CN113793400B - Construction method of gas concentration thermodynamic diagram - Google Patents
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Abstract
The invention discloses a construction method of a gas concentration thermodynamic diagram, which is characterized by comprising the following steps: acquiring a plane map as a background map of the concentration heat map; positioning the plane map to acquire current position coordinate information in real time, and acquiring gas concentration data in real time at the same time; aligning the gas concentration data with the coordinate information to obtain concentration information with position information; generating two-dimensional Gaussian kernels with different degrees and sizes at corresponding positions on a map through concentration information with position information; using the coordinate point attached to the gas concentration data as a central coordinate point, multiplying the concentration data by each element in the two-dimensional Gaussian kernel, and filling the two-dimensional Gaussian kernel into surrounding coordinates; and marking different concentrations by different colors to form a thermodynamic diagram, and then overlapping the thermodynamic diagram with a background map to generate a gas concentration thermodynamic map.
Description
Technical Field
The invention relates to a construction method of a gas concentration thermodynamic diagram, and belongs to the technical field of positioning and mapping.
Background
The thermodynamic diagram is a form of highlighting some specific data, and by means of the thermodynamic diagram, the size, change and distribution of the data can be intuitively seen, and the thermodynamic diagram, the human flow thermodynamic diagram and the like are mainly applied at present. Such as the temperature thermodynamic diagram displayed in the weather forecast, the temperature conditions of different areas can be intuitively seen. And the flow thermodynamic diagram can intuitively see the flow of people in a certain scene. It follows that thermodynamic diagrams have a strong visual advantage over conventional data.
The traditional gas concentration detection is carried out by a fixed device, the detection result is displayed in a data form or a waveform diagram form, the common digital data can not record the gas concentration change, the waveform diagram can record the historical gas concentration change, but a single waveform diagram can not give the gas concentration information of different areas. Therefore, the detection result is not visual enough and the analysis and judgment are not quick enough. The thermodynamic diagram is applied to gas concentration monitoring to make up for the defects, a planar map is generated by utilizing SLAM technology, the gas concentration is detected by utilizing a gas concentration sensor, and the map and the concentration data are combined to generate a gas concentration thermodynamic map. On the gas concentration thermodynamic map, the gas concentration of different places on the map can be intuitively observed, and once dangerous or inflammable and explosive gas leaks, the position of a leakage area can be quickly found on the thermodynamic map, the nearest safe area can be also found, surrounding personnel are guided to evacuate, and the leakage area is sealed and salvaged.
Disclosure of Invention
The invention aims to provide a construction method of a gas concentration thermodynamic diagram, which aims to solve the defects that the detection result of the existing gas concentration monitoring mode is not visual enough and the analysis and judgment are not quick enough.
A method of constructing a gas concentration thermodynamic diagram, the method comprising:
acquiring a plane map as a background map of the concentration heat map;
positioning the plane map to acquire current position coordinate information in real time, and acquiring gas concentration data in real time at the same time;
aligning the gas concentration data with the coordinate information to obtain concentration information with position information;
generating two-dimensional Gaussian kernels with different degrees and sizes at corresponding positions on a map through concentration information with position information;
using the coordinate point attached to the gas concentration data as a central coordinate point, multiplying the concentration data by each element in the two-dimensional Gaussian kernel, and filling the two-dimensional Gaussian kernel into surrounding coordinates;
and marking different concentrations by different colors to form a thermodynamic diagram, and then overlapping the thermodynamic diagram with a background map to generate a gas concentration thermodynamic map.
Further, the method for acquiring the plane map comprises the following steps:
generating a map in a PGM format through a laser radar; and converting the picture in the PGM format into a picture in the PNG format as a plane map.
Further, the gas concentration data is acquired by a gas concentration sensor.
