768 results about "Urban rail" patented technology

The invention discloses a virtual marshalling multi-train cooperative adaptive sliding mode control method based on an extended state observer, and the method comprises the steps: firstly constructing an urban rail multi-train tracking error dynamic model based on a unit train dynamic equation; secondly, establishing a nonsingular terminal sliding mode surface according to the urban rail multi-train tracking error dynamical model to obtain an equivalent control law, combining the equivalent control law with a switching control law to obtain a nonsingular terminal sliding mode control law, and introducing a gain adaptive law and a saturation function into the nonsingular terminal sliding mode control law to obtain an adaptive nonsingular terminal sliding mode control law; and finally, an extended state observer is adopted to estimate external interference, an observation value of the interference is added to the output of the adaptive nonsingular terminal sliding mode controller to serve as feed-forward compensation, and the adaptive nonsingular terminal sliding mode control method based on the extended state observer is constructed. According to the method, the multi-train cooperative operation tracking control precision and stability can be remarkably improved under the external interference condition.

The invention belongs to the field of urban rail transit network passenger flow simulation, and particularly relates to an urban rail transit passenger travel behavior characteristic estimation method. Obtaining, analyzing and processing travel data; searching a feasible path set meeting the maximum transfer frequency constraint between the OD pairs by adopting a breadth-first algorithm with constraint; reconstructing a passenger trip chain; through a path time similarity index, passengers on a non-transfer single path and passengers on a single-transfer single path are distributed to a train, and then a travel chain of the two types of travel passengers is obtained; and taking the standard deviation of the train arrival time as the standard deviation of the waiting time of the passengers on the platform, and calculating to obtain passenger in-out walking time distribution and transfer walking time distribution. According to the passenger travel behavior characteristic estimation method for the urban rail transit, station entering and exiting walking time distribution of passengers in all stations in an urban rail transit network, transfer walking time distribution of the passengers in each transfer direction of a transfer station and waiting time distribution of each station can be accurately estimated.

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.

A coordinated control method for an urban rail passenger flow, and an electronic device and a storage medium, which belong to the technical field of rail transit control. In order to realize passenger flow management and control without increasing the transport capacity, in the present application, a rail simulation system is used to deduce a predicted passenger flow of an urban rail line during rush hour, which involves data such as entry station IDs, exit station IDs, entry time periods and passenger flow volumes; performing division on the basis of different time periods t and stations s, so as to obtain a flow of passengers arriving at a station s and preparing to enter the station to take a train within the time period t; on the basis of historical passenger flow distribution data of subway operating companies, compiling statistics on passenger flows in various directions among historical passenger flows, and calculating the proportions of the passenger flows in the various directions among the historical passenger flows; constructing a mixed integer programming model for multi-station passenger flow coordinated control; and using a branch and bound algorithm to solve the mixed integer programming model for multi-station passenger flow coordinated control constructed in step S3, so as to obtain an optimal passenger entry flow scheme for stations. The present application realizes the effect of prioritizing the evacuation in a station with a large passenger flow.

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 belongs to the technical field of steel rail collecting and unloading, and discloses a modularized urban rail car which comprises a bottom frame, a steel rail lifting device, a raceway beam, a steel rail guiding anti-overturning device, a safety door and a rail pulling winding device, and a notch is reserved in the middle of the bottom frame so that a steel rail can be put down or taken back in the middle of the bottom frame. Comprising a plurality of modularized urban rail steel rail vehicles, and the invention further discloses a rail collecting and unloading method which comprises the following steps that S1, the urban rail steel rail vehicle set is adopted; s2, when the middle old steel rail is recycled, a rail pulling winding device is started, and a steel rail lifting device is started to synchronously lift the steel rail to a raceway beam; s3, when a new steel rail in the middle is unloaded, the end of the steel rail is dragged to be in complete contact with the ground, the vehicle set moves forwards, and the remaining part of the steel rail is unloaded; and S4, when the steel rails are recovered or unloaded, the steel rails are lifted to the raceway beam or lifted to the ground. The method has the advantages that long and short steel rails are recovered and unloaded at the same time, and the requirement for centrally and laterally recovering and unloading the rails is met.

