CN117934709A - Intelligent ballasting method and device based on three-dimensional point cloud - Google Patents
Intelligent ballasting method and device based on three-dimensional point cloud Download PDFInfo
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Abstract
The invention relates to the fields of railway trucks, sensors and automation, and particularly discloses an intelligent ballasting method and device based on three-dimensional point cloud, wherein the method comprises the following steps: performing ad hoc network grouping on a plurality of ballasted carriages of the locomotive; collecting parameter data of the ballast unloading carriages and acquiring unordered point cloud data of the section of the ballast bed; calculating to obtain the real-time three-dimensional point cloud profit and loss of the ballast bed based on the preprocessed unordered point cloud data; and controlling the ballast unloading carriage to perform ballast unloading operation based on the ballast bed real-time three-dimensional point cloud profit and loss, the ad hoc network grouping information and the parameter data. According to the method provided by the invention, the self-networking grouping is carried out on the plurality of ballast unloading carriages of the locomotive, the unordered point cloud data is preprocessed, and the real-time three-dimensional point cloud surplus and shortage of the ballast bed is calculated, so that intelligent and accurate control on ballast unloading operation is realized, more ballast repairing operations can be completed in a shorter time, and the labor cost is reduced.
Description
Technical Field
The invention relates to the fields of railway trucks, sensors and automation, in particular to an intelligent ballasting method and device based on three-dimensional point cloud.
Background
The existing railway line ballasting operation in China at present mainly comprises the following steps: the ballast is supplemented to the ballast bed by adopting the engineering truck to pull the ballast hopper car, and the whole ballast supplementing process depends on manual control and has lower efficiency. Meanwhile, a large amount of dust can be generated in the ballast unloading operation of the ballast hopper car, so that the environment is polluted. The existing manual ballasting has the defects that (1) the efficiency of manual ballasting is limited by the speed and precision of manual labor, and uneven and unstable conditions can exist. (2) The manual ballasting needs to be put into a large amount of manpower and material resources, the cost is high, and the requirement on personnel safety is high. (3) In the manual ballasting process, potential safety hazards exist in the vicinity of a railway operated by a worker.
In recent years, along with the rapid development of artificial intelligence and automation technology, the field of railway maintenance has also gradually introduced intelligent solutions. Therefore, the technical scheme of the ballast bed intelligent ballasting system is provided. The system refers to an automated device or system for railway track maintenance, a system for detecting and supplementing railway ballast. These systems can use various sensors and techniques to monitor the condition of the ballast and then automatically or semi-automatically supplement the ballast to maintain the flatness and safety of the railway.
In order to improve the problems that the whole ballasting process mainly depends on manual control, is low in efficiency and the like, the required quantity, ballasting times and ballasting quantity of each time are determined for ballasting operation, then a locomotive pulls a ballast hopper car to perform ballasting operation, the application of other technologies such as a computer vision technology, an image processing technology and a laser detection technology is utilized, the state of the ballasting/ballasting quantity of the whole ballast bed is mastered macroscopically, and an automatic detection system is associated with an automatic ballasting control system, so that the maintenance time of the ballast bed is shortened in real time, the requirements of speed acceleration and high-density driving are met, the operation safety of a train after the speed acceleration is ensured, and great economic and social benefits are achieved.
Based on the technical background, the invention researches an intelligent ballasting method and device based on three-dimensional point cloud.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent ballasting method and device based on three-dimensional point cloud, which are characterized in that the method comprises the steps of carrying out self-networking grouping on a plurality of ballasting carriages of a locomotive, preprocessing and calculating unordered point cloud data to obtain the real-time three-dimensional point cloud surplus and shortage of a ballast bed, realizing intelligent and accurate control on ballasting operation, completing more ballasting work in a shorter time, and reducing labor cost.
In order to achieve the above object, a first aspect of the present invention provides an intelligent ballasting method based on three-dimensional point cloud, comprising:
Performing ad hoc network grouping on a plurality of ballasted carriages of the locomotive;
collecting parameter data of the ballast unloading carriages and acquiring unordered point cloud data of the section of the ballast bed;
Calculating to obtain the real-time three-dimensional point cloud profit and loss of the ballast bed based on the preprocessed unordered point cloud data;
And controlling the ballast unloading carriage to perform ballast unloading operation based on the ballast bed real-time three-dimensional point cloud profit and loss, the ad hoc network grouping information and the parameter data.
The second aspect of the invention provides an intelligent ballasting device based on three-dimensional point cloud, comprising:
The grouping module is used for performing ad hoc network grouping on a plurality of ballasted carriages of the locomotive;
The point cloud acquisition module is used for acquiring parameter data of the ballasted carriages and acquiring unordered point cloud data of the section of the ballast bed;
the point cloud processing module is used for calculating and obtaining the real-time three-dimensional point cloud profit and loss of the ballast bed based on the preprocessed unordered point cloud data;
and the ballast unloading control module is used for controlling the ballast unloading carriage to carry out ballast unloading operation based on the real-time three-dimensional point cloud profit and loss of the ballast bed, the ad hoc network grouping information and the parameter data.
The beneficial effects of the invention include:
(1) According to the intelligent ballast unloading method based on the three-dimensional point cloud, the plurality of ballast unloading carriages of the locomotive are subjected to self-networking grouping, unordered point cloud data are preprocessed, and the real-time three-dimensional point cloud surplus and shortage of the ballast bed are calculated, so that intelligent and accurate control over ballast unloading operation is realized, more ballast repairing operations can be completed in a shorter time, and the labor cost is reduced.
