A method for tracing the source of high-risk gas-related enterprises based on urban VOCs mobile vehicles

Abstract

The present invention relates to the field of environmental monitoring technology, and in particular to a method for tracing the source of high-risk air-related enterprises based on an urban VOCs UAV, including obtaining the cruise monitoring data of the VOCs UAV; screening high-risk monitoring points according to the cruise monitoring data; matching air-related pollution sources according to the high-risk monitoring points; calculating the relative pollution contribution index of the air-related pollution source by using the inverse distance weighted method; determining the weight factor by using wind speed and wind direction, and correcting the relative pollution contribution index of the air-related pollution source; and classifying the pollution level according to the corrected relative pollution contribution index of the air-related pollution source. The present invention can improve the accuracy of the tracing results of high-risk air-related enterprises and the monitoring efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a method for tracing the source of high-risk VOCs-related enterprises based on an urban VOCs mobile vehicle. Background Art

[0002] The existing urban VOCs cruise vehicles are mainly faced with efficiency bottlenecks and excessive resource consumption when performing routine monitoring tasks. In the current VOCs cruise monitoring practice, the vehicle needs to conduct extensive and detailed carpet scanning of industrial parks along the established route. Although this process can fully cover the monitoring area, when faced with high-frequency monitoring needs, it will not only lead to high human and material investment, but also may cause a large number of invalid operations due to repeated monitoring of areas with no significant changes, thus affecting the overall monitoring efficiency and the rationality of resource allocation. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for tracing the source of high-risk air-related enterprises based on an urban VOCs UAV.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] A method for tracing the source of high-risk gas-related enterprises based on an urban VOCs mobile vehicle includes the following steps:

[0006] Obtain the VOCs cruise monitoring data of the cruise vehicle;

[0007] Screen high-risk monitoring points based on cruise monitoring data;

[0008] Match air pollution sources according to high-risk monitoring points;

[0009] The inverse distance weighted method is used to calculate the relative pollution contribution index of air pollution sources;

[0010] The weight factor is determined by wind speed and wind direction to correct the relative pollution contribution index of air pollution sources;

[0011] Pollution levels are classified according to the corrected relative pollution contribution index of air pollution sources.

[0012] Preferably, the navigation monitoring data includes:

[0013] Monitoring values ​​of volatile organic compounds, monitoring longitude and latitude, and release time.

[0014] As a preferred method, screening high-risk monitoring points based on cruise monitoring data includes:

[0015] Set the TVOC artificial threshold to determine whether the TVOC monitoring value in the cruise monitoring data of each monitoring point is greater than or equal to the TVOC artificial threshold; if so, the monitoring point is determined to be a high-risk monitoring point; otherwise, it is a non-high-risk monitoring point and the corresponding monitoring point is deleted.

[0016] Preferably, matching the air pollution sources according to the high-risk monitoring points includes the following steps:

[0017] Calculate the distance coefficient between each pollution source and the high-risk monitoring point respectively;

[0018] The radius coefficient is set, and pollution sources with a distance coefficient less than or equal to the radius coefficient are screened for each high-risk monitoring point to match the air pollution sources.

[0019] As a preferred method, the relative pollution contribution index of air pollution sources is calculated by using the inverse distance weighted method as follows:

[0020]

[0021] in, Represents the relative pollution contribution index of air pollution sources, Indicates l The distance coefficient, k represents the power coefficient, h represents the number of distance coefficients, Indicates the TVOC monitoring value of the high-risk monitoring point.

[0022] Preferably, the weight factor is determined by using wind speed and wind direction to correct the relative pollution contribution index of the air pollution source, including the following steps:

[0023] Calculate the angle between each high-risk monitoring point and the pollution source, and determine the position of each high-risk monitoring point in the pollution source based on the corresponding relationship between wind direction and angle;

[0024] Calculate the wind speed weight factor based on the wind speed data at the pollution source;

[0025] Determine the wind direction weight factor based on the wind direction data at the pollution source and the location of the high-risk monitoring point at the pollution source;

[0026] The relative pollution contribution index of air pollution sources is corrected according to the wind speed weight factor and wind direction weight factor.

[0027] Preferably, the wind speed weight factor is calculated based on the wind speed data at the pollution source as follows:

[0028]

[0029] in, represents the wind speed weight factor, Represents the wind speed data of pollution source s.

[0030] Preferably, according to the wind direction data at the pollution source and the location of the high-risk monitoring point at the pollution source, determining the wind direction weight factor includes:

[0031] If the wind direction data at the pollution source is consistent with the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is set to the first weight;

[0032] If the wind direction data at the pollution source is inconsistent with the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is set to the second weight;

[0033] The first weight is greater than the second weight.

