Train operation optimization system based on real-time data acquisition and processing

Abstract

The invention belongs to the technical field of traffic transport locomotive assemblies, and discloses a train operation optimization system based on real-time data acquisition and processing. The system comprises a real-time data acquisition and processing module, a dynamic adjustment and optimization module, an operation guidance module, an energy-saving effect evaluation module, a human-computer interaction interface optimization module and a system self-learning and optimization module. According to the invention, through a train operation optimization algorithm, based on line data, train parameters and train marshalling static information, dynamically changing traffic conditions and environmental factors are fully considered, so that the train can better adapt to a complex traffic environment, the operation efficiency and safety are remarkably improved, and the train operation safety is improved through vehicle-mounted display or voice prompt. Meanwhile, detailed economic operation curve information and economic level frame sequence prompts of a controller handle can be provided for a train driver in real time, the influence of subjective judgment of the driver on operation is effectively reduced, and the error risk caused by misoperation is greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation locomotive components, and in particular is a train operation optimization system based on real-time data collection and processing. Background Art

[0002] In the field of modern transportation, with the increase in train speed and transportation volume, improving operating efficiency and safety, and reducing energy consumption and environmental pollution have become urgent tasks. At the same time, data processing and artificial intelligence technologies provide new ways to optimize train operations. Existing technologies collect train operation data in real time through on-board sensors and communication equipment, covering lines, parameters, marshaling, traffic and environmental factors, and process and analyze them to provide accurate input for train operation optimization algorithms. Based on these data, the algorithm dynamically adjusts the train operation curve and time allocation to enhance adaptability and flexibility. In addition, some solutions also provide drivers with economic operation curve information and controller handle economic level prompts in real time, helping drivers to operate accurately and smoothly, reduce errors, and further improve the overall efficiency and safety of train operation. However, existing technologies still face some challenges in the field of train operation optimization. First, the train operation optimization algorithm is mainly based on static data, such as lines, train parameters and marshaling information, but ignores the dynamically changing traffic conditions and environmental factors, which makes it difficult for the algorithm to adapt to the complex traffic environment, affecting the efficiency and safety of train operation. Secondly, existing technologies rely too much on the subjective judgment of train drivers and lack an immediate evaluation mechanism for the energy-saving effect of driver manipulation, which may lead to unnecessary waste of energy and increased environmental pollution. Finally, the self-learning and optimization capabilities of the technology are insufficient, and it is impossible to make full use of the accumulation of time and data to continuously optimize the train operation algorithm and guidance strategy, thereby limiting the further improvement of train operation efficiency and safety, so it needs to be improved. Summary of the invention

[0003] The purpose of the present invention is to provide a train operation optimization system based on real-time data collection and processing to solve the problems raised in the above background technology.

[0004] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a train operation optimization system based on real-time data acquisition and processing, the system comprising a real-time data acquisition and processing module, a dynamic adjustment and optimization module, an operation guidance module, an energy-saving effect evaluation module, a human-computer interaction interface optimization module, and a system self-learning and optimization module;

[0005] The real-time data acquisition and processing module provides accurate input for the dynamic adjustment and optimization module, which uses model predictive control to optimize energy saving based on these data; the operation guidance module provides operation suggestions to the driver according to the optimization results, and the energy-saving effect evaluation module evaluates the driver's operation to promote energy saving; the human-computer interaction interface optimization module improves information readability and reduces operational errors; the system self-learning and optimization module continuously optimizes algorithms and strategies through machine learning.

[0006] Preferably, the real-time data acquisition and processing module includes a data acquisition unit, a data processing and analysis unit and a communication unit;

[0007] The data acquisition unit is mainly responsible for collecting various data during train operation, such as line data, train parameters, train formation data, and intersection signal light information;

[0008] The data processing and analysis unit is mainly responsible for processing and analyzing the collected data to extract information useful for optimizing train operation;

[0009] The communication unit is responsible for sending the processed data to the train control system or other related systems.

