86 results about "Rail infrastructure" patented technology

The invention discloses a railway infrastructure health state prediction method, device and platform. The method comprises the following steps: collecting multi-mode sensor data reflecting the state of the railway infrastructure; the data of the multi-mode sensor are preprocessed; constructing and training a multi-modal data fusion deep learning model; and inputting preprocessed data collected in real time into the trained multi-modal data fusion deep learning model to obtain a health state prediction result of the railway infrastructure, and comparing the health state prediction result with a preset health threshold. According to the technical scheme, data standardization is achieved by collecting the data of the multi-modal sensor and adopting the preprocessing technology; a multi-task deep neural network self-organizing mapping and a graph neural network are used for fusing modal features, and a multi-task long-short-term memory network and a Bayesian uncertainty quantification module are used for assisting, so that the railway health state is accurately predicted.

The invention relates to the technical field of carbon emission accounting, and particularly discloses a carbon emission calculation method and system for railway infrastructure construction, and the method comprises the steps: firstly, calling basic data of a railway infrastructure from a background database and corresponding carbon emission which is verified to be real to construct a reference sample set; and carrying out embedded coding and dynamic semantic search on the basic data of the to-be-verified railway infrastructure uploaded by the user and each reference sample by adopting a data processing technology based on deep learning, so as to query response information based on the semantic query between the to-be-verified basic data and each reference sample. And the reasonable carbon emission amount of the railway infrastructure to be verified is intelligently estimated, and whether the total carbon emission amount of the railway infrastructure to be verified uploaded by the user is true or not is judged based on the reasonable carbon emission amount of the railway infrastructure to be verified. According to the method, rapid and accurate calculation and verification of the carbon emission in the railway infrastructure construction process can be realized, false declaration is avoided, and the authenticity and reliability of the carbon emission data are improved.

The invention relates to the technical field of rail traffic safety monitoring, and discloses a railway foreign matter invasion real-time monitoring and alarm system based on an SAR radar, and the system comprises an SAR multi-dimensional information depth perception module which extracts polarization, coherence, texture and other multi-dimensional features from SAR data; the multi-scale space-time attention fusion module adaptively focuses the key space-time change through a space and time attention mechanism to generate space-time fusion features; and the railway foreign matter invasion evolution knowledge graph construction and reasoning module integrates the space-time fusion features and multi-source information such as railway infrastructure, environment and historical events to construct a dynamic knowledge graph, and performs risk reasoning and alarming by using a graph neural network. According to the invention, through deep fusion of multi-dimensional perception, intelligent attention and knowledge graph reasoning, the accuracy, robustness and intelligent level of foreign matter invasion monitoring are significantly improved, and deep understanding and adaptive early warning of risks are realized.

The invention relates to the technical field of carbon emission prediction, and particularly discloses a railway infrastructure construction process carbon emission prediction method and system.The method comprises the steps that firstly, carbon emission basic data in the railway infrastructure construction process are extracted from railway infrastructure related IFC files, and meanwhile a related carbon emission feature database is constructed; on the basis of the Hash similarity between each carbon emission basic data and each carbon emission characteristic data element in the database, querying a corresponding carbon emission factor to calculate the carbon emission, and further introducing a deep learning algorithm; a plurality of candidate carbon emission characteristic data elements with relatively high similarity are subjected to semantic analysis and dynamic query matching fitting to deduce corresponding carbon emission factors, so that carbon emission calculation is realized. According to the method, the carbon emission characteristics of novel composite materials and construction activities can be effectively handled, and the generalization ability of carbon emission prediction is improved, so that powerful support is provided for carbon emission management in the railway industry.

The invention provides an online rail fault diagnosis method and system based on knowledge transfer learning, and belongs to the technical field of crossing of intelligent monitoring and artificial intelligence of railway infrastructures. The method comprises the steps that S1, a server trains a model framework through a source domain data set to obtain a teacher diagnosis model; based on the target domain data set, training the teacher diagnosis model by adopting a mixed training strategy to obtain a student diagnosis model; s2, acquiring real-time multi-modal monitoring data from a target domain line by the edge computing equipment, compensating to obtain corrected data, and dynamically selecting the most important feature subset from the corrected data to form a simplified feature set; and S3, inputting the simplified feature set into a student diagnosis model to obtain a student fault diagnosis result and confidence thereof, and introducing a D-S evidence theory to generate a student fault diagnosis report. The method has the advantages that the accuracy, the real-time performance, the cross-domain adaptability and the overall system safety of railway track fault diagnosis are greatly improved.

