572 results about "Urban rail" patented technology

The invention discloses a construction and verification method and system for a digital twinborn model of major equipment in an urban rail field section, which adopts an advanced data fusion algorithm to fuse data from different sensors, improves the accuracy and comprehensiveness of the data and provides a more reliable basis for construction of the digital twinborn model. Machine learning and deep learning algorithms are utilized to construct a data-driven digital twinborn model, so that the future state and performance of the equipment can be accurately predicted, and powerful support is provided for intelligent management and maintenance of the urban rail field equipment; and a set of complete virtual-real consistency verification index system and verification method is designed, so that the accuracy and reliability of the digital twin model can be effectively ensured, and a guarantee is provided for the application of the digital twin technology in the urban rail field.

An urban rail train pantograph abrasion loss lightweight prediction method based on twin data is used for urban rail train pantograph abrasion loss prediction. The invention provides a lightweight model which is high in calculation efficiency and simple in structure aiming at the problem of wear loss prediction of a pantograph carbon contact strip. According to the model, the prediction robustness is ensured, and meanwhile, the wear loss prediction of the pantograph carbon contact strip can be quickly and accurately realized. In addition, twin data of pantograph abrasion are calculated through cyclic simulation by means of material parameters of the carbon slide plate, the weight and offset of the model are adjusted and optimized, and the prediction precision is improved. According to the method, firstly, collected wear data are preprocessed and then input into a lightweight network for pre-training; and then, performing parameter tuning on the pre-trained lightweight model by using twin data, and finally storing the adjusted parameters to complete prediction of the wear life. The method achieves the precise prediction of the wear loss of the pantograph carbon slide plate, and has the advantages of being small in parameter quantity and short in operation time.

The embodiment of the invention provides an urban rail signal system fault diagnosis method based on a knowledge graph and a large model, and the method comprises the steps: collecting system operation logs, state parameters and fault information in real time, and carrying out the data preprocessing and standardization; based on historical fault data, a fault classification and prediction model is constructed through feature extraction and mode recognition, and automatic fault recognition and risk early warning are achieved; constructing a fault diagnosis knowledge graph, and establishing an association relationship among entities such as equipment, faults, reasons, maintenance schemes and the like; a pre-training large language model and a LoRA technology are adopted for efficient fine tuning, a fault diagnosis reasoning model is trained, and end-to-end generation from fault description to diagnosis and maintenance suggestions is achieved; the knowledge graph and large model output are fused, real-time and historical data are combined, multi-path fault analysis and comprehensive diagnosis are carried out, and a diagnosis report is generated and displayed. The intelligent level, accuracy and efficiency of fault diagnosis can be improved, and technical support is provided for intelligent operation and maintenance management.

The invention relates to the technical field of power monitoring, in particular to an urban rail power supply system fault early warning method based on multi-source data fusion, which comprises the following steps: acquiring a thermogram, extracting a temperature drift trajectory, marking a thermal abnormal point, identifying abnormal data by combining voltage sampling, analyzing the overlapping property of abnormal time and direction, and positioning a fault overlapping point. And identifying the linkage early warning area through spatial statistics. According to the method, the continuous drift trajectory of the temperature gravity center in the traction transformer thermogram is recognized, the thermal abnormal points are extracted, the voltage sudden change features are analyzed in combination with the voltage sampling sequences in the same time period, the composite recognition mode of time synchronization and trend direction comparison is achieved, the fault symptom positioning accuracy is effectively improved, and the fault symptom positioning accuracy is improved. By aggregating the fault coincidence points in the plurality of power supply sections and executing the spatio-temporal clustering operation, the capability of sensing common anomalies in the region can be significantly improved, and a potential linkage fault region can be clearly identified based on joint calculation of a spatial adjacent structure and a statistical aggregation degree.

