4961 results about "Rail transit" patented technology

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The invention relates to a rail transit full-scene intelligent construction cooperative control method and system, and the method comprises the steps: constructing a three-layer closed-loop architecture of a unified data base, a dynamic digital twin engine and a cooperative control platform, carrying out the real-time collection and fusion of multi-source heterogeneous data through the unified data base, and carrying out the synchronous processing based on a space-time alignment protocol; constructing a real-time digital twinning model by utilizing a dynamic digital twinning engine, and performing dynamic evolution prediction on a construction scene by adopting a mode of fusing a graph neural network and a physical constraint model; based on a real-time digital twinborn model, autonomous path planning, task allocation and fault prediction are performed on a plurality of construction devices through a cooperative control platform by adopting a multi-agent reinforcement learning algorithm, and cooperative operation and risk pre-control between the devices are realized. According to the invention, a data barrier can be broken, high-fidelity real-time twinning is realized, an autonomous cooperative control capability is provided, the construction efficiency is obviously improved, the safety risk is reduced, and manual intervention is reduced.

The invention provides a rail transit dispatching analysis method and system based on artificial intelligence, and belongs to the technical field of rail transit dispatching.The method comprises the steps that firstly, a dynamic dispatching baseline of a rail transit system is established, the dynamic dispatching baseline comprises reference operation features, rule constraint features and scene association features, and then real-time dispatching data is collected; the method comprises the following steps: performing multi-dimensional comparative analysis on a dynamic scheduling baseline to generate a baseline deviation feature set, calling a pre-trained scheduling collaborative evaluation model to perform dynamic correlation analysis on the baseline deviation feature set to generate a scheduling collaborative evaluation result, and constructing a multi-target scheduling optimization model based on the scheduling collaborative evaluation result to perform collaborative optimization processing. According to the method, an initial scheduling adjustment scheme set is generated, finally, dynamic constraint verification processing is performed on the initial scheduling adjustment scheme set, and a final rail transit scheduling instruction is generated and used for triggering a scheduling control system to execute scheduling parameter updating operation, so that the running efficiency, safety and stability of a rail transit system can be improved.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention discloses a rail transit hub emergency regulation and control method and system based on real-time simulation, and relates to the technical field of intelligent traffic and rail transit emergency management, and the method comprises the steps: carrying out the time synchronization processing of real-time passenger flow data and train operation state data, and generating a hub state data set; constructing a multi-level simulation model, and simulating a passenger flow distribution evolution process by presetting passenger flow evacuation behaviors and train operation constraint conditions; based on an output result of the multi-level simulation model, positioning capacity limiting nodes in each evacuation path by adopting a bottleneck identification algorithm, calculating passenger flow carrying capacity, and generating an optimal path regulation and control parameter with shortest evacuation time as a target; and the optimal path regulation and control parameters are converted into a train automatic control system instruction and a passenger flow guide display instruction, the instructions are issued to each line control unit through a hub centralized dispatching system, and the train operation frequency and the passenger flow evacuation direction are adjusted. According to the invention, intelligence and collaboration of emergency regulation and control of the rail transit hub are realized.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention provides a multi-modal fusion and semantic enhancement train positioning method and system, and belongs to the technical field of rail transit, and the method comprises the steps: carrying out the time-space alignment of data, obtaining a dense point cloud, constructing a dense semantic point cloud, and dynamically estimating the confidence coefficient weight of each type of sensors; a set residual error and a Manhattan structure constraint residual error of a plane are constructed, laser radar point cloud parameters are obtained, and visual projection constraints are constructed at the same time; constructing a comprehensive degradation scoring function to carry out degradation judgment on the current environment; when the degradation result is yes, introducing a structure and motion information independent of an external environment, maintaining trajectory estimation, and constructing a compensation constraint; introducing a prior semantic constraint and a large model semantic factor constraint; and constructing a global optimization objective function, dynamically adjusting the weight of each modal factor, obtaining an optimal estimation state, and outputting a high-precision train positioning result. According to the method, high-precision and robust track estimation in an extreme scene is realized, so that the continuity, safety and intelligence of train positioning are guaranteed.

The invention provides a rail transit equipment fault prediction method and system, and the system comprises a data perception and fusion layer which is used for collecting and fusing multi-source heterogeneous data; the digital twinning and intelligent prediction layer is used for realizing accurate prediction and early fault early warning of the health state of the equipment by constructing a lightweight digital twinning model of the equipment and combining a space-time diagram attention network and a self-adaptive learning algorithm; the interpretable analysis and decision support layer is used for analyzing the root cause of the prediction result and providing visual analysis; and the maintenance linkage and execution layer is used for automatically generating a maintenance strategy and scheduling resources according to a prediction result, relates to the technical field of rail transit operation and maintenance, and overcomes the defects of single data, static model, weak early warning capability, poor interpretability and disjunction between prediction and maintenance in the prior art. And the full-life-cycle, self-adaptive and intelligent management of the fault prediction of the rail transit equipment is realized.

The invention discloses an abnormal detection method and system for a splayed anti-loosening line of a rail train, and relates to the technical field of rail traffic detection, and the method comprises the steps: image collection and processing: shooting an image of a fixed point region containing the anti-loosening line, and preprocessing the image to obtain a target image; line integrity judgment: extracting a bolt, a bolt marking line and an anti-loosening line through an analysis model to form a target area graph, and judging whether the anti-loosening line is complete or not according to line segment continuity and form; performing line deformation depth analysis, and synchronously obtaining the position of an anti-loosening line intersection point and deformation data through the model in combination with the loosening angle of the bolt marking line when the line deformation is complete; performing line deformation safety judgment, setting a safety threshold group based on historical data, and comparing deformation data to output a signal; after line deformation abnormity judgment and comparison signal receiving, actual data are compared with theoretical data output by the mechanical model, and abnormal or normal signals are output. The anti-loose line full-dimension high-precision detection is achieved, automation and real-time performance are improved, and train operation safety is guaranteed.

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.

The invention provides a rail transit vehicle-ground security encryption control method based on a national cryptographic algorithm. The method comprises the following steps: realizing rail transit vehicle-ground security encryption control enabled by the national cryptographic algorithm by adopting a layered and segmented three-level security encryption architecture of a vehicle-mounted terminal layer-a network transmission layer-a core network layer; cooperative control of a national cryptographic algorithm is realized through three stages of terminal authentication, data encryption and tunnel encapsulation; in order to solve the problem of encryption interruption caused by wireless link switching such as 5G signal attenuation, the technical scheme of the patent realizes lossless switching through a three-layer linkage mechanism of signal monitoring, switching decision and key synchronization; self-adaptive intelligent adjustment of a key strategy is realized through a three-layer closed-loop architecture of environmental perception, intelligent evaluation and dynamic regulation and control; and the self-adaptive intelligent adjustment of the key strategy is realized through a three-layer closed-loop architecture of environmental perception, intelligent evaluation and dynamic regulation and control. And the national secret algorithm is combined with the block chain evidence storage technology to realize data tampering prevention, and the speed is high.

The invention provides a rail transit power supply digital twin system and a construction method, and relates to the technical field of data processing, and the method comprises the steps: constructing an urban rail transit power supply system digital twin model, achieving the digital virtualization associated with a physical entity, completing the model rendering, and displaying the state of the entity through a virtual model; according to the entity state, operating parameters of a substation and key equipment of a contact network are collected through a sensor and a hardware device; the operation parameters are cached by the station-level monitoring device body through the double-ring network architecture and then scheduled to a cloud platform communication ring network node, a data transmission path is optimized based on a load balancing strategy, and the data are uploaded to a cloud platform in real time. The management level can be improved, the operation and maintenance cost can be reduced, and a foundation is laid for intelligent operation and maintenance of a rail transit power supply system.

