A track monitoring method, device, medium, and product

Abstract

The application relates to the technical field of monitoring control, in particular to a track monitoring method, device, medium and product, the method comprising the following steps: building a track model based on three-dimensional coordinate data; then inputting first track measurement data and second track measurement data into a track three-dimensional digital model; and controlling the track three-dimensional digital model to update a track state. Furthermore, the first track measurement data and the second track measurement data are subjected to abnormal monitoring by using an abnormal detection algorithm to determine track monitoring information. Finally, the track monitoring information and the track three-dimensional digital model are used to present a monitoring result. The track measurement data is combined with the track three-dimensional digital model, so that the efficiency and effect of maintenance work are improved. The combination of the laser measurement technology and the infrared thermal imaging technology improves the comprehensiveness and accuracy of collected data, and the abnormal detection algorithm is used to automatically detect the track state, so that the accuracy of the track monitoring result is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring control, and in particular to a track monitoring method, device, medium and product. BACKGROUND

[0002] With the gradual expansion of the scale of each city, the continuous increase of urban population, the demand for urban rail transit construction is getting larger and larger, and it promotes the development of railway in the direction of high speed, large capacity and short interval. Under this development background, the operation burden of the track structure is inevitably increased, which affects the service state of the track. The load of the train acting on the track structure is more and more complex, which intensifies the dynamic interaction force of wheel and rail, to some extent, accelerates the damage of track parts, so that the track parts need to be frequently replaced and maintained, which seriously affects the safety and stability of the railway track operation.

[0003] At present, the track state monitoring methods at home and abroad mainly include the following methods: manual inspection, video detection and the like. The manual inspection not only occupies a large amount of human resources, but also is easily affected by the judgment, so that the quality of the inspection result cannot be guaranteed. In the video detection process, dirt, rain and snow and the like often block the camera lens or the detected components, so that accurate data information cannot be obtained, which affects the detection result. Therefore, the track monitoring result accuracy in the related art is low.

[0004] Therefore, how to improve the accuracy of the track monitoring result is a problem to be solved by the person skilled in the art. SUMMARY

[0005] The purpose of the present application is to provide a track monitoring method, device, medium and product, which can solve at least one of the above technical problems.

[0006] The above purpose of the present application is achieved by the following technical scheme:

[0007] In a first aspect, the present application provides a track monitoring method, which adopts the following technical scheme:

[0008] A track monitoring method comprises:

[0009] Obtaining three-dimensional coordinate data collected by a laser, building a track model based on the three-dimensional coordinate data, and obtaining a track three-dimensional digital model, wherein the track three-dimensional digital model is used to accurately reflect the geometric shape of the track and provides a visual tool for track monitoring;

[0010] acquire first track measurement data collected by a laser and second track measurement data collected by an infrared sensor in real time, wherein the track measurement data includes surface morphology data and dynamic change data, and the second track measurement data includes temperature distribution data and temperature trend change data;

[0011] input the first track measurement data and the second track measurement data into the track three-dimensional digital model, and control the track three-dimensional digital model to perform track state updating, wherein the track state updating is used to reflect the current condition of the track in real time;

[0012] when performing the track state updating, use an anomaly detection algorithm to perform anomaly monitoring on the first track measurement data and the second track measurement data, and determine track monitoring information;

[0013] present the monitoring result based on the track monitoring information and the track three-dimensional digital model, so that monitoring personnel can know the working condition of the track.

[0014] By adopting the above technical solution, in order to realize automatic and high-precision track monitoring, improve the efficiency and accuracy of track monitoring, a track model is built based on three-dimensional coordinate data to obtain a track three-dimensional digital model, which can truly and accurately reproduce the actual condition of the track and provide a solid foundation for subsequent track state analysis and evaluation. Then, the first track measurement data collected by the laser and the second track measurement data collected by the infrared sensor are acquired in real time, and the first track measurement data and the second track measurement data are input into the track three-dimensional digital model to control the track three-dimensional digital model to perform track state updating. Through visualization technology, the track three-dimensional digital model is displayed to relevant personnel, which is convenient for intuitively understanding the track state and making decision analysis. Further, when performing the track state updating, the anomaly detection algorithm is used to perform anomaly monitoring on the first track measurement data and the second track measurement data to determine the track monitoring information. Finally, the monitoring result is presented based on the track monitoring information and the track three-dimensional digital model, so that monitoring personnel can know the working condition of the track. The track measurement data is combined with the track three-dimensional digital model, and the anomaly detection algorithm automatically performs anomaly monitoring, which realizes dynamic monitoring of the track state, and is convenient for maintenance personnel to intuitively understand the specific position and range of the track that needs to be maintained, thereby improving the efficiency and effect of maintenance work. The combination of laser measurement technology and infrared thermal imaging technology improves the comprehensiveness and accuracy of the collected data, and the anomaly detection algorithm is used to automatically perform track state detection, thereby improving the accuracy of the track monitoring result.

[0015] The application can be further configured in a preferred example as follows: after the track monitoring information is determined by using the anomaly detection algorithm to perform anomaly monitoring on the first track measurement data and the second track measurement data, the application further comprises:

[0016] When the preliminary monitoring result in the track monitoring information is abnormal, based on the abnormal position in the track monitoring information, a target ultrasonic flaw detector is determined, and the target ultrasonic flaw detector is controlled to perform secondary detection on the abnormal position of the track;

[0017] Obtaining ultrasonic detection data collected by the target ultrasonic flaw detector, wherein the ultrasonic detection data comprises echo information and sound wave intensity change information;

[0018] Based on the ultrasonic detection data, defect analysis processing is performed to obtain defect detail information;

[0019] Correspondingly, the monitoring result presentation based on the track monitoring information and the track three-dimensional digital model comprises:

[0020] The monitoring result presentation is based on the track monitoring information, the defect detail information, and the track three-dimensional digital model.

[0021] The application can be further configured in a preferred example as follows: the track monitoring method further comprises:

[0022] Obtaining vibration data collected by a vibration sensor, wherein the vibration sensor is arranged at a key position of the track and is used to capture vibration characteristics at different positions of the track;

[0023] Obtaining standard vibration characteristics, performing vibration data analysis based on the vibration data and the standard vibration characteristics, and determining a vibration data analysis result;

[0024] When the vibration data analysis result is abnormal, then based on abnormal vibration data and sensor deployment information, a vibration abnormal position and an estimated abnormal reason are determined, wherein the abnormal vibration data is data in the vibration data that does not match the standard vibration characteristics.