The invention relates to the technical field of fusion management, in particular to an urban rail train multi-subsystem intelligent fusion management method and system, and the method comprises the steps: obtaining operation data through multiple types of sensing terminals inside and outside each carriage of a train; constructing a situation awareness model of a passenger and environment interaction relationship, and predicting passenger interaction data in a future time period; adopting an anomaly detection model to identify emergencies and classifying emergencies; the method comprises the following steps: constructing a hierarchical fusion management architecture comprising a compartment-level local management unit and a train-level global coordination unit, wherein the local management unit dynamically adjusts the configuration of an operation subsystem in each compartment; and the global coordination unit constructs a global coordination model, and sets a priority regulation and control weight for a compartment area corresponding to an emergency by taking the passenger comfort and the system energy consumption as targets. According to the invention, the operation state of each subsystem can be sensed in real time and intelligently adjusted according to passenger flow and demand changes, so that the passenger experience is optimized, the energy consumption is reduced, and the overall system efficiency is improved.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention discloses a railway bogie bearing fault diagnosis method and equipment based on a feature extraction network, and relates to the field of intelligent diagnosis and maintenance guarantee of urban rail trains. The method comprises the following steps: data acquisition: acquiring vibration signals of a bogie axle box bearing under the same rotation speed and load combination; performing data preprocessing: performing frequency spectrum adaptive decomposition on each section of signal based on improved empirical wavelet transform, introducing a Fisher score and frequency spectrum entropy joint scoring mechanism, screening out key modal signals, and then performing image mapping to reconstruct a diagnosis sequence; model construction: introducing a topology perception attention mechanism module on the basis of the lightweight convolutional neural network, and constructing a fault diagnosis model; model training: training the fault diagnosis model to obtain an optimized fault diagnosis model; and diagnosis result output: using the optimized fault diagnosis model to diagnose the fault signal of the bogie axle box bearing, and outputting the diagnosis result. The method can improve the accuracy of fault diagnosis.

The invention discloses an urban rail vehicle simulation driving training method and system based on scene simulation, and belongs to the technical field of driving simulation, and the method specifically comprises the steps: collecting rail line parameters, weather data, passenger flow density, vehicle equipment state and driving data in real time, carrying out the preprocessing, constructing a dynamic personalized simulation scene, and simulating driving operation. According to operation of a driver and a preset simulation scene, micro features of eye-brow areas of the driver are recognized, the psychological stress of the driver is analyzed in combination with historical data, the simulation scene is dynamically adjusted according to a psychological stress analysis result, the performance of the driver in the training is scored according to a preset evaluation standard, and a detailed training report is generated. According to the method, pressure analysis is carried out on the eyebrow area, pressure evaluation is carried out in combination with the physiological dimension, the pressure of the driver in a simulation scene can be judged in real time, different simulation scenes are provided for different drivers, and the training effect is greatly improved.

The invention relates to the technical field of traffic planning and management optimization, and provides a regional hub and urban rail transit interchange connection optimization method, which comprises the following steps of S1, adopting different models for performing short-time arrival passenger flow prediction aiming at different travel modes to obtain respective interchange passenger flow; s2, modeling is conducted on the environment layout and pedestrian behaviors of an actual station through Anylogic software, a dynamic evaluation index system is constructed on the basis of the whole hub and the basic transfer facility state, passenger flow prediction data are input into simulation software, and transfer connection efficiency is obtained; and S3, constructing a maximum comprehensive benefit function from three aspects of a comfort index, an economic index and a facility traffic capacity balance degree, and optimizing transfer service facility configuration by adopting a genetic algorithm to optimize transfer connection efficiency. According to the optimization method provided by the invention, the transfer passenger flow volume can be accurately predicted, and the resource allocation is reasonably optimized, so that the transfer efficiency is improved, and the experience feeling of passengers is improved.

The invention relates to the technical field of intelligent management of urban rail transit equipment, in particular to a resource management method for containerization deployment of urban rail equipment. Aiming at the problems of static extensive resource management and response lagging in the existing containerization deployment, dynamic optimization is realized through the following innovations: deploying a multi-service subsystem on intelligent fusion host equipment in a containerization manner; collecting container resource data with space-time labels in real time to construct a multi-source monitoring cube; a container resource dependency relationship is modeled based on a federated learning framework and a graph neural network, and a load dynamic state is predicted in combination with a time sequence attention mechanism; dynamic resource allocation is executed through the computing power unit; adopting a priority guarantee mechanism to identify burst loads and preferentially deploying resources; and evaluating the migration cost by using a deep learning model to realize smooth container migration. According to the method, the resource utilization rate and the system stability are remarkably improved, the key service interruption risk is reduced, and high-reliability intelligent resource management support is provided for urban rail equipment.