The invention relates to the technical field of battery management, and discloses a high-voltage lithium battery cluster voltage balance control method and system for rail transit. The method comprises the following steps: acquiring battery voltage and tunnel environment parameters, and obtaining environment characteristic data through Kalman filtering; predicting a voltage deviation trend through LSTM in combination with a UPS load rule; calculating a dynamic equilibrium threshold according to the predicted value; when the voltage difference value exceeds a threshold value, the bidirectional energy transmission equalization circuit is started; and monitoring the battery state change in the equalization process, and outputting a maintenance instruction through a fault recognition algorithm. The problem that battery balance control in a rail transit UPS system cannot adapt to complex environments and dynamic load changes is solved, and the precision and the intelligent level of battery cluster voltage balance control are improved.

The invention discloses a rail transit dispatching cooperation system and a rail transit dispatching system, and relates to the technical field of rail transit, the rail transit dispatching cooperation system comprises a time phase observation module, a time sequence analysis module, a conflict prediction module, an alignment correction module, a state determination module and a closed-loop extinguishing module, and fusing the time service link with the time mark data of the train control system to generate a continuous time sequence fingerprint baseline for identifying a scheduling switching boundary. According to the method, frequency mutation characteristics are extracted by constructing a time sequence fingerprint baseline, scheduling dislocation risks are identified, phase correction budget is generated, cross-interval scheduling alignment is achieved through double mirror image anchor points, judgment accuracy is guaranteed by combining buffer cone delay signal collection and double-channel verification, and finally, conflicts are absorbed through inverse phase traction and shadow periods, so that scheduling accuracy is improved. And scheduling rhythm closed-loop extinguishing control is realized.

The invention relates to the technical field of rail transit intelligence, and discloses a rail transit intelligent scheduling management method and system, and the method comprises the steps: collecting the entering and exiting data of passengers, the number of people in a waiting area, the train load factor and the platform congestion degree through an automatic fare collection system, a video monitor and a sensor of each station of rail transit; the collected passenger flow data are preprocessed, a box plot about passenger flow distribution is constructed, and sudden passenger flow fluctuation areas are identified in different time windows; a K-means clustering algorithm is adopted to classify passenger flow modes in peak periods, and the distribution type of passenger flow fluctuation is analyzed; a time sequence prediction model is constructed by adopting a Transform model in combination with weather, holidays and festivals and emergencies, the future short-term and medium-and-long-term passenger flow trend is predicted, and the train departure interval is optimized; and based on the predicted passenger flow distribution, a scheduling optimization objective function is constructed, and a reinforcement learning algorithm is combined. The method has the advantage of improving the passenger flow prediction precision in the peak period.

The invention discloses a rail transit fastener model identification system and method, and relates to the technical field of fastener overhaul, an initial image of a rail is acquired through an image acquisition module, and an image preprocessing module is used for preprocessing to obtain a reference image; the image analysis module inputs the reference image into a preset model for processing, completes segmentation of the fastener assembly and the steel rail assembly, and obtains segmented images; the positioning module is used for screening the images of part of fastener assemblies and the images of the rail bottom of the steel rail and restoring the images to the reference images; the feature acquisition module is used for acquiring a reference adjustment line based on the segmented image of the rail bottom, acquiring feature parameters of the partial components based on the segmented images of the partial components, and correcting by using the reference adjustment line to obtain reference feature parameters; and the model identification module is used for matching the reference characteristic parameters of the screening component with the data in the constructed database and outputting a corresponding fastener model. According to the invention, efficient identification of the models of the fasteners can be realized, and the identification accuracy is also guaranteed.

