CN119223196B - A digital twin method and system for track information based on Beidou track inspection instrument - Google Patents
A digital twin method and system for track information based on Beidou track inspection instrumentInfo
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- CN119223196B CN119223196B CN202411297578.8A CN202411297578A CN119223196B CN 119223196 B CN119223196 B CN 119223196B CN 202411297578 A CN202411297578 A CN 202411297578A CN 119223196 B CN119223196 B CN 119223196B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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Abstract
本发明公开了一种基于北斗轨道检查仪的轨道信息数字孪生方法，包括：根据线路设计信息构建相应的轨道三维模型；通过搭载多种传感器的轨道检查仪移动检测收集轨道及周边数据；对轨道及周边数据进行高精度里程定位；将经过高精度里程定位的轨道及周边数据与轨道三维模型匹配；基于经过匹配后的轨道及周边数据对应的密集点云数据与轨道三维模型间的差异智能识别轨道异常情况，并将轨道异常情况进行多维表达。还公开了对应的系统、电子设备和计算机可读存储介质，通过提升现有的方案中的数据表达形式，提高数据与轨道实景信息的关联程度，将检测数据与轨道模型关联起来，从而解决当前检测过程中历史溯源性不足，综合多种检测结果能力弱的问题。
The present invention discloses a digital twin method for track information based on a Beidou track inspection instrument, comprising: constructing a corresponding three-dimensional track model according to line design information; collecting track and surrounding data through mobile detection of a track inspection instrument equipped with multiple sensors; performing high-precision mileage positioning on the track and surrounding data; matching the track and surrounding data that has undergone high-precision mileage positioning with the track three-dimensional model; intelligently identifying track anomalies based on the differences between the dense point cloud data corresponding to the matched track and surrounding data and the track three-dimensional model, and expressing the track anomalies in multiple dimensions. Also disclosed are corresponding systems, electronic devices, and computer-readable storage media. By improving the data expression form in existing solutions, the degree of correlation between data and track real-life information is improved, and the detection data is associated with the track model, thereby solving the problems of insufficient historical traceability and weak ability to integrate multiple detection results in the current detection process.
Description
Technical Field
The invention relates to the technical field of railway operation and maintenance management, in particular to a digital twin method and system for track information based on a Beidou track inspection instrument.
Background
The railway is the main artery in the traffic development and economic construction of China. In the large environments of digital China, digital economy, intellectualization and informatization, the development of railway informatization naturally becomes one of the main trends of modern railway development. At present, railway informatization has made great progress in aspects of passenger transport, freight transport, scheduling and the like, but a great deal of progress space exists in aspects of engineering operation and maintenance technology informatization. Track smoothness is an important basic condition for ensuring safe and stable running of railways. Along with the increase of the railway operation mileage and the improvement of the operation speed in China, the pressure is increased for the operation and maintenance of the railway track. Seven points of importance in rail operation and maintenance are rail direction, track gauge, height, level, rail bottom slope and triangle pits. In addition, the problems of invasion of foreign matters, damage of track plates, damage of fasteners and the like of the building limit are also required to be paid attention to in the running process of the train. These data need to be acquired prior to repair as a reference basis for repair.
First, the maintenance point location needs to be precisely located, and the railway adopts mileage to represent the location, so that precise mileage data is needed, which requires precise mileage data to be obtained in the detection process. However, at the same time, a certain degree of damage occurs at a part of mileage positions in the detection process, but the repair standard is not met due to the lower degree, and the part of information is not fully utilized, but further remedial measures are taken until the problem occurs, so that the record and analysis of historical data are lacking, and the whole life cycle management in the railway operation and maintenance process is not facilitated.
The method is characterized in that a track inspection vehicle is used for detecting track diseases, dynamic data are collected, irregularity characteristics of the track are analyzed, a measuring section is selected for static measurement, however, the method is high in cost, relatively fixed in detection period and insufficient in flexibility, the method is used for detecting the track diseases, the method is used for detecting the static detection, the method is divided into three types, the first type is used for measuring by means of a suspension wire, a track ruler and other tools manually, the second type is used for measuring by means of a track inspection device and a total station device in a 'stop-and-go' mode, and the third type is used for realizing mobile measurement by means of a track inspection device and GNSS and inertia inspection devices. The rail inspection instrument is matched with the total station to obtain comprehensive measurement data of the rail, the detection method is more accurate than that of a manual tool, but the detection speed of the rail inspection instrument under the single-point condition needs to be improved. The adoption of the track inspection instrument matched with the GNSS mobile measurement can improve the speed of track measurement on the premise of ensuring the precision, and is relatively more suitable for detection and use of staff. However, the above detection scheme focuses on the presentation of the results of a single detection, and lacks effective management of multi-stage achievements. During the detection process, the staff pay attention to the overrun data which needs to be treated, but does not pay attention to the development process, and does not pay attention to the fine abnormality which does not affect the operation.
Thirdly, the prior art lacks full life cycle management of multiple detection data, generally adopts a one-dimensional and two-dimensional mode such as a table, a wave line graph and the like as a presentation mode of a result, has insufficient relevance with a track, is difficult to intuitively reflect the track disease position, has insufficient historical traceability of the detection data, is generally organized and managed according to a file management method in a table, an image and the like mode, has poor capability of integrating various detection data results, and is generally analyzed independently during data acquisition.
Along with the development of fine operation, the development of the fine operation is required to be known, and the position which reaches the maintenance standard is emphasized, and the area where the disease seedling occurs is emphasized, so that a new informatization method and system for the engineering operation and maintenance technology are required to be constructed.
Disclosure of Invention
The invention aims to construct a digital twin method and a digital twin system for track information based on a Beidou track inspection instrument aiming at the defects of the prior art, and is a digital twin method and a digital twin system suitable for track inspection instrument detection data in the railway operation and maintenance process, wherein the digital twin comprises modules of track inspection instrument data acquisition, track inspection instrument data analysis, track inspection instrument data storage and the like, the association degree of data and track live-action information is improved by improving the data expression form in the existing scheme, the detection data and a track model are associated, the effect of finding is realized, the quick positioning and the visual perception can be realized, and the characteristics of original data expression analysis are maintained, so that the problems of insufficient historical traceability and weak comprehensive various detection result capacities in the current detection process can be solved.
The first aspect of the invention provides a track information digital twin method based on a Beidou track inspection tester, which comprises the following steps:
s1, constructing a corresponding track three-dimensional model according to line design information;
s2, collecting the track and peripheral data through the movement detection of a track inspection instrument carrying various sensors;
S3, performing high-precision mileage positioning on the track and the peripheral data through a GNSS+inertial navigation algorithm;
s4, matching the track and peripheral data subjected to high-precision mileage positioning with the track three-dimensional model;
and S5, intelligently identifying the abnormal condition of the track based on the difference between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model, and carrying out multidimensional expression on the abnormal condition of the track.
Preferably, the S1 includes:
s11, providing the railway line design information by a designer, wherein the railway line design information comprises a track section, a fastener type and line construction information;
And S12, constructing a track three-dimensional model according to the track section, the fastener type and the line construction information, wherein the track three-dimensional model is a discrete three-dimensional model formed by combining different constituent units, the track three-dimensional model adopts an OBJ format, a GLTF format or a 3D Ti les format, the track three-dimensional model is not a complete three-dimensional model, the different constituent units are manufactured according to a design drawing or corresponding products, and then the different constituent units are assembled together according to a position matching relation to serve as an initial track three-dimensional model in the method.
Preferably, the GNSS receiver and the inertial navigation measurement device are fixedly arranged on the track inspection instrument, and the plurality of sensors comprise a three-dimensional laser scanner and a structured light module, wherein the three-dimensional laser scanner is used for acquiring information of a steel rail and/or acquiring environmental information around the track.
Preferably, the S2 includes:
S21, after the orbit checking instrument is placed at the starting point of the area to be checked, a GNSS receiver is turned on, satellite signals with a certain duration are received, current position information is determined, and current mileage information is recorded;
s22, moving and measuring at a constant speed according to the set speed of the orbit checking instrument, and continuously using a GNSS receiver to perform satellite signal receiving test for a certain duration at the end position of the measurement to determine the position information and mileage position information of the end point;
s23, acquiring scene information around the track through the three-dimensional laser scanner in the operation process of the track inspection instrument, and acquiring dense point cloud information of the left and right steel rails of the track through the structural optical module, wherein the dense point cloud information comprises fasteners and rail surfaces.
Preferably, the S3 includes:
s31, unifying the coordinate system of the dense point cloud information;
S32, based on the initial point position and mileage information, and alignment prisms arranged on two sides of the track, completing the position registration of the whole data;
S33, filtering out corresponding noise and outlier points by adopting a method of combining direct filtering and cloth filtering for the rail point cloud data acquired by the line structure optical module, and determining whether foreign matters invade the minimum building limit at different mileage positions according to the limit requirements at different heights aiming at the building limit.
