NRT Global: How Smart Maintenance Systems Improve Road Management Efficiency.

With the continuous advancement of urbanization and the continued expansion of the highway network, road maintenance and management face unprecedented challenges. Traditional maintenance and management models rely primarily on manual inspections and empirical judgment, resulting in low efficiency, poor accuracy, and delayed response. Against the backdrop of digital transformation, smart maintenance systems have emerged. By integrating modern information technologies such as the Internet of Things, big data, artificial intelligence, and digital twins, they enable real-time monitoring of road health and intelligent maintenance management, providing a new solution for improving road management efficiency.

As an intelligent road maintenance and management model based on modern information technologies such as big data, the Internet of Things, and cloud computing, the smart maintenance system enables real-time monitoring of urban road health and intelligent maintenance management through real-time data collection, intelligent analysis, precise decision-making, and efficient execution. It revolutionizes traditional maintenance models by replacing human eyes and ears with technologies like intelligent perception and machine vision, and replacing human neural networks with 5G and the Internet of Things. This system transmits all types of road information in a timely, comprehensive, and accurate manner to an autonomously learning and thinking artificial intelligence brain, assisting or even replacing highway managers in making scientific decisions, thereby comprehensively improving road performance and vehicle traffic quality.

01. Composition and key technologies of intelligent maintenance system

1. System Architecture

From the system level, the smart maintenance system mainly includes the following aspects:

1) Data collection layer: Through sensors and monitoring equipment, real-time data such as road conditions, traffic flow, and weather conditions are collected and sent to the system server.

2) Data processing layer: Process, analyze and store the collected data, extract effective information, and perform maintenance needs analysis and plan formulation based on algorithms.

3) Software application layer: This layer provides a user interface, including maintenance task dispatch, resource management, statistical analysis, and other functions. It also provides mobile applications to facilitate staff in viewing and processing maintenance tasks.

4) Database layer: used to store various types of data, including road information, maintenance records, resource management, etc.

5) Communication layer: responsible for data transmission and communication between various modules within the system to ensure the real-time and accuracy of the system.

In addition, the advanced version of the intelligent maintenance system will also adopt the system architecture of "one database, one base, one map + N applications" to build a "space-time big database" to provide full-factor digital support for highway maintenance management.

2. Key technologies

The core of the smart maintenance system lies in the advanced technologies it uses, which together form the intelligent foundation of the system. The main key technologies include:

1) Internet of Things (IoT) technology: By deploying various sensors at key road locations, real-time monitoring is provided for road wear, climate impacts, traffic flow, and vehicle loads. This data not only helps assess the current health of the road but also enables trend prediction through machine learning algorithms, enabling proactive maintenance measures and preventing accidents.

2) Artificial Intelligence (AI) Technology: AI is widely used in smart highway maintenance. For example, image recognition technology is used to analyze road cracks, and drones are used for regular inspections, reducing labor costs and errors. These automated solutions significantly improve inspection efficiency and accuracy, providing a crucial basis for maintenance decision-making.

3) Big Data Analysis Technology: The Smart Highway Maintenance Management System uses big data analysis to enable real-time monitoring and prediction of road conditions, vehicle flow, and other information. Leveraging machine learning and deep learning algorithms, it analyzes large amounts of historical data to establish predictive models for road conditions and traffic flow, and implements appropriate maintenance based on these predictions.

4) Cloud Computing and IoT Technologies: Utilizing cloud computing and IoT technologies, we enable remote data storage, resource sharing, and scheduling for the intelligent highway maintenance management system. By building a cloud platform, we integrate dispersed monitoring equipment and maintenance resources, improving the system's overall management efficiency and responsiveness.

5) Intelligent Decision Support: Combining artificial intelligence and data analysis technologies, this technology provides decision support for road maintenance plans. By analyzing collected data, relevant departments can formulate maintenance plans in a timely manner, optimize resource inputs, and reduce operating costs.

6) Digital Twin Technology: Digital twin technology plays a key role in smart maintenance systems. By creating digital twin models of roads and bridges, it enables real-time monitoring and predictive maintenance of infrastructure. Leveraging historical data and predictive models, digital twins can predict the remaining useful life and maintenance needs of road assets, optimizing maintenance plans, avoiding unplanned downtime, and extending asset lifespan.

The comprehensive application of these key technologies enables the smart maintenance system to achieve comprehensive perception, accurate analysis and intelligent decision-making of road conditions, thereby greatly improving road management efficiency.