[0025] The application can be further configured in a preferred example as follows: the track monitoring method further comprises:

[0026] Based on the defect detail information, the vibration abnormal position, and the estimated abnormal reason, track anomaly analysis is performed to determine a track anomaly level and a fault solving method;

[0027] Based on the track anomaly level and the fault solving method, a track early warning report is generated, wherein the track early warning report records the processing priority and the processing method of each anomaly.

[0028] The application can be further configured in a preferred example to further include, after the presenting of the monitoring result based on the track monitoring information and the track three-dimensional digitized model:

[0029] Obtaining monitoring track deployment information, performing section identification based on the monitoring track deployment information, and determining a target track section;

[0030] Obtaining an exploration characteristic corresponding to the target track section, performing section monitoring analysis based on the exploration characteristic, and determining a target monitoring type and a target monitoring method;

[0031] Periodically detecting the target track section according to the target monitoring type and the target monitoring method, so as to improve the accuracy of track monitoring.

[0032] The application can be further configured in a preferred example to utilize an abnormality detection algorithm to perform abnormality monitoring on the first track measurement data and the second track measurement data, and determine track monitoring information, including:

[0033] Performing key feature extraction based on the surface morphology data and the temperature distribution data to obtain surface morphology features corresponding to the surface morphology data and temperature distribution features corresponding to the temperature distribution data;

[0034] Obtaining static data monitoring standards, performing abnormality identification based on the static data monitoring standards, the surface morphology features, and the temperature distribution features, and determining static data monitoring information;

[0035] Obtaining a dynamic data prediction model and a temperature change prediction model, inputting the dynamic change data into the dynamic data prediction model, inputting the temperature trend change data into the temperature change prediction model, and obtaining dynamic data monitoring information;

[0036] Integrating the static data monitoring information and the dynamic data monitoring information to determine track monitoring information.

[0037] In a second aspect, the application provides an electronic device, which adopts the following technical solution:

[0038] At least one processor;

[0039] A memory;

[0040] At least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute the track monitoring method described above.

[0041] In a third aspect, the application provides a computer-readable storage medium, which adopts the following technical solution:

[0042] A computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the track monitoring method described above.

[0043] In a fourth aspect, the present application provides a computer program product, which adopts the technical scheme as follows:

[0044] A computer program product comprising a computer program which, when executed by a processor, implements the track monitoring method described above.

[0045] In summary, the present application includes at least one of the following beneficial technical effects: In order to realize automatic and high-precision track monitoring, improve the efficiency and accuracy of track monitoring, based on three-dimensional coordinate data, the track model is built, and the track three-dimensional digital model is obtained. The track three-dimensional digital model can truly and accurately reproduce the actual situation of the track, and provide a solid foundation for subsequent track state analysis and evaluation. Then, the first track measurement data collected by the laser and the second track measurement data collected by the infrared sensor are obtained in real time, and the first track measurement data and the second track measurement data are input into the track three-dimensional digital model, and the track three-dimensional digital model is controlled to update the track state. Through the visualization technology, the track three-dimensional digital model is displayed to the relevant personnel, which is convenient for intuitive understanding of the track state and decision analysis. Further, when performing track state updating, the first track measurement data and the second track measurement data are monitored by using the anomaly detection algorithm, and the track monitoring information is determined. Finally, based on the track monitoring information and the track three-dimensional digital model, the monitoring result is presented, so that the monitoring personnel can know the working condition of the track. The track measurement data is combined with the track three-dimensional digital model, and the anomaly detection algorithm is automatically monitored, which realizes the dynamic monitoring of the track state, and the maintenance personnel can intuitively understand the specific position and range of the track that needs to be maintained, which improves the efficiency and effect of the maintenance work. The combination of laser measurement technology and infrared thermal imaging technology improves the comprehensiveness and accuracy of the collected data, and the anomaly detection algorithm is used to automatically detect the track state, which improves the accuracy of the track monitoring result.

[0046] When the preliminary monitoring result in the track monitoring information is that there is an abnormality, based on the abnormal position in the track monitoring information, a target ultrasonic flaw detector is determined, and the target ultrasonic flaw detector is controlled to perform secondary detection on the abnormal position of the track. The secondary detection can not only more accurately locate the specific position of the track defect, but also accurately know the detailed situation of the track defect. Then, based on the ultrasonic detection data collected by the target ultrasonic flaw detector, defect analysis processing is performed to obtain defect detail information. Based on the track monitoring information, the defect detail information, and the track three-dimensional digital model, a monitoring result is presented, so that the defect condition of the track can be detailed and accurately displayed on the track three-dimensional digital model. BRIEF DESCRIPTION OF DRAWINGS

[0047] Fig. 1 is a flowchart of a track monitoring method according to an embodiment of the present application;

[0048] Fig. 2 is a structural diagram of a track monitoring device according to an embodiment of the present application;

[0049] Fig. 3 is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be described in detail. Figs. 1 to 3 The present application will be further described in detail.

[0051] The present embodiment is merely an explanation of the present application, and is not a limitation of the present application. Those skilled in the art can make modifications to the present embodiment without creative contribution, as long as the modifications are within the scope of the present application.

[0052] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative contribution are within the scope of protection of the present application.

[0053] In addition, the term "and / or" herein merely describes the association relationship of the associated objects, and can represent the existence of three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B together, and the existence of B alone. In addition, the character " / " herein, unless otherwise specified, generally represents an "or" relationship between the associated objects.

[0054] The embodiments of the present application will be described in further detail below with reference to the drawings of the specification.

[0055] The embodiment of the present application provides a track monitoring method, which is executed by an electronic device, and the electronic device can be a server or a terminal device. The server can be an independent physical server, a server cluster composed of multiple physical servers or a distributed system, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected through wired or wireless communication, and the embodiment of the present application does not make any limitation here, for example, as shown in the figure, the method comprises the following steps: Fig. 1 The method comprises the following steps:

[0056] In step S101, three-dimensional coordinate data collected by a laser device is acquired, and a track model is built based on the three-dimensional coordinate data to obtain a track three-dimensional digital model. The track three-dimensional digital model is used to accurately reflect the geometric shape of the track and provides a visual tool for track monitoring.

[0057] For the embodiment of the present application, the traditional track monitoring method often depends on manual measurement or two-dimensional image analysis, but often has the problems of insufficient precision and low efficiency. In order to realize automatic and high-precision track monitoring and improve the efficiency and accuracy of track monitoring, a track three-dimensional digital model is built to truly and accurately reproduce the actual condition of the track, thereby providing a solid foundation for subsequent track state analysis and evaluation.