Preferably, the S4 includes:
s41, matching high-precision mileage positioning information based on the track and peripheral data with the mileage of the track three-dimensional model;
S42, properly performing thinning treatment on point cloud data of the surrounding environment by using a nearest neighbor algorithm according to the loading frame rate;
S43, matching point clouds at the tunnel and the like with the standard model according to a least square method.
Preferably, the step S5 includes:
s51, intelligently identifying abnormal conditions of the track based on differences between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model;
S52, classifying the abnormal conditions and carrying out color assignment through a color imparting component according to the classification;
S53, storing detection data of track irregularity in the track inspection instrument, wherein the detection data of track irregularity comprises track gauge, track direction, height, level and triangle pit data, matching the detection data of track irregularity with track mileage positions, selecting corresponding indexes, and generating corresponding three-dimensional lines right above a central line Z axis of the three-dimensional track model;
and S54, clicking the mileage position in the track three-dimensional model to view and download a common two-dimensional line graph, and simultaneously matching the relevant point cloud screenshot of the place where the track is abnormal with the color-imparting component.
A second aspect of the present invention is to provide a digital twin system for track information based on a beidou track inspection meter, for implementing the method of the first aspect, including:
the model construction module (101) is used for constructing a corresponding track three-dimensional model according to the line design information;
the data acquisition module (102) is used for detecting and collecting the track and peripheral data through the movement of the track inspection instrument carrying various sensors;
the mileage positioning module (103) is used for performing high-precision mileage positioning on the track and the peripheral data through a GNSS (Global navigation satellite System) inertial navigation algorithm;
the data matching module (104) is used for matching the track subjected to high-precision mileage positioning and peripheral data with the track three-dimensional model;
And the anomaly identification module (105) is used for intelligently identifying the track anomaly based on the difference between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model, and carrying out multidimensional expression on the track anomaly.
A third aspect of the invention provides an electronic device comprising a processor and a memory, the memory storing a plurality of instructions, the processor being for reading the instructions and performing the method according to the first aspect.
A fourth aspect of the invention provides a computer readable storage medium storing a plurality of instructions readable by a processor and for performing the method of the first aspect.
The method and the system have the beneficial effects that:
(1) Compared with the traditional method, the track information digital twin method has the advantages that the track model is related to the detection result, the problem points are marked through the grading colors, the method is focused on long-term operation and maintenance management, particularly, through the layering color setting method, the problem areas are marked with red, the abnormal areas are marked with yellow, and more visual display and history tracing of the detection result can be realized.
(2) The invention partially solves the problem of weak capability of integrating various detection results in the current detection process by integrating the various detection results on the steel rail model, solves the problem of insufficient historical traceability in the current detection by using a method of color grading and multi-period historical data recording, and can help railway staff to position key sections.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the related art, the drawings that are required to be used in the description of the embodiments or the related art will be briefly described, and it is apparent that the drawings in the description below are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
Fig. 1 is a flowchart of a track information digital twin method based on a Beidou track inspection tester provided according to the prior art;
Fig. 2 is a diagram of a track information digital twin system architecture based on a Beidou track inspection tester, provided by an embodiment of the invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected via an intervening medium, or in communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
As shown in fig. 1, this embodiment provides a track information digital twin method based on a beidou track inspection tester, including:
s1, constructing a corresponding track three-dimensional model according to line design information;
in this embodiment, this step is the first step in the method, which serves to provide the model basis for the method, and is the prior work in all steps.
As a preferred embodiment, the S1 includes:
s11, providing the railway line design information by a designer, wherein the railway line design information comprises a track section, a fastener type and line construction information;
S12, constructing a track three-dimensional model according to the track section, the fastener type and the line construction information, wherein the track three-dimensional model is a discrete three-dimensional model formed by combining different constituent units, so that the track three-dimensional model corresponds to a track actually, and is convenient for updating part of models when a subsequent track model changes;
In a preferred embodiment, the track three-dimensional model is in an OBJ format, GLTF format or 3D Ti les format, and is not a complete three-dimensional model, and the different constituent units are manufactured according to a design drawing or a corresponding product and then assembled together according to a position matching relationship to serve as an initial track three-dimensional model in the method.
S2, collecting the track and peripheral data through the movement detection of a track inspection instrument carrying various sensors;
The method for detecting the environment of the rail comprises the steps of fixing a GNSS receiver and an inertial navigation measurement device on a rail inspection instrument, and carrying out various sensors comprising a three-dimensional laser scanner and a structured light module, wherein the three-dimensional laser scanner is used for acquiring information of a steel rail and/or acquiring environment information around the rail (in the method of the embodiment, the environment information around the rail mainly corresponds to the acquisition of building limit information).
