Traffic Data Warehouse Spatial Coupling Management
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Solution Overview
Problem
Current traffic informatization platforms face challenges in efficient data organization and resource sharing due to decentralized systems, trans-regional and inter-departmental information barriers, making it difficult to ensure overall road network operation efficiency and provide accurate services, especially in managing complex coupling relationships between monitoring objects, tasks, and indexes.
Innovation Solution
A traffic data warehouse construction method that creates labeled monitoring objects by determining spatial coupling relationships between monitoring objects and tasks, using a preset monitoring calculation function to generate and distribute monitoring index calculation results to task databases, thereby reducing data coupling and improving system service efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a decentralized system with multiple monitoring tasks and objects is implemented, then the system can handle diverse monitoring requirements, but the coupling relationships between monitoring objects, tasks, and indexes become complex and difficult to manage
Solution Approach 1:
The patent segments the complex monitoring system into independent modular components: monitoring tasks, monitoring objects, monitoring indexes, and their relationships are separated into distinct data structures. This allows each component to be managed independently, reducing the complexity of coupling relationships while maintaining the system's ability to handle diverse monitoring requirements.
Solution Approach 2:
The patent introduces an intermediary data warehouse layer that mediates between monitoring tasks and monitoring objects. This intermediate structure standardizes the coupling relationships through unified data models and association rules, making the complex interactions manageable while preserving system versatility.
2Reliability
If comprehensive monitoring data is collected and stored, then accurate road network operation monitoring is achieved, but data organization efficiency decreases and resource sharing becomes difficult
Solution Approach 1:
The patent segments monitoring data into different categories (task data, object data, index data, relationship data) and stores them in structured tables within the data warehouse. This segmentation enables efficient querying and organization while maintaining comprehensive monitoring capabilities, thus improving both reliability and productivity.
Solution Approach 2:
The patent creates a universal data warehouse structure that serves multiple functions: storing comprehensive monitoring data, enabling efficient organization through standardized schemas, and facilitating resource sharing across different monitoring tasks. The unified data model allows the same infrastructure to support diverse monitoring requirements.
3Productivity
If trans-regional and inter-departmental information barriers are removed through integration, then overall road network operation efficiency can be ensured, but system complexity and data management difficulty increase
Solution Approach 1:
The patent introduces a data warehouse as an intermediary layer that integrates data from multiple sources (different regions and departments) without requiring complex point-to-point connections. This intermediate structure standardizes data exchange through unified schemas and association rules, reducing integration complexity while enabling comprehensive road network monitoring.
Solution Approach 2:
The patent creates a universal integrated platform that handles multiple functions: data collection from diverse sources, standardized data organization, relationship management, and resource sharing. This multi-functional system achieves comprehensive integration while managing complexity through a unified architectural approach.
Data Source
AI summary
Disclosed are a traffic data warehouse construction method, a storage medium, and a terminal. The method includes: creating a target monitoring task based on a creation instruction; loading a monitoring object and spatial range corresponding to the target monitoring task, and obtaining a sampling unit set of the monitoring object; calculating a spatial attribution relationship between a spatial range of each sampling unit in the sampling unit set and the spatial range of the target monitoring task, and determining a spatial coupling relationship; setting a label of the monitoring object based on the spatial coupling relationship, generating a labeled monitoring object, inputting the labeled monitoring object to a preset monitoring calculation function, and outputting a calculation result; and configuring the calculation result in the labeled monitoring object, and distributing the configured monitoring object to a task database corresponding to the target monitoring task.