The present invention relates to the technical field of rail transit evacuation mechanisms, and particularly to a new type of side door lifting evacuation ladder for urban rail vehicles; it includes a frame assembly, a driving mechanism, a rotating arm assembly, a manual operation device, and a pedal assembly: the frame assembly includes a frame structure, a guide rail slider pair, and a gas spring. The guide rail slider pairs are respectively fixed on both sides of the frame structure to guide the up and down sliding of the pedal assembly. One end of the gas spring is hinged to the frame structure, and the other end of the gas spring is hinged to the first-level pedal of the pedal assembly. The structure achieves the effect that the lifting evacuation ladder can be automatically opened and closed, with simple operation and high automation degree. At the same time, it can be used in emergency situations such as power failure, greatly improving the train's escape and evacuation ability.

The invention relates to an oil-proof emulsification control method and system for an air compressor of an urban rail vehicle, and solves the problem of lubricating oil emulsification caused by insufficient working efficiency of the air compressor and environment humidity change by constructing a comprehensive control system comprising the compressor, an oil-proof emulsification device and a controller. The system monitors lubricating oil temperature and environment humidity in real time through a temperature sensor and a humidity sensor; the working rate of the air compressor is accurately calculated, wherein the vehicle operation time t2 takes the operation speed larger than 10 km / h as the calculation starting point; the controller comprehensively judges whether the oil-proof emulsification device is started or not based on temperature, humidity and working rate data, and the control strategy comprises the step of dynamically adjusting the working rate judgment node according to the real-time temperature and humidity. According to the method, accurate and reliable control over the oil-proof emulsification process is achieved, emulsification of the lubricating oil is effectively prevented, and energy consumption and maintenance cost can be reduced.

The invention discloses an urban rail vehicle door intelligent monitoring and early warning method and system based on a PHM technology, and belongs to the technical field of intelligent monitoring and early warning. The method comprises the following steps: deploying a multi-modal sensor at a key mechanical node of an urban rail vehicle door, collecting operation data of the urban rail vehicle door, performing timestamp synchronization, and constructing a multi-source data interaction diagram; based on the multi-source data interaction diagram, the vehicle door health degree of a single urban rail vehicle door at a single timestamp is calculated; constructing a timestamp-vehicle door health degree curve of a single urban rail vehicle door; based on the timestamp-vehicle door health degree curve, the vehicle door health abnormity degree of a single urban rail vehicle door is calculated; calculating the similarity between different urban rail vehicle doors; and a threshold value is preset, and early warning is carried out. According to the method, the multi-source data interaction diagram is constructed, dynamic evaluation of the health degree and the health abnormality degree is combined, and similarity analysis and early warning threshold judgment between the vehicle doors are assisted, so that the accuracy of vehicle door system health state recognition and the timeliness of abnormality detection are effectively improved.

The invention relates to the technical field of urban rail train safety detection, and discloses a mechanical rail train detection method and system with a real-time detection function, and the method comprises the steps: collecting the states of obstacles on the two sides of a bogie and normally open and normally closed contacts of derailment detection travel switches; calculating a trigger state code; an emergency or non-emergency braking signal is output and an alarm is given; and when the state returns to normal and a reset instruction is received, the alarm is released and the initial state is recovered. In the prior art, obstacle or derailment detection is realized by depending on a multi-stage sensor and an electronic control unit, and especially in complex operation environments of high electromagnetic interference, high humidity dust and the like, stable and low-fault-rate real-time detection cannot be realized. Due to the fact that a redundancy judgment mechanism combining a pure mechanical structure and the vehicle programmable logic control unit LCU is adopted, high-reliability real-time detection of the obstacle and derailment state is achieved, and the safety and maintainability of a train system are improved.

The invention provides an improved DBSCAN dry-type transformer vibration signal fault early warning method and system, and belongs to the technical field of dry-type transformer fault diagnosis, and the method comprises the steps: obtaining a traction rectifier transformer vibration signal of a to-be-detected urban rail power supply system; processing the acquired vibration signal by using a pre-trained fault diagnosis model to obtain a fault diagnosis result; and if the number of the abnormal points in the fault diagnosis result exceeds a set threshold value, performing early warning. According to the method, the DBSCAN parameters are automatically optimized through multi-objective optimization and a simulated annealing algorithm, so that the judgment of abnormal points reaches a most accurate state, and the workload is reduced.

