Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
Prompt: > A hand-drawn, minimalist infographic in the style of Excalidraw, on a clean light-cream background. The style uses simple marker sketches, thin black lines, and a "Virgil" handwritten font. Layout: Two comparison panels side-by-side. Left Panel (Title: "Traditional Skills"): Sketch of an AI robot holding multiple messy, hard-coded wires. Each wire is labeled with "Custom API", "Email Tool", or "Database Script". It looks complex and rigid. Text below: "Fixed, Manual, High Maintenance". Right Panel (Title: "MCP (Model Context Protocol)"): Sketch of the same AI robot plugging a single universal "MCP plug" into a standardized hub. Various data sources like "GitHub", "Local Files", and "Slack" are connected to this hub via the same interface. It looks clean and modular. Text below: "Universal, Scalable, Plug-and-Play". Style Details: Marker sketch, stick figures, pastel blue and yellow accent colors, very clean white space, professional yet casual.
An image depicting the fundamental concept of building a successful side business. The scene includes a minimalist home office setup with a laptop displaying a simple yet effective business plan. Nearby, a small whiteboard lists key milestones and objectives. The environment is tidy and efficient, symbolizing the clarity and focus needed for a successful side business. The background is intentionally plain, emphasizing the theme of starting small but thinking big.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
A clean and modern infographic illustrating the five key considerations for building a robust Chart of Accounts. A visual representation of a well-structured Chart of Accounts, with clear headings and subheadings for different account categories. A simple graphic comparing the financial performance of a business with a well-structured Chart of Accounts versus one that is disorganized. A business owner reviewing a well-structured Chart of Accounts on a tablet, with a smile on their face. A team of accountants working collaboratively on a large whiteboard, with a Chart of Accounts diagram in the center.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
An image depicting the fundamental concept of building a successful side business. The scene includes a minimalist home office setup with a laptop displaying a simple yet effective business plan. Nearby, a small whiteboard lists key milestones and objectives. The environment is tidy and efficient, symbolizing the clarity and focus needed for a successful side business. The background is intentionally plain, emphasizing the theme of starting small but thinking big.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
A clean and modern infographic illustrating the five key considerations for building a robust Chart of Accounts. A visual representation of a well-structured Chart of Accounts, with clear headings and subheadings for different account categories. A simple graphic comparing the financial performance of a business with a well-structured Chart of Accounts versus one that is disorganized. A business owner reviewing a well-structured Chart of Accounts on a tablet, with a smile on their face. A team of accountants working collaboratively on a large whiteboard, with a Chart of Accounts diagram in the center.
Prompt: > A hand-drawn, minimalist infographic in the style of Excalidraw, on a clean light-cream background. The style uses simple marker sketches, thin black lines, and a "Virgil" handwritten font. Layout: Two comparison panels side-by-side. Left Panel (Title: "Traditional Skills"): Sketch of an AI robot holding multiple messy, hard-coded wires. Each wire is labeled with "Custom API", "Email Tool", or "Database Script". It looks complex and rigid. Text below: "Fixed, Manual, High Maintenance". Right Panel (Title: "MCP (Model Context Protocol)"): Sketch of the same AI robot plugging a single universal "MCP plug" into a standardized hub. Various data sources like "GitHub", "Local Files", and "Slack" are connected to this hub via the same interface. It looks clean and modular. Text below: "Universal, Scalable, Plug-and-Play". Style Details: Marker sketch, stick figures, pastel blue and yellow accent colors, very clean white space, professional yet casual.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
A clean and modern infographic illustrating the five key considerations for building a robust Chart of Accounts. A visual representation of a well-structured Chart of Accounts, with clear headings and subheadings for different account categories. A simple graphic comparing the financial performance of a business with a well-structured Chart of Accounts versus one that is disorganized. A business owner reviewing a well-structured Chart of Accounts on a tablet, with a smile on their face. A team of accountants working collaboratively on a large whiteboard, with a Chart of Accounts diagram in the center.
Prompt: > A hand-drawn, minimalist infographic in the style of Excalidraw, on a clean light-cream background. The style uses simple marker sketches, thin black lines, and a "Virgil" handwritten font. Layout: Two comparison panels side-by-side. Left Panel (Title: "Traditional Skills"): Sketch of an AI robot holding multiple messy, hard-coded wires. Each wire is labeled with "Custom API", "Email Tool", or "Database Script". It looks complex and rigid. Text below: "Fixed, Manual, High Maintenance". Right Panel (Title: "MCP (Model Context Protocol)"): Sketch of the same AI robot plugging a single universal "MCP plug" into a standardized hub. Various data sources like "GitHub", "Local Files", and "Slack" are connected to this hub via the same interface. It looks clean and modular. Text below: "Universal, Scalable, Plug-and-Play". Style Details: Marker sketch, stick figures, pastel blue and yellow accent colors, very clean white space, professional yet casual.
An image depicting the fundamental concept of building a successful side business. The scene includes a minimalist home office setup with a laptop displaying a simple yet effective business plan. Nearby, a small whiteboard lists key milestones and objectives. The environment is tidy and efficient, symbolizing the clarity and focus needed for a successful side business. The background is intentionally plain, emphasizing the theme of starting small but thinking big.
An image depicting the fundamental concept of building a successful side business. The scene includes a minimalist home office setup with a laptop displaying a simple yet effective business plan. Nearby, a small whiteboard lists key milestones and objectives. The environment is tidy and efficient, symbolizing the clarity and focus needed for a successful side business. The background is intentionally plain, emphasizing the theme of starting small but thinking big.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
A clean and modern infographic illustrating the five key considerations for building a robust Chart of Accounts. A visual representation of a well-structured Chart of Accounts, with clear headings and subheadings for different account categories. A simple graphic comparing the financial performance of a business with a well-structured Chart of Accounts versus one that is disorganized. A business owner reviewing a well-structured Chart of Accounts on a tablet, with a smile on their face. A team of accountants working collaboratively on a large whiteboard, with a Chart of Accounts diagram in the center.
Prompt: > A hand-drawn, minimalist infographic in the style of Excalidraw, on a clean light-cream background. The style uses simple marker sketches, thin black lines, and a "Virgil" handwritten font. Layout: Two comparison panels side-by-side. Left Panel (Title: "Traditional Skills"): Sketch of an AI robot holding multiple messy, hard-coded wires. Each wire is labeled with "Custom API", "Email Tool", or "Database Script". It looks complex and rigid. Text below: "Fixed, Manual, High Maintenance". Right Panel (Title: "MCP (Model Context Protocol)"): Sketch of the same AI robot plugging a single universal "MCP plug" into a standardized hub. Various data sources like "GitHub", "Local Files", and "Slack" are connected to this hub via the same interface. It looks clean and modular. Text below: "Universal, Scalable, Plug-and-Play". Style Details: Marker sketch, stick figures, pastel blue and yellow accent colors, very clean white space, professional yet casual.
Prompt: > A hand-drawn, minimalist infographic in the style of Excalidraw, on a clean light-cream background. The style uses simple marker sketches, thin black lines, and a "Virgil" handwritten font. Layout: Two comparison panels side-by-side. Left Panel (Title: "Traditional Skills"): Sketch of an AI robot holding multiple messy, hard-coded wires. Each wire is labeled with "Custom API", "Email Tool", or "Database Script". It looks complex and rigid. Text below: "Fixed, Manual, High Maintenance". Right Panel (Title: "MCP (Model Context Protocol)"): Sketch of the same AI robot plugging a single universal "MCP plug" into a standardized hub. Various data sources like "GitHub", "Local Files", and "Slack" are connected to this hub via the same interface. It looks clean and modular. Text below: "Universal, Scalable, Plug-and-Play". Style Details: Marker sketch, stick figures, pastel blue and yellow accent colors, very clean white space, professional yet casual.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
A clean and modern infographic illustrating the five key considerations for building a robust Chart of Accounts. A visual representation of a well-structured Chart of Accounts, with clear headings and subheadings for different account categories. A simple graphic comparing the financial performance of a business with a well-structured Chart of Accounts versus one that is disorganized. A business owner reviewing a well-structured Chart of Accounts on a tablet, with a smile on their face. A team of accountants working collaboratively on a large whiteboard, with a Chart of Accounts diagram in the center.
An image depicting the fundamental concept of building a successful side business. The scene includes a minimalist home office setup with a laptop displaying a simple yet effective business plan. Nearby, a small whiteboard lists key milestones and objectives. The environment is tidy and efficient, symbolizing the clarity and focus needed for a successful side business. The background is intentionally plain, emphasizing the theme of starting small but thinking big.
An image depicting the fundamental concept of building a successful side business. The scene includes a minimalist home office setup with a laptop displaying a simple yet effective business plan. Nearby, a small whiteboard lists key milestones and objectives. The environment is tidy and efficient, symbolizing the clarity and focus needed for a successful side business. The background is intentionally plain, emphasizing the theme of starting small but thinking big.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架;给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法;揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系;建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证;形成可推广的低通信、高可靠、可验证协同控制方法;服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1(G1)” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具,降低收敛性与有界性结论的保守性;推导误差上界、无Zeno条件与IET下界;刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2(G2)” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架;研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响;设计权值更新律与触发机制,实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3(G3)” Body: “面向模型信息不完备与参数不确定等情形,研究模型弱依赖的学习辨识–事件触发一致性控制方法;给出一致性误差收敛性与有界性条件,以及DRNN权值与内部递归状态的有界性结论;提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成:低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
A clean and modern infographic illustrating the five key considerations for building a robust Chart of Accounts. A visual representation of a well-structured Chart of Accounts, with clear headings and subheadings for different account categories. A simple graphic comparing the financial performance of a business with a well-structured Chart of Accounts versus one that is disorganized. A business owner reviewing a well-structured Chart of Accounts on a tablet, with a smile on their face. A team of accountants working collaboratively on a large whiteboard, with a Chart of Accounts diagram in the center.
Prompt: > A hand-drawn, minimalist infographic in the style of Excalidraw, on a clean light-cream background. The style uses simple marker sketches, thin black lines, and a "Virgil" handwritten font. Layout: Two comparison panels side-by-side. Left Panel (Title: "Traditional Skills"): Sketch of an AI robot holding multiple messy, hard-coded wires. Each wire is labeled with "Custom API", "Email Tool", or "Database Script". It looks complex and rigid. Text below: "Fixed, Manual, High Maintenance". Right Panel (Title: "MCP (Model Context Protocol)"): Sketch of the same AI robot plugging a single universal "MCP plug" into a standardized hub. Various data sources like "GitHub", "Local Files", and "Slack" are connected to this hub via the same interface. It looks clean and modular. Text below: "Universal, Scalable, Plug-and-Play". Style Details: Marker sketch, stick figures, pastel blue and yellow accent colors, very clean white space, professional yet casual.