AI Agent Operational Architecture
PropBlock’s AI Agent operates based on a three-layer architecture, comprising the Data Layer, Analytics Layer, and Application Layer. These layers are responsible for data collection, data analysis, and project execution, respectively. The operational structure of the AI Agent is as follows:
1. Core Functional Module Expansion
1.1 Data Layer
The Data Layer serves as the AI Agent’s “sensory system”, responsible for collecting and structuring real-world data from multiple sources, providing the fuel for upper-layer analysis.
Data Source Integration
Market Data
Real-time API access to global real estate prices, updated monthly; rental data is scraped from platforms like Airbnb, with deduplication applied.
Scrapy, Apify, Restful API
Geographical Data
Integration of real-time traffic data from Google Maps API; POI data categorized (Education/Healthcare) and analyzed within a 500m buffer zone.
Google Maps API, GeoPandas
Policy Data
Dynamic monitoring of government websites; OCR-based parsing of PDF policy documents, extracting key terms (e.g., “purchase restriction,” “FAR adjustment”).
Tesseract OCR, BERT Keyword Extraction
Economic Indicators
Connection to local National Statistics Bureau API for quarterly GDP data; integration with third-party data platforms (e.g., Wind) for employment rate indicators via Snowflake.
Snowflake, Tableau
Data Preprocessing
Data Cleaning
Housing price anomaly detection: Identifying data deviating from market trends by more than three standard deviations using Isolation Forest.
PyOD, Scikit-learn
Standardization
Geocoding unified to WGS84 coordinate system; housing price units converted to “USD/m² per month.”
GDAL, Python RegEx
Knowledge Graph
Constructs “Land Parcel - Policy - Economic Indicator” triplets, e.g., <Land A, Affected by Policy, Purchase Restriction 2023>.
Neo4j, Apache Jena
1.2 Analytics Layer
The Analytics Layer functions as the “brain” of the AI Agent, leveraging machine learning and operations research models to transform data into actionable insights. It upgrades traditional experience-driven decision-making into a data-driven dynamic optimization process, balancing returns and risks.
Market Dynamics Modeling
Time Series Forecasting
Prophet model incorporates custom seasonal factors (e.g., policy cycles); LSTM utilizes an Attention mechanism to capture long- and short-term dependencies.
Facebook Prophet, TensorFlow + Keras
Clustering Analysis
K-means cluster optimization based on silhouette coefficient; features include “Price/Rent Ratio,” “Metro Station Density,” “School District Rating.”
Scikit-learn, Yellowbrick
Location Decision Engine
Multi-Objective Optimization
Defines objective functions: Maximize (ROI), Minimize (Policy Risk), Maximize (Transport Accessibility); constraints include “Budget ≤ USD 100M.”
Pyomo, NSGA-II Algorithm
Spatial Weight Analysis
ArcGIS Network Analyst calculates commuting time from land parcel to CBD, with weighted allocation: Transport (40%), Schools (30%), Commercial Amenities (30%).
ArcGIS Pro, Python ArcPy
Risk Assessment Models
Policy Sensitivity Analysis
Monte Carlo simulation of policy variables (e.g., interest rate increase of 0.5%–2%), outputting probability density distribution of returns.
NumPy, Matplotlib
Market Stress Testing
Extreme scenario settings: GDP growth decreases by 3%, unemployment rate rises by 5%, testing probability of cash flow breakdown in asset portfolios.
StressTesting Library, Custom Python Simulator
1.3 Application Layer
The Application Layer serves as the AI Agent’s “interaction interface,” translating complex analytical results into user-friendly solutions. It hides technological complexity behind intuitive interfaces, ensuring a seamless Analyze-Decision-Execution loop.
Interactive Interface
Natural Language Query
User input parsing: SpaCy for entity recognition + Rasa for dialogue management, supporting multi-turn interactions (e.g., “Filter out old properties before 2020”).
Rasa Framework, SpaCy NLP Library
Visualization Dashboard
Power BI integrates Folium map plugin, supporting interactive heatmaps (colored based on ROI/risk level).
Power BI Embedded, Folium
Automated Decision Support
Intelligent Recommendation System
Item-based collaborative filtering, similarity calculation: Euclidean Distance (Price Trends) + Jaccard Index (Policy Tags).
Surprise, Redis
Compliance Checking
Drools rule engine verifies “Land Use Type matches Planning,” e.g., “Commercial land plot must have an FAR ≥ 2.0.”
Drools, MySQL
2.Technical Architecture Expansion
The AI Agent enables end-to-end automation from data collection to decision support, ensuring technical robustness and user experience friendliness for complex business scenarios.
A well-structured technical architecture is essential to support real-world project execution.
2.1 Infrastructure
Data Lake
AWS S3 bucket storage: raw_data (raw scraped data), processed_data (cleaned data in Parquet format).
High scalability; supports spatial data partitioning (by city/date).
Compute Engine
Databricks cluster configuration: Driver Node (16-core, 64GB RAM for GIS analysis); Worker Nodes (auto-scaling based on workload).
Integrated with MLflow for model tracking; Delta Lake ensures ACID transactions.
Model Deployment
Kubernetes deployment strategy: - Predictive models (Stateless Pods, HPA auto-scaling).
- GIS Services (StatefulSet fixed nodes).
High availability; Istio enables model canary releases.
2.2 Tech Stack
Geospatial Analysis
Spatial DB: PostGIS (supports GIS topology queries).
Dev Tools: QGIS Plugin (Python + QT Framework).
PostGIS spatial indexing accelerates queries; QGIS plugin enables custom land scoring tools.
Machine Learning
ML Pipeline: Scikit-learn Pipeline.
Hyperparameter Tuning: Optuna + Dask distributed search.
Optuna’s TPE algorithm improves efficiency by 50%+ over Grid Search.
Business Logic
Web Framework: Django REST Framework.
Asynchronous Tasks: Celery + RabbitMQ (Priority Queues).
Celery workers separate urgent tasks (e.g., real-time risk assessment) from batch processing.
2.3 Security & Compliance
Data Masking
Homomorphic Encryption (Microsoft SEAL) protects user phone numbers/IDs while enabling encrypted computation on aggregate indicators (e.g., average age in an area).
Encryption Algorithm: CKKS Scheme (supports floating-point operations).
Audit Tracking
Hyperledger Fabric blockchain records key operations (e.g., model parameter changes), ensuring immutable audit logs.
Chaincode defines data notarization rules; nodes include developers, regulators, and third-party auditors.
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