CN114136194B - Method, device, monitoring equipment and storage medium for monitoring volume of materials in warehouse - Google Patents
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Abstract
The application relates to a method, a device, monitoring equipment and a storage medium for monitoring the volume of materials in a warehouse. According to the method, radar data of a plurality of scanning points obtained by scanning a line scanning radar arranged on the top of a material bin are obtained, and according to the radar data and a height prediction model, the average height of the line scanning radar relative to the surface of a material in the material bin is obtained, and then the volume of the material in the material bin is determined according to the characteristic data and the average height of the material bin, so that the volume of the material in the material bin can be rapidly and accurately monitored.
Description
Technical Field
The application relates to the technical field of warehouse monitoring, in particular to a method and a device for monitoring the volume of materials in a warehouse, monitoring equipment and a storage medium.
Background
The material storage bin in the feed production workshop is a link which is started up and down in the production process, has an indispensable adjusting effect on the production beat connection of equipment, and whether the bin design is reasonable relates to whether a factory or a production line can run smoothly or not. When the bin capacity is large enough, for production, only the threshold values of the high bin space and the low bin space are needed to support the control logic to run smoothly, but if the bin capacity is optimally designed for the purposes of reducing the construction cost or improving the automation degree of a factory, namely the bin capacity is reduced to a certain extent, the control disorder can be generated by only adopting the high bin space and the low bin space for monitoring the materials in the bin, so that the production cannot be smoothly carried out. Therefore, the measurement data of the real-time bin capacity level is an important support for further improving the automation level of the whole factory and is also an important component of the construction of the future intelligent factory.
For a long time, the material storage bin in the feed production workshop is monitored by the level meter at the upper limit and the lower limit, namely, only the high and low material level early warning is carried out, the stock data of the materials in the bin between the high and low material levels is not known, and in recent years, along with the development of technology, the means for realizing the on-line monitoring of the bin capacity mainly comprise the following steps. Firstly, a capacitance type material level gauge is adopted, the material level gauge is arranged at the top of a storage bin, a cable vertically penetrates through the inside of a bin body, and the volume fraction of materials in the bin is approximate to the length of the cable which is covered by the materials; secondly, detecting the surface of the material in the bin by adopting high-frequency radar waves (80-90 GHz), obtaining the shape of the material on the surface of the material, and further calculating the volume fraction of the material; thirdly, a weighing mode is adopted, namely a weighing sensor is used for measuring the weight of materials in the warehouse.
However, the capacitive level gauge estimates the whole volume by a single point, ignoring the material morphology features, and thus leading to large errors in measured data. The method for detecting the high-frequency radar waves can effectively avoid the defects of single point type of a level gauge, material limitation and the like, but is limited by the beam angle of the radar waves and the size of a bin, has severe requirements on the installation position, and has high manufacturing cost due to the adoption of the wave band of 80GHz-90 GHz. In contrast, the weighing mode is a relatively mature and reliable metering mode at present. However, since most of storage bins in the feed workshops are designed and built in the form of bin groups, each bin group can only be weighed integrally, and thus, the information of the stock of materials in the single bin in the bin group cannot be obtained. And because the required measuring range of the weighing scale is more than 10 tons, and the weighing scale needs to be checked regularly, the checking flow is very complicated, and if each single bin is provided with one weighing scale, the cost is high. Therefore, a technical means capable of rapidly and accurately monitoring the volume of the material in a single bin is needed.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a method, an apparatus, a monitoring device and a storage medium for monitoring the volume of material in a bin, which can rapidly and accurately monitor the volume of material in a single bin.
A method of monitoring the volume of material in a bin, the method comprising:
acquiring radar data, wherein the radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged on the top of a material bin;
obtaining average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and the height prediction model;
And determining the volume of the material in the material bin according to the characteristic data of the material bin and the average predicted height.
In one embodiment, the obtaining the average predicted height of the line scan radar relative to the material surface in the material bin according to the radar data and the height prediction model includes: mapping the radar data into a plane coordinate system to obtain corresponding plane coordinate data, wherein the plane coordinate data comprises horizontal coordinates and vertical coordinates of each scanning point relative to the line scanning radar; sampling is carried out based on the plane coordinate data, and the target vertical coordinates of the corresponding sampling points are obtained; and inputting the target vertical coordinates of the corresponding sampling points into the height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
In one embodiment, the sampling based on the plane coordinate data to obtain the target vertical coordinate of the corresponding sampling point includes: extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to a set coordinate threshold range; performing interpolation fitting based on plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path; and uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter.
In one embodiment, the characteristic data of the material bin includes a bottom area of the material bin and a height of the material bin; the determining the material volume in the material bin according to the characteristic data of the material bin and the average predicted height comprises the following steps: determining the difference between the height of the material bin and the average height as the average height of the materials in the material bin; and determining the volume of the material in the material bin according to the bottom area of the material bin and the average height of the material in the material bin.