[0010] Preferably, the dynamic adjustment and optimization module includes a data acquisition and preprocessing unit, a dynamic model building unit, a model prediction control unit, a control strategy optimization and execution unit, a real-time monitoring and feedback unit, and a human-computer interaction interface unit;

[0011] The data acquisition and preprocessing unit is responsible for real-time acquisition of train operation data, including locomotive parameters, train and line data and external environmental factors, and preprocessing to ensure that the data is accurate and available;

[0012] The dynamic model building unit constructs an electric locomotive dynamic model based on the TBT 1407.1-2018 standard and combines locomotive, train and line data. The model reveals the relationship between train speed, acceleration, braking force and external environment, laying a solid foundation for subsequent prediction and control.

[0013] The model prediction control unit uses a model prediction control method to predict the train status and line conditions in the future based on the current locomotive status and historical data, and calculates the optimal control strategy in the future, such as acceleration, deceleration, and speed maintenance, to minimize energy consumption according to the prediction results;

[0014] The control strategy optimization and execution unit optimizes the control strategy according to the output of the model prediction control unit, generates specific control instructions, and sends the control instructions to the train control system to instruct the driver to accelerate or decelerate, or automatically adjust the train operation curve and time allocation;

[0015] The real-time monitoring and feedback unit is mainly responsible for real-time monitoring of the driver's operating behavior, such as handle level adjustment, acceleration and deceleration, and comparing it with the model predictive control suggestions, analyzing the deviation and making dynamic adjustments. In case of speed deviation or abnormal signal, the system can immediately identify and process it, and recalculate the economic operation curve and time allocation accordingly to ensure operation safety and efficiency;

[0016] The human-machine interaction interface unit provides the driver with a clear and intuitive human-machine interaction interface, displaying economic operation curves, time allocation, and handle level recommendation information; and then receives the driver's input and feedback, such as confirmation instructions and adjustment suggestions, to ensure that the driver can accurately understand and execute control strategies.

[0017] Preferably, the operation guidance module refers to the system's ability to provide detailed economic operation curve information to the train driver in real time. The information comprehensively considers multiple factors such as line conditions, train performance and external environment. At the same time, the system will intelligently generate economic level frame sequence prompts for the driver's controller and brake controller handle based on the calculation results of the economic operation curve, and provide the driver with precise operation guidance. Through these real-time information and prompts, the train driver can control the train more accurately and smoothly, effectively reduce errors caused by improper operation, thereby further improving the safety and economy of train operation.

[0018] Preferably, the energy-saving effect evaluation module includes a real-time data monitoring unit, an energy-saving effect evaluation unit, a driver operation guidance and improvement unit, and a crew management unit;

[0019] The real-time data monitoring unit is responsible for real-time monitoring of various data in train operation, including but not limited to speed, acceleration, braking force, power consumption, line conditions and external environmental factors; the energy-saving effect evaluation unit is mainly based on the data provided by the real-time data monitoring unit. Under the same line conditions and train traffic volume, the unit calculates the power consumed by the locomotive to complete a specific transportation task within a certain period of time, and compares and evaluates it according to unit energy consumption. By comparing the energy consumption data under different operating modes, the driver's energy-saving effect is evaluated;

[0020] The driver operation guidance and improvement unit mainly guides and changes the driver's operation mode based on the results of the energy-saving effect evaluation unit. For drivers who operate according to experience and habits, energy-saving tips and suggestions are provided; for drivers who have already operated according to the energy-saving tips, their operation strategies are further optimized to further reduce energy consumption;

[0021] The crew management unit is responsible for managing and recording the crew's operating methods and their corresponding energy-saving effect evaluation results, providing a reference for subsequent driver training and selection.

[0022] Preferably, the human-computer interaction interface optimization module refers to improving the readability of information by improving the layout and color matching of the on-board display screen; at the same time, optimizing the clarity and logic of the voice prompts to make it easier for the driver to understand and operate. Such improvement measures effectively reduce operational errors caused by inaccurate or difficult to understand information, and provide train drivers with a more intuitive and convenient control experience.

[0023] Preferably, the system self-learning and optimization module includes a data acquisition and processing unit, a machine learning model training unit, and a strategy optimization and output unit;

[0024] The data acquisition and processing unit is responsible for collecting train operation data and driver operation data, including speed, acceleration, braking force, handle level adjustment, acceleration and deceleration operations, and performing preprocessing to ensure the accuracy and consistency of the data;

[0025] The machine learning model training unit uses machine learning technology to learn and analyze the preprocessed data, and continuously optimizes the train operation optimization algorithm and operation guidance strategy;

[0026] The strategy optimization and output unit optimizes the train operation guidance strategy based on the analysis results of the machine learning model and outputs the optimized strategy.

