CN113793400A - Construction method of gas concentration thermodynamic diagram - Google Patents
Construction method of gas concentration thermodynamic diagram Download PDFInfo
- Publication number
- CN113793400A CN113793400A CN202111073092.2A CN202111073092A CN113793400A CN 113793400 A CN113793400 A CN 113793400A CN 202111073092 A CN202111073092 A CN 202111073092A CN 113793400 A CN113793400 A CN 113793400A
- Authority
- CN
- China
- Prior art keywords
- map
- gas concentration
- data
- concentration
- dimensional gaussian
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/206—Drawing of charts or graphs
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—Specially adapted to detect a particular component
- G01N33/004—Specially adapted to detect a particular component for CO, CO2
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/60—Editing figures and text; Combining figures or text
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Combustion & Propulsion (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a method for constructing a gas concentration thermodynamic diagram, which is characterized by comprising the following steps of: acquiring a plane map as a background map of the concentration thermodynamic map; positioning a plane map to acquire current position coordinate information in real time and simultaneously acquire gas concentration data in real 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 according to concentration information with position information; using a 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 concentration data into surrounding coordinates; different concentrations are marked through different colors to form a thermodynamic map, and the thermodynamic map is superposed 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 diagram construction.
Background
The thermodynamic diagram is a method for displaying specific data in a highlighted form, the size, change and distribution of the data can be visually seen through the thermodynamic diagram, and the thermodynamic diagram and the human flow thermodynamic diagram are mainly applied at present. Such as a temperature thermodynamic diagram shown in weather forecast, the temperature conditions in different regions can be visually seen. And the people flow thermodynamic diagram can intuitively see the people flow in a certain scene. It follows that thermodynamic diagrams have a strong intuitive advantage over traditional data.
The conventional gas concentration detection is performed by a fixed device at a fixed point, and the detection result is displayed in the form of data or a waveform diagram, so that the common digital data cannot record the change of the gas concentration, and although the waveform diagram can record the historical change of the gas concentration, a single waveform diagram cannot give the information of the gas concentration in different areas. Therefore, the detection result is not intuitive enough and the analysis and judgment are not rapid enough. The thermodynamic diagram is applied to gas concentration monitoring to overcome the defects, a planar map is generated by utilizing the 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 observed visually, and if dangerous or flammable and explosive gas leaks, the position of the leaking area can be found quickly on the thermodynamic map, the nearest safety area can be found, surrounding personnel can be instructed to evacuate, and the leaking area can be subjected to closed rush repair.
Disclosure of Invention
The invention aims to provide a method for constructing a gas concentration thermodynamic diagram, which aims to overcome the defects that the detection result of the existing gas concentration monitoring mode is not intuitive enough and the analysis and judgment are not rapid enough.
A method of constructing a gas concentration thermodynamic diagram, the method comprising:
acquiring a plane map as a background map of the concentration thermodynamic map;
positioning a plane map to acquire current position coordinate information in real time and simultaneously acquire gas concentration data in real 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 according to concentration information with position information;
using a 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 concentration data into surrounding coordinates;
different concentrations are marked through different colors to form a thermodynamic map, and the thermodynamic map is superposed 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 comprises the following steps:
firstly, coordinate translation transformation matrix information contained in a yaml file of a map, namely XY axis offset X1 and Y1 of a map coordinate system relative to a pixel coordinate system, is obtained; 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 at this point are X2, Y2, then the pixel coordinates are as follows:
further, the method of acquiring concentration data includes:
sampling the gas concentration value for i times in a small period to obtain:
{ci}1≤i≤20
and (3) quickly sequencing concentration values, and taking out a median to obtain:
Ci＝mid{ci}
the data in each small period are stored in the same queue after being processed, and the following steps are obtained:
{Ci}1≤i≤20
when the number of the data in the queue is 20, namely a large period of 100ms, the queue is sorted again to obtain a median value, so that a stable gas concentration value is obtained:
C＝mid{Ci}。
further, the method for obtaining the concentration information with the position information by aligning the gas concentration data and the coordinate information comprises the following steps:
forming a data structure by the position information and the concentration information and storing the data structure into a txt file, wherein each datum is separated by a comma; the expression is as follows:
[n，xn，yn，Cn]
the first data is a serial number of the acquired data, the second is X-axis data of coordinates, the third is Y-axis data of coordinates, and the fourth is concentration data.