The invention relates to the technical field of rail traffic equipment maintenance and artificial intelligence image recognition, in particular to a rail vehicle cloud edge cooperative detection method and system and inspection equipment. The method comprises the following steps: acquiring image data of an area to be detected, synchronously recording attitude information of shooting equipment, carrying out image quality scoring on the image data, if the image quality score reaches a preset threshold value, calling a local target detection model to carry out defect identification on an image, and outputting an identification result containing a target type, a position and confidence; if the confidence coefficient of the recognition result is lower than a set threshold value, the image data and the posture information are uploaded to a cloud server, a cloud high-precision recognition model is called for secondary recognition, and a cloud recognition result is obtained. The real-time requirement is met through quick response of the local model, the cloud model rechecks a low-confidence result, efficiency and precision are both considered, and the contradiction that in the prior art, manual judgment standards are different, and speed and precision are difficult to consider is solved.

The invention discloses an extruded and insulated flame-retardant power cable for on-line monitoring of rail transit, and relates to the technical field of power cables, serpentine springs are connected between two adjacent cavity frames and outside corresponding five conductors on the outer side, an outer plastic pipe is connected between two adjacent serpentine springs, the outer plastic pipe is filled with silicone fireproof adhesive, and the outer plastic pipe is connected with the outer side of the serpentine springs. According to the invention, through the cooperation of the cavity frame, the serpentine spring, the inner plastic pipe and the expanding and supporting strip, the outer side of the conductor is provided with a sufficient heat dissipation circulation gap, the heat dissipation speed of the cable is improved, and the inner parts of the outer plastic pipe and the inner plastic pipe are filled with silicone fireproof glue, so that the heat dissipation performance of the cable is improved. The silicone fireproof adhesive expands when meeting fire to form a compact fireproof layer which tightly wraps the outer side of the conductor, and the expanded fireproof layer can fill heat dissipation gaps among the conductors to prevent flame from spreading, so that the cable has good heat dissipation capability, and meanwhile, the flame retardant property of the cable is also ensured.

The embodiment of the invention provides an unmanned aerial vehicle inspection method and a rail transit line inspection system, and the method comprises the steps: obtaining multi-source inspection data corresponding to an inspection unmanned aerial vehicle, the multi-source inspection data being obtained based on the collection data of a plurality of sensors carried by the inspection unmanned aerial vehicle and based on the time-space consistency fusion; based on the multi-source inspection data, an anomaly detection result is obtained through an anomaly detection model of at least one level, and the anomaly detection result comprises at least one anomaly area; and on the basis of spatial correlation characteristics, mapping an abnormal region in the abnormal detection result to a corresponding actual geographic position to obtain an inspection evaluation result, the spatial correlation characteristics being used for indicating a mapping relationship between the abnormal region and the actual geographic position of the rail transit line. The multi-source fusion improves the detection integrity, the hierarchical model enhances the environmental adaptability, the space mapping improves the positioning accuracy, and the overall inspection effect is optimized.

The invention relates to a multi-modal data-driven rail transit construction potential safety hazard identification method and system, and solves the problem that the accuracy and real-time performance of potential hazard identification are insufficient due to the fact that multi-source data cannot realize deep collaboration, and the method comprises the following steps: obtaining multi-modal data containing a BIM + GIS model, an unmanned aerial vehicle image stream and Internet of Things data; according to an edge end-cloud architecture, by taking BIM + GIS coordinates as a reference, the edge end associates and binds data and cleans and transmits the data to the cloud, and the cloud corrects and generates a standardized data set; and the comprehensive risk index is calculated by using an entropy weight method, and a report is generated and pushed to a construction management terminal. The method has the following effects: the multi-modal data barrier is broken, the integrated accurate identification of the hidden hidden danger of the rail transit construction is realized, and the accuracy and efficiency of the construction safety management and control are fundamentally improved.

The invention discloses a graph neural network-based train path collaborative scheduling method, which relates to the technical field of rail transit transportation scheduling, and comprises the following steps: S1, constructing a train path topological graph; s2, extracting a node embedding feature vector; s3, extracting context feature vectors of the computational nodes; s4, extracting a conflict node pair set; s5, introducing a fitness sharing mechanism and an elitist strategy, and adopting an improved binary whale optimization algorithm to generate an optimized multi-train collaborative path scheduling scheme; and S6, performing feedback and iterative optimization. The method overcomes the limitations of strong subjectivity of manual decision, slow response, difficulty in coping with complex path conflict problems and difficulty in accurately capturing path dynamic change rules in a traditional class path scheduling method, and provides an intelligent, efficient and accurate solution for real-time collaborative scheduling of class paths.