The invention discloses a medium-low traffic volume train operation plan optimization method based on adaptive large neighborhood search, and relates to the technical field of medium-low traffic volume urban rail transit transportation organizations, and the method comprises the steps: obtaining rail operation basic data information based on a selected target urban rail line; a train operation scheme optimization mechanism is used for processing, and a train candidate set is generated; according to the train candidate set and the pre-processed integrated optimization decision variable, pre-configuring a train operation optimization target and a train scheduling constraint condition, and constructing a train collaborative optimization model; the train collaborative optimization model sequentially executes corresponding processing operations on a train candidate set, a pre-configured train scheduling constraint condition and the preprocessed neighborhood operator to obtain an adaptive large neighborhood search algorithm; and analyzing by using the train collaborative optimization model and the adaptive large neighborhood search algorithm to obtain an optimal train operation plan scheme. According to the invention, the flexibility of medium-and-low-traffic-volume urban rail driving and the passenger flow space-time distribution coupling are improved.

The invention relates to the technical field of electrical fault positioning, in particular to an urban rail train relay state online monitoring method and system. The specific implementation process comprises the following steps: collecting coil current, contact voltage, load current and environment vibration signals by using an electrical sensor group; when the coil current is subjected to step change, intercepting an electromechanical characteristic data set; arc energy and contact resistance fluctuation characteristics are calculated, correlation analysis is carried out in combination with environment vibration signals, and passive fluctuation and active fluctuation are distinguished; and establishing a multi-factor health degree evaluation model, dynamically adjusting the arc energy and the weight coefficient of the contact resistance according to the magnitude of the load current and the vibration intensity, monitoring the health state of the relay in real time, and predicting the residual life. Through multi-dimensional data synchronous acquisition and vibration decoupling, the interference of mechanical vibration on electrical performance monitoring is effectively eliminated, the problem that a dynamic working condition cannot be reproduced in an off-line test is improved, and the accuracy of relay state evaluation and service life prediction is remarkably improved.

The invention relates to the technical field of rail traffic monitoring, and discloses an urban rail vehicle driving analysis system based on digital twinning, which is characterized in that vehicle vibration and traction motor current signals are collected in real time through a vehicle-mounted sensing unit, and an edge feature extraction unit calculates energy ratio features of a predefined fault frequency band; the data transmission unit transmits the characteristic data and the working condition information to a digital twin platform, a digital fault tree model is arranged in the platform, and a cross-physical domain fault conduction path is analyzed and recognized through multi-source data coupling. Through combination of frequency band energy ratio characteristics and fault tree logical reasoning, electromechanical coupling fault risks which cannot be found by traditional isolated monitoring are effectively captured, meanwhile, a diagnosis threshold value is dynamically adjusted according to the wheel-rail adhesion state, and the diagnosis reliability under the complex working condition is improved.

The invention provides an urban rail system emergency scheduling method and system combined with big data analysis, and relates to the technical field of urban rail transit, and the method comprises the steps: firstly, converging dynamic monitoring data of an urban rail system, including train real-time working conditions, station region people flow distribution, rail structure vibration, power supply network load and external environment interference data; secondly, constructing an abnormal evolution topology based on the data, taking data fluctuation characteristics as nodes, connecting the nodes into a network by reflecting evolution association strength of a mutual induction relationship, and then identifying key conduction nodes and diffusion boundaries from the abnormal evolution topology; then, key conduction nodes, diffusion boundaries and emergency resource layout data are combined to generate a collaborative scheduling matrix which comprises multiple sequences such as resource pre-scheduling, train path reconstruction, station load evacuation and system function compensation and is linked through time sequence correlation parameters; and finally, converting the cooperative scheduling matrix into executable scheduling instruction sets, and pushing the executable scheduling instruction sets to corresponding control terminals according to execution subject classification, thereby realizing efficient emergency scheduling.