[0058] Specifically, in order to ensure that the three-dimensional data of the track surface can be accurately captured, a high-precision and high-stability laser scanner is deployed on the track site to scan the track site, and the scanning path and scanning point density are reasonably planned according to the length, curvature and other characteristics of the track, so as to ensure the comprehensiveness and accuracy of the data collected by the laser. Further, the electronic device and the laser are connected wirelessly to transmit the collected three-dimensional coordinate data, wherein the three-dimensional coordinate data is used to represent the geometric shape and position information of the track in three-dimensional space, that is, each data point includes its X, Y, Z coordinate values in three-dimensional space, which collectively define the specific position of the point in space. Then, based on the three-dimensional coordinate data, the track model is built to obtain the track three-dimensional digital model, which can be used for real-time monitoring and evaluation of the track state, and provides a scientific basis for subsequent track maintenance, repair or reconstruction. For track model building, the three-dimensional coordinate data is preprocessed to improve the quality and usability of the data, including but not limited to: denoising, filtering, registration, etc. Then, the preprocessed three-dimensional coordinate data is input into a three-dimensional modeling software, and a three-dimensional model of the track is constructed through the modeling tools (e.g., surface fitting) in the software. In the modeling process, attention should be paid to the geometric shape, dimensional accuracy and connection relationship between different components of the track to ensure that the model can truly reflect the actual condition of the track. Preferably, the initially constructed track three-dimensional model can also be optimized, including smoothing, detail repair, size checking and other steps, to improve the accuracy and aesthetics of the model. Further, the optimized track three-dimensional model is visualized to realize all-around and multi-angle display of the track three-dimensional digital model.

[0059] Step S102: Real-time acquisition of first track measurement data collected by the laser and second track measurement data collected by the infrared sensor, wherein the track measurement data includes surface shape data and dynamic change data; the second track measurement data includes temperature distribution data and temperature trend change data.

[0060] For the embodiments of the present application, the electronic device is wirelessly connected with the laser and the infrared sensor, so that the electronic device can receive the data sent by the laser and the infrared sensor in real time. The first track measurement data is used to represent the surface morphology and its dynamic changes of the track, including the geometric shape, surface roughness, structural features of the track, and the changes of these features over time or external conditions (such as train passing, weather changes, etc.). The first track measurement data is crucial for evaluating the integrity, stability and safety of the track. The surface morphology data includes but is not limited to: geometric shape, surface roughness and surface flatness, and the dynamic change data includes but is not limited to: displacement change and stress change. The second track measurement data is used to represent the temperature distribution and its change trend of the track. Temperature is one of the important factors affecting the performance, stability and safety of the track material. By monitoring the temperature distribution and change trend of the track in real time, track diseases or accidents caused by temperature changes can be found and prevented in time. The temperature distribution data is used to show the temperature distribution of the track along its length, width and depth directions, and the temperature trend change data is used to represent the change of the track temperature over time.

[0061] Step S103: input the first track measurement data and the second track measurement data into the track three-dimensional digital model, and control the track three-dimensional digital model to update the track state, wherein the track state update is used to reflect the current condition of the track in real time.

[0062] For the embodiments of the present application, since the first track measurement data and the second track measurement data come from different measurement devices, there is a difference between the time stamp and the coordinate system. Therefore, the two kinds of data are time-synchronized and spatially aligned to ensure that the two kinds of data can accurately reflect the track state at the same time and at the same position. At the same time, a data interface is provided in the three-dimensional digital model, which is used to receive and process the real-time measurement data of the track, and dynamically adjust the related parameters in the track three-dimensional digital model according to the first track measurement data and the second track measurement data received in real time. For example, update the geometric shape and displacement of the track according to the surface morphology data and the dynamic change data; update the temperature field and stress state of the track according to the temperature distribution data and the temperature trend change data. Then, after updating the three-dimensional digital model, the three-dimensional digital model is re-rendered to reflect the current state of the track in real time. Finally, through visualization technology (such as virtual reality, augmented reality, etc.), the track three-dimensional digital model is displayed to relevant personnel so that they can intuitively understand the track state and make decision analysis.

[0063] Step S104: when performing track state update, use an anomaly detection algorithm to monitor the first track measurement data and the second track measurement data for abnormalities, and determine track monitoring information;

[0064] Step S105: based on the track monitoring information and the track three-dimensional digital model, a monitoring result is presented, so that the monitoring personnel can know the working condition of the track.

[0065] For the embodiment of the present application, when performing track state updating, the first track measurement data and the second track measurement data are monitored for abnormalities by using an abnormality detection algorithm, and track monitoring information is determined, wherein the track monitoring information is used to represent whether the current working state of the track is abnormal or deviates from the normal state, so that the maintenance personnel can timely understand the working state of the track and timely maintain in the case of abnormality. There are various ways for abnormality monitoring, and the embodiment of the present application is not limited in this regard. In one realizable way, key features are extracted based on surface morphology data and temperature distribution data to obtain surface morphology features corresponding to the surface morphology data and temperature distribution features corresponding to the temperature distribution data; a static data monitoring standard is obtained, and based on the static data monitoring standard, the surface morphology features and the temperature distribution features, abnormality recognition is performed to determine static data monitoring information; a dynamic data prediction model and a temperature change prediction model are obtained, dynamic change data is input into the dynamic data prediction model, and temperature trend change data is input into the temperature change prediction model to obtain dynamic data monitoring information; the static data monitoring information and the dynamic data monitoring information are integrated to determine the track monitoring information, wherein the track monitoring information includes: preliminary monitoring results (used to represent whether the current working state of the track is abnormal), abnormality types and abnormality positions. Finally, the track monitoring information obtained by abnormality monitoring processing is fused with the track three-dimensional digital model, that is, the current working condition of the track is visually displayed in the track three-dimensional digital model, so that the maintenance personnel can intuitively see the working condition and abnormal information of the track. For example, according to the track monitoring information, the positions, types and severity of abnormalities and other information are marked in the track three-dimensional digital model. Different colors, icons or labels can be used to represent different abnormal states. Of course, the form of the monitoring result presentation can be adjusted according to actual conditions, and the embodiment of the present application is not limited in this regard.

[0066] The track measurement data is combined with the track three-dimensional digital model, and the abnormality detection algorithm automatically performs abnormality monitoring, realizing dynamic monitoring of the track state, facilitating the maintenance personnel to intuitively understand the specific position and range of the track that needs to be maintained, and improving the efficiency and effect of the maintenance work. Compared with the track monitoring methods of manual inspection and video detection, the combination of laser measurement technology and infrared thermal imaging technology improves the comprehensiveness and accuracy of the collected data, and automatically detects the track state by using the abnormality detection algorithm, improving the accuracy of the track monitoring result.

[0067] It can be seen that, in the embodiment of the present application, in order to realize automatic and high-precision track monitoring and improve the efficiency and accuracy of track monitoring, a track model is built based on three-dimensional coordinate data to obtain a track three-dimensional digital model, which can truly and accurately reproduce the actual state of the track and provide a solid foundation for subsequent track state analysis and evaluation. Then, the first track measurement data collected by the laser and the second track measurement data collected by the infrared sensor are acquired in real time, and the first track measurement data and the second track measurement data are input into the track three-dimensional digital model to control the track three-dimensional digital model to update the track state. Through visualization technology, the track three-dimensional digital model is displayed to relevant personnel, which facilitates intuitive understanding of the track state and decision analysis. Further, when performing track state updating, an abnormality detection algorithm is used to monitor the first track measurement data and the second track measurement data to determine track monitoring information. Finally, based on the track monitoring information and the track three-dimensional digital model, monitoring results are presented to enable monitoring personnel to know the working condition of the track. The combination of track measurement data and track three-dimensional digital model, and automatic abnormality monitoring by the abnormality detection algorithm, realizes dynamic monitoring of the track state, facilitates maintenance personnel to intuitively understand the specific position and range of the track that needs to be maintained, and improves the efficiency and effect of maintenance work. The combination of laser measurement technology and infrared thermal imaging technology improves the comprehensiveness and accuracy of the collected data, and the use of the abnormality detection algorithm for automatic track state detection improves the accuracy of the track monitoring results.

[0068] Further, in order to more accurately locate the specific position of the track defect and accurately know the detailed information of the track defect, in the embodiment of the present application, after the abnormality detection algorithm is used to monitor the first track measurement data and the second track measurement data to determine the track monitoring information, the method further includes:

[0069] When the preliminary monitoring result in the track monitoring information is that there is an abnormality, based on the abnormal position in the track monitoring information, a target ultrasonic flaw detector is determined, and the target ultrasonic flaw detector is controlled to perform secondary detection on the abnormal position of the track;

[0070] Acquiring ultrasonic detection data collected by the target ultrasonic flaw detector, the ultrasonic detection data including: echo information, sound wave intensity change information;

[0071] Based on the ultrasonic detection data, defect analysis and processing are performed to obtain defect detail information;

[0072] Correspondingly, based on the track monitoring information and the track three-dimensional digital model, monitoring results are presented, including:

[0073] Based on the track monitoring information, the defect detail information and the track three-dimensional digital model, monitoring results are presented.

[0074] For the embodiments of the present application, since the detailed situation of the track defect is not detected in the preliminary monitoring result, only the abnormal position of the track is preliminarily located, therefore, the secondary detection of the abnormal position of the track by using the ultrasonic detection technology can not only more accurately locate the specific position of the track defect, but also accurately know the detailed situation of the track defect, such as the defect shape and size, which is helpful to provide strong support for the subsequent maintenance strategy.

[0075] Specifically, when the preliminary monitoring result in the track monitoring information is abnormal, the target ultrasonic flaw detector is determined based on the abnormal position in the track monitoring information by using the position correspondence relationship between the flaw detector and the detection position, wherein the position correspondence relationship is pre-stored in the electronic device, and the target ultrasonic flaw detector is controlled to perform secondary detection on the abnormal position of the track. The target ultrasonic flaw detector will collect echo information and sound wave intensity change information during the detection process, therefore, the target ultrasonic flaw detector sends the ultrasonic detection data to the electronic device by wireless transmission. Then, the ultrasonic detection data is filtered to remove noise and interference signals and improve data quality, wherein the filtering method includes but is not limited to low-pass filtering, high-pass filtering, band-pass filtering, etc. Further, the echo signal is analyzed, the first characteristic parameter related to the defect is extracted based on the echo signal, including but not limited to echo time, echo amplitude, echo shape, etc., wherein the first characteristic parameter can reflect the position, size, shape and other information of the defect; the sound wave intensity change information is analyzed, and the second characteristic parameter related to the defect is extracted, including but not limited to sound wave attenuation rate, sound wave reflection intensity, etc., wherein the second characteristic parameter can reflect the influence degree of the defect on the sound wave propagation. Then, the first characteristic parameter and the second characteristic parameter are identified by using a defect identification algorithm to determine the defect type, wherein the defect identification algorithm includes but is not limited to threshold judgment method, pattern recognition method, machine learning method, etc. Finally, according to the echo time and the sound wave propagation speed, the specific position of the defect on the track is calculated, and by using the width, amplitude and other parameters of the echo signal, the speed of sound and the acoustic characteristics of the material, the size of the defect (such as length, width, depth, etc.) is calculated. The defect detail information is determined by comprehensively considering the defect type, specific position and size information. Correspondingly, the monitoring result presentation is performed based on the track monitoring information, the defect detail information and the track three-dimensional digital model, so that the defect condition of the track can be displayed in detail and accurately on the track three-dimensional digital model.

[0076] It can be seen that in the embodiment of the present application, when the preliminary monitoring result in the track monitoring information is abnormal, the target ultrasonic flaw detector is determined based on the abnormal position in the track monitoring information, and the target ultrasonic flaw detector is controlled to perform secondary detection on the abnormal position of the track. The secondary detection not only can more accurately locate the specific position of the track defect, but also can accurately know the detailed situation of the track defect. Then, based on the ultrasonic detection data collected by the target ultrasonic flaw detector, defect analysis and processing are performed to obtain defect detail information. Based on the track monitoring information, the defect detail information and the track three-dimensional digital model, the monitoring result is presented, so that the defect condition of the track can be displayed in detail and accurately on the track three-dimensional digital model.

[0077] Further, in order to improve the accuracy of the track monitoring result, in the embodiment of the present application, further comprising:

[0078] Obtaining vibration data collected by a vibration sensor, wherein the vibration sensor is arranged at a key position of the track and is used to capture vibration characteristics of different positions of the track;

[0079] Obtaining standard vibration characteristics, performing vibration data analysis based on the vibration data and the standard vibration characteristics, and determining a vibration data analysis result.

[0080] When the vibration data analysis result is abnormal, the vibration abnormal position and the estimated abnormal reason are determined based on the abnormal vibration data and the sensor deployment information, wherein the abnormal vibration data is the data in the vibration data that does not match the standard vibration characteristics.

