Lorn.ADSP是一个开源的智能广告投放系统，致力于通过AI技术和大数据分析，实现广告投放的精准化、智能化和自动化。该系统融合了先进的人工智能大模型技术，为广告创意生成、用户画像分析、投放策略优化提供强大的AI驱动能力。系统可用于网站、视频播放的所有页面广告、视频广告以及无线客户端、TV广告的管理、播放、定向和统计。
Lorn.ADSP is an open-source intelligent advertising platform dedicated to achieving precision, intelligence, and automation in ad delivery through AI technology and big data analytics. The system integrates advanced artificial intelligence large model technology, providing powerful AI-driven capabilities for ad creative generation, user profiling analysis, and delivery strategy optimization. This system can be used for managing, playing, targeting, and analyzing all types of page ads, video ads on websites and video platforms, as well as mobile client and TV advertising.
打造智能化、一站式的数字广告投放平台，助力广告主实现精准营销，为用户提供优质广告体验，推动Lorn.ADSP开源广告平台商业生态可持续发展。通过融合AI大模型技术，实现广告创意的智能化生成、用户意图的精准理解和投放策略的自动优化。
Build an intelligent, one-stop digital advertising platform that helps advertisers achieve precision marketing, provides users with high-quality advertising experiences, and promotes the sustainable development of the Lorn.ADSP open-source advertising platform business ecosystem. By integrating AI large model technology, achieve intelligent ad creative generation, precise user intent understanding, and automatic delivery strategy optimization.
- 广告业务管理：客户关系管理、合同管理、商机跟踪
- 售前计划管理：广告位管理、流量预测分析、库存智能调控
- 广告活动管理：计划和单元管理、定向策略配置、预算和出价控制
- AI大模型智能引擎：智能创意生成、用户意图理解、投放策略优化、多模态内容分析
- 广告投放引擎：实时竞价系统、智能投放决策、多目标平衡优化
- 多媒体广告支持：播放器内视频广告、展示广告、原生广告等多种形式
- 数据分析与商业智能：多维数据分析、业务预测洞察、自定义报表
- 用户定向系统：多维用户画像构建、场景化投放策略、智能定向优化
- 第三方广告平台对接：支持DSP/SSP/ADX广告生态对接
- Advertising Business Management: Customer relationship management, contract management, opportunity tracking
- Pre-sales Planning Management: Ad slot management, traffic forecasting analysis, intelligent inventory control
- Campaign Management: Plan and unit management, targeting strategy configuration, budget and bidding control
- AI Large Model Intelligent Engine: Intelligent creative generation, user intent understanding, delivery strategy optimization, multimodal content analysis
- Ad Delivery Engine: Real-time bidding system, intelligent delivery decisions, multi-objective balanced optimization
- Multimedia Advertising Support: In-player video ads, display ads, native ads and other formats
- Data Analytics & Business Intelligence: Multi-dimensional data analysis, business predictive insights, custom reports
- User Targeting System: Multi-dimensional user profiling, scenario-based delivery strategies, intelligent targeting optimization
- Third-party Platform Integration: Support for DSP/SSP/ADX advertising ecosystem integration
- 文案创作：基于产品特性和目标受众，自动生成吸引力强的广告文案
- 视觉设计：结合品牌元素和用户偏好，智能生成广告视觉创意
- 视频制作：自动剪辑和生成短视频广告，支持多种风格和场景
- 素材优化：根据投放效果反馈，持续优化创意素材表现
- 行为预测：基于用户历史行为，预测未来行为趋势和购买概率
- 兴趣挖掘：深度分析用户内容消费习惯，发现潜在兴趣点
- 情感分析：理解用户对广告内容的情感反应，优化投放策略
- 价值评估：智能评估用户商业价值和转化潜力
- 策略推荐：基于历史投放数据，智能推荐最优投放策略
- 实时调优：监控投放效果，实时调整投放参数和策略
- 异常检测：识别投放过程中的异常情况，及时预警和处理
- 效果预测：预测不同投放策略的预期效果和ROI
- 多模态理解：同时理解文本、图像、视频等多种内容形式
- 合规检测：自动检测广告内容是否符合法规和平台规范
- 质量评估：评估广告创意的质量和吸引力
- 风险识别：识别可能存在的品牌风险和负面影响
- Copywriting: Automatically generate compelling ad copy based on product features and target audience
- Visual Design: Intelligently generate advertising visual creatives combining brand elements and user preferences
- Video Production: Automatically edit and generate short video ads supporting various styles and scenarios
- Material Optimization: Continuously optimize creative material performance based on delivery effectiveness feedback
- Behavior Prediction: Predict future behavioral trends and purchase probability based on user historical behavior
- Interest Mining: Deep analysis of user content consumption habits to discover potential interest points
- Sentiment Analysis: Understand user emotional reactions to ad content to optimize delivery strategies
- Value Assessment: Intelligently assess user commercial value and conversion potential
- Strategy Recommendations: Intelligently recommend optimal delivery strategies based on historical delivery data
- Real-time Optimization: Monitor delivery effectiveness and adjust delivery parameters and strategies in real-time
- Anomaly Detection: Identify anomalies in the delivery process with timely alerts and handling
- Performance Prediction: Predict expected effectiveness and ROI of different delivery strategies
- Multimodal Understanding: Simultaneously understand multiple content forms including text, images, and videos
- Compliance Detection: Automatically detect whether ad content complies with regulations and platform standards
- Quality Assessment: Evaluate the quality and attractiveness of advertising creatives
- Risk Identification: Identify potential brand risks and negative impacts
- 多模态内容生成：支持文本、图像、视频广告创意的AI自动生成
- 品牌风格适配：基于品牌调性和目标受众，自动调整创意风格和表现形式
- 创意效果预测：利用大模型分析历史数据，预测创意表现和用户响应
- 动态创意优化：实时分析用户反馈，自动优化创意元素和展现方式
- 深度用户理解：通过自然语言处理和行为分析，构建立体化用户画像
- 意图识别预测：基于用户行为序列，预测用户购买意图和兴趣变化
- 个性化推荐：为每个用户提供个性化的广告内容和投放时机
- 用户生命周期管理：智能识别用户价值和生命周期阶段，优化投放策略
- 智能竞价策略：基于实时市场数据和用户价值，自动调整竞价策略
- 预算智能分配：利用大模型预测不同时段和渠道的投放效果，优化预算分配
- 反作弊检测：通过异常行为模式识别，智能检测和防范广告作弊
- 效果归因分析：多维度分析广告投放效果，提供智能化的归因分析和优化建议
- AI大模型驱动：集成GPT、BERT等先进大模型技术，实现智能化的广告投放决策
- 智能化能力：AI驱动的投放决策、机器学习的效果优化、自然语言理解的用户分析
- 高性能架构：毫秒级广告投放响应、支持百万级QPS竞价、GPU加速的AI推理
- 安全可靠性：完善的权限管理、严格的数据加密、防作弊监控体系、AI模型安全防护
- 可扩展性：插件化架构设计、灵活的策略配置、多渠道快速接入、AI模型热更新
- AI Large Model Driven: Integrate advanced large model technologies like GPT and BERT for intelligent ad delivery decisions
- AI-Powered Capabilities: AI-driven delivery decisions, machine learning-based performance optimization, natural language understanding for user analysis
- High-Performance Architecture: Millisecond-level ad delivery response, support for million-level QPS bidding, GPU-accelerated AI inference
- Security & Reliability: Comprehensive permission management, strict data encryption, anti-fraud monitoring system, AI model security protection
- Scalability: Plugin-based architecture design, flexible strategy configuration, rapid multi-channel integration, AI model hot updates
本项目基于.NET 9技术栈构建，采用C#和F#混合编程，支持多云平台部署，技术架构包括：
- .NET 9: 主要开发框架，支持现代C#特性和高性能运行时
- ASP.NET Core 9.0: 高性能Web API和MVC框架，支持跨平台部署
- Entity Framework Core 9.0: ORM框架，支持Code First和Database First
- F# 9.0: 函数式编程语言，用于广告投放策略算法实现
- WPF (.NET 9): 桌面应用开发，用于监控和管理工具
- .NET MAUI: 跨平台移动应用开发，支持iOS、Android、Windows
- Blazor: Web应用开发，支持Server、WebAssembly、Hybrid模式
- TypeScript: 小程序SDK开发，支持微信、支付宝、快应用等平台
- 关系型数据库:
- 阿里云：RDS MySQL/PostgreSQL、PolarDB
- Azure：Azure SQL Database、Azure Database for MySQL/PostgreSQL
- AWS：RDS SQL Server/MySQL/PostgreSQL、Aurora
- 分布式缓存: Redis 7.0集群，支持阿里云Redis、Azure Cache、AWS ElastiCache
- 搜索引擎: Elasticsearch 8.0，支持全文搜索和日志分析
- 消息队列: Apache Kafka、RabbitMQ，支持多云托管服务
- 微服务架构: 基于领域驱动设计(DDD)的微服务拆分
- API网关: Ocelot网关，支持路由、限流、熔断
- 服务治理: Consul服务发现和配置中心
- 容器化部署: Docker容器化，Kubernetes编排
- 管道模式: 可配置的广告处理流程，支持策略动态组合
- F#策略引擎: 召回、筛选、排序策略的函数式实现
- OpenRTB协议: 严格遵循IAB标准的实时竞价实现
- VAST/VMAP: 视频广告服务模板标准支持
- 指标监控: Prometheus + Grafana监控体系
- 链路追踪: Jaeger分布式追踪系统
- 日志分析: ELK Stack (Elasticsearch + Logstash + Kibana)
- 性能测试: NBomber负载测试框架
- 单元测试: xUnit测试框架，Moq Mock框架
- 集成测试: TestContainers容器化测试
- 性能测试: NBomber压力测试
- 代码质量: SonarQube静态代码分析
This project is built on the .NET 9 technology stack, using a hybrid of C# and F# programming, supporting multi-cloud platform deployment. The technical architecture includes:
- .NET 9: Primary development framework supporting modern C# features and high-performance runtime
- ASP.NET Core 9.0: High-performance Web API and MVC framework with cross-platform deployment support
- Entity Framework Core 9.0: ORM framework supporting Code First and Database First approaches
- F# 9.0: Functional programming language for advertising delivery strategy algorithm implementation
- WPF (.NET 9): Desktop application development for monitoring and management tools
- .NET MAUI: Cross-platform mobile development supporting iOS, Android, Windows
- Blazor: Web application development supporting Server, WebAssembly, Hybrid modes
- TypeScript: Mini-program SDK development supporting WeChat, Alipay, QuickApp platforms
- Relational Databases:
- Alibaba Cloud: RDS MySQL/PostgreSQL, PolarDB
- Azure: Azure SQL Database, Azure Database for MySQL/PostgreSQL
- AWS: RDS SQL Server/MySQL/PostgreSQL, Aurora
- Distributed Cache: Redis 7.0 clusters supporting Alibaba Cloud Redis, Azure Cache, AWS ElastiCache
- Search Engine: Elasticsearch 8.0 supporting full-text search and log analysis
- Message Queues: Apache Kafka, RabbitMQ with multi-cloud managed service support
- Microservice Architecture: Domain-driven design (DDD) based microservice decomposition
- API Gateway: Ocelot gateway supporting routing, rate limiting, circuit breaking
- Service Governance: Consul service discovery and configuration center
- Containerized Deployment: Docker containerization with Kubernetes orchestration
- Pipeline Pattern: Configurable ad processing workflows supporting dynamic strategy composition
- F# Strategy Engine: Functional implementation of recall, filtering, and ranking strategies
- OpenRTB Protocol: Real-time bidding implementation strictly following IAB standards
- VAST/VMAP: Video Ad Serving Template standard support
- Metrics Monitoring: Prometheus + Grafana monitoring system
- Distributed Tracing: Jaeger distributed tracing system
- Log Analysis: ELK Stack (Elasticsearch + Logstash + Kibana)
- Performance Testing: NBomber load testing framework
- Unit Testing: xUnit testing framework, Moq mocking framework
- Integration Testing: TestContainers containerized testing
- Performance Testing: NBomber stress testing
- Code Quality: SonarQube static code analysis
本项目提供完整的文档体系，分为三个主要目录：
产品设计文档 - docs_ProductDesign目录：
- 项目背景与业务流程
- 系统架构设计
- 各模块功能详解
- 广告投放引擎设计
技术设计文档 - docs_TecDesign目录：
- 技术架构实现方案
- API接口规范
- 数据库设计文档
- 系统集成指南
项目管理文档 - docs_ProjectManage目录：
- 开发路线图
- 产品需求清单
- 风险管理计划
- Sprint计划模板
This project provides a comprehensive documentation system organized into three main directories:
Product Design Documentation - docs_ProductDesign directory:
- Project background and business processes
- System architecture design
- Detailed module functionality
- Ad delivery engine design
Technical Design Documentation - docs_TecDesign directory:
- Technical architecture implementation
- API interface specifications
- Database design documentation
- System integration guide
Project Management Documentation - docs_ProjectManage directory:
- Development roadmap
- Product requirements list
- Risk management plan
- Sprint planning templates
本项目采用LICENSE许可证开源。
This project is open-sourced under the LICENSE license.