[0027] Preferably, based on the above system, the specific steps of using the system are as follows:

[0028] Step 1: Collect the line, parameters, formation, traffic and environmental data of the train in real time through on-board sensors and data acquisition devices, and provide comprehensive and accurate information support for train operation optimization;

[0029] Step 2: Process and analyze the collected data to provide accurate input for the train operation optimization algorithm;

[0030] Step 3: Use optimization algorithms to dynamically adjust train operation curves and time allocations based on real-time traffic and environmental conditions to ensure efficient and adaptable train operation;

[0031] Step 4: The system provides the train driver with the economic operation curve and the economic level prompt of the controller handle in real time, helping the driver to accurately and smoothly control the train, effectively reducing operating errors and improving operation efficiency and safety;

[0032] Step 5: Real-time monitoring of train operation data, timely evaluation of the energy-saving effect of driver operations, and provision of guidance and improvement suggestions to reduce energy consumption, pollution, and achieve green and efficient train operation;

[0033] Step 6: Optimize on-board display and voice prompts to improve information readability and operability, reduce driver operational errors caused by information misunderstanding, and ensure train operation safety and efficiency;

[0034] Step 7: Use machine learning technology to self-learn train operation and driver operation data, continuously optimize algorithms and strategies, improve train operation efficiency and safety, and achieve intelligent management

[0035] The beneficial effects of the present invention are as follows:

[0036] 1. The present invention adopts a train operation optimization algorithm, based on line data, train parameters, and static information of train formation, and more fully considers dynamically changing traffic conditions and environmental factors, so that trains can better adapt to complex traffic environments and significantly improve operating efficiency and safety. Through on-board displays or voice prompts, the present invention can provide detailed economic operation curve information and economic level frame sequence prompts of the controller handle in real time to the train driver. This innovative design effectively reduces the influence of the driver's subjective judgment on the operation and greatly reduces the risk of errors caused by improper operation. This improvement not only further enhances the safety and stability of train operation, but also provides passengers with a more comfortable and efficient travel experience.

[0037] 2. The present invention can quickly discover and correct unreasonable operations by real-time evaluation of the energy-saving effect of the driver's operation, thereby achieving more energy-saving and environmentally friendly train operation, effectively reducing energy consumption and environmental pollution, and optimizing the train operation curve and time allocation, helping train drivers to complete transportation tasks in an energy-saving manner while ensuring safety, punctuality and stability, thereby greatly improving transportation efficiency, reducing transportation costs, and enhancing the competitiveness of railway transportation companies. It is of far-reaching significance for improving operating efficiency. With high adaptability, accuracy and real-time performance, as well as significant energy-saving and efficiency-improving advantages, it demonstrates its excellence over existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a logic diagram of the system of the present invention;

[0039] Figure 2 The figure is a schematic diagram of the operation flow of the system of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] like Figure 1 to Figure 2As shown, an embodiment of the present invention provides a train operation optimization system based on real-time data collection and processing, the system includes a real-time data collection and processing module, a dynamic adjustment and optimization module, an operation guidance module, an energy-saving effect evaluation module, a human-computer interaction interface optimization module, and a system self-learning and optimization module;

[0042] The real-time data acquisition and processing module provides accurate input for the dynamic adjustment and optimization module, which uses model predictive control to optimize energy conservation based on these data; the operation guidance module provides operation suggestions to the driver according to the optimization results, and the energy-saving effect evaluation module evaluates the driver's operation to promote energy conservation; the human-machine interaction interface optimization module improves information readability and reduces operational errors; the system self-learning and optimization module continuously optimizes algorithms and strategies through machine learning. Each module cooperates with each other to form a closed-loop optimization system to continuously improve the efficiency and safety of train operation.