Further, the method for acquiring the position coordinate information relative to the pixel coordinate system includes:
firstly, coordinate translation transformation matrix information contained in yaml files of a map is obtained, namely XY axis offset X1 and Y1 of a map coordinate system relative to a pixel coordinate system; the unit of the map coordinate system can be converted into pt through M/Z, and the position relative to the map coordinate system is converted into the position relative to the pixel coordinate system;
let TF query that the coordinates are X2, Y2 at this point, then the pixel coordinates are as follows:
further, the method for acquiring the concentration data comprises the following steps:
sampling the gas concentration value for i times in a small period to obtain:
{c i }1≤i≤20
and (3) rapidly sequencing the concentration values, and taking out the median to obtain:
C i ＝mid{c i }
the data in each small period is stored in the same queue after the processing, and the data is obtained:
{C i }1≤i≤20
when the number of data in the queue is 20, namely, a large period of 100ms passes, sequencing the queue again, and obtaining a median value to obtain a stable gas concentration value:
C＝mid{C i }。
further, the method for aligning the gas concentration data with the coordinate information to obtain the concentration information with the position information comprises the following steps:
the position information and the concentration information form a data structure, the data structure is stored in a txt file, and each data is separated by commas; the expression is:
[n，x n ，y n ，C n ]
the first data is serial number of collected data, the second data is X-axis data of coordinates, the third data is Y-axis data of coordinates, and the fourth data is concentration data.
Further, the two-dimensional Gaussian kernel production method comprises the following steps:
the coordinate point attached to each concentration data is used as a central coordinate point, which is also called a point source, and two-dimensional Gaussian kernels with different degrees and sizes are generated according to different concentration sizes, wherein the two-dimensional Gaussian kernels have the following formulas:
wherein, sigma is standard deviation, the larger the sigma is, the flatter the Gaussian curve is, the lower the center point is, and the gas concentration similar to a point source is low; the smaller the σ, the steeper the gaussian curve, the higher the center point, and the higher the gas concentration similar to a point source; definition: n represents the transverse and longitudinal dimensions of the two-dimensional gaussian kernel;
the size of n is selected in proportion to the size of the concentration C, namely:
n＝KC
wherein K is a constant;
generating a one-dimensional Gaussian kernel by using a getGaussian Kerne1 function of an OpenCV library, wherein the formula of the one-dimensional Gaussian kernel is as follows:
firstly, generating two one-dimensional Gaussian kernels G (x) and G (y), wherein the obtained one-dimensional Gaussian kernel is a row vector, and then, carrying out cross multiplication on the row vector and the transpose of the other vector to obtain a matrix, namely, the two-dimensional Gaussian kernel to be obtained:
G(x，y)＝G(x)×G(y) T 。
further, the specific expression mode of the generated two-dimensional Gaussian kernel is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
g XY a center element representing a two-dimensional gaussian kernel;
n represents the size of the two-dimensional gaussian kernel;
and multiplying the concentration data by each element in the two-dimensional Gaussian kernel, filling each element value of the processed Gaussian kernel into each pixel point on the map, wherein the central element of the Gaussian kernel corresponds to the point source coordinate of the map:
wherein A is a constant, and when filling is performed
Concentration data C 1 The attached coordinate point is a= (x) 1 ，y 1 )，
Concentration data C 2 The attached coordinate point is b= (x) 2 ，y 2 )，
If the two-dimensional Gaussian kernels generated by the points A and B are filled on the map and then an overlapping area is formed around the two-dimensional Gaussian kernels, each point of the overlapping area adopts an overlapping strategy, namely gas concentration data on the same coordinate point are added.
Further, the method further comprises: converting the values of pixel points in the picture into corresponding different colors by using a pseudo-color applymmap function of an OpenCV library, processing an original picture containing simple values into a thermodynamic diagram with color data, and using a color_JET chromaticity table in the OpenCV library, wherein when the gas concentration is smaller, the pixel data is smaller, and the color is blue; when the gas concentration is larger, the pixel data is larger, the color is reddish, and the gas concentration visualization is achieved;
and superposing the thermodynamic diagram with the color data and the background map by using an addWeighted function of the OpenCV library, setting reasonable transparency, and superposing the information of the two pictures to generate a gas concentration thermodynamic map.
Compared with the prior art, the invention has the beneficial effects that: the invention automatically generates a gas concentration thermodynamic diagram by combining SLAM technology and gas detection technology, and solves the defects that the traditional data form can not intuitively see the change condition of gas concentration data and the waveform form can not intuitively observe the gas concentration distribution conditions of different areas at the same time. The gas concentration thermodynamic diagram not only contains gas concentration information, but also contains gas concentration change information and regional gas concentration information, and is more visual and reliable than the traditional data and waveform diagrams.