The invention discloses a bolt color marking line detection method based on image segmentation and classification fusion, and belongs to the field of track maintenance. According to the method, a color mark line area of a bolt head is accurately segmented by collecting a track bolt image and combining image preprocessing and mask extraction. On the basis, through deep semantic feature extraction and fusion, pixel-level masks and global context information are integrated and sent to a classification network to judge whether the bolt color graticules are aligned or not. And finally, the whole network structure is optimized through a joint training mechanism, so that the accuracy and robustness of the system in a complex environment are improved. According to the method, the bolt color marking line can be accurately extracted under the complex conditions of weak light, light reflection, shielding and the like, the fine offset with the alignment deviation smaller than 15 degrees can be effectively recognized, and the method can be widely applied to the fields of subway lines, urban rail vehicle maintenance, automatic inspection robots and the like.

The invention relates to the technical field of urban rail transit operation optimization, and provides an energy consumption peak value optimization method and system for a peak period of an urban rail train group, and the method comprises the steps: obtaining a historical train operation timetable, real-time position data and an energy consumption curve, and generating a training sample set; classifying the operation intervals in the sample set based on the operation interval difference, and determining the peak type of each interval; training a time sequence classification model by utilizing interval data and peak types to obtain an energy consumption feature classification model, and training a multi-objective optimization neural network model to obtain an energy consumption peak value optimization model; and inputting real-time operation data into the classification model to identify a current peak type, and inputting the data into the optimization model according to the type to generate a dynamic timetable adjustment strategy so as to smooth an energy consumption peak value. According to the method, the energy consumption peak value of the urban rail train group can be reduced, the energy utilization efficiency is improved, departure interval fluctuation and delay are reduced, and the operation stability is improved.

The present application provides a kind of urban rail vehicle electronic history data processing method, the method establishes vehicle operation configuration in combination with vehicle structure design, and vehicle configuration data structure is structured using database technology;Vehicle history data granularity refined to the smallest maintainable unit is used, the correctness and timeliness of data are guaranteed through data analysis and integration technology based on business process, the whole life cycle stage document of vehicle is stored using data storage technology, and preliminary vehicle knowledge framework is established in combination with full-text search technology, to provide basic data support for later subway vehicle procurement, technical reform, operation.In history data management, block chain technology is added to ensure the confidentiality and integrity of data flow, to prevent sensitive data from being leaked and tampered with during transmission, and to enhance the security and correctness of vehicle history data, to provide reliable data security guarantee for subsequent history sharing.

The embodiment of the invention provides an urban rail engineering construction material carbon emission analysis method and device, and the method comprises the steps: obtaining carbon emission data, and carrying out the data quality evaluation based on the carbon emission data, and determining a quality evaluation result; probability distribution modeling is carried out based on the quality evaluation result, and a probability distribution model is determined; analyzing statistical distribution characteristics of the carbon emission based on a probability distribution model; and performing process optimization based on the statistical distribution characteristics, and determining a process optimization scheme. Based on the above scheme, the problems of dynamic adaptation, accurate traceability and decision support in rail transit engineering carbon emission accounting are systematically solved, and a quantifiable, traceable and optimizable complete technical system is provided for green rail transit construction.

The invention discloses a public transportation network reconstruction method under urban rail networking operation, and the method comprises the steps: obtaining multivariate travel data, carrying out the data-driven calculation of passenger flow OD, and providing scientific data support for network planning; meanwhile, when the whole line network is established, a backbone network and a connection network are adopted to simplify levels, technical indexes and service standards of all the levels are defined, actual passenger flow distribution is accurately measured and calculated, and the reasonability of line planning is ensured; and meanwhile, by establishing a three-dimensional matching index system, quantifying the difference between the current situation and the ideal network, and by differentiating schemes and implementing priority ranking, lines with large difference are optimized according to priorities, so that gradual optimization of the whole network is realized, large-scale oscillation is avoided, and finally reconstruction of the public transport network is realized. A complementary situation with urban rail transit is formed, redundant waste of public traffic design is avoided, and meanwhile, residents can go out more conveniently by optimizing lines.

