Railway modularized intelligent emergency training system based on TRIZ theory

Abstract

The invention discloses a railway modularized intelligent emergency training system based on a TRIZ theory, and belongs to the technical field of railway emergency training. The system is composed of a data acquisition module, a scene simulation module, an intelligent interaction module, a practical training evaluation module and a modular management unit. The data acquisition module acquires railway operation data and historical emergency case data in real time; the scene simulation module constructs a vivid and parameter-adjustable practical training scene by using VR, AR and other technologies; the intelligent interaction module realizes natural interaction and real-time feedback between trainees and scenes; the practical training evaluation module scientifically evaluates the practical training performance of the trainee from multiple dimensions; and the modular management unit flexibly manages and dispatches each functional module. According to the invention, innovative design is carried out based on the TRIZ theory, the authenticity of practical training is improved, the flexibility and expansibility of the system are enhanced, the evaluation scientificity of the practical training effect is improved, and the emergency processing capability of railway workers can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of railway emergency training technology, specifically to a modular intelligent emergency training system for railways based on TRIZ theory. Background Technology

[0002] With the rapid development of the railway transportation industry, the railway system is expanding in scale and the operating environment is becoming increasingly complex, which places higher demands on the ability of railway staff to respond to various emergencies. However, there are currently many problems in railway emergency training.

[0003] On the one hand, traditional practical training methods mostly rely on theoretical lectures and simple simulations. The training scenarios are limited and lack realism, failing to effectively improve the staff's ability to respond to complex and ever-changing real-world emergency situations. For example, in simulating emergency response to a railway bridge collapse, relying solely on pictures and text descriptions makes it difficult for trainees to truly experience the complex situation and decision-making pressure on site.

[0004] On the other hand, existing training systems lack flexibility and scalability. Different railway departments and positions have different training content and requirements, but existing training systems are difficult to adjust quickly to meet diverse needs. Moreover, with the continuous updating of railway technology, such as the application of new train control systems and intelligent dispatching systems, existing training systems cannot incorporate new technological elements in a timely manner for training.

[0005] Furthermore, the evaluation methods for practical training effectiveness are not scientific or comprehensive enough. They rely heavily on students' written test scores and simple operational performance, failing to accurately measure improvements in key abilities such as emergency decision-making and teamwork. Summary of the Invention

[0006] Purpose of the invention This invention aims to provide a modular intelligent emergency training system for railways based on TRIZ theory. By applying TRIZ theory, it addresses the problems existing in current railway emergency training, improves the authenticity, flexibility, scalability, and scientific rigor of training effectiveness evaluation, thereby effectively enhancing the emergency response capabilities of railway staff. Technical solution

[0007] System Overall Architecture This training system mainly consists of a data acquisition module, a scenario simulation module, an intelligent interaction module, a training evaluation module, and a modular management unit.

[0008] Innovative Design Based on TRIZ Theory Optimizing module design using the TRIZ principle: Based on the TRIZ principle of segmentation, railway emergency training content is divided into multiple independent functional modules according to different scenarios (such as train malfunctions, track disasters, station emergencies, etc.), job positions (such as dispatchers, train attendants, and maintenance personnel), and handling procedures (emergency response, on-site handling, and restoration of operations, etc.). Each module can be independently developed, maintained, and upgraded, while also being flexibly combined according to training needs. For example, the train fire emergency response scenario can be divided into sub-modules such as fire alarm, personnel evacuation, and fire extinguishing operations, allowing trainees to select the appropriate sub-module for targeted training based on their own job requirements.

[0009] Functional integration is achieved through the principle of versatility: By adopting the principle of versatility, some hardware devices and software functions in the system have multiple uses. For example, the display devices in the training system can be used to display training scene images and also serve as a terminal for displaying evaluation results after the training; the intelligent interactive devices can not only receive the trainees' operation commands, but also realize dialogue and communication with the trainees through voice recognition technology, providing real-time training guidance and prompts.

[0010] Enhancing training effectiveness by applying the principle of pre-emptive reaction: Based on the principle of pre-emptive reaction, potential risk factors and interference items are set up in the training system to simulate unexpected situations that may occur in actual emergency scenarios. For example, in simulating emergency response to train derailment, interference factors such as communication equipment failure and insufficient rescue materials are set up in advance to train trainees' ability to deal with sudden and complex situations, enabling them to prepare for various unexpected events in advance during training.

[0011] Specific functions of each module Data Acquisition Module: This module collects various data in real time during railway operation through sensors installed at the railway site (such as temperature sensors, pressure sensors, and displacement sensors) and data interfaces with the railway information system. This includes train operating parameters, track status information, and equipment operating conditions. Simultaneously, it collects historical emergency event case data to provide realistic data support for scenario simulations.

[0012] Scenario Simulation Module: Based on collected data and historical cases, highly realistic railway emergency training scenarios are constructed using virtual reality (VR), augmented reality (AR), and 3D modeling technologies. These scenarios cover a variety of common and rare emergency situations, and parameters can be adjusted according to different training needs, such as changing weather conditions, fault types, and severity. For example, in simulating railway line maintenance training under blizzard conditions, VR technology allows trainees to experience firsthand the impact of severe weather on line inspection and maintenance work.

[0013] Intelligent Interaction Module: Employing advanced human-computer interaction technologies, including gesture recognition, voice interaction, and touch operation, this module enables natural interaction between trainees and the training environment. Trainees can issue emergency response decisions via voice commands, such as dispatching trains and allocating rescue resources; they can also perform equipment checks and maintenance in the virtual environment using gestures. Simultaneously, the system provides real-time feedback based on the trainees' actions, offering correct operational guidance and error prompts.

[0014] The practical training evaluation module assesses trainees' performance from multiple dimensions. It evaluates not only the accuracy of their operations and the rationality of their decisions, but also their emergency response speed, teamwork ability (for multi-person collaborative training scenarios), and communication and coordination skills. By establishing a scientific evaluation indicator system and utilizing big data analytics and artificial intelligence technology to analyze and process trainees' training data, detailed evaluation reports are generated, providing trainees with improvement suggestions and training directions.

[0015] Modular Management Unit: Responsible for managing and scheduling the various functional modules within the system. It quickly combines and configures corresponding modules based on different training tasks and student needs, enabling flexible customization of the training system. Simultaneously, it manages module version updates and maintenance to ensure the system can promptly incorporate new railway technologies and emergency response methods.

Claims

1. A modular intelligent emergency training system for railways based on TRIZ theory, characterized in that, include: The data acquisition module is used to collect railway operation data and historical emergency event case data in real time through various sensors installed at the railway site and data interface with the railway information system; The scenario simulation module, based on the data collected by the data acquisition module and historical cases, uses virtual reality, augmented reality and 3D modeling technologies to construct a railway emergency training scenario whose parameters can be adjusted according to training needs; The intelligent interaction module uses advanced human-computer interaction technologies such as gesture recognition, voice interaction, and touch operation to enable students to interact naturally with the training scenario and provide real-time feedback based on the students' operations. The practical training assessment module evaluates trainees’ performance from multiple dimensions, including operational accuracy, decision-making rationality, emergency response speed, teamwork ability, and communication and coordination ability, using big data analysis and artificial intelligence technology to generate assessment reports. The modular management unit is responsible for managing and scheduling the various functional modules in the system. It can quickly combine and configure modules according to training tasks and student needs, and manage module version updates and maintenance.

2. The railway modular intelligent emergency training system based on TRIZ theory according to claim 1, characterized in that, The data acquisition module collects data such as temperature, humidity, pressure, and displacement in real time by installing high-precision sensors at key locations along the railway line. The data is then transmitted to the data processing center of the training system via a wireless communication network, and the module also interfaces with the railway dispatching and command system and equipment management system.

3. The railway modular intelligent emergency training system based on TRIZ theory according to claim 1, characterized in that, The scenario simulation module uses professional 3D modeling software to construct virtual scenario models, combines collected data and historical cases to set emergency event triggering conditions and parameters, and uses virtual reality and augmented reality technologies to provide trainees with an immersive or virtual-real combined training experience.

4. The railway modular intelligent emergency training system based on TRIZ theory according to claim 1, characterized in that, The intelligent interaction module uses gesture recognition algorithms and a speech recognition engine to convert students' gestures into operation commands, recognize students' voice commands and respond through speech synthesis technology, and establishes an operation command database to optimize recognition accuracy and response speed.

5. The railway modular intelligent emergency training system based on TRIZ theory according to claim 1, characterized in that, The training evaluation module collects students' operation data, decision records, and interaction information in real time during the training process. It quantitatively evaluates students' training performance based on a pre-set evaluation index system and uses big data analysis technology to identify students' weaknesses and potential problems in emergency response.