[0043] Wherein, the real-time data acquisition and processing module includes a data acquisition unit, a data processing and analysis unit and a communication unit;

[0044] The data acquisition unit is mainly responsible for collecting various data during train operation, such as line data, train parameters, train formation data, and intersection signal light information. This is usually achieved through various sensors and receivers, including speed sensors, track circuit information receiving units (STMs) and transponder information receiving units (BTMs).

[0045] The data processing and analysis unit is mainly responsible for processing and analyzing the collected data to extract information useful for optimizing train operations; this unit may include a computer module and specialized data processing software for cleaning, organizing, analyzing and visualizing the collected data.

[0046] The communication unit is responsible for sending the processed data to the train control system or other related systems. This may include a CAN communication module, a GPRS communication module, etc., for realizing wireless transmission of data and Internet access.

[0047] The real-time data acquisition and processing module integrates data acquisition, processing analysis and communication functions to provide comprehensive and accurate data support for train operation. Through high-speed and accurate sensors and receivers, various key data in train operation are collected in real time. After fine processing and analysis, information that is crucial to train operation optimization is extracted. With the help of advanced communication modules, wireless data transmission and Internet access are realized to ensure that data is delivered to the train control system and related systems in a timely manner. The application of this module not only improves the intelligence level of train operation, but also provides a solid data foundation for optimizing train operation strategies and improving operation efficiency and safety.

[0048] The dynamic adjustment and optimization module includes a data acquisition and preprocessing unit, a dynamic model building unit, a model prediction control unit, a control strategy optimization and execution unit, a real-time monitoring and feedback unit, and a human-computer interaction interface unit.

[0049] The data acquisition and preprocessing unit is responsible for real-time acquisition of train operation data, including locomotive parameters, train and line data and external environmental factors, and preprocessing to ensure that the data is accurate and available;

[0050] The dynamic model building unit constructs an electric locomotive dynamic model based on the TBT 1407.1-2018 standard and combines locomotive, train and line data. The model reveals the relationship between train speed, acceleration, braking force and external environment, laying a solid foundation for subsequent prediction and control.

[0051] The model prediction control unit uses a model prediction control method to predict the train status and line conditions in the future based on the current locomotive status and historical data, and calculates the optimal control strategy in the future, such as acceleration, deceleration, and speed maintenance, to minimize energy consumption according to the prediction results;

[0052] The control strategy optimization and execution unit optimizes the control strategy according to the output of the model prediction control unit, generates specific control instructions, and sends the control instructions to the train control system to instruct the driver to accelerate or decelerate, or automatically adjust the train operation curve and time allocation;

[0053] The real-time monitoring and feedback unit is mainly responsible for real-time monitoring of the driver's operating behavior, such as handle level adjustment, acceleration and deceleration, and comparing it with the model predictive control recommendations, analyzing deviations and dynamically adjusting them. In the event of speed deviation or signal abnormality, the system can immediately identify and process it, and recalculate the economic operation curve and time allocation accordingly to ensure operational safety and efficiency.

[0054] The human-machine interaction interface unit provides the driver with a clear and intuitive human-machine interaction interface, displaying economic operation curves, time allocation, and handle level recommendation information; and then receives the driver's input and feedback, such as confirmation instructions and adjustment suggestions, to ensure that the driver can accurately understand and execute control strategies.

[0055] The dynamic adjustment and optimization module provides accurate prediction and optimal control strategy for train operation through real-time data acquisition and preprocessing, combined with dynamic models and model predictive control. The module can monitor the driver's operation in real time and dynamically adjust the control instructions to ensure safe and efficient operation of the train. By optimizing the control strategy, energy consumption can be effectively reduced and the operation economy can be improved. At the same time, the clear and intuitive human-computer interaction interface enables the driver to accurately understand and execute the control strategy, improving operating efficiency.

[0056] Among them, the operation guidance module refers to the system's ability to provide detailed economic operation curve information to the train driver in real time. This information comprehensively considers multiple factors such as line conditions, train performance and external environment. At the same time, the system will also intelligently generate economic level frame sequence prompts for the driver controller and brake controller handle based on the calculation results of the economic operation curve, providing the driver with precise operation guidance. Through these real-time information and prompts, the train driver can control the train more accurately and smoothly, effectively reduce errors caused by improper operation, and further improve the safety and economy of train operation.

