Intelligent dispatching and monitoring system for railway vehicles

Abstract

The invention relates to the technical field of intelligent scheduling, in particular to a railway vehicle intelligent scheduling and monitoring system which comprises a data acquisition module, a data processing module, an intelligent scheduling module, a priority adjustment module, a scheduling scheme generation module and a visualization module. The data acquisition module acquires multi-source data related to train operation in real time through a sensor and monitoring equipment, the data processing module performs preprocessing and feature extraction on the multi-source data, the intelligent scheduling module generates a preliminary scheduling scheme by adopting an intelligent algorithm based on the extracted feature data, the priority adjustment module performs priority adjustment, and the scheduling module performs scheduling according to the preliminary scheduling scheme. The scheduling scheme is ensured to adapt to real-time demand changes, the scheduling scheme generation module generates a complete train scheduling scheme, and the visualization module displays the scheduling scheme to a dispatcher and an administrator through a graphical interface; according to the method, through real-time dynamic scheduling and simulation optimization, the operation efficiency and safety of railway transportation are improved, delay is reduced, and the satisfaction degree of passengers is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent dispatching, and in particular to an intelligent dispatching and monitoring system for railway vehicles. Background Art

[0002] As an important mode of transportation, railways play a vital role in passenger and freight transportation. Railway transportation faces extremely complex scheduling tasks, especially during peak hours or in severe weather conditions. The stability and timeliness of train operation are crucial to the travel of passengers and the efficiency of the overall transportation system.

[0003] In traditional railway dispatching systems, train dispatching plans usually rely on fixed timetables and manual dispatching methods, and cannot dynamically respond to changes in actual operations, such as sudden weather changes, equipment failures, passenger flow fluctuations, and other factors. Although some railway dispatching systems have adopted computer-aided dispatching and automated dispatching systems, these systems still have many problems in dealing with complex and dynamically changing actual operating environments. For example, most existing systems lack sufficient flexibility during peak hours or special periods (such as holidays, bad weather, etc.), and cannot dynamically adjust train priorities and operating times. At the same time, most existing dispatching plans are based on static timetables or single factor optimization, ignoring the comprehensive analysis of multi-source data (such as real-time train location, speed, number of passengers, weather conditions, etc.). Summary of the invention

[0004] The present invention provides a railway vehicle intelligent dispatching and monitoring system to solve the technical problems mentioned in the background technology section;

[0005] To this end, the present invention provides a railway vehicle intelligent dispatching and monitoring system, including a data acquisition module, a data processing module, an intelligent dispatching module, a priority adjustment module, a dispatching scheme generation module and a visualization module, wherein;

[0006] The data acquisition module is used to collect multi-source data related to train operation, including train speed, location, meteorological data and the number of passengers on board;

[0007] The data processing module is used to perform real-time preprocessing on the collected multi-source data, the preprocessing includes data cleaning, denoising and data standardization, and feature extraction on the preprocessed multi-source data;

[0008] The intelligent scheduling module uses intelligent algorithms to calculate and generate preliminary train scheduling plans based on the extracted features to ensure efficient allocation and scheduling of railway resources;

[0009] The priority adjustment module is used to dynamically adjust the priority of each train based on real-time multi-sources during peak hours (such as holidays) to ensure that the train scheduling plan adapts to changes in transportation demand during the period;

[0010] The scheduling scheme generation module generates a complete train scheduling scheme according to the outputs of the intelligent scheduling module and the priority adjustment module;

[0011] The visualization module is used to present the generated train dispatching plan to dispatchers and administrators through a visualization interface.

[0012] Optionally, the data acquisition module includes:

[0013] Install speed sensors and GPS positioning devices in railway vehicles to collect train speed data and geographic location data in real time;

[0014] Install passenger flow monitoring equipment in train carriages to collect the number of passengers on board in real time;

[0015] Meteorological monitoring equipment is installed along the track to collect meteorological data along the track in real time;

[0016] The collected multi-source data is transmitted to the data processing module in real time through the wireless communication network.