As a preferred embodiment, the S2 includes:
S21, after the orbit checking instrument is placed at the starting point of the area to be checked, a GNSS receiver is turned on, satellite signals with a certain time length (five minutes in the embodiment) are received, current position information is determined, and current mileage information is recorded;
S22, according to the set speed (3 km/h in the embodiment) of the orbit checking instrument, moving at a constant speed for measurement, continuously using a GNSS receiver for satellite signal receiving test for a certain continuous time (five minutes in the embodiment) at the end position of the measurement, and determining the position information and mileage position information of the end point;
s23, acquiring scene information around the track through the three-dimensional laser scanner in the operation process of the track inspection instrument, and acquiring dense point cloud information of the left and right steel rails of the track through the structural optical module, wherein the dense point cloud information comprises fasteners and rail surfaces.
S3, performing high-precision mileage positioning on the track and the peripheral data through a GNSS+inertial navigation algorithm;
in this embodiment, after the track and the peripheral data are collected, a technician is required to post-process the collected data, so as to complete high-precision positioning of the data.
As a preferred embodiment, the S3 includes:
s31, unifying the coordinate system of the dense point cloud information;
S32, based on the initial point position and mileage information, and alignment prisms arranged on two sides of the track, completing the position registration of the whole data;
And S33, filtering out corresponding noise and outlier points by adopting a method of combining direct filtering and cloth filtering for the rail point cloud data acquired by the line structure optical module, and determining whether foreign matters invade the minimum building limit at different mileage positions according to the limit requirements at building limit positions (such as the tunnel position of the embodiment).
S4, matching the track and peripheral data subjected to high-precision mileage positioning with the track three-dimensional model;
as a preferred embodiment, the S4 includes:
s41, matching high-precision mileage positioning information based on the track and peripheral data with the mileage of the track three-dimensional model;
S42, properly performing thinning treatment on point cloud data of the surrounding environment by using a nearest neighbor algorithm according to the loading frame rate;
S43, matching point clouds at the tunnel and the like with the standard model according to a least square method.
In this embodiment, the least squares method is often used to solve the curve fitting problem. It finds the best functional match for the data by minimizing the sum of squares of the errors.
S5, intelligently identifying abnormal conditions of the track (such as abnormal conditions of track abrasion, fastener missing and the like in the embodiment of the invention) based on differences between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model, and carrying out multidimensional expression on the abnormal conditions of the track.
As a preferred embodiment, the S5 includes:
s51, intelligently identifying abnormal conditions of the track based on differences between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model;
S52, classifying the abnormal conditions and carrying out color assignment through a color imparting component according to the classification;
in this embodiment, the part unit to be repaired is set to red, but is not so serious that the traffic safety is marked yellow while abnormal, and the normal area is not colored.
S53, storing detection data of track irregularity in the track inspection instrument, wherein the detection data of track irregularity comprises track gauge, track direction, height, level and triangle pit data, matching the detection data of track irregularity with track mileage positions, selecting corresponding indexes, and generating corresponding three-dimensional lines right above a central line Z axis of the three-dimensional track model;
and S54, clicking the mileage position in the track three-dimensional model to view and download a common two-dimensional line graph, and simultaneously matching the relevant point cloud screenshot of the place where the track is abnormal with the color-imparting component.
Example two
As shown in fig. 2, this embodiment provides a track information digital twin system based on a beidou track inspection tester, including:
the model construction module 101 is used for constructing a corresponding track three-dimensional model according to the line design information;
the data acquisition module 102 is used for detecting and collecting the track and peripheral data through the movement of a track inspection instrument carrying various sensors;
The mileage positioning module 103 is used for performing high-precision mileage positioning on the track and the peripheral data through a GNSS+inertial navigation algorithm;
The data matching module 104 is configured to match the track and the peripheral data subjected to high-precision mileage positioning with the track three-dimensional model;
the anomaly identification module 105 is configured to intelligently identify an anomaly of the track based on a difference between the matched dense point cloud data corresponding to the track and the surrounding data and the track three-dimensional model, and perform multidimensional expression on the anomaly of the track.
The invention also provides a memory storing a plurality of instructions for implementing the method according to the first embodiment.
As shown in fig. 3, the present invention further provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, where the memory 302 stores a plurality of instructions, and the instructions may be loaded and executed by the processor, so that the processor can execute the method according to the first embodiment.
It should be noted that the above embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solution described in the above embodiments may be modified or some or all of the technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of the present invention.