[0034] As a preferred embodiment, the relative pollution contribution index of the air pollution source is corrected according to the wind speed weight factor and the wind direction weight factor as follows:

[0035]

[0036] in, It represents the relative pollution contribution index after correction of air pollution sources. Represents the relative pollution contribution index of air pollution sources, represents the wind speed weight factor, Represents the wind direction weight factor.

[0037] Preferably, the pollution level classification according to the corrected relative pollution contribution index of the air pollution source includes the following steps:

[0038] Normalize the corrected relative pollution contribution index of air pollution sources;

[0039] Based on the preset pollution classification strategy, the pollution level of air pollution sources is determined according to the normalized relative pollution contribution index.

[0040] The present invention has the following beneficial effects:

[0041] The patent of this invention is based on the data of urban VOCs UAVs and uses big data analysis technology. First, a scientific pollution diffusion threshold is set, and potential high-risk areas are screened out based on this. On this basis, the system deeply analyzes the pollution source data in these areas, and uses the inverse distance weighting method (IDW, Inverse Distance Weighting) to accurately calculate the pollution concentration contribution index of each pollution source to the surrounding environment, ensuring that the evaluation results can truly reflect the impact of each emission source. More importantly, the present invention cleverly integrates meteorological factors, especially the real-time data of wind speed and wind direction, to dynamically correct the calculated pollution concentration index. This strategy can effectively respond to changes in atmospheric diffusion conditions, improve the accuracy of tracing sources, and ensure that the source of VOCs pollution can be accurately tracked even under complex and changeable meteorological conditions. Finally, the concentration index of all pollution sources is standardized and classified using normalization processing, making data comparison intuitive and easy for decision makers to identify high-risk hotspots of VOCs emissions. The application of this technology not only greatly improves the pertinence and efficiency of monitoring operations and reduces the ineffective allocation of resources, but also lays a solid data foundation for urban managers to formulate more accurate VOCs emission reduction strategies and long-term environmental governance plans. It is an important technological innovation to promote the construction of smart city environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The figure is a flow chart of a method for tracing the source of high-risk air-related enterprises based on an urban VOCs UAV. DETAILED DESCRIPTION

[0043] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0044] like Figure 1 As shown, the embodiment of the present invention provides a method for tracing the source of high-risk gas-related enterprises based on an urban VOCs mobile vehicle, including the following steps S1 to S6:

[0045] S1. Obtaining the VOCs cruise monitoring data of the cruise vehicle;

[0046] In an optional embodiment of the present invention, step S1 obtains the VOCs UAV monitoring data, including the monitoring values ​​of volatile organic compounds, monitoring longitude and latitude, and release time; wherein the volatile organic compounds include TVOC, trimethylbenzene, toluene, formaldehyde, etc.

[0047] This embodiment performs data type conversion after acquiring the VOCs cruise monitoring data of the cruise vehicle, converts the monitoring value and the monitoring longitude and latitude into floating point numbers, and converts the release time into a time type, recorded as DATA.

[0048] S2. Screen high-risk monitoring points based on cruise monitoring data;

[0049] In an optional embodiment of the present invention, step S2 of screening high-risk monitoring points according to the cruise monitoring data includes:

[0050] Set the TVOC artificial threshold to determine whether the TVOC monitoring value in the cruise monitoring data of each monitoring point is greater than or equal to the TVOC artificial threshold; if so, the monitoring point is determined to be a high-risk monitoring point; otherwise, it is a non-high-risk monitoring point and the corresponding monitoring point is deleted.

[0051] Exemplarily, this embodiment defines the TVOC artificial threshold , set to 600 μg / m 3 (unit is micrograms per cubic meter). If the monitoring point is greater than or equal to the artificial threshold, it is determined to be a high-risk monitoring point. Otherwise, it is determined to be pollution-free data. Finally, the result data is recorded as , expressed as:

[0052]

[0053] in, Indicates the pollution point. Indicates high-risk pollution points. Indicates a non-polluting point. i It is the point sequence number.

[0054] S3. Match air pollution sources according to high-risk monitoring points;

[0055] In an optional embodiment of the present invention, step S3 matches the air pollution source according to the high-risk monitoring point location, including the following steps:

[0056] Calculate the Euclidean distance between each pollution source and the high-risk monitoring point;

[0057] The radius coefficient is set, and pollution sources with a Euclidean distance less than or equal to the radius coefficient are screened for each high-risk monitoring point to match the air pollution sources.

[0058] Exemplarily, this embodiment defines the radius coefficient , set to 100m, filter out the pollution sources in the target area, including air-related enterprise locations, restaurant fume locations, gas stations, and body repair shops. The distance coefficient is calculated using the Euclidean distance, expressed as:

[0059]

[0060] in, Represents the distance coefficient between the pollution source and the high-risk monitoring point, represents the longitude of the pollution source, represents the latitude of the pollution source, Indicates the longitude of the high-risk monitoring point, Indicates the latitude of the high-risk monitoring point.

