Solution Architecture

First, from the road-network perspective

For road operating conditions collected by front-end cameras, intelligently monitor specific vehicles, form multidimensional analysis, support decisions such as whether a segment needs widening, and warn on emergencies in advance. Besides real-time front-end video alerts, large numbers of algorithms from deep learning can form early-warning models to support decisions. For example, if rural-road radar meteorology indicates heavy rain in the next three days, the algorithm automatically raises comparison frequency of that segment with the past, learns from historical data, generates a warning at the first moment, and carries out decision support, planning ahead.

Second, from the maintenance perspective

Collect year-over-year maintenance work input and results, analyze and compare, and help users make decisions. Through historical data + technical indicators + maintenance recommendations, greatly raise user work efficiency.

Through an early-warning decision-support system, dynamically monitor structural-safety performance indicators of bridges and roads, transmit measurement data to the cloud platform via wired or wireless communication, assess bridge and road status through monitoring-data analysis, grasp long-term performance changes, discover structural safety hazards early, and save lives in time.

Third, the overall picture

Finally collect all business-line information, present a highway overview on a large screen, and help decision-makers intuitively grasp highway network status. For important information such as annual road-investment budgets and trunk-road repair frequency, compare past presentations with newly collected data, predict through algorithms, and ultimately realize the goal of decision support.