CN113554759B - Intelligent monitoring and analyzing method, device and equipment for coal transportation and scattering - Google Patents
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Abstract
The invention discloses an intelligent monitoring and analyzing method, device and equipment for coal transportation and scattering, which comprises the following steps: collecting first point cloud data of the current train in the transportation process by utilizing laser radar scanning; data cleaning is carried out on the first point cloud data to obtain second point cloud data; registering the second point cloud data by using a registration algorithm model to obtain third point cloud data; fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and according to the numerical value change of at least two fourth point cloud data, monitoring and analyzing the scattering condition of coal in the transportation process. According to the invention, by monitoring each train in real time, reliable monitoring data can be provided for the scattering condition in the coal transportation process, so that the effective monitoring of the dust suppression effect of the coal dust suppressant is realized, and further the effective monitoring of the standard operation of the dust suppression station is realized.
Description
Technical Field
The invention belongs to the technical field of coal transportation monitoring, and particularly relates to an intelligent monitoring and analyzing method, device and equipment for coal transportation scattering.
Background
With the increase of railway coal transportation, the environmental pollution of railway transportation is gradually scheduled. However, although the existing railway transportation adopts dust suppression measures such as spraying dust suppressant, the dust suppression effect cannot be guaranteed to be continuous until the transportation process is finished, and the coal is scattered and pollutes the environment along the line in the transportation process.
In order to reduce coal transportation loss and environmental pollution in the transportation process, each railway coal station performs dust suppressant spraying operation after coal loading. However, whether the dust suppressant is sprayed plays a role or not is an effective supervision measure at present. The vehicles originating from different loading stations have great differences in the degree of scattering produced during transportation.
In the prior art, the supervision measures for coal transportation mainly comprise: TSP (total suspended particulate, total suspended particulate matter) and PM 10 (inhalable particles), inhalable particulate matter are monitored and video monitoring facilities are synchronously adopted. However, it is impossible to judge whether the dust pollution of the coal dust generated when the train passes is instantaneous dust or deposited dust, so that the current vehicle scattering condition cannot be judged. And for coal scattering, specific scattering vehicles, scattering sites and scattering degrees cannot be accurately judged only through conventional online particle monitoring and online high-definition video monitoring, so that the effect of the conventional supervision measures is poor.
Disclosure of Invention
The invention aims to provide an intelligent monitoring and analyzing method, device and equipment for coal transportation scattering, which are used for solving at least one problem in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the invention provides an intelligent monitoring and analyzing method for coal transportation spills, comprising the following steps:
Collecting first point cloud data of the current train in the transportation process by utilizing laser radar scanning; the laser radar is erected in a plurality of radar observation points along the coal transportation railway;
performing data cleaning on the first point cloud data to obtain second point cloud data;
Registering the second point cloud data by using a registration algorithm model to obtain third point cloud data;
Fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and according to the numerical value change of at least two fourth point cloud data, monitoring and analyzing the scattering condition of coal in the transportation process.
In one possible design, the data cleaning of the first point cloud data to obtain second point cloud data includes:
acquiring position coordinates of rails on two sides below each radar observation point, and filtering irrelevant point data acquired outside the rails on two sides in the first point cloud data;
And calculating discrete point data in the first point cloud data by utilizing Kdtree algorithm model, and filtering the discrete point data in the first point cloud data to obtain second point cloud data.
In one possible design, registering the second point cloud data with a registration algorithm model to obtain third point cloud data includes:
Selecting the first two frames of point cloud data of the same carriage collected at the same radar observation point from the second point cloud data;
registering the second frame of point cloud data by using the NDT algorithm model and taking the first frame of point cloud data as target point cloud data to obtain offset vectors of the first two frames of point cloud data;
Assuming that the train does uniform linear motion in the scanning range of the same radar observation point;
Registering other frame point cloud data acquired at the same radar observation point of the same carriage based on the offset vector;
And repeating the steps, and registering the point cloud data of other carriages of the current train to obtain third point cloud data.
In one possible design, after registering the second point cloud data with the registration algorithm model, the method further includes:
acquiring the number of frames of the acquired point cloud data of each carriage;
When the difference of the frames between the first-frame and the second-frame carriages is greater than the difference of the frames between the second-frame carriage and any other carriage, the first-frame carriage is judged to be stopped below the current radar observation point;
and filtering out the repeated point cloud data frames acquired by the carriage with the first frame number.
In one possible design, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical variation of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, including:
Acquiring fourth point cloud data Y i1 of a current train at a loading point and fourth point cloud data Y i2 of a terminal point from the third point cloud data;
Point data a i＝(a1,a2...,an of the fourth point cloud data Y i1 projected to the yOz plane and point data b i＝(b1,b2...,bn of the fourth point cloud data Y i2 projected to the yOz plane in the Δx range) are compared one by one;
Wherein Δx is a differential distance amount in the movement direction of the current train, a i＝(a1,a2...,an) is point data in the ith carriage point cloud data in the fourth point cloud data Y i1, and b i＝(b1,b2...,bn) is point data in the ith carriage point cloud data in the fourth point cloud data Y i2;
When the change T between the point data a i and the point data b i exceeds the threshold T Threshold value , performing curve fitting on the point data a i and the point data b i respectively to obtain a fitted curve f 1 and a fitted curve f 2; wherein,
And integrating the fitting curve f 1 and the fitting curve f 2 on the y axis and the z axis together, calculating the volume change values of the train at the loading point and the end point according to the integration result, and judging whether the coal is scattered or not in the transportation process according to the volume change values.
