BAIC Group Powers the Internet of Vehicles With MongoDB

Gabriel Woo
February 19, 2025

The Internet of Vehicles (IoV) is revolutionizing the automotive industry by connecting vehicles to the Internet. Vehicle sensors generate a wealth of data, affording manufacturers, vehicle owners, and traffic departments deep insights. This unlocks new business opportunities and enhances service experiences for both enterprises and consumers.

BAIC Research Institute, a subsidiary of Beijing Automotive Group Co. (BAIC Group), is a backbone enterprise of the Chinese auto industry. Headquartered in Beijing, BAIC Group is involved in everything from R&D and manufacturing of vehicles and parts to the automobile service trade, comprehensive traveling services, financing, and investments. BAIC Group is a Fortune Global 500 company with more than 67 billion USD of annual revenue.

The Institute is also heavily invested in the IoV industry. It plays a pivotal role in the research and development of the two major independent passenger vehicle products in China: Arcfox and Beijing Automotive. It is also actively involved in building vehicle electronic architecture, intelligent vehicle controls, smart cockpit systems, and smart driving technologies.

To harness cutting-edge, data-driven technologies such as cloud computing, the Internet of Things, and big data, the Institute has built a comprehensive IoV cloud platform based on ApsaraDB for MongoDB. The platform collects, processes, and analyzes data generated by over a million vehicles, providing intelligent and personalized services to vehicle owners, automotive companies, and traffic management departments.

At MongoDB.local Beijing in September 2024, BAIC Group’s Deputy Chief Engineer Chungang Zuo said that the BAIC IoV cloud platform facilitates data access for over a million vehicles. It also supports online services for hundreds of thousands of vehicles.

Data technology acts as a key factor for IoV development

With a rapid increase of vehicle ownership in recent years, the volume of data on BAIC Group’s IoV cloud platform quickly surged. This led to several data management challenges, namely the need to handle the following:

  • Large data volumes

  • High update frequencies

  • Complex data formats

  • High data concurrency

  • Low query efficiency

  • Data security issues

The IoV platform also needed to support automotive manufacturers who must centrally store and manage a large amount of diverse transactional data. Finally, the platform is needed to enable manufacturers to leverage AI and analytical capabilities to interpret and create value from this data.

BAIC Group’s IoV cloud platform reached a breaking point because the legacy databases it employed were incapable of handling the deluge of exponential vehicle data nor supporting planned AI-driven capabilities.

The Institute identified MongoDB as the solution to support its underlying data infrastructure. By using MongoDB, BAIC would gain a robust core to enhance data management efficiency from the business layer to the application layer.

The power of MongoDB as a developer data platform offered a wide range of capabilities. This was a game-changer for the Institute.

MongoDB’s document model makes managing complex data simple

Unlike traditional relational database models, MongoDB’s JSON data structure and flexible schema model are well suited for the variety and scale of the ever-changing data produced by connected vehicles.

In traditional databases, vehicle information is spread across multiple tables, each with nearly a hundred fields, leading to redundancy, inflexibility, and complexity.

With MongoDB, all vehicle information can be stored in a single collection, simplifying data management. Migrating vehicle information to MongoDB has significantly improved the Institute’s data application efficiency.

MongoDB’s GeoJSON supports location data management

The ability to accurately calculate vehicle location within the IoV cloud platform is a key benefit offered by MongoDB. Particularly, MongoDB’s GeoJSON (geospatial indexing) supports important features, such as the ability to screen vehicle parking situations.

Zuo explained that during the data cleaning phase, the Institute formats raw vehicle data for MongoDB storage and outputs it as standardized cleaned data. In the data calculation phase, GeoJSON filters vehicles in a specific range. This is followed by algorithmic clustering analysis of locations to gain vehicle parking information. Finally, the Institute retrieves real-time data from the MongoDB platform to classify and display vehicle parking situations on a map for easy viewing.

