NRT Digital Twin + DeepSeek: Exploring the Unknown

With the rapid development of artificial intelligence technology, the domestic AI big model DeepSeek has attracted extensive and in-depth discussions worldwide due to its significantly lower training costs and open-source features, making it the focus of attention in the industry. In the face of this far-reaching technological change, efang has made a rapid strategic response. Fully integrating DeepSeek's technical strengths in core areas such as data analysis, model training and intelligent decision-making, DeepSeek actively undertook technical integration research work on NRT Digital Twin, a 3D transportation simulation platform, aiming to realize the first systematic application of cognitive intelligence in traffic management. This innovative initiative of technology integration marks that China's intelligent transportation system has officially entered a new era of "simulation as reality," providing new technological pathways and solutions for solving increasingly complex transportation problems.

At present, we have completed significant phased tests in the following key technical areas:

Multi-source data fusion

Deepseek's advanced Natural Language Processing (NLP) and Computer Vision (CV) technologies allow deep interpretation of many sources of unstructured data such as traffic camera video, social media commentary and meteorological data. Through the construction of multi-dimensional traffic environment input fusion processing algorithm system, and its continuous optimization, to achieve the efficient integration and deep use of multi-source data, for the subsequent traffic simulation and decision-making to provide comprehensive and accurate data support.

Generative AI modeling

Based on deep generative models, such as Generative Adversarial Networks (GANs) and Diffusion Models, we can simulate complex traffic scenarios, such as sudden accidents, holiday peak traffic, etc. By generating a large amount of simulation data, it effectively bridges the bottleneck problem of insufficient data in traditional simulation methods, provides a wealth of data resources for the analysis and optimization of transportation systems, and greatly improves the reliability and reference value of simulation results.

Real-time data assimilation

Using online learning technology, simulation model parameters are dynamically updated, and real-time data such as roadside sensors such as geomagnetic coils, radar, and floating car data such as taxi GPS are combined to achieve real-time calibration of simulation models and real-world traffic conditions. This ensures that the simulation results can accurately reflect the current traffic situation, providing a timely and accurate basis for traffic management decisions.

Traffic situation forecast

Relying on the DeepSeek reinforcement learning model (RL), the NRT Digital Twin access to large-scale real-time traffic data for efficient processing and in-depth analysis. The model is able to detect traffic problems in a timely manner and issue accurate warnings, while effectively predicting potential traffic risk points. The application of this technology has significantly improved the efficiency and precision of traffic incident management, providing strong support for road clearing and traffic safety.

Simulation parameter optimization

Through deep training of DeepSeek Enhanced Long-Term and Short-Term Memory Model (LSTM), the driver's behavior parameters, weather parameters, road environment parameters and other key simulation parameters are refined adjustment. The accuracy and reliability of traffic simulation inference have been further improved, so that the simulation results can more realistically reflect the actual traffic operation, providing a more scientific basis for decision-making in traffic planning and management.

Diversified simulation scenarios

Based on Deep Seek Transportation, NRT Digital Twin achieves more accurate communication and transportation decisions in areas such as bus emulation, emergency / secret route planning, etc. Through in-depth simulation and analysis of different traffic scenarios, the utilization efficiency of traffic resources has been effectively improved, providing a strong guarantee for the efficient operation of urban traffic.

Multi-perspective simulation

With DeepSeek AR / VR technology, the NRT Digital Twin platform enables a full-view traffic simulation experience. Users can immerse themselves in traffic conditions from a first-person perspective, providing a more intuitive and comprehensive perspective for traffic management decisions, and helping to enhance the scientificity and rationality of decisions.

In addition, NRT Digital Twin uses DeepSeek's near-end policy optimization algorithm (PPO) or deep Q network algorithm (DQN) reinforcement learning model to deeply optimize intelligent transportation organizations. Through reinforcement learning signal control and network-level coordinated optimization, the real-time optimization of signal timing scheme and the coordinated control of multiple intersection signals have been achieved, effectively alleviating the global congestion problem caused by local optimization and improving the overall operation efficiency of the urban transportation network.

Through these series of technological innovations and practices, we have gradually explored the utilization of AI big models to achieve full-stack capability enhancement, from microscopic traffic flow simulation to macroscopic traffic organization optimization, thus driving the intelligent transportation system to a deeper stage of cognitive intelligence.

The deep integration of NRT Digital Twin and DeepSeek will provide us with more precise and efficient technical means to address complex challenges in the field of communications management. We firmly believe that with the widespread application of this innovative technology, the intelligent transportation system of the future will be more intelligent, efficient and safe, creating a more convenient and comfortable transportation environment for people's travel.