In one embodiment, the method for obtaining the height prediction model includes: acquiring a training data set for model training, wherein the training data set comprises a plurality of groups of sample data and corresponding tag data, each group of sample data comprises a plurality of sample sampling points and sample vertical coordinate data corresponding to each sample sampling point, the tag data comprises sample average heights of the plurality of sample sampling points in the sample data relative to a line scanning radar, and the sample sampling points are determined from a plurality of sample scanning points obtained by scanning the line scanning radar arranged on the top of a material bin; respectively inputting each group of sample data in the training data set into a height prediction model to be trained to obtain the average sample prediction height of a plurality of sample sampling points in each group of sample data relative to the line scanning radar; and optimizing the height prediction model to be trained according to the average sample height and the average sample height corresponding to each group of sample data to obtain an optimized height prediction model.
In one embodiment, the highly predictive model to be trained includes any one of a linear regression model, a machine learning model, and a neural network model.
A bin material volume monitoring device, the device comprising:
The radar data acquisition module is used for acquiring radar data, wherein the radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged on the top of a material bin;
the height prediction module is used for obtaining the average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and the height prediction model;
and the material volume determining module is used for determining the material volume in the material bin according to the characteristic data of the material bin and the average predicted height.
A monitoring device comprising a memory storing a computer program, and a processor implementing the steps of the method as described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method as described above.
A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method as described above.
According to the method, the device, the monitoring equipment and the storage medium for monitoring the volume of the materials in the bin, the radar data of a plurality of scanning points obtained by scanning the line scanning radar installed on the top of the bin of the material bin are obtained, the average height of the line scanning radar relative to the surface of the materials in the bin of the material is obtained according to the radar data and the height prediction model, and then the volume of the materials in the bin of the material is determined according to the characteristic data and the average height of the bin of the material, so that the volume of the materials in the bin of the material can be monitored rapidly and accurately.
Drawings
FIG. 1 is a flow chart of a method of monitoring the volume of material in a bin according to one embodiment;
FIG. 2 is a schematic view of a material bin in one embodiment;
FIG. 3 is a flow diagram of the step of obtaining an average height in one embodiment;
FIG. 4 is a flow chart illustrating the steps of sampling the vertical coordinates of a target in one embodiment;
FIG. 5 is a flow chart illustrating the steps for obtaining a height prediction model in one embodiment;
FIG. 6 is a schematic diagram of raw radar data in one embodiment;
FIG. 7 is a schematic diagram of the filter calibration of FIG. 6;
FIG. 8 is a schematic illustration of interpolation fitting of data in the longest path;
FIG. 9 is a block diagram of an embodiment of an apparatus for monitoring the volume of material in a bin;
fig. 10 is an internal structural diagram of the monitoring device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, a method for monitoring the volume of material in a warehouse is provided, and the embodiment is exemplified by the method applied to a monitoring device, wherein the monitoring device is in communication with a line scanning radar installed on the top of the warehouse of the material warehouse through a network. In this embodiment, the method includes the steps of:
Step 110, radar data is acquired.
The radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged on the top of the material bin. Specifically, the material bin refers to a storage bin for storing or transferring materials in an industrial processing process. The line scanning radar is arranged at the top of the material bin and is used for scanning along the diameter of the material bin so as to obtain a plurality of scanning points. Each scan point may be represented by (ρ, θ) in a polar coordinate system, where ρ refers to a linear distance of the scan point relative to the line scan radar, and θ refers to an angle between the linear distance and a perpendicular distance of the scan point relative to the line scan radar. In this embodiment, the polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar installed on the top of the material bin are obtained, so that the polar coordinate data of the plurality of scanning points can be analyzed and processed through subsequent steps, and the material volume in the material bin is obtained.
And 120, obtaining the average predicted height of the line scanning radar relative to the surface of the material in the material bin according to the radar data and the height prediction model.
The height prediction model is used for predicting the average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data obtained by scanning. Specifically, the height prediction model is obtained after training in advance, and in this example, the height prediction model includes any one of a linear regression model, a machine learning model, and a neural network model. Since the material may take on a rough form in the material bin as shown on the left side of fig. 2, it is often difficult to calculate the actual volume of the material in this form. The average predicted height is equivalent to the distance from the predicted material surface to the line scanning radar after flattening the original rugged material, namely h_hat in the right side of fig. 2. And because the line scanning radar scans along the diameter of the material bin, the radar data obtained by scanning comprises scanning points corresponding to the uneven form of the material surface in the left side of the figure 2. Thus, from the polar data of these scan points and the height prediction model, an average predicted height of the model predicted line scan radar relative to the material surface within the material bin may be obtained.
And 130, determining the volume of the material in the material bin according to the characteristic data of the material bin and the average predicted height.
The characteristic data of the material bin refers to data capable of reflecting the characteristics of the material bin, for example, but not limited to data capable of reflecting the shape of the material bin, data capable of reflecting the volume of the material bin and the like. In particular, the characteristic data of the material bin includes, but is not limited to, radius, diameter, length, width, height, etc. of the material bin. In this embodiment, according to the characteristic data of the material bin and the obtained average predicted height, the material volume in the material bin can be determined through calculation.