[0017] Optionally, the data processing module receives multi-source data collected by the data acquisition module, performs preprocessing, and performs feature extraction on the preprocessed data, where the extracted features include speed change rate, acceleration, snowfall, and passenger density in the vehicle, and the extracted feature data is transmitted to the intelligent scheduling module.

[0018] Optionally, the intelligent scheduling module includes:

[0019] Based on the characteristic data extracted by the data processing module, the intelligent scheduling module defines the scheduling objectives and constraints;

[0020] Generate preliminary train dispatch plans using intelligent algorithms;

[0021] The intelligent dispatching module optimizes the generated preliminary train dispatching plan;

[0022] The optimized preliminary train scheduling plan is passed to the scheduling plan generation module.

[0023] Optionally, the priority adjustment module makes priority adjustment decisions based on real-time multi-source data, evaluates the transportation needs of each train, and uses an intelligent algorithm to dynamically adjust the priority of the train, and reschedules the train scheduling time according to the dynamically adjusted priority.

[0024] Optionally, the scheduling scheme generation module obtains output data of the intelligent scheduling module and the priority adjustment module, and generates a complete train scheduling scheme based on the obtained preliminary scheduling scheme and priority adjustment results.

[0025] Optionally, the scheduling plan generation module verifies and optimizes the generated complete scheduling plan, and transmits the optimized complete scheduling plan to the dispatcher and administrator through the system interface for implementation.

[0026] Optionally, the visualization module displays the train scheduling plan through a graphical user interface, dynamically updates the train scheduling information in real time and provides user interaction functions.

[0027] Optionally, the visualization module automatically generates a dispatch report based on the train dispatch situation, including detailed information such as train departure, stop, route, delay, etc., and supports the export of dispatch reports in multiple formats.

[0028] Beneficial effects of the present invention:

[0029] The present invention performs dynamic scheduling and priority adjustment based on real-time multi-source data (such as vehicle speed, location, weather conditions and number of passengers), so that the train scheduling plan can respond to dynamic changing factors such as peak hours and bad weather in a timely manner. Through the coordinated work of the intelligent scheduling module and the priority adjustment module, the train departure time, stop time and travel path can be optimized, the utilization efficiency of railway resources and the accuracy of scheduling can be effectively improved, and it can be ensured that trains can arrive on time and minimize delays in a complex operating environment.

[0030] The present invention, through the combination of intelligent algorithms and Monte Carlo simulation, can simulate the performance of different dispatching schemes in the face of uncertain factors (such as weather changes, equipment failures, emergencies, etc.), and optimize and adjust the schemes. This real-time simulation and dynamic optimization method makes the train dispatching system highly adaptable, and can quickly respond to various unforeseen situations that occur during operation, such as bad weather, train failures and other emergencies, and reduce the impact of these factors on train operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 Schematic diagram of a system flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0034] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0035] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0036] like Figure 1 As shown, a railway vehicle intelligent dispatching and monitoring system includes a data acquisition module, a data processing module, an intelligent dispatching module, a priority adjustment module, a dispatching scheme generation module and a visualization module, wherein;

[0037] The data acquisition module is used to collect multi-source data related to train operation, including train speed, location, meteorological data and the number of passengers on board;

[0038] The data processing module is used to perform real-time preprocessing on the collected multi-source data. The preprocessing includes data cleaning, denoising and data standardization, and feature extraction on the preprocessed multi-source data.

[0039] The intelligent scheduling module uses intelligent algorithms to calculate and generate preliminary train scheduling plans based on the extracted features to ensure efficient allocation and scheduling of railway resources;

[0040] The priority adjustment module is used to dynamically adjust the priority of each train based on real-time multi-sources during peak hours (such as holidays) to ensure that the train scheduling plan adapts to changes in transportation demand during the period;

[0041] The scheduling plan generation module generates a complete train scheduling plan based on the outputs of the intelligent scheduling module and the priority adjustment module;

[0042] The visualization module is used to present the generated train dispatching plan to dispatchers and administrators through a visualization interface.

