An information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar

Abstract

The present invention discloses an information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar, and relates to the technical field of atmospheric environmental protection. The pollutant automatic monitoring and identification module of the present invention obtains pollutant components and meteorological factors collected in the atmosphere from each station for real-time 3D observation; the original observation data is subjected to S-shaped cropping, and binarization and morphological change processing are performed to frame the specific area where the pollutants appear on the map; the pollutant spatial transmission trend measurement module standardizes each observation index, and the projected wind speed and standard pollutant concentration data are integrated in time and space dimensions to obtain the pollutant transmission flux in the specified direction. The present invention performs 3D regional precise observation of the air around the clock and in real time, and automatically identifies the spatiotemporal distribution of abnormal periods of atmospheric components based on parsing and analysis of multi-source radar observation data, thereby realizing automated 3D real-time joint monitoring of the monitoring area.

Description

Technical Field

[0001] The invention belongs to the technical field of atmospheric environmental protection, and in particular relates to an information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar. Background Art

[0002] Environmental quality monitoring is inseparable from environmental protection. A real-time and efficient monitoring system is a solid fortress for the stable and healthy operation of the ecological environment. How to conduct 3D accurate monitoring of the target area in a relatively real-time manner has become the top priority of the work. At present, the main monitoring methods in the industry include single-site monitoring, satellite image analysis, and drone remote sensing.

[0003] At present, the main monitoring methods in the industry include single-site monitoring, satellite image analysis, and drone remote sensing. However, traditional isolated site monitoring is one-sided; in order to solve this problem, some scholars have proposed using satellite remote sensing images for monitoring. Although the field of view of satellite images is large, their flight cycle and trajectory are very inflexible; in order to make up for the shortcomings of satellite images, some scholars have further proposed using drone aerial images for observation, but this of course needs to be based on high costs and cannot solve the problem of all-weather real-time observation.

[0004] To this end, we proposed a dynamic spatiotemporal environmental monitoring system based on multi-source radar. The system combines the flexibility and real-time performance of a single site, and has high efficiency and accuracy under the large-area vision of "connecting points into a network", as well as rich scalability. At present, the monitoring system mainly provides single-site pollutant display and automatic identification alarm services and spatial transmission law analysis services of atmospheric components under multi-site joint. It can monitor the spatial change laws of each component in real time from the perspective of time and space, and provide scientific decision-making basis for ecological and environmental protection issues. Summary of the invention

[0005] The purpose of the present invention is to provide an information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar, which can realize automated 3D real-time joint monitoring of the monitoring area by conducting accurate 3D regional observation of the air in real time and in all weather conditions, and automatically identifying the spatiotemporal distribution of abnormal periods of atmospheric components based on parsing and analyzing the data observed by multi-source radar, thereby solving the problems of high cost and insufficient accuracy of existing meteorological environmental monitoring caused by the inability to monitor in real time.

[0006] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention is an information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar, comprising a pollutant automatic monitoring and identification module and a pollutant spatial transmission trend measurement module, characterized in that:

[0008] The workflow of the pollutant automatic monitoring and identification module is as follows:

[0009] Step S1: Each station collects pollutant components and meteorological factors in the atmosphere for real-time 3D observation;

[0010] Step S2: Use the Gauss-3σ principle to perform S-shaped clipping on the original observation data;

[0011] Step S3: Binarize the original data according to the noise-suppressed data and the pollution concentration threshold ε specified by the industry;

[0012] Step S4: performing morphological change processing on the observation map after binarization processing;

[0013] Step S5: Perform 8-connectivity detection on the processing results, and display the start and end time and start and end height of the detected large-area individuals in the form of a report to warn the user and frame the specific area where the pollutants appear on the map;

[0014] The workflow of the pollutant space transmission trend measurement module is as follows:

[0015] Step K1: Standardize each observation indicator;

[0016] Step K2: After data normalization, preprocess the wind data;

[0017] Step K3: according to the specified flux direction angle, calculate the projection wind speed of the average wind speed in the direction;

[0018] Step K4: Integrate the projected wind speed and standard pollutant concentration data in time and space dimensions to obtain the pollutant transmission flux in the specified direction.

[0019] As a preferred technical solution, in step S2, the probability density function of the Gaussian-3σ distribution is as follows:

[0020]

[0021] Where μ is the mean of the observed variable x, σ is the standard deviation of the observed variable x, and the observed variable x is the original data set observed by each device.