[0057] The operation guidance module provides detailed economic operation curve information in real time and comprehensively considers multiple factors to provide accurate operation guidance for train drivers. The module can intelligently generate economic level frame sequence prompts to help drivers operate trains more accurately and smoothly, effectively reducing operational errors. This not only improves the safety of train operation, but also significantly enhances economy, reduces energy consumption and operating costs. With the assistance of the operation guidance module, train drivers can make more scientific driving decisions, provide passengers with more comfortable and efficient travel services, and promote the sustainable development of railway transportation.

[0058] The energy-saving effect evaluation module includes a real-time data monitoring unit, an energy-saving effect evaluation unit, a driver operation guidance and improvement unit, and a crew management unit;

[0059] The data real-time monitoring unit is responsible for real-time monitoring of various data in train operation, including but not limited to speed, acceleration, braking force, power consumption, line conditions and external environmental factors; and provides basic data for subsequent energy-saving effect evaluation.

[0060] The energy-saving effect evaluation unit is mainly based on the data provided by the real-time data monitoring unit. Under the same line conditions and train traffic volume, the unit calculates the power consumed by the locomotive to complete a specific transportation task within a certain period (such as a month or a quarter), and compares and evaluates it according to unit energy consumption (power consumption per thousand ton-kilometer). By comparing the energy consumption data under different operating modes, the driver's energy-saving effect is evaluated;

[0061] The driver operation guidance and improvement unit mainly guides and improves the driver's operation mode based on the results of the energy-saving effect evaluation unit. For drivers who operate according to experience and habits, energy-saving tips and suggestions are provided; for drivers who have already operated according to the energy-saving tips, their operation strategies are further optimized to further reduce energy consumption;

[0062] The crew management unit is responsible for managing and recording the crew's operating methods and their corresponding energy-saving effect evaluation results, providing a reference for subsequent driver training and selection.

[0063] The energy-saving effect evaluation module accurately evaluates the driver's energy-saving effect by monitoring the train operation data in real time, providing a scientific basis for optimizing the operation mode. This module not only guides the driver to improve the operation strategy and reduce energy consumption, but also records the energy-saving effect through the crew management unit, providing strong support for driver training and selection. These measures jointly promote the improvement of energy efficiency of train operation, reduce resource waste, and help achieve green and low-carbon transportation. At the same time, by continuously optimizing the operation mode, the stability and safety of train operation are also improved, providing passengers with a more comfortable and efficient travel experience.

[0064] Among them, the human-computer interaction interface optimization module refers to improving the readability of information by improving the layout and color matching of the on-board display screen; at the same time, it optimizes the clarity and logic of the voice prompts, making it easier for the driver to understand and operate. Such improvement measures effectively reduce operational errors caused by inaccurate or difficult to understand information, and provide train drivers with a more intuitive and convenient control experience.

[0065] The human-machine interaction interface optimization module significantly improves the readability of information by improving the layout and color matching of the on-board display screen, allowing train drivers to quickly capture key information. At the same time, the optimized voice prompts are clear and logical, which reduces the difficulty of understanding and the risk of operating errors. These improvements provide drivers with a more intuitive and convenient control experience, which not only improves work efficiency but also enhances driving safety. By optimizing the human-machine interaction interface, trains run more smoothly and drivers can operate more easily, providing a strong guarantee for the safe and efficient operation of railway transportation.

[0066] The system self-learning and optimization module includes a data acquisition and processing unit, a machine learning model training unit, and a strategy optimization and output unit;

[0067] The data acquisition and processing unit is responsible for collecting train operation data and driver operation data, including speed, acceleration, braking force, handle level adjustment, acceleration and deceleration operations, and performing preprocessing to ensure the accuracy and consistency of the data;

[0068] The machine learning model training unit uses machine learning technology to learn and analyze the preprocessed data, and continuously optimizes the train operation optimization algorithm and operation guidance strategy; the core of this unit is the continuous iteration and optimization of the algorithm to improve the accuracy and generalization ability of the model.

[0069] The strategy optimization and output unit optimizes the train operation guidance strategy based on the analysis results of the machine learning model and outputs the optimized strategy; these strategies will be directly used to guide the operation of the train driver and improve the efficiency and safety of the train operation.