[0061] The pollution sources whose distance coefficient is less than or equal to the radius coefficient are screened and the air pollution sources are matched as the polluting enterprise set, denoted as , expressed as:

[0062]

[0063] S4. Use the inverse distance weighted method to calculate the relative pollution contribution index of air pollution sources;

[0064] In an optional embodiment of the present invention, since during the atmospheric monitoring process, the pollution source at a relatively far distance has a relatively low contribution to the current pollution point, this embodiment adopts the inverse distance weighting method (IDW) to convert the relative pollution contribution index according to the distance coefficient, which is recorded as , expressed as:

[0065]

[0066] in, Represents the relative pollution contribution index of air pollution sources, Indicates l The distance coefficient, k represents the power coefficient, h represents the number of distance coefficients, Indicates the TVOC monitoring value of the high-risk monitoring point.

[0067] This embodiment adopts the inverse distance weighted method, and converts the relative pollution contribution index through the distance coefficient between the pollution source and the pollution point, which can fully describe the diffusion index of the data on the plane.

[0068] S5. Use wind speed and wind direction to determine the weight factor and correct the relative pollution contribution index of air pollution sources;

[0069] In an optional embodiment of the present invention, step S5 uses wind speed and wind direction to determine a weight factor to correct the relative pollution contribution index of the air pollution source, including the following steps:

[0070] Calculate the angle between each high-risk monitoring point and the pollution source, and determine the position of each high-risk monitoring point in the pollution source based on the corresponding relationship between wind direction and angle;

[0071] Calculate the wind speed weight factor based on the wind speed data at the pollution source;

[0072] According to the wind direction data at the pollution source and the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is determined, including:

[0073] If the wind direction data at the pollution source is consistent with the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is set to the first weight;

[0074] If the wind direction data at the pollution source is inconsistent with the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is set to the second weight;

[0075] The first weight is greater than the second weight;

[0076] The relative pollution contribution index of air pollution sources is corrected according to the wind speed weight factor and wind direction weight factor.

[0077] For example, in the process of atmospheric pollution diffusion, wind speed and wind direction are one of the most important factors in meteorological conditions, and play a decisive role in the spread and diffusion of pollution sources. The greater the wind speed, the stronger the ability of pollutants to spread to a distance, the pollutants will spread rapidly, and the concentration will gradually weaken. When the wind speed is low, pollutants tend to accumulate in the source area, resulting in an increase in pollution concentration in the area. In addition, changes in wind direction will affect the pollution concentration in different areas. When the wind direction coincides with the direction of the source of the pollutants, pollutants will be concentrated in a specific area, causing serious pollution in the area.

[0078] This embodiment first performs angle calculation, that is, calculates the position of the pollution point in the pollution source, and calculates according to the longitude and latitude. First, the radians function in the scientific computing Math tool is used to calculate the radian of the longitude and latitude data, and then the degress angle calculation function is used to calculate the angle between the high-risk monitoring point and the pollution source.

[0079] This embodiment then performs azimuth matching, that is, based on the azimuth and angle comparison table, calculates the azimuth to which the angle between the high-risk monitoring point and the pollution source belongs, recorded as , as shown in Table 1.

[0080] Table 1. Comparison table of orientation and angle

[0081]

[0082] This embodiment then calculates the wind speed weight factor. That is, in the process of pollution diffusion, the current pollution point data is often positively correlated with the historical wind speed factor. Pollution diffusion also takes time. Therefore, this embodiment selects the historical 2-hour wind speed data of the grid where the pollution source is located, averages the historical wind speed data, and then calculates the wind speed weight, which is expressed as:

[0083]

[0084] in, represents the wind speed weight factor, Represents the wind speed data of pollution source s.

[0085] This embodiment then defines the wind direction weight, that is, the wind direction plays a key role in the pollution diffusion process. If the wind direction of pollution source A to pollution point B is the opposite wind direction, it does not mean that pollution source A has no contribution relationship to pollution point B. It is just that the contribution value has a lower weight in terms of wind direction angle. With this idea, this embodiment first obtains the historical 1-hour wind direction data of the grid where the pollution source is located, recorded as , and then the wind direction data is defined, recorded as , expressed as:

[0086]

[0087] In this embodiment, The first weight, which indicates that the wind direction data at the pollution source is consistent with the direction of the high-risk monitoring point at the pollution source, is set to 1.25; The second weight, which indicates that the wind direction data at the pollution source is inconsistent with the direction of the high-risk monitoring point at the pollution source, is set to 1; Indicates wind direction data at the pollution source; Indicates that the high-risk monitoring point is located in the direction of the pollution source.

[0088] This embodiment finally performs a pollution index correction, that is, using summation and averaging to balance each weight factor, including wind speed factor, wind direction factor and other indexes, and finally calculates the corrected pollution index with the pollution index. