绍兴市智慧快速路车路协同数据

车路协同智能驾驶（1、信息共享与协同行驶：车辆通过车路协同系统，与道路基础设施、其他车辆进行实时通信，共享位置、速度、方向等信息，从而协同行驶，避免碰撞，提高整体交通的安全性。实时监测与预测：车辆通过车路协同系统实时获取周围车辆的行驶状态，并预测潜在的交通事故风险。2、当检测到危险情况时，及时发送警报给驾驶人，提醒其采取避免事故的措施。3、实时路况信息：基于车路协同技术，车辆可以实时获取路况信息，包括拥堵情况、交通事故等，为驾驶人提供最优的车道级导航路线。）主要运用AIOT大数据立体监测技术，应用LUA语言作为各类传感器硬件数据协议解析框架，采集的数据采用流计算引擎，利用SQL和NOSQL结合的模式进行数据存储。
数据采集集成平台中，应用LUA语言作为各类传感器硬件数据协议解析的框架。基于LUA嵌入式语言设计，系统实现了传感器协议的强大解析能力，实现可配置化的解析协议脚本。同时系统兼容串行、TCP/UDP、GPRS/3G/LTE等多样化的数据采集接口，以适应不同的物联网部署环境。
采用SQL 和 NOSQL结合的模式存储数据，拟通过集群中多个数据库提供用户的数据库操作，利用大数据技术，对内外部海量数据进行统一采集、计算、存储，并使用统一的数据规范进行管理，也是核心研究技术。包括：数据规范包括数据口径、数据模型、元数据规范、参考数据标准、主数据标准、业务规则等。

Vehicle-Road Cooperative Intelligent Driving: The system mainly applies AIoT big data stereo monitoring technology, uses Lua language as the data protocol parsing framework for various sensor hardware, adopts stream computing engines for collected data, and uses a combined SQL and NoSQL mode for data storage. Its specific functions include:
1. Information Sharing and Cooperative Driving: Vehicles communicate in real time with road infrastructure and other vehicles via vehicle-road cooperative systems, sharing information such as location, speed, and direction to enable cooperative driving, avoid collisions, and improve overall traffic safety.
2. Real-time Monitoring and Prediction: Vehicles acquire real-time driving states of surrounding vehicles through vehicle-road cooperative systems and predict potential traffic accident risks. When dangerous situations are detected, timely alerts are sent to drivers to remind them to take accident-avoiding measures.
3. Real-time Traffic Information: Based on vehicle-road cooperative technology, vehicles can obtain real-time traffic information including congestion, traffic accidents and other conditions, providing drivers with optimal lane-level navigation routes.

In the data collection and integration platform, Lua language is employed as the framework for parsing data protocols of various sensor hardware. Designed based on the embedded Lua language, the system realizes powerful parsing capabilities for sensor protocols and develops configurable parsing protocol scripts. Meanwhile, the system is compatible with diverse data collection interfaces such as serial port, TCP/UDP, GPRS/3G/LTE to adapt to different IoT deployment environments.

Adopting a combined SQL and NoSQL mode for data storage, the system intends to provide users with database operation services through multiple databases in the cluster. Using big data technologies to uniformly collect, compute, store massive internal and external data, and manage them with unified data specifications is also a core research technology. The data specifications include data caliber, data models, metadata specifications, reference data standards, master data standards, business rules, etc.