According to the method for monitoring the volume of the materials in the bin, the radar data of a plurality of scanning points obtained by scanning the line scanning radar arranged on the top of the bin of the material bin are obtained, the average height of the line scanning radar relative to the surface of the materials in the bin of the material is obtained according to the radar data and the height prediction model, and then the volume of the materials in the bin of the material is determined according to the characteristic data and the average height of the bin of the material, so that the volume of the materials in the bin of the material can be monitored rapidly and accurately.
In one embodiment, as shown in fig. 3, the method for obtaining the average predicted height of the line scan radar relative to the surface of the material in the material bin according to the radar data and the height prediction model specifically includes the following steps:
And step 310, mapping the radar data into a plane coordinate system to obtain corresponding plane coordinate data.
Since the radar data is polar coordinate data in which the scanning points are located in polar coordinates, in this embodiment, the radar data in the polar coordinate system is mapped into the planar coordinate system based on a mapping relationship between the polar coordinate system and the planar coordinate system, so as to obtain planar coordinate data corresponding to each scanning point. Wherein the plane coordinate data includes a horizontal coordinate x and a vertical coordinate y of each scanning point with respect to the line scanning radar. Specifically, the polar coordinate data (ρ, θ) of a certain scanning point in the polar coordinate system is mapped to the planar coordinate system, and the corresponding planar coordinate data (x, y) = (ρ×cos (θ), ρ×sin (θ)) is obtained. Based on this, each scanning point is mapped, thereby obtaining corresponding plane coordinate data.
Step 320, sampling is performed based on the plane coordinate data, so as to obtain the target vertical coordinate of the corresponding sampling point.
Since the average predicted height of the line scanning radar relative to the material surface in the material bin is required to be predicted, sampling can be performed from the scanning points corresponding to the uneven form of the material surface in the left side of fig. 2, so as to obtain the target vertical coordinates of the corresponding sampling points, namely, the y value of each sampling point in the plane coordinate system, namely, the vertical height of each sampling point relative to the line scanning radar.
And 330, inputting the target vertical coordinates of the corresponding sampling points into a height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
Specifically, the average predicted height of the line scanning radar output by the model relative to the material surface in the material bin is obtained by inputting the target vertical coordinates of each sampling point into the height prediction model.
In the above embodiment, the corresponding planar coordinate data is obtained by mapping the radar data into the planar coordinate system, sampling is performed based on the planar coordinate data to obtain the target vertical coordinates of the corresponding sampling points, and the target vertical coordinates of the corresponding sampling points are input into the height prediction model, so that the average height of the line scanning radar relative to the surface of the material in the material bin is obtained. According to the embodiment, the average predicted height of the line scanning radar relative to the material surface in the material bin is predicted through the height prediction model, so that the prediction accuracy and the prediction efficiency are high.
In one embodiment, as shown in fig. 4, the sampling is performed based on the plane coordinate data to obtain the target vertical coordinate of the corresponding sampling point, which specifically includes the following steps:
and 410, extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to the set coordinate threshold range.
Where the longest diameter is the longest distance between two opposite points on the bin wall in the cross section of the bin determined based on the morphological characteristics of the bin, for example, for a round bin the longest diameter may be the diameter on the cross section of the bin and for a square bin the longest diameter may be the diagonal on the cross section of the bin. The coordinate threshold range refers to the value range of the horizontal coordinate axis x in the plane coordinate system. In particular, the coordinate threshold range may be determined based on the size morphology of the material bin and the installation location of the line scan radar. In this embodiment, based on the set coordinate threshold range, the scanning points in the range are extracted from the plane coordinate data, so that the plane coordinate data of the scanning points on the longest diameter in the material bin can be obtained.
And step 420, performing interpolation fitting based on the plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path.
Because the plurality of scanning points obtained by scanning the line scanning radar are discrete points, in the embodiment, in order to ensure the sampling uniformity and improve the prediction capability of the model, interpolation fitting can be performed based on plane coordinate data of the scanning points on the longest path, so that a continuous fitting curve of the longest path is obtained, and even sampling is performed through subsequent steps.
And 430, uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter.
The number of samples can be set based on actual scene requirements, and the higher the number of samples is set, the higher the corresponding prediction accuracy is, and the lower the number of samples is set, the lower the corresponding prediction accuracy is. In this embodiment, the obtained fitting curve with the longest diameter is uniformly sampled based on the preset sampling number, so as to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter, that is, the y value of each sampling point on the longest diameter in the plane coordinate system, that is, the vertical height of each sampling point on the longest diameter relative to the line scanning radar.
In the above embodiment, according to the set coordinate threshold range, the plane coordinate data of the scanning point on the longest path in the material bin is extracted, interpolation fitting is performed based on the plane coordinate data of the scanning point on the longest path, a fitting curve of the longest path is obtained, and according to the preset sampling number, uniform sampling is performed on the fitting curve, so as to obtain the target vertical coordinate of the corresponding sampling point on the longest path, and further, height prediction is performed based on the target vertical coordinate of the corresponding sampling point on the longest path, so that higher prediction accuracy can be realized.
