Method for identifying and quantifying a voc gas cloud using a total hydrocarbon fid module

Abstract

The application discloses a kind of VOC gas cloud imaging methods for identification and quantification using total hydrocarbon FID module, belong to VOC leakage monitoring field.The method is by obtaining imaging corresponding relation table, constructs total hydrocarbon real-time data network, determines the FID module set of coverage, obtains background image, when monitoring VOC leakage, the historical and real-time data of FID module are used to calculate background concentration, concentration increment and gray increment, the concentration value corresponding to unit gray is obtained, and then the total hydrocarbon distribution concentration of leakage air mass is calculated, combined with twin model rendering diffusion distribution and marking high concentration point position.The method realizes the quantification identification and distribution presentation of VOC leakage air mass total hydrocarbon, provides effective data support for pollution control and economic loss assessment.

Description

Technical Field

[0001] This invention belongs to the field of VOC leakage monitoring technology and relates to a method for identifying and quantifying VOC gas cloud imaging using a total hydrocarbon FID module. Background Technology

[0002] Hydrocarbons are precursors to photochemical smog and a major component of VOCs. VOC leaks and emissions from petroleum, petrochemical and other chemical enterprises not only cause a series of environmental pollution problems such as photochemical smog, but also bring huge economic losses to these enterprises.

[0003] VOC cloud imaging analysis, based on mid-wave infrared absorption technology, has been increasingly widely used in orbital mode due to its ability to perform infrared imaging of VOC leaks from production facilities. To expand the application of VOC cloud imaging in total hydrocarbon (THC) monitoring, a method needs to be developed to integrate with existing THC FID monitoring systems in chemical plants. This would enable quantitative identification of THC leaks through VOC cloud imaging, thereby facilitating the control of photochemical pollution and the assessment of economic losses for the enterprise. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying and quantifying VOC gas cloud imaging using a total hydrocarbon FID module. The specific technical solution is as follows.

[0005] A method for identifying and quantifying VOC cloud imaging using a total hydrocarbon FID module includes the following steps: S1: Construct a real-time monitoring network for total hydrocarbon FID in the monitoring area; S2: Obtain the table T(d) showing the correspondence between the size and distance of the imaged object and the image pixels in the VOC cloud imaging system; S3: Determine the total hydrocarbon FID module set F covered by the current VOC gas cloud imaging scan; S4: Periodically scan each module in set F to obtain the background infrared image B(f); S5: When VOC leakage is detected: S5-1: Query the historical hourly data D(f,h) and real-time data D(f,r) of each module in set F; S5-2: Calculate the total background hydrocarbon concentration d; S5-3: The FID module that identifies the maximum concentration increment d(p) captures its infrared image and compares it with B(f) to obtain the grayscale increment d(g); S5-4: Calculate the concentration-grayscale conversion factor Si = d(p) / d(g); S5-5: Generate the total hydrocarbon concentration distribution map of the leaking gas cloud based on the formula F(d,i) = G(d,i) × Si - d; where F(d,i) represents the total hydrocarbon concentration of each pixel in the VOC leaking gas cloud, and G(d,i) represents the gray value of each pixel in the infrared image of the VOC leaking gas cloud. S6: Combine digital twin model to render the leakage diffusion distribution and mark high concentration points.

[0006] Furthermore, the total hydrocarbon FID monitoring network in step S1 includes: Portable total hydrocarbon FID modules distributed at monitoring points, each containing a unique code and three-dimensional coordinates; The FID data server receives real-time data through a communication network and stores it in a database. The data interaction interface supports basic information query and data query functions.

[0007] Furthermore, the method for determining set F in step S3 is as follows: A circular area with a scanning radius R centered on the latitude and longitude of VOC cloud imaging was constructed using ArcGIS; Call the Intersects function to obtain all total hydrocarbon FID modules within the region.

[0008] Furthermore, the calculation method for the grayscale increment d(g) in step S5-3 is as follows: Control the VOC cloud imaging gimbal to aim at the target FID module and capture infrared images; Compare the grayscale differences between the leaked image and the background image B(f) pixel by pixel.

[0009] Furthermore, the method for rendering the concentration distribution map in step S5-5 is as follows: F(d,i) was chromatographically mapped using color scale calipers; Generate a gridded diffusion distribution map in the digital twin model.

[0010] Furthermore, the background concentration d in step S5-2 is calculated by taking the arithmetic mean of the historical hourly data D(f,h).

[0011] A system for implementing the above method includes: VOC cloud imaging analyzer, including infrared camera and gimbal control system; Total hydrocarbon FID monitoring module network; FID data server, including data receiving module and interaction interface; Digital twin model platform.

[0012] Furthermore, the FID data server's interaction interface supports: Total hydrocarbon FID basic information query: returns module code, coordinates and status; Total hydrocarbon FID data query: Returns real-time / historical concentration data.

[0013] A computer-readable storage medium storing a computer program that, when executed, implements the steps of the above-described method.

[0014] A computer program product, which, when executed, implements the steps of the above method.

[0015] The present invention has the following beneficial technical effects: by constructing a data network, acquiring background information, real-time monitoring and calculation, rendering distribution and other operations, it realizes the quantitative identification and diffusion distribution of total hydrocarbons in VOC leakage gas clouds, providing comprehensive data support for pollution control and economic loss assessment. Attached Figure Description

[0016] Figure 1 This is a diagram of the total hydrocarbon FID monitoring network architecture; Figure 2 This is a flowchart of the spatial matching process between VOC cloud imaging and the FID module; Figure 3 This is a rendering of a leak in a digital twin model after a certain monitoring. Detailed Implementation

[0017] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0018] A method for identifying and quantifying VOC cloud imaging using a total hydrocarbon FID module includes the following steps: Step 1: Construct a real-time total hydrocarbon (THC) data network based on the existing total hydrocarbon FID monitoring system in the monitoring area, as shown in Figure 1. This network includes terminal equipment and an FID data server. The terminal equipment comprises several THC FID monitoring modules distributed across various monitoring points in the monitoring area, including portable THC FID monitoring modules. Each THC FID monitoring module has a unique code and longitude, latitude, and altitude information. Each THC FID monitoring module transmits data to the data receiving module of the FID data server via a 5G network. After receiving the real-time monitoring data from the terminal THC FID monitoring modules, the FID data server's data receiving module automatically formats the data according to the point number information and then stores it in the database. Simultaneously, the FID data server provides a data interaction interface, supporting data requests and data returns. Its interface includes basic THC FID information query functions and THC FID data query functions.

[0019] Step 2: Obtain the correspondence table T(d) between the size and distance of the imaged object and the image pixels in the VOC cloud imaging system. By imaging a standard object of known size at different distances, the correspondence between the size and distance of the imaged object and the image pixels is recorded, and this correspondence table is established to clarify the relationship between the actual size and distance of the object in the imaging system and the image pixels.

[0020] Step 3: Obtain the total hydrocarbon FID module set F covered by the current VOC gas cloud imaging scan, specifically as follows: Figure 2 As shown. The VOC cloud imaging analysis accesses the data interaction interface on the FID server to query the basic information of total hydrocarbon FID. Based on the input VOC cloud imaging latitude and longitude information and scanning radius R, it constructs a circular scanning area in conjunction with ArcGIS, and obtains all total hydrocarbon FID modules within the area covered by the current VOC cloud imaging analyzer through the Intersects function in the ArcGIS library, denoted as set F.

[0021] Step 4: In practical operation, VOC cloud imaging periodically scans each total hydrocarbon FID module in the alignment set F to acquire infrared images, which are denoted as background image B(f). The timing period can be set according to actual needs to ensure that the background image accurately reflects the normal state when there is no VOC leakage.

[0022] Step 5: When the VOC cloud imaging detects several VOC gas leaks in the device in real time, proceed with the following steps: 5-1: Access the Total Carbon-hydrogen FID data query function in the data interaction interface shown in Figure 1 to obtain the historical hourly data D(f,h) and real-time monitoring data D(f,r) corresponding to each Total Carbon-hydrogen FID module in set F. Among them, the historical hourly data D(f,h) is the monitoring data of the past 24 hours.

[0023] 5-2: Calculate the mean of D(f,h) by summing all historical hourly data and dividing by the number of data points to obtain the background concentration d of total hydrocarbons in the current monitoring area.

[0024] 5-3: As VOC leaks continue, the real-time concentration of the total hydrocarbon FID module in the current monitoring area will gradually increase due to the diffusion of the leaked gas. The monitoring increment of each total hydrocarbon FID module is calculated, and the module with the maximum concentration increment d(p) is identified. The VOC cloud imaging analyzer's pan-tilt unit is then rotated and aligned with this module to capture an infrared image. This image is then compared with the background image B(f) in grayscale. The grayscale comparison is performed by calculating the difference in grayscale values ​​between corresponding pixels in the infrared image and the background image B(f) to calculate the grayscale increment d(g).

[0025] 5-4: Calculate the ratio Si of d(p) to d(g), which represents the total hydrocarbon concentration value corresponding to a unit gray level in the infrared image.

[0026] 5-5: Obtain the infrared image of the VOC leaking gas cloud. Calculate the total hydrocarbon concentration of the VOC leaking gas cloud using the following formula and render a chromatographic diffusion map: F(d,i) = G(d,i) × Si - d. Where F(d,i) represents the total hydrocarbon concentration at each pixel of the VOC leaking gas cloud; G(d,i) represents the grayscale value of each pixel in the infrared image of the VOC leaking gas cloud; and d represents the total hydrocarbon background concentration.

[0027] Step 6: Use color calipers to render F(d,i). Different total hydrocarbon concentrations correspond to different colors, forming total hydrocarbon leakage and diffusion distribution data. Combine this with a twin model for grid rendering. The grid size can be adjusted according to the monitoring accuracy requirements. Finally, mark high-concentration points in the twin model. As shown in Figure 3, in a VOC cloud imaging track inspection mode, the total hydrocarbon FID model is used to quantify and identify the monitoring results of the VOC cloud imaging analyzer, and the total hydrocarbon leakage rendering effect is combined with the twin model.

[0028] Although embodiments of the present invention have been shown and described above, it is understood that these embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and alterations to the above embodiments within the scope of the present invention without departing from its principles and spirit. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. A method for identifying and quantifying VOC cloud imaging using a total hydrocarbon FID module, characterized in that, Includes the following steps: S1: Construct a real-time monitoring network for total hydrocarbon FID in the monitoring area; S2: By imaging a standard object of known size at different distances, record the correspondence between the size of the imaged object, the distance, and the image pixels, and establish this correspondence table to clarify the relationship between the actual size and distance of the object in the imaging system and the image pixels, and obtain the correspondence table between the size of the imaged object, the distance, and the image pixels of the VOC cloud imaging system. S3: Determine the total hydrocarbon FID module set F covered by the current VOC gas cloud imaging scan; S4: Periodically scan each module in set F to obtain the background infrared image B(f); S5: When VOC leakage is detected: S5-1: Query the historical hourly data D(f,h) and real-time data D(f,r) of each module in set F; S5-2: Calculate the total background hydrocarbon concentration d; S5-3: Calculate the monitoring increment of each total hydrocarbon FID module, find the total hydrocarbon FID module with the maximum concentration increment d (p), capture its infrared image and compare it with B (f) to obtain the grayscale increment d (g); S5-4: Calculate the concentration-grayscale conversion factor Si = d(p) / d(g); S5-5: Generate the total hydrocarbon concentration distribution map of the leaking gas cloud based on the formula F(d,i) = G(d,i) × Si - d; where F(d,i) represents the total hydrocarbon concentration of each pixel in the VOC leaking gas cloud, and G(d,i) represents the gray value of each pixel in the infrared image of the VOC leaking gas cloud. S6: Combine digital twin model to render the leakage diffusion distribution and mark high concentration points.

2. The method as described in claim 1, characterized in that, The total hydrocarbon FID monitoring network in step S1 includes: Portable total hydrocarbon FID modules distributed at monitoring points, each containing a unique code and three-dimensional coordinates; The FID data server receives real-time data through a communication network and stores it in a database. The data interaction interface supports basic information query and data query functions.

3. The method as described in claim 1, characterized in that, The method for determining set F in step S3 is as follows: A circular area with a scanning radius R centered on the latitude and longitude of VOC cloud imaging was constructed using ArcGIS; Call the Intersects function to obtain all total hydrocarbon FID modules within the region.

4. The method as described in claim 1, characterized in that, The calculation method for grayscale increment d(g) in step S5-3 is as follows: Control the VOC cloud imaging gimbal to aim at the target FID module and capture infrared images; Compare the grayscale differences between the leaked image and the background image B(f) pixel by pixel.

5. The method as described in claim 1, characterized in that, The method for rendering the concentration distribution map in step S5-5 is as follows: F(d,i) was chromatographically mapped using color scale calipers; Generate a gridded diffusion distribution map in the digital twin model.

6. The method as described in claim 1, characterized in that, The background concentration d in step S5-2 is calculated by taking the arithmetic mean of the historical hourly data D(f,h).

7. A system for implementing the method according to any one of claims 1-6, characterized in that, include: VOC cloud imaging analyzer, including infrared camera and gimbal control system; Total hydrocarbon FID monitoring module network; FID data server, including data receiving module and interaction interface; Digital twin model platform.

8. The system as described in claim 7, characterized in that, The FID data server's interactive interface supports: Total hydrocarbon FID basic information query: returns module code, coordinates and status; Total hydrocarbon FID data query: Returns real-time / historical concentration data.

9. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed, it performs the steps of the method according to any one of claims 1-6.

10. A computer program product, characterized in that, When the program is executed, it performs the steps of the method according to any one of claims 1-6.

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