Improvement of vehicle-ground joint control system and method based on real-time data

Abstract

The invention provides an innovative train-ground joint control system and method based on real-time data aiming at the requirements of high efficiency and high safety in the current railway transportation field. According to the system, comprehensive intelligent management and optimization of the railway transportation process are realized by fusing advanced big data analysis technology, artificial intelligence algorithm, Internet of Things technology and cloud computing platform. The system can efficiently process and analyze mass transportation data, and uses an artificial intelligence algorithm to automatically learn and adjust a scheduling strategy, thereby realizing more accurate route planning and risk early warning. Meanwhile, information exchange among locomotives, vehicles and rail facilities is more real-time and accurate due to application of the Internet of Things technology, powerful data processing capacity and flexible resource allocation are provided for the system due to introduction of the cloud computing platform, and efficient operation and easy expansion of the system are ensured. The implementation of the invention is expected to obviously improve the railway transportation automation level, reduce the operation cost and enhance the reliability and stability of the system.

Description

Improvements to Vehicle-to-Ground Control Systems and Methods Based on Real-Time Data Technical Field

[0001] This invention relates to the field of railway transportation automation and intelligent management technology, and in particular to a vehicle-to-ground joint control system and method based on real-time data. Background Technology

[0002] With the rapid development of the global economy and the acceleration of urbanization, railway transportation, as one of the important modes of transportation, undertakes an ever-increasing task of transporting people and goods. Traditional railway transportation systems rely heavily on manual operation and experience-based judgment, which to some extent limits the improvement of transportation efficiency and the control of safety risks. Especially when facing complex and ever-changing transportation environments and continuously increasing transportation demands, traditional methods are no longer sufficient to meet the requirements of modern railway transportation for high efficiency, high safety, and high reliability.

[0003] To address these issues, the railway transportation industry has begun exploring and applying various advanced information technologies and management methods. Among them, the real-time data-based train-to-ground interlocking system has become a significant development direction in the railway transportation field in recent years. This system integrates microcomputer-based interlocking station map functions, real-time location tracking technology, digital shunting command transmission, and safety early warning systems to achieve automated planning and processing of railway transport routes, thereby improving transportation efficiency and safety. However, despite the progress made by existing technologies in railway transport management, some problems still urgently need to be solved.

[0004] First, existing vehicle-to-ground control systems have limitations in data processing capabilities, making it difficult to handle large-scale, high-frequency data processing demands. Second, the system's intelligence level needs improvement; current scheduling strategies rely heavily on preset rules and lack adaptability to complex situations and autonomous learning capabilities. Furthermore, the system's scalability and flexibility also need further enhancement to adapt to the ever-changing transportation environment and operational needs.

[0005] To address the aforementioned issues, this invention proposes an improved scheme for a vehicle-to-ground joint control system based on real-time data. By introducing big data analytics, artificial intelligence algorithms, Internet of Things (IoT) technology, and cloud computing platforms, it aims to achieve comprehensive intelligent management and optimization of the railway transportation system, improve transportation efficiency, reduce operating costs, and enhance the system's reliability and stability. Summary of the Invention

[0006] The core of this invention lies in proposing a novel real-time data-based vehicle-to-ground interlocking control system and its operation method, aiming to achieve efficient, intelligent, and safe operation of railway transportation through the comprehensive application of various cutting-edge technologies. The specific design and implementation process of this system is as follows: The microcomputer interlocking station map module: This module is the foundation of the system, responsible for integrating and updating station map information, including key information such as track layout and signaling equipment, and uniformly planning train routes based on real-time data and historical records to ensure the continuity and efficiency of train travel between stations.

[0007] The real-time position tracking module utilizes satellite differential positioning technology, enabling it to accurately track the position information of locomotives and trains in real time. Through the cooperation of onboard GPS equipment and ground base stations, the system can monitor the train's operating status in real time, providing precise data for subsequent route control and scheduling decisions.

[0008] The digital shunting instruction transmission module converts traditional shunting instructions into digital signals, which are then transmitted in real time to the driver and shunting operator via the onboard information system. The digitization of shunting instructions not only improves the accuracy and timeliness of information transmission but also enhances the driver's understanding and execution of instructions through voice broadcasting.

[0009] The safety early warning module, combining real-time location data, gate signals, and microcomputer interlocking status, can predict and warn of potential safety risks. Through real-time data analysis, the system can promptly issue warning signals to drivers and dispatchers when possible conflicts or dangerous situations occur, allowing for preventative measures to be taken.