Drawings
FIG. 1 shows a flow chart for obtaining concentration according to an embodiment of the present invention;
FIG. 2 is a flowchart of acquiring position coordinate information according to an embodiment of the present invention;
FIG. 3 is a general flow chart of a concentration heat map generation method according to an embodiment of the present invention;
fig. 4 shows a combined block diagram of a concentration heat map generating apparatus according to an embodiment of the present invention.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
The embodiment of the invention provides a method for constructing a gas concentration thermodynamic map, which is shown in figures 3-4 and comprises the following specific steps:
(1) Generating a factory indoor plane map by using a laser radar as a background map of the concentration heat map;
(2) Positioning on the generated map to acquire current position coordinate information in real time;
(3) The carbon dioxide concentration data is obtained in real time by using a carbon dioxide concentration sensor while positioning;
(4) Aligning the carbon dioxide gas concentration data with the coordinate information to obtain concentration information with position information;
(5) Generating two-dimensional Gaussian kernels with different degrees and sizes according to the corresponding positions of the carbon dioxide concentration data with the position information on the map;
(6) Using a coordinate point attached to the carbon dioxide gas concentration data as a central coordinate point, multiplying the concentration data by each element in the two-dimensional Gaussian kernel, and filling the two-dimensional Gaussian kernel into surrounding coordinates;
(7) And marking different concentrations by using different colors to form a thermodynamic diagram, and then overlapping the thermodynamic diagram with a background map to generate a carbon dioxide gas concentration thermodynamic map.
The method (1) generates a plane map through a laser radar, and the method further comprises the following steps of: the map generated by the lidar is in PGM format, which needs to be converted into PNG picture format. The pixel width and height information of the PGM format is obtained through the matplotin packet of python, the DPI of the PGM is calculated through the pixel width and the pixel height, and the PNG picture is saved by using the savefag () function.
As shown in fig. 2, the method (2) performs positioning on the generated map to obtain the current position coordinate information in real time, and further includes: because the conversion from the map coordinate system to the image coordinate system has no rotational relationship, only translational relationship and scale transformation. The coordinate information obtained by positioning is in meters, but we need to load the yaml file obtained by constructing the map firstly by taking the origin of the map as a reference, the yaml file contains rotation translation information and resolution information of the map, and the X-axis offset X1 and Y1 of the map and the resolution Z of the map pair are obtained in the yaml file. Assuming that the TF inquires that the coordinates are X2, Y2 at this point, the pixel coordinates are as follows:
as shown in fig. 1, the method (3) for acquiring the carbon dioxide gas concentration data in real time by using the carbon dioxide gas concentration sensor while positioning further comprises: sampling the gas concentration value for 20 times in a small period of 5ms to obtain:
{c i }1≤i≤20
and (3) rapidly sequencing the concentration values, and taking out the median to obtain:
C i ＝mid{c i }
the data in each small period is stored in the same queue after the processing, and the data is obtained:
{C i }1≤i≤20
when the number of data in the queue is 20, namely, a large period of 100ms passes, sequencing the queue again, and obtaining a median value to obtain a stable gas concentration value:
C＝mid{C i }
as shown in fig. 2, the method (4) aligning the gas concentration data with the coordinate information to obtain concentration information with position information further includes: the location information and the concentration information are formed into a data structure and stored in a txt file, and each data is separated by commas. Such as:
[n，x n ，y n ，C n ]
the first data is serial number of collected data, the second data is X-axis data of coordinates, the third data is Y-axis data of coordinates, and the fourth data is concentration data.
The method (5) for generating two-dimensional Gaussian kernels with different degrees and sizes according to corresponding positions on a map with position information concentration data further comprises: for an average gas flow of a continuous source, the concentration profile is gaussian, i.e. the gas enters the atmosphere through a point source, and the gas is spread to a distribution which is gaussian on a flat surface. Based on the above principle, with the coordinate point attached to each concentration data as a central coordinate point, also referred to as a point source, two-dimensional gaussian kernels of different degrees and sizes are generated according to the concentration size, and the diffusion range is also very large because of the gas with higher concentration. The formula of the two-dimensional gaussian kernel is as follows:
wherein, sigma is standard deviation, the larger the sigma is, the flatter the Gaussian curve is, the lower the center point is, and the gas concentration similar to a point source is low; the smaller σ, the steeper the gaussian curve, the higher the center point, and the higher the gas concentration similar to a point source. Definition: n represents the transverse and longitudinal dimensions of the two-dimensional gaussian kernel.
