112 results about "Track geometry" patented technology

The invention provides a railway track periodical intelligent monitoring method based on the Internet of Things. The railway track periodical intelligent monitoring method comprises the steps that track geometric deformation data and track temperature distribution data are collected through sensing nodes deployed beside a track and on the track; extracting abnormal sections in the track geometric deformation data based on time window segmentation, and mapping the abnormal sections to a space grid of the track temperature distribution data to generate a deformation-temperature incidence matrix; calculating a track stress change gradient by combining the matrix and historical track temperature distribution data, and generating a track health degree evaluation index; and when the track health degree evaluation index meets a preset degradation condition, inputting the time sequence multi-source fusion data into a deep learning track state prediction module, and outputting a track failure risk level. According to the invention, the efficiency, accuracy and intelligent level of railway track monitoring can be improved, and the safety and reliability of railway transportation are guaranteed.

The invention discloses a thunder-vision fusion multi-sensor collaborative sensing system along a railway and an installation and calibration method, and belongs to the technical field of intelligent traffic and environment sensing. Aiming at the problems of low target detection precision, poor multi-source data fusion timeliness, dependence on manpower on sensor installation and calibration and the like in a complex scene along a railway, the system provides an innovative architecture of'layered sensing-dynamic fusion-autonomous calibration '. The multi-modal sensor array of a laser radar, a millimeter-wave radar, a visible light camera and a thermal infrared imager is deployed; autonomous optimization of sensor pose parameters is realized by using track geometric constraint and a deep learning model; an edge computing node and a cloud collaboration platform are integrated, and real-time target tracking, intrusion early warning and equipment state diagnosis are supported. Experiments show that the target detection accuracy rate of the system is greater than or equal to 98.5%, the false alarm rate is less than or equal to 0.3% and the self-calibration error of sensor installation parameters is less than 0.05 degree in the scenes of rain and fog, night, high-speed movement and the like, which are improved by more than 40% compared with the traditional scheme.

The invention discloses a multifunctional modularized track comprehensive detection system, which relates to the technical field of track detection, takes a lightweight moving trolley as a carrier, and integrates a system body, a positioning module, six multifunctional detection modules and a data processing module, and the system body is used for providing a walking platform, power supply and operation control. Control and synchronization of different function detection modules are realized through differential pulse signals; the positioning module is used for realizing position alignment of module data in the system; the multifunctional detection module covers visual inspection, track geometric parameter detection, steel rail profile detection, steel rail crawling displacement detection, turnout area detection and high-precision fastener detection; and the data processing module is used for processing the multi-source detection data and outputting a detection result and a maintenance suggestion. The problems that traditional equipment is single in function, low in detection efficiency and accuracy, high in manpower dependence and poor in data collaboration are solved, and scientificity and efficiency of track maintenance are improved.

The invention relates to the technical field of rail transit, and provides a multifunctional rail detection system, which comprises a multi-source data acquisition unit, an optical fiber inertial navigation sensor, a laser array module, a dual-mode camera group, a radar speed measurement module and a temperature and humidity sensor, the data acquisition module is used for synchronously acquiring track geometric parameters, three-dimensional surface topography, inertial navigation data, real-time vehicle speed and environmental parameters; the dynamic processing unit is arranged in the compartment and comprises a data fusion controller, a self-adaptive Kalman filtering module, an anomaly diagnosis engine and an edge computing host, and the data fusion controller integrates multi-source data through a multi-thread parallel processing technology. On the basis of self-adaptive Kalman filtering and vehicle speed-parameter dynamic mapping, the track gauge detection error under the variable speed working condition is reduced to + / -0.3 mm from + / -3 mm, and the curvature radius calculation error lt is obtained; and the predictive maintenance model prolongs the overhaul period of the track by 40%, and reduces the operation and maintenance cost by 35%.