The invention relates to the technical field of railway infrastructure disease identification, and discloses a railway infrastructure crack segmentation method, a readable storage medium and computer equipment, and the segmentation method comprises the steps: obtaining a data set of a railway infrastructure crack image; marking images in the data set, and dividing the data set according to a proportion to obtain a training set and a test set; a multi-head self-attention module and a triple attention module are embedded in a YOLO11 cross-scale trunk in parallel, a data enhancement strategy is combined, a decoupling Anchor-Free instance segmentation head is designed at a detection head, and an improved YOLO11 model is obtained; training the improved YOLO11 detection model based on the training set to obtain a railway infrastructure crack segmentation model; and inputting the test set into the railway infrastructure crack segmentation model to obtain a test result and a railway infrastructure crack segmentation result. According to the invention, the accuracy of detection and segmentation can be effectively improved, and the practicability of detection and the engineering application value are remarkably improved.

The invention discloses a high-speed rail infrastructure health state monitoring method, system and device and a medium, and relates to the technical field of high-speed rail infrastructure monitoring. The method comprises the following steps: constructing a monitoring framework based on a monitoring category; arranging a data acquisition scheme based on the monitoring framework, and obtaining monitoring data; predicting the health state of the infrastructure along the high-speed rail according to the monitoring data and the state monitoring model, and determining a risk prediction result; the risk prediction result comprises a potential risk area and a corresponding type; the state monitoring model comprises a prediction model and a key area monitoring model based on a prediction result. The method can improve the precision of an abnormal state prediction scheme, and improves the data transmission and processing efficiency. And meanwhile, flaw detection train monitoring items are expanded, and the monitoring precision is enhanced, so that the health state of the whole infrastructure along the high-speed railway is monitored.

The invention relates to the technical field of railways and rail transit, and provides a railway infrastructure maintenance resource and plan associated scheduling method and system, and the method comprises the following steps: S1, analyzing the maintenance plan compilation optimization and maintenance resource scheduling problems of a railway service depot, and summarizing the problems as maintenance resources and maintenance resource combined scheduling problems; s2, integrating the organization structure, the disease type, the maintenance resource information and the maintenance plan information of the railway work depot, designing a combined dispatching system database of the maintenance resources and plans of the railway infrastructure, and developing a data interface in a railway work management system; s3, on the basis of actual requirements, a software framework and a system interface of the combined dispatching system for the maintenance and repair resources and plans of the railway infrastructure are developed, and the system interface comprises a resource management module, a resource configuration module and an intelligent work order module; according to the invention, association scheduling can be well carried out.

The invention belongs to the technical field of railway multi-source data fusion, and particularly relates to a railway infrastructure multi-source data fusion system and method. According to the method, the time features of each reference data source are analyzed, the data sources with the centralized features and the dispersive features can be accurately recognized, the corresponding fusion rules are formulated accordingly, the scientificity and reasonability of the data fusion process are ensured, abnormal fusion nodes can be found and processed in time, and the fusion efficiency is improved. In addition, the abnormal fusion nodes can be dynamically adjusted according to the fusion deviation value, the continuity and stability of the data fusion process are ensured, the deviation in the data fusion process can be found and corrected in time through real-time monitoring and backtracking analysis, and the data fusion accuracy is improved. Therefore, decision errors caused by data fusion errors are avoided, and accurate and timely data support is provided for maintenance and management of railway infrastructures.

The method S100 can include: determining a remote operation request Silo; optionally determining a priority of the remote operation request S120; providing vehicle data to a remote operator S130; responding to the remote operation request S140; and optionally training a model based on the response S150. However, the method S100 can additionally or alternatively include any other suitable elements. The method S100 functions to facilitate remote assistance of a vehicle operating within a rail network (e.g., operation of unmanned vehicles within restricted track regions). Additionally or alternatively, the system can function to facilitate remote validation / verification of vehicle operations and / or rail infrastructure status.

The present invention provides a monitoring system (104) comprising at least two interconnected sensors (102a, b, c) for monitoring events or situations in a traffic infrastructure (100) by detecting vibration at multiple sensor locations, wherein each sensor (102a, b, c) is integrated in a housing of an electrical connector directly connected to a traffic infrastructure equipment for deriving vibration patterns (109a, b, c) from the detected vibration, a method for monitoring events or situations in a traffic infrastructure (100) using a monitoring system (104) and a traffic infrastructure (100), in particular railway infrastructure, provided with such monitoring system (104).

