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Land Transport

Special Vehicle Distributed Monitoring Multi-dimensional Visualization System

 

Solution Background

As modern special vehicles develop towards intelligence, informatization, and complexity, the demand for status monitoring and data analysis during their test runs is increasing. Traditional monitoring systems often adopt a centralized architecture, which suffers from problems such as low data processing efficiency, poor real-time performance, and insufficient scalability, making it difficult to meet multi-parameter, high-dimensional, and cross-regional testing requirements. At the same time, the massive data generated during the testing process (e.g., mechanical performance, electronic system status, environmental parameters, battlefield simulation data) lacks effective visualization methods, leading to difficulties in extracting key information and affecting the efficiency of fault diagnosis and performance evaluation.

 

Solution Introduction

Solution Overview

Through distributed sensing networks, edge computing, and cloud-edge collaborative processing technology, real-time collection, fusion, and analysis of multi-source heterogeneous data are achieved. Multi-dimensional dynamic visualization technologies (such as 3D modeling, spatiotemporal situation mapping, interactive dashboards, etc.) are used to intuitively present vehicle status, test environment, and historical trends. This solution will significantly enhance the interpretability of test data, providing intelligent support for equipment R&D, fault early warning, and combat effectiveness evaluation, aligning with the development direction of future digital testing and support systems for special equipment.

Adopting an overall solution of "structural separation, functional layering". The system is divided into two parts: vehicle-mounted and ground-based, to adapt to long-term harsh vehicle testing environments. The vehicle-mounted part of the system completes the acquisition of test data, and the ground-based part of the system completes the management, display, processing, and application of test data.

Applicable Scenarios

  • Special vehicle testing and performance verification;
  • Dynamic health status monitoring during railway special vehicle transportation;
  • Simulation training and effectiveness evaluation;

Core Functions

Function Module Description
Multi-source heterogeneous data access Supports real-time collection of data from sensors (temperature, pressure, vibration, etc.), buses (CAN, Ethernet), video/images, GPS/Beidou positioning, etc.
Spatiotemporal alignment and fusion Unifies timestamps and coordinate benchmarks for data from different dimensions such as mechanical, electronic, and environmental, ensuring analysis consistency.
3D Vehicle Model Interaction Real-time mapping of vehicle component states (e.g., power system heat maps, suspension stress distribution).
Spatiotemporal Situation Display Displays vehicle trajectories, speed curves, and terrain adaptability combined with GIS maps; historical data playback function to reproduce key events throughout the test.
Data Analysis Dashboard Visual display of data such as transportation mileage, risk events, and energy consumption to assist management decisions.

Technical Advantages

  • Data Security: Encrypted data transmission, meeting classified transportation requirements.
  • High Data Synchronization Accuracy: Synchronization accuracy ≤100μs
  • High Data Acquisition Channels: 128 channels
  • Multiple Data Acquisition Dimensions: 12 dimensions of data
  • Low Latency Response: 5G + Edge Computing, warning latency <1 second.

Application Case Studies

Case One: High-Precision Vehicle Data Synchronous Acquisition and Analysis System

A certain type of special vehicle needs to undergo extreme environmental reliability testing, requiring synchronous acquisition of vehicle bus data, environmental data, driving dynamic data, etc., with time synchronization accuracy better than ±1ms, to accurately analyze multi-system coupling faults. The solution is based on PTP (Precision Time Protocol) for high-precision distributed synchronous acquisition, reorganizing multi-source data according to a unified time base (UTC + sensor local offset), with a system-wide time synchronization error of ≤100μs.

 

 

 

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Solution Introduction

Application Cases

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