The invention discloses an anti-label noise urban rail train gearbox incremental fault diagnosis method, which comprises the following steps of: segmenting and standardizing collected gearbox fault data of an urban rail train bogie according to a preset length to obtain a fault sample; based on the fault sample, constructing a data set which can be used for class incremental learning; constructing a fault diagnosis model, and performing initial training on the fault diagnosis model by using the data set to obtain a fault diagnosis model after initial training; performing incremental training on the fault diagnosis model after the initial training to obtain a trained fault diagnosis model; and performing fault identification on a to-be-detected vibration signal by using the trained fault diagnosis model to obtain a diagnosis result of the gearbox. According to the method, disastrous forgetting and noise interference can be effectively handled, and the accuracy of fault diagnosis of the gearbox of the urban rail train is improved.

The invention discloses an urban rail energy consumption scheduling method based on artificial intelligence multi-objective optimization, and the method comprises the steps: effectively solving a problem of scheduling decision limitation caused by a single optimization objective in the prior art through comprehensive real-time data processing, multi-objective collaborative optimization and intelligent algorithm iteration solution; the optimal control of the overall operation energy consumption of the rail transit system is realized; the train operation energy efficiency, the regenerated energy utilization rate and the operation punctuality rate are effectively improved, meanwhile, the peak load of a traction power supply system is reduced, the overall energy utilization efficiency and the operation performance of rail transit are remarkably improved, and the dual purposes of energy conservation, consumption reduction and efficient operation of the system are achieved.

The invention discloses an urban rail hybrid energy storage system power distribution method based on LPF-MPC, and the method comprises the steps: employing a hybrid energy storage system (HESS) composed of a super capacitor and a battery for the frequent starting and braking of an urban rail train to generate a large amount of braking energy; on the basis that a low-pass filter (LPF) distributes power required by HESS, the rolling prediction and optimization control capability of model prediction control (MPC) is utilized to control the minimum value of the state of charge (SOC) of a super-capacitor to be close to a preset expected value, so that power distribution is optimized, and loads are reasonably distributed to the super-capacitor and a battery; according to the method, the working interval of the super-capacitor SOC is dynamically adjusted, the capacity advantage of the super-capacitor is fully played, the charging and discharging times of the battery are reduced, the energy management efficiency of the urban rail hybrid energy storage system is effectively improved, the system stability is enhanced, and the service life of the battery is prolonged.

The invention discloses an urban rail station full life cycle carbon accounting method, and relates to the technical field of urban rail transit engineering low carbon. The method comprises the following steps: constructing and regularly updating a carbon emission factor library, collecting and storing full-life-cycle activity level data and time information, determining an accounting dimension according to a scene, screening an accounting cardinal number of unit time from the data, accounting carbon emission increment of each dimension of the unit time in combination with the factor library, and finally accumulating to obtain the total amount of full-life-cycle carbon emission. According to the method, complete-cycle carbon emission accurate accounting of the urban rail station is realized, data support is provided for low-carbon decision, and the efficiency and scientificity of low-carbon management of the urban rail station are improved.

The invention provides an automatic test question generation method and device for the urban rail industry and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining question setting information input by a user, wherein the question setting information comprises knowledge point information, question setting parameters and question setting rules; obtaining a target knowledge fragment from a knowledge base according to the knowledge point information and the question setting parameters; according to the target knowledge fragment, the question setting parameters and the question setting rules, generating at least one question by adopting a question generation model; evaluating each question in at least one evaluation dimension by adopting a plurality of question evaluation models to obtain a plurality of scores and modification suggestions of each question in at least one evaluation dimension; according to the multiple scores of each question in the at least one evaluation dimension, calculating a total score and a confidence coefficient of each question; and optimizing each question by adopting a question generation model according to the total score, the confidence coefficient and the modification suggestion of each question. The test questions can be automatically generated, and the quality of the test questions is ensured.

An urban rail passenger flow and train collaborative organization optimization method based on deep reinforcement learning comprises the following steps: firstly, based on automatic fare collection (AFC) system data of rail transit, analyzing an arrival rule and a trip chain of passengers, and generating an OD matrix of a rail transit network by adopting a diffusion model; secondly, a simulation model reflecting train operation and passenger flow states is constructed, and a verification environment is provided for the optimization process; for a train operation scheme and passenger flow scheduling, a deep Q network (DQN) model is designed, and a simulation model is used for training and learning. The method can reasonably match the passenger flow demand and the transport capacity resource, effectively shorten the waiting time of passengers, and reduce the operation cost of the train.