02. How Smart Maintenance Systems Improve Road Management Efficiency

Smart maintenance systems improve road management efficiency in a variety of ways, including real-time monitoring and rapid response, predictive and preventive maintenance, resource optimization, and decision support. These aspects are discussed in detail below.

1. Real-time monitoring and rapid response

Traditional road maintenance and management models rely primarily on manual inspections and empirical judgment, which is not only inefficient but also poses safety risks. However, the intelligent maintenance system, through real-time monitoring technology, has significantly improved the efficiency and safety of road management.

First, the intelligent maintenance system monitors road conditions in real time, including surface roughness, cracks, and deformation. It uses data analysis and algorithms to predict potential failures and accidents, issuing timely warnings and improving the efficiency and safety of highway maintenance. Compared to traditional manual inspections, intelligent inspection robots demonstrate significant advantages, nearly doubling inspection efficiency.

Secondly, the smart maintenance system can quickly respond to emergencies. In the event of a traffic accident, rainstorm, or landslide, the smart road administration system is a lifesaver. The system also intelligently plans routes, ensuring the fastest arrival of ambulances and fire trucks, minimizing losses. Furthermore, the smart maintenance system can quickly address road damage, significantly reducing the workload for maintenance personnel in screening and assessing damage clues.

2. Predictive maintenance and preventive maintenance

An important feature of the smart maintenance system is its ability to perform predictive maintenance, shifting from passive response to active prevention and significantly improving maintenance efficiency.

In practical applications, intelligent maintenance systems can shift from static damage analysis to multi-dimensional analysis across time and space, leveraging AI models and historical data, providing optimal repair recommendations. This technology enables traffic management departments to more accurately formulate maintenance plans and improve the scientific and accurate allocation of resources. Furthermore, intelligent maintenance systems can leverage digital twin technology to enable preventive maintenance, such as providing early warning of damage risks from natural disasters like typhoons, shifting maintenance efforts from reactive to proactive.

3. Resource optimization and decision support

NRT's intelligent maintenance system has significantly improved road management efficiency by optimizing resource allocation and providing scientific decision-making support.

In terms of resource allocation, the smart maintenance system can achieve rational allocation of resources, including the allocation and scheduling of manpower, material resources and funds, and improve resource utilization.

In terms of decision support, the smart maintenance system uses big data analysis and artificial intelligence technology to deeply mine and analyze road usage and maintenance data, optimize maintenance management strategies, and improve road usage efficiency and service quality.

In addition, the intelligent maintenance system can also provide statistical and analytical reports on maintenance work through statistics and analysis, which are used to evaluate maintenance effectiveness and resource utilization. By establishing dynamic monitoring, event management, and quantitative assessment mechanisms, the system construction has achieved the expected results of improving maintenance efficiency and shortening repair cycles. The system converts complex data into easy-to-understand charts, making it easy to intuitively view the trends of relevant road diseases and promptly identify potential risks.

The positions of personnel, vehicles and handling procedures are recorded in real time to realize closed-loop management of the entire process of "discovery - handling - completion - evaluation" of safety hazards; the maintenance team is assessed according to the completion rate of hazard handling and the analysis of hazard handling conditions, to help achieve "minor hazards are handled overnight, and major hazards are precisely controlled."

4. Specific ways to improve efficiency

NRT's intelligent maintenance system improves road management efficiency in a variety of ways:

Reduced Inspection Time: The intelligent maintenance system significantly reduces inspection time. For example, intelligent inspection robots can autonomously inspect approximately 50 kilometers of roads daily, nearly doubling inspection efficiency compared to traditional manual inspection methods.

Improved defect identification accuracy: Smart maintenance systems can more accurately identify road defects. NRT Global has AI-powered detection capabilities for dozens of road defect and infrastructure categories, with an accuracy rate exceeding 90%. Leveraging deep learning algorithms, the system automatically classifies over 20 defect types (cracks, ruts, potholes, etc.), increasing efficiency by dozens of times.

Shortened response time: The intelligent maintenance system can respond to road problems more quickly. For example, when inspection vehicles discover a problem, their immediate response significantly improves road safety.

Reduce maintenance costs: The smart maintenance system can reduce maintenance costs. Through the intelligent decision-making system, maintenance costs are greatly reduced.

Improved maintenance quality: Smart maintenance systems can improve maintenance quality. For example, the closed-loop processing and evaluation of intelligent maintenance systems significantly improves maintenance quality, solving problems such as low efficiency and safety hazards of traditional manual inspections.