MongoDB provides scalability and high-performance

MongoDB’s sharded cluster enhances data capacity and processing performance, enabling the Institute to effectively manage exponential IoV data growth.

The querying and result-returning processes are executed concurrently in a multi-threaded manner. This facilitates continuous horizontal expansion without any downtime as data needs grow.

Zuo said that a significant advantage for developers is the high self-healing capability of the sharded cluster; if a primary node fails, MongoDB automatically switches to a backup. This ensures seamless service and process integrity.

Security features meet data regulatory requirements

MongoDB’s built-in security features enable the IoV platform to meet rigorous data protection standards, helping the Institute stay compliant with regulatory requirements and industry standards.

With MongoDB, the Institute can ensure end-to-end data encryption throughout the entire data lifecycle, including during transmission, storage, and processing, with support for executing queries directly on encrypted data. For example, during storage, MongoDB encrypts sensitive data, such as vehicle identification numbers and phone numbers. Sharding and replication mechanisms establish a robust data security firewall. Furthermore, MongoDB’s permission control mechanism enables secure database management with decentralized authority.

Zuo said that MongoDB’s sharded storage and clustered deployment features ensure the platform’s reliability exceeds the 99.99% service-level agreement. MongoDB’s high concurrency capabilities enable the Institute to share real-time vehicle status updates with vehicle owners’ apps, enhancing user experience and satisfaction.

In addition, MongoDB’s unique compression technology and flexible cloud server configurations reduce data storage space and resource waste. This significantly lowers data storage and application costs.

BAIC uses MongoDB to prepare for future opportunities

Looking ahead, Zuo Chungang stated that the BAIC IoV cloud platform has expanding demands for data development and application in three areas: vehicle data centers, application scenario implementation, and AI applications.

MongoDB’s capabilities will remain core to helping address the Institute’s upcoming needs and challenges.

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Smarter Care: MongoDB & Microsoft