In one possible design, curve fitting is performed on point data a i and point data b i together to obtain a fitted curve f 1 and a fitted curve f 2, including:
Performing curve fitting on point data a i by using a least square method to obtain a B-spline curve f 1 =p (t);
Using objective functions Optimizing the B-spline curve f 1;
Wherein, For the square of the point data a i to the B-spline curve f 1, f s is a function controlling the smoothness of the curve, λ is the coefficient corresponding to f s, and when the objective function reaches a minimum value, the B-spline curve f 1 can be solved;
The solution process of the fitting curve f 2 of the point data b i is the same as that of f 1.
In one possible design, integrating the fitted curve f 1 and the fitted curve f 2 on the y axis and the z axis respectively, calculating the volume change values of the train at the loading point and the end point according to the integration result, and judging whether the coal is scattered in the transportation process according to the volume change values, including:
The fitted curve F 1 and the fitted curve F 2 are integrated together on the y axis and the z axis to obtain an intermediate curve area F i, wherein the calculation formula of F i is as follows:
Wherein D is the range of values in the y direction, y min≤D≤ymax;fi(t)＝f1-f2;
According to the intermediate curve area F i, calculating an intermediate volume difference V 1 between the volume of the train at the loading point and the volume at the end point, wherein the calculation formula is as follows:
Wherein k is the total number of sections of the cars in the train;
And judging whether the train is scattered in the running process according to the intermediate volume difference V 1.
In one possible design, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical variation of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, including:
Respectively acquiring fourth point cloud data Z i1 and fourth point cloud data Z i2 of the same carriage acquired by two adjacent radar detection points;
Performing surface fitting on the fourth point cloud data Z i1 and the fourth point cloud data Z i2 to obtain a fitting surface P 1 and a fitting surface P 2;
acquiring the whole sinking distance mu h of the current train in the transportation process;
Translational lowering of the fitting surface P 1 by mu h to obtain a fitting surface P '1, wherein P' 1＝P1-μh;
calculating a first volume difference delta 1 between the fitting surface P 1 and the fitting surface P' 1;
Δ 1＝∫∫(P1-P'1) dδ; wherein dδ is the differentiation in the xy direction;
calculating a second volume difference delta 2 of the fitting surface P 1 and the fitting surface P 2;
Δ2＝∫∫(P1-P2)dδ；
Calculating a volume change value Deltav of the coal caused by scattering in the transportation process according to the first volume difference value Delta 1 and the second volume difference value Delta 2;
Δv＝Δ2-Δ1。
In a second aspect, the invention provides an intelligent monitoring and analyzing device for coal transportation and scattering, comprising:
The first data acquisition module is used for scanning and acquiring first point cloud data of the current train in the transportation process by using a laser radar; the laser radar is erected in a plurality of radar observation points along the coal transportation railway;
The second data acquisition module is used for carrying out data cleaning on the first point cloud data to obtain second point cloud data;
The third data acquisition module is used for registering the second point cloud data by using a registration algorithm model to obtain third point cloud data;
and the coal scattering analysis module is used for acquiring fourth point cloud data of the current train at least two radar observation points from the third point cloud data, and carrying out monitoring analysis on the scattering condition of the coal in the transportation process according to the numerical change of at least two pieces of the fourth point cloud data.
In one possible design, the data cleaning is performed on the first point cloud data to obtain second point cloud data, where the second data obtaining module is specifically configured to:
acquiring position coordinates of rails on two sides below each radar observation point, and filtering irrelevant point data acquired outside the rails on two sides in the first point cloud data;
And calculating discrete point data in the first point cloud data by utilizing Kdtree algorithm model, and filtering the discrete point data in the first point cloud data to obtain second point cloud data.
In one possible design, the second point cloud data is registered by using a registration algorithm model to obtain third point cloud data, where the third data obtaining module is specifically configured to:
Selecting the first two frames of point cloud data of the same carriage collected at the same radar observation point from the second point cloud data;
registering the second frame of point cloud data by using the NDT algorithm model and taking the first frame of point cloud data as target point cloud data to obtain offset vectors of the first two frames of point cloud data;
Assuming that the train does uniform linear motion in the scanning range of the same radar observation point;
Registering other frame point cloud data acquired at the same radar observation point of the same carriage based on the offset vector;
And repeating the steps, and registering the point cloud data of other carriages of the current train to obtain third point cloud data.
In one possible design, the apparatus further comprises:
the frame number acquisition unit is used for acquiring the frame number of the point cloud data acquired by each carriage;
The judging unit is used for judging that the carriage with the first frame number stops below the current radar observation point when the difference of the frame number between the carriage with the first frame number and the carriage with the second frame number is larger than the difference of the frame number between the carriage with the second frame number which is N times larger than the difference of the frame number between any other carriages;
and the repeated data filtering unit is used for filtering the repeated point cloud data frames acquired by the carriage with the first frame number.
In one possible design, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical change of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, and the coal scattering analysis module is specifically configured to:
Acquiring fourth point cloud data Y i1 of a current train at a loading point and fourth point cloud data Y i2 of a terminal point from the third point cloud data;
Point data a i＝(a1,a2...,an of the fourth point cloud data Y i1 projected to the yOz plane and point data b i＝(b1,b2...,bn of the fourth point cloud data Y i2 projected to the yOz plane in the Δx range) are compared one by one;
Wherein Δx is a differential distance amount in the movement direction of the current train, a i＝(a1,a2...,an) is point data in the ith carriage point cloud data in the fourth point cloud data Y i1, and b i＝(b1,b2...,bn) is point data in the ith carriage point cloud data in the fourth point cloud data Y i2;
When the change T between the point data a i and the point data b i exceeds the threshold T Threshold value , performing curve fitting on the point data a i and the point data b i respectively to obtain a fitted curve f 1 and a fitted curve f 2; wherein,
And integrating the fitting curve f 1 and the fitting curve f 2 on the y axis and the z axis together, calculating the volume change values of the train at the loading point and the end point according to the integration result, and judging whether the coal is scattered or not in the transportation process according to the volume change values.
In one possible design, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical change of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, and the coal scattering analysis module is specifically configured to:
Respectively acquiring fourth point cloud data Z i1 and fourth point cloud data Z i2 of the same carriage acquired by two adjacent radar detection points;
Performing surface fitting on the fourth point cloud data Z i1 and the fourth point cloud data Z i2 to obtain a fitting surface P 1 and a fitting surface P 2;
acquiring the whole sinking distance mu h of the current train in the transportation process;
Translational lowering of the fitting surface P 1 by mu h to obtain a fitting surface P '1, wherein P' 1＝P1-μh;
calculating a first volume difference delta 1 between the fitting surface P 1 and the fitting surface P' 1;
Δ 1＝∫∫(P1-P'1) dδ; wherein dδ is the differentiation in the xy direction;
calculating a second volume difference delta 2 of the fitting surface P 1 and the fitting surface P 2;
Δ2＝∫∫(P1-P2)dδ；
Calculating a volume change value Deltav of the coal caused by scattering in the transportation process according to the first volume difference value Delta 1 and the second volume difference value Delta 2;
Δv＝Δ2-Δ1。
In a third aspect, the present invention provides a computer device, comprising a memory, a processor and a transceiver in communication with each other in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to send and receive messages, and the processor is configured to read the computer program and perform the intelligent monitoring and analysis method for coal transportation spill as described in any one of the possible designs of the first aspect.
In a fourth aspect, the invention provides a computer readable storage medium having instructions stored thereon which, when executed on a computer, perform a method of intelligent monitoring and analysis of coal transportation spill as described in any one of the possible designs of the first aspect.
In a fifth aspect, the invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of intelligent monitoring and analysis of coal transportation spill as described in any one of the possible designs of the first aspect.
The beneficial effects are that: the method comprises the steps of utilizing a laser radar to scan and collect first point cloud data of a current train in the transportation process; performing data cleaning on the first point cloud data to obtain second point cloud data; registering the second point cloud data by using a registration algorithm model to obtain third point cloud data; fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and according to the numerical value change of at least two fourth point cloud data, monitoring and analyzing the scattering condition of coal in the transportation process. According to the invention, by monitoring each train in real time, reliable monitoring data can be provided for the scattering condition in the coal transportation process, so that the effective monitoring of the dust suppression effect of the coal dust suppressant is realized, and further the effective monitoring of the standard operation of the dust suppression station is realized; in addition, through the data monitoring of the whole process of the coal transportation train, the controlled factors of different dust suppressants and different dust suppressing operations can be obtained, and data support is provided for further improving the performance of the dust suppressants and standardizing the operation management of dust suppressing stations.
Drawings
FIG. 1 is a flow chart of an intelligent monitoring and analyzing method for coal transportation spill in an embodiment of the invention;
FIG. 2 is a flowchart of registering second point cloud data according to an embodiment of the present invention;
FIG. 3 is a flow chart of fitting a train curved surface in the practice of the present invention;
Fig. 4 is a block diagram of a device for intelligently monitoring and analyzing coal transportation scattering in an embodiment of the invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present specification more clear, the technical solutions of the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are some embodiments of the present specification, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present invention based on the embodiments herein.
Examples
As shown in fig. 1-3, in a first aspect, the present embodiment provides an intelligent monitoring and analyzing method for coal transportation spill, including, but not limited to, implementation by steps S101-S104:
s101, scanning and collecting first point cloud data of a current train in the transportation process by using a laser radar; the laser radar is erected in a plurality of radar observation points along the coal transportation railway;
S102, carrying out data cleaning on the first point cloud data to obtain second point cloud data;
It should be noted that, because the first point cloud data collected by laser radar includes not only train point cloud data and rail point cloud data, but also point cloud data of unrelated objects such as land, trees, wires, etc., the system needs to filter the unrelated point cloud data before analyzing and processing the train point cloud data and the rail point cloud data containing the coal point cloud data.
