A linear interpolation-based vocs concentration prediction method and device

Abstract

This invention discloses a VOCs concentration prediction method and device based on linear interpolation. The method includes the following steps: S1, using a drone equipped with a gas analyzer to collect VOCs concentration data of the space to be measured; S2, based on the collected data, performing data preprocessing, and using two-dimensional linear interpolation to fill the VOCs concentration data of the outer frame of the space to be measured; S3, based on the collected data and the outer frame data obtained in the previous step, using three-dimensional spatial linear interpolation to interpolate and fill the points inside the rectangular body to be measured. This invention provides a VOCs concentration prediction method based on three-dimensional spatial interpolation using a drone. This method is rapid to implement, has high prediction accuracy, and has low requirements for equipment and site, offering significant advantages over traditional methods.

Description

Technical Field

[0001] This invention relates to the field of air pollution prediction technology in the field of environmental monitoring, specifically to a method and device for predicting VOCs concentration based on linear interpolation. Background Technology

[0002] Industrial development is inseparable from mining, but the mining process inevitably causes environmental pollution, among which air pollution is particularly prominent and has a significant impact on the health of workers. Mining operations generate various particulate matter pollutants, such as PM1.0, PM2.5, PM10, and some heavy metal particles, as well as various harmful gases, such as SO2, CO2, and CO. Accurate measurement of the distribution of pollutants is necessary for air pollution prevention.

[0003] In general, many existing methods for measuring the distribution of polluting gases, such as manual analysis and measurement, and continuous analyzers, require a significant amount of time and manpower. Summary of the Invention

[0004] The present invention proposes a VOCs concentration prediction method based on linear interpolation, which can quickly predict the pollution distribution in a three-dimensional cuboid space.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for predicting VOC concentrations based on linear interpolation includes the following steps:

[0007] Step 1: Use a drone equipped with a gas analyzer to collect VOCs concentration data in the space to be tested;

[0008] Step 2: Based on the collected data, perform data preprocessing and use two-dimensional linear interpolation to fill the VOCs concentration data on the outer frame of the space to be measured;

[0009] Step 3: Based on the collected data and the outer frame data obtained in the previous step, use three-dimensional spatial linear interpolation to interpolate and fill the internal points of the rectangle to be measured.

[0010] Further, step S1 above: using a drone equipped with a gas analyzer to analyze and measure the air in the space to be tested, specifically includes the following sub-steps S11 to S13:

[0011] S11: Install the snorkeling gas analyzer onto the DJI M300 drone.

[0012] S12: Control the drone to collect VOCs concentration data on the six faces of the rectangular space to be tested.

[0013] S13: Control the drone to collect VOCs concentration data inside the rectangular space to be tested. The more data collected and the more uniformly distributed, the more accurate the interpolation.

[0014] Further, step S2 above: based on the collected data, data preprocessing is performed, and two-dimensional linear interpolation is used to complete the data filling of the outer frame of the space to be measured, specifically including the following sub-steps S21 to S22:

[0015] S21: Based on the collected data, use the nearest neighbor interpolation method to interpolate and fill the data on the twelve edges of the rectangular space to be measured.

[0016] The core idea of ​​nearest neighbor interpolation is to select the point closest to the target point as the new value point to be inserted. In an application example of this invention, a three-dimensional array of size L*W*H can be created, where L is the sum of the number of data points collected and the number of data points to be interpolated in the length of the space to be measured, W is the sum of the number of data points collected and the number of data points to be interpolated in the width of the space to be measured, and H is the sum of the number of data points collected and the number of data points to be interpolated in the height of the space to be measured.

[0017] For the point P to be interpolated, iterate through the array to find the point closest to P, use the air pollution concentration at that point as the VOCs concentration at P, and store the air pollution data of point P in the corresponding position of the array.

[0018] S22: Based on the edge data obtained in S21 and the data collected by the UAV, establish an xOy coordinate system on the outer frame surface, Q 11 Q 12 Q 21 Q 22 This should be the point closest to the point to be interpolated in both the x-axis and y-axis directions. The data for the outer frame of the rectangular space to be measured are obtained using a two-dimensional linear interpolation method, with the specific formula as follows:

[0019]

[0020] x, y are the x-axis coordinates and y-axis coordinates of the point P to be interpolated;

[0021] (x1,y1) and (x2,y2) are the coordinates of the two nearest points to the point P to be interpolated in two dimensions.

