背景，与整体画面形成鲜明对比。通过扫描二维码，观众可以快速进入故障报修系统，体验科技的便利性和智能化。这幅图片将现代科技与智慧运维完美结合，呈现出一幅充满活力和专业感的画面，让人感受到科技进步所带来的便利和效率。
030
DreamShaper
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.
138
Nano Banana
在这幅现代科技风格的图片中，一台高科技的服务器房展现在眼前。这个服务器房内部布置得井井有条，设备整齐地排列在机架上。灯光昏暗，透过柔和的光线，可以看到一些工程师正在专注地操作着控制台。在图片中心，一块巨大的显示屏上出现了一张精确的故障报修处理流程图，清晰地展示了从故障报修的接收到处理的整个流程。而在图像的右侧，一名年轻的工程师正佩戴着专业的耳麦，手持便携式设备，专注地巡视着服务器房。背景中的墙壁上挂满了各种监控摄像头，保障着整个服务器房的安全。整个画面以现代科技的冷色调为主，突出了智慧运维的主题。在一侧的显示屏旁边，预留了一个合适的区域，放置了一个简洁明亮的故障报修二维码，以黑色为背景，与整体画面形成鲜明对比。通过扫描二维码，观众可以快速进入故障报修系统，体验科技的便利性和智能化。这幅图片将现代科技与智慧运维完美结合，呈现出一幅充满活力和专业感的画面，让人感受到科技进步所带来的便利和效率。
030
DreamShaper
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.
138
Nano Banana
在这幅现代科技风格的图片中，一台高科技的服务器房展现在眼前。这个服务器房内部布置得井井有条，设备整齐地排列在机架上。灯光昏暗，透过柔和的光线，可以看到一些工程师正在专注地操作着控制台。在图片中心，一块巨大的显示屏上出现了一张精确的故障报修处理流程图，清晰地展示了从故障报修的接收到处理的整个流程。而在图像的右侧，一名年轻的工程师正佩戴着专业的耳麦，手持便携式设备，专注地巡视着服务器房。背景中的墙壁上挂满了各种监控摄像头，保障着整个服务器房的安全。整个画面以现代科技的冷色调为主，突出了智慧运维的主题。在一侧的显示屏旁边，预留了一个合适的区域，放置了一个简洁明亮的故障报修二维码，以黑色为背景，与整体画面形成鲜明对比。通过扫描二维码，观众可以快速进入故障报修系统，体验科技的便利性和智能化。这幅图片将现代科技与智慧运维完美结合，呈现出一幅充满活力和专业感的画面，让人感受到科技进步所带来的便利和效率。
030
DreamShaper
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.
138
Nano Banana
在这幅现代科技风格的图片中，一台高科技的服务器房展现在眼前。这个服务器房内部布置得井井有条，设备整齐地排列在机架上。灯光昏暗，透过柔和的光线，可以看到一些工程师正在专注地操作着控制台。在图片中心，一块巨大的显示屏上出现了一张精确的故障报修处理流程图，清晰地展示了从故障报修的接收到处理的整个流程。而在图像的右侧，一名年轻的工程师正佩戴着专业的耳麦，手持便携式设备，专注地巡视着服务器房。背景中的墙壁上挂满了各种监控摄像头，保障着整个服务器房的安全。整个画面以现代科技的冷色调为主，突出了智慧运维的主题。在一侧的显示屏旁边，预留了一个合适的区域，放置了一个简洁明亮的故障报修二维码，以黑色为背景，与整体画面形成鲜明对比。通过扫描二维码，观众可以快速进入故障报修系统，体验科技的便利性和智能化。这幅图片将现代科技与智慧运维完美结合，呈现出一幅充满活力和专业感的画面，让人感受到科技进步所带来的便利和效率。
030
DreamShaper
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.
138
Nano Banana