The size of n is selected in proportion to the size of the concentration C, namely:
n＝KC
where K is a constant.
Generating a one-dimensional Gaussian kernel by using a getGaussian Kernel function of an OpenCV library, wherein the formula of the one-dimensional Gaussian kernel is as follows:
firstly, generating two one-dimensional Gaussian kernels G (x) and G (y) by using a complaint method, wherein the obtained one-dimensional Gaussian kernel is a row vector, and then, carrying out cross multiplication on the row vector and the transposition of the other vector to obtain a matrix, namely, the two-dimensional Gaussian kernel to be obtained:
G(x，y)＝G(x)×G(t) T
the method (6) of multiplying the concentration data by each element in the two-dimensional gaussian kernel using the coordinate point attached to the gas concentration data as a center coordinate point, and filling the coordinates around the concentration data further includes: the specific expression mode for generating the two-dimensional Gaussian kernel is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
g XY a center element representing a two-dimensional gaussian kernel;
n represents the size of the two-dimensional gaussian kernel;
and multiplying the concentration data by each element in the two-dimensional Gaussian kernel, filling each element value of the processed Gaussian kernel into each pixel point on the map, and enabling the central element of the Gaussian kernel to correspond to the point source coordinate of the map.
Wherein A is a constant. The purpose of this is to be able to predict the gas concentration data of points around the center coordinates and to fill the data into the corresponding coordinate points. When filling, it is assumed that
Concentration data C 1 The attached coordinate point is a= (x) 1 ，y 1 )，
Concentration data C 2 The attached coordinate point is b= (x) 2 ，y 2 )，
If the two-dimensional Gaussian kernels generated by the points A and B are filled on the map and an overlapping area is formed around the two-dimensional Gaussian kernels, each point of the overlapping area adopts an overlapping strategy, and the overlapping strategy is equivalent to that the gas concentration generates mixed overlapping when the gas concentration is diffused.
The method (7) uses different colors to mark different concentrations to form a thermodynamic diagram, and then the thermodynamic diagram is overlapped with a background map to generate a gas concentration thermodynamic map, which comprises the following steps: firstly, converting values of pixel points in a picture into corresponding different colors by using a pseudo-color applymmap function of an OpenCV library, processing an original picture containing simple values into a thermodynamic diagram with color data, and using a color_jet chromaticity table in the OpenCV library, wherein when the gas concentration is smaller, the pixel data is smaller, and the color is blue; when the gas concentration is larger, the pixel data is larger, the color is reddish, and the effect of visualizing the gas concentration is achieved.
And then, superposing a thermodynamic diagram with color data and a background map by using an addWeighted function of an OpenCV library, setting reasonable transparency, and superposing information of the two pictures, so that a gas concentration thermodynamic map is generated, and the gas concentration sizes of different places on the map can be observed on the gas concentration thermodynamic map at the same time, thereby realizing the optimal visual effect.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (8)
1. A method of constructing a thermodynamic diagram of a gas concentration, the method comprising:
acquiring a plane map as a background map of the concentration heat map;
positioning the plane map to acquire current position coordinate information in real time, and acquiring gas concentration data in real time at the same time;
aligning the gas concentration data with the coordinate information to obtain concentration information with position information;
generating two-dimensional Gaussian kernels with different degrees and sizes at corresponding positions on a map through concentration information with position information;
using the coordinate point attached to the gas concentration data as a central coordinate point, multiplying the concentration data by each element in the two-dimensional Gaussian kernel, and filling the two-dimensional Gaussian kernel into surrounding coordinates;
marking different concentrations by different colors to form a thermodynamic diagram, and then superposing the thermodynamic diagram with a background map to generate a gas concentration thermodynamic map;
the two-dimensional Gaussian kernel production method comprises the following steps:
the coordinate point attached to each concentration data is used as a central coordinate point, which is also called a point source, and two-dimensional Gaussian kernels with different degrees and sizes are generated according to different concentration sizes, wherein the two-dimensional Gaussian kernels have the following formulas:
wherein, sigma is standard deviation, the larger the sigma is, the flatter the Gaussian curve is, the lower the center point is, and the gas concentration representing the point source is low; the smaller the sigma, the steeper the gaussian curve, the higher the center point, and the higher the gas concentration representing the point source; definition: n represents the transverse and longitudinal dimensions of the two-dimensional gaussian kernel;
the size of n is selected in proportion to the size of the concentration C, namely:
n＝KC
wherein K is a constant;
generating a one-dimensional Gaussian kernel by using a getGaussian Kemel function of an OpenCV library, wherein the formula of the one-dimensional Gaussian kernel is as follows:
firstly, generating two one-dimensional Gaussian kernels G (x) and G (y), wherein the obtained one-dimensional Gaussian kernel is a row vector, and then, carrying out cross multiplication on the row vector and the transpose of the other vector to obtain a matrix, namely, the two-dimensional Gaussian kernel to be obtained:
G(x，y)＝G(x)×G(y) T 。
2. the method for constructing a gas concentration thermodynamic diagram according to claim 1, wherein the method for acquiring a plan map comprises:
generating a map in a PGM format through a laser radar; and converting the picture in the PGM format into a picture in the PNG format as a plane map.