[0022] As a preferred technical solution, in step S4, the morphological change processing of the observed graph includes corrosion operator processing and dilation operator processing;

[0023] The formula of the corrosion operator is:

[0024] [θ B (X)](x)=min{X B};

[0025] Where, X B is the set of pixels in the Moore neighborhood of the central pixel, [δ B (X)](x) corresponds to X B The original pixel set of the index;

[0026] The formula of the dilation operator is:

[0027] [δ C (X)](x)=max{X C};

[0028] Where, X C is the set of pixels in the Moore neighborhood of the central pixel, [δ C (X)](x) corresponds to X C The original set of pixels indexed.

[0029] As a preferred technical solution, in step K1, the standardization processing of each observation index includes spatial normalization processing and time interval normalization processing. By using bilinear interpolation method, the interpolation value of point P is obtained. The specific steps are as follows:

[0030] Step K11: Set four points Q in space 11 =(x1,y1),Q 12 =(x1,y2),Q 21 =(x2,y1),Q 22 =(x2,y2),;

[0031] Step K12: Perform linear interpolation in the x direction:

[0032]

[0033] Step K13: Calculate the value of the final point P based on the obtained R1 and R2:

[0034]

[0035] As a preferred technical solution, in step K2, the wind data is preprocessed to obtain the average wind speed and average wind direction angle in the time period from the observed wind speed and wind direction angle. The specific formula is as follows:

[0036]

[0037]

[0038]

[0039]

[0040] In the formula, A i represents the i-th angle observed during this period, μ represents the average component of the east-west direction, v represents the average component of the north-south direction, θ represents the average wind direction of the unit vector, δ represents the quadrant correction angle, τ represents the average wind speed during this period, and S i Indicates the magnitude of the i-th wind speed observed during this period of time.

[0041] As a preferred technical solution, in step K3, the projection wind speed calculation formula of the average wind speed in this direction is as follows:

[0042]

[0043] In the formula, It is the angle converted according to the direction specified by the user, γ represents the projection in the direction specified by the user, and τ represents the average wind speed.

[0044] As a preferred technical solution, in step K4, the pollutant transmission flux calculation formula is as follows:

[0045]

[0046] Where h1 and h2 represent the initial observation starting point and the final observation end point, respectively; t1 and t2 represent the initial observation time and the final observation time, respectively; v(t) represents the amount of material passing through per unit time; γ(t) represents the projected wind speed; and ρ(t) represents the corresponding amount of pollutants.

[0047] The present invention has the following beneficial effects:

[0048] (1) The present invention conducts accurate 3D regional observations of the ground and the air in real time around the clock, and automatically identifies the spatiotemporal distribution of abnormal periods of atmospheric components based on parsing and analysis of data observed by multi-source radars, thereby achieving automated 3D real-time joint monitoring of the monitoring area.

[0049] (2) The information system of the present invention has a higher time resolution for space observation and can be actively adjusted according to subjective will, and can be scientifically and flexibly regulated. Unlike satellites, which are constrained by strict orbits and rigorous observation cycles, the system is a real-time monitoring system in the true sense.

[0050] (3) The information system of the present invention can overcome extreme weather conditions, such as clouds, fog, rain and snow, etc. Unlike satellite images, which are often covered and weakened by clouds in the atmosphere, the system is a truly all-weather monitoring system.

[0051] (4) The information system of the present invention is more regionally targeted and less affected by multipath effects at close range, so the signal is less subject to interference during propagation. Therefore, the system is a truly precise regional monitoring system.

[0052] (5) The information system of the present invention integrates observation modes such as "vertical sky observation, horizontal scanning observation, and V-shaped scanning observation". In the single-site mode, it can still ensure the scientificity, integrity and three-dimensionality of the data. Therefore, the system is a true 3D monitoring system.

[0053] (6) The "automatic detection and identification of pollutants" module provided by the information system of the present invention can realize automatic identification, monitoring and alarm of atmospheric components; the "flux analysis" module provided can study the transmission laws of atmospheric components in the region in time and space under the networking platform.

[0054] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 The workflow diagram for the automatic pollutant monitoring and identification module;

[0057] Figure 2 The workflow diagram of the pollutant space transmission trend measurement module;

[0058] Figure 3 Schematic diagram before and after corrosion;

[0059] Figure 4 Schematic diagram before and after expansion. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] See also Figure 1As shown, the present invention is an information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar, including a pollutant automatic monitoring and identification module and a pollutant spatial transmission trend measurement module;

[0062] The workflow of the pollutant automatic monitoring and identification module is as follows:

[0063] Step S1: Each station collects pollutant components and meteorological factors in the atmosphere for real-time 3D observation; each station uses the configured ozone radar, differential spectrometer radar, micro-pulse radar and wind radar and other equipment to conduct real-time 3D observation of components such as O3, SO2, NO2, CH2O, aerosols, particulate matter and meteorological factors such as wind speed and wind direction in the atmosphere, thereby ensuring robust access to data sources.