As one possible implementation manner of step S102, performing data cleaning on the first point cloud data to obtain second point cloud data, including:
S1021, acquiring position coordinates of rails on two sides below each radar observation point, and filtering irrelevant point data acquired outside the rails on two sides in the first point cloud data;
The point cloud data collected by the laser radar can be primarily cleaned through the position coordinates of the rails at the two sides, and the method specifically comprises the following steps:
Firstly, selecting point data with a z-axis close to 0, eliminating the z-axis coordinate of the point data and converting the z-axis coordinate into two-dimensional point data; the z axis is a coordinate axis parallel to the plane where the rails on two sides are located, and the point data close to 0 is selected to ensure that the point data selected by the laser radar is the point data located between the rails on two sides on the premise of correct installation, and the reason for eliminating the z axis is that the position coordinates of the detected rails are irrelevant to the z axis.
Secondly, the point data with the x coordinate between (0, 1.0) are orderly sequenced from small to large according to the size of the y-axis coordinate, wherein the x coordinate of the point with the smallest y coordinate is the position coordinate of one side rail, and the width of the railway in China is 1.435m, so that the position coordinate of the other side rail can be calculated according to the position coordinate of one side rail;
And finally, taking the position coordinates of the rails at the two sides as a limiting condition, and filtering irrelevant point data positioned outside the rails at the two sides in the first point cloud data.
As another optional implementation manner, in addition to the filtering manner of the irrelevant point data in step S1021, this embodiment further provides another filtering manner of the irrelevant point data, which specifically includes:
and limiting the scanning range of the laser radars between the train and the rail according to the hemispherical cover body on each laser radar, so as to filter out other irrelevant point data.
And S1022, calculating discrete point data in the first point cloud data by utilizing Kdtree algorithm models, and filtering the discrete point data in the first point cloud data to obtain second point cloud data.
It should be noted that, the generation of the discrete point data is usually caused by an accuracy error of the laser radar during scanning, so that a small part of the point data deviates from a position where the laser radar should be originally located, or because an interference object such as an electric wire or a bird is arranged below the laser radar, the second cleaning is required for the first point cloud data, which is specifically as follows:
Calculating 30 points nearest to each point data p i by using Kdtree algorithm model, and calculating the distance m ik from each point to the 30 points according to the following calculation formula:
mik＝||pi-pik||；
Then, the average d i of the distances from p i for these 30 points is calculated:
For each distance average d i, the mean μ and variance δ are found:
all points d i outside (μ - δ, μ+δ) are discrete points that need to be filtered out from the first point cloud data seed.
S103, registering the second point cloud data by using a registration algorithm model to obtain third point cloud data;
in an optional implementation manner of step S103, registering the second point cloud data with a registration algorithm model to obtain third point cloud data includes:
S1031, selecting the first two frames of point cloud data of the same carriage collected at the same radar observation point from the second point cloud data;
S1032, registering second frame point cloud data by using an NDT (Normal Distributions Transform, normal distribution conversion) algorithm model and using the first frame point cloud data as target point cloud data to obtain offset vectors of the first two frames of point cloud data, wherein the method specifically comprises the following steps:
Obtaining a conversion vector by using an NDT algorithm model:
Wherein/> Respectively representing the offset of the second frame point cloud data in the directions of x, y and z axes,/>, respectivelyRespectively representing the rotation angles of the second frame point cloud data on x, y and z axes respectively;
passing the second frame point cloud data through a conversion vector After conversion, cost functionTaking a minimum value; wherein/>Representing points in the second frame point cloud data, the T function being/>At the transformation vector/>Conversion results under action; wherein:
Wherein d 1,d2 is a constant value, Is covariance matrix,/>Representing the point cloud center.
To accelerate the registration process, for the same car, the first two frames of point cloud data may be registered only by using NDT algorithm, because there is a greater difference between the frame of the car start part and the frame of the car end part relative to the frame of the car middle part, and the stationarity of the lidar and the linear travelling property of the train together result in no rotation amount, but only a translation amount, which may be expressed as:
S1032, assuming that the train does uniform linear motion in the scanning range of the same radar observation point;
s1033, registering other frame point cloud data acquired at the same radar observation point of the same carriage based on the offset vector;
Because the train does uniform linear motion in a short time, the difference between two frames of all frames at the back can be considered to be identical to the difference between the two frames at the front, and therefore, the transformation method of the point cloud data of the second frame can be as follows:
all point cloud data for the same car may be registered.
And S1034, repeating the steps, and registering the point cloud data of other carriages of the current train to obtain third point cloud data.
As an alternative embodiment, after step S103, the method further includes:
acquiring the number of frames of the acquired point cloud data of each carriage;
When the difference of the frames between the first-frame and the second-frame carriage is greater than the difference of the frames between the second-frame carriage and any other carriage, the first-frame carriage is judged to be stopped below the current radar observation point, wherein the calculation formula is as follows:
T max1|-|tmax2|＞(|tmax2|-|tmax3 |) x N; wherein, |t max1 | represents the car with the first frame number, |t max2 | represents the car with the second frame number, |t max3 | represents the car with the third frame number, and so on. Preferably, N is an integer of 2.
And filtering out the repeated point cloud data frames acquired by the carriage with the first frame number.
Specifically, registering frame data acquired by the whole vehicle by using an NDT algorithm model, wherein every two adjacent frames are registered once by using the NDT algorithm to obtain displacement vectorsWill/>The length |d| of the two frames is used as the similarity between the two frames, and then the two frames are arranged according to the size of the similarity from small to large, and because the two adjacent frames are completely repeated when stopping and the similarity is almost zero, the point cloud data arranged in front can be deleted until the average value of the current carriage point cloud frame number and other carriage frame numbers is basically the same.
And S104, acquiring fourth point cloud data of the current train at least two radar observation points from the third point cloud data, and monitoring and analyzing the scattering condition of coal in the transportation process according to the numerical change of at least two pieces of fourth point cloud data.
In an optional implementation manner, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical change of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, including:
Acquiring fourth point cloud data Y i1 of a current train at a loading point and fourth point cloud data Y i2 of a terminal point from the third point cloud data;
Point data a i＝(a1,a2...,an of the fourth point cloud data Y i1 projected to the yOz plane and point data b i＝(b1,b2...,bn of the fourth point cloud data Y i2 projected to the yOz plane in the Δx range) are compared one by one;
Wherein Δx is a differential distance amount in the movement direction of the current train, a i＝(a1,a2...,an) is point data in the ith carriage point cloud data in the fourth point cloud data Y i1, and b i＝(b1,b2...,bn) is point data in the ith carriage point cloud data in the fourth point cloud data Y i2;
When the change T between the point data a i and the point data b i exceeds the threshold T Threshold value , performing curve fitting on the point data a i and the point data b i respectively to obtain a fitted curve f 1 and a fitted curve f 2; wherein,
Of course, it is understood that if the change T between the point data a i and the point data b i does not exceed the threshold T Threshold value , then curve fitting is not performed on the point data a i and the point data b i.
And integrating the fitting curve f 1 and the fitting curve f 2 on the y axis and the z axis respectively, calculating the volume change values of the train at the loading point and the end point according to the integration result, and judging whether the coal is scattered or not in the transportation process according to the volume change values.
In an alternative embodiment, curve fitting is performed on the point data a i and the point data b i to obtain a fitted curve f 1 and a fitted curve f 2, respectively, including:
Performing curve fitting on point data a i by using a least square method to obtain a B-spline curve f 1 =p (t);
Using objective functions Optimizing the B-spline curve f 1;
Wherein, For the square of the point data a i to the B-spline curve f 1, f s is a function controlling the smoothness of the curve, λ is the coefficient corresponding to f s, and when the objective function reaches a minimum value, the B-spline curve f 1 can be solved;
The solution process of the fitting curve f 2 of the point data b i is the same as that of f 1.
In an alternative embodiment, integrating the fitting curve f 1 and the fitting curve f 2 on the y axis and the z axis respectively, calculating the volume change values of the train at the loading point and the end point according to the integration result, and judging whether the coal is scattered in the transportation process according to the volume change values, including:
The fitted curve F 1 and the fitted curve F 2 are integrated together on the y axis and the z axis to obtain an intermediate curve area F i, wherein the calculation formula of F i is as follows:
Wherein D is the range of values in the y direction, y min≤D≤ymax;fi(t)＝f1-f2;
According to the intermediate curve area F i, calculating an intermediate volume difference V 1 between the volume of the train at the loading point and the volume at the end point, wherein the calculation formula is as follows:
Wherein k is the total number of sections of the cars in the train;
And judging whether the train is scattered in the running process according to the intermediate volume difference V 1.
As the volume change of the train is caused during operation, not only is the scattering and sedimentation occur, but also the smaller particles above the coal gradually move downwards during the jolt of the train, so that the whole volume of the coal is changed. Under the action of the dust suppressant, the shape of the coal surface is generally not greatly deformed, so that the coal surface is wholly sunk by a certain amount under the condition of sufficient dust suppressant. And for the place where the dust suppressant is insufficient, the coal can be scattered, so that air pollution is caused, and the part can be deformed greatly. Since this method is used to detect the coal scattering, it is necessary to exclude the volume change occurring during sedimentation.
In an optional implementation manner, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical value change of at least two fourth point cloud data, the monitoring and analysis are performed on the scattering condition of coal in the transportation process, including:
Respectively acquiring fourth point cloud data Z i1 and fourth point cloud data Z i2 of the same carriage acquired by two adjacent radar detection points;
Performing surface fitting on the fourth point cloud data Z i1 and the fourth point cloud data Z i2 to obtain a fitting surface P 1 and a fitting surface P 2;
acquiring the whole sinking distance mu h of the current train in the transportation process;
Wherein the overall dip distance μ h can be obtained by:
For any coordinate (x, y) of the point cloud data, the current heights h 1 and h 2 of the fitting curved surface P 1 and the fitting curved surface P 2 A kind of electronic device can be obtained, and the height difference Δh=h 1-h2 can be obtained;
For a train, the area with small deformation amount should be the majority, so the height difference of each random point can be obtained according to a large number of randomly generated coordinates of the train, the height differences are ordered, and the average value mu h of the height differences with the middle K% is taken as the whole sinking distance, so that the points with overlarge or overlarge deformation amount caused by sedimentation can be eliminated; preferably, the average of the height differences of the middle 30% is taken.
Translational lowering of the fitting surface P 1 by mu h to obtain a fitting surface P '1, wherein P' 1＝P1-μh;
calculating a first volume difference delta 1 between the fitting surface P 1 and the fitting surface P' 1;
Δ 1＝∫∫(P1-P'1) dδ; wherein dδ is the differentiation in the xy direction;
calculating a second volume difference delta 2 of the fitting surface P 1 and the fitting surface P 2;
Δ2＝∫∫(P1-P2)dδ；
Calculating a volume change value Deltav of the coal caused by scattering in the transportation process according to the first volume difference value Delta 1 and the second volume difference value Delta 2;
Δv＝Δ2-Δ1。
Based on the disclosure, the embodiment can provide reliable monitoring data for the scattering condition in the coal transportation process by monitoring each train in real time, so that the effective monitoring of the dust suppression effect of the coal dust suppressant is realized, and further the effective monitoring of the standard operation of the dust suppression station is realized; in addition, through the data monitoring of the whole process of the coal transportation train, the controlled factors of different dust suppressants and different dust suppressing operations can be obtained, and data support is provided for further improving the performance of the dust suppressants and standardizing the operation management of dust suppressing stations.
As shown in fig. 4, in a second aspect, the present embodiment provides an intelligent monitoring and analyzing device for coal transportation and scattering, including:
The first data acquisition module is used for scanning and acquiring first point cloud data of the current train in the transportation process by using a laser radar; the laser radar is erected in a plurality of radar observation points along the coal transportation railway;
The second data acquisition module is used for carrying out data cleaning on the first point cloud data to obtain second point cloud data;
The third data acquisition module is used for registering the second point cloud data by using a registration algorithm model to obtain third point cloud data;
and the coal scattering analysis module is used for acquiring fourth point cloud data of the current train at least two radar observation points from the third point cloud data, and carrying out monitoring analysis on the scattering condition of the coal in the transportation process according to the numerical change of at least two pieces of the fourth point cloud data.
In one possible design, the data cleaning is performed on the first point cloud data to obtain second point cloud data, where the second data obtaining module is specifically configured to:
acquiring position coordinates of rails on two sides below each radar observation point, and filtering irrelevant point data acquired outside the rails on two sides in the first point cloud data;
And calculating discrete point data in the first point cloud data by utilizing Kdtree algorithm model, and filtering the discrete point data in the first point cloud data to obtain second point cloud data.
In one possible design, the second point cloud data is registered by using a registration algorithm model to obtain third point cloud data, where the third data obtaining module is specifically configured to:
Selecting the first two frames of point cloud data of the same carriage collected at the same radar observation point from the second point cloud data;
registering the second frame of point cloud data by using the NDT algorithm model and taking the first frame of point cloud data as target point cloud data to obtain offset vectors of the first two frames of point cloud data;
Assuming that the train does uniform linear motion in the scanning range of the same radar observation point;
Registering other frame point cloud data acquired at the same radar observation point of the same carriage based on the offset vector;
And repeating the steps, and registering the point cloud data of other carriages of the current train to obtain third point cloud data.
In one possible design, the apparatus further comprises:
the frame number acquisition unit is used for acquiring the frame number of the point cloud data acquired by each carriage;
The judging unit is used for judging that the carriage with the first frame number stops below the current radar observation point when the frame number difference between the carriage with the first frame number and the carriage with the second frame number is larger than the frame number difference between any two other carriages with N times;
and the repeated data filtering unit is used for filtering the repeated point cloud data frames acquired by the carriage with the first frame number.
In one possible design, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical change of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, and the coal scattering analysis module is specifically configured to:
Acquiring fourth point cloud data Y i1 of a current train at a loading point and fourth point cloud data Y i2 of a terminal point from the third point cloud data;
Point data a i＝(a1,a2...,an of the fourth point cloud data Y i1 projected to the yOz plane and point data b i＝(b1,b2...,bn of the fourth point cloud data Y i2 projected to the yOz plane in the Δx range) are compared one by one;
Wherein Δx is a differential distance amount in the movement direction of the current train, a i＝(a1,a2...,an) is point data in the ith carriage point cloud data in the fourth point cloud data Y i1, and b i＝(b1,b2...,bn) is point data in the ith carriage point cloud data in the fourth point cloud data Y i2;
When the change T between the point data a i and the point data b i exceeds the threshold T Threshold value , performing curve fitting on the point data a i and the point data b i respectively to obtain a fitted curve f 1 and a fitted curve f 2; wherein, />
And integrating the fitting curve f 1 and the fitting curve f 2 on the y axis and the z axis together, calculating the volume change values of the train at the loading point and the end point according to the integration result, and judging whether the coal is scattered or not in the transportation process according to the volume change values.
In one possible design, fourth point cloud data of the current train at least two radar observation points is obtained from the third point cloud data, and according to the numerical change of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, and the coal scattering analysis module is specifically configured to:
Respectively acquiring fourth point cloud data Z i1 and fourth point cloud data Z i2 of the same carriage acquired by two adjacent radar detection points;
Performing surface fitting on the fourth point cloud data Z i1 and the fourth point cloud data Z i2 to obtain a fitting surface P 1 and a fitting surface P 2;
acquiring the whole sinking distance mu h of the current train in the transportation process;
Translational lowering of the fitting surface P 1 by mu h to obtain a fitting surface P '1, wherein P' 1＝P1-μh;
calculating a first volume difference delta 1 between the fitting surface P 1 and the fitting surface P' 1;
Δ 1＝∫∫(P1-P'1) dδ; wherein dδ is the differentiation in the xy direction;
calculating a second volume difference delta 2 of the fitting surface P 1 and the fitting surface P 2;
Δ2＝∫∫(P1-P2)dδ；
Calculating a volume change value Deltav of the coal caused by scattering in the transportation process according to the first volume difference value Delta 1 and the second volume difference value Delta 2;
Δv＝Δ2-Δ1。
In a third aspect, the present invention provides a computer device, comprising a memory, a processor and a transceiver in communication with each other in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to send and receive messages, and the processor is configured to read the computer program and perform the intelligent monitoring and analysis method for coal transportation spill as described in any one of the possible designs of the first aspect.
In a fourth aspect, the invention provides a computer readable storage medium having instructions stored thereon which, when executed on a computer, perform a method of intelligent monitoring and analysis of coal transportation spill as described in any one of the possible designs of the first aspect.
In a fifth aspect, the invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of intelligent monitoring and analysis of coal transportation spill as described in any one of the possible designs of the first aspect.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An intelligent monitoring and analyzing method for coal transportation spills is characterized by comprising the following steps:
Collecting first point cloud data of the current train in the transportation process by utilizing laser radar scanning; the laser radar is erected in a plurality of radar observation points along the coal transportation railway;
performing data cleaning on the first point cloud data to obtain second point cloud data;
Registering the second point cloud data by using a registration algorithm model to obtain third point cloud data;
Fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and according to the numerical value change of at least two pieces of fourth point cloud data, monitoring and analyzing the scattering condition of coal in the transportation process;
Fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and according to the numerical value change of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, and the method comprises the following steps:
Acquiring fourth point cloud data of the current train at a loading point from the third point cloud data And fourth point cloud data at endpoint/>；
For the fourth point cloud data within the range of DeltaxDot data projected to yOz plane/>And the fourth point cloud data/>Dot data projected to yOz plane/>Comparing one by one;
wherein Deltax is the differential distance quantity of the movement direction of the current train, For the fourth point cloud data/>Point data in the ith node carriage point cloud data,/>For the fourth point cloud data/>Point data in the ith node carriage point cloud data;
point-of-time data Sum point data/>When the change T between the two exceeds a threshold T Threshold value , the point data/>Sum point data/>Respectively performing curve fitting to obtain a fitted curve/>And fitting a curve/>; Wherein/>；
Fitting a curve toAnd fitting a curve/>Integrating on a y axis and a z axis, calculating the volume change values of the train at a loading point and an end point according to an integration result, and judging whether the coal is scattered in the transportation process according to the volume change values;
or acquiring fourth point cloud data of the current train at least two radar observation points from the third point cloud data, and monitoring and analyzing the scattering condition of coal in the transportation process according to the numerical value change of at least two pieces of fourth point cloud data, wherein the method comprises the following steps:
respectively acquiring fourth point cloud data of the same carriage acquired by two adjacent radar detection points And fourth point cloud data；
Respectively for fourth point cloud dataAnd fourth point cloud data/>Performing surface fitting to obtain a fitting surface P 1 and a fitting surface P 2;
Obtaining the whole sinking distance of the current train in the transportation process ；
The fitting curved surface P 1 is moved down in a translation wayObtaining a fitting curved surface/>Wherein/> =P1-/>；
Calculating a fitting surface P 1 and a fitting surfaceIs a first volume difference delta 1;
; wherein/> Is the differentiation in xy direction;
calculating a second volume difference delta 2 of the fitting surface P 1 and the fitting surface P 2;
；
Calculating a volume change value Deltav of the coal caused by scattering in the transportation process according to the first volume difference value Delta 1 and the second volume difference value Delta 2;
Δv=Δ2-Δ1。
2. the method for intelligently monitoring and analyzing the scattering of coal transportation according to claim 1, wherein the step of performing data cleaning on the first point cloud data to obtain second point cloud data comprises the following steps:
acquiring position coordinates of rails on two sides below each radar observation point, and filtering irrelevant point data acquired outside the rails on two sides in the first point cloud data;
And calculating discrete point data in the first point cloud data by utilizing Kdtree algorithm model, and filtering the discrete point data in the first point cloud data to obtain second point cloud data.
3. The intelligent monitoring and analyzing method for coal transportation spill as claimed in claim 1, wherein registering the second point cloud data by using a registration algorithm model to obtain third point cloud data comprises:
Selecting the first two frames of point cloud data of the same carriage collected at the same radar observation point from the second point cloud data;
registering the second frame of point cloud data by using the NDT algorithm model and taking the first frame of point cloud data as target point cloud data to obtain offset vectors of the first two frames of point cloud data;
Assuming that the train does uniform linear motion in the scanning range of the same radar observation point;
Registering other frame point cloud data acquired at the same radar observation point of the same carriage based on the offset vector;
And repeating the steps, and registering the point cloud data of other carriages of the current train to obtain third point cloud data.
4. The intelligent monitoring and analyzing method for coal transportation spill as claimed in claim 1, wherein after registering the second point cloud data by using a registration algorithm model to obtain third point cloud data, the method further comprises:
acquiring the number of frames of the acquired point cloud data of each carriage;
When the difference of the frames between the first-frame and the second-frame carriages is greater than the difference of the frames between the second-frame carriage and any other carriage, the first-frame carriage is judged to be stopped below the current radar observation point;
and filtering out the repeated point cloud data frames acquired by the carriage with the first frame number.
5. The intelligent monitoring and analyzing method for coal transportation spills according to claim 1, wherein the point data isSum point data/>Respectively performing curve fitting to obtain a fitted curve/>And fitting a curve/>Comprising:
Point data using least squares Performing curve fitting to obtain a B-spline curve f 1 =P (t);
Using objective functions Optimizing the B-spline curve f 1;
Wherein, For dot data/>To the square of the B-spline curve f 1, f s is a function controlling the smoothness of the curve, λ is the coefficient corresponding to f s, and when the objective function reaches a minimum value, the B-spline curve f 1 can be solved;
Wherein the point data is Is a fitting curve/>Solution process and/>The same applies.
6. The intelligent monitoring and analyzing method for coal transportation spills according to claim 5, wherein the method is characterized by fitting a curveAnd fitting a curve/>Integrating on a y axis and a z axis, calculating the volume change values of the train at a loading point and an end point according to the integration result, judging whether the coal is scattered in the transportation process according to the volume change values, and comprising the following steps:
Fitting a curve to And fitting a curve/>Integration is carried out on the y axis and the z axis together to obtain an intermediate curve area F i, wherein the calculation formula of F i is as follows:
; wherein D is the range of values in the y-axis direction, y min≤D≤ymax; /(I) ；
According to the intermediate curve area F i, calculating an intermediate volume difference V 1 between the volume of the train at the loading point and the volume at the end point, wherein the calculation formula is as follows:
; wherein k is the total number of sections of the cars in the train;
And judging whether the train is scattered in the running process according to the intermediate volume difference V 1.
7. An intelligent monitoring and analyzing device for coal transportation and scattering, which is characterized by comprising:
The first data acquisition module is used for scanning and acquiring first point cloud data of the current train in the transportation process by using a laser radar; the laser radar is erected in a plurality of radar observation points along the coal transportation railway;
The second data acquisition module is used for carrying out data cleaning on the first point cloud data to obtain second point cloud data;
The third data acquisition module is used for registering the second point cloud data by using a registration algorithm model to obtain third point cloud data;
the coal scattering analysis module is used for acquiring fourth point cloud data of the current train at least two radar observation points from the third point cloud data, and carrying out monitoring analysis on the scattering condition of coal in the transportation process according to the numerical value change of at least two pieces of the fourth point cloud data;
Fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and according to the numerical value change of at least two fourth point cloud data, the scattering condition of coal in the transportation process is monitored and analyzed, and the method comprises the following steps:
Acquiring fourth point cloud data of the current train at a loading point from the third point cloud data And fourth point cloud data at endpoint/>；
For the fourth point cloud data within the range of DeltaxDot data projected to yOz plane/>And the fourth point cloud data/>Dot data projected to yOz plane/>Comparing one by one;
wherein Deltax is the differential distance quantity of the movement direction of the current train, For the fourth point cloud data/>Point data in the ith node carriage point cloud data,/>For the fourth point cloud data/>Point data in the ith node carriage point cloud data;
point-of-time data Sum point data/>When the change T between the two exceeds a threshold T Threshold value , the point data/>Sum point data/>Respectively performing curve fitting to obtain a fitted curve/>And fitting a curve/>; Wherein/>；
Fitting a curve toAnd fitting a curve/>Integrating on a y axis and a z axis, calculating the volume change values of the train at a loading point and an end point according to an integration result, and judging whether the coal is scattered in the transportation process according to the volume change values;
or acquiring fourth point cloud data of the current train at least two radar observation points from the third point cloud data, and monitoring and analyzing the scattering condition of coal in the transportation process according to the numerical value change of at least two pieces of fourth point cloud data, wherein the method comprises the following steps:
respectively acquiring fourth point cloud data of the same carriage acquired by two adjacent radar detection points And fourth point cloud data；
Respectively for fourth point cloud dataAnd fourth point cloud data/>Performing surface fitting to obtain a fitting surface P 1 and a fitting surface P 2;
Obtaining the whole sinking distance of the current train in the transportation process ；
The fitting curved surface P 1 is moved down in a translation wayObtaining a fitting curved surface/>Wherein/> =P1-/>；
Calculating a fitting surface P 1 and a fitting surfaceIs a first volume difference delta 1;
; wherein/> Is the differentiation in xy direction;
calculating a second volume difference delta 2 of the fitting surface P 1 and the fitting surface P 2;
；
Calculating a volume change value Deltav of the coal caused by scattering in the transportation process according to the first volume difference value Delta 1 and the second volume difference value Delta 2;
Δv=Δ2-Δ1。
8. A computer device comprising a memory, a processor and a transceiver in communication with each other in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to send and receive messages, and the processor is configured to read the computer program and perform the intelligent monitoring and analysis method for coal transportation spill as claimed in any one of claims 1 to 6.
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