[0043] The data acquisition module includes:

[0044] Speed ​​sensors and GPS positioning devices are installed in railway vehicles to collect train speed data and geographic location data in real time to ensure accurate tracking of train travel status;

[0045] The speed sensor measures the speed of the train using a wheel speed sensor, and the GPS positioning device includes a GPS system or a differential GPS system to provide the real-time position of the train;

[0046] Install passenger flow monitoring equipment in train carriages to collect the number of passengers on board in real time, so as to judge the passenger flow of the train;

[0047] The passenger flow monitoring device is an infrared sensor that determines the number of passengers by detecting changes in infrared heat radiation when passengers pass through the doors or carriage passages;

[0048] Meteorological monitoring equipment is installed along the track to collect real-time meteorological data along the track (including snowfall and freezing rain probability);

[0049] Meteorological monitoring equipment includes radar snow meters to monitor the intensity and amount of snowfall, and the probability of freezing rain is estimated through weather station data and temperature-humidity curve models;

[0050] The collected multi-source data is transmitted to the data processing module in real time through the wireless communication network to provide real-time data support for the subsequent intelligent scheduling module.

[0051] The data processing module includes:

[0052] Receive multi-source data collected by the data acquisition module and perform preprocessing. The preliminary preprocessing includes:

[0053] Data cleaning: remove outliers, fill in empty values, remove duplicate data, etc. from the collected multi-source data to ensure data integrity and consistency;

[0054] Data denoising: Use filtering algorithms (such as median filtering, Kalman filtering, etc.) to remove noise from multi-source data to ensure data accuracy and reliability;

[0055] Data standardization: Standardize the different types of data collected (such as vehicle speed, weather data, number of passengers, etc.) to make them have a unified dimension and scale to avoid inaccurate data processing due to dimensional differences;

[0056] The preprocessed data is subjected to feature extraction. The extracted features include speed change rate, acceleration, snowfall, and passenger density in the vehicle, including:

[0057] Extract driving state characteristics from the speed and position data, such as speed change rate, position offset, acceleration, etc., to reflect the driving state of the train;

[0058] Extract weather impact characteristics from meteorological data, especially the severity and duration of freezing rain and snowfall, and assess their potential impact on train operations;

[0059] Extract passenger flow characteristics from the passenger number data, such as passenger density in the train, frequency of passengers getting on and off the train, etc., to evaluate the passenger flow change trend of the train;

[0060] The extracted feature data is transmitted to the intelligent scheduling module to support the generation of subsequent scheduling decisions;

[0061] The data processing module performs pre-processing operations such as cleaning, denoising, and standardization on the collected multi-source data to ensure data quality while extracting features that are important for train scheduling optimization, providing reliable input data for the intelligent scheduling module and helping to improve the accuracy and execution efficiency of the scheduling plan.

[0062] The intelligent scheduling module includes:

[0063] Based on the feature data extracted by the data processing module, the intelligent scheduling module defines the scheduling objectives and constraints. The specific steps include:

[0064] According to the extracted characteristic data, such as speed change rate, acceleration, snowfall and passenger density, the train dispatching objectives are set. The dispatching objectives mainly include: improving train punctuality, maximizing railway resource utilization, reducing delays between trains, and ensuring maximum train operation efficiency;

[0065] Set scheduling constraints, including train spacing restrictions, station capacity restrictions, track usage restrictions, and safe travel speeds, to ensure that the scheduling plan meets the basic requirements of railway operation safety and passenger demand;

[0066] Use intelligent algorithms to generate preliminary train scheduling plans. The specific steps include:

[0067] A mixed integer linear programming model is used to optimize the running time, departure interval, stop time, etc. of trains to ensure efficient allocation of railway resources. The steps are as follows:

[0068] (1) Train scheduling variable definition: Define scheduling decision variables for each train, mainly including:

[0069] The departure time of the train is T start ;

[0070] The train stop time T stop ;

[0071] The running path of the train (selection of track sections);

[0072] The speed distribution of the train (possibly represented by discretized speed intervals).