[0081] For the embodiment of the present application, unlike the static data provided by the laser measurement and infrared thermal imaging technology, vibration detection can capture the response of the track under dynamic load, which is crucial for evaluating the stability and safety of the track. Therefore, based on the laser measurement technology and the infrared thermal imaging technology, the vibration detection method is combined to further improve the accuracy of the track monitoring result. For the execution order of the above steps, the embodiment of the present application is not limited, and can be performed before or after any step of steps S101 to S105.

[0082] Specifically, the vibration sensor is a device for converting mechanical vibration into an electrical signal, which can accurately convert the vibration characteristics (such as amplitude, frequency, phase, etc.) of the track into an electrical signal, wherein the vibration sensor is arranged at a key position of the track, and the key position is usually a position that can reflect the overall vibration characteristics of the track or a potential problem area, for example, the support structure (such as a sleeper, a fastener, etc.), a curve, a joint, a bridge or a tunnel, etc. of the track are important monitoring points. Obtain the vibration data collected by the vibration sensor, wherein the vibration data includes but is not limited to: vibration frequency, vibration amplitude, vibration speed, vibration acceleration, vibration duration, etc. The standard vibration characteristics are pre-stored in the electronic device, that is, the numerical range of each item of vibration information under normal conditions is recorded in the standard vibration characteristics, and the standard vibration characteristics are standard data suitable for track monitoring according to industry specifications, technical standards or historical data. Then, based on the vibration data and the standard vibration characteristics, vibration data analysis is performed to determine the vibration data analysis result, that is, if the vibration data does not match the standard vibration characteristics, the vibration data analysis result is determined to be abnormal; otherwise, the vibration data analysis result is determined to be normal. Further, when the vibration data analysis result is abnormal, the data segment that does not match the standard vibration characteristics is automatically identified as abnormal vibration data, which usually takes the form of abnormal amplitude increase, frequency decrease and waveform distortion, etc., and based on the abnormal vibration data and the sensor deployment information, the vibration abnormal position is determined. Then, in-depth analysis is performed based on the abnormal vibration data, including but not limited to: time domain analysis, frequency domain analysis and waveform analysis, etc., to extract abnormal feature information in the abnormal vibration data, such as main frequency, harmonic component, waveform shape, etc., and match the abnormal feature information with known fault modes to determine the estimated abnormal reason, wherein the estimated abnormal reason includes but is not limited to: imbalance, looseness, bearing failure, gear failure, motor problem, etc., and the known fault modes usually depend on a fault mode library or expert experience, which contains various fault types and their corresponding vibration characteristics.

[0083] It can be seen that in the embodiment of the present application, the vibration data collected by the vibration sensor is obtained, and vibration data analysis is performed based on the vibration data and the standard vibration characteristics to determine the vibration data analysis result. When the vibration data analysis result is abnormal, the vibration abnormal position and the estimated abnormal reason are determined based on the abnormal vibration data and the sensor deployment information. Thus, on the basis of laser measurement technology and infrared thermal imaging technology, combined with the vibration detection method, the accuracy of the track monitoring result is further improved.

[0084] Further, in order to efficiently configure resources and prioritize important abnormalities, and improve fault solving efficiency, in the embodiment of the present application, it further includes:

[0085] perform track anomaly analysis based on the defect detail information, the vibration abnormal position and the estimated abnormal cause, determine a track anomaly level and a fault resolution method;

[0086] generate a track warning report based on the track anomaly level and the fault resolution method, wherein the track warning report records a processing priority and a processing method for each anomaly.

[0087] For the embodiments of the present application, after determining the defect detail information, the vibration abnormal position and the estimated abnormal cause, track anomaly analysis can be performed, and the track anomaly can be scientifically and reasonably classified, which helps efficient allocation of resources and prioritization of important anomalies. At the same time, formulating a fault resolution method can reduce unnecessary maintenance work and improve fault resolution efficiency.

[0088] Specifically, the track anomaly level classification standard is pre-stored in the electronic device, therefore, according to the track anomaly level classification standard, the track anomaly level is determined based on the defect detail information, the vibration abnormal position and the estimated abnormal cause. Then, according to the defect detail information and the estimated abnormal cause, the specific type of track fault is further identified, for example, crack, wear, loosening, and for different types of track faults, corresponding fault resolution methods are formulated, including but not limited to: replacing damaged parts, adjusting track parameters, and increasing inspection frequency. Finally, based on the track anomaly level and the fault resolution method, a track warning report is generated, which records the basic information of the track anomaly (including: abnormal position, abnormal performance, estimated cause and anomaly level), the processing priority and the specific fault resolution method for each anomaly.

[0089] As can be seen, in the embodiments of the present application, based on the defect detail information, the vibration abnormal position and the estimated abnormal cause, track anomaly analysis is performed to determine the track anomaly level and the fault resolution method. Then, based on the track anomaly level and the fault resolution method, a track warning report is generated. Scientific and reasonable classification of track anomalies helps efficient allocation of resources and prioritization of important anomalies. At the same time, formulating a fault resolution method can reduce unnecessary maintenance work and improve fault resolution efficiency.

[0090] Further, in order to be able to discover small changes and potential problems in the track in a timely manner, periodic detection helps prevent major accidents and ensures the safe and stable operation of the track system, in the embodiments of the present application, after presenting the monitoring results based on the track monitoring information and the track three-dimensional digital model, further includes:

[0091] obtain monitoring track deployment information, perform section identification based on the monitoring track deployment information, and determine a target track section;

[0092] obtain survey characteristics corresponding to the target track section, perform section monitoring analysis based on the survey characteristics, and determine a target monitoring type and a target monitoring manner;

[0093] According to the target monitoring type and the target monitoring manner, the target track section is periodically detected to improve the accuracy of track monitoring.

[0094] For the embodiments of the present application, the monitoring track step information is used to represent key information such as the specific layout of the track system, the device configuration, the running state, and the monitoring requirements. The monitoring track deployment information is built by track design documents, construction drawings, and geographic information system (GIS) data. Then, based on the monitoring track deployment information, the target track section is identified, wherein the target track section is a specific position of the track (for example, a curve, a bridge, a tunnel, a curve section, a turnout section), a specific engineering area (for example, a curve section, a turnout section), and a part that is complex in structure, special in stress condition, and prone to problems. For section identification, the section identification standard is pre-stored in the electronic device. The section identification standard records the characteristics possessed by the target track section. The section identification standard is matched with the monitoring track deployment information, that is, the consistency of the geographic position, the characteristic parameters, and other information of the track with the section identification standard is compared to determine the target track section. Then, the survey characteristics corresponding to the target track section are obtained, including but not limited to geological conditions, climate conditions, traffic flow, track structure, etc. The survey characteristics of the target track section will affect the type of track abnormalities to some extent, and special abnormal types require corresponding monitoring manners. Therefore, based on the survey characteristics, the section monitoring analysis is performed to determine the target monitoring type and the target monitoring manner, wherein the target monitoring type includes but is not limited to structural safety monitoring, geological disaster monitoring, environmental monitoring, etc. The target monitoring manner is a technical means suitable for the target monitoring type, for example, installing a displacement sensor to monitor the deformation of the track structure, using a seismograph to monitor the dynamic changes of underground rock layers, setting up a video monitoring device to monitor the environment around the track in real time, etc. Finally, according to the target monitoring type and the target monitoring manner, the target track section is periodically detected to improve the accuracy of track monitoring. According to the target monitoring type and the target monitoring manner, the target track section is periodically detected, which can timely discover the small changes and potential problems of the track. Periodic detection helps to prevent major accidents and ensures the safe and stable operation of the track system.