[0070] The system self-learning and optimization module brings significant benefits to train operation by integrating data collection and processing, machine learning model training, and strategy optimization and output. It ensures the accuracy and consistency of data, laying a solid foundation for subsequent analysis. Through the continuous iteration and optimization of machine learning technology, the model can more accurately analyze train operation data and improve the accuracy of algorithms and operation guidance strategies. The optimized strategy directly guides the driver's operation, which not only improves the efficiency of train operation, but also significantly enhances safety.

[0071] Among them, based on the above system, the specific steps of using the system are as follows:

[0072] Step 1: Collect the line, parameters, formation, traffic and environmental data of the train in real time through on-board sensors and data acquisition devices, and provide comprehensive and accurate information support for train operation optimization;

[0073] For example, the temperature sensor collects the current ambient temperature of the train, the pressure sensor collects the pressure when the train is running, the GPS device collects the location information of the train, the speed sensor collects the speed information of the train, and the communication equipment exchanges data with the ground control center through the wireless network.

[0074] Step 2: Process and analyze the collected data to provide accurate input for the train operation optimization algorithm;

[0075] For example, the position information is parsed to obtain the current position of the train and the position of the obstacles ahead; the speed information is processed to obtain the real-time speed and acceleration of the train; the ambient temperature and pressure information is processed to obtain the environmental parameters of the train operation.

[0076] Step 3: Use optimization algorithms to dynamically adjust train operation curves and time allocations based on real-time traffic and environmental conditions to ensure efficient and adaptable train operation;

[0077] For example, when an obstacle is detected ahead, the train's running curve is adjusted to avoid the obstacle; when traffic congestion is detected, the train's time allocation is adjusted to avoid staying in the congested section for too long.

[0078] Step 4: The system provides the train driver with the economic operation curve and the economic level prompt of the controller handle in real time, helping the driver to accurately and smoothly control the train, effectively reducing operating errors and improving operation efficiency and safety;

[0079] For example, the train economic operation curve is displayed on the on-board display screen, and the economic level frame sequence of the driver's controller and brake controller handle is prompted on the screen.

[0080] Step 5: Monitor train operation data in real time, promptly evaluate the energy-saving effects of the driver's operations, and provide guidance and improvement suggestions to reduce energy consumption, reduce pollution, and achieve green and efficient train operation.

[0081] For example, the train's speed, acceleration and energy consumption data can be monitored to evaluate the energy-saving effect of the driver's operation and guide the driver's operation based on the evaluation results.

[0082] Step 6: Optimize on-board display and voice prompts to improve information readability and operability, reduce driver operational errors caused by information misunderstanding, and ensure train operation safety and efficiency;

[0083] For example, optimize the interface layout and color matching of the display screen to improve the readability of information; optimize the volume and tone of voice prompts to improve the operability of information.

[0084] Step 7: Use machine learning technology to self-learn train operation and driver operation data, continuously optimize algorithms and strategies, improve train operation efficiency and safety, and achieve intelligent management;

[0085] For example, the collected train operation data and driver operation data are input into the machine learning model, and the train operation optimization algorithm and operation guidance strategy are continuously optimized through the self-learning function of the model. The above steps can be adjusted and optimized according to specific circumstances in actual applications to ensure the reliability and practicality of the system.

[0086] Application prospects of this technology

[0087] This technical solution, by virtue of its advanced nature, has shown broad application potential in multiple application fields such as transportation, data processing and artificial intelligence. In the field of transportation, this solution can effectively reduce the interference of human factors by dynamically optimizing the train operation algorithm, realize energy-saving effect evaluation, human-computer interaction interface optimization, and system self-learning and optimization, and help train drivers complete transportation tasks safely, on time, smoothly and energy-efficiently. This not only significantly improves transportation efficiency and reduces transportation costs, but also fully considers dynamically changing traffic conditions and environmental factors, so that trains can better adapt to complex traffic environments, thereby improving operation efficiency and safety. This innovation not only meets the urgent needs of modern transportation for efficiency, safety and environmental protection, but also provides the railway department with new technical means, greatly improving the service quality and market competitiveness of railway transportation.