(2) According to the intelligent ballasting method based on the three-dimensional point cloud, the efficiency and the flexibility are improved by performing ad hoc network grouping on a plurality of ballasting carriages of a locomotive, the rapid and efficient carriage grouping is realized, and the railway transportation efficiency is greatly improved; the grouping sequence and mode can be flexibly adjusted according to actual conditions, and different transportation requirements and schedule arrangement can be adapted; meanwhile, the dependence on manpower can be reduced; ② Labor and time cost of manual grouping are reduced; this allows railway companies to save significant human resources and use them for other more important transportation and maintenance tasks; ③ The safety is improved; the self-grouping technique can reduce the possibility of accidents caused by human errors and improper operation; it meets strict safety standards and regulations, and ensures the safety and stability in the railway transportation process.
(3) The intelligent ballasting method based on the three-dimensional point cloud, which is provided by the invention, is used for solving the problem of different vehicle states in each marshalling, determining the vehicle states through self-checking before the vehicle marshalling, and collecting vehicle data such as expected running speed, carriage length parameters and railway ballast parameters; for the acquired disordered three-dimensional point cloud, the disordered point cloud is preprocessed by adopting a random sampling consistency algorithm, and interference of abnormal values can be effectively eliminated through a random sampling and iteration process, so that more accurate model parameter estimation is obtained; the robustness, flexibility and applicability of the system are improved integrally.
(4) According to the intelligent ballast unloading method based on the three-dimensional point cloud, mathematical statistics is carried out on ballast filling/ballasting quantity of the ballast bed through a continuous integration method, so that the change trend of the ballast bed in a period of time can be known, and further maintenance conditions and future maintenance requirements of the ballast bed can be evaluated.
(5) According to the intelligent ballasting method based on the three-dimensional point cloud, ① calculation results are accurate when mathematical statistics is carried out on ballast filling/ballasting quantity of a ballast bed: smoother data can be obtained through continuous integration, noise and fluctuation in the data are reduced, and the change trend of the ballast bed is clearer and more discernable; ② Periodic analysis: the continuous integration method can also help analyze periodic characteristics of track bed changes, such as influence of seasonal changes or other periodic factors on the track bed, and provide reference basis for making corresponding maintenance plans; ③ Abnormality detection: the abnormal change of the ballast bed condition can be found in time by comparing the actual ballast bed surplus/unballasted quantity with the trend prediction obtained by continuous integral analysis so as to take corresponding emergency maintenance measures and avoid further damage; ④ And (3) establishing a model: the continuous integration method can establish a mathematical model for the track bed condition, is used for predicting the future track bed ballasting/ballasting quantity change, and provides a quantitative basis for the establishment and adjustment of a railway maintenance plan.
(6) The intelligent ballasting method based on the three-dimensional point cloud provided by the invention uses a fusion positioning method to determine the position of the vehicle; ① Positioning accuracy is improved: by fusing the data of various sensors, the GPS and sensor fusion positioning can improve the positioning precision and accuracy; under the environment that GPS signals are blocked or signals are weaker, the sensor fusion technology can provide more reliable positioning results; ② Enhancing the anti-interference capability: the sensor fusion positioning technology can reduce positioning errors caused by environmental interference and multipath effects, improve the anti-interference capability of a positioning system and ensure the stability and reliability of positioning results; ③ The multi-environment adaptation is realized: the sensor fusion technology not only depends on GPS signals, but also combines the information of other sensors, so that the sensor fusion technology has stronger adaptability; ④ Real-time and stability: the sensor fusion positioning can realize real-time monitoring and tracking of the position, and meanwhile, the stability and the continuity of a positioning result are maintained; furthermore, the GPS and sensor fusion positioning technology improves positioning accuracy and reliability by integrating data of various sensors, and simultaneously enhances adaptability and stability of a positioning system.
(7) The intelligent ballast unloading method based on the three-dimensional point cloud provided by the invention enables railway companies to monitor and maintain the conditions of ballast beds better, thereby improving the safety of railway transportation, and meanwhile, the intelligent ballast supplementing system can improve the transportation efficiency of railways by guaranteeing the stability and the flatness of the railway ballast beds; the train operation faults caused by the ballast bed problem are reduced, and the normal operation of the railway is ensured.
(8) The intelligent ballasting method based on the three-dimensional point cloud realizes automatic monitoring and supplementation of the railway ballast bed, and greatly improves the maintenance efficiency; compared with the traditional manual ballasting mode, the intelligent system can complete more ballasting work in a shorter time, and the manual labor cost is reduced.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 is a schematic flow chart of an intelligent ballasting method based on three-dimensional point cloud.
Fig. 2 is a schematic flow chart of a specific embodiment of an intelligent ballasting method based on three-dimensional point cloud.
Fig. 3 is a schematic diagram showing the comparison of curves of an actual ballast bed and a theoretical ballast bed in a specific embodiment of the intelligent ballast unloading method based on the three-dimensional point cloud.
Fig. 4 is a schematic flow chart of a random sampling and consistent algorithm in a specific implementation mode of the intelligent ballasting method based on the three-dimensional point cloud.
Fig. 5 is a schematic diagram of sensor data fusion in a specific embodiment of an intelligent ballasting method based on three-dimensional point cloud.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
The invention provides an intelligent ballasting method based on three-dimensional point cloud, as shown in figure 1, comprising the following steps:
Performing ad hoc network grouping on a plurality of ballasted carriages of the locomotive;
Collecting parameter data of a plurality of ballasted carriages, and obtaining unordered point cloud data of a ballast bed section;
Calculating to obtain the real-time three-dimensional point cloud profit and loss of the ballast bed based on the preprocessed unordered point cloud data;
And controlling the ballasting carriage to perform ballasting operation based on the ballast bed real-time three-dimensional point cloud profit and loss, the ad hoc network grouping information and the parameter data.
According to the invention, the ballast unloading carriages of the locomotive are subjected to self-networking grouping, the unordered point cloud data is preprocessed and calculated to obtain the real-time three-dimensional point cloud surplus and shortage of the ballast bed, intelligent and accurate control over the ballast unloading operation is realized, more ballasting works can be completed in a shorter time, and the labor cost is reduced.
According to the invention, the self-networking grouping of a plurality of ballasted carriages comprises:
installing a positioner, an identification card, a wireless ad hoc network terminal and corresponding networking communication equipment on a funnel of each ballasted carriage;
Sequentially transmitting the ID and position relation information of each ballasted carriage to a base station and a background monitoring center, and then grouping;
and identifying, positioning and tracking a plurality of grouped ballasted carriages and inquiring tracks through a background monitoring center.
In the invention, the efficiency and the flexibility are improved by carrying out self-networking grouping on a plurality of ballasted carriages of the locomotive, the rapid and efficient carriage grouping is realized, and the railway transportation efficiency is greatly improved; the grouping sequence and mode can be flexibly adjusted according to actual conditions, and different transportation requirements and schedule arrangement can be adapted; meanwhile, the dependence on manpower can be reduced; ② Labor and time cost of manual grouping are reduced; this allows railway companies to save significant human resources and use them for other more important transportation and maintenance tasks; ③ The safety is improved; the self-grouping technique can reduce the possibility of accidents caused by human errors and improper operation; it meets strict safety standards and regulations, and ensures the safety and stability in the railway transportation process.
According to the invention, the self-networking grouping of a plurality of ballasting carriages is performed:
Modeling the gate flow of each ballasted carriage and the construction speed determined in advance;
Based on the modeling model, measuring actual ballasting flow of each ballasting carriage under different construction conditions and different gate opening degrees.
According to the invention, the locomotive is provided with a tractor;
The front end of the tractor is provided with a laser section scanner, a speed sensor, a positioner and an odometer;
the collecting of the parameter data of a plurality of ballasting carriages comprises the following steps:
according to the structural size of a hopper of the ballast unloading carriage, obtaining the relative mileage between a ballast unloading port of each ballast unloading carriage and an odometer;
obtaining a designed ballast bed structure of each mileage section according to the design data;
measuring time consumption of an opening process and a closing process of a ballast unloading opening of each ballast unloading carriage;
determining the length of a ballast unloading unit based on the maximum running speed of the locomotive preset by ballasting;
respectively storing the number of the railway ballasts of each railway ballast unloading carriage, and establishing a railway ballast stock database;
The ballast bed structure comprises: the ballast top width, the ballast thickness, the slope of the turnout side slope and the ballast pike high size;
the railway ballast stock database is synchronously updated along with the ballast unloading operation.
According to the invention, the acquisition of unordered point cloud data of the section of the ballast bed comprises the following steps:
Scanning by using a laser section scanner to obtain unordered point cloud data of the section of the ballast bed;
The preprocessing sequentially comprises filtering, segmentation and contour extraction;
Dividing into points according to space, geometry and texture characteristics;
The method adopted for segmentation is a random sampling coincidence algorithm.
The method of the invention is to solve the problem of different vehicle states in each marshalling, determine the vehicle state by self-checking before the marshalling of the vehicle, and collect the vehicle data such as the expected running speed, carriage length parameter and railway track bed parameter; for the acquired disordered three-dimensional point cloud, the disordered point cloud is preprocessed by adopting a random sampling consistency algorithm, and interference of abnormal values can be effectively eliminated through a random sampling and iteration process, so that more accurate model parameter estimation is obtained; the robustness, flexibility and applicability of the system are improved integrally.
Preferably, the calculating the real-time three-dimensional point cloud surplus and shortage of the ballast bed based on the preprocessed unordered point cloud data includes:
obtaining an actual track bed contour curve based on the pretreated unordered point cloud data;
and comparing the actual ballast profile curve with a standard theoretical ballast profile curve, and calculating the real-time three-dimensional point cloud profit and loss quality of the ballast between the actual ballast and the theoretical ballast within the set distance length range.
Preferably, comparing the actual ballast profile curve with the standard theoretical ballast profile curve comprises;
in a two-dimensional coordinate system, obtaining a plurality of closed curves formed by intersecting an actual ballast bed profile curve with a standard theoretical ballast bed profile curve;
the calculation of the real-time three-dimensional point cloud profit and loss quality of the ballast bed between the actual ballast bed and the theoretical ballast bed within the set distance length range comprises the following steps:
In a two-dimensional coordinate system, carrying out unitary integration on each closed curve to obtain the area of the closed curve;
Calculating the area sum of all closed curves;
and calculating the product of the area and the set distance length and the ballast density as the real-time three-dimensional point cloud profit and loss quality of the ballast bed.
According to the invention, mathematical statistics is carried out on ballast filling/ballasting quantity of the ballast bed by a continuous integration method, so that the change trend of the ballast bed in a period of time can be known, and further the maintenance condition and future maintenance requirements of the ballast bed can be evaluated.
In the invention, ① calculation results are accurate when mathematical statistics is carried out on ballast filling/ballasting quantity of the ballast bed: smoother data can be obtained through continuous integration, noise and fluctuation in the data are reduced, and the change trend of the ballast bed is clearer and more discernable; ② Periodic analysis: the continuous integration method can also help analyze periodic characteristics of track bed changes, such as influence of seasonal changes or other periodic factors on the track bed, and provide reference basis for making corresponding maintenance plans; ③ Abnormality detection: the abnormal change of the ballast bed condition can be found in time by comparing the actual ballast bed surplus/unballasted quantity with the trend prediction obtained by continuous integral analysis so as to take corresponding emergency maintenance measures and avoid further damage; ④ And (3) establishing a model: the continuous integration method can establish a mathematical model for the track bed condition, is used for predicting the future track bed ballasting/ballasting quantity change, and provides a quantitative basis for the establishment and adjustment of a railway maintenance plan.
According to the invention, based on the ballast bed real-time three-dimensional point cloud profit and loss, the ad hoc network grouping information and the parameter data, the control of the ballast unloading carriage to perform the ballast unloading operation comprises the following steps:
a plurality of ballasting carriages are pulled to a ballasting starting point, and an odometer is adjusted to be the railway mileage of the ballasting starting point;
Based on the relative mileage between the laser section scanner and each ballast unloading opening of the ballast unloading carriage and the odometer, calculating and obtaining the absolute mileage of the laser section scanner and each ballast unloading opening of the ballast unloading carriage;
Measuring the current locomotive speed by using an odometer, and calculating the maximum ballasting amount of a ballasting opening of a single ballasting carriage in one ballasting unit at the current locomotive speed;
Scanning the ballast bed in real time by using a laser scanner to obtain a default ballast quantity of a ballast unloading unit;
Calculating to obtain the number of the ballasted carriages and the opening of the ballasted opening of the current ballasting unit based on the default railway ballast quantity and the maximum ballasting quantity;
According to the number of the ballasts and the number of the ballasts in each ballasting carriage, obtaining the numbers of the ballasting carriages participating in the ballasting of the corresponding ballasting unit;
when the ballast unloading port of the ballast unloading carriage participating in ballast unloading reaches the position of the ballast unloading unit, the ballast unloading port is opened to a specified opening degree to unload the ballast.
According to the invention, the positioning of each ballasting carriage adopts the integration of the GPS and the inertial measurement unit for positioning;
the inertial measurement unit includes a plurality of accelerometers and a plurality of gyroscopes for performing supplemental positioning when GPS positioning accuracy is degraded.
The method of the invention uses a fusion positioning method to determine the position of the vehicle; ① Positioning accuracy is improved: by fusing the data of various sensors, the GPS and sensor fusion positioning can improve the positioning precision and accuracy; under the environment that GPS signals are blocked or signals are weaker, the sensor fusion technology can provide more reliable positioning results; ② Enhancing the anti-interference capability: the sensor fusion positioning technology can reduce positioning errors caused by environmental interference and multipath effects, improve the anti-interference capability of a positioning system and ensure the stability and reliability of positioning results; ③ The multi-environment adaptation is realized: the sensor fusion technology not only depends on GPS signals, but also combines the information of other sensors, so that the sensor fusion technology has stronger adaptability; ④ Real-time and stability: the sensor fusion positioning can realize real-time monitoring and tracking of the position, and meanwhile, the stability and the continuity of a positioning result are maintained; furthermore, the GPS and sensor fusion positioning technology improves positioning accuracy and reliability by integrating data of various sensors, and simultaneously enhances adaptability and stability of a positioning system.
The method of the invention ensures that railway companies can monitor and maintain the status of the ballast bed better, thereby improving the safety of railway transportation, and meanwhile, the intelligent ballasting system can improve the transportation efficiency of the railway by ensuring the stability and the flatness of the railway ballast bed; the train operation faults caused by the ballast bed problem are reduced, and the normal operation of the railway is ensured.
The method of the invention realizes the automatic monitoring and supplementation of the railway ballast bed, and greatly improves the maintenance efficiency; compared with the traditional manual ballasting mode, the intelligent system can complete more ballasting work in a shorter time, and the manual labor cost is reduced.
The present invention will be described in more detail with reference to examples.
Embodiment one:
As shown in fig. 2, the present embodiment provides an intelligent ballast unloading method based on three-dimensional point cloud, and in the present embodiment, the intelligent ballast unloading method is implemented by using an intelligent ballast unloading vehicle system, and the system includes a ballast bed section detection system and a ballast unloading control system; the track bed section detection system mainly comprises a laser section scanner, a speed sensor, a positioner and the like and is arranged at the front end of the tractor; carrying out scanning along with running of a tractor, obtaining position and speed parameters, comparing an actual ballast bed structure obtained by scanning with a designed ballast bed structure, thereby determining the cross-sectional area of a section where the ballast needs to be supplemented, and obtaining the default ballast quantity of a ballast unloading unit through integral calculation; the ballast unloading control system calculates and obtains the number of vehicles and the number of vehicles participating in ballast unloading in the corresponding ballast unloading unit interval according to the vehicle load and the vehicle train position; controlling each vehicle bottom door control system through a corresponding algorithm, so that when a vehicle reaches the position of a ballast unloading unit interval, opening the bottom door, adjusting the opening of the bottom door according to a bottom door position sensor and position information, and closing the corresponding vehicle bottom door after ballast unloading is finished;
in this embodiment, the intelligent ballasting method based on the three-dimensional point cloud specifically includes the following steps:
Step one, determining an ad hoc network grouping scheme and input parameters of a vehicle terminal;
the method comprises the following steps of 1, self-grouping engineering vehicles;
Because the grouping condition of the railway engineering vehicles is not completely fixed, namely, the current position of the vehicles is required to be grouped according to factors such as the type and the characteristics of railway cargoes, the transportation distance and the destination, the loading efficiency, the balance and the like before each construction; the current marshalling method mainly carries out marshalling manually; in order to realize efficient and rapid automatic ballasting of railways, through an automatic grouping system, railway carriages can be independently grouped and connected according to a specific algorithm and a grouping rule; different types of carriages are accurately identified and positioned through RFID (radio frequency identification), sensor identification and the like, and the carriages are ensured to be capable of being automatically grouped at the correct positions; through modern communication technology and control system, real-time communication and coordination can be carried out between different carriages so as to ensure the smooth proceeding of the marshalling process; the ad hoc network is used as a short-distance wireless communication technology, has very strong applicability in the aspect of quick disassembly due to the convenience of network construction, and is very suitable for short-distance transmission in the technical scheme, so that the embodiment uses an ad hoc network method for automatic grouping;
The wireless self-networking terminal adopts a wireless communication technology, but has larger transmission distance (the visible transmission distance reaches 10 KM), and has the characteristics of low power consumption, low cost, low speed, short distance, support of a large number of nodes, support of relay, support of encryption, high speed and the like; in order to realize quick disassembly of automatic networking, in the embodiment, corresponding locators, identification cards, wireless ad hoc network terminals and corresponding networking communication equipment are arranged on each hopper car, the ad hoc network wireless communication technology is utilized to transmit the ID information of the locating hopper car to a base station, the base station uploads the received wireless information to a background monitoring center, and vehicle position analysis is carried out according to regional information set in a software system to realize locating; thereby realizing the functions of identifying, positioning and tracking, track inquiry and the like of each hopper car;
performing object identification by using RFID (radio frequency identification technology), connecting all current vehicles to a central server by using 5G communication, identifying front and rear hopper car identification card IDs by using the RFID when a certain part of hopper cars are connected, and transmitting the front and rear hopper car IDs connected by each hopper car handle to the central server, so that the central server can directly obtain the specific vehicle grouping sequence and relative relation;
2, determining input parameters;
After the railway engineering vehicles are grouped, modeling is carried out on the engineering vehicle quantity gate flow and the construction speed of the engineering vehicles determined in advance, and a corresponding mathematical model is established in advance, so that the actual ballasting flow of the engineering vehicles under different construction conditions and different gate openings is accurately metered, and further the follow-up scanning and the better feedforward control are facilitated;
Step two, vehicle data acquisition and unordered point cloud preprocessing;
the method comprises the following steps of 1, collecting vehicle data;
According to the structural size of the ballast hopper car, calculating and obtaining the relative mileage between the section scanner, ballast unloading ports of each carriage and the odometer; obtaining a designed ballast bed structure of each mileage section according to design data, wherein the ballast bed structure comprises: the ballast top width, the ballast thickness, the slope of a turnout side slope, the high size of ballast pike and the like; measuring time consumption of an opening process and a closing process of each ballast unloading opening of each vehicle, and determining the length s of a ballast unloading unit according to the maximum running speed of the locomotive preset by ballasting supplement; respectively storing the number of the railway ballast of each railway ballast hopper car, and establishing a railway ballast stock database; the ballast stock data is synchronously updated along with ballast unloading operation;
2, preprocessing unordered point cloud data;
Scanning the section of the ballast bed by a laser radar to obtain disordered point clouds, and filtering, dividing and extracting the point clouds to serve as input of a subsequent algorithm; when the point cloud data is acquired, due to the influences of equipment precision, environmental factors and the like, electromagnetic wave diffraction characteristics and the influence of a data splicing and registering operation process, noise points inevitably appear in the point cloud data, and the noise points belong to random errors; in addition, due to the influence of external interference such as line of sight shielding, obstacles and other factors, some discrete points far away from the subject point cloud, namely outliers, often exist in the point cloud data; the filtering process is used as the first step of the point cloud process, and is important for the subsequent process; only in the filtering process flow, noise points, outliers, holes, data compression and the like are customized according to subsequent processing, so that subsequent application processing such as registration, feature extraction, curved surface reconstruction, visualization and the like can be better performed;
The embodiment adopts a random sampling consensus algorithm (RandomSample Consensus, RANSAC) to divide the point cloud; the point cloud segmentation refers to the step of dividing point clouds according to the characteristics of space, geometry, texture and the like, wherein the point clouds in the same division have similar characteristics; the RANSAC algorithm assumes that the data contains the correct number;
Data and anomaly data (or referred to as noise); the correct data is marked as an inner point, and the abnormal data is marked as an outer point; at the same time RANSAC also assumes that given a correct set of data, there is a way in which model parameters that fit these data can be calculated; the core ideas of the algorithm are randomness and supposition: the randomness is to randomly select sampling data according to the occurrence probability of correct data, and the randomness simulation can approximately obtain a correct result according to the law of large numbers; supposition is that the selected sampling data are all correct data, then the correct data are used for calculating other points through a model which is satisfied by the problem, and then the result is scored; the algorithm flow is shown in figure 4;
Step three, calculating the profit and loss of the ballast bed real-time three-dimensional point cloud scanning;
In the embodiment, mathematical statistics is carried out on ballast filling/ballasting quantity of a ballast bed by a continuous integration method; a track bed contour curve is obtained by establishing a system mathematical model through actually collected continuous point clouds, and then the track bed contour curve is compared with various standard theoretical track bed contours to obtain the volume and the mass of ballast between an actual track bed and a theoretical track bed within a certain distance length range;
In the embodiment, a unitary integral solution is used, firstly, the condition that the coverage area surrounded by an actual track bed and a theoretical track bed of each integral step length in the scanning travelling direction is equal is assumed, and the volume of the ballast of each integral step length can be considered as the product obtained by multiplying the coverage area and the step length; secondly, respectively solving a theoretical ballast bed contour straight line segment equation; analyzing the conventional ballast bed schematic diagram as shown in figure 3, and dividing the straight line section of the section profile of the left theoretical ballast bed into 4 sections;
The method comprises the following steps of: y=l 1(x),y＝L2(x),y＝L3(x),y＝L4 (x) assuming that the track bed profile curves detected by actual scanning are y=f (x), y=g (x), y=h (x), y=i (x), and y=j (x) are theoretical track bed profile feature points;
.A0(xa0,ya0),A(xa,ya),B(xb,yb),C(xc,yc)D(xd,yd) For known conditions, the equation for each theoretical contour straight line segment can be found as follows:
L1(x)＝(ya-yd0)/(xa-xa0)(x-xa0)+ya0 (1-1)；
L2(x)＝(yb-ya)/(xb-xa)(x-xa)+ya (1-2)；
L3(x)＝(yc-yb)/(xc-xb)(x-xb)+yb (1-3)；
L4(x)＝(yd-yc)/(xd-xc)(x-xc)+yc (1-4)；
Knowing the profile straight line segment y=l 1(x),y＝L2(x),y＝L3(x),y＝L4 (x) of the profile of the theoretical ballast bed and the profile curve of the ballast bed detected by actual scanning: y=f (x), y=g (x), y=h (x), y=i (x), y=j (x) are continuous integrable functions within the respective domain intervals;
The area of the S 1 portion is surrounded by y=f (x), y=l 1 (x), and the intersections are a 0(xa0,ya0),A(xa,ya respectively;
The same principle can be obtained:
thus, the sum of consolidated profit & loss deficiency areas of the actual track bed profile curve and the theoretical standard profile curve of the left track bed is obtained as follows:
Stotal＝S1+S2+S3+S4+S5 (1-10)；
assuming consolidated profit & loss full areas per integration step are equal then the volume per step:
V i is V i＝Stotal L (1-11);
where i=1, 2,..n, L is the step size;
assuming that the ballast density is d, the corresponding ballast profit and loss mass is as follows:
Mi＝Vid (1-12)；
constructing point cloud edge computing equipment through the mathematical model, debugging and testing an algorithm, and constructing a real-time three-dimensional point cloud scanning profit and loss estimation scheme;
step four, GPS fusion positioning;
the embodiment uses a fusion positioning method, namely, a GPS and a multi-sensor fusion are used for positioning the vehicle;
The GPS obtains the arrival time difference from the satellite to the receiver by using the accurate position of each GPS satellite and the navigation information generated by the continuously transmitted satellite atomic clock; the GPS satellite continuously transmits radio signals with time and position information in the air for a GPS receiver to receive; because of the distance factor of transmission, the time when the signal is received by the receiver is delayed from the time when the signal is transmitted by the satellite, which is commonly called time delay, so that the distance can be determined by the time delay; the satellite and the receiver simultaneously generate the same pseudo-random code, and once the two codes realize time synchronization, the receiver can measure the time delay; multiplying the time delay by the speed of light to obtain a distance; the satellite-to-user distance measurement is based on the difference between the time of transmission of the satellite signal and the time of arrival at the receiver, called pseudorange; in order to calculate the three-dimensional position of the user and the receiver clock bias, the pseudorange measurements require at least receiving signals from 4 satellites; the GPS positioning has the advantages of all weather, global coverage, high fixed point, fixed speed and timing precision, and the defects of low positioning precision caused by easy loss of signals when obstacles such as tunnels, high buildings and the like exist, and in addition, the civil GPS positioning frequency is generally 10Hz, and the positioning frequency is low;
In order to make up for the defects of GPS positioning, the embodiment adopts a method of integrating GPS and IMU positioning; the IMU is typically composed of 3 accelerometers and 3 gyroscopes, the accelerometers and gyroscopes being mounted on mutually perpendicular measuring axes; the IMU integrates the acceleration of the carrier in an inertial reference system by measuring the acceleration of the carrier in an inertial reference system, and transforms the acceleration into a navigation coordinate system to obtain information such as speed, yaw angle, position and the like in the navigation coordinate system; the update frequency of the IMU is high, the working frequency can reach more than 100Hz, the calculation accuracy in a short time is high, but the accumulated error of the IMU is larger and larger along with the time; the advantages and disadvantages of the IMU are complemented with the GPS, and when the two sensors work together, the positioning output with the frequency of 100Hz can be given; FIG. 5 is a schematic diagram of two sensor data fusion;
fifthly, ballast unloading operation is achieved;
And (3) ballast unloading operation: the ballast hopper car is pulled to a ballast unloading starting point by a locomotive, and an odometer is adjusted to be the railway mileage of the ballast unloading starting point; calculating and obtaining absolute mileage of the section scanner and the ballast unloading ports of each carriage according to the section scanner and the relative mileage between the ballast unloading ports of each carriage and the odometer; measuring the current locomotive speed v according to an odometer, and calculating to obtain the maximum ballasting quantity N of a ballasting unit with the length s of a single carriage ballasting port at the current locomotive speed v according to the current locomotive speed v; along with the running of a locomotive, a laser scanner scans a ballast bed in real time, and the actual ballast bed structure obtained by scanning is compared with the designed ballast bed structure to obtain the default ballast quantity M of a ballast unloading unit; calculating to obtain the number P of carriages participating in the ballasting of the current ballasting unit and the opening K of a ballasting opening of the current ballasting unit according to the default railway ballast quantity M of one ballasting unit and the maximum ballasting quantity N of a single carriage in one ballasting unit; calculating and obtaining each carriage number participating in ballast unloading of the corresponding ballast unloading unit according to the number of the ballasts in each carriage and the number P of the carriages; when a ballast unloading opening of a carriage participating in ballast unloading reaches the position of a ballast unloading unit, opening the ballast unloading opening to a specified opening degree for ballast unloading; and further realizes the accurate ballast unloading technology in the embodiment.
Embodiment two:
as shown in fig. 1, the intelligent ballasting method based on the three-dimensional point cloud in this embodiment includes:
Performing ad hoc network grouping on a plurality of ballasted carriages of the locomotive;
Collecting parameter data of a plurality of ballasted carriages, and obtaining unordered point cloud data of a ballast bed section;
Calculating to obtain the real-time three-dimensional point cloud profit and loss of the ballast bed based on the preprocessed unordered point cloud data;
based on the ballast bed real-time three-dimensional point cloud profit and loss, ad hoc network grouping information and parameter data, controlling a ballast unloading carriage to perform ballast unloading operation;
In this embodiment, performing ad hoc network grouping on a plurality of ballasting carriages includes:
installing a positioner, an identification card, a wireless ad hoc network terminal and corresponding networking communication equipment on a funnel of each ballasted carriage;
Sequentially transmitting the ID and position relation information of each ballasted carriage to a base station and a background monitoring center, and then grouping;
Identifying, positioning and tracking a plurality of grouped ballasted carriages and inquiring tracks through a background monitoring center;
In this embodiment, the ad hoc networking and grouping of the plurality of ballast unloading carriages is performed:
Modeling the gate flow of each ballasted carriage and the construction speed determined in advance;
Based on the modeling model, measuring actual ballasting flow of each ballasting carriage under different construction conditions and different gate openings;
in the embodiment, the locomotive is provided with a tractor;
The front end of the tractor is provided with a laser section scanner, a speed sensor, a positioner and an odometer;
the collecting of the parameter data of a plurality of ballasting carriages comprises the following steps:
according to the structural size of a hopper of the ballast unloading carriage, obtaining the relative mileage between a ballast unloading port of each ballast unloading carriage and an odometer;
obtaining a designed ballast bed structure of each mileage section according to the design data;
measuring time consumption of an opening process and a closing process of a ballast unloading opening of each ballast unloading carriage;
determining the length of a ballast unloading unit based on the maximum running speed of the locomotive preset by ballasting;
respectively storing the number of the railway ballasts of each railway ballast unloading carriage, and establishing a railway ballast stock database;
The ballast bed structure comprises: the ballast top width, the ballast thickness, the slope of the turnout side slope and the ballast pike high size;
The railway ballast stock database is synchronously updated along with the ballast unloading operation;
In this embodiment, obtaining unordered point cloud data of a track bed section includes:
Scanning by using a laser section scanner to obtain unordered point cloud data of the section of the ballast bed;
The preprocessing sequentially comprises filtering, segmentation and contour extraction;
Dividing into points according to space, geometry and texture characteristics;
the method adopted by the segmentation is a random sampling coincidence algorithm;
in this embodiment, the calculating the real-time three-dimensional point cloud profit and loss amount of the ballast bed based on the preprocessed unordered point cloud data includes:
obtaining an actual track bed contour curve based on the pretreated unordered point cloud data;
comparing the actual ballast bed profile curve with a standard theoretical ballast bed profile curve, and calculating the real-time three-dimensional point cloud profit and loss quality of the ballast bed between the actual ballast bed and the theoretical ballast bed within the range of the set distance length;
Comparing the actual ballast profile curve with the standard theoretical ballast profile curve comprises;
in a two-dimensional coordinate system, obtaining a plurality of closed curves formed by intersecting an actual ballast bed profile curve with a standard theoretical ballast bed profile curve;
the calculation of the real-time three-dimensional point cloud profit and loss quality of the ballast bed between the actual ballast bed and the theoretical ballast bed within the set distance length range comprises the following steps:
In a two-dimensional coordinate system, carrying out unitary integration on each closed curve to obtain the area of the closed curve;
Calculating the area sum of all closed curves;
Calculating the product of the area and the set distance length and the ballast density as the real-time three-dimensional point cloud profit and loss quality of the ballast bed;
In this embodiment, based on the ballast bed real-time three-dimensional point cloud profit and loss, ad hoc network grouping information and parameter data, the controlling the ballast unloading carriage to perform the ballast unloading operation includes:
a plurality of ballasting carriages are pulled to a ballasting starting point, and an odometer is adjusted to be the railway mileage of the ballasting starting point;
Based on the relative mileage between the laser section scanner and each ballast unloading opening of the ballast unloading carriage and the odometer, calculating and obtaining the absolute mileage of the laser section scanner and each ballast unloading opening of the ballast unloading carriage;
Measuring the current locomotive speed by using an odometer, and calculating the maximum ballasting amount of a ballasting opening of a single ballasting carriage in one ballasting unit at the current locomotive speed;
Scanning the ballast bed in real time by using a laser scanner to obtain a default ballast quantity of a ballast unloading unit;
Calculating to obtain the number of the ballasted carriages and the opening of the ballasted opening of the current ballasting unit based on the default railway ballast quantity and the maximum ballasting quantity;
According to the number of the ballasts and the number of the ballasts in each ballasting carriage, obtaining the numbers of the ballasting carriages participating in the ballasting of the corresponding ballasting unit;
When a ballast unloading port of a ballast unloading carriage participating in ballast unloading reaches the position of a ballast unloading unit, opening the ballast unloading port to a specified opening degree for ballast unloading;
in the embodiment, the positioning of each ballasting carriage adopts the fusion of a GPS and an inertial measurement unit for positioning;
the inertial measurement unit includes a plurality of accelerometers and a plurality of gyroscopes for performing supplemental positioning when GPS positioning accuracy is degraded.
According to the intelligent ballast unloading method based on the three-dimensional point cloud, which is provided by the embodiment of the invention, the self-networking grouping is carried out on a plurality of ballast unloading carriages of a locomotive, the unordered point cloud data are preprocessed, and the real-time three-dimensional point cloud surplus and shortage of the ballast bed is calculated, so that intelligent and accurate control on ballast unloading operation is realized, more ballast repairing operations can be completed in a shorter time, and the labor cost is reduced.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (10)
1. An intelligent ballasting method based on three-dimensional point cloud is characterized by comprising the following steps:
Performing ad hoc network grouping on a plurality of ballasted carriages of the locomotive;
collecting parameter data of the ballast unloading carriages and acquiring unordered point cloud data of the section of the ballast bed;
Calculating to obtain the real-time three-dimensional point cloud profit and loss of the ballast bed based on the preprocessed unordered point cloud data;
And controlling the ballast unloading carriage to perform ballast unloading operation based on the ballast bed real-time three-dimensional point cloud profit and loss, the ad hoc network grouping information and the parameter data.
2. The method of claim 1, wherein ad hoc grouping a plurality of ballasted cars comprises:
installing a positioner, an identification card, a wireless ad hoc network terminal and corresponding networking communication equipment on a funnel of each ballasted carriage;
Sequentially transmitting the ID and position relation information of each ballasted carriage to a base station and a background monitoring center, and then grouping;
and identifying, positioning and tracking a plurality of grouped ballasted carriages and inquiring tracks through a background monitoring center.
3. The method of claim 2, wherein the ad hoc grouping of the plurality of ballasting cars is further performed:
Modeling the gate flow of each ballasted carriage and the construction speed determined in advance;
and based on the modeling model, measuring actual ballasting flow of each ballasting carriage under different construction conditions and different gate opening degrees.
4. A method according to claim 3, wherein the locomotive is provided with a tractor;
the front end of the tractor is provided with a laser section scanner, a speed sensor, a positioner and an odometer;
the collecting of the parameter data of the plurality of ballasting carriages comprises the following steps:
according to the structural size of a hopper of the ballast unloading carriage, obtaining the relative mileage between a ballast unloading port of each ballast unloading carriage and an odometer;
obtaining a designed ballast bed structure of each mileage section according to the design data;
measuring time consumption of an opening process and a closing process of a ballast unloading opening of each ballast unloading carriage;
determining the length of a ballast unloading unit based on the maximum running speed of the locomotive preset by ballasting;
respectively storing the number of the railway ballasts of each railway ballast unloading carriage, and establishing a railway ballast stock database;
the ballast bed structure comprises: the ballast top width, the ballast thickness, the slope of the turnout side slope and the ballast pike high size;
and synchronously updating the railway ballast stock database along with the ballast unloading operation.
5. The method of claim 4, wherein obtaining unordered point cloud data for a ballast bed section comprises:
Scanning by using a laser section scanner to obtain unordered point cloud data of the section of the ballast bed;
the preprocessing sequentially comprises filtering, segmentation and contour extraction;
The segmentation refers to the division of point cloud data according to space, geometry and texture characteristics;
the method adopted by the segmentation is a random sampling coincidence algorithm.
6. The method of claim 5, wherein calculating the ballast real-time three-dimensional point cloud surplus based on the preprocessed out-of-order point cloud data comprises:
obtaining an actual track bed contour curve based on the pretreated unordered point cloud data;
and comparing the actual ballast bed profile curve with a standard theoretical ballast bed profile curve, and calculating the real-time three-dimensional point cloud profit and loss quality of the ballast bed between the actual ballast bed and the theoretical ballast bed within the length range of the set distance.
7. The method of claim 6, wherein comparing the actual ballast profile with a standard theoretical ballast profile comprises;
In a two-dimensional coordinate system, obtaining a plurality of closed curves formed by intersecting the actual ballast bed profile curve and a standard theoretical ballast bed profile curve;
the calculation of the real-time three-dimensional point cloud profit and loss quality of the ballast bed between the actual ballast bed and the theoretical ballast bed within the set distance length range comprises the following steps:
In a two-dimensional coordinate system, carrying out unitary integration on each closed curve to obtain the area of the closed curve;
Calculating the area sum of all closed curves;
And calculating the product of the area and the set distance length and the railway ballast density as the real-time three-dimensional point cloud profit and loss quality of the railway ballast.
8. The method of claim 7, wherein controlling the ballasting car to perform ballasting operations based on the ballast bed real-time three-dimensional point cloud profit and loss, ad hoc network grouping information and parameter data comprises:
a plurality of ballasting carriages are pulled to a ballasting starting point, and an odometer is adjusted to be the railway mileage of the ballasting starting point;
Based on the relative mileage between the laser section scanner and each ballast unloading opening of the ballast unloading carriage and the odometer, calculating and obtaining the absolute mileage of the laser section scanner and each ballast unloading opening of the ballast unloading carriage;
Measuring the current locomotive speed by using an odometer, and calculating the maximum ballasting amount of a ballasting opening of a single ballasting carriage in one ballasting unit at the current locomotive speed;
Scanning the ballast bed in real time by using a laser scanner to obtain a default ballast quantity of a ballast unloading unit;
Calculating to obtain the number of the ballasted carriages and the opening of the ballasted opening of the current ballasting unit based on the default railway ballast quantity and the maximum ballasting quantity;
According to the number of the ballasts and the number of the ballasts in each ballasting carriage, obtaining the numbers of the ballasting carriages participating in the ballasting of the corresponding ballasting unit;
when the ballast unloading port of the ballast unloading carriage participating in ballast unloading reaches the position of the ballast unloading unit, the ballast unloading port is opened to a specified opening degree to unload the ballast.
9. The method of claim 8, wherein each ballasting carriage location is located using a combination of GPS and inertial measurement units;
The inertial measurement unit comprises a plurality of accelerometers and a plurality of gyroscopes for performing supplementary positioning when GPS positioning accuracy is reduced.
10. Intelligent ballasting device based on three-dimensional point cloud, characterized by comprising:
The grouping module is used for performing ad hoc network grouping on a plurality of ballasted carriages of the locomotive;
The point cloud acquisition module is used for acquiring parameter data of the ballasted carriages and acquiring unordered point cloud data of the section of the ballast bed;
the point cloud processing module is used for calculating and obtaining the real-time three-dimensional point cloud profit and loss of the ballast bed based on the preprocessed unordered point cloud data;
and the ballast unloading control module is used for controlling the ballast unloading carriage to carry out ballast unloading operation based on the real-time three-dimensional point cloud profit and loss of the ballast bed, the ad hoc network grouping information and the parameter data.
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