Further, the two-dimensional Gaussian kernel production method comprises the following steps:
and (3) generating two-dimensional Gaussian kernels with different degrees and sizes according to different concentrations by taking a coordinate point attached to each concentration data as a central coordinate point, also called as a point source, wherein the formula of the two-dimensional Gaussian kernels is as follows:
wherein, σ is standard deviation, the larger σ is, the gentler the Gaussian curve is, the lower the central point is, and the gas concentration similar to a point source is low; the smaller the sigma is, the steeper the Gaussian curve is, the higher the central point is, and the gas concentration similar to a point source is high; defining: n represents the transverse and longitudinal sizes of the two-dimensional Gaussian kernel;
the size of n is selected to be in direct 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 getGaussianKerne1 function of an OpenCV library, wherein the formula of the one-dimensional Gaussian kernel is as follows:
firstly, two one-dimensional Gaussian kernels G (x) and G (y) are generated, the obtained one-dimensional Gaussian kernel is a row vector, and then cross multiplication is carried out on the transpose of the row vector and 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 generated two-dimensional gaussian kernel is expressed in the following specific manner:
wherein the content of the first and second substances,
gXYa central 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 to 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, it is set
Concentration data C1The coordinate point is A ═ x1，y1)，
Concentration data C2The coordinate point is B ═ x2，y2)，
If an overlapping area exists around the map after the two-dimensional Gaussian kernels generated by the point A and the point B are filled in the map, each point in the overlapping area adopts a superposition strategy, namely gas concentration data on the same coordinate point are added.
Further, the method further comprises: converting the numerical value of a pixel point in a picture into corresponding different colors by using a pseudo-color applyColorMap function of an OpenCV (open computer vision library), processing an original image containing simple numerical values into a thermodynamic diagram with color data, and using a COLORMAP _ JET chromaticity table in the OpenCV, wherein the smaller the gas concentration is, the smaller the pixel data is, the more the color is blue; when the gas concentration is higher, the pixel data is higher, the color is reddish, and the visualization of the gas concentration is achieved;
and superposing the thermodynamic diagram with color data and a background map by using an addWeighted function of an OpenCV (open dynamic vehicle library), setting reasonable transparency, and overlapping the information of the two pictures to generate a gas concentration thermodynamic map.
Compared with the prior art, the invention has the following beneficial effects: the invention combines the SLAM technology and the gas detection technology to automatically generate a gas concentration thermodynamic diagram, and solves the defects that the traditional data form can not visually see the change condition of gas concentration data and the waveform graph can not simultaneously and visually observe the gas concentration distribution conditions in different areas. 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 traditional data and oscillograms.
Drawings
Fig. 1 shows a flow chart for obtaining concentration according to an embodiment of the present invention;
fig. 2 shows a flowchart for acquiring position coordinate information according to an embodiment of the present invention;
fig. 3 shows an overall flowchart of a concentration thermodynamic map generation method proposed by an embodiment of the present invention;
fig. 4 shows a combined block diagram of a concentration thermal map generation apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
The embodiment of the invention provides a method for constructing a gas concentration thermodynamic map, which comprises the following specific steps as shown in fig. 3-4:
(1) generating a plant indoor plane map as a background map of the concentration thermodynamic map by using a laser radar;
(2) positioning on the generated map to acquire the current position coordinate information in real time;
(3) the method comprises the following steps of positioning, and simultaneously acquiring carbon dioxide gas concentration data in real time by using a carbon dioxide gas concentration sensor;
(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 at corresponding positions on a map according to the carbon dioxide concentration data with the position information;
(6) a coordinate point attached to the carbon dioxide gas concentration data is used as a central coordinate point, the concentration data is multiplied by each element in the two-dimensional Gaussian core, and the obtained product is filled into surrounding coordinates;
(7) different colors are used for marking different concentrations to form a thermodynamic map, and the thermodynamic map is superposed with a background map to generate a thermodynamic map of the concentration of the carbon dioxide gas.