The invention provides a train positioning method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: constructing a multi-objective optimization model; the multi-objective optimization model comprises a first objective function taking GNSS positioning error minimization as a target, a second objective function taking INS accumulative error minimization as a target, a third objective function taking INS attitude error minimization as a target and constraint conditions; the constraint conditions comprise a GNSS error boundary constraint condition, an INS error boundary constraint condition and a track geometric polynomial constraint condition, and the track geometric polynomial constraint condition is used for constraining that the position deviation of the train position obtained through current optimization is smaller than or equal to a preset deviation value; and performing optimization solution on the multi-objective optimization model by adopting a differential evolution algorithm to obtain optimal train position and attitude information. According to the invention, the positioning precision and reliability of the train in a complex environment can be improved, and the real-time requirement of train positioning is met.

Described herein are techniques for determining motion characteristics of trains traveling along a train track. In some embodiments, a processor may determine an estimated position of a train using an observed position obtained using one or more UWB antennas and an observed position obtained using one or more GNSS receivers. In some embodiments, a processor may access information specifying a geometry of a train track and determining the position of a train along the train track using an observed position determined using one or more UWB antennas and / or GNSS receiver(s) and the information specifying the geometry of the train track. In some embodiments, a processor may determine estimated positions of a train using the geometry of the train track and at least one observation of the train obtained using one or more positioning devices and select the position of the train from among the estimated positions.

The invention discloses a railway construction simulation model generation method based on full-line design parameters, and relates to the technical field of model simulation. Line track geometric parameters, geological exploration data, bridge and tunnel structure parameters, construction mechanical performance indexes and environment constraint conditions are collected, a multi-source heterogeneous database is constructed, and a simulation model is generated; the method comprises the following steps: constructing full-line design parameters after space coordinate unification and data cleaning, establishing a space-time dependency relationship of construction elements based on a railway line segmentation topological structure, generating an initial construction logic network, and optimizing the construction logic network according to construction efficiency, a resource scheduling rule and a dynamic constraint condition, so as to obtain a construction result; parallel construction events in the railway construction process are defined, full-line design parameters and a construction logic network are mapped into a three-dimensional visual simulation model of railway construction through a parametric modeling engine, real-time railway construction data are introduced, the construction delay probability of railway construction is predicted, and the construction delay probability of railway construction is calculated. And determining whether to carry out construction process adjustment and construction resource redistribution.

The invention discloses a geometric irregularity state deterioration characterization method for a longitudinally-connected ballastless track on a high-speed railway bridge under a transverse earthquake, and the method comprises the steps: building a refined finite element model of a longitudinally-connected ballastless track-bridge system, and analyzing the damage characteristics and distribution rules of key components of the earthquake-caused track-bridge system; the characteristic of damage state degradation of key components of the earthquake-induced track-bridge system and a mapping mechanism are disclosed; then, based on the second theorem of Castlliano and the superposition principle, a geometric irregularity state deterioration characterization model of the longitudinally-connected ballastless track on the high-speed railway bridge under the action of the transverse earthquake is constructed, the geometric irregularity state of the longitudinally-connected ballastless track on the simply-supported / continuous beam bridge is mapped based on the deterioration characterization model, deformation data of the steel rail under the action of the earthquake are accurately quantified, and the accuracy of the deformation of the steel rail under the action of the transverse earthquake is improved. The method provides a theoretical basis for quickly judging the post-earthquake traffic capacity and maintenance engineering of a track-bridge system, ensures the operation safety of a high-speed railway, provides a theoretical basis for the design of a high-speed railway track-bridge, is beneficial to post-earthquake track repair and train operation recovery, and has a good practical value and a good popularization prospect.

Systems and techniques for generating track geometry data (TGD) for railroad track analysis. In embodiments, rail profile data for each of the rails of a track may be obtained at regular intervals as a vehicle carrying the system traverses the track. The rail profile data may be validated, and reference points on the profiles of each of the rails corresponding to specific geometric features of the rails may be identified. The rail profile data may be filtered and processed to generate track geometry data. The track geometry data may include data for a plurality of data channels. The track geometry data may be transmitted to a backend system receives for further processing to determine rail defects, and to generate notifications and alerts based on the determined defects. The system utilizes machine learning algorithms and models for defect detection and validation.

Systems and techniques for railway track monitoring and defect detection. In embodiments, a system captures and processes rail profile data for detecting defects on a railroad track. An on-board rail profile capture system, mounted to a railroad vehicle, captures rail profile data at regular intervals as the vehicle traverses the track. The system includes profile capture sensors and an inertial measurement unit to capture rates and angles of the vehicle's orientation. A main computer receives the rail profile data and generates track geometry data. A backend rail profile processing system receives the track geometry data, processes it to determine rail defects, and generates notifications and alerts based on the determined defects. The system utilizes machine learning algorithms and models for defect detection and validation.

The invention relates to the technical field of train aided driving control, and discloses a train aided driving control method and system based on four-eye vision. The method comprises the steps that a forward four-eye camera array is used for synchronously collecting a front track image sequence, multi-scale feature synchronous extraction is carried out, and a unified sensing data block containing track geometry, identification semantics and an obstacle contour form is directly generated. On the basis, a space-time probability distribution diagram of collision threats is generated by constructing a virtual track reference surface and analyzing the spatial relationship between the dynamic target and the virtual track reference surface, so that the upgrading from discrete alarm to continuous risk prediction is realized. A preliminary control instruction is generated by combining speed limiting constraint and system deduction, then the instruction is cooperatively adjusted by combining the real-time traction braking state and a load signal of a train, and finally an executable closed-loop control instruction set is output. According to the method, the efficiency of environment perception and the perspectiveness and accuracy of decision control are improved.

The application provides a geometric position real-time correction method and system for railway ballastless track construction, and relates to the technical field of track construction. The method comprises: analyzing the design coordinates of the railway ballastless track, and dividing the construction line; extracting the geometric position gradient period of the pouring construction; anchoring the target monitoring feature space-time distribution of each time sequence stage; connecting the monitoring equipment for corresponding pouring state monitoring by taking the space-time monitoring feature chain as the regulation and control activation demand of the time sequence period; analyzing the geometric position correction parameters based on the monitoring state data to generate correction regulation and control information, and performing real-time correction control on the geometric position. The application solves the technical problem that the existing technology lacks recognition of the dynamic evolution of the track geometric position in the concrete pouring process, resulting in a mismatch between the correction opportunity and the material physical state, and further affecting the construction efficiency of the railway ballastless track. The correction is changed from post-correction to process adaptive regulation and control, and the forming quality and construction efficiency are improved.

The invention provides a magnetic levitation traffic rail irregularity monitoring and adjustable complete assembly. The magnetic levitation traffic rail irregularity monitoring and adjustable complete assembly comprises an adjusting mechanism and a rail irregularity monitoring device. The adjusting mechanism is arranged in the large pedestal below the track plate and is used for dynamically adjusting the height of the track in a hydraulic driving manner; the monitoring device is composed of wireless vision displacement sensing equipment and a receiving target, is installed on large pedestals on the two sides of a track plate, and captures track geometric deformation signals in real time. In the working process, the sensing device recognizes the displacement change of the receiving target, the data processing unit is combined to generate a track smoothness curve and calculate deviation parameters, and when irregularity is detected, the adjusting mechanism is automatically triggered to conduct accurate compensation. According to the assembly, high-smoothness intelligent maintenance of the whole period of rail installation and operation is achieved, the manual maintenance requirement is remarkably reduced, and the train operation safety and operation continuity are effectively improved.

1. The name of the design product: track detector. 2. The use of the design product: for track geometry detection. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1.

The invention relates to the technical field of track characteristic data analysis, and discloses a track mileage calibration method and system based on track geometric dynamic and static waveform matching, the system comprises a track geometric waveform parameter processing module, a track mileage calibration analysis module and a track mileage calibration execution module; based on numerical analysis science, track static and dynamic waveform sampling frequency consistency is detected, and meanwhile, an interpolation algorithm is combined to carry out track geometric static waveform sampling frequency intelligent adjustment, so that track geometric waveform sampling frequency autonomous identification and correction in track geometric dynamic waveform mileage calibration are realized; track mileage calibration algorithm type fine matching is carried out on the basis of the static and dynamic waveform combination information of the track and the static and dynamic waveform combination information of the standard track corresponding to different track mileage calibration algorithms based on big data storage in combination with an artificial intelligence bionic algorithm, and the precision of track geometric dynamic waveform mileage calibration is improved.