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 belongs to the field of passenger flow simulation of an urban rail transit network, and particularly relates to a multi-level passenger flow state estimation system of the urban rail transit network. The method comprises the following steps: defining a trip chain of passengers by acquiring, analyzing and processing related data of urban rail transit and mobile phone signaling; random stratified sampling is carried out according to a proportion by using real-time OD passenger flow estimated by a BP neural network to obtain an OD path passenger flow distribution result, a passenger trip chain reconstruction algorithm model is established, the time similarity of a passenger trip sub-chain and a train operation sub-chain on a feasible path between different path OD pairs is calculated, passengers are matched to a train, and a complete trip chain of the passengers is reconstructed; and a multi-level passenger flow state index system of the urban rail transit system is constructed, so that the system can output microscopic, mesoscopic and macroscopic passenger flow indexes of the urban rail transit system in real time.

Disclosed are a coordinated control method for urban rail transit passenger flow, an electronic device, and a storage medium. The method includes: predicting passenger flow in peak hours of urban rail lines using a simulation deduction function in a rail simulation system, including station entry ID, station exit ID, station entry time period, and passenger number data; obtaining passenger flow of passengers arriving at stations s and preparing to board during time periods t by dividing according to the different time periods t and the stations s; counting passenger flow in each direction of historical passenger flow, and calculating a proportion of the passenger flow in each direction of the historical passenger flow; constructing a mixed integer programming model of multi-station passenger flow coordinated control; and obtaining an optimal station entry passenger flow scheme by solving the mixed integer programming model of multi-station passenger flow coordinated control.

The invention discloses a visual arrangement system and method for urban rail cloud simulation test resources, and aims to assist a user to conveniently and efficiently arrange a simulation test service scene, convert the service scene into a template, namely a reusable service scene architecture script, and enable a scheduling engine to schedule the simulation test service scene according to template data information. The method comprises the following steps: respectively docking with an on-cloud platform, an under-cloud system and a simulation interface, establishing a simulation test scene instance of a service system from the on-cloud to the under-cloud, and enabling a user to carry out the simulation test work of the service system on the scene instance. According to the method, a certain resource can be selected as a template to be stored, when needed in the follow-up simulation test process, a template file is flexibly called, the environment is pulled up and deployed through a scheduling engine, the problem that the resources of the test environment are limited is solved, and the convenience and efficiency of cloud simulation environment scene construction are 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 provides an urban rail train working diagram adjusting method and device based on a genetic algorithm, and relates to the technical field of urban rail train dispatching.The urban rail train working diagram adjusting method comprises the steps that firstly, historical data including a train operation scheme, station information data, passenger flow data and train operation data are obtained; the train punctuality rate, the stopping time, the passenger flow congestion degree, the train average delay time and the passenger flow satisfaction rate are analyzed, and train operation performance indexes and passenger experience indexes are generated. Then, the two indexes are used for constructing a riding state model and training the riding state model; a historical train operation scheme is used as an initial population, fitness values of individuals in the population are calculated, genetic algorithm iterative optimization is carried out, and finally the individual with the highest fitness value is selected as the optimal operation scheme. And finally, the current train operation scheme is adjusted based on the optimal scheme, and a new train scheduling scheme is formulated to improve the operation efficiency and the passenger experience.

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 provides a rail transit station classification method and device based on multi-source data, and the method comprises the steps: integrating urban land utilization and planning data, rail transit operation data, geographic information data, mobile phone signaling data and other multi-source information; a characteristic index system including five dimensions of passenger flow scale, resident demand, district development, station connection and peripheral facilities is constructed, and 17 characteristic indexes are extracted to comprehensively calibrate rail station characteristics. Then, analyzing the characteristic indexes by using a K-means clustering method, and accurately classifying the urban rail transit stations into seven categories, namely a core area comprehensive station, a living function leading station, a business office leading station, a transfer hub station, a suburban comprehensive station, a leisure tourism function station and a general station; therefore, fine division and scientific evaluation of functions of urban rail stations are realized.