[0010] The automatic route planning module automatically calculates and plans the optimal route based on real-time shunting plans and transportation demands. The system optimizes route selection through intelligent algorithms, automatically handles route conflicts, and ensures that trains can complete transportation tasks safely and efficiently.

[0011] The big data analytics module is responsible for collecting, storing, and analyzing large amounts of transportation data, including historical and real-time data. By applying big data analytics technology, the system can deeply mine patterns and trends in the data, providing a scientific basis for transportation planning and decision-making.

[0012] The aforementioned artificial intelligence algorithm module integrates advanced AI algorithms such as deep learning and reinforcement learning. This module is capable of autonomously learning and optimizing scheduling strategies. By simulating different transportation scenarios, the system continuously adjusts and improves the scheduling scheme, enhancing the intelligence and adaptability of the scheduling process.

[0013] The IoT communication module, through IoT technology, enables comprehensive interconnection and interoperability of transportation elements such as locomotives, vehicles, and rail facilities. The system can collect and transmit equipment status information in real time, achieving comprehensive monitoring and management of the transportation process.

[0014] The cloud computing resource management module provides robust cloud computing support for the system, enabling elastic allocation and efficient utilization of resources. The system can dynamically adjust computing and storage resources based on real-time changes in transportation tasks, ensuring efficient operation and rapid expansion.

[0015] Through the coordinated operation of the above modules, the system of the present invention can realize the full-process automation and intelligent management of railway transportation, improve transportation efficiency, reduce operating costs, and significantly enhance the safety and reliability of railway transportation.

[0016] This invention proposes innovative solutions to a series of technical challenges in the railway transportation field, specifically including the following aspects: 1. Enhanced data processing capabilities: Traditional train-to-ground control systems suffer from performance bottlenecks when processing large-scale, high-frequency transportation data. This invention, by introducing big data analytics, significantly improves the system's data processing capabilities, enabling it to quickly and accurately analyze and respond to complex transportation scenarios, thereby effectively improving transportation efficiency and decision-making accuracy. 2. Implementation of intelligent scheduling strategies: Previous railway transportation scheduling strategies relied heavily on manual experience and preset rules, lacking adaptability to complex situations and self-learning capabilities. The artificial intelligence algorithm module integrated in this invention enables the system to autonomously learn and optimize scheduling strategies, adapting to constantly changing transportation demands and improving the intelligence and flexibility of scheduling. 3. Improved real-time monitoring and early warning systems: Safety is the primary task of railway transportation. This invention, through the collaborative work of a real-time location tracking module and a safety early warning module, achieves real-time monitoring of train operation status and early warning of potential risks, significantly improving the safety of the transportation process. 4. Enhanced System Scalability and Flexibility: With the continuous expansion of railway transportation networks and the increasing demand for transportation, system scalability and flexibility have become particularly important. This invention utilizes IoT communication technology and cloud computing resource management modules to achieve rapid system expansion and flexible deployment, reducing operating and maintenance costs while ensuring system stability and reliability. 5. Optimized Transportation Efficiency: Improving transportation efficiency while ensuring safety is one of the goals pursued by railway transportation systems. The automatic route planning module of this invention can automatically calculate and plan the optimal route scheme based on real-time shunting plans and transportation demands, effectively avoiding route conflicts and improving the smoothness and efficiency of transportation. Through these innovative technical solutions, this invention successfully solves several technical challenges in the field of railway transportation, providing strong technical support for the modernization and intelligent development of the railway transportation industry.

[0017] Key points requiring protection include: 1. Real-time data processing and intelligent scheduling algorithms: This invention integrates advanced big data analytics and artificial intelligence algorithms to achieve efficient processing and intelligent analysis of railway transportation data. Protection should focus on the algorithms and system architecture for data acquisition, real-time processing, intelligent analysis, and scheduling decision optimization, ensuring the proprietary nature and competitive advantage of these core technologies. 2. Train-to-ground communication and safety early warning mechanisms: The digital shunting instruction transmission and safety early warning modules in this invention achieve real-time communication and data exchange between the train and the ground through IoT technology, significantly improving transportation safety. Protection should cover communication protocols, data transmission security, early warning logic, and emergency response mechanisms to ensure the efficient and reliable operation of the system. 3. Automatic route planning and conflict resolution strategies: The automatic route planning module is one of the core innovations of this invention. It automatically plans and manages train routes through intelligent algorithms, effectively avoiding potential conflicts and delays. Protection should include the algorithm design for route planning, conflict detection and resolution strategies, and integration methods with existing railway signaling systems.