[0095] It can be seen that in the embodiment of the present application, the monitoring track deployment information is acquired, the section identification is performed based on the monitoring track deployment information, the target track section is determined, and the target track section is a part that is complex in structure and special in stress condition and prone to problems. Then, the survey characteristics corresponding to the target track section are acquired, the section monitoring analysis is performed based on the survey characteristics, and the target monitoring type and the target monitoring method are determined. Further, the target track section is periodically detected according to the target monitoring type and the target monitoring method, so as to improve the accuracy of track monitoring. The target track section is periodically detected according to the target monitoring type and the target monitoring method, so as to timely find the slight changes and potential problems of the track, and the periodic detection helps to prevent major accidents and ensures the safe and stable operation of the track system.

[0096] Further, in order to more comprehensively reflect the actual condition of the track and improve the accuracy and reliability of track monitoring, in the embodiment of the present application, the first track measurement data and the second track measurement data are monitored for abnormalities by using an abnormality detection algorithm, and track monitoring information is determined, including:

[0097] Based on the surface morphology data and the temperature distribution data, key features are extracted to obtain surface morphology features corresponding to the surface morphology data and temperature distribution features corresponding to the temperature distribution data;

[0098] The static data monitoring standard is acquired, the abnormality identification is performed based on the static data monitoring standard, the surface morphology features and the temperature distribution features, and the static data monitoring information is determined;

[0099] The dynamic data prediction model and the temperature change prediction model are acquired, the dynamic change data is input into the dynamic data prediction model, the temperature trend change data is input into the temperature change prediction model, and the dynamic data monitoring information is obtained;

[0100] The static data monitoring information and the dynamic data monitoring information are integrated to determine the track monitoring information.

[0101] For the embodiment of the present application, when performing abnormality monitoring, the static data monitoring information and the dynamic data monitoring information are integrated to form comprehensive track monitoring information, which can more comprehensively reflect the actual condition of the track. The comprehensive monitoring method not only improves the accuracy and reliability of track monitoring, but also provides strong data support for the maintenance and management of the track.

[0102] Specifically, feature parameters reflecting the track state are extracted from the surface morphology data and the temperature distribution data, that is, feature extraction is performed based on the surface morphology data to determine surface morphology features, which include but are not limited to geometric features (for reflecting the geometric morphology of the track) and material features; feature extraction is performed based on the temperature distribution data to determine temperature distribution features, which include but are not limited to temperature gradient features and locked rail temperature, wherein the temperature gradient features are the temperature difference and temperature change trend between different parts of the track (such as the rail, the sleeper, and the track bed), which can reflect the thermal stability of the track; the locked rail temperature reflects the temperature state of the rail under certain conditions (such as no external force), which is an important parameter for evaluating the thermal expansion and contraction effect and stability of the track. Further, static data monitoring standards are obtained, and abnormality identification is performed based on the static data monitoring standards, the surface morphology features, and the temperature distribution features to determine static data monitoring information, wherein the static data monitoring standards give corresponding monitoring indicators for each item of static data information, and the static indicators are matched with the surface morphology features and the temperature distribution features; if there are unmatched features, it is determined that the preliminary monitoring result in the static data monitoring information is abnormal, and the position corresponding to the unmatched features is recorded as an abnormal position. If there are no unmatched features, it is determined that the preliminary monitoring result in the static data monitoring information is normal.

[0103] For dynamic data, a dynamic data prediction model and a temperature change prediction model are obtained, both of which are obtained by training a neural network model using a large amount of historical dynamic data and can be used to predict the dynamic data and the temperature change. Thus, the real-time collected dynamic change data is input into the dynamic data prediction model, and the dynamic data prediction model generates a prediction result according to the input data, which includes the dynamic change trend of the dynamic data and abnormal value warning information, etc. At the same time, the real-time collected temperature trend change data is input into the temperature change prediction model, and the temperature change prediction model generates a prediction result according to the input data, which includes the dynamic change trend of the temperature data and abnormal value warning information, etc. The output results of the dynamic data prediction model and the temperature change prediction model are combined to obtain dynamic data monitoring information. Finally, the static data monitoring information and the dynamic data monitoring information are combined to determine the track monitoring information.

[0104] It can be seen that, in the embodiment of the present application, the key features are extracted based on the surface morphology data and the temperature distribution data to obtain the surface morphology features corresponding to the surface morphology data and the temperature distribution features corresponding to the temperature distribution data. Then, the abnormality is identified based on the static data monitoring standard, the surface morphology features and the temperature distribution features to determine the static data monitoring information. Further, the dynamic change data is input into the dynamic data prediction model, and the temperature trend change data is input into the temperature change prediction model to obtain the dynamic data monitoring information. Finally, the static data monitoring information and the dynamic data monitoring information are comprehensively determined to obtain the track monitoring information. The static data monitoring information and the dynamic data monitoring information are comprehensively formed to obtain comprehensive track monitoring information, which can more comprehensively reflect the actual condition of the track, and the comprehensive monitoring method not only improves the accuracy and reliability of the track monitoring, but also provides strong data support for the maintenance and management of the track.

[0105] The above embodiment introduces a track monitoring method from the perspective of method flow, and the following embodiment introduces a track monitoring device from the perspective of virtual module or virtual unit. For details, see the following embodiment.

[0106] The embodiment of the present application provides a track monitoring device, as shown in the figure, which specifically can include: Fig. 2

[0107] The track model building module 210 is configured to acquire three-dimensional coordinate data collected by the laser, build a track model based on the three-dimensional coordinate data, and obtain a track three-dimensional digital model. The track three-dimensional digital model is used to accurately reflect the geometric shape of the track and provides a visual tool for track monitoring.

[0108] The real-time acquisition module 220 is configured to acquire first track measurement data collected by the laser and second track measurement data collected by the infrared sensor in real time. The track measurement data includes surface morphology data and dynamic change data. The second track measurement data includes temperature distribution data and temperature trend change data.

[0109] The track state updating module 230 is configured to input the first track measurement data and the second track measurement data into the track three-dimensional digital model and control the track three-dimensional digital model to update the track state. The track state updating is used to reflect the current condition of the track in real time.

[0110] The abnormality monitoring module 240 is configured to perform abnormality monitoring on the first track measurement data and the second track measurement data by using an abnormality detection algorithm when the track state updating is performed, and determine track monitoring information.

[0111] ​The monitoring result presentation module 250 is configured to perform monitoring result presentation based on the track monitoring information and the track three-dimensional digitized model, so that the monitoring personnel can know the working condition of the track.

[0112] For the embodiments of the present application, in order to realize automatic and high-precision track monitoring and improve the efficiency and accuracy of track monitoring, a track model is built based on three-dimensional coordinate data to obtain a track three-dimensional digitized model, which can truly and accurately reproduce the actual condition of the track and provide a solid foundation for subsequent track state analysis and evaluation. Then, the first track measurement data collected by the laser and the second track measurement data collected by the infrared sensor are acquired in real time, and the first track measurement data and the second track measurement data are input into the track three-dimensional digitized model to control the track three-dimensional digitized model to update the track state. Through visualization technology, the track three-dimensional digitized model is displayed to relevant personnel, which facilitates intuitive understanding of the track state and decision analysis. Furthermore, when the track state update is performed, the first track measurement data and the second track measurement data are monitored for abnormalities by using an abnormality detection algorithm to determine track monitoring information. Finally, monitoring result presentation is performed based on the track monitoring information and the track three-dimensional digitized model, so that the monitoring personnel can know the working condition of the track. The combination of track measurement data and the track three-dimensional digitized model and automatic abnormality monitoring by the abnormality detection algorithm realize dynamic monitoring of the track state, which facilitates maintenance personnel to intuitively understand the specific position and range of the track that needs to be maintained, and improves the efficiency and effect of maintenance work. The combination monitoring method of the laser measurement technology and the infrared thermal imaging technology improves the comprehensiveness and accuracy of the collected data, and the track state is detected automatically by using the abnormality detection algorithm, which improves the accuracy of the track monitoring result.

[0113] In a possible implementation of the embodiments of the present application, the track monitoring device further includes:

[0114] The ultrasonic detection module is configured to, when the preliminary monitoring result in the track monitoring information is that there is an abnormality, determine a target ultrasonic flaw detector based on the abnormal position in the track monitoring information, and control the target ultrasonic flaw detector to perform secondary detection on the abnormal position of the track.

[0115] The ultrasonic detection data collected by the target ultrasonic flaw detector is acquired, and the ultrasonic detection data includes echo information and sound wave intensity change information.

[0116] Defect analysis and processing are performed based on the ultrasonic detection data to obtain defect detail information.

[0117] Correspondingly, when performing monitoring result presentation based on the track monitoring information and the track three-dimensional digitized model, the monitoring result presentation module 250 is configured to:

[0118] The monitoring result is presented based on the track monitoring information, the defect detail information and the track three-dimensional digital model.

[0119] In a possible implementation of the embodiment of the present application, the track monitoring device further includes:

[0120] The vibration detection module is configured to acquire vibration data collected by the vibration sensor, wherein the vibration sensor is arranged at a key position of the track and is configured to capture vibration characteristics at different positions of the track.

[0121] The standard vibration characteristics are acquired, and vibration data analysis is performed based on the vibration data and the standard vibration characteristics to determine a vibration data analysis result.

[0122] When the vibration data analysis result is abnormal, the vibration abnormal position and the estimated abnormal cause are determined based on the abnormal vibration data and the sensor deployment information, wherein the abnormal vibration data is data in the vibration data that does not match the standard vibration characteristics.

[0123] In a possible implementation of the embodiment of the present application, the track monitoring device further includes:

[0124] The track anomaly analysis module is configured to perform track anomaly analysis based on the defect detail information, the vibration abnormal position and the estimated abnormal cause to determine a track anomaly level and a fault solution mode.

[0125] Based on the track anomaly level and the fault solution mode, a track early warning report is generated, wherein the track early warning report records a processing priority and a processing mode of each anomaly.

[0126] In a possible implementation of the embodiment of the present application, the track monitoring device further includes:

[0127] The track section monitoring module is configured to acquire monitoring track deployment information, perform section identification based on the monitoring track deployment information, and determine a target track section.

[0128] The survey characteristics corresponding to the target track section are acquired, and section monitoring analysis is performed based on the survey characteristics to determine a target monitoring type and a target monitoring mode.

[0129] The target track section is periodically detected according to the target monitoring type and the target monitoring mode, so as to improve the accuracy of track monitoring.

[0130] In a possible implementation of the embodiment of the present application, when the anomaly monitoring module 240 performs anomaly monitoring on the first track measurement data and the second track measurement data by using the anomaly detection algorithm to determine the track monitoring information, the anomaly monitoring module 240 is configured to:

[0131] Based on the surface morphology data and the temperature distribution data, key feature extraction is performed to obtain surface morphology features corresponding to the surface morphology data and temperature distribution features corresponding to the temperature distribution data.

[0132] Obtain static data monitoring criteria, based on the static data monitoring criteria, the surface morphology features and the temperature distribution features, perform anomaly identification, and determine static data monitoring information.

[0133] Obtain a dynamic data prediction model and a temperature change prediction model, input the dynamic change data into the dynamic data prediction model, input the temperature trend change data into the temperature change prediction model, and obtain dynamic data monitoring information.

[0134] Integrate the static data monitoring information and the dynamic data monitoring information to determine track monitoring information.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0136] An electronic device is provided in the embodiments of the present application, as shown in Fig. 3 The electronic device 300 shown in Fig. 3 The electronic device 300 shown in

[0137] The processor 301 can be a CPU (Central Processing Unit, central processor), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure content of the present application. The processor 301 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0138] The bus 302 can include a path that transmits information between the above-described components. The bus 302 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Fig. 3 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0139] The memory 303 can be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0140] The memory 303 is used to store application program codes for implementing the scheme of the present application, and is controlled to execute by the processor 301. The processor 301 is used to execute the application program codes stored in the memory 303 to realize the content shown in the foregoing method embodiments.

[0141] The electronic device includes, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a vehicle terminal (for example, a car navigation terminal), etc., and a fixed terminal such as a digital TV, a desktop computer, etc. It can also be a server, etc. Fig. 3 The electronic device shown is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.

[0142] The embodiments of the present application provide a computer readable storage medium, which stores computer programs, and when the computer programs run on a computer, the computer can execute the corresponding content in the foregoing method embodiments.

[0143] The embodiment of the present application provides a computer program product, which comprises a computer program, and the computer program realizes the method in any of the above embodiments when executed by a processor. Compared with the related art, in order to realize automatic and high-precision track monitoring, improve the efficiency and accuracy of track monitoring, the track model is built based on three-dimensional coordinate data to obtain a track three-dimensional digital model, and the track three-dimensional digital model can truly and accurately reproduce the actual state of the track, thereby providing a solid foundation for subsequent track state analysis and evaluation. Then, the first track measurement data collected by the laser and the second track measurement data collected by the infrared sensor are acquired in real time, and the first track measurement data and the second track measurement data are input into the track three-dimensional digital model to control the track three-dimensional digital model to update the track state. Through the visualization technology, the track three-dimensional digital model is displayed to the relevant personnel, so that the track state can be intuitively understood and decision analysis can be performed. Further, when the track state updating is performed, the first track measurement data and the second track measurement data are monitored by using the anomaly detection algorithm to determine track monitoring information. Finally, the track monitoring information and the track three-dimensional digital model are used for monitoring result presentation, so that the monitoring personnel can know the working state of the track. The track measurement data is combined with the track three-dimensional digital model, and the anomaly detection algorithm is used for automatic anomaly monitoring, so that the dynamic monitoring of the track state is realized, the specific position and range of the track that needs to be maintained can be intuitively understood by the maintenance personnel, and the efficiency and effect of the maintenance work are improved. The combination of the laser measurement technology and the infrared thermal imaging technology improves the comprehensiveness and accuracy of the collected data, and the anomaly detection algorithm is used for automatic track state detection, so that the accuracy of the track monitoring result is improved.

[0144] It should be understood that, although each step in the flowchart of the accompanying drawings is displayed in sequence according to the indication of the arrow, these steps are not necessarily executed in sequence according to the indication of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0145] The above is only part of the embodiments of the present application, and it should be pointed out that, for ordinary skilled persons in the technical field, some improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method of track monitoring, characterized in that, The method comprises the following steps: acquiring three-dimensional coordinate data collected by a laser, and building an orbit model based on the three-dimensional coordinate data to obtain an orbit three-dimensional digital model, wherein the orbit three-dimensional digital model is used to accurately reflect the geometric shape of the orbit and provides a visual tool for orbit monitoring; acquiring first orbit measurement data collected by the laser and second orbit measurement data collected by an infrared sensor in real time, wherein the orbit measurement data includes surface shape data and dynamic change data, and the second orbit measurement data includes temperature distribution data and temperature trend change data; inputting the first orbit measurement data and the second orbit measurement data into the orbit three-dimensional digital model to control the orbit three-dimensional digital model to update the orbit state, wherein the orbit state update is used to reflect the current state of the orbit in real time; when the orbit state update is performed, using an anomaly detection algorithm to monitor the first orbit measurement data and the second orbit measurement data for abnormalities to determine orbit monitoring information; the method of monitoring the first orbit measurement data and the second orbit measurement data for abnormalities to determine orbit monitoring information comprises: extracting key features based on the surface shape data and the temperature distribution data to obtain surface shape features corresponding to the surface shape data and temperature distribution features corresponding to the temperature distribution data; acquiring static data monitoring standards, and identifying abnormalities based on the static data monitoring standards, the surface shape features and the temperature distribution features to determine static data monitoring information; acquiring a dynamic data prediction model and a temperature change prediction model, inputting the dynamic change data into the dynamic data prediction model, and inputting the temperature trend change data into the temperature change prediction model to obtain dynamic data monitoring information; comprehensively determining orbit monitoring information based on the static data monitoring information and the dynamic data monitoring information; after the orbit monitoring information is determined, the method further comprises: when the preliminary monitoring result in the orbit monitoring information indicates that there is an abnormality, determining a target ultrasonic flaw detector based on the abnormal position in the orbit monitoring information, and controlling the target ultrasonic flaw detector to perform secondary detection on the abnormal position of the orbit; acquiring ultrasonic detection data collected by the target ultrasonic flaw detector, wherein the ultrasonic detection data includes echo information and sound wave intensity change information; performing defect analysis and processing based on the ultrasonic detection data to obtain defect detail information; correspondingly, the method of presenting the monitoring result based on the orbit monitoring information and the orbit three-dimensional digital model comprises: presenting the monitoring result based on the orbit monitoring information and the orbit three-dimensional digital model so that monitoring personnel can know the working condition of the orbit.

2. The track monitoring method of claim 1, wherein, the method further comprises: acquiring vibration data collected by a vibration sensor, wherein the vibration sensor is arranged at a key position of the orbit and is used to capture vibration characteristics at different positions of the orbit. acquire a standard vibration feature, perform vibration data analysis based on the vibration data and the standard vibration feature, and determine a vibration data analysis result; when the vibration data analysis result is abnormal, determine a vibration abnormal position and an estimated abnormal cause based on abnormal vibration data and sensor deployment information, wherein the abnormal vibration data is data in the vibration data that does not match the standard vibration feature.

3. The track monitoring method of claim 2, wherein, Further comprising: perform track abnormality analysis based on the defect detail information, the vibration abnormal position, and the estimated abnormal cause, and determine a track abnormality level and a failure resolution method; generate a track early warning report based on the track abnormality level and the failure resolution method, wherein the track early warning report records a processing priority and a processing method for each abnormality.

4. The track monitoring method of claim 1, wherein, After the monitoring result presentation based on the track monitoring information and the track three-dimensional digital model, further comprising: acquire monitoring track deployment information, perform section identification based on the monitoring track deployment information, and determine a target track section; acquire an exploration feature corresponding to the target track section, perform section monitoring analysis based on the exploration feature, and determine a target monitoring type and a target monitoring method; perform periodic detection on the target track section according to the target monitoring type and the target monitoring method, so as to improve the accuracy of track monitoring.

5. An electronic device, comprising: comprise: at least one processor; a memory; at least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute the track monitoring method in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, a computer program is stored thereon, and when the computer program is executed in a computer, the computer program causes the computer to execute the track monitoring method in any one of claims 1-4.

7. A computer program product, characterised in that, a computer program is stored thereon, and when the computer program is executed in a computer, the computer program causes the computer to execute the track monitoring method in any one of claims 1-4.

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