[0022] Further, step S3 above: Based on the collected data and the outer frame data obtained in the previous step, three-dimensional spatial linear interpolation is used to interpolate and fill the internal points of the measured spatial volume, specifically including the following sub-steps S31 to S33:

[0023] S31: For the point P(x,y,z) to be interpolated, use the nearest neighbor interpolation method to obtain point c. 000 (x+1,y+1,z+1),c001 (x+1,y+1,z-1),c 010 (x+1,y-1,z+1),c 011 (x+1,y-1,z-1),c 100 (x-1,y+1,z+1),c 101 (x-1,y+1,z-1),c 110 (x-1,y-1,z+1),c 111 VOCs concentrations in (x-1, y-1, z-1).

[0024] S32: Based on the data obtained in S31, the VOCs concentration data for all points in the space to be measured are obtained using a three-dimensional spatial interpolation method. The specific formula is as follows:

[0025]

[0026]

[0027]

[0028] c = c 000 (1-x d (1-y) d (1-z) d )+

[0029] c 100 x d (1-y d (1-z) d )+

[0030] c 010 (1-x d )y d (1-z d )+

[0031] c 001 (1-x d (1-y) d )z d +

[0032] c 101 x d (1-y d )z d +

[0033] c 011 (1-x d )y d z d +

[0034] c 110 x d yd (1-z d )+

[0035] c 111 x d y d z d

[0036] (xd, yd, zd) are the spatial coordinates of any pollution interpolation point to be estimated in space, and (x0, y0, z0) and (x1, y1, z1) are the coordinates of the two nearest points in space to the interpolation point P.

[0037] C000, C100, C010, C001, C101, C011, C110, and C111 are the pollution concentration values ​​of the eight nearest vertices of the cube containing the point to be interpolated;

[0038] Update the VOCs concentration data obtained from the formula to the array.

[0039] S33: For each interpolation point in the space, repeat S31 and S32 to obtain the VOCs concentration at all points in the space to be measured.

[0040] As can be seen from the above technical solution, the VOCs concentration prediction method based on linear interpolation of the present invention specifically proposes a VOCs concentration prediction method based on three-dimensional spatial interpolation using UAVs. This method is quick to implement, has high prediction accuracy, and has low requirements for equipment and site, which has great advantages compared with traditional methods.

[0041] The spatial interpolation method for pollution data proposed in this invention can take into account the influence of the spatial heterogeneity of pollution monitoring data distribution, and can accurately generate spatial variation distribution data that covers the real pollution monitoring data. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method of the present invention;

[0043] Figure 2 This is a comparison chart of the interpolation effects of HC pollution monitoring data in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0045] like Figure 1 As shown in this embodiment, the VOCs concentration prediction method based on linear interpolation includes:

[0046] Step 1: Use a drone equipped with a gas analyzer to collect the VOCs concentration in the space to be tested.

[0047] Step 2: Based on the collected data, perform data preprocessing and use two-dimensional linear interpolation to fill the VOCs concentration data on the outer frame of the space to be measured.

[0048] Step 3: Based on the collected data and the outer frame data obtained in the previous step, use three-dimensional spatial linear interpolation to interpolate and fill the internal points of the rectangle to be measured.

[0049] The following is a detailed explanation:

[0050] Step S1 above: Using a drone equipped with a gas analyzer to analyze and measure the air in the space to be tested, specifically including the following sub-steps S11 to S13:

[0051] S11: Install the snorkeling gas analyzer onto the DJI M300 drone.

[0052] S12: Control the drone to collect VOCs concentration data on the six faces of the rectangular space to be tested.

[0053] S13: Control the drone to collect VOCs concentration data inside the rectangular space to be tested. The more data collected and the more uniformly distributed, the more accurate the interpolation.

[0054] Step S2 above: Based on the collected data, perform data preprocessing and use two-dimensional linear interpolation to complete the data filling of the outer frame of the space to be measured. Specifically, it includes the following sub-steps S21 to S22:

[0055] S21: Based on the collected data, use the nearest neighbor interpolation method to interpolate and fill the data on the twelve edges of the rectangular space to be measured.

