产品设计与创新领域最大的人工智能提示目录
欢迎访问全球最大的人工智能提示目录，该目录致力于先进的产品设计、工程、科学、创新、质量和制造。虽然在线人工智能工具正在通过增强人类能力迅速改变工程领域，但其真正的威力是通过精确和专业的指令来释放的。本综合目录为您提供了一系列此类提示，使您能够指挥人工智能系统处理海量数据、识别复杂模式并生成新颖的解决方案，其效率远远超过传统方法。
发现并微调所需的准确提示，利用在线人工智能代理优化设计，以达到最佳性能和可制造性，加速复杂模拟，准确预测材料特性，并自动执行各种关键分析任务。
通过高级搜索过滤器可以快速访问这个内容广泛的目录，涵盖现代工程学的所有领域。
考虑到服务器资源和时间，提示信息本身仅供注册会员使用，如果您没有登录，则无法在下方看到。 您可以免费注册 100%：
- 代码生成和调试
人工智能提示 MATLAB Script for 2D Truss FEA
- 方差分析, 面向制造设计 (DfM), 优化设计, 工程, 有限元法（FEM）, 材料科学, 机械工业, 模拟, 结构工程
Generates a basic MATLAB script to perform a Finite Element Analysis (FEA) on a 2D truss structure. The script will take nodal coordinates, element connectivity, material properties, loads, and boundary conditions as input.
输出：
- MATLAB
- 不需要实时互联网
- Fields: {truss_geometry_and_properties_json} {load_and_boundary_conditions_json}
- Best for: Generating a foundational MATLAB script for 2D truss analysis, useful for educational purposes or as a starting point for custom FEA tools.
- 数据生成或扩充
人工智能提示 Create Construction Schedule Variations
- 敏捷方法论, 建筑信息模型（BIM）, 建筑工程, 精益制造, 项目管理, 风险分析, 风险管理
Generates multiple plausible variations of a construction schedule by introducing delays or accelerations to activities based on specified risk factors and their potential impacts. Helps in Monte Carlo simulations or risk analysis.
输出：
- CSV
- 不需要实时互联网
- Fields: {baseline_schedule_csv_activities_durations_dependencies} {risk_event_descriptions_and_potential_impacts_on_duration} {number_of_scenario_variations}
- Best for: Generating data for schedule risk analysis, Monte Carlo simulations, and contingency planning.
- 信息提取
人工智能提示 Extract Material Properties from Text
- 快速成型制造, 复合材料, 复合材料, 增材制造设计（DfAM）, 材料, 机械性能, 产品设计, 产品开发, 可持续发展实践
Extracts specified material properties for given materials from a block of unstructured text like a report or specification. This helps in quickly populating material databases or creating comparison sheets without manual searching.
输出：
- JSON
- 不需要实时互联网
- Fields: {document_text} {list_of_materials_and_their_properties_to_find}
- Best for: Populating material databases or comparison sheets from unstructured text, saving manual effort.
- 数据生成或扩充
人工智能提示 Create Synthetic Soil Bearing Capacity Data
- 土木工程, 建筑工程, 可持续性设计, 优化设计, 环境影响评估, 岩土工程, 材料科学, 质量控制, 结构工程
This prompt generates synthetic soil bearing capacity data based on input soil parameters {soil_properties_json}. The AI should produce a JSON array with multiple data points showing allowable bearing capacity values under varied depths and footing sizes for civil engineering foundation design.
输出：
- JSON
- 不需要实时互联网
- Fields: {soil_properties_json}
- Best for: Best for creating varied soil bearing capacity datasets for foundation design.
- 信息提取
人工智能提示 Identify Key Structural Design Codes Cited
- 土木工程, 建筑工程, 设计分析, 设计文档, 面向制造设计 (DfM), 可持续性设计, 质量保证, 质量管理, 结构工程
This prompt scans through the provided civil engineering document text {document_text} to identify and list all references to structural design codes (e.g., ACI, Eurocode, IS codes), including version/year if available. The AI must list codes uniquely and give a brief description of their scope if known.
输出：
- Markdown
- 不需要实时互联网
- Fields: {document_text}
- Best for: Best for extracting references to design standards from technical documents.
- 故障排除和诊断
人工智能提示 Troubleshoot Heat Exchanger Efficiency Loss
- 腐蚀, 效率, 热处理, 泄漏检测, 维护, 流程改进, 工艺优化, 质量控制, 质量管理
This prompt evaluates heat exchanger operational data and symptoms to diagnose causes of efficiency loss. The AI provides a markdown report outlining potential issues like fouling, leaks, or flow maldistribution with corrective recommendations.
输出：
- Markdown
- 不需要实时互联网
- Fields: {heat_exchanger_data} {symptoms_description}
- Best for: Diagnose and address heat exchanger performance problems
- 故障排除和诊断
人工智能提示 Troubleshoot Distillation Column Anomalies
- 化学品回收, 持续改进, 故障模式和影响分析（FMEA）, 流程改进, 工艺优化, 质量管理, 根本原因分析, 统计过程控制 (SPC)
This prompt takes detailed operational parameters and symptoms related to a distillation column and generates a structured diagnostic report identifying likely malfunctions, their causes, and recommended fixes.
输出：
- Markdown
- 需要实时互联网
- Fields: {operational_parameters} {symptoms}
- Best for: Identify and fix distillation column issues efficiently
- 故障排除和诊断
人工智能提示 Diagnose Reactor Performance Issues
- 持续改进, 纠正措施, 故障模式和影响分析（FMEA）, 流程改进, 工艺优化, 质量控制, 质量管理, 根本原因分析, 统计过程控制 (SPC)
This prompt helps diagnose common reactor performance problems by analyzing user-provided operational data and observed symptoms. The AI outputs a prioritized list of probable root causes along with suggested diagnostic tests or corrective actions.
输出：
- Markdown
- 需要实时互联网
- Fields: {operational_data} {symptoms_list}
- Best for: Systematic diagnosis of reactor malfunctions
- 假设的产生
人工智能提示 Generate Hypotheses from Literature Summary
- 快速成型制造, 化学品回收, 持续改进, 环境影响评估, 创新, 工艺优化, 研究与开发, 可持续发展实践, 可持续发展
This prompt ingests a user-provided summary of recent literature on a chemical engineering topic and generates a list of potential hypotheses for further research, highlighting gaps or inconsistencies discovered. The output is a JSON array with hypothesis statements and supporting notes.
输出：
- JSON
- 不需要实时互联网
- Fields: {literature_summary}
- Best for: Discover new research directions from literature analysis
- 假设的产生
人工智能提示 Suggest Novel Process Optimization Hypotheses
- 持续改进, 可持续性设计, 效率, 创新, 精益制造, 流程改进, 工艺优化, 质量管理, 可持续发展实践
This prompt takes a brief description of a chemical process and suggests innovative, testable process optimization hypotheses that could improve efficiency, yield, or sustainability. The output is a markdown report detailing each hypothesis with rationale and expected benefits.
输出：
- Markdown
- 不需要实时互联网
- Fields: {process_description}
- Best for: Generate innovative ideas for process improvement
没有人讨论这些目录在人工智能选择方面可能存在的偏见吗？人工智能无法避免偏见，各位。