Healthcare is on the cusp of a revolution powered by data and AI. Microsoft, with innovations like Azure OpenAI, Microsoft Fabric, and Power BI, has become a leading force in this transformation. MongoDB Atlas complements these advancements with a flexible and scalable platform for unifying operational, metadata, and AI data, enabling seamless integration into healthcare workflows. By combining these technologies, healthcare providers can enhance diagnostics, streamline operations, and deliver exceptional patient care. In this blog post, we explore how MongoDB and Microsoft AI technologies converge to create cutting-edge healthcare solutions through our “Leafy Hospital” demo—a showcase of possibilities in breast cancer diagnosis. The healthcare data challenge The healthcare industry faces unique challenges in managing and utilizing massive datasets. From mammograms and biopsy images to patient histories and medical literature, making sense of this data is often time-intensive and error-prone. Radiologists, for instance, must analyze vast amounts of information to deliver accurate diagnoses, while ensuring sensitive patient data is handled securely. MongoDB Atlas addresses these challenges by providing a unified view of disparate data sources, offering scalability, flexibility, and advanced features like Search and Vector search. When paired with Microsoft AI technologies, the potential to revolutionize healthcare workflows becomes limitless. The leafy hospital solution: A unified ecosystem Our example integrated solution, Leafy Hospital, showcases the transformative potential of MongoDB Atlas and Microsoft AI capabilities in healthcare. Focused on breast cancer diagnostics, this demo explores how the integration of MongoDB’s flexible data platform with Microsoft’s cutting-edge features—such as Azure OpenAI, Microsoft Fabric, and Power BI—can revolutionize patient care and streamline healthcare workflows. The solution takes a three-pronged approach to improve breast cancer diagnosis and patient care: Predictive AI for early detection Generative AI for workflow automation Advanced BI and analytics for actionable insights Figure 1. Leafy hospital solution architecture If you’re interested in discovering how this solution could be applied to your organization’s unique needs, we invite you to connect with your MongoDB account representative. We’d be delighted to provide a personalized demonstration of the Leafy Hospital solution and collaborate on tailoring it for your specific use case. Key capabilities Predictive AI for early detection Accurate diagnosis is critical in breast cancer care. Traditional methods rely heavily on radiologists manually analyzing mammograms and biopsies, increasing the risk of errors. Predictive AI transforms this process by automating data analysis and improving accuracy. BI-RADS prediction BI-RADS (Breast Imaging-Reporting and Data System) is a standardized classification for mammogram findings, ranging from 0 (incomplete) to 6 (malignant). To predict BI-RADS scores, deep learning models like VGG16 and EfficientNetV2L are trained on mammogram images dataset. Fabric Data Science simplifies the training and experimentation process by enabling: Direct data uploads to OneLake for model training Easy comparison of multiple ML experiments and metrics Auto-logging of parameters with MLflow for lifecycle management These models are trained on a significant number of epochs until a reliable accuracy is achieved, offering reliable predictions for radiologists. Biopsy classification In the case of biopsy analysis, classification models such as the random forest classifier are trained on biopsy features like cell size, shape uniformity, and mitoses counts. Classification models attain high accuracy when trained on scalar data, making it highly effective for classifying cancers as malignant or benign. Data ingestion, training, and prediction cycles are well managed using Fabric Data Science and the MongoDB Spark Connector , ensuring a seamless flow of metadata and results between Azure and MongoDB Atlas. Generative AI for workflow automation Radiologists often spend hours documenting findings, which could be better spent analyzing cases. Generative AI streamlines this process by automating report generation and enabling intelligent chatbot interactions. Vector search: The foundation of semantic understanding At the heart of these innovations lies MongoDB Atlas Vector Search , which revolutionizes how medical data is stored, accessed, and analyzed. By leveraging Azure OpenAI’s embedding models, clinical notes and other unstructured data are transformed into vector embeddings—mathematical representations that capture the meaning of the text in a high-dimensional space. Similarity search is a key use case, enabling radiologists to query the system with natural language prompts like “Show me cases where additional tests were recommended.” The system interprets the intent behind the question, retrieves relevant documents, and delivers precise, context-aware results. This ensures that radiologists can