[0064] Step S2: Use the Gaussian-3σ principle to perform S-shaped cropping on the original observation data; Due to the "noise" phenomenon of the spectrum that is inevitably caused by the "signal-to-noise ratio" factor, this application document uses the "Gaussian-3σ" principle to perform S-shaped cropping on the original observation data. Because when the hardware equipment is operating normally, the noise data is a very small part compared to the vast majority of normal data, so the prior assumption of Gaussian distribution is met.

[0065] Step S3: Binarize the original data according to the noise-suppressed data and the pollution concentration threshold ε specified by the industry. The significance of binarization is to shield the influence of discontinuous isolated high-value points in the data.

[0066] Step S4: performing morphological change processing on the observation map after binarization processing;

[0067] Step S5: Perform 8-connectivity detection on the processing results, and display the start and end time and start and end height of the detected large-area individuals in the form of a report to warn the user and frame the specific area where the pollutants appear on the map;

[0068] See also Figure 2 As shown, the workflow of the pollutant space transmission trend measurement module is as follows:

[0069] Step K1: Standardize each observation index; because the observation frequency and step of each satellite may be inconsistent in time nodes and spatial heights due to factors such as the hardware itself and the delay of settings and transmission, bilinear interpolation is used here to normalize the data and facilitate data fusion analysis.

[0070] Step K2: After data normalization, preprocess the wind data;

[0071] Step K3: according to the specified flux direction angle, calculate the projection wind speed of the average wind speed in the direction;

[0072] Step K4: Integrate the projected wind speed and standard pollutant concentration data in time and space dimensions to obtain the pollutant transmission flux in the specified direction.

[0073] In step S2, the probability density function of the Gaussian-3σ distribution is as follows:

[0074]

[0075] In the formula, μ is the mean of the observed variable x, σ is the standard deviation of the observed variable x, and the observed variable x is the original data set observed by each device;

[0076] According to the Gaussian probability density function, integrated in the μ±3σ region: We can understand that this process eliminates a few "outliers" outside the 0.27% range and retains 99.73% of the original valid observation data, thereby ensuring the objective authenticity of the data.

[0077] In step S4, the observed image is subjected to morphological change processing including corrosion operator processing and dilation operator processing;

[0078] like Figure 3 As shown, the left figure is a schematic diagram before corrosion, and the right figure is a schematic diagram after corrosion. The formula of the corrosion operator is:

[0079] [θ B (X)](x)=min{X B};

[0080] Where, X B is the set of pixels in the Moore neighborhood of the central pixel, [δ B (X)](x) corresponds to X B The original pixel set of the index;

[0081] like Figure 4 As shown, the formula of the expansion operator is:

[0082] [δ C (X)](x)=max{X C};

[0083] Where, X C is the set of pixels in the Moore neighborhood of the central pixel, [δ C (X)](x) corresponds to X C The original set of pixels indexed.

[0084] In step K1, the standardization processing of each observation index includes spatial normalization processing and time interval normalization processing. By using the bilinear interpolation method, the interpolation value of point P is obtained. The specific steps are as follows:

[0085] Step K11: Set four points Q in space 11 =(x1,y1),Q 12 =(x1,y2),Q 21 =(x2,y1),Q 22 =(x2,y2);

[0086] Step K12: Perform linear interpolation in the x direction:

[0087]

[0088] Step K13: Calculate the value of the final point P based on the obtained R1 and R2:

[0089]

[0090] In step K2, the wind data is preprocessed, and the observed wind speed and wind direction angle are used to obtain the average wind speed and average wind direction angle within the time period. Due to the high observation frequency of the wind radar, after many experiments, we unified the time interval of the data to 10 minutes, and used the wind speed and wind direction angle observed in 10 minutes to calculate the average wind speed and average wind direction angle within the period. The specific formula is as follows:

[0091]

[0092]

[0093]

[0094]

[0095] In the formula, A i represents the i-th angle observed during this period, μ represents the average component of the east-west direction, v represents the average component of the north-south direction, θ represents the average wind direction of the unit vector, δ represents the quadrant correction angle, τ represents the average wind speed during this period, and S i Indicates the magnitude of the i-th wind speed observed during this period of time.