[0073] Among them, T start and T stop is an integer variable, representing the departure and stop times of trains, and other parameters such as speed and path selection can be used as continuous variables.

[0074] (2) Objective function design: Design an objective function to minimize the total delay time of train scheduling, maximize the utilization rate of railway resources, or comprehensively consider multiple objectives, such as:

[0075]

[0076] Where N is the total number of trains, delay i is the delay time of the i-th train, M is the total number of railway resources (tracks, stations, etc.), resource_usage j is the usage of the jth resource, and λ is the weight coefficient for adjusting the trade-off between resource usage and delay.

[0077] (3) Definition of constraints:

[0078] Train interval constraint: Ensure that the train interval is not less than the set minimum safety interval Δt min , to prevent collision or overcrowding, expressed as:

[0079]

[0080] Station capacity constraint: The station has limited capacity to accommodate a train, ensuring that at most one train stops at the same time, expressed as:

[0081]

[0082] Track usage constraint: Only one train can pass through the same track section at the same time, expressed as:

[0083]

[0084] Safety speed constraint: the speed of the train v i Do not exceed the safe driving speed v max , expressed as:

[0085]

[0086] (4) Solution process: A mixed integer linear programming solver (such as Gurobi, CPLEX, etc.) is used to solve the problem and obtain a preliminary train scheduling plan, i.e., the train departure time, stop time, route selection, and speed distribution;

[0087] The intelligent scheduling module optimizes the generated preliminary train scheduling plan. The specific steps include:

[0088] Calculate the possible delays and their impacts during train operation, use the Monte Carlo simulation method to simulate different scheduling plans, evaluate the reliability of the scheduling plans, and make adjustments based on the simulation results. The steps include:

[0089] (1) Identify and define uncertainties: In actual train operation, many factors may lead to delays or scheduling problems. First, it is necessary to identify and define these uncertainties and incorporate them into the simulation model. Common uncertainties include:

[0090] Relative delays between trains: Due to the delay of the previous train, the subsequent trains may be affected, resulting in a chain reaction;

[0091] Changes in weather conditions: Weather factors such as snow, freezing rain, and strong winds may cause trains to slow down or stop running;

[0092] Unexpected events: such as equipment failure, signal problems or traffic accidents, which may cause unplanned train suspension or delay;

[0093] Passenger flow changes: The level of crowding when passengers board, alight, or get off a train affects the train's stopping time;

[0094] Using these factors as input conditions, we define random variables in the simulation model, such as train delays, weather impacts, and failure probability.

[0095] (2) Establishing Monte Carlo simulation model: Monte Carlo simulation is a method of evaluating system performance and reliability through random sampling and statistical analysis. In this process, Monte Carlo simulation is used to simulate different scheduling schemes, run multiple times, and analyze the results of each run. The specific steps include:

[0096] (2.1) Set the number of simulations. Usually, thousands to tens of thousands of random simulations are used to obtain a sufficient sample size to ensure the reliability of the simulation results.

[0097] (2.2) In each simulation, random variables (such as train delays, weather conditions, failures, etc.) are input, and each scheduling scheme is calculated to obtain the operating results under different scheduling schemes, including delay time, resource utilization, passenger flow, etc.;

[0098] (2.3) Based on the simulation results, the expected delay time, delay probability, reliability and other indicators of each plan are calculated to form the evaluation results of multiple scheduling plans;

[0099] (3) Evaluate and optimize the reliability of the scheduling scheme: Through Monte Carlo simulation, the evaluation results of different scheduling schemes are obtained, mainly through the following indicators:

[0100] (3.1) Expected delay time: The average delay time of each scheduling scheme in multiple simulations, reflecting the efficiency and reliability of the scheme, expressed as:

[0101]

[0102] Where E[delay] is the expected delay time, delay i is the delay time of the train in the i-th simulation, and N is the total number of simulations;

[0103] (3.2) Delay probability: The probability of delay for each scheduling scheme, that is, the frequency of delay in multiple simulations, is expressed as:

[0104]

[0105] Where P[delay] is the delay probability, count delay is the number of simulations in which delay occurs, and N is the total number of simulations;

[0106] (3.3) Stability of the plan: This measures the adaptability of the scheduling plan to various uncertain factors. The stability of the scheduling plan is evaluated by observing the performance of the scheduling plan in each simulation (whether it is often delayed or does not meet the target);

[0107] (4) Adjust the preliminary scheduling plan: Based on the results of the Monte Carlo simulation, identify the potential risks and unstable factors in the preliminary scheduling plan and make adjustments:

[0108] For scheduling plans with longer delays or higher delay probabilities, the intelligent scheduling module can automatically adjust parameters such as departure time, stop time, and vehicle speed, or reduce delay risks by adding spare trains and adjusting train routes.

[0109] For high-risk areas or periods where serious delays may occur (such as peak hours, bad weather, etc.), optimize scheduling plans and take corresponding preventive measures, such as speeding up train scheduling and adjusting train priorities.

[0110] (5) Outputting the optimized dispatching plan: Through the above adjustments, the optimized dispatching plan can better cope with uncertain factors, improve the reliability and stability of train operation, and finally output an optimized preliminary train dispatching plan;

[0111] The optimized preliminary train scheduling plan is passed to the scheduling plan generation module.

[0112] The priority adjustment module specifically includes:

[0113] Priority adjustment decisions are made based on real-time multi-source data. The priority adjustment module evaluates the transportation demand of each train. The specific steps include:

[0114] (1) Based on real-time multi-source data monitoring results (such as train location, speed, number of passengers, etc.) and external factors (such as weather conditions, holidays, etc.), the priority needs of trains in the current period are evaluated. For example, during peak hours or in severe weather conditions, some trains may need to depart earlier or speed up to ensure that passengers arrive on time;

[0115] (2) Based on real-time data updates and in combination with the strategic objectives of railway operations, a preliminary priority score is assigned to each train, which is based on the following factors:

[0116] Number of passengers: Trains with a high number of passengers on board have higher priority;

[0117] Travel time: Trains with long travel time and serious delays have higher priority;

[0118] External conditions: For example, weather factors, holidays and other special periods require that certain trains be dispatched first;

[0119] Predetermined destination: such as departure station, final destination, important stations, etc.;

[0120] Intelligent algorithms are used to dynamically adjust the priority of trains. The specific steps include:

[0121] (1) Using a priority adjustment algorithm based on reinforcement learning, the adjustment strategy is trained based on historical data and real-time data. In this process, the priority of each train is regarded as an action in the environment, and the reward function is evaluated based on train punctuality, passenger satisfaction, railway resource utilization, etc., thereby determining the scheduling priority;

[0122] (2) Introducing fuzzy logic control, combined with real-time uncertainties (such as traffic accidents, equipment failures and other emergencies), to make fuzzy adjustments to priorities. The fuzzy control algorithm can handle these uncertainties and ensure that train priorities can be reasonably adjusted under complex circumstances.

[0123] (3) Dynamically adjust train priorities based on changes in the real-time dispatching environment (such as weather changes). The priority adjustment module ensures that the dispatching plan can be optimized and adjusted as the actual situation changes through continuous monitoring and feedback mechanisms.

[0124] The train scheduling time is rescheduled according to the dynamically adjusted priority. The specific steps include:

[0125] (1) Sort all trains by priority and adjust the departure time and stop order according to the trains with higher priority;

[0126] (2) During the adjustment process, ensure that constraints such as train intervals, track capacity, and station capacity are not exceeded to avoid train congestion or other resource conflicts;

[0127] (3) Output the final priority adjustment results and update the dispatch plan for implementation by the dispatcher and automatic control system;

[0128] By real-time monitoring of multiple sources of data such as train operation status, weather conditions, and passenger flow, the priority adjustment module can dynamically adjust the priority of each train, thereby effectively responding to changes in transportation demand during peak hours or special periods (such as holidays, bad weather, etc.). The module combines intelligent algorithms such as reinforcement learning and fuzzy logic control, which can not only respond to changes in the dispatching environment in real time, but also ensure the efficient allocation of railway resources, reduce delays, and improve passenger satisfaction.