[0056] The core idea of ​​nearest neighbor interpolation is to select the point closest to the target point as the new value point to be inserted. In an application example of this invention, a three-dimensional array of size L*W*H can be created, where L is the sum of the number of data points collected and the number of data points to be interpolated in the length of the space to be measured, W is the sum of the number of data points collected and the number of data points to be interpolated in the width of the space to be measured, and H is the sum of the number of data points collected and the number of data points to be interpolated in the height of the space to be measured.

[0057] For the point P to be interpolated, iterate through the array to find the point closest to P, use the air pollution concentration at that point as the VOCs concentration at P, and store the air pollution data of point P in the corresponding position of the array.

[0058] S22: Based on the edge data obtained in S21 and the data collected by the UAV, establish an xOy coordinate system on the outer frame surface, Q 11 Q12 Q 21 Q 22 This should be the point closest to the point to be interpolated in both the x-axis and y-axis directions. The data for the outer frame of the rectangular space to be measured are obtained using a two-dimensional linear interpolation method, with the specific formula as follows:

[0059]

[0060] x, y are the x-axis coordinates and y-axis coordinates of the point P to be interpolated;

[0061] (x1,y1) and (x2,y2) are the coordinates of the two nearest points to the point P to be interpolated in two dimensions.

[0062] Step S3 above: Based on the collected data and the outer frame data obtained in the previous step, use three-dimensional spatial linear interpolation to interpolate and fill the internal points of the measured spatial volume, specifically including the following sub-steps S31 to S33:

[0063] S31: For the point P(x,y,z) to be interpolated, use the nearest neighbor interpolation method to obtain point c. 000 (x+1, y+1, z+1), c 001 (x+1, y+1, z-1), c 010 (x+1, y-1, z+1), c 011 (x+1, y-1, z-1), c 100 (x-1, y+1, z+1), c 101 (x-1, y+1, z-1), c 110 (x-1, y-1, z+1), c 111 VOCs concentrations at (x-1, y-1, z-1).

[0064] S32: Based on the data obtained in S31, the VOCs concentration data for all points in the space to be measured are obtained using a three-dimensional spatial interpolation method. The specific formula is as follows:

[0065]

[0066]

[0067]

[0068] c = c 000 (1-x d (1-y) d (1-z) d )+

[0069] c 100 x d (1-y d (1-z)d )+

[0070] c 010 (1-x d )y d (1-z d )+

[0071] c 001 (1-x d (1-y) d )z d +

[0072] c 101 x d (1-y d )z d +

[0073] c 011 (1-x d )y d z d +

[0074] c 110 x d y d (1-z d )+

[0075] c 111 x d y d z d

[0076] (xd, yd, zd) are the spatial coordinates of any pollution interpolation point to be estimated in space, and (x0, y0, z0) and (x1, y1, z1) are the coordinates of the two nearest points in space to the interpolation point P.

[0077] C000, C100, C010, C001, C101, C011, C110, and C111 are the pollution concentration values ​​of the eight nearest vertices of the cube containing the point to be interpolated;

[0078] Update the VOCs concentration data obtained from the formula to the array.

[0079] S33: For each interpolation point in the space, repeat S31 and S32 to obtain the VOCs concentration at all points in the space to be measured.

[0080] Figure 2 The following is a comparison of the interpolation effects of HC pollution monitoring data according to embodiments of the present invention: the left figure is the interpolation effect diagram of the method of the embodiment of the present invention; the middle figure is the result diagram of the Kriging interpolation method; and the right figure is the result diagram of the IDW interpolation method.

[0081] In summary, the pollution data spatial interpolation method proposed in this embodiment of the invention can take into account the influence of the spatial heterogeneity of pollution monitoring data distribution, and can accurately generate spatial variation distribution data that covers the real pollution monitoring data.

[0082] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of any of the methods described above.

[0083] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of any of the methods described above.

[0084] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the steps of any of the methods described in the above embodiments.