[0096] In step K3, the projection wind speed of the average wind speed in this direction is calculated as follows:

[0097]

[0098] In the formula, It is the angle converted according to the direction specified by the user. The angle is in the range of [0,180]. γ represents the projection in the direction specified by the user, and τ represents the average wind speed.

[0099] In step K4, the pollutant transmission flux calculation formula is as follows:

[0100]

[0101] Where h1 and h2 represent the initial observation starting point and the final observation end point, respectively; t1 and t2 represent the initial observation time and the final observation time, respectively; v(t) represents the amount of material passing through per unit time; γ(t) represents the projected wind speed; and ρ(t) represents the corresponding amount of pollutants (concentration or amount of material).

[0102] It is worth noting that in the above system embodiment, the various units included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0103] In addition, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by instructing related hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.

[0104] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar, comprising a pollutant automatic monitoring and identification module and a pollutant spatial transmission trend measurement module, characterized in that: The workflow of the pollutant automatic monitoring and identification module is as follows: Step S1: Each station collects pollutant components and meteorological factors in the atmosphere for real-time 3D observation; Step S2: Use the Gauss-3σ principle to perform S-shaped clipping on the original observation data; Step S3: Binarize the original data according to the noise-suppressed data and the pollution concentration threshold ε specified by the industry; Step S4: performing morphological change processing on the observation map after binarization processing; Step S5: Perform 8-connectivity detection on the processing results, and display the start and end time and start and end height of the detected large-area individuals in the form of a report to warn the user and frame the specific area where the pollutants appear on the map; The workflow of the pollutant space transmission trend measurement module is as follows: Step K1: Standardize each observation indicator; Step K2: After data normalization, preprocess the wind data; Step K3: according to the specified flux direction angle, calculate the projection wind speed of the average wind speed in the direction; Step K4: Integrate the projected wind speed and standard pollutant concentration data in time and space dimensions to obtain the pollutant transmission flux in the specified direction.

2. The information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar according to claim 1 is characterized in that: In step S2, the probability density function of the Gaussian-3σ distribution is as follows: Where μ is the mean of the observed variable x, σ is the standard deviation of the observed variable x, and the observed variable x is the original data set observed by each device.

3. The information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar according to claim 1 is characterized in that: In the step S4, the observed graph is subjected to morphological change processing including corrosion operator processing and dilation operator processing; The formula of the corrosion operator is: [i B (X)](x)=min{X B }; Where, X B is the set of pixels in the Moore neighborhood of the central pixel, [δ B (X)](x) corresponds to X B The original pixel set of the index; The formula of the dilation operator is: [δ C (X)](x)=max{X C }; Where, X C is the set of pixels in the Moore neighborhood of the central pixel, [δ C (X)](x) corresponds to X C The original set of pixels indexed.

4. The information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar according to claim 1, characterized in that: In step K1, the standardization processing of each observation index includes space standardization processing and time interval standardization processing. By using the bilinear interpolation method, the interpolation value of point P is obtained. The specific steps are as follows: Step K11: Set four points Q in space 11 =(x1,y1),Q 12 =(x1,y2),Q 21 =(x2,y1),Q 22 =(x2,y2); Step K12: Perform linear interpolation in the x direction: Step K13: Calculate the value of the final point P based on the obtained R1 and R2: P： 5. The information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar according to claim 1 is characterized in that: In step K2, the wind data is preprocessed to obtain the average wind speed and average wind direction angle within the time period from the observed wind speed and wind direction angle. The specific formula is as follows: In the formula, A i represents the i-th angle observed during this period, μ represents the average component of the east-west direction, v represents the average component of the north-south direction, θ represents the average wind direction of the unit vector, δ represents the quadrant correction angle, τ represents the average wind speed during this period, and S i Indicates the magnitude of the i-th wind speed observed during this period of time.

6. The information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar according to claim 1 is characterized in that: In step K3, the projection wind speed of the average wind speed in this direction is calculated as follows: In the formula, It is the angle converted according to the direction specified by the user, γ represents the projection in the direction specified by the user, and τ represents the average wind speed.

7. The information system for dynamic spatiotemporal environmental quality monitoring based on multi-source radar according to claim 1 is characterized in that: In step K4, the pollutant transmission flux calculation formula is as follows: In the formula, h1 and h2 represent the starting point of observation and the ending point of observation respectively; t1 and t2 represent the starting observation time and the ending observation time respectively, v(t) represents the amount of material passing through per unit time, γ(t) represents the projected wind speed, and ρ(t) represents the corresponding amount of pollutants.

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