[0088] In the field of data processing, the solution needs to process massive amounts of train operation and driver control data, opening up a broad application space for data processing technology. Through real-time processing and analysis of these data, it can accurately grasp the real-time status and trends of train operation, and provide solid data support for train operation optimization algorithms. At the same time, the self-learning and optimization functions of the data continuously improve the accuracy and efficiency of algorithms and strategies, which has effectively promoted the innovation and development of data processing technology.

[0089] In the field of artificial intelligence, this solution uses machine learning technology to continuously optimize train operation algorithms and guidance strategies through deep self-learning of train operation and driver operation data, opening up a new direction for the application of artificial intelligence in the field of transportation. In addition, this solution also involves the optimization of the human-computer interaction interface, promoting the progress of artificial intelligence in the field of user interface design and human-computer interaction. In summary, this technical solution has broad application prospects and huge market demand in the fields of transportation, data processing, and artificial intelligence, and is expected to lead technological progress and industrial upgrading in related fields.

[0090] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0091] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A train operation optimization system based on real-time data collection and processing, characterized by: The system includes real-time data acquisition and processing module, dynamic adjustment and optimization module, operation guidance module, energy-saving effect evaluation module, human-computer interaction interface optimization module, system self-learning and optimization module; The real-time data acquisition and processing module provides accurate input for the dynamic adjustment and optimization module, which uses model predictive control to optimize energy saving based on these data; the operation guidance module provides operation suggestions to the driver according to the optimization results, and the energy-saving effect evaluation module evaluates the driver's operation to promote energy saving; the human-computer interaction interface optimization module improves information readability and reduces operational errors; the system self-learning and optimization module continuously optimizes algorithms and strategies through machine learning.

2. The train operation optimization system based on real-time data acquisition and processing according to claim 1 is characterized in that: The real-time data acquisition and processing module includes a data acquisition unit, a data processing and analysis unit and a communication unit; The data acquisition unit is mainly responsible for collecting various data during train operation, such as line data, train parameters, train formation data, and intersection signal light information; The data processing and analysis unit is mainly responsible for processing and analyzing the collected data to extract information useful for optimizing train operation; The communication unit is responsible for sending the processed data to the train control system or other related systems.

3. The train operation optimization system based on real-time data acquisition and processing according to claim 1 is characterized in that: The dynamic adjustment and optimization module includes a data acquisition and preprocessing unit, a dynamic model building unit, a model prediction control unit, a control strategy optimization and execution unit, a real-time monitoring and feedback unit, and a human-computer interaction interface unit; The data acquisition and preprocessing unit is responsible for real-time acquisition of train operation data, including locomotive parameters, train and line data and external environmental factors, and preprocessing to ensure that the data is accurate and available; The dynamic model building unit constructs an electric locomotive dynamic model based on the TBT 1407.1-2018 standard and combines locomotive, train and line data. The model reveals the relationship between train speed, acceleration, braking force and external environment, laying a solid foundation for subsequent prediction and control. The model prediction control unit uses a model prediction control method to predict the train status and line conditions in the future based on the current locomotive status and historical data, and calculates the optimal control strategy in the future, such as acceleration, deceleration, and speed maintenance, to minimize energy consumption according to the prediction results; The control strategy optimization and execution unit optimizes the control strategy according to the output of the model prediction control unit, generates specific control instructions, and sends the control instructions to the train control system to instruct the driver to accelerate or decelerate, or automatically adjust the train operation curve and time allocation; The real-time monitoring and feedback unit is mainly responsible for real-time monitoring of the driver's operating behavior, such as handle level adjustment, acceleration and deceleration, and comparing it with the model predictive control suggestions, analyzing the deviation and making dynamic adjustments. In case of speed deviation or abnormal signal, the system can immediately identify and process it, and recalculate the economic operation curve and time allocation accordingly to ensure operation safety and efficiency; The human-machine interaction interface unit provides the driver with a clear and intuitive human-machine interaction interface, displaying economic operation curves, time allocation, and handle level recommendation information; and then receives the driver's input and feedback, such as confirmation instructions and adjustment suggestions, to ensure that the driver can accurately understand and execute control strategies.