The method (1) generates a plane map through a laser radar, and further comprises the following steps as a background map of the concentration thermal map: the map generated by the laser radar is in a PGM format, and the PGM format needs to be converted into a PNG picture format. The pixel width and height information of the PGM format is acquired through a matplotib packet of python, then the DPI of the PGM format is calculated through the pixel width and height, and finally the PNG picture is stored by using a savefig () 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 between the map coordinate system to the image coordinate system has no rotation relationship, only translation relationship and scale transformation. The coordinate information obtained by positioning is in meters, but pixel coordinates based on the origin of mapping are needed, so a mapping yaml file is loaded, the yaml file contains map rotational and translational information and resolution information, the X-axis and Y-axis offsets X1 and Y1 of the map and the resolution Z of the map pair are obtained in the yaml file. Assuming the TF queries that the coordinates at this point are X2, Y2, then the pixel coordinates are as follows:
as shown in fig. 1, the method (3) for acquiring carbon dioxide concentration data in real time by using a carbon dioxide concentration sensor while locating further includes: sampling 20 times of gas concentration values within a small period of 5ms to obtain:
{ci}1≤i≤20
and (3) quickly sequencing concentration values, and taking out a median to obtain:
Ci＝mid{ci}
the data in each small period are stored in the same queue after being processed, and the following steps are obtained:
{Ci}1≤i≤20
when the number of the data in the queue is 20, namely a large period of 100ms, the queue is sorted again to obtain a median value, so that a stable gas concentration value is obtained:
C＝mid{Ci}
as shown in fig. 2, the method (4) aligning the gas concentration data and the coordinate information to obtain the concentration information with the position information further includes: and forming a data structure by the position information and the concentration information and storing the data structure into a txt file, wherein each datum is separated by a comma. Such as:
[n，xn，yn，Cn]
the first data is a serial number of the acquired data, the second is X-axis data of coordinates, the third is Y-axis data of coordinates, and the fourth is concentration data.
The method (5) for generating two-dimensional Gaussian kernels with different degrees and sizes according to corresponding positions with position information concentration data on the map further comprises the following steps: for an average gas flow from a continuous source, the concentration distribution is gaussian, i.e. the gas enters the atmosphere through a point source, and the gas diffusion distribution is gaussian in the plane. Based on the above principle, two-dimensional gaussian kernels of different degrees and sizes are generated according to the concentration, with the coordinate point attached to each concentration data as the central coordinate point, also called point source, because of the high concentration gas, the diffusion range is also very large. The formula for the two-dimensional gaussian kernel is as follows:
wherein, σ is standard deviation, the larger σ is, the gentler the Gaussian curve is, the lower the central 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. Defining: n represents the size of the transverse and longitudinal dimensions of the two-dimensional Gaussian kernel.
The size of n is selected to be in direct 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 getGaussianKernel 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 the method, wherein the obtained one-dimensional Gaussian kernel is a row vector, and then cross-multiplying the transpose of the row vector and 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 a coordinate point attached to the gas concentration data as a center coordinate point, and filling the surrounding coordinates with the concentration data further includes: the specific expression mode for generating the two-dimensional Gaussian kernel is as follows:
wherein the content of the first and second substances,
gXYa central 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 to 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. The purpose of this is to be able to predict gas concentration data for points around the central coordinate and to fill in the data to the corresponding coordinate points. When filling is performed, it is assumed
Concentration data C1The coordinate point is A ═ x1，y1)，
Concentration data C2The coordinate point is B ═ x2，y2)，
If an overlapping area exists around the map after two-dimensional Gaussian kernels generated by the point A and the point B are filled on the map, each point in the overlapping area adopts a superposition strategy, which is equivalent to the fact that the gas concentration generates mixed superposition during diffusion.