Safety test results generated in a safety testing system 31 are displayed to an operator for input, by receiving the results at a server 34; using a high integrity safety test reporting system 32 hosted thereon to pre-filter the results into a log file; passing the log file to a low integrity report viewer (e.g. commercial off-the-shelf (COTS) viewer or browser 35) embedded in the server; and operating the reporting system and viewer with a display device 33 to display the log file to an operator for input. The server may be cloud-based. The test results may relate to a rail infrastructure safety system 39,40. The safety test reporting system may meet EN 50129 SIL4 requirements whereas the report viewer need not meet any SIL requirements.

The invention relates to a method for determining a fault (18) in railway infrastructure by means of a detection device (12) of a train (10), comprising the steps of detecting the train's (10's) surroundings (14) by means of the detection device (12); determining an anomaly (16) associated with the railway infrastructure as a function of the detected surroundings (14) by means of an electronic computing unit (20) of the detection device (12); and determining the fault (18) as a function of the determined anomaly (16) by means of the electronic computing unit (20). The invention further relates to a computer program product, a computer-readable storage medium, and a detection device (12).

Energy system (1) for supplying a railway infrastructure with electrical energy, wherein the energy system (1) comprises: - a plurality of wind energy installations (2) arranged along a railway section of the railway infrastructure and in the immediate vicinity of a track of the railway section and designed to generate electrical energy by means of the wind generated by a passing rail vehicle (3), and - a plurality of energy transmission installations (4) coupled to the plurality of wind energy installations (2) and designed to supply the generated electrical energy directly to the railway infrastructure.

The invention discloses a collection system and method for railway infrastructure detection and monitoring data. The system comprises a data acquisition module, a dynamic execution control module, a data quality verification module and a database. The data acquisition module is used for acquiring multi-source data by adopting a corresponding transmission mode according to a data source; the transmission mode comprises wireless communication, a publishing and subscribing mode and client-side batch uploading; the dynamic execution control module is used for configuring verification tasks with different priorities for the multi-source data, calling the data quality verification module to execute the verification tasks and generating a data quality report; the data quality verification is used for verifying whether the normalization, uniqueness, integrity, consistency, availability and timeliness of the multi-source data meet preset specification requirements or not; checking whether the multi-source data meets a preset service requirement or not; checking whether the multi-source data meets a preset process compliance requirement or not; the database is used for storing multi-source data. According to the invention, unified collection management of all-path detection monitoring data can be realized.

A portable rail clamp mount assembly configured to be installed on a railway without requiring any modification to the existing rail infrastructure is described herein. In various embodiments, the rail clamp mount assembly may comprise a set of one or more horizontal base components removably attachable to and positioned beneath a set of tracks of a railway and at least one vertical component attached to the set of one or more horizontal base components and configured to support railway equipment positioned proximate to the railway. For example, the railway equipment may include one or more devices for operating a railroad or railyard. Each of the set of one or more horizontal base components may include at least one tie configured to engage an outside edge of the set of tracks when attached to the set of tracks.

Visibility of a railway infrastructure asset (e.g. trackside railway signal 104) to a driver 107 of a train 103 on a railway track 102 is determined using a computer model modelling (e.g. in three dim

1. The name of the design product: double track flaw detector single probe wheel alignment structure. 2. The use of the design product: for ultrasonic flaw detection of steel rails in railway infrastructure. 3. The design features of the design product: in the frame structure. 4. The picture or photo that best indicates the design features: perspective view.

The invention discloses a high-speed railway roadbed hidden danger intelligent identification system based on infrared image analysis, and belongs to the technical field of railway infrastructure detection. The system comprises an infrared image acquisition module, a visible light image acquisition module, an environment parameter acquisition module, an image fusion module, an environment correction module, a thermal anomaly detection module, a hidden danger classification module and a space positioning module. The system fuses infrared and visible light images based on an attention mechanism, eliminates the influence of environmental factors based on environmental parameter adaptive correction, extracts spatio-temporal features from an image sequence to detect temperature anomaly, and intelligently classifies hidden danger types and severity based on form and temperature features. And mapping the abnormal region to an actual spatial position and generating a visual distribution map. According to the invention, high-precision, high-efficiency and high-reliability intelligent identification of the hidden danger of the high-speed rail roadbed is realized, the detection rate reaches more than 95%, and technical guarantee is provided for safe operation of the high-speed rail.