[0018] The beneficial effects of this invention lie in its significant improvement of the automation level and intelligent management capabilities of railway transportation systems through the comprehensive application of cutting-edge technologies such as big data analysis, artificial intelligence algorithms, Internet of Things (IoT) technology, and cloud computing platforms. The system can process and analyze large amounts of transportation data in real time, intelligently optimize scheduling strategies, and improve transportation efficiency and response speed. Simultaneously, real-time monitoring and early warning mechanisms greatly enhance transportation safety and reduce the risk of accidents. Furthermore, the system's scalability and flexibility are significantly improved, enabling it to adapt to constantly changing transportation demands and operating environments, thereby reducing operating costs and improving the overall service quality and user satisfaction of railway transportation. Overall, this invention brings revolutionary technological progress to the railway transportation industry, promoting its modernization and intelligent development. Attached Figure Description

[0019] Figure 1 is a flowchart of the system operation method of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to Figure 1. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In specific embodiments of the present invention, we will describe in detail the implementation process of the vehicle-to-ground control system and its operation method based on real-time data, including key parameters and expected results.

[0022] System Setup and Configuration: 1. Microcomputer Interlocking Station Map Module: Configure the microcomputer interlocking system to integrate station layout and signaling equipment data. Parameters include the number of tracks, signal locations, and turnout types. The expected result is the formation of a unified station map database, providing foundational data for subsequent modules.

[0023] 2. Real-time Position Tracking Module: Deploys an onboard GPS receiver and a ground base station, and configures a satellite differential positioning system. Key parameters include positioning accuracy (±0.5m) and update frequency (1Hz). The expected result is to achieve real-time position tracking of locomotives and trains.

[0024] 3. Digital Shunting Command Transmission Module: Develop an onboard information system to digitize shunting commands. Parameters include command transmission rate (10Mbps) and system response time (≤300ms). The expected result is accurate and error-free transmission of shunting commands.

[0025] 4. Safety Early Warning Module: Integrates gate signals, microcomputer interlocking status monitoring, and locomotive positioning data, and sets early warning thresholds. Parameters include early warning response time (≤1s) and early warning accuracy (≥99%). The expected result is to provide timely safety protection early warnings.

[0026] 5. Automatic Route Planning Module: Develop intelligent algorithms to automatically plan routes. Parameters include the planning algorithm complexity (polynomial time) and route conflict rate (≤1%). The expected result is automatic and efficient route planning.

[0027] 6. Big Data Analytics and Processing Module: Deploy the big data analytics system and configure data processing capabilities. Parameters include data throughput (≥1TB / s) and analysis latency (≤5s). The expected result is the rapid and accurate processing and analysis of transportation data.

[0028] 7. Artificial Intelligence Algorithm Module: Configure deep learning and reinforcement learning algorithms, and set learning parameters. Parameters include learning rate (0.01) and number of iterations (1000). The expected result is that the system can autonomously learn and optimize scheduling strategies.

[0029] 8. IoT Communication Module: Establishes an IoT communication network and configures the communication protocol. Parameters include communication distance (≥10km) and data transmission rate (≥100Mbps). The expected result is to achieve comprehensive interconnection between locomotives, rolling stock, and rail infrastructure.

[0030] 9. Cloud Computing Resource Management Module: Configure cloud computing resources and set resource allocation strategies. Parameters include resource utilization (≥90%) and system expansion time (≤1 min). The expected result is to support rapid system expansion and flexible deployment.

[0031] Operation method flow: S1. Data collection: Collect status data of locomotives, trains and track facilities through real-time location tracking module and IoT communication module.

[0032] S2. Data Processing and Analysis: Utilize the big data analysis and processing module to process and analyze the collected data in real time and extract useful information.

[0033] S3. Intelligent Scheduling: The artificial intelligence algorithm module automatically learns and optimizes scheduling strategies based on the analysis results, and generates scheduling instructions.

[0034] S4. Route Planning: The automatic route planning module automatically plans routes based on scheduling instructions and real-time data to avoid conflicts.

[0035] S5. Command Transmission: The digital shunting command transmission module transmits the planning results and dispatching commands to the onboard equipment.

[0036] S6. Safety Monitoring and Early Warning: The safety early warning module monitors the transportation status in real time and issues an early warning immediately once a potential risk is detected.

[0037] S7. Resource Management: The cloud computing resource management module dynamically allocates computing and storage resources according to system requirements to ensure efficient system operation.

[0038] S8. System Feedback and Optimization: Collect system operation data and feed it back to the artificial intelligence algorithm module for continuous learning and optimization.

[0039] Through the above-described implementation methods, it is expected that comprehensive automation and intelligent management of railway transportation will be achieved. The system will be able to efficiently process large amounts of real-time data, intelligently optimize transportation scheduling, and significantly improve transportation efficiency and safety. Simultaneously, the system's flexibility and scalability will be enhanced, enabling it to quickly adapt to changes in transportation demand and reduce operating costs. Furthermore, real-time monitoring and early warning mechanisms will significantly improve the safety of the transportation process and reduce the probability of accidents. Ultimately, this invention will bring revolutionary technological progress to the railway transportation industry, promoting its modernization and intelligent development.

[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A vehicle-to-ground control system based on real-time data, characterized in that, It includes the following modules: a microcomputer interlocking station map module, used to integrate station map functions and unify route planning; and a real-time position tracking module, which tracks the position of locomotives and trains in real time through satellite differential positioning technology. The digital shunting instruction transmission module digitizes and transmits shunting instructions to the onboard equipment; The system includes: a safety early warning module that provides vehicle safety protection early warnings and voice alarms by combining gate signals, microcomputer interlocking status, and precise locomotive positioning; an automatic route planning module that automatically handles routes based on shunting plans; a big data analysis and processing module that processes and analyzes transportation data to optimize route planning; an artificial intelligence algorithm module that integrates deep learning and reinforcement learning algorithms to automatically learn and optimize scheduling strategies; an IoT communication module that enables comprehensive interconnection and interoperability among locomotives, vehicles, and track facilities; and a cloud computing resource management module responsible for the flexible allocation and efficient utilization of resources.

2. The vehicle-to-ground control system according to claim 1, wherein the big data analysis and processing module is further used to analyze transportation trends and predict potential transportation problems in real time, so as to improve transportation efficiency and decision-making accuracy.

3. The vehicle-to-ground control system according to claim 1, wherein the artificial intelligence algorithm module is further used to adjust the scheduling strategy in real time to adapt to the ever-changing transportation demands and conditions, thereby improving the system's flexibility and response speed.

4. The vehicle-to-ground control system according to claim 1, wherein the Internet of Things communication module is further used to collect and transmit the status information of locomotives, vehicles and track facilities in real time, so as to realize comprehensive monitoring and management of the transportation process.

5. The vehicle-to-ground control system according to claim 1, wherein the cloud computing resource management module is further used to dynamically allocate computing and storage resources to support the efficient operation and rapid expansion of the system, while reducing operating and maintenance costs.

6. A railway transportation management method based on the vehicle-to-ground control system according to claim 1, characterized in that, Includes the following steps: s1. Integrate the station map and plan routes through the microcomputer interlocking station map module; s2. Track the real-time position of locomotives and trains using the real-time position tracking module; s3. Transmit shunting commands to onboard equipment via a digital shunting command transmission module; s4. The safety early warning module provides safety protection early warnings based on real-time data; s5. The automatic route planning module automatically handles routes according to the shunting plan; s6. The big data analysis and processing module analyzes transportation data and optimizes route planning; s7. The artificial intelligence algorithm module automatically learns and optimizes scheduling strategies; s8. The IoT communication module enables real-time monitoring of devices and the environment; s9. The cloud computing resource management module enables efficient resource utilization and rapid system expansion.

7. The railway transportation management method according to claim 6, wherein the big data analysis and processing step further includes in-depth mining of historical transportation data to discover potential improvement points and optimization opportunities.

8. The railway transportation management method according to claim 6, wherein the artificial intelligence algorithm module learning step further includes simulating and evaluating scheduling strategies under different transportation scenarios to select the optimal scheduling scheme.

9. The railway transportation management method according to claim 6, wherein the IoT communication module monitoring step further includes real-time detection and reporting of abnormal situations during transportation so as to take timely countermeasures.

10. The railway transportation management method according to claim 6, wherein the cloud computing resource management step further includes real-time monitoring and analysis of the system operating status to ensure the stability and reliability of the system.

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