The method (7) is characterized in that different colors are used for marking different concentrations to form a thermodynamic map, and the thermodynamic map is superposed with a background map to generate a gas concentration thermodynamic map, and comprises the following steps: firstly, converting the numerical value of a pixel point in a picture into corresponding different colors by using a pseudo-color applyColorMap function of an OpenCV (open computer vision library), processing an original image containing simple numerical values into a thermodynamic diagram with color data, and using a COLORMAP _ JET chromaticity table in the OpenCV, wherein the smaller the gas concentration is, the smaller the pixel data is, the more the color is blue; when the gas concentration is larger, the pixel data is larger, the color is more red, and the effect of gas concentration visualization is achieved.
And then, an addWeighted function of an OpenCV (open vehicle vision library) is utilized to superpose the thermodynamic diagram with color data and a background map, reasonable transparency is set, and the information of the two pictures is superposed, so that a gas concentration thermodynamic map is generated, the gas concentrations of different places on the map can be observed on the gas concentration thermodynamic map at the same time, and the optimal visualization effect is realized.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (9)
1. A method of constructing a gas concentration thermodynamic diagram, the method comprising:
acquiring a plane map as a background map of the concentration thermodynamic map;
positioning a plane map to acquire current position coordinate information in real time and simultaneously acquire gas concentration data in real 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 according to concentration information with position information;
using a 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 concentration data into surrounding coordinates;
different concentrations are marked through different colors to form a thermodynamic map, and the thermodynamic map is superposed with a background map to generate a gas concentration thermodynamic map.
2. The method for constructing a gas concentration thermodynamic diagram according to claim 1, wherein the method for acquiring the planar 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.
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 the position coordinate information with respect to the pixel coordinate system includes:
firstly, coordinate translation transformation matrix information contained in a yaml file of a map, namely XY axis offset X1 and Y1 of a map coordinate system relative to a pixel coordinate system, is obtained; 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 at this point are X2, Y2, then the pixel coordinates are as follows:
5. the method of constructing a gas concentration thermodynamic diagram according to claim 1, wherein the method of acquiring concentration data comprises:
sampling the gas concentration value for i times in a small period to obtain:
{ci}1≤i≤20
and (3) quickly sequencing concentration values, and taking out a median to obtain:
Ci＝mid{ci}
the data in each small period are stored in the same queue after being processed, and the following steps are obtained:
{Ci}1≤i≤20
when the number of the data in the queue is 20, namely a large period of 100ms, the queue is sorted again to obtain a median value, so that a stable gas concentration value is obtained:
C＝mid{Ci}。
6. the method for constructing a gas concentration thermodynamic diagram according to claim 1, wherein the method for obtaining concentration information with position information by aligning gas concentration data and coordinate information comprises:
forming a data structure by the position information and the concentration information and storing the data structure into a txt file, wherein each datum is separated by a comma; the expression is as follows:
[n，xn，yn，Cn]
the first data is a serial number of the acquired data, the second is X-axis data of coordinates, the third is Y-axis data of coordinates, and the fourth is concentration data.
7. The method for constructing a gas concentration thermodynamic diagram according to claim 1, wherein the two-dimensional Gaussian kernel production method comprises:
and (3) generating two-dimensional Gaussian kernels with different degrees and sizes according to different concentrations by taking a coordinate point attached to each concentration data as a central coordinate point, also called as a point source, wherein the formula of the two-dimensional Gaussian kernels is as follows:
wherein, σ is standard deviation, the larger σ is, the gentler the Gaussian curve is, the lower the central point is, and the gas concentration similar to a point source is low; the smaller the sigma is, the steeper the Gaussian curve is, the higher the central point is, and the gas concentration similar to a point source is high; defining: n represents the transverse and longitudinal sizes of the two-dimensional Gaussian kernel;
the size of n is selected to be in direct 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 getGaussianKernel function of an OpenCV library, wherein the formula of the one-dimensional Gaussian kernel is as follows:
firstly, two one-dimensional Gaussian kernels G (x) and G (y) are generated, the obtained one-dimensional Gaussian kernel is a row vector, and then cross multiplication is carried out on the transpose of the row vector and the other vector to obtain a matrix, namely the two-dimensional Gaussian kernel to be obtained:
G(x，y)＝G(x)×G(y)T。
8. the method for constructing a gas concentration thermodynamic diagram according to claim 7, wherein the generated two-dimensional Gaussian kernel is expressed in the following manner:
wherein the content of the first and second substances,
gXYa central 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 to 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, it is set
Concentration data C1The coordinate point is A ═ x1，y1)，
Concentration data C2The coordinate point is B ═ x2，y2)，
If an overlapping area exists around the map after the two-dimensional Gaussian kernels generated by the point A and the point B are filled in the map, each point in the overlapping area adopts a superposition strategy, namely gas concentration data on the same coordinate point are added.
9. The method of constructing a gas concentration thermodynamic diagram according to claim 1, further comprising:
converting the numerical value of a pixel point in a picture into corresponding different colors by using a pseudo-color applyColorMap function of an OpenCV (open computer vision library), processing an original image containing simple numerical values into a thermodynamic diagram with color data, and using a COLORMAP _ JET chromaticity table in the OpenCV, wherein the smaller the gas concentration is, the smaller the pixel data is, the more the color is blue; when the gas concentration is higher, the pixel data is higher, the color is reddish, and the visualization of the gas concentration is achieved;
and superposing the thermodynamic diagram with color data and a background map by using an addWeighted function of an OpenCV (open dynamic vehicle library), setting reasonable transparency, and overlapping the information of the two pictures to generate a gas concentration thermodynamic map.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111073092.2A CN113793400B (en) | 2021-09-14 | 2021-09-14 | Construction method of gas concentration thermodynamic diagram |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111073092.2A CN113793400B (en) | 2021-09-14 | 2021-09-14 | Construction method of gas concentration thermodynamic diagram |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113793400A true CN113793400A (en) | 2021-12-14 |
| CN113793400B CN113793400B (en) | 2023-05-09 |
Family
ID=78880246
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111073092.2A Active CN113793400B (en) | 2021-09-14 | 2021-09-14 | Construction method of gas concentration thermodynamic diagram |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113793400B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080288182A1 (en) * | 2007-05-15 | 2008-11-20 | Cline Richard L | Energy Flow Measurement In Gas Pipelines |
| CN109686050A (en) * | 2019-01-18 | 2019-04-26 | 桂林电子科技大学 | Environment inside car monitoring and pre-alarming method based on cloud service and deep neural network |
| CN111369657A (en) * | 2020-03-09 | 2020-07-03 | 腾讯科技（深圳）有限公司 | Three-dimensional thermodynamic diagram generation method and device, computer equipment and storage medium |
| CN111538799A (en) * | 2020-04-17 | 2020-08-14 | 平安城市建设科技(深圳)有限公司 | Thermodynamic diagram construction method, thermodynamic diagram construction equipment, thermodynamic diagram construction storage medium and thermodynamic diagram construction device |
| CN112100299A (en) * | 2020-08-20 | 2020-12-18 | 四川大学 | Visualization method for emergency early warning of sudden toxic gas leakage |
| CN112198280A (en) * | 2020-09-27 | 2021-01-08 | 深圳市水务科技有限公司 | Gas pollution source monitoring method, system, equipment and storage medium |
| CN112232333A (en) * | 2020-12-18 | 2021-01-15 | 南京信息工程大学 | Real-time passenger flow thermodynamic diagram generation method in subway station |
| CN112304890A (en) * | 2020-11-27 | 2021-02-02 | 重庆商勤科技有限公司 | Method and system for monitoring ozone concentration by airborne spectrum remote sensing |
| CN112668752A (en) * | 2020-11-27 | 2021-04-16 | 合肥泽众城市智能科技有限公司 | Safe evacuation method and system for gas pipeline leakage emergency |
-
2021
- 2021-09-14 CN CN202111073092.2A patent/CN113793400B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080288182A1 (en) * | 2007-05-15 | 2008-11-20 | Cline Richard L | Energy Flow Measurement In Gas Pipelines |
| CN109686050A (en) * | 2019-01-18 | 2019-04-26 | 桂林电子科技大学 | Environment inside car monitoring and pre-alarming method based on cloud service and deep neural network |