The invention discloses an operation optimization method, system and equipment for cooperative carbon reduction of an intelligent rail line network, and relates to the technical field of energy-saving scheduling and low-carbon operation of urban public transport. A refined section-level energy consumption and carbon emission quantitative model is constructed based on dynamic characteristics of intelligent rail vehicles and carbon emission factors of a power grid; a deep learning network is used for fusing multi-source data to carry out cross-line short-time passenger flow prediction, real-time passenger flow demands, energy consumption and carbon emission indexes and intersection signal phase time window constraints are uniformly incorporated into a multi-target collaborative optimization framework, and solving is carried out through mixed integer linear programming in a rolling vision field. According to the method, combined optimization and dynamic closed-loop control of departure intervals, section operation speeds and road right strategies are realized, so that the total operation energy consumption and carbon emission of the intelligent rail line network are remarkably reduced on the premise of ensuring passenger flow transportation requirements and service quality, and the line network level energy-saving and carbon-reducing operation targets are achieved.

The invention provides an urban rail transit comprehensive monitoring method and system based on multi-source heterogeneous data, relates to the field of rail transit, and solves the technical problems of low fault diagnosis accuracy and difficult root cause tracing caused by splitting of physical monitoring data and operation and maintenance knowledge in an existing rail transit system. The method comprises the following steps: collecting multi-source heterogeneous data from a rail transit system, preprocessing the multi-source heterogeneous data, and constructing a rail physical topological graph and a rail operation and maintenance knowledge graph; performing feature fusion on the track physical topological graph and the track operation and maintenance knowledge graph through a cross-graph attention mechanism to obtain a fusion feature vector; and inputting the fusion feature vector into a track risk assessment model, executing joint reasoning, identifying an abnormal type and assessing a risk level. The method is used in the urban rail transit monitoring process.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention relates to the technical field of urban rail transit, in particular to an urban rail transit passenger monitoring data processing and analyzing system and method, and the system comprises a data collection layer which is used for collecting video streams, gate passing records, passenger positioning data and environment parameters of temperature, humidity and illumination in real time; the spatio-temporal feature fusion layer is used for converting the video image data into analyzable feature vectors and integrating card swiping records and position information to form spatio-temporal trajectory data of passengers; the three-level anomaly detection layer comprises an individual layer detection unit, a group layer detection unit and a system layer prediction unit; the dynamic decision-making layer is used for calculating an abnormal score based on a dynamic threshold value, and dynamically adjusting a behavior coefficient according to a historical disposal effect through a PPO reinforcement learning algorithm; and the execution layer comprises an edge computing node and a cloud analysis platform. Therefore, the problems of single data acquisition, lack of comprehensive anomaly detection means, fixed and lagged decision response, ineffective utilization of resources and the like in the prior art are solved.

The invention discloses a vehicle-mounted fault diagnosis system, method and device and a storage medium, and relates to the technical field of rail transit, and the method comprises the steps: configuring system basic parameters through a data preloading module, so as to control the system to start and operate according to the system basic parameters; the log analysis module receives the running log of the train, analyzes the log, obtains abnormal events of a train subsystem and a vehicle-mounted controller, and obtains basic fault information based on the abnormal events; the alarm module performs verification and fault hardware positioning on the basic fault information, obtains a fault positioning result and generates alarm information; the database management module queries a downtime log or / and an event log according to a fault positioning result, and constructs an event curve to determine a fault reason; the parameter tracking module tracks the event curve and judges whether the fault has cross-system influence or not; accurate and reliable fault correlation analysis in a complex scene of multi-system interaction of the train is realized, so that the fault diagnosis and operation and maintenance efficiency is improved.