quickly access information without sifting through irrelevant data. Beyond similarity search, vector search facilitates the development of RAG architectures , which combine semantic understanding with external contextual data. This architecture allows for the creation of advanced features like automated report generation and intelligent chatbots, which further streamline decision-making and enhance productivity. Automated report generation Once a mammogram or biopsy is analyzed, Azure OpenAI’sLarge Language models can be used to generate detailed clinical notes, including: Findings: Key observations from the analysis Conclusions: Diagnoses and suggested next steps Standardized codes: Using SNOMED terms for consistency This automation enhances productivity by allowing radiologists to focus on verification rather than manual documentation. Chatbots with retrieval-augmented generation Chatbots can be another approach to support radiologists, when they need quick access to historical patient data or medical research. Traditional methods can be inefficient, particularly when dealing with older records or specialized cases. Our retrieval-augmented generation-based chatbot, powered by Azure OpenAI, Semantic Kernel, and MongoDB Atlas, provides: Patient-specific insights: Querying MongoDB for 10 years of patient history, summarized and provided as context to the chatbot Medical literature searches: Using vector search to retrieve relevant documents from indexed journals and studies Secure responses: Ensuring all answers are grounded in validated patient data and research The chatbot improves decision-making and enhances the user experience by delivering accurate, context-aware responses in real-time. Advanced BI and analytics for actionable insights In healthcare, data is only as valuable as the insights it provides. MongoDB Atlas bridges real-time transactional analytics and long-term data analysis, empowering healthcare providers with tools for informed decision-making at every stage. Transactional analytics Transactional, or in-app, analytics deliver insights directly within applications. For example, MongoDB Atlas enables radiologists to instantly access historical BI-RADS scores and correlate them with new findings, streamlining the diagnostic process. This ensures decisions are based on accurate, real-time data. Advanced clinical decision support (CDS) systems benefit from integrating predictive analytics into workflows. For instance, biopsy results stored in MongoDB are enriched with machine learning predictions generated in Microsoft Fabric , helping radiologists make faster, more precise decisions. Long-term analytics While transactional analytics focus on operational efficiency, long-term analytics enable healthcare providers to step back and evaluate broader trends. MongoDB Atlas, integrated with Microsoft Power BI and Fabric, facilitates this critical analysis of historical data. For instance, patient cohort studies become more insightful when powered by a unified dataset that combines MongoDB Atlas’ operational data with historical trends stored in Microsoft OneLake. Long-term analytics also shine in operational efficiency assessments. By integrating MongoDB Atlas data with Power BI, hospitals can create dashboards that track key performance indicators such as average time to diagnosis, wait times for imaging, and treatment start times. These insights help identify bottlenecks, streamline processes, and ultimately improve the patient experience. Furthermore, historical data stored in OneLake can be combined with MongoDB’s real-time data to train machine learning models, enhancing future predictive analytics. OLTP vs OLAP This unified approach is exemplified by the distinction between OLTP and OLAP workloads. On the OLTP side, MongoDB Atlas handles real-time data processing, supporting immediate tasks like alerting radiologists to anomalies. On the OLAP side, data stored in Microsoft OneLake supports long-term analysis, enabling hospitals to identify trends, evaluate efficiency, and train advanced AI models. This dual capability allows healthcare providers to “run the business” through operational insights and “analyze the business” by uncovering long-term patterns. Figure 2. Real-time analytics data pipeline MongoDB’s Atlas SQL Connector plays a crucial role in bridging these two worlds. By converting MongoDB’s flexible document model into a relational format, it allows tools like Power BI to work seamlessly with MongoDB data. Next steps For a detailed, technical exploration of the architecture, including ML notebooks, chatbot implementation code, and dataset resources, visit our Solution Library Building Advanced Healthcare Solutions with MongoDB and Microsoft . Whether you’re a developer, data scientist, or healthcare professional, you’ll find valuable insights to replicate and expand upon this solution! To learn more about how MongoDB can power healthcare solutions, visit our solutions page . Check out our Atlas Vector Search Quick Start guide to get started with MongoDB Atlas Vector Search today.