| CN111369657A (en) * | 2020-03-09 | 2020-07-03 | 腾讯科技（深圳）有限公司 | Three-dimensional thermodynamic diagram generation method and device, computer equipment and storage medium |
| CN111538799A (en) * | 2020-04-17 | 2020-08-14 | 平安城市建设科技(深圳)有限公司 | Thermodynamic diagram construction method, thermodynamic diagram construction equipment, thermodynamic diagram construction storage medium and thermodynamic diagram construction device |
| CN112100299A (en) * | 2020-08-20 | 2020-12-18 | 四川大学 | Visualization method for emergency early warning of sudden toxic gas leakage |
| CN112198280A (en) * | 2020-09-27 | 2021-01-08 | 深圳市水务科技有限公司 | Gas pollution source monitoring method, system, equipment and storage medium |
| CN112304890A (en) * | 2020-11-27 | 2021-02-02 | 重庆商勤科技有限公司 | Method and system for monitoring ozone concentration by airborne spectrum remote sensing |
| CN112668752A (en) * | 2020-11-27 | 2021-04-16 | 合肥泽众城市智能科技有限公司 | Safe evacuation method and system for gas pipeline leakage emergency |
| CN112232333A (en) * | 2020-12-18 | 2021-01-15 | 南京信息工程大学 | Real-time passenger flow thermodynamic diagram generation method in subway station |
Non-Patent Citations (1)
| Title |
|---|
| 刘世泽等: "区域环境质量可视化分析方法" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113793400B (en) | 2023-05-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109872401B (en) | Unmanned aerial vehicle video augmented reality implementation method | |
| US6208353B1 (en) | Automated cartographic annotation of digital images | |
| JPS62231380A (en) | Picture synthesizing device | |
| JPH06231275A (en) | Picture simulation method | |
| CN105023287A (en) | Laser radar imaging and coloring method used for dynamic three dimensional scene | |
| CN106448432A (en) | Wind-field multi-element fusion visualization method for map service | |
| GB2578510A (en) | Environment map generation and hole filling | |
| CN110633339A (en) | GIS-based petrochemical enterprise gas leakage continuous dynamic simulation display method | |
| KR20190076188A (en) | Fusion dual IR camera and image fusion algorithm using LWIR and SWIR | |
| CN104517313B (en) | The method of ambient light masking based on screen space | |
| CN111199584A (en) | Target object positioning virtual-real fusion method and device | |
| CN113793400A (en) | Construction method of gas concentration thermodynamic diagram | |
| CN108269303B (en) | Three-dimensional meteorological radar display method | |
| CN116384145A (en) | Multi-source and multi-element data independent layer rendering method of combat scene based on GPU | |
| CN116385894A (en) | Coastline identification method, device and equipment based on remote sensing image | |
| CN108269301B (en) | Three-dimensional meteorological radar display method | |
| Billingsley et al. | Interactive image processing for meteorological applications at NASA/Goddard Space Flight Center | |
| CN112558017B (en) | Polarization target three-component decomposition result color visualization method and system | |
| JPH10239255A (en) | Three-dimensional display method for crystal particle | |
| KR102237382B1 (en) | Method of harmonic rendering on augmented reality environment, and augmented reality system and recoding medium for performing thereof | |
| CN112580729A (en) | Deep learning simulation data set manufacturing method based on CAD model | |
| CN102724535A (en) | Displaying method of stereo-scanning 3D (three-dimensional) display | |
| Thalmann et al. | Temporal and spatial relationship of flare signatures and the force-free coronal magnetic field | |
| Torson | Interactive image cube visualization and analysis | |
| CN112764651B (en) | Method and system for drawing three-dimensional point cloud profile of browser |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |