Technology ▏ Discussion on the key elements of large model deployment in the vertical field of architecture - knowledge graph

Perceptual intelligence mainly solves the problem of "what", such as image recognition, speech recognition, etc. Cognitive intelligence further solves the problems of "why" and "how", such as reasoning, decision-making, etc. Although general large models such as DeepSeek already have good reasoning performance, in the field of construction engineering, they are still far from being deeply applied in actual projects . Knowledge graphs provide a bridge from perception to cognition for AI, enabling AI to perform more complex tasks. The paths to achieve cognitive intelligence mainly include:

Multimodal fusion :

Combining knowledge graphs and deep learning models, we can achieve the integration of perception and cognitive capabilities. For example, based on image recognition, we can understand the design intention and make decision inferences through the knowledge graph of construction drawings.

Continuous learning and updating:

The knowledge graph needs to be constantly updated and expanded to adapt to changes in the construction industry and technological development. For example, with the emergence of green and low-carbon materials, smart construction site systems, and construction robots, the relevant content in the knowledge graph needs to be updated in a timely manner.

In the field of construction, whether it is training large language models or developing RAG (retrieval augmentation generation) agents, data is always the core element. When processing these data, knowledge graphs and vector databases undoubtedly play a key role. Knowledge graphs and vector databases each have their own unique advantages and limitations. For example, a solid semantic foundation can be built with the help of knowledge graphs to clearly present the associations and meanings between data; at the same time, the powerful similarity search capabilities of vector databases can be used to quickly locate data similar to the target. The two complement each other and provide strong support for the complex and changing needs in the field of intelligent construction . The following is a brief comparison of knowledge graphs and vector databases:

Knowledge graphs intuitively display complex semantic relationships between entities through the structure of nodes and edges, and are particularly suitable for representing hierarchical and highly correlated data. Vector databases map data to points in a high-dimensional space, relying on numerical features to capture data similarities, and are suitable for processing unstructured or high-dimensional feature data.

Knowledge graphs use graph query languages ​​(such as SPARQL) to achieve semantic reasoning through path traversal and pattern matching, but query complexity grows with the size of the graph. Vector databases are based on vector similarity (such as cosine similarity and Euclidean distance) and accelerate queries through approximate nearest neighbor search (ANN), which is suitable for fast retrieval of large-scale data.

In the field of architecture, knowledge graphs can be applied to intelligent design decisions (such as design optimization based on standard atlases), construction management assistance (such as construction process inference) and intelligent diagnosis (such as complex construction problem analysis). Vector databases are good at similarity search (such as building component parameter matching), recommendation systems (such as material/process recommendations) and anomaly detection (such as energy consumption pattern anomaly recognition).

The advantage of knowledge graphs is that they can provide rich semantic information and support complex reasoning and decision-making. However, they are difficult to build and their performance may be affected when processing large-scale data. The advantages of vector databases are high query efficiency, strong scalability, and suitability for processing large-scale vector data. However, they lack semantic information and have shortcomings in complex reasoning.

By building a professional semantic network in the field of construction through knowledge graphs and combining the deep learning capabilities of vertical large models such as Deepseek and Tongyi Qianwen, data-driven intelligent decision-making can be achieved in all aspects of the building life cycle, promoting the transformation of the construction industry towards intelligence and refinement. The following are representative application scenarios.

1. Architectural design and optimization

• Environmentally responsive design: Build a “climate - energy consumption - comfort ” knowledge graph, use AI big models to predict the annual energy consumption performance of different design schemes, and provide building form optimization solutions based on climate adaptability.

• Intelligent solution generation: Establish a knowledge network of architectural style, functional requirements and spatial layout, and use AI big models to support designers to quickly generate conceptual solutions through language interaction, greatly improving the efficiency of solution generation.

2. Construction full cycle management

• Intelligent material selection: Build a material performance - cost - supply chain knowledge graph, and use the AI ​​big model to automatically recommend the optimal material combination based on the BIM model and construction progress to reduce material loss rate .

• Progress risk warning: Integrate real-time information such as construction logs and meteorological data, and use AI big models to build a construction progress risk prediction model to provide early warning of potential risks.

3. Operation and maintenance management upgrade

• Maintenance decision optimization: Integrate equipment maintenance history and spare parts inventory data, and use AI big models to provide cost-based maintenance strategy recommendations to shorten maintenance response time .

4.Environmental and Energy Management

• Dynamic energy-saving control: Build a knowledge graph of “building energy consumption - environmental parameters - user behavior ”, and use AI big models to optimize the operation strategies of air conditioning and lighting systems in real time to improve the overall energy saving rate .

5. Smart building control

• Abnormal behavior detection: Establish a knowledge graph of equipment operation modes, and use AI large model recognition technology to detect abnormal equipment operation status in real time, significantly reducing the false alarm rate .

6. Project risk management

• Emergency plan generation: The knowledge graph associates risk types with response measures, and uses the AI ​​big model to automatically generate graded emergency plans based on real-time risk levels to quickly respond to various risks .

IV . Conclusion