In one embodiment, the characteristic data of the material bin includes a bottom area of the material bin and a height of the material bin; determining the volume of the material in the material bin according to the characteristic data and the average predicted height of the material bin, including: determining the difference between the height of the material bin and the average predicted height as the average height of the materials in the material bin; and determining the volume of the materials in the material bin according to the bottom area of the material bin and the average height of the materials in the material bin. Since the average predicted height is the average height of the predicted line scanning radar relative to the surface of the material in the material bin, namely the height of the empty bin part in the material bin, and the overall height of the material bin is fixed, the difference between the height of the material bin and the average predicted height is the average height of the material in the material bin. And because the physical data such as radius, diameter, length, width, height and the like of the material bin are known, the volume of the material in the material bin can be calculated based on the known data and the average height of the material in the material bin. For example, taking a regular straight cylinder shape as an example of the material bin, the volume of the material in the material bin can be rapidly calculated based on the bottom area of the material bin and the average height of the material in the material bin. Of course, if the material bin is an irregular shape, the volume of the material in the material bin can be solved based on the known characteristic data of the material bin and the average height of the material in the material bin.
Specifically, taking a more complex cylindrical material bin as shown in fig. 2 as an example, the actual volume of the material in the material bin is as follows:
V＝Vp1+Vp2
Since the material is in a rugged form within the bin, it is often difficult to calculate the actual volume of the material in this form. As shown on the right side of fig. 2, by installing a line scanning radar R on the top of the material bin and predicting the average height h_hat of the radar to the surface of the material based on the radar data obtained by the line scanning radar scanning, which is equivalent to flattening the originally rugged material, the equivalent volume is obtained The method comprises the following steps:
For the material bin, V p1 is known (can be calculated based on the characteristic data of the material bin), the bottom surface area of the middle column portion and h 2 are also known, and the height of p2 'can be obtained based on h 2 and the predicted average height h_hat, and then the volume V p2′ of p2' can be calculated according to the known data, so as to obtain the volume of the material in the bin.
In one embodiment, as shown in fig. 5, the method for obtaining the altitude prediction model may include the following steps:
Step 510, a training dataset for model training is obtained.
The training data set includes a plurality of sets of sample data and corresponding tag data, each set of sample data may include a plurality of sample sampling points and sample vertical coordinate data corresponding to each sample sampling point, and the tag data includes a sample average height, i.e. an actual average height, of a plurality of sample sampling points in the sample data relative to the line scanning radar. And the sample sampling point is determined from a plurality of sample scanning points obtained by scanning a line scanning radar installed on the top of the material bin.
First, for the collection of tag data, in order to obtain more accurate collected data, data of an independent bin with a load cell is generally collected, so as to be used for calibration and comparison of real data (i.e. calibration of a real value corresponding to the average predicted height h_hat). When the method of the application is actually applied to monitor the volume of the materials in the bin, the weighing sensor can be omitted in the bin.
In this embodiment, the weighing sensor is used to weigh the material in the bin, so as to obtain the actual weight m of the material in the bin, and the volume weight γ can be sampled and measured, so that the actual volume of the material in the bin is:
V＝m/γ
The volume of the P2 portion is:
Vp2＝m/γ-Vp1
the average height of the radar sample to the material is further obtained:
h＝h2-Vp2/s
＝h2-(m/γ-Vp1)/s
Wherein s is the bottom area of the middle column, V p1 is the volume of p1, and the volumes are constant values, so that the average height h of a radar sample to a material, namely label data, can be obtained.
And for the acquisition of the sample data, the sample radar data obtained based on the radar scanning of the top of the material bin is determined. Specifically, as shown in fig. 6, the original sample radar data is polar coordinate data of a plurality of sample scanning points obtained by radar scanning, and is denoted by (ρ, θ), where ρ represents a linear distance of the scanning point relative to the radar, and θ refers to an angle of the scanning point relative to the radar. And the point of theta epsilon [45, 180] is the bin top data, which has no reference value and can be deleted. Since the relative position ρ and the angle θ can be measured, further filtering and correction can be performed on the data, i.e. the data is rotated by a certain angle to make the bin wall vertical and the point of the bin top area is removed to obtain fig. 7. In fig. 7, the straight lines on two opposite sides and parallel with each other are the scanned wall data, and the curve connecting the two straight lines is the longest diameter data obtained by scanning the surface of the material.
In the present embodiment, the polar coordinate data of fig. 7 is mapped into a planar coordinate system, thereby obtaining corresponding planar coordinate data. Specifically, the polar coordinate data (ρ, θ) of a certain scanning point in the polar coordinate system is mapped to the planar coordinate system, and the corresponding planar coordinate data (x, y) = (ρ×cos (θ), ρ×sin (θ)) is obtained.