欢迎对互联网广告系统感兴趣的开发者参与贡献！
Developers interested in internet advertising systems are welcome to contribute!
产品经理独孤虾拥有20年互联网广告与商业化产品经验，专注于广告平台架构设计和流量变现策略优化。他曾在百度、优酷、苏宁等多家头部互联网公司担任产品总监，主导5个亿级DAU广告平台的AI化升级，构建了完整的实时竞价(RTB)系统和多目标优化算法体系，实现eCPM提升38%以上。
在广告技术领域，他打造的广告中台架构支撑日均500万次竞价，推动平台年收入从12亿增长至35亿。他精通CTR/CVR预估模型、广告投放策略、流量变现机制，并拥有3项广告技术相关发明专利，参与制定《智能广告技术白皮书》行业标准。
作为资深广告产品架构师，他出版了《智能营销—大模型如何为运营与产品经理赋能》等专著，系统性地探讨了AI技术在广告营销领域的创新应用。他创建的Lorn.ADSP开源广告平台，旨在为行业提供一套覆盖广告主管理、媒体资源优化、实时竞价和多维度数据分析的完整解决方案。
📧 联系方式：[email protected]
💻 更多开源项目请访问作者GitHub
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Product Manager Dugu Xia has 20 years of experience in internet advertising and commercial products, focusing on advertising platform architecture design and traffic monetization strategy optimization. He has served as Product Director at leading internet companies such as Baidu, Youku, and Suning, leading AI upgrades for 5 advertising platforms with hundreds of millions of DAU, building complete real-time bidding (RTB) systems and multi-objective optimization algorithm frameworks, achieving eCPM improvements of over 38%.
In the advertising technology field, the advertising middle platform architecture he built supports an average of 5 million daily bidding transactions, driving platform annual revenue growth from 1.2 billion to 3.5 billion. He is proficient in CTR/CVR prediction models, advertising delivery strategies, and traffic monetization mechanisms, holds 3 advertising technology-related invention patents, and participated in formulating the "Intelligent Advertising Technology White Paper" industry standard.
As a senior advertising product architect, he has published books such as "Intelligent Marketing - How Large Models Empower Operations and Product Managers," systematically exploring innovative applications of AI technology in advertising and marketing. The Lorn.ADSP open-source advertising platform he created aims to provide the industry with a complete solution covering advertiser management, media resource optimization, real-time bidding, and multi-dimensional data analysis.
📧 Contact: [email protected]
💻 Visit the author's GitHub for more open-source projects
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- Video Production: Automatically edit and generate short video ads supporting various styles and scenarios
- Material Optimization: Continuously optimize creative material performance based on delivery effectiveness feedback
- Behavior Prediction: Predict future behavioral trends and purchase probability based on user historical behavior
- Interest Mining: Deep analysis of user content consumption habits to discover potential interest points
- Sentiment Analysis: Understand user emotional reactions to ad content to optimize delivery strategies
- Value Assessment: Intelligently assess user commercial value and conversion potential
- Strategy Recommendations: Intelligently recommend optimal delivery strategies based on historical delivery data
- Real-time Optimization: Monitor delivery effectiveness and adjust delivery parameters and strategies in real-time
- Anomaly Detection: Identify anomalies in the delivery process with timely alerts and handling
- Performance Prediction: Predict expected effectiveness and ROI of different delivery strategies
- Multimodal Understanding: Simultaneously understand multiple content forms including text, images, and videos
- Compliance Detection: Automatically detect whether ad content complies with regulations and platform standards
- Quality Assessment: Evaluate the quality and attractiveness of advertising creatives
- Risk Identification: Identify potential brand risks and negative impacts