Operation data monitoring system for pilot test platform of TACS system

Abstract

The invention relates to the field of signal system testing, in particular to an operation data monitoring system for a TACS pilot test platform, which comprises a TACS tested piece, a TACS iVP pilot test platform and a monitoring platform, the tested pieces are connected with the TACS iVP pilot test platform through corresponding communication protocols and data interfaces, and the tested pieces are connected with one another through corresponding communication protocols and data interfaces; the monitoring platform acquires data between the tested pieces and data between the tested pieces and the TACS iVP pilot plant test platform in a mode of capturing network packets; and communicating with the TACS iVP pilot test platform through a network protocol and acquiring data. Compared with the prior art, on the basis that existing TACS system software and hardware are not modified, operation data collection can be completed, ultra-low coupling of a data collection system and an existing system is guaranteed, the collected data are connected into an AI large model and a digital intelligent large screen to be processed and displayed, and a manager can visually know the test process and state.

Description

Technical Field

[0001] The invention relates to the technical field of signal system testing, and in particular to an operation data monitoring system for a TACS system pilot test platform. Background Art

[0002] With the development of China's urban rail transit industry, the Train Autonomous Signaling System (TACS) based on vehicle-to-vehicle communication has gradually become the technical direction of the next generation of rail transit control systems. Compared with the widely used CBTC system, TACS realizes autonomous resource management and active interval protection through operation plans and real-time location information. With the three major innovations of platform optimization, system simplification and refined resource management, TACS simplifies the system structure, makes functions more efficient, and enhances safety protection.

[0003] As a fully automatic test platform, the TACS iVP pilot platform has excellent capabilities in simulating subway operation in the laboratory. The platform can not only realize the mixed running of real and fake vehicles, but also is not limited by resources. There is almost no upper limit on the number of RC (train controller), IC (interface controller) and TC (track circuit controller) in the system. This feature completely gets rid of the constraints of the French TestBench platform.

[0004] However, the composition of the test system of the TACS iVP pilot platform is extremely complex, including nearly 20 software components. When real and fake cars are running together, several physical entity components are also involved. These components include not only the software of the test platform, but also the DUT and related software such as ATS that adapts to the data version of the DUT. Each component is distributed and deployed on multiple Windows computers, both physical and virtual. All these components communicate data through the TCP / IP protocol, forming a complex network data flow. Due to the complex data flow, this poses a great challenge to data monitoring and problem location. Moreover, due to the large number of components and their wide distribution, monitoring the data flow of each component has become an arduous task. Timely capture and analysis of abnormal situations in these data flows requires highly professional monitoring tools and technologies. What is more difficult is that when a problem occurs in the system, it becomes very difficult to locate the root cause of the problem. The interdependence and data interaction between the components make the troubleshooting process extremely complicated. Technicians must check the operating status and data communication of each component one by one to find possible fault points. This distributed architecture increases the difficulty of problem location. Failure in any link may trigger a chain reaction, resulting in a decline in the overall performance of the system or even a crash.

[0005] Therefore, although the TACS test system is powerful, its complex structure and data flow pose severe challenges to data monitoring and problem location. This requires a lot of resources and technical means to ensure the stable operation of the system and rapid response to faults. Summary of the invention

[0006] The object of the present invention is to overcome the defects of the above-mentioned prior art and provide an operation data monitoring system for a TACS system pilot platform.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] An operation data monitoring system for a TACS system pilot platform, the system comprising: a TACS system tested device, a TACS iVP pilot platform and a monitoring platform;

[0009] The device under test is connected to the TACS iVP pilot platform and each device under test via a corresponding communication protocol and data interface;

[0010] The monitoring platform obtains data between the devices under test and between the devices under test and the TACS iVP pilot platform by capturing network packets;

[0011] The monitoring platform communicates with the TACS iVP pilot platform through network protocols and obtains data.

[0012] As a preferred technical solution, the tested parts include: a train controller, an interface controller, a track circuit controller, a log collection software, a train automatic monitoring system, and a train control onboard equipment human-machine interface DMI device of a train signal system.

[0013] As a preferred technical solution, the system deploys packet capture software on each communication data flow between each device under test and the TACS iVP pilot platform to capture network packets of running data.

[0014] As a preferred technical solution, the packet capture software is distributedly deployed on the physical machines and virtual machines corresponding to each component under test, and can identify network protocols and process data communications between different components to form standardized data packets.

[0015] As a preferred technical solution, the data packet captured by the packet capture software includes an error checking mechanism.

[0016] As a preferred technical solution, the monitoring platform includes:

[0017] The data collection and analysis module is responsible for receiving the data packets sent back from the corresponding packet capture software of each device under test, analyzing and sorting them, and storing the processed data in the MySQL database;

[0018] Database, used to store data collected and processed by the data collection and analysis module;

[0019] The web server is deployed with web applications to provide an intuitive monitoring interface for technicians;

[0020] The terminal accesses the web server through the network to obtain TACS system test data and warning information.

[0021] As a preferred technical solution, the data collection and analysis module is used to identify and analyze different types of data packets, and perform data cleaning and formatting processing;

[0022] The data collection and analysis module is also equipped with an error handling mechanism to detect and process abnormal data during the data analysis process.

[0023] As a preferred technical solution, the data collection and analysis module includes multiple independent data collection and analysis modules distributed on multiple hosts.

[0024] As a preferred technical solution, the database is a MySQL database, which is provided with a form structure for storing the operating data collected by the packet capture software, capable of high-concurrency writing and reading of data; and is provided with data sharding and index optimization.

[0025] As a preferred technical solution, the web application deployed in the web server provides chart and data visualization tools, has abnormal warning and status notification functions and interactivity, and users can provide their own defined monitoring views and reports.

[0026] As a preferred technical solution, the web application is deployed on a server and is provided with access control and data encryption mechanisms to prevent unauthorized access and data leakage.

[0027] As a preferred technical solution, the web application is designed and developed using a low-code platform designer.

[0028] As a preferred technical solution, the monitoring platform also includes a large data processing model, into which the collected data is injected for fault recording and analysis.

[0029] As an optimal technical solution, the data processing big model gradually acquires the ability to predict faults and locate problems through the training of the AI ​​big model; during the model training process, the model parameters are continuously adjusted and optimized to ensure that its prediction and location of faults are highly accurate and reliable, and at the same time, a self-learning and updating mechanism of the model is established to enable continuous improvement and enhancement of the model.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1) The solution proposed by the present invention realizes non-sensing data detection: complete data collection is completed without modifying the system software. This means that the normal operation of the system will not be affected in any way, while ensuring the ultra-low coupling between the data acquisition system and the existing system.

[0032] 2) The solution proposed in this invention can realize efficient web interface development: using the FineReport low-code design tool, large-screen application development is efficiently completed. This tool not only significantly reduces development time and cost, but also makes the development process easier, providing the possibility for rapid deployment and updating.

[0033] 3) The solution proposed by the present invention provides a large-screen display with excellent viewing value: The large-screen display is very ornamental and can not only be used as a tool for daily system monitoring, but also can be displayed on various large screens in the laboratory anytime and anywhere. Through intuitive and beautiful visualization effects, technicians and management can understand the system operation status more clearly and improve decision-making efficiency.

[0034] 4) The solution proposed by the present invention can realize monitoring and alarming without time and region restrictions: system status monitoring and alarm push are not restricted by time and region. No matter where they are, technicians can access the system through the network, monitor its operating status in real time, and receive alarm notifications in time. This flexible monitoring method greatly enhances the management ability and response speed of the system, ensuring the continuous and stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a schematic diagram of the structure of the operation data monitoring system used in the TACS system pilot platform;

[0036] The numbers in the figure show: 1. Train signal system components, 2. Log collection software, 3. Automatic train monitoring system ATS, 4. Human-machine interface DMI of train control on-board equipment, 5. TACS iVP pilot platform, 6. Data collection and analysis module, 7. Database, 8. Large data processing model, 9. Web server, 10. Terminal. DETAILED DESCRIPTION

[0037] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0038] Example 1

[0039] The purpose of this invention is to collect and organize the data generated by the TACS system pilot platform, write it into a database, and connect it to a digital intelligence screen and an AI big model, so that testers and managers can understand the test progress and status in real time and visualize it, and intelligently analyze the cause of the problem when an abnormality occurs, thereby improving the efficiency of testing and problem location.

[0040] like Figure 1 As shown, the module composition of this test system and the data flow of this solution are presented. The test system test pieces include: module vehicle controller CC, train controller RC, track circuit controller TC and other train signal system components 1, log collection software 2, train automatic monitoring system ATS 3 and other supporting software, train control vehicle equipment human-machine interface DMI 4 and other equipment; TACS iVP pilot platform 5 is a set of iVP platform software built for testing the test pieces. The monitoring platform includes: data collection and analysis module 6 is a collection of data collection and analysis modules, which are distributed on each host of the system under test according to actual conditions; database 7 is a MySQL database for storing detection data; monitoring platform data processing model 8 adopts big data model for fault recording and analysis; web server 9 is deployed with large screen application and provides intuitive monitoring interface for technicians; terminal 10 includes multiple web terminals for obtaining TACS system test data and warning information, and each terminal accesses the web server (9) through the network. Since the protocols between the tested devices cannot be changed at will, the present invention obtains data by capturing network packets. The modules in the TACS iVP pilot platform 5 can directly communicate with the data collection and analysis module 6 through the network protocol to obtain data.

[0041] The technical solution includes the following steps:

[0042] Step 1: Write packet capture software and deploy it in a distributed manner

[0043] Write packet capture software for each data flow. Since the various components under test of the test system are distributed on multiple physical machines and virtual machines, these packet capture software also need to be deployed on each machine to ensure that all data packets can be fully captured. Carefully design the deployment strategy of the packet capture software to ensure that each machine can run the packet capture program efficiently and can capture and transmit data in real time.

[0044] Step 2: Define network protocol and data interface

[0045] Define the network protocol and data interface with the test system software. Ensure that the packet capture software can accurately identify and process data communications between different components to form a standardized data packet format. The structure, transmission method, and error checking mechanism of each data packet need to be defined in detail to ensure data integrity and reliability. It is also necessary to consider the multiple communication protocols that different components may use and ensure that the packet capture software is compatible and can handle these differences.

[0046] Step 3: Set up the MySQL database

[0047] Build a MySQL database and design and create a suitable table structure. This database will be used to store and manage the massive data collected from the packet capture software, providing a basis for subsequent data analysis and processing. The database design needs to take into account the high concurrent writing and reading performance of the data to ensure high efficiency in a large-scale data environment. If necessary, data sharding and index optimization can also be set to improve query and storage efficiency.

[0048] Step 4: Write data receiving and parsing module

[0049] Write a data receiving and parsing module. This module is responsible for receiving data packets from various packet capture software, parsing and organizing them, and storing the processed data in the previously built MySQL database. The parsing module must have efficient data processing capabilities, be able to quickly identify and parse different types of data packets, and perform data cleaning and formatting. It is also necessary to set up an error handling mechanism to ensure that abnormal data can be discovered and processed in a timely manner during the data parsing process.

[0050] Step 5: Introduce large model for fault analysis

[0051] The collected data is injected into the Taichu Zhiyan big model for fault recording and analysis. Through the training of the AI ​​big model, the fault prediction and problem location capabilities are gradually acquired. This will greatly enhance the intelligence level of the system and improve the efficiency and accuracy of fault handling. During the model training process, it is necessary to continuously adjust and optimize the model parameters to ensure that the prediction and location of faults are highly accurate and reliable. At the same time, it is also necessary to establish a self-learning and update mechanism for the model so that it can be continuously improved and enhanced.

[0052] Step 6: Develop a visualization web application

[0053] Develop a web application and design rich charts and data visualization tools. Select core data from massive data for display, and implement abnormal warning and status notification functions. The application will provide technicians with an intuitive monitoring interface to help them quickly understand and analyze the system status. Visual design needs to take into account the user experience, making the interface concise and easy to operate. At the same time, it must also have good interactivity, allowing users to customize monitoring views and reports to meet different needs. In this step, a low-code platform designer (FineReport was used in this practice) was also used for design, which greatly reduced the technical requirements and learning costs. Developers do not need to master too many front-end technologies to develop beautiful web applications and are very efficient.

[0054] Step 7: Deploy the web application and implement remote monitoring

[0055] Finally, deploy the web application on the server. In this way, users can monitor the test status and get notifications and warnings anytime and anywhere on any terminal accessible by the network, whether it is a PC or a mobile phone. This monitoring capability anytime and anywhere will greatly enhance the flexibility and responsiveness of the system. During the deployment process, it is necessary to ensure the security and stability of the application, set up access control and data encryption mechanisms to prevent unauthorized access and data leakage. At the same time, it is also necessary to configure the high availability and load balancing of the server to ensure stable operation under high traffic conditions.

[0056] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An operation data monitoring system for a TACS system pilot platform, characterized in that: The system comprises: a TACS system device under test, a TACS iVP pilot platform (5) and a monitoring platform; The device under test is connected to the TACS iVP pilot platform (5) and each device under test via a corresponding communication protocol and data interface; The monitoring platform obtains data between the devices under test and between the devices under test and the TACS iVP pilot platform (5) by capturing network packets; The monitoring platform communicates with the TACS iVP pilot platform (5) through a network protocol and obtains data.

2. The operation data monitoring system for the TACS system pilot platform according to claim 1, characterized in that: The tested parts include: a train controller of a train signal system, an interface controller, a track circuit controller, log collection software (2), a train automatic monitoring system (3), and a train control onboard equipment human-machine interface DMI device (4).

3. The operation data monitoring system for a TACS system pilot platform according to claim 1, characterized in that: The system deploys packet capture software in each communication data flow direction between each device under test and the TACS iVP pilot platform (5) to capture network packets of running data.

4. The operation data monitoring system for the TACS system pilot platform according to claim 3, characterized in that: The packet capture software is distributedly deployed on the physical machines and virtual machines corresponding to the components under test, and can identify network protocols and process data communications between different components to form standardized data packets.

5. The operation data monitoring system for the TACS system pilot platform according to claim 4, characterized in that: The data packet captured by the packet capture software includes an error checking mechanism.

6. The operation data monitoring system for a TACS system pilot platform according to claim 1, characterized in that: The monitoring platform includes: The data collection and analysis module (6) is responsible for receiving the data packets sent back from the corresponding packet capture software of each device under test, analyzing and sorting them, and storing the processed data in the MySQL database; A database (7) for storing data collected and processed by the data collection and analysis module (6); A web server (9) is deployed with a web application to provide an intuitive monitoring interface for technicians; The terminal (10) accesses the web server (9) through the network to obtain TACS system test data and early warning information.

7. The operation data monitoring system for the TACS system pilot platform according to claim 6, characterized in that: The data collection and analysis module (6) is used to identify and analyze different types of data packets, and perform data cleaning and formatting processing; The data collection and analysis module (6) is also provided with an error handling mechanism to detect and handle abnormal data during the data analysis process.

8. The operation data monitoring system for the TACS system pilot platform according to claim 7, characterized in that: The data collection and analysis module (6) includes multiple independent data collection and analysis modules distributed on multiple hosts.

9. The operation data monitoring system for a TACS system pilot platform according to claim 6, characterized in that: The database (7) is a MySQL database, which is provided with a form structure for storing the operation data collected by the packet capture software, and is capable of high-concurrency writing and reading of data; and is provided with data segmentation and index optimization.

10. The operation data monitoring system for a TACS system pilot platform according to claim 6, characterized in that: The web application deployed in the web server (9) provides chart and data visualization tools, has abnormal warning and status notification functions and interactivity, and users can provide their own defined monitoring views and reports.

11. The operation data monitoring system for a TACS system pilot platform according to claim 10, characterized in that: The web application is deployed on a server (9) and is provided with access control and data encryption mechanisms to prevent unauthorized access and data leakage.

12. The operation data monitoring system for a TACS system pilot platform according to claim 10, characterized in that: The web application is designed and developed using a low-code platform designer.

13. The operation data monitoring system for a TACS system pilot platform according to claim 6, characterized in that: The monitoring platform also includes a data processing large model (8), into which the collected data is injected for fault recording and analysis.

14. The operation data monitoring system for a TACS system pilot platform according to claim 13, characterized in that: The data processing large model (8) gradually acquires the ability to predict faults and locate problems through the training of the AI ​​large model; during the model training process, the model parameters are continuously adjusted and optimized to ensure that the prediction and location of faults are highly accurate and reliable, and at the same time, a self-learning and updating mechanism of the model is established to continuously improve and enhance the model.

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