The present invention discloses a rail infrastructure safety assurance method, system, device, and storage medium, relating to the field of intelligent safety monitoring and control technology for rail transit. The method comprises: collecting raw track data through a multi-source perception module; processing the raw track data using a bilateral filter and region growing method to obtain track data containing semantic tags; extracting features from the track data through a data fusion hub to construct a spatiotemporal correlation feature matrix; performing thermal-mechanical coupling simulation based on the track data through a digital twin decision platform to obtain simulation results; and generating optimized control instructions and alarm information through an adaptive control module based on the spatiotemporal correlation feature matrix and the simulation results. The present invention solves the problems of perception system fragmentation, insufficient intelligent diagnosis and decision-making, and passive risk management in related technologies.

The invention relates to the technical field of high-speed rail virtual tests, in particular to a novel high-speed rail infrastructure virtual test system. The method comprises the following steps: screening out speed reduction demand track sections; for any speed reduction demand track segment, randomly generating a position point as a bird nest position, and executing a cuckoo search algorithm; the fitness of each nest position is obtained according to the position feature of each nest position on the track and the maximum operation adjusting speed of the track segment where the nest position is located and the next track segment; judging whether the bird nest position is abandoned or not by combining tunnel parameters and position features of different position points in a neighborhood range; if it is judged that the bird nest position is abandoned, iteratively updating the bird nest position to obtain a new bird nest position until the maximum number of iterations is reached, and taking the bird nest position corresponding to the maximum fitness value as an optimal coasting position point; and the high-speed rail is operated. According to the method, by obtaining the accurate fitness and the abandoning mode when the algorithm is carried out, the effectiveness of the operation decision is improved, and the effect of the virtual test is optimized.

The application provides a high-speed railway infrastructure detection data processing method and device, and belongs to the high-speed railway data processing field. The high-speed railway infrastructure detection data processing method comprises the following steps: acquiring detection data, wherein the detection data comprises structured data and unstructured data; performing mile calibration on the structured data by using a dynamic functionalization feature modeling alignment method; performing data cleaning on the detection data; constructing an intermediate data model for mixed storage of the structured data and the unstructured data, and performing mile correction on the unstructured data; constructing a data quality scoring model for scoring the detection data; and storing the detection data through the intermediate data model when the score of the detection data meets the requirements. The method optimizes the mile correction method of the detection data, and improves the mile correction accuracy of the detection data. Meanwhile, the intermediate data model is constructed for storing the detection data, and the problem of multi-source data fusion difficulty can be overcome.

The invention discloses a railway special line intelligent inspection robot system and a multi-mode sensing method thereof, and the system comprises a crawler-type mobile platform which is used for autonomously moving on a railway track and under complex road conditions along the line; the multi-mode sensing module is installed on the crawler-type mobile platform and used for collecting multi-source heterogeneous data of the railway infrastructure and the environment, therefore, the multi-mode sensing module is adopted, full-dimension recognition is achieved, the missing detection rate and the false detection rate are greatly reduced, the crawler-type mobile platform is adopted, the crawler-type structure is high in road holding force, the anti-interference capacity is outstanding, and the reliability of the system is improved. The robot can flexibly adapt to scene seamless switching of a track, a soil road along the line and a ramp, can complete operations such as close-range review and slight disposal of hidden dangers through the mechanical arm, makes up for the defect that existing equipment has no execution capability, does not need manual intervention into a closed loop, and greatly improves the inspection and disposal efficiency.

The invention provides a railway cargo transportation network vulnerability assessment method based on cargo flow data. The method comprises the following steps: constructing an initialized RFTN; a pre-interruption model is adopted to distribute a cargo flow / traffic flow to a network, and the cargo flow / traffic flow is mapped to # imgabs0 #; initial network performance indexes # imgabs 1 # and # imgabs 2 # are calculated; distributing the cargo flow / traffic flow to the network again to obtain an invalid network # imgabs3 #; according to an output result, calculating network performance indexes # imgabs4 # and # imgabs5 # after interruption; respectively calculating three vulnerability indexes and a normalized and coupled comprehensive vulnerability index; repeating the steps until all interrupt scenes are simulated; and respectively obtaining RFTN vulnerabilities, key stations, intervals and areas under different interruption scenes and different vulnerability index measurements. According to the method, four different recovery strategies are introduced, partial elastic stages are reflected, network structures and performance fluctuation conditions of longer time before and after interruption events are captured, and the obtained results and conclusions are beneficial to formulating railway infrastructure strengthening strategies, determining maintenance resource allocation and formulating emergency response plans.

The application discloses a kind of frozen soil area railway subgrade and underlying stratum multi-physical field on-line detection evaluation method, through periodic excitation, observation direct current electric field and seismic wave field, network upload data, fast processing direct current electric field and seismic wave field observation data, realize the real-time detection of railway subgrade structure density and the upper and lower limit of underground frozen soil layer, short interval, nondestructive, fast, accurately grasp the change situation of road basic body structure and underground frozen soil layer, provide safety early warning for railway infrastructure, and provide early warning reference standard.Observation system cost is low, installation is convenient, once installation benefits for life, detection method has the advantages such as large detection depth, real-time, few people, intelligent detection, can meet the engineering operation detection demand of plateau railway, highway and the like, provide safety early warning method and equipment for railway, highway and the like subgrade engineering.

The application provides a high-speed railway rail irregularity polishing strategy making method for sound and vibration comfort, and belongs to the technical field of intelligent operation and maintenance of high-speed railway infrastructure, and the method comprises the following steps: (1) establishing a vehicle-track coupling dynamics model; (2) collecting field synchronous test data; (3) model verification; (4) sound comfort index calculation; (5) vibration comfort index calculation; (6) comprehensive comfort index fusion; (7) determining a target rail irregularity spectrum; and (8) polishing strategy generation. The application takes human sound and vibration comprehensive comfort as an optimization target, and realizes a more scientific, accurate and economic rail polishing strategy making method.

The invention discloses an unsupervised polygonal structural member profile fitting method, which belongs to the technical field of railway infrastructure intelligent monitoring and computer vision, and comprises the following steps: acquiring a profile contour point set of a structural member in an image, and describing a straight line where the edge of the structural member is located by a slope parameter and a translation parameter corresponding to the slope parameter; constructing an objective function based on the distance between the contour point and the straight line, optimizing a slope parameter and a translation parameter, minimizing the objective function to train a neural network model, generating a slope parameter and a translation parameter corresponding to the predicted optimal straight line, and obtaining a contour point set corresponding to the contour image of the to-be-detected structural member. And generating an optimal slope parameter and an optimal translation parameter through the trained neural network model, generating an optimal straight line, and obtaining the appearance of the polygonal structural member through the intersection points between the straight lines. According to the invention, accurate fitting and state detection can be carried out on the appearance of the polygonal structural member, so that accurate and efficient real-time monitoring of railway infrastructures is realized.

The invention discloses a roadbed freezing damage diagnosis method based on satellite remote sensing data enhancement and SCNN-BN. The method comprises the following steps: firstly, acquiring PS-InSAR settlement time sequence data of a railway roadbed, and preprocessing the data to eliminate noise and improve data quality; then, performing time sequence alignment and enhancement on the original data by adopting an FDTW algorithm, and generating a training data set with higher representativeness and balance; then, a convolutional neural network model based on a sequence structure is designed, and the convergence speed, stability and diagnosis precision of the model are remarkably improved in combination with a batch normalization technology; and finally, through experimental verification, the performance of the proposed method is comprehensively evaluated. Experimental results show that the method has a high accuracy rate of 96.76% in railway roadbed freeze injury diagnosis, has excellent performance in a noise environment, and is significantly superior to that of a traditional model. The method provides an efficient and reliable solution for intelligent monitoring of the railway infrastructure.

The invention provides a DETR-based end-to-end catenary component defect detection method, and relates to the technical field of railway infrastructure equipment detection, the DETR-based end-to-end catenary component defect detection method comprises a Backbone module, an Encoder module and a Decoder module, the Backbone module, the Encoder module and the Decoder module are all based on a DETR detection model, the Backbone module is used for extracting texture and semantic features in an acquired image, and the Encoder module is used for extracting the texture and semantic features in the acquired image; converting an input two-dimensional image into a feature vector; the Encoder module is an encoder of a network and is used for encoding the image features extracted by the Backbone module into a state vector required by decoding; according to the method, an end-to-end target detection method based on DETR is used, the image understanding and target recognition capability of the model is enhanced, small target detection is improved, and therefore the purpose of automatic detection of catenary defects is better achieved.

An electric rail infrastructure for a work machine (100) is disclosed. The electric rail infrastructure comprises: a plurality of powered rails (104); a plurality of energy interfaces positioned throughout the plurality of powered rails (104); a power module (108) connected to the plurality of energy interfaces for providing electrical power to the plurality of powered rails (104); a plurality of electric clamps (200) positioned on each end of the powered rails (104), each electric clamp (200) having at least one resistor (204).