February 18, 2025
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Multi-Agent Collaboration for Manufacturing Operations Optimization

While there are some naysayers across the media landscape who doubt the potential impact of AI innovations, for those of us immersed in implementing AI on a daily basis, there’s wide agreement that its potential is huge and world-altering. It’s now generally accepted that Large Language Models (LLMs) will eventually be able to perform tasks as well—if not better—than a human. And the size of the potential AI market is truly staggering. Bain’s AI analysis estimates that the total addressable market (TAM) for AI and gen AI-related hardware and software will grow between 40% and 55% annually, reaching between $780 billion and $990 billion by 2027. This growth is especially relevant to industries like manufacturing, where generative AI can be applied across the value chain. From inventory categorization to product risk assessments, knowledge management, and predictive maintenance strategy generation, AI's potential to optimize manufacturing operations cannot be overstated. But in order to realize the transformative economic potential of AI, applications powered by LLMs need to evolve beyond chatbots that leverage retrieval-augmented generation (RAG). Truly transformative AI-powered applications need to be objective-driven, not just responding to user queries but also taking action on behalf of the user. This is crucial in complex manufacturing processes. In other words, they need to act like agents. Agentic systems, or compound AI systems, are currently emerging as the next frontier of generative AI applications. These systems consist of a single or multiple AI agents that collaborate with each other and use tools to provide value. An AI agent is a computational entity containing short- and long-term memory, which enables it to provide context to an LLM. It also has access to tools, such as web search and function calling, that enable it to act upon the response from an LLM or provide additional information to the LLM. Figure 1. Basic components of an agentic system. An agentic system can have more than one AI agent. In most cases, AI agents may be required to interact with other agents within the same system or external systems., They’re expected to engage with humans for feedback or review of outputs from execution steps. AI agents can also comprehend the context of outputs from other agents and humans, and change their course of action and next steps. For example, agents can monitor and optimize various facets of manufacturing operations simultaneously, such as supply chain logistics and production line efficiency. There are certain benefits of having a multi-agent collaboration system instead of having one single agent. You can have each agent customized to do one thing and do it well. For example, one agent can create meeting minutes while another agent writes follow-up emails. It can also be implemented on predictive maintenance, with one agent analyzing machine data to find mechanical issues before they occur while another optimizes resource allocation, ensuring materials and labor are utilized efficiently. You can also provision dedicated resources and tools for different agents. For example, one agent uses a model to analyze and transcribe videos while the other uses models for natural language processing (NLP) and answering questions about the video. Figure 2. Multi-agent collaboration system. MongoDB can act as the memory provider for an agentic system. Conversation history alongside vector embeddings can be stored in MongoDB leveraging the flexible document model. Atlas Vector Search can be used to run semantic search on stored vector embeddings, and our sharding capabilities allow for horizontal scaling without compromising on performance. Our clients across industries have been leveraging MongoDB Atlas for their generative AI use cases , including agentic AI use cases such as Questflow , which is transforming work by using multi-agent AI to handle repetitive tasks in strategic roles. Supported by MiraclePlus and MongoDB Atlas, it enables startups to automate workflows efficiently. As it expands to larger enterprises, it aims to boost AI collaboration and streamline task automation, paving the way for seamless human-AI integration. The concept of a multi-agent collaboration system is new, and it can be challenging for manufacturing organizations to identify the right use case to apply this cutting-edge technology. Below, we propose a use case where three agents collaborate with each other to optimize the performance of a machine. Multi-agent collaboration use case in manufacturing In manufacturing operations, leveraging multi-agent collaboration for predictive maintenance can significantly boost operational efficiency. For instance, consider a production environment where three distinct agents—predictive maintenance, process optimization, and quality assurance—collaborate in real-time to refine machine operations and maintain the factory at peak performance. In Figure 3, the predictive maintenance agent is focused on machinery maintenance. Its main tasks are to monitor equipment health by analyzing sensor data generated from the machines. It predicts machine failures and recommends maintenance actions to extend machinery lifespan and prevent downtime as much as possible. Figure 3. A multi-agent system for production optimization. The process optimization agent is designed to enhance production efficiency. It analyzes production parameters to identify inefficiencies and bottlenecks, and it optimizes said parameters by adjusting them (speed, vibration, etc.) to maintain product quality and production efficiency. This agent also incorporates feedback from the other two agents while making decisions on what production parameter to tune. For instance, the predictive maintenance agent can flag an anomaly in a milling machine temperature sensor reading; for example, if temperature values are going up, the process optimization agent can review the cutting speed parameter for adjustment. The quality assurance agent is responsible for evaluating product quality. It analyzes optimized production parameters and checks how those parameters can affect the quality of the product being fabricated. It also provides feedback for the other two agents. The three agents constantly exchange feedback with each other, and this feedback is also stored in the MongoDB Atlas database as agent short-term memory. In contrast, vector embeddings and sensor data are persisted as long-term memory. MongoDB is an ideal memory provider for agentic AI use case development thanks to its flexible document model, extensive security and data governance features, and horizontal scalability. All three agents have access to a "search_documents" tool, which leverages Atlas Vector Search to query vector embeddings of machine repair manuals and old maintenance work orders. The predictive maintenance agent leverages this tool to figure out additional insights while performing machine root cause diagnostics. Set up the use case shown in this article using our repo . To learn more about MongoDB’s role in the manufacturing industry, please visit our manufacturing and automotive webpage . To learn more about AI agents, visit our Demystifying AI Agents guide .

February 19, 2025