And then extracting plane coordinate data of the scanning point on the longest diameter in the material bin according to the set coordinate threshold range. The coordinate threshold range may be determined based on the size and shape of the material bin and the installation position of the line scanning radar, and specifically, the coordinate threshold range refers to a value range of a horizontal coordinate axis x in the plane coordinate system. For example, as shown in fig. 7, the distance between the radar and the left wall and the right wall of the bin is [ -2,2], i.e. the range of the horizontal coordinate axis x corresponding to all the scanning points, and the horizontal coordinates corresponding to the scanning points on the bin walls at both sides are the end points of the range. And only the data of the scanning points on the longest path is helpful for height prediction, so that the data of the bin wall is filtered out by setting a threshold range, and only the data on the longest path is reserved. In this embodiment, the set threshold range may be about 90% of the distance between the radar and the left and right walls of the material bin, and in this embodiment, taking 90% as an example, the coordinate threshold range is [ -1.8,1.8]. Only the plane coordinate data of the scanning point with the horizontal coordinate x within the range of [ -1.8,1.8] is extracted, so that the plane coordinate data of the scanning point on the longest diameter in the material bin is obtained.
And because the scanning points obtained by radar scanning are discrete points, interpolation fitting is performed on the basis of the extracted plane coordinate data of the scanning points on the longest path, so that a continuous fitting curve of the longest path as shown in fig. 8 is obtained. In particular, spline interpolation may be used for interpolation fitting, for example, by interpolation function h dia (x), which has an input of horizontal coordinate x and an output of y, i.e. the vertical height of the point to the radar.
Further, the fitting curve is uniformly sampled according to the preset sampling number, so that the sample vertical coordinate of the corresponding sampling point on the longest diameter is obtained. For example, if the longest diameter range is [ min, max ] and the preset sampling number is n, the uniform sampling based on the sampling number to obtain a set of sampling points may be expressed as:
x＝[x1,x2,...,xi]，i∈(0，n]∩Z
Wherein: x i＝hdia (min+ (max-min)) i/n. Thus, the y-coordinate of the corresponding sample point, i.e., the sample vertical coordinate, can be obtained. The above x is that the whole longest diameter is uniformly sampled, so that the model can adapt to material bins with different sizes.
Based on the method, m groups of sample data and corresponding label data can be acquired for subsequent model training and optimization.
And step 520, respectively inputting each group of sample data in the training data set into a height prediction model to be trained, and obtaining the average sample prediction height of a plurality of sample sampling points in each group of sample data relative to the line scanning radar.
Specifically, the vertical coordinates of samples of each set of sample data corresponding to the sampling points are input into a height prediction model to be trained, so that the average height of samples of each set of sample data relative to the radar is obtained. In this embodiment, taking the height prediction model as a linear regression model as an example, there is a regression equation:
havg(x)＝ξx
Wherein x is an array, that is, the sample vertical coordinates of n sampling points in the obtained set of sample data, h avg represents the values of the n sampling points, the average height of the samples from the radar to the surface of the material estimated at the moment is output, and ζ is a model parameter, which can be optimized through the subsequent steps.
And step 530, optimizing the height prediction model to be trained according to the average sample height and the average sample height corresponding to each group of sample data to obtain an optimized height prediction model.
Specifically, in the present embodiment, optimization of the above model parameter ζ by minimizing a loss function may be employed, wherein the minimizing a loss function may employ a gradient descent method, a least square method, or the like. The gradient descent method will be described as an example, specifically:
Wherein m is the number of training sets, h is the average height of the actual samples of the radar to the material corresponding to the acquired sample data, h avg represents the value of n sampling points (i.e. a group of sample data) input, the estimated average height of the radar to the sample of the material at this time is output, and the height is made to approach the average height h of the actual samples so as to optimize the height prediction model to be trained, thereby obtaining the optimized height prediction model.
Further, again taking fig. 2 as an example, further describing the conversion of the volume, the equivalent volume:
Except for the input parameter x, the other quantities are known values. Wherein x is the target vertical coordinate of the sampling point, and the acquisition mode is similar to training, and this embodiment will not be described in detail.
For example, if the radius of the cylindrical portion of the bin shown in fig. 2 is r, the height of the truncated cone of the p1 portion is h p1, and the radius of the bottommost portion is r p1, the volume of the material in the bin is:
V＝πhp1(rp1 2+r2+rp1r)/3+(h2-havg(x))*πr2
thereby the volume of the materials in the warehouse can be calculated conveniently and rapidly.
It should be understood that, although the steps in the flowcharts of fig. 1 to 8 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps of fig. 1-8 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with other steps or at least a portion of the steps or stages in other steps.
In one embodiment, as shown in FIG. 9, there is provided an in-bin material volume monitoring device comprising: a module, B module and C module, wherein:
The radar data acquisition module 902 is configured to acquire radar data, where the radar data is polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar installed on a bin top of a material bin;
The height prediction module 904 is configured to obtain an average predicted height of the line scan radar relative to a surface of the material in the material bin according to the radar data and a height prediction model;
and a material volume determining module 906, configured to determine a material volume in the material bin according to the characteristic data of the material bin and the average predicted height.