The invention relates to the technical field of data processing, in particular to an anomaly identification method, device and equipment for urban rail vehicle trend data, and the method comprises the steps: determining a difference value between time adjacent data in a trend data set of a target category; determining whether single-point mutation exists or not according to the obtained difference value sequence; performing feature calculation on multiple pieces of data in each sliding window of the trend data set to obtain a first feature set; determining whether continuous mutation exists or not according to the first feature set corresponding to the adjacent sliding windows; performing feature calculation on multiple pieces of data of the target sliding windows of the trend data sets corresponding to the multiple positions to obtain second feature sets corresponding to the multiple positions; according to the corresponding data of the second feature set and the trend data set, whether the same-condition comparison significant difference exists or not is determined; and generating an abnormal result according to the three results. According to the method, various abnormal conditions in the data are considered, category division is carried out, and the analysis mode is simple and reliable.

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 belongs to the technical field of vehicle intelligent anti-pinch, and discloses an urban rail vehicle door intelligent anti-pinch system which comprises a bioelectricity monitoring module, a multi-mode sensor fusion module, a virtual reality warning module and an intelligent control center. The bioelectricity monitoring module detects human body bioelectricity signals through an electrode type sensor to judge the human clamping risk. The multi-mode sensor fusion module is combined with pressure, infrared and ultrasonic sensors, and the detection accuracy is improved through data preprocessing and a fusion algorithm. The intelligent control center processes data, judges risks and generates control instructions, and can communicate with a train and station system to achieve remote monitoring. And the virtual reality warning module projects warning images and information, so that the safety awareness of passengers is improved. The system has the advantages of innovative detection mode, visual warning and multi-sensor fusion, and can effectively improve the safety and operation efficiency of the door of the urban rail vehicle. The method has a wide application prospect, and can provide safety guarantee for more fields.

The invention discloses an urban rail anti-torsion cable and a production process, and belongs to the technical field of cables, the urban rail anti-torsion cable comprises a core wire, a stranded wire layer is wound on the outer side of the core wire, and a shielding layer is arranged on the outer side of the stranded wire layer. The winding pitch of the first armor layer and the second armor layer is one half of the width of the steel belt, so that the cable can be normally bent and is convenient to wind and roll, and the left-right twisting of the cable is blocked through the first armor layer and the second armor layer which are opposite in spiral direction, so that the cable is prevented from being twisted in the left-right direction, and the cable is prevented from being twisted in the right-left direction. The cable main body is limited to twist integrally, axial twisting of the cable main body is avoided, the anti-twisting performance of the urban rail cable is greatly improved, the influence on transformation and change of an existing cable production line is small, and only the steel belt winding device needs to be additionally arranged to wind two layers of steel belts with opposite spiral directions on the cable. The method has the characteristics of low transformation cost and low difficulty.

The present invention discloses an automatic compilation method and system for urban rail train operation diagrams for large and small route modes. The present invention includes: configuring basic parameters for the compilation of train operation diagrams, re-dividing the flat-peak transition period based on the basic parameters and adjusting the flat-peak period, and drawing the train operation diagrams for each period; aiming to have a small number of train connection pairs in adjacent periods and uniform train entry and exit sections, and compiling the operation line connection plans for different periods in sequence; compiling the entry and exit plans for the operation lines that need to enter and exit sections; checking the operation diagram conflicts, and establishing a relief network to relieve the conflicts; and obtaining the train operation diagram that can be used in actual operations. The present invention improves the efficiency of train operation diagram compilation in complex operating environments, effectively solves the situation of sudden changes in departure time caused by unreasonable flat-peak transition, and can effectively relieve the conflicts of the operation lines at the turnaround station or the entry and exit stations.

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.