CN113554759A - 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, a device and equipment for coal transportation and scattering, which comprises the following steps: scanning and collecting first point cloud data of the current train in the transportation process by using a laser radar; carrying out 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; and 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 the at least two fourth point cloud data. According to the invention, each train is monitored in real time, reliable monitoring data can be provided for the scattering condition in the coal transportation process, the effective monitoring of the dust suppression effect of the coal dust suppressant is realized, and the effective monitoring of the standard operation of the dust suppression station is further 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 the transportation amount of railway coal, the environmental pollution of railway transportation is gradually promoted. However, although dust suppression measures such as spraying of dust suppressant have been adopted in the current railway transportation, the dust suppression effect cannot be guaranteed to be continued until the transportation process is finished, and scattering is generated in the coal transportation process and pollutes the environment along the line.
In order to reduce the coal transportation loss and the environmental pollution in the transportation process, the dust suppressant is sprayed after coal is loaded in each railway coal car station. However, whether the spraying of the dust suppressant plays a due role is not enough, and an effective supervision measure is not available at present. Vehicles from different loading stations have different scattering degrees in the transportation process.
In the prior art, the supervision measures for coal transportation mainly include: for TSP (total suspended particulate matter) and PM10(inhalable particles) and synchronously adopting video monitoring facilities. However, the situation that the next vehicle is left without being scattered cannot be judged because the coal dust pollution generated when the train passes through cannot be judged to be instant dust or deposited dust. And for coal scattering, the specific scattering vehicles, scattering places and scattering degree can not be accurately judged only by conventional online particulate matter monitoring and online high-definition video monitoring, so that the existing supervision is causedThe effect of the measures is not good.
Disclosure of Invention
The invention aims to provide an intelligent monitoring and analyzing method, device and equipment for coal transportation and scattering, which are used for solving at least one problem in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides an intelligent monitoring and analyzing method for coal transportation and scattering, which comprises the following steps:
scanning and collecting 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;
carrying out 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;
and 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 the at least two fourth point cloud data.
In one possible design, data cleaning is performed on the first point cloud data to obtain second point cloud data, and the method includes:
obtaining position coordinates of rails on two sides below each radar observation point, and filtering out data of irrelevant points collected outside the rails on two sides in the first point cloud data;
calculating discrete point data in the first point cloud data by using a 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 by using a registration algorithm model to obtain third point cloud data, including:
selecting the first two frames of point cloud data of the same compartment collected at the same radar observation point from the second point cloud data;
using an NDT algorithm model, taking the first frame point cloud data as target point cloud data, and registering the second frame point cloud data to obtain offset vectors of the first two frames of point cloud data;
the train is assumed to do uniform linear motion in the scanning range of the same radar observation point;
registering other frame point cloud data acquired by the same carriage at the same radar observation point 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 the second point cloud data is registered by using a registration algorithm model to obtain third point cloud data, the method further includes:
acquiring the frame number of point cloud data acquired by each carriage;
when the frame number difference between the carriages with the first frame number and the second frame number is larger than the frame number difference between the carriages with the second frame number which is N times of the frame number difference and any other carriages, judging that the carriage with the first frame number stops below the current radar observation point;
and filtering out repeated point cloud data frames acquired by the carriage with the first frame number.
In one possible design, the method includes the steps of obtaining fourth point cloud data of a 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 the at least two fourth point cloud data, and includes the following steps:
obtaining fourth point cloud data Y of the current train at the loading point from the third point cloud datai1And fourth point cloud data Y at the end pointi2；
For the fourth point cloud data Y in the range of delta xi1Point data a projected onto the yOz planei＝(a1,a2...,an) And the fourth point cloud data Yi2Point data b projected onto yOz planei＝(b1,b2...,bn) Comparing one by one;
wherein, Deltax is the differential distance quantity of the current train moving direction, ai＝(a1,a2...,an) For the fourth point cloud data Yi1Point data in the point cloud data of the ith carriage, bi＝(b1,b2...,bn) For the fourth point cloud data Yi2Point data in the ith carriage point cloud data;
when point data aiAnd point data biExceeds a threshold value TThreshold valueTime, point to point data aiSum data biRespectively carrying out curve fitting to obtain a fitting curve f1And fitting curve f2(ii) a Wherein the content of the first and second substances,
to the fitted curve f1And fitting curve f2And integrating on the y axis and the z axis, calculating the volume change values of the train at the loading point and the terminal point according to the integration result, and judging whether the coal is scattered in the transportation process according to the volume change values.