3. The method of constructing a gas concentration thermodynamic diagram according to claim 1, wherein the gas concentration data is acquired by a gas concentration sensor.
4. The method for constructing a gas concentration thermodynamic diagram according to claim 1, wherein the method for acquiring positional coordinate information with respect to a pixel coordinate system includes:
firstly, coordinate translation transformation matrix information contained in yaml files of a map is obtained, namely XY axis offset X1 and Y1 of a map coordinate system relative to a pixel coordinate system; the unit of the map coordinate system can be converted into pt through M/Z, and the position relative to the map coordinate system is converted into the position relative to the pixel coordinate system;
let TF query that the coordinates are X2, Y2 at this point, then the pixel coordinates are as follows:
5. the method of constructing a gas concentration thermodynamic diagram of claim 1, wherein the method of acquiring concentration data comprises:
sampling the gas concentration value for i times in a small period to obtain:
{c i }1≤i≤20
and (3) rapidly sequencing the concentration values, and taking out the median to obtain:
C i ＝mid{c i }
the data in each small period is stored in the same queue after the processing, and the data is obtained:
{C i }1≤i≤20
when the number of data in the queue is 20, namely, a large period of 100ms passes, sequencing the queue again, and obtaining a median value to obtain a stable gas concentration value:
C＝mid｛C i }。
6. the method for constructing a gas concentration thermodynamic diagram according to claim 1, wherein the method for aligning the gas concentration data with the coordinate information to obtain the concentration information with the position information comprises:
the position information and the concentration information form a data structure, the data structure is stored in a txt file, and each data is separated by commas; the expression is:
[n，x n ，y n ，C n ]
the first data is serial number of collected data, the second data is X-axis data of coordinates, the third data is Y-axis data of coordinates, and the fourth data is concentration data.
7. The method for constructing a gas concentration thermodynamic diagram according to claim 1, wherein the specific expression of the generated two-dimensional gaussian kernel is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
g XY a center element representing a two-dimensional gaussian kernel;
n represents the size of the two-dimensional gaussian kernel;
and multiplying the concentration data by each element in the two-dimensional Gaussian kernel, filling each element value of the processed Gaussian kernel into each pixel point on the map, wherein the central element of the Gaussian kernel corresponds to the point source coordinate of the map:
wherein A is a constant, and when filling is performed
Concentration data C 1 The attached coordinate point is a= (x) 1 ，y 1 )，
Concentration data C 2 The attached coordinate point is b= (x) 2 ，y 2 )，
If the two-dimensional Gaussian kernels generated by the points A and B are filled on the map and then an overlapping area is formed around the two-dimensional Gaussian kernels, each point of the overlapping area adopts an overlapping strategy, namely gas concentration data on the same coordinate point are added.
8. The method of constructing a thermodynamic diagram of a gas concentration according to claim 1, further comprising:
converting the values of pixel points in the picture into corresponding different colors by using a pseudo-color applymmap function of an OpenCV library, processing an original picture containing simple values into a thermodynamic diagram with color data, and using a color_JET chromaticity table in the OpenCV library, wherein when the gas concentration is smaller, the pixel data is smaller, and the color is blue; when the gas concentration is larger, the pixel data is larger, the color is reddish, and the gas concentration visualization is achieved;
and superposing the thermodynamic diagram with the color data and the background map by using an addWeighted function of the OpenCV library, setting reasonable transparency, and superposing the information of the two pictures to generate a gas concentration thermodynamic map.
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