This invention relates to the field of solid deformation monitoring technology, specifically to an automated track geometry monitoring system and method. The method includes: determining the deformation significance of each monitoring point at the current acquisition time based on pre-acquired lateral and longitudinal displacements; segmenting the track to be monitored; determining the deformation gradient dispersion rate within each segment based on the deformation significance and the bridge / culvert jacking distance; determining the deformation differences between segments; determining local deformation parameters based on the inter-segment deformation differences and the bridge / culvert jacking distance; thereby performing real-time monitoring of track geometry deviations for each monitored segment to achieve automated track geometry monitoring. This invention, by analyzing the deformation at different monitoring points, achieves real-time monitoring of track geometry deviations in monitored segments and improves the rationality of track geometry deviation monitoring, thus enhancing the overall rationality of track geometry monitoring.

The utility model provides a self-cleaning device for carrying type track geometric dynamic detection equipment. The self-cleaning device comprises a sensor protection shell, a driver and a sliding cleaning block. A sensor light-transmitting hole is formed in the surface of the sensor protective shell, and protective glass is arranged in the sensor light-transmitting hole; a sliding rail is arranged on the surface of the sensor protection shell, the sliding rail is arranged beside the sensor light hole, and the sliding cleaning block is arranged on the sliding rail in a sliding mode; the driver is arranged on the surface of the sensor protection shell, the sliding cleaning block is arranged at the output end of the driver, and the driver drives the sliding cleaning block to move along the sliding rail so that the sliding cleaning block can abut against the surface of the protection glass. The driver is used for driving the sliding cleaning block to slide in the first direction, the sliding cleaning block abuts against the surface of the protective glass, the sliding cleaning block scrapes off dust adhering to the surface of the protective glass, the self-cleaning device with the simple structure is used for achieving the self-cleaning purpose, and cleaning and maintenance operation is simpler.

According to the line dynamic equivalent conicity measuring system and method, detection beams are arranged on axle boxes on the left side and the right side of a vehicle respectively, speed sensors and laser sensors are arranged on the detection beams, the speed sensors are used for detecting vehicle speed signals, and the laser sensors are used for dynamically scanning the profile of a steel rail; the controller matches the speed signal with the laser contour data and sends the speed signal and the laser contour data to an upper computer, and the upper computer obtains a line dynamic equivalent taper curve through calculation; high-precision, continuous and dynamic monitoring on the geometric state of the track under the normal operation condition of a train is realized.

This utility model discloses a novel prestressed concrete railway sleeper, comprising a sleeper body laid on the railway track bed, a reinforcing cage inside the sleeper body for increasing strength, two diagonally distributed "J"-shaped pre-embedded bolt assemblies located inside the sleeper body and extending upwards to both sides of the rail mounting position, two diagonally distributed fixing holes on the upper surface of the sleeper body located on both sides of the rail mounting position, a steel base fixed to the upper surface of the sleeper body by the pre-embedded bolt assemblies, and a steel pad installed on the upper end of the steel base through the fixing holes. This novel prestressed concrete railway sleeper effectively improves the track geometry retention capacity and load-bearing strength of the sleeper body; moreover, the two-layer steel plate structure has suitable deformation capacity, attenuating the impact energy transmitted to the concrete sleeper, greatly reducing the ballast pulverization rate, and ensuring safe vehicle operation.

The invention relates to a method for tamping sleepers (5) of a track (3) using a tamping unit (7) with several pairs of tamping arms (17) arranged side by side, wherein the tamping picks (12) of each pair of tamping arms (17) are immersed in a ballast bed (4) on both sides of a sleeper (5) and aligned relative to each other by means of an adjusting cylinder (18) by applying an adjusting pressure (p). In a track curve, a control unit (20) automatically sets different adjusting pressures (p) for at least two of the adjacent pairs of tamping arms (17) depending on a superelevation (u) of the track (3) or a proportional value thereto. In this way, optimized properties are achieved for the compacted ballast bed (4), so that uneven loads on the track (3) do not cause premature or uneven deterioration of the track geometry.

The present application belongs to the technical field of traffic track detection, and particularly relates to a locomotive external track geometric position detection device and a detection method. The method comprises the following steps: real-time acquisition of the lateral acceleration and vertical acceleration of the vehicle and comparison with the corresponding preset threshold values, respectively, when any acceleration exceeds the threshold value, determination of excessive vibration interference; when the vibration interference is excessive, acquisition of the geometric appearance of the track, the distance information of the track relative to the vehicle, the position information of the vehicle and the motion state of the vehicle; based on the geometric appearance of the track, the distance information of the track relative to the vehicle, the position information of the vehicle and the motion state of the vehicle, real-time track gauge calculation, track horizontal calculation, track height calculation and triangular pit calculation of the vehicle are carried out to obtain the dynamic geometric position parameters of the track. The method realizes on-demand detection, avoids data redundancy, improves detection pertinence and resource utilization, and is suitable for accurate identification and normal operation and maintenance of track disease sections.

The application discloses a railway construction simulation model generation method based on full-line design parameters, relates to the technical field of model simulation, collects line track geometric parameters, geological exploration data, bridge and tunnel structure parameters, construction mechanical performance indexes and environmental constraint conditions, constructs a multi-source heterogeneous database, constructs full-line design parameters after spatial coordinate unification and data cleaning, establishes a time and space dependency relationship of construction elements based on a line section topological structure of the railway, generates an initial construction logic network, optimizes the construction logic network according to construction efficiency, resource scheduling rules and dynamic constraint conditions, defines parallel construction events in a railway construction process, maps the full-line design parameters and the construction logic network into a three-dimensional visual simulation model of railway construction through a parameterized modeling engine, introduces real-time railway construction data, predicts a construction delay probability of the railway construction, and determines whether to make construction process adjustment and construction resource redistribution.

The invention discloses a tunnel-rail cooperative detection method and system based on multi-source data fusion. The method comprises the following steps: acquiring tunnel three-dimensional point cloud data and track geometric data at the same mileage section; preprocessing the tunnel three-dimensional point cloud data and the track geometric data, and uniformly converting the tunnel three-dimensional point cloud data and the track geometric data into a track plane coordinate system in combination with the space attitude information to obtain a section fusion point cloud; calculating the minimum space distance from each evaluation reference point to the surface of the tunnel structure in the section fusion point cloud, and generating a safety space map continuously distributed along the mileage of the line; and calculating a real-time safety margin according to the safety space map, and outputting a tunnel-rail collaborative evaluation result in combination with the deformation characteristics of the rail and the tunnel. According to the invention, the transformation from discrete detection to integrated perception of the railway tunnel infrastructure and from over-limit alarm to intelligent diagnosis can be realized, and the operation and maintenance efficiency, accuracy and safety are significantly improved.

The present application relates to the field of data processing technology, and discloses a method and system for processing railway construction data. The method comprises: semantically annotating track laying, line measurement and construction monitoring data through RDF semantic association to obtain a semantic data set; setting smoothness constraints based on gauge deviation and elevation deviation to filter qualified data; inputting qualified data into an improved fireworks algorithm, and optimizing track geometry parameters through a curvature adaptive explosion mechanism; using improved Kriging interpolation to complete track centerline, rail surface elevation and gauge change data; establishing a construction quality constraint matrix, and collaboratively adjusting track alignment, elevation control and gauge accuracy through a sequential quadratic programming algorithm. The present application solves the technical problems of the lack of semantic association of multi-source data, insufficient adaptability of professional algorithms, and insufficient parameter collaborative optimization capabilities in railway construction.

The invention provides an intelligent rail smoothness adjusting method and system based on dynamic detection and big data analysis, and belongs to the technical field of railway engineering maintenance. The method comprises the steps that track geometric data, vehicle dynamics response data and environment data are collected in real time through a multi-dimensional sensor array installed on an operating train; uploading the data to a cloud big data platform for fusion and deep mining, constructing a track state degradation prediction model by using a machine learning algorithm, and accurately predicting the development trend of track irregularity; and based on the prediction result, automatically generating a targeted track fine adjustment scheme, and issuing the scheme to an intelligent maintenance robot deployed on the line or guiding a manual maintenance team. The system comprises a data acquisition layer, a cloud analysis layer and an intelligent execution layer. According to the method, the change of the track state from'regular maintenance 'to'predictive maintenance' is realized, the maintenance efficiency, the precision and the line smoothness are remarkably improved, and the maintenance cost of the whole life cycle is reduced.

The application discloses a track geometry large value overrun monitoring method and device, and relates to the field of track geometry large value overrun monitoring.The method comprises the following steps: establishing a wheel-rail interaction model of an operation vehicle and a target track according to target track and operation vehicle design parameters; establishing a correlation between different speed levels of the operation vehicle and a vertical acceleration threshold value of a frame and a vertical displacement threshold value of the frame; determining a target vertical acceleration threshold value of the frame and a target vertical displacement threshold value of the frame at a speed level corresponding to a vehicle speed of a target operation vehicle of the target track; and issuing an alarm information of a track geometry large value overrun event of the target track when a target vertical acceleration of the frame of the operation vehicle is greater than or equal to the target vertical acceleration threshold value of the frame and a target vertical displacement of the frame of the operation vehicle is greater than or equal to the target vertical displacement threshold value of the frame.The application is used to improve the efficiency and accuracy of track geometry large value overrun monitoring.

The utility model discloses a spherical steel support with a lateral bouncing function. The spherical steel support comprises an upper support plate assembly, a spherical crown assembly, a plane sliding plate, a spherical sliding plate, a lateral bouncing assembly, a lateral sliding plate and a lower support plate assembly. The side elastic assembly comprises a fixing block, a fixing bolt, a plate spring and a limiting shaft. One end of the plate spring is fixedly installed between the two fixing blocks, and the other end is inserted into the upper seat plate guide rail opening. When the upper seat plate slides, the plate spring is driven to be deformed and stressed, so that the plate spring has certain rigidity in the sliding direction. When the plate spring deforms by a certain value, the elastic force is enough to overcome the friction resistance of the same bridge relative supports, so that the adjacent pier supports are driven to slide, the beam body freely and symmetrically stretches and deforms from the midspan to the two sides, and the device has the advantages of simple structure, easiness in processing and maintenance and the like. Relates to the technical field of bridge construction, and solves the problems that the manufacturing and laying process of a steel rail telescopic regulator and a curve is complicated, and the geometric shape and position of a track are difficult to maintain in operation.

A forward monitoring device includes a map information storage unit that stores track information representing a location and a track geometry of a track, a train information acquisition unit that obtains train location information on a train, and an interceptor presence-absence determination unit that determines, based on the track information, the train location information, and interceptor candidate information, whether there is an interceptor between the train and a first monitoring scope, when the forward monitoring device monitors an obstacle on the track in the first monitoring scope, the interceptor being not the obstacle, the interceptor blocking a view of the first monitoring scope from the train, the interceptor candidate information indicating candidates for the interceptor that blocks a view from the train, the first monitoring scope including a spot on the track at a first distance from the train along the track in the travelling direction of the train.

This invention relates to the field of mobile track geometry state detection technology, and discloses a real-time track state detection method based on multi-sensor fusion and dynamic compensation. It collects multi-source synchronous data from a global navigation satellite system receiver, inertial measurement unit, and laser profilometer mounted on the vehicle body, as well as accelerometers mounted on the bogie, and performs time synchronization and coordinate system unification. Through dynamic error correction and extended Kalman filtering fusion, it achieves high-precision estimation of vehicle attitude, velocity, and position. First-level point cloud motion compensation eliminates spatial distortion caused by the rigid body motion of the vehicle itself on the point cloud collected by the laser profilometer. Second-level point cloud motion compensation eliminates residual geometric errors caused by high-frequency vibration of the bogie. Finally, track geometric parameters are extracted and smoothness analysis is performed. This invention achieves high-precision reconstruction of track point clouds, providing technical support for real-time, high-precision track state monitoring on operating trains.