In one embodiment, the height prediction module is specifically configured to: mapping the radar data into a plane coordinate system to obtain corresponding plane coordinate data, wherein the plane coordinate data comprises horizontal coordinates and vertical coordinates of each scanning point relative to the line scanning radar; sampling is carried out based on the plane coordinate data, and the target vertical coordinates of the corresponding sampling points are obtained; and inputting the target vertical coordinates of the corresponding sampling points into the height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
In one embodiment, the height prediction module is specifically further configured to: extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to a set coordinate threshold range; performing interpolation fitting based on plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path; and uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter.
In one embodiment, the characteristic data of the bin includes a bottom area of the bin and a height of the bin; the material volume determination module is specifically configured to: determining the difference between the height of the material bin and the average height as the average height of the materials in the material bin; and determining the volume of the material in the material bin according to the bottom area of the material bin and the average height of the material in the material bin.
In one embodiment, the system further comprises a height prediction model acquisition module, wherein the height prediction model acquisition module is used for acquiring a training data set for model training, the training data set comprises a plurality of groups of sample data and corresponding label data, each group of sample data comprises a plurality of sample sampling points and sample vertical coordinate data corresponding to each sample sampling point, the label data comprises sample average heights of the plurality of sample sampling points in the sample data relative to a line scanning radar, and the sample sampling points are determined from a plurality of sample scanning points obtained by scanning the line scanning radar arranged on the top of a material bin; respectively inputting each group of sample data in the training data set into a height prediction model to be trained to obtain the average sample prediction height of a plurality of sample sampling points in each group of sample data relative to the line scanning radar; and optimizing the height prediction model to be trained according to the average sample height and the average sample height corresponding to each group of sample data to obtain an optimized height prediction model.
In one embodiment, the highly predictive model to be trained includes any one of a linear regression model, a machine learning model, and a neural network model.
For specific limitations on the in-bin material volume monitoring device, reference may be made to the above limitations on the in-bin material volume monitoring method, and no further description is given here. The modules in the bin material volume monitoring device can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a monitoring device is provided, which may be a server or a terminal, and the internal structure of the monitoring device may be as shown in fig. 10. The monitoring device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the monitoring device is configured to provide computing and control capabilities. The memory of the monitoring device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the monitoring device is used to store model data. The network interface of the monitoring device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method of monitoring the volume of material in a bin.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the monitoring device to which the present inventive arrangements are applied, and that a particular monitoring device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a monitoring device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring radar data, wherein the radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged on the top of a material bin;
obtaining average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and the height prediction model;
And determining the volume of the material in the material bin according to the characteristic data of the material bin and the average predicted height.
In one embodiment, the processor when executing the computer program further performs the steps of: mapping the radar data into a plane coordinate system to obtain corresponding plane coordinate data, wherein the plane coordinate data comprises horizontal coordinates and vertical coordinates of each scanning point relative to the line scanning radar; sampling is carried out based on the plane coordinate data, and the target vertical coordinates of the corresponding sampling points are obtained; and inputting the target vertical coordinates of the corresponding sampling points into the height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
In one embodiment, the processor when executing the computer program further performs the steps of: extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to a set coordinate threshold range; performing interpolation fitting based on plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path; and uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter.
In one embodiment, the characteristic data of the bin includes a bottom area of the bin and a height of the bin; the processor when executing the computer program also implements the steps of: determining the difference between the height of the material bin and the average height as the average height of the materials in the material bin; and determining the volume of the material in the material bin according to the bottom area of the material bin and the average height of the material in the material bin.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a training data set for model training, wherein the training data set comprises a plurality of groups of sample data and corresponding tag data, each group of sample data comprises a plurality of sample sampling points and sample vertical coordinate data corresponding to each sample sampling point, the tag data comprises sample average heights of the plurality of sample sampling points in the sample data relative to a line scanning radar, and the sample sampling points are determined from a plurality of sample scanning points obtained by scanning the line scanning radar arranged on the top of a material bin; respectively inputting each group of sample data in the training data set into a height prediction model to be trained to obtain the average sample prediction height of a plurality of sample sampling points in each group of sample data relative to the line scanning radar; and optimizing the height prediction model to be trained according to the average sample height and the average sample height corresponding to each group of sample data to obtain an optimized height prediction model.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring radar data, wherein the radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged on the top of a material bin;
obtaining average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and the height prediction model;
And determining the volume of the material in the material bin according to the characteristic data of the material bin and the average predicted height.
In one embodiment, the computer program when executed by the processor further performs the steps of: mapping the radar data into a plane coordinate system to obtain corresponding plane coordinate data, wherein the plane coordinate data comprises horizontal coordinates and vertical coordinates of each scanning point relative to the line scanning radar; sampling is carried out based on the plane coordinate data, and the target vertical coordinates of the corresponding sampling points are obtained; and inputting the target vertical coordinates of the corresponding sampling points into the height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
In one embodiment, the computer program when executed by the processor further performs the steps of: extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to a set coordinate threshold range; performing interpolation fitting based on plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path; and uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter.