[0129] The scheduling scheme generation module includes:

[0130] The scheduling scheme generation module obtains the output data of the intelligent scheduling module and the priority adjustment module, including:

[0131] Receive the preliminary train dispatch plan output by the intelligent dispatch module, which includes dispatching decisions such as train departure time, stop time, speed distribution and running path selection;

[0132] Receive the priority adjustment result output by the priority adjustment module, and adjust the departure time, stop time, etc. in the preliminary scheduling plan according to the priority adjustment of different trains in specific time periods (such as peak hours or bad weather);

[0133] Based on the obtained preliminary dispatching plan and priority adjustment results, a complete train dispatching plan is generated. The specific steps include:

[0134] Combined with the output of priority adjustment, the departure order of each train is dynamically adjusted. For high-priority trains, the departure is arranged in advance or the stop time is reduced. For low-priority trains, the departure is delayed or the stop time is increased.

[0135] Combine the train's route selection and real-time traffic conditions to reallocate or adjust the routes of some trains to avoid congestion or intersection conflicts when possible;

[0136] Considering the safety interval between trains and train scheduling constraints, the interval between trains is optimized to avoid collisions or delays caused by overly dense schedules;

[0137] Based on the actual dispatch results, the dispatch plan is updated in real time, and the departure time, stop time and speed settings are dynamically adjusted to ensure that all trains can run smoothly and meet transportation needs.

[0138] The scheduling scheme generation module also includes:

[0139] Verify and optimize the generated complete scheduling plan. The specific steps include:

[0140] Conduct simulation tests on the generated train dispatching plan to simulate the operating effects under different operating conditions (such as peak hours, bad weather, equipment failure, etc.);

[0141] If simulation results indicate potential scheduling conflicts or delays, adjust the scheduling plan to ensure that it is highly robust and adaptable.

[0142] The optimized complete dispatching plan is transmitted to dispatchers and administrators through the system interface for implementation;

[0143] The main function of the scheduling scheme generation module is to combine the output information from the intelligent scheduling module and the priority adjustment module to generate a complete train scheduling scheme. This scheme not only meets the transportation needs, but also effectively responds to peak hours and emergencies, ensuring the optimal use of railway resources by dynamically adjusting departure times, stop times, speed distribution and path selection. At the same time, the generated scheduling scheme is optimized through real-time simulation and verification to ensure its high reliability and flexibility in complex actual operating environments.

[0144] The visualization modules include:

[0145] The visualization module displays the train scheduling plan through a graphical user interface, including:

[0146] Design and implement a graphical interface that can display various information of train dispatch in real time. The interface includes:

[0147] Train timeline: used to display the departure time, stop time, running time and other information of each train;

[0148] Train Position and Track Map: Displays the current position and projected path of a train on a map or track diagram;

[0149] Station and platform information: Displays the station dispatching situation, including the arrival and departure times of each station and the congestion situation of the current station;

[0150] Priority identification: The priorities of different trains are marked by colors, labels, etc., to help dispatchers quickly identify the trains that are given priority.

[0151] Real-time dynamic update of train dispatch information, the specific steps include:

[0152] During the actual operation of the train, it receives data from the data acquisition module (such as GPS positioning, speed sensor, etc.) and the intelligent scheduling module (such as train status update, route adjustment, etc.) in real time, and dynamically updates the train information on the visual interface;

[0153] If there is a change in the train's operating status (such as delays, suspensions, speed adjustments, etc.), the visualization module automatically refreshes and displays the relevant changes on the interface, reminding dispatchers and administrators to pay attention to abnormal situations;

[0154] The visualization module provides user interaction functions, including:

[0155] Dispatchers and administrators can directly modify or adjust train departure times, stop times and other parameters through a visual interface, and update the dispatch plan in real time;

[0156] Provide multiple view switching functions, such as viewing the dispatch plan by train, station, time period and other different dimensions to meet different operation requirements;

[0157] A visual interface is used to provide intuitive dispatch abnormality alarms. For example, when the train delay exceeds a certain threshold, the system automatically sends an alarm signal to remind the dispatcher to take measures.

