Dual-Database Big Data: High Integration with Hybrid Architecture

Dual-Database Hybrid (Data Architecture): This adopts a dual-database mode combining InfluxDB (Time-series Database) and PostgreSQL (Business Database). InfluxDB is dedicated to storing and analyzing high-frequency time-series data, while PostgreSQL handles relational data such as user profiles and order information to guarantee core business logic control. By decoupling time-series data from business data and utilizing the most suitable database engine for each, overall performance and stability are remarkably enhanced. This architecture reduces average query latency by 76% and achieves a data write success rate of 99.98% (fewer than 20 lost entries per million). Leveraging AI capabilities for collaborative coordination and redundancy, the system employs a distributed architecture combined with AI-driven node scheduling algorithms. This design ensures 99.99% availability, limiting annual downtime to less than 52 minutes. Key performance metrics include regulatory platform query latencies under 10 seconds, terminal data upload retry intervals within 30 seconds, the capability to detect weight data fluctuations exceeding 20%, and a 100% deduplication rate for redundant data uploads. The team achieved real-time fusion of multi-source heterogeneous data. By developing efficient cleaning and processing algorithms for multi-modal heterogeneous data, they designed a smart analysis framework that integrates time-series modeling with feature extraction to enable the automated extraction of key process indicators. Ultimately, the team constructed a real-time data fusion engine, establishing a high-quality, low-latency unified data view to support upper-level decision-making.

Middleware Scheduling (Transaction Architecture): The integrated RabbitMQ safety valve reduced failure propagation rates by over 70% and brought the message loss rate to near zero. The Redis data accelerator achieved data read/write speeds exceeding 100,000 operations per second, while the Elasticsearch data spotlight enabled millisecond-level searches across terabytes of unstructured data. Middleware acts as an urban central traffic control center, uniformly manages “traffic” (data flows), sets “traffic lights” (rate limiting), and handles “congestion” (peak load shifting). This guarantees smooth system operations.

SpringCloud Microservices (Application Architecture): Each microservice employs a dual-database pattern to optimize data access performance, supporting hot-swappable service modules and dynamic horizontal scaling. This architecture effortlessly accommodates growth from 306 to over 1,000 enterprises. By providing isolated access environments, it achieves data segregation and granular permission control, thereby ensuring enterprise data security.

Cloud-Edge-Device Collaborative Computing (Computing Architecture): Device-side sensing aggregates AIoT terminals to guarantee data quality at the source, remarkably reducing the costs associated with transmitting invalid data. Edge processing supports offline transmission resumption with a buffer capacity of ≥72 hours, elevating anomaly response from minutes to seconds, drastically minimizing the spread of environmental risks. Cloud decision-making leverages the integrated advantages of cloud-based AI computing power to support comprehensive global control capabilities. Collaborative supervision establishes a three-tier regulatory system spanning enterprise, provincial, and national levels.

National Cryptography-level Encrypted Communication (Data Security): The entire link adheres to National Cryptography Standards (e.g., GM/T0002-2012, GM/T0003-2012). Data is encrypted throughout the process using national cryptography algorithms such as SM4 and SM2, complemented by SHA-256 hash verification. This technology generates a unique 256-bit (32-byte) “data fingerprint” for inputs of arbitrary length.