[0085] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0086] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0087] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting VOCs concentration based on linear interpolation, characterized in that, Includes the following steps, S1. Use a drone equipped with a gas analyzer to collect VOCs concentration data of the space to be tested; wherein, the collection includes: controlling the drone to collect VOCs concentration data of the six sides of the rectangular space to be tested, and controlling the drone to collect VOCs concentration data inside the rectangular space to be tested. S2. Based on the collected data, perform data preprocessing and use two-dimensional linear interpolation to fill the VOCs concentration data on the outer frame of the space to be measured. S3. Based on the collected data and the outer frame data obtained in the previous step, use three-dimensional spatial linear interpolation to interpolate and fill the internal points of the rectangular body to be measured. Step S2 specifically includes the following sub-steps: S21: Based on the collected data, use the nearest neighbor interpolation method to interpolate and fill the data on the twelve edges of the rectangular space to be measured; S22: Based on the edge data obtained in S21 and the data collected by the UAV, the data of the outer frame of the rectangular space to be measured is obtained by two-dimensional linear interpolation. Step S3: Use the nearest neighbor interpolation method to obtain the pollution concentration values ​​of the eight nearest vertices of the cube of the point to be interpolated, and use the three-dimensional spatial interpolation method to obtain the VOCs concentration data of all points in the space to be measured.

2. The VOCs concentration prediction method based on linear interpolation according to claim 1, characterized in that: Step S1 specifically includes, S11: Install the Lingxiu gas analyzer onto the drone; S12: Control the drone to collect VOCs concentration data on the six faces of the rectangular space to be tested; S13: Control the drone to collect VOCs concentration data inside the rectangular space to be tested.

3. The VOCs concentration prediction method based on linear interpolation according to claim 2, characterized in that: The above-mentioned step S2 specifically includes the following sub-steps S21 to S22: S21: Based on the collected data, use the nearest neighbor interpolation method to interpolate and fill the data on the twelve edges of the rectangular space to be measured; Create a three-dimensional array of size L*W*H, where L is the sum of the number of data points collected and the number of data points to be interpolated in the length of the space to be tested, W is the sum of the number of data points collected and the number of data points to be interpolated in the width of the space to be tested, and H is the sum of the number of data points collected and the number of data points to be interpolated in the height of the space to be tested. For the point P to be interpolated, traverse the array, find the point closest to P, use the air pollution concentration at that point as the VOCs concentration at P, and store the air pollution data of point P in the corresponding position of the array. S22: Based on the edge data obtained in S21 and the data collected by the UAV, establish an xOy coordinate system on the outer frame surface, Q 11 Q 12 Q 21 Q 22 This should be the point closest to the point to be interpolated in both the x-axis and y-axis directions; the data of the outer frame of the rectangular space to be measured are obtained using a two-dimensional linear interpolation method, with the specific formula as follows: x, y are the x-axis coordinates and y-axis coordinates of the point P to be interpolated; (x1,y1) and (x2,y2) are the coordinates of the two nearest points to the point P to be interpolated in two dimensions.

4. The VOCs concentration prediction method based on linear interpolation according to claim 3, characterized in that: The above-mentioned step S3 specifically includes the following sub-steps S31 to S33: S31: For the point P(x,y,z) to be interpolated, use the nearest neighbor interpolation method to obtain point c. 000 (x+1,y+1,z+1),c 001 (x+1,y+1,z-1),c 010 (x+1,y-1,z+1),c 011 (x+1,y-1,z-1),c 100 (x-1,y+1,z+1),c 101 (x-1,y+1,z-1),c 110 (x-1,y-1,z+1),c 111 VOCs concentrations in (x-1, y-1, z-1); S32: Based on the data obtained in S31, the VOCs concentration data of all points in the space to be measured are obtained using three-dimensional spatial interpolation; the specific formula is as follows: c=c 000 (1-x d )(1-y d )(1-z d )+c 100 x d (1-y d )(1-z d )+c 010 (1-x d )y d (1-z d )+c 001 (1-x d )(1-y d )z d +c 101 x d (1-y d )z d +c 011 (1-x d )y d z d +c 110 x d y d (1-z d )+c 111 x d y d z d (xd,yd,zd) are the spatial coordinates of any pollution interpolation point to be estimated in space, and (x0,y0,z0) and (x1,y1,z1) are the coordinates of the two nearest points in space to the interpolation point P. C000, C100, C010, C001, C101, C011, C110, and C111 are the pollution concentration values ​​of the eight nearest vertices of the cube containing the point to be interpolated; Update the VOCs concentration data obtained from this formula to the array; S33: For each interpolation point in the space, repeat S31 and S32 to obtain the VOCs concentration at all points in the space to be measured.

5. A computer-readable device storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 4.

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