In one embodiment, the characteristic data of the bin includes a bottom area of the bin and a height of the bin; the computer program when executed by the processor also performs the steps of: determining the difference between the height of the material bin and the average height as the average height of the materials in the material bin; and determining the volume of the material in the material bin according to the bottom area of the material bin and the average height of the material in the material bin.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a training data set for model training, wherein the training data set comprises a plurality of groups of sample data and corresponding tag data, each group of sample data comprises a plurality of sample sampling points and sample vertical coordinate data corresponding to each sample sampling point, the tag data comprises sample average heights of the plurality of sample sampling points in the sample data relative to a line scanning radar, and the sample sampling points are determined from a plurality of sample scanning points obtained by scanning the line scanning radar arranged on the top of a material bin; respectively inputting each group of sample data in the training data set into a height prediction model to be trained to obtain the average sample prediction height of a plurality of sample sampling points in each group of sample data relative to the line scanning radar; and optimizing the height prediction model to be trained according to the average sample height and the average sample height corresponding to each group of sample data to obtain an optimized height prediction model.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring radar data, wherein the radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged on the top of a material bin;
obtaining average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and the height prediction model;
And determining the volume of the material in the material bin according to the characteristic data of the material bin and the average predicted height.
In one embodiment, the computer program when executed by the processor further performs the steps of: mapping the radar data into a plane coordinate system to obtain corresponding plane coordinate data, wherein the plane coordinate data comprises horizontal coordinates and vertical coordinates of each scanning point relative to the line scanning radar; sampling is carried out based on the plane coordinate data, and the target vertical coordinates of the corresponding sampling points are obtained; and inputting the target vertical coordinates of the corresponding sampling points into the height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
In one embodiment, the computer program when executed by the processor further performs the steps of: extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to a set coordinate threshold range; performing interpolation fitting based on plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path; and uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter.
In one embodiment, the characteristic data of the bin includes a bottom area of the bin and a height of the bin; the computer program when executed by the processor also performs the steps of: determining the difference between the height of the material bin and the average height as the average height of the materials in the material bin; and determining the volume of the material in the material bin according to the bottom area of the material bin and the average height of the material in the material bin.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a training data set for model training, wherein the training data set comprises a plurality of groups of sample data and corresponding tag data, each group of sample data comprises a plurality of sample sampling points and sample vertical coordinate data corresponding to each sample sampling point, the tag data comprises sample average heights of the plurality of sample sampling points in the sample data relative to a line scanning radar, and the sample sampling points are determined from a plurality of sample scanning points obtained by scanning the line scanning radar arranged on the top of a material bin; respectively inputting each group of sample data in the training data set into a height prediction model to be trained to obtain the average sample prediction height of a plurality of sample sampling points in each group of sample data relative to the line scanning radar; and optimizing the height prediction model to be trained according to the average sample height and the average sample height corresponding to each group of sample data to obtain an optimized height prediction model.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (9)
1. A method of monitoring the volume of material in a bin, the method comprising:
acquiring radar data, wherein the radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged at the top of a material bin along the diameter of the material bin;
Obtaining average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and a height prediction model, wherein the height prediction model is used for predicting the average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data obtained by scanning, and the average predicted height is equivalent to the distance between the predicted material surface and the line scanning radar after flattening the original rugged material;
Determining the volume of the material in the material bin according to the characteristic data of the material bin and the average predicted height;
The characteristic data of the material bin comprises the bottom area of the material bin and the height of the material bin; the determining the material volume in the material bin according to the characteristic data of the material bin and the average predicted height comprises the following steps: determining the difference between the height of the material bin and the average height as the average height of the materials in the material bin; determining the volume of the materials in the material bin according to the bottom area of the material bin and the average height of the materials in the material bin;
the obtaining the average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and the height prediction model comprises the following steps:
Mapping the radar data in a polar coordinate system into a plane coordinate system based on a mapping relation between the polar coordinate system and the plane coordinate system to obtain corresponding plane coordinate data, wherein the plane coordinate data comprises horizontal coordinates and vertical coordinates of each scanning point relative to the line scanning radar;
extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to a set coordinate threshold range, wherein the longest diameter is the longest distance between two opposite points on a bin wall in a cross section of the material bin determined based on morphological characteristics of the material bin, and the coordinate threshold range is determined based on the size and the shape of the material bin and the installation position of the line scanning radar; performing interpolation fitting based on plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path; uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter;
And inputting the target vertical coordinates of the corresponding sampling points into the height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
In the case that the material bin is a circular material bin, the longest diameter is the diameter on the cross section of the material bin;
In the case that the material bin is a square material bin, the longest diameter is a diagonal line on the cross section of the material bin.
3. The method of claim 1, wherein the threshold range is used to characterize a range of values for horizontal coordinate axes in the planar coordinate system, the threshold range being determined from a distance between the line scan radar and the left and right walls of the material bin.