4. The train operation optimization system based on real-time data acquisition and processing according to claim 1 is characterized in that: The operation guidance module refers to the system's ability to provide detailed economic operation curve information to the train driver in real time. This information comprehensively considers multiple factors such as line conditions, train performance and external environment. At the same time, the system will intelligently generate economic level frame sequence prompts for the driver's controller and brake controller handle based on the calculation results of the economic operation curve, providing the driver with accurate operation guidance. Through these real-time information and prompts, the train driver can control the train more accurately and smoothly, effectively reduce errors caused by improper operation, and further improve the safety and economy of train operation.

5. The train operation optimization system based on real-time data collection and processing according to claim 1 is characterized in that: The energy-saving effect evaluation module includes a real-time data monitoring unit, an energy-saving effect evaluation unit, a driver operation guidance and improvement unit, and a crew management unit; The real-time data monitoring unit is responsible for real-time monitoring of various data in train operation, including but not limited to speed, acceleration, braking force, power consumption, line conditions and external environmental factors; the energy-saving effect evaluation unit is mainly based on the data provided by the real-time data monitoring unit. Under the same line conditions and train traffic volume, the unit calculates the power consumed by the locomotive to complete a specific transportation task within a certain period of time, and compares and evaluates it according to unit energy consumption. By comparing the energy consumption data under different operating modes, the driver's energy-saving effect is evaluated; The driver operation guidance and improvement unit mainly guides and changes the driver's operation mode based on the results of the energy-saving effect evaluation unit. For drivers who operate according to experience and habits, energy-saving tips and suggestions are provided; for drivers who have already operated according to the energy-saving tips, their operation strategies are further optimized to further reduce energy consumption; The crew management unit is responsible for managing and recording the crew's operating methods and their corresponding energy-saving effect evaluation results, providing a reference for subsequent driver training and selection.

6. The train operation optimization system based on real-time data acquisition and processing according to claim 1 is characterized by: The human-computer interaction interface optimization module refers to improving the readability of information by improving the layout and color matching of the on-board display screen; at the same time, it optimizes the clarity and logic of the voice prompts, making it easier for the driver to understand and operate. Such improvement measures effectively reduce operational errors caused by inaccurate or difficult to understand information, and provide train drivers with a more intuitive and convenient control experience.

7. The train operation optimization system based on real-time data collection and processing according to claim 1 is characterized by: The system self-learning and optimization module includes a data acquisition and processing unit, a machine learning model training unit, and a strategy optimization and output unit; The data acquisition and processing unit is responsible for collecting train operation data and driver operation data, including speed, acceleration, braking force, handle level adjustment, acceleration and deceleration operations, and performing preprocessing to ensure the accuracy and consistency of the data; The machine learning model training unit uses machine learning technology to learn and analyze the preprocessed data, and continuously optimizes the train operation optimization algorithm and operation guidance strategy; The strategy optimization and output unit optimizes the train operation guidance strategy based on the analysis results of the machine learning model and outputs the optimized strategy.

8. The train operation optimization system based on real-time data collection and processing according to claim 1 is characterized by: Based on the above system, the specific steps of using the system are as follows: Step 1: Collect the line, parameters, formation, traffic and environmental data of the train in real time through on-board sensors and data acquisition devices, and provide comprehensive and accurate information support for train operation optimization; Step 2: Process and analyze the collected data to provide accurate input for the train operation optimization algorithm; Step 3: Use optimization algorithms to dynamically adjust train operation curves and time allocations based on real-time traffic and environmental conditions to ensure efficient and adaptable train operation; Step 4: The system provides the train driver with the economic operation curve and the economic level prompt of the controller handle in real time, helping the driver to accurately and smoothly control the train, effectively reducing operating errors and improving operation efficiency and safety; Step 5: Real-time monitoring of train operation data, timely evaluation of the energy-saving effect of driver operations, and provision of guidance and improvement suggestions to reduce energy consumption, pollution, and achieve green and efficient train operation; Step 6: Optimize on-board display and voice prompts to improve information readability and operability, reduce driver operational errors caused by information misunderstanding, and ensure train operation safety and efficiency; Step 7: Use machine learning technology to self-learn train operation and driver operation data, continuously optimize algorithms and strategies, improve train operation efficiency and safety, and realize intelligent management.

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