The invention relates to a rail transit multi-scene intelligent inspection device, method and equipment based on reinforcement learning, and the device comprises a sensing and state characterization module which is responsible for collecting multi-modal information from a rail transit inspection environment, and processing and fusing the multi-modal information into a structured environment state; the reinforcement learning decision-making module is used for selecting and outputting a current optimal decision-making action by utilizing the trained strategy network and value network based on the current structured environment state and the inspection task; the action execution and control module is used for converting the decision action into a bottom layer physical instruction sequence and controlling an intelligent agent to execute; the reinforcement learning training and optimization module is used for carrying out iterative optimization on the strategy network and the value network according to the inspection task and empirical data, and updating model parameters; and the task planning and scheduling module is used for defining inspection tasks and task allocation. Compared with the prior art, the method has the advantages of improving inspection efficiency and quality, realizing risk active early warning and the like.

The invention discloses an urban rail transit equipment fault prediction method based on big data analysis, and relates to the technical field of industrial big data analysis, and the method comprises the steps: building a health state index, a degradation trend index and a risk characteristic index according to the equipment type based on a standardized operation data set, and forming an index sequence; based on the index sequence, performing fault prediction calculation in a big data analysis environment to obtain a fault occurrence probability, a health state score and a residual life evaluation result; the fault occurrence probability, the health state score, the residual life evaluation result and the risk level are summarized, and fault prediction information and risk early warning information of operation and maintenance management are generated; a multi-dimensional dynamic index sequence fusing the health state, the deterioration trend and the risk characteristics is constructed, an intelligent operation and maintenance decision with clear priority and reasonable time sequence is supported and formed, and the safety, reliability and resource utilization efficiency of operation and maintenance of urban rail transit equipment are greatly improved.

The invention discloses an automatic identification and state evaluation method for rail transit unmanned aerial vehicle inspection, particularly relates to the field of rail transit unmanned aerial vehicle inspection, and is used for solving the problem that existing unmanned aerial vehicle inspection is influenced by rail attitude change and environment interference, so that rail defect identification and positioning are not comprehensive. An attitude time sequence matrix is constructed by controlling an unmanned aerial vehicle cluster to fly in the track direction, and an analysis unit is obtained by adopting space-time alignment and sliding window segmentation; based on principal component analysis, attitude cooperative oscillation characteristics are extracted, an attitude reference curved surface is constructed, and individual attitude dissimilation degree indexes are calculated; identifying an abnormal section by using a dynamic threshold value to realize track defect positioning, and controlling a cluster to execute matrix scanning and cross validation flight according to the defect distribution density; and finally, outputting a continuous track smoothness evaluation map. The method can keep stable inspection performance in a complex environment, improves the accuracy and coverage range of track anomaly recognition, and has high engineering application value.

The invention discloses an urban rail transit pull-in passenger flow prediction method fusing multi-source spatio-temporal data. The method comprises the following steps: collecting multi-source spatio-temporal data of a target station and a corresponding associated station; kernel density estimation is carried out on the surrounding POI density corresponding to each site, the spatial thermal characteristics of each site are generated, one-hot coding is carried out on the real-time weather index corresponding to each site, the weather influence characteristics of each site are generated, segmented coding is carried out on the date type identifier corresponding to each site, and the periodic effect characteristics of each site are generated. Performing differential stabilization on the historical pull-in passenger flow sequence of each station to generate a time sequence fluctuation characteristic of each station; tensor splicing is carried out on the space thermal characteristics, the weather influence characteristics, the periodic effect characteristics and the time sequence fluctuation characteristics of all the stations, a space-time fusion characteristic tensor is constructed and input into a space-time self-adaptive prediction model, and an inbound passenger flow prediction value of the target station is generated. The accuracy and timeliness of urban rail transit pull-in passenger flow prediction can be improved.