In one possible design, the point-to-point data aiAnd point data biPerforming curve fitting to obtain a fitting curve f1And fitting curve f2The method comprises the following steps:
using least square method to point data aiPerforming curve fitting to obtain a B spline curve f1＝P(t)；
Using an objective function
For the B spline curve f1Optimizing;
wherein the content of the first and second substances,
is point data aiTo the B-spline curve f1Square of (a), (b), (c), (d)sIs a function of the smoothness of the control curve, and λ is fsCorresponding coefficients, said B-spline curve f being solved when said objective function reaches a minimum value1；
Wherein the point data biFitted curve f of2Solution process of (a) and (f)1The same is true.
In one possible design, the fitted curve f is fitted1And fitting curve f2Respectively integrating on a y axis and a z axis, calculating volume change values of the train at a loading point and a terminal point according to an integration result, and judging whether coal is scattered in the transportation process according to the volume change values, wherein the method comprises the following steps:
to the fitted curve f1And fitting curve f2The integration is carried out on the y axis and the z axis together to obtain the middle curve area F1Wherein F is1The calculation formula of (a) is as follows:
According to the intermediate curve area F1Calculating the intermediate volume difference V between the volume of the train at the loading point and the volume at the end point1The calculation formula is as follows:
according to the intermediate volume difference V1And judging whether the train is scattered in the running process.
In one possible design, the method includes the steps of obtaining fourth point cloud data of a 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 the at least two fourth point cloud data, and includes the following steps:
respectively acquiring fourth point cloud data Z of the same carriage acquired by two adjacent radar detection pointsi1And fourth point cloud data Zi2；
Respectively to the fourth point cloud data Zi1And fourth point cloud data Zi2Performing surface fitting to obtain a fitted surface P1And fitting the surface P2；
Acquiring the integral sinking distance mu of the current train in the transportation processh；
Will fit to the surface P1Translational descent muhTo obtain a fitted surface P'1Wherein, P' 1 ═ P1-μh；
Calculating a fitted surface P1And fitting surface P'1First volume difference value delta of1；
Δ1＝∫∫(P1-P'1) d δ; wherein d δ is the differential in the xy direction;
calculating a fitted surface P1And fitting the surface P2Second volume difference value Δ2；
Δ2＝∫∫(P1-P2)dδ；
According to the first volume difference value delta1And a second volume difference value delta2Calculating the volume change value delta v caused by scattering of the coal in the transportation process;
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 monitoring and analyzing the scattering condition of the coal in the transportation process according to the numerical change of the at least two fourth point cloud data.
In one possible design, the first point cloud data is subjected to data cleaning to obtain second point cloud data, and the second data acquisition module is specifically configured to:
obtaining position coordinates of rails on two sides below each radar observation point, and filtering out data of irrelevant points collected outside the rails on two sides in the first point cloud data;
calculating discrete point data in the first point cloud data by using a 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, and the third data acquisition module is specifically configured to:
selecting the first two frames of point cloud data of the same compartment collected at the same radar observation point from the second point cloud data;
using an NDT algorithm model, taking the first frame point cloud data as target point cloud data, and registering the second frame point cloud data to obtain offset vectors of the first two frames of point cloud data;
the train is assumed to do uniform linear motion in the scanning range of the same radar observation point;
registering other frame point cloud data acquired by the same carriage at the same radar observation point 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 includes:
the system comprises a frame number acquisition unit, a data acquisition unit and a data acquisition unit, wherein the frame number acquisition unit is used for acquiring the frame number of point cloud data acquired by each carriage;
the judging unit is used for judging that the compartment with the first frame number stops below the current radar observation point when the frame number difference value between the compartment with the first frame number and the compartment with the second frame number is larger than the frame number difference value between the compartment with the second frame number and any other compartment;
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 the coal scattering condition in the transportation process is monitored and analyzed according to the numerical change of the at least two fourth point cloud data, wherein the coal scattering analysis module is specifically used for:
obtaining fourth point cloud data Y of the current train at the loading point from the third point cloud datai1And fourth point cloud data Y at the end pointi2；
For the fourth point cloud data Y in the range of delta xi1Point data a projected onto the yOz planei＝(a1,a2...,an) And the fourth point cloud data Yi2Point data b projected onto yOz planei＝(b1,b2...,bn) Comparing one by one;
wherein, Deltax is the differential distance quantity of the current train moving direction, ai＝(a1,a2...,an) For the fourth point cloud data Yi1Point data in the point cloud data of the ith carriage, bi＝(b1,b2...,bn) For the fourth point cloud data Yi2Point data in the ith carriage point cloud data;
when point data aiAnd point data biExceeds a threshold value TThreshold valueTime, point to point data aiSum data biRespectively carrying out curve fitting to obtain a fitting curve f1And fitting curve f2(ii) a Wherein the content of the first and second substances,
to the fitted curve f1And fitting curve f2And integrating on the y axis and the z axis, calculating the volume change values of the train at the loading point and the terminal point according to the integration result, and judging whether the coal is scattered 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 the coal scattering condition in the transportation process is monitored and analyzed according to the numerical change of the at least two fourth point cloud data, wherein the coal scattering analysis module is specifically used for:
respectively acquiring fourth point cloud data Z of the same carriage acquired by two adjacent radar detection pointsi1And fourth point cloud data Zi2；
Respectively to the fourth point cloud data Zi1And fourth point cloud data Zi2Performing surface fitting to obtain a fitted surface P1And fitting the surface P2；
Acquiring the integral sinking distance mu of the current train in the transportation processh；
Will fit to the surface P1Translational descent muhTo obtain a fitted surface P'1Wherein, P' 1 ═ P1-μh；
Calculating a fitted surface P1And fitting surface P'1First volume difference value delta of1；
Δ1＝∫∫(P1-P'1) d δ; wherein d δ is the differential in the xy direction;
calculating a fitted surface P1And fitting the surface P2Second volume difference value Δ2；
Δ2＝∫∫(P1-P2)dδ；
According to the first volume difference value delta1And a second volume difference value delta2Calculating the volume change value delta v caused by scattering of the coal in the transportation process;
Δv＝Δ2-Δ1。
in a third aspect, the present invention provides a computer device, which includes a memory, a processor and a transceiver, which are sequentially connected in communication, wherein the memory is used for storing a computer program, the transceiver is used for transceiving a message, and the processor is used for reading the computer program and executing the intelligent monitoring and analyzing method for coal transportation loss as described in any one of the possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon instructions for executing the method for intelligent monitoring and analysis of coal transportation loss as described in any one of the possible designs of the first aspect.
In a fifth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method for intelligent monitoring and analysis of coal transportation spillage as described in any one of the possible designs of the first aspect.
Has the advantages that: the method comprises the steps of scanning and collecting first point cloud data of a current train in a transportation process by using a laser radar; carrying out 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; and 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 fourth point cloud data. The invention can provide reliable monitoring data for the scattering condition in the coal transportation process by monitoring each train in real time, realizes effective monitoring of the dust suppression effect of the coal dust suppressant, and further realizes effective monitoring of the standard operation of the dust suppression station; in addition, through data monitoring of the whole process of the coal transport train, controlled factors of different dust suppression agents and different dust suppression operations can be obtained, and data support is provided for further improving the performance of the dust suppression agents and standardizing the operation management of the dust suppression stations.
Drawings
FIG. 1 is a flow chart of an intelligent monitoring and analyzing method for coal transportation and scattering in the embodiment of the invention;
FIG. 2 is a flow chart of registration of second point cloud data in an embodiment of the invention;
FIG. 3 is a flow chart of fitting a curved surface of a train in the practice of the present invention;
fig. 4 is a block diagram of a structure of an intelligent monitoring and analyzing device for coal transportation and scattering in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments obtained by a person skilled in the art based on the embodiments in the present description without any inventive step are within the scope of the present invention.
Examples
As shown in fig. 1-3, in a first aspect, the present embodiment provides an intelligent monitoring and analyzing method for coal transportation missing, which includes but is not limited to steps S101 to S104:
s101, scanning and collecting 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;
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 acquired by scanning with the laser radar includes not only train point cloud data and rail point cloud data, but also point cloud data of irrelevant objects such as land, trees, wires, and the like, the system needs to filter the irrelevant 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 includes:
s1021, acquiring position coordinates of rails on two sides below each radar observation point, and filtering out irrelevant point data collected outside the rails on the two sides in the first point cloud data;
wherein, it is required to explain that because laser radar erects the top at both sides rail, consequently, can carry out preliminary washing, specifically include to the point cloud data of laser radar collection through the position coordinate of both sides rail:
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 reason why the z axis is eliminated is because the position coordinate of the detection rail is not related to the z axis.
Secondly, sorting the point data of the x coordinate between (0, 1.0) in sequence from small to large according to the size of the y-axis coordinate, wherein the x coordinate of the point with the minimum y coordinate is the position coordinate of the rail on one side, and the position coordinate of the rail on the other side can be calculated according to the position coordinate of the rail on one side because the width of the railway in China is 1.435 m;
and finally, filtering out the data of the irrelevant point outside the rails on the two sides in the first point cloud data by taking the position coordinates of the rails on the two sides as a limiting condition.
As another optional implementation, in addition to the manner of filtering the unrelated point data in step S1021, this embodiment also provides another manner of filtering the unrelated point data, which specifically includes:
the scanning range of the laser radar is limited between the train and the rail according to a hemispherical cover body on each laser radar, so that other irrelevant point data are filtered.
Step S1022, calculating discrete point data in the first point cloud data by using a Kdtree algorithm model, 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 generally causes a small amount of point data to deviate from an original position due to an accuracy error occurring during scanning of the laser radar, or requires secondary cleaning of the first point cloud data due to an interfering object such as a wire or a bird below the laser radar, which is specifically as follows:
calculating the data p of each point in distance by using Kdtree algorithm modeliThe nearest 30 points, the distance m from each point to the 30 pointsikThe calculation formula of (2) is as follows:
mik＝||pi-pik||；
then, these 30 points and p are calculatediAverage value d of distancesi：
Average value d for each distanceiThe mean μ and variance δ are found:
then all diPoints outside (μ - δ, μ + δ) are discrete points that need to be filtered out of the first point cloud data.
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 by using a registration algorithm model to obtain third point cloud data, including:
step S1031, selecting the first two frames of point cloud data collected by the same compartment at the same radar observation point from the second point cloud data;
step s1032, registering the second frame of point cloud data by using an NDT (Normal distribution Transform) algorithm model and using the first frame of point cloud data as target point cloud data to obtain an offset vector of the first two frames of point cloud data, specifically including:
and (3) obtaining a conversion vector by using an NDT algorithm model:
subjecting the second frame point cloud data to vector conversion
After conversion, the cost function
Taking the minimum value; wherein the content of the first and second substances,
representing a number of points in the second frame of point cloud data, the T function being
In transforming vectors
The conversion result under action; wherein:
In order to accelerate the registration process, for the same car, only the previous two frames of point cloud data can be registered by using an NDT algorithm, because the frame of the beginning part and the frame of the ending part of the car have greater difference relative to the frame of the middle part of the car, and the stationarity of the lidar and the linear traveling property of the train jointly cause that the registration result has no rotation amount, but only has a translation amount, which can 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 frames of point cloud data of the same compartment collected at the same radar observation point based on the offset vector;
because the train does uniform linear motion in a short time, the difference between two frames of all the following frames can be considered to be completely the same as the difference between the two frames, and therefore, the transformation method of the second frame point cloud data can be as follows:
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 optional implementation manner, after step S103, the method further includes:
acquiring the frame number of point cloud data acquired by each carriage;
when the difference between the number of frames of the first carriage and the second carriage is larger than the difference between the number of frames of the second carriage which is N times of the number of frames of the second carriage and the number of frames of any other carriages, the first carriage of the number of frames is judged to stop below the current radar observation point, wherein the calculation formula is as follows:
|tmax1|-|tmax2|＞(|tmax2|-|tmax3|) × N; wherein, | tmax1I represents the first car of frame number, | tmax2I represents the car with the second number of frames, | tmax3And | represents the car with the third frame number, and so on. Among them, N is preferably an integer of 2.
And filtering out repeated point cloud data frames acquired by the carriage with the first frame number.
Specifically, the NDT algorithm model is utilized to register the collected frame data of the whole vehicle, wherein each two adjacent frames are registered once by the NDT algorithm, and the in-place motion vector can be obtained
Will be provided with
The length | d | of the point cloud data is used as the similarity between two frames, and then the point cloud data is arranged from small to large according to the similarity, because two adjacent frames are completely repeated when the vehicle stops and the similarity is almost zero, the point cloud data arranged in the front can be deleted until the point cloud frame number of the current carriage is basically the same as the average value of the point cloud frame numbers of other carriages.
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 fourth point cloud data.
In an optional implementation manner, the method for 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 fourth point cloud data includes:
obtaining fourth point cloud data Y of the current train at the loading point from the third point cloud datai1And fourth point cloud number at the end pointAccording to Yi2；
For the fourth point cloud data Y in the range of delta xi1Point data a projected onto the yOz planei＝(a1,a2...,an) And the fourth point cloud data Yi2Point data b projected onto yOz planei＝(b1,b2...,bn) Comparing one by one;
wherein, Deltax is the differential distance quantity of the current train moving direction, ai＝(a1,a2...,an) For the fourth point cloud data Yi1Point data in the point cloud data of the ith carriage, bi＝(b1,b2...,bn) For the fourth point cloud data Yi2Point data in the ith carriage point cloud data;
when point data aiAnd point data biExceeds a threshold value TThreshold valueTime, point to point data aiSum data biRespectively carrying out curve fitting to obtain a fitting curve f1And fitting curve f2(ii) a Wherein the content of the first and second substances,
of course, it will be understood that if the point data a isiAnd point data biDoes not exceed the threshold value TThreshold valueWhen it is, the point data a is not alignediAnd point data biAnd (6) performing curve fitting.
To the fitted curve f1And fitting curve f2And respectively integrating on a y axis and a z axis, calculating volume change values of the train at a loading point and a terminal point according to an integration result, and judging whether the coal is scattered in the transportation process according to the volume change values.
In an alternative embodiment, the point-to-point data aiAnd point data biRespectively carrying out curve fitting to obtain a fitting curve f1And fitting curve f2The method comprises the following steps:
using least square method to point data aiPerforming curve fitting to obtainTo B-spline curve f1＝P(t)；
Using an objective function
For the B spline curve f1Optimizing;
wherein the content of the first and second substances,
is point data aiTo the B-spline curve f1Square of (a), (b), (c), (d)sIs a function of the smoothness of the control curve, and λ is fsCorresponding coefficients, said B-spline curve f being solved when said objective function reaches a minimum value1；
Wherein the point data biFitted curve f of2Solution process of (a) and (f)1The same is true.
In an alternative embodiment, the fitted curve f is fitted1And fitting curve f2Respectively integrating on a y axis and a z axis, calculating volume change values of the train at a loading point and a terminal point according to an integration result, and judging whether coal is scattered in the transportation process according to the volume change values, wherein the method comprises the following steps:
to the fitted curve f1And fitting curve f2The integration is carried out on the y axis and the z axis together to obtain the middle curve area F1Wherein F is1The calculation formula of (a) is as follows:
According to the intermediate curve area F1Calculating the intermediate volume difference V between the volume of the train at the loading point and the volume at the end point1The calculation formula is as follows:
according to the intermediate volume difference V1And judging whether the train is scattered in the running process.
The volume of the train is changed during running, so that the volume of the coal is not only lost but also settled, namely, small particles above the coal gradually move downwards in the bumping of the train, so that the whole volume of the coal is changed. Under the action of the dust suppressant, the surface shape of the coal generally cannot be greatly deformed, so that the surface of the coal integrally sinks a certain amount under the condition of sufficient dust suppressant. And the coal is scattered in the place with insufficient dust suppressant, so that air pollution is caused, and the part can generate large deformation. Since the method is used for detecting the coal scattering condition, the volume change in the sedimentation process needs to be eliminated.
In an optional implementation manner, the fourth point cloud data of the current train at the at least two radar observation points is obtained from the third point cloud data, and the monitoring and analysis of the coal scattering condition in the transportation process is performed according to the numerical change of the at least two fourth point cloud data, including:
respectively acquiring fourth point cloud data Z of the same carriage acquired by two adjacent radar detection pointsi1And fourth point cloud data Zi2；
Respectively to the fourth point cloud data Zi1And fourth point cloud data Zi2Performing surface fitting to obtain a fitted surface P1And fitting the surface P2；
Acquiring the integral sinking distance mu of the current train in the transportation processh；
Wherein the overall subsidence distance muhCan be obtained by the following method:
for any point cloud data coordinate (x, y), a fitting surface P can be obtained1And fitting the curved surface P2 ofCurrent height h1And h2Then, the height difference Δ h ═ h can be obtained1-h2；
For a train, the area with small deformation amount should be the majority, so a large amount of coordinates generated randomly for the train can be generatedCalculating the height difference of each random point, sorting the height differences, and taking the average value mu of the height differences of the middle K%hAs the overall sinking distance, points with too large or too small deformation amount caused by the sinking can be eliminated; preferably, the height difference is averaged over the middle 30%.
Will fit to the surface P1Translational descent muhTo obtain a fitted surface P'1Wherein, P'1＝P1-μh；
Calculating a fitted surface P1And fitting surface P'1First volume difference value delta of1；
Δ1＝∫∫(P1-P'1) d δ; wherein d δ is the differential in the xy direction;
calculating a fitted surface P1And fitting the surface P2Second volume difference value Δ2；
Δ2＝∫∫(P1-P2)dδ；
According to the first volume difference value delta1And a second volume difference value delta2Calculating the volume change value delta v caused by scattering of the coal in the transportation process;
Δv＝Δ2-Δ1。
based on the above disclosure, the embodiment can provide reliable monitoring data for the scattering condition in the coal transportation process by monitoring each train in real time, thereby realizing effective monitoring of the dust suppression effect of the coal dust suppressant and further realizing effective monitoring of the standard operation of the dust suppression station; in addition, through data monitoring of the whole process of the coal transport train, controlled factors of different dust suppression agents and different dust suppression operations can be obtained, and data support is provided for further improving the performance of the dust suppression agents and standardizing the operation management of the dust suppression stations.
As shown in fig. 4, in a second aspect, the present embodiment provides an intelligent monitoring and analyzing apparatus for coal transportation left 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 monitoring and analyzing the scattering condition of the coal in the transportation process according to the numerical change of the at least two fourth point cloud data.
In one possible design, the first point cloud data is subjected to data cleaning to obtain second point cloud data, and the second data acquisition module is specifically configured to:
obtaining position coordinates of rails on two sides below each radar observation point, and filtering out data of irrelevant points collected outside the rails on two sides in the first point cloud data;
calculating discrete point data in the first point cloud data by using a 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, and the third data acquisition module is specifically configured to:
selecting the first two frames of point cloud data of the same compartment collected at the same radar observation point from the second point cloud data;
using an NDT algorithm model, taking the first frame point cloud data as target point cloud data, and registering the second frame point cloud data to obtain offset vectors of the first two frames of point cloud data;
the train is assumed to do uniform linear motion in the scanning range of the same radar observation point;
registering other frame point cloud data acquired by the same carriage at the same radar observation point 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 includes:
the system comprises a frame number acquisition unit, a data acquisition unit and a data acquisition unit, wherein the frame number acquisition unit is used for acquiring the frame number of 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 carriages with the first frame number and the second frame number is larger than the frame number difference between any two other carriages which is N times larger than the frame number difference between the carriages with the first frame number;
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 the coal scattering condition in the transportation process is monitored and analyzed according to the numerical change of the at least two fourth point cloud data, wherein the coal scattering analysis module is specifically used for:
obtaining fourth point cloud data Y of the current train at the loading point from the third point cloud datai1And fourth point cloud data Y at the end pointi2；
For the fourth point cloud data Y in the range of delta xi1Point data a projected onto the yOz planei＝(a1,a2...,an) And the fourth point cloud data Yi2Point data b projected onto yOz planei＝(b1,b2...,bn) Comparing one by one;
wherein, Deltax is the differential distance quantity of the current train moving direction, ai＝(a1,a2...,an) For the fourth point cloud data Yi1Point data in the point cloud data of the ith carriage, bi＝(b1,b2...,bn) For the fourth point cloud data Yi2Point data in the ith carriage point cloud data;
when point data aiAnd point data biExceeds a threshold value TThreshold valueTime, point to point data aiSum data biRespectively carrying out curve fitting to obtain a fitting curve f1And fitting curve f2(ii) a Wherein the content of the first and second substances,
to the fitted curve f1And fitting curve f2And integrating on the y axis and the z axis, calculating the volume change values of the train at the loading point and the terminal point according to the integration result, and judging whether the coal is scattered 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 the coal scattering condition in the transportation process is monitored and analyzed according to the numerical change of the at least two fourth point cloud data, wherein the coal scattering analysis module is specifically used for:
respectively acquiring fourth point cloud data Z of the same carriage acquired by two adjacent radar detection pointsi1And fourth point cloud data Zi2；
Respectively to the fourth point cloud data Zi1And fourth point cloud data Zi2Performing surface fitting to obtain a fitted surface P1And fitting the surface P2；
Acquiring the integral sinking distance mu of the current train in the transportation processh；
Will fit to the surface P1Translational descent muhTo obtain a fitted surface P'1Wherein, P'1＝P1-μh；
Calculating a fitted surface P1And fitting surface P'1First volume difference value delta of1；
Δ1＝∫∫(P1-P'1) d δ; wherein d δ is the differential in the xy direction;
calculating a fitted surface P1And fitting the surface P2Second volume difference value Δ2；
Δ2＝∫∫(P1-P2)dδ；
According to the first volume difference value delta1And a second volume difference value delta2Calculating the volume change value delta v caused by scattering of the coal in the transportation process;
Δv＝Δ2-Δ1。
in a third aspect, the present invention provides a computer device, which includes a memory, a processor and a transceiver, which are sequentially connected in communication, wherein the memory is used for storing a computer program, the transceiver is used for transceiving a message, and the processor is used for reading the computer program and executing the intelligent monitoring and analyzing method for coal transportation loss as described in any one of the possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon instructions for executing the method for intelligent monitoring and analysis of coal transportation loss as described in any one of the possible designs of the first aspect.
In a fifth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method for intelligent monitoring and analysis of coal transportation spillage as described in any one of the possible designs of the first aspect.
Finally, it should be noted that: the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent monitoring and analyzing method for coal transportation and scattering is characterized by comprising the following steps:
scanning and collecting 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;
carrying out 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;
and 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 the at least two fourth point cloud data.
2. The intelligent monitoring and analyzing method for coal transportation and scattering 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:
obtaining position coordinates of rails on two sides below each radar observation point, and filtering out data of irrelevant points collected outside the rails on two sides in the first point cloud data;
calculating discrete point data in the first point cloud data by using a 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 missing and scattering according to claim 1, wherein the registration of the second point cloud data is performed by using a registration algorithm model to obtain third point cloud data, and the method comprises the following steps:
selecting the first two frames of point cloud data of the same compartment collected at the same radar observation point from the second point cloud data;
registering second frame point cloud data by using the NDT algorithm model and taking the first frame point cloud data as target point cloud data to obtain offset vectors of the first two frames of point cloud data;
the train is assumed to do uniform linear motion in the scanning range of the same radar observation point;
registering other frame point cloud data acquired by the same carriage at the same radar observation point 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 missing scattering according to claim 1, wherein after the second point cloud data is registered by using a registration algorithm model to obtain third point cloud data, the method further comprises:
acquiring the frame number of point cloud data acquired by each carriage;
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 the carriage with the second frame number which is N times of the frame number difference and any other carriage, judging that the carriage with the first frame number stops below the current radar observation point;
and filtering out repeated point cloud data frames acquired by the carriage with the first frame number.
5. The intelligent monitoring and analysis method for coal transportation scattering according to claim 1, wherein fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and the scattering condition of coal in the transportation process is monitored and analyzed according to the numerical change of at least two fourth point cloud data, and the method comprises the following steps:
obtaining fourth point cloud data Y of the current train at the loading point from the third point cloud datai1And fourth point cloud data Y at the end pointi2；
For the fourth point cloud data Y in the range of delta xi1Point data a projected onto the yOz planei＝(a1,a2...,an) And the fourth point cloud data Yi2Point data b projected onto yOz planei＝(b1,b2...,bn) Comparing one by one;
wherein, Deltax is the differential distance quantity of the current train moving direction, ai＝(a1,a2...,an) For the fourth point cloud data Yi1Section i of ChinaPoint data in car point cloud data, bi＝(b1,b2...,bn) For the fourth point cloud data Yi2Point data in the ith carriage point cloud data;
when point data aiAnd point data biExceeds a threshold value TThreshold valueTime, point to point data aiAnd point data biRespectively carrying out curve fitting to obtain a fitting curve f1And fitting curve f2(ii) a Wherein the content of the first and second substances,
to the fitted curve f1And fitting curve f2And integrating on the y axis and the z axis, calculating the volume change values of the train at the loading point and the terminal point according to the integration result, and judging whether the coal is scattered in the transportation process according to the volume change values.
6. The intelligent analysis method for coal transportation missing scattering according to claim 5, characterized in that point data a is point dataiAnd point data biRespectively carrying out curve fitting to obtain a fitting curve f1And fitting curve f2The method comprises the following steps:
using least square method to point data aiPerforming curve fitting to obtain a B spline curve f1＝P(t)；
Using an objective function
For the B spline curve f1Optimizing;
wherein the content of the first and second substances,
is point data aiTo the B-spline curve f1Square of (a), (b), (c), (d)sIs a function of the smoothness of the control curve, and λ is fsCorresponding coefficients, said B-spline curve f being solved when said objective function reaches a minimum value1；
Wherein the point data biFitted curve f of2Solution process of (a) and (f)1The same is true.
7. The intelligent analysis method for coal transportation scattering according to claim 6, wherein fitting curve f is fitted1And fitting curve f2The method comprises the following steps of integrating on a y axis and a z axis together, calculating volume change values of a train at a loading point and a terminal point according to an integration result, and judging whether coal is scattered in a transportation process according to the volume change values, wherein the method comprises the following steps:
to the fitted curve f1And fitting curve f2The integration is carried out on the y axis and the z axis together to obtain the middle curve area F1Wherein F is1The calculation formula of (a) is as follows:
According to the intermediate curve area F1Calculating the intermediate volume difference V between the volume of the train at the loading point and the volume at the end point1The calculation formula is as follows:
according to the intermediate volume difference V1And judging whether the train is scattered in the running process.
8. The intelligent analysis method for coal transportation scattering according to claim 1, wherein fourth point cloud data of the current train at least two radar observation points are obtained from the third point cloud data, and the scattering condition of coal in the transportation process is monitored and analyzed according to the numerical change of at least two fourth point cloud data, and the method comprises the following steps:
respectively acquiring two adjacent radarsFourth point cloud data Z of same carriage collected at detection pointi1And fourth point cloud data Zi2；
Respectively to the fourth point cloud data Zi1And fourth point cloud data Zi2Performing surface fitting to obtain a fitted surface P1And fitting the surface P2；
Acquiring the integral sinking distance mu of the current train in the transportation processh；
Will fit to the surface P1Translational descent muhTo obtain a fitted surface P'1Wherein, P'1＝P1-μh；
Calculating a fitted surface P1And fitting surface P'1First volume difference value delta of1；
Δ1＝∫∫(P1-P'1) d δ; wherein d δ is the differential in the xy direction;
calculating a fitted surface P1And fitting the surface P2Second volume difference value Δ2；
Δ2＝∫∫(P1-P2)dδ；
According to the first volume difference value delta1And a second volume difference value delta2Calculating the volume change value delta v caused by scattering in the transportation process of the coal;
Δv＝Δ2-Δ1。
9. the utility model provides a coal transportation loses and spills intelligent monitoring analytical equipment which characterized in that includes:
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 monitoring and analyzing the scattering condition of coal in the transportation process according to the numerical change of the at least two fourth point cloud data.
10. A computer device, comprising a memory, a processor and a transceiver, which are connected in sequence in communication, wherein the memory is used for storing a computer program, the transceiver is used for sending and receiving messages, and the processor is used for reading the computer program and executing the intelligent monitoring and analyzing method for coal transportation spillage according to any one of claims 1-8.
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