Claims (5)
1. The track information digital twin method based on the Beidou track inspection instrument is characterized by comprising the following steps of:
s1, constructing a corresponding track three-dimensional model according to line design information;
s2, collecting the track and peripheral data through the movement detection of a track inspection instrument carrying various sensors;
S3, performing high-precision mileage positioning on the track and the peripheral data through a GNSS+inertial navigation algorithm;
s4, matching the track and peripheral data subjected to high-precision mileage positioning with the track three-dimensional model;
s5, intelligently identifying the abnormal condition of the track based on the difference between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model, and carrying out multidimensional expression on the abnormal condition of the track;
the S1 comprises the following steps:
s11, providing railway line design information by a designer, wherein the railway line design information comprises a track section, a fastener type and line construction information;
S12, constructing a track three-dimensional model according to the track section, the fastener type and the line construction information, wherein the track three-dimensional model is a discrete three-dimensional model formed by combining different constituent units, the track three-dimensional model adopts an OBJ format, a GLTF format or a 3D (three-dimensional) Tiles format, and the track three-dimensional model is not a complete three-dimensional model;
The step S2 comprises the following steps:
S21, after the orbit checking instrument is placed at the starting point of the area to be checked, a GNSS receiver is turned on, satellite signals with a certain duration are received, current position information is determined, and current mileage information is recorded;
s22, moving and measuring at a constant speed according to the set speed of the orbit checking instrument, and continuously using a GNSS receiver to perform satellite signal receiving test for a certain duration at the end position of the measurement to determine the position information and mileage position information of the end point;
s23, acquiring scene information around a track through the three-dimensional laser scanner and acquiring dense point cloud information of left and right steel rails of the track through a structural optical module in the operation process of the track inspection instrument, wherein the dense point cloud information comprises fasteners and rail surfaces;
The step S3 comprises the following steps:
s31, unifying the coordinate system of the dense point cloud information;
S32, based on the initial point position and mileage information, and alignment prisms arranged on two sides of the track, completing the position registration of the whole data;
S33, filtering out corresponding noise and outliers by adopting a method of combining direct filtering and cloth filtering for the rail point cloud data acquired by the line structure optical module;
the step S4 comprises the following steps:
s41, matching high-precision mileage positioning information based on the track and peripheral data with the mileage of the track three-dimensional model;
S42, properly performing thinning treatment on point cloud data of the surrounding environment by using a nearest neighbor algorithm according to the loading frame rate;
s43, matching point clouds at the tunnel and the like with a standard model according to a least square method;
The step S5 comprises the following steps:
s51, intelligently identifying abnormal conditions of the track based on differences between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model;
S52, classifying the abnormal conditions and carrying out color assignment through a color imparting component according to the classification;
S53, storing detection data of track irregularity in the track inspection instrument, wherein the detection data of track irregularity comprises track gauge, track direction, height, level and triangle pit data, matching the detection data of track irregularity with track mileage positions, selecting corresponding indexes, and generating corresponding three-dimensional lines right above a central line Z axis of the three-dimensional track model;
and S54, clicking the mileage position in the track three-dimensional model to view and download a common two-dimensional line graph, and simultaneously matching the relevant point cloud screenshot of the place where the track is abnormal with the color-imparting component.
2. The digital twin method for track information based on the Beidou track inspection instrument is characterized in that a GNSS receiver and an inertial navigation measuring device are fixedly installed on the track inspection instrument, the plurality of sensors comprise a three-dimensional laser scanner and a structured light module, and the three-dimensional laser scanner is used for acquiring information of steel rails and/or acquiring environmental information around the tracks.
3. A digital twin system of track information based on a Beidou track inspection machine for implementing the method of any one of claims 1-2, comprising:
the model construction module (101) is used for constructing a corresponding track three-dimensional model according to the line design information;
the data acquisition module (102) is used for detecting and collecting the track and peripheral data through the movement of the track inspection instrument carrying various sensors;
the mileage positioning module (103) is used for performing high-precision mileage positioning on the track and the peripheral data through a GNSS (Global navigation satellite System) inertial navigation algorithm;
the data matching module (104) is used for matching the track subjected to high-precision mileage positioning and peripheral data with the track three-dimensional model;
And the anomaly identification module (105) is used for intelligently identifying the track anomaly based on the difference between the matched dense point cloud data corresponding to the track and the peripheral data and the track three-dimensional model, and carrying out multidimensional expression on the track anomaly.
4. An electronic device comprising a processor and a memory, the memory storing a plurality of instructions, the processor configured to read the instructions and perform the method of any of claims 1-2.
5. A computer readable storage medium storing a plurality of instructions readable by a processor and for performing the method of any one of claims 1-2.
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