4. A method according to any one of claims 1 to 3, wherein the method of obtaining the height prediction model comprises:
Acquiring a training data set for model training, wherein the training data set comprises a plurality of groups of sample data and corresponding tag data, each group of sample data comprises a plurality of sample sampling points and sample vertical coordinate data corresponding to each sample sampling point, the tag data comprises sample average heights of the plurality of sample sampling points in the sample data relative to a line scanning radar, and the sample sampling points are determined from a plurality of sample scanning points obtained by scanning the line scanning radar arranged on the top of a material bin;
respectively inputting each group of sample data in the training data set into a height prediction model to be trained to obtain the average sample prediction height of a plurality of sample sampling points in each group of sample data relative to the line scanning radar;
And optimizing the height prediction model to be trained according to the average sample height and the average sample height corresponding to each group of sample data to obtain an optimized height prediction model.
5. The method of claim 4, wherein the highly predictive model to be trained comprises any one of a linear regression model and a neural network model.
6. A device for monitoring the volume of material in a warehouse, the device comprising:
The radar data acquisition module is used for acquiring radar data, wherein the radar data are polar coordinate data of a plurality of scanning points obtained by scanning a line scanning radar arranged at the top of a material bin along the diameter of the material bin;
The height prediction module is used for obtaining average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data and a height prediction model, wherein the height prediction model is used for predicting average predicted height of the line scanning radar relative to the material surface in the material bin according to the radar data obtained by scanning, and the average predicted height is equivalent to the distance between the predicted material surface and the line scanning radar after flattening the original uneven material;
The material volume determining module is used for determining the material volume in the material bin according to the characteristic data of the material bin and the average predicted height;
The characteristic data of the material bin comprises the bottom area of the material bin and the height of the material bin; the material volume determining module is specifically used for: determining the difference between the height of the material bin and the average height as the average height of the materials in the material bin; determining the volume of the materials in the material bin according to the bottom area of the material bin and the average height of the materials in the material bin;
the height prediction module is specifically configured to:
Mapping the radar data in a polar coordinate system into a plane coordinate system based on a mapping relation between the polar coordinate system and the plane coordinate system to obtain corresponding plane coordinate data, wherein the plane coordinate data comprises horizontal coordinates and vertical coordinates of each scanning point relative to the line scanning radar;
extracting plane coordinate data of a scanning point on the longest diameter in the material bin according to a set coordinate threshold range, wherein the longest diameter is the longest distance between two opposite points on a bin wall in a cross section of the material bin determined based on morphological characteristics of the material bin, and the coordinate threshold range is determined based on the size and the shape of the material bin and the installation position of the line scanning radar; performing interpolation fitting based on plane coordinate data of the scanning points on the longest path to obtain a fitting curve of the longest path; uniformly sampling the fitting curve according to the preset sampling number to obtain the target vertical coordinate of the corresponding sampling point on the longest diameter;
And inputting the target vertical coordinates of the corresponding sampling points into the height prediction model to obtain the average predicted height of the line scanning radar relative to the material surface in the material bin.
7. A monitoring device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
9. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721367A (en) * | 2012-07-02 | 2012-10-10 | 吉林省粮油科学研究设计院 | Method for measuring volume of large irregular bulk grain pile based on dynamic three-dimensional laser scanning |
| CN211033818U (en) * | 2019-10-22 | 2020-07-17 | 成都中成华瑞科技有限公司 | Powder material level measuring device |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105950807B (en) * | 2016-06-02 | 2018-10-16 | 燕山大学 | A kind of blast furnace material distribution process shape of charge level modeling method of Multi-information acquisition |
| JP2019100719A (en) * | 2017-11-28 | 2019-06-24 | 株式会社キーエンス | Optical scan height measuring device |
| CN109490861B (en) * | 2018-10-29 | 2020-06-02 | 北京科技大学 | Blast furnace burden line extraction method |
| EP3730908B1 (en) * | 2019-04-26 | 2023-06-07 | VEGA Grieshaber KG | Method for determining a linearisation curve for determining the fill level in a container and use of a fill level measuring device for this method |
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| CN116930933A (en) * | 2020-03-27 | 2023-10-24 | 深圳市速腾聚创科技有限公司 | Attitude correction method and device for laser radar |
| CN111552289B (en) * | 2020-04-28 | 2021-07-06 | 苏州高之仙自动化科技有限公司 | Detection method, virtual radar device, electronic apparatus, and storage medium |
| CN111695440B (en) * | 2020-05-21 | 2022-07-26 | 河海大学 | GA-SVR lake level measurement and prediction method based on radar altimeter |
| CN111784718B (en) * | 2020-07-11 | 2021-09-10 | 吉林大学 | Intelligent online prediction device and prediction method for discrete material accumulation state |
| CN213580544U (en) * | 2020-11-02 | 2021-06-29 | 中国神华能源股份有限公司哈尔乌素露天煤矿 | Material bulk density detection device, quantitative bin assembly and quantitative loading assembly |
| CN112595383A (en) * | 2020-12-16 | 2021-04-02 | 安徽海螺水泥股份有限公司 | Cement bin material level height detection device |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721367A (en) * | 2012-07-02 | 2012-10-10 | 吉林省粮油科学研究设计院 | Method for measuring volume of large irregular bulk grain pile based on dynamic three-dimensional laser scanning |
| CN211033818U (en) * | 2019-10-22 | 2020-07-17 | 成都中成华瑞科技有限公司 | Powder material level measuring device |
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