[0158] The visualization module automatically generates dispatch reports based on train dispatch conditions, including detailed information such as train departures, stops, routes, delays, etc., and supports the export of dispatch reports in multiple formats.

[0159] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0160] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A railway vehicle intelligent dispatching and monitoring system, characterized in that: It includes data acquisition module, data processing module, intelligent scheduling module, priority adjustment module, scheduling scheme generation module and visualization module, among which; The data acquisition module is used to collect multi-source data related to train operation, the multi-source data including train speed, location, meteorological data and the number of passengers on board; The data processing module is used to perform real-time preprocessing on the collected multi-source data, the preprocessing includes data cleaning, denoising and data standardization, and feature extraction on the preprocessed multi-source data; The intelligent scheduling module uses an intelligent algorithm to calculate and generate a preliminary train scheduling plan based on the extracted features; The priority adjustment module dynamically adjusts the priority of each train based on real-time multi-source; The scheduling scheme generation module generates a complete train scheduling scheme according to the outputs of the intelligent scheduling module and the priority adjustment module; The visualization module is used to present the generated train dispatching plan to dispatchers and administrators through a visualization interface.

2. A railway vehicle intelligent dispatching and monitoring system according to claim 1, characterized in that: The data acquisition module comprises: Install speed sensors and GPS positioning devices in railway vehicles to collect train speed data and geographic location data in real time; Install passenger flow monitoring equipment in train carriages to collect the number of passengers on board in real time; Meteorological monitoring equipment is installed along the track to collect meteorological data along the track in real time; The collected multi-source data is transmitted to the data processing module in real time through the wireless communication network.

3. A railway vehicle intelligent dispatching and monitoring system according to claim 2, characterized in that: The data processing module comprises: Receive multi-source data collected by the data acquisition module and perform pre-processing; Perform feature extraction on the preprocessed data, and the extracted features include speed change rate, acceleration, snowfall, and passenger density in the vehicle; The extracted feature data is transmitted to the intelligent scheduling module.

4. A railway vehicle intelligent dispatching and monitoring system according to claim 3, characterized in that: The intelligent scheduling module includes: Based on the characteristic data extracted by the data processing module, the intelligent scheduling module defines the scheduling objectives and constraints; Use intelligent algorithms to generate preliminary train scheduling plans and optimize the generated preliminary train scheduling plans; The optimized preliminary train scheduling plan is passed to the scheduling plan generation module.

5. A railway vehicle intelligent dispatching and monitoring system according to claim 4, characterized in that: The priority adjustment module makes priority adjustment decisions based on real-time multi-source data, and uses an intelligent algorithm to dynamically adjust the priority of the train, and rearranges the train scheduling time according to the dynamically adjusted priority.

6. A railway vehicle intelligent dispatching and monitoring system according to claim 5, characterized in that: The scheduling scheme generating module obtains the output data of the intelligent scheduling module and the priority adjustment module, and generates a complete train scheduling scheme based on the obtained output data.

7. A railway vehicle intelligent dispatching and monitoring system according to claim 6, characterized in that: The scheduling scheme generation module verifies and optimizes the generated complete scheduling scheme, and transmits the optimized complete scheduling scheme to the dispatcher and administrator through the system interface for implementation.

8. The intelligent dispatching and monitoring system for railway vehicles according to claim 7, characterized in that: The visualization module displays the train scheduling plan through a graphical user interface and dynamically updates the train scheduling information in real time, and the visualization module provides user interaction function.

9. The intelligent dispatching and monitoring system for railway vehicles according to claim 8, characterized in that: The visualization module automatically generates a dispatch report based on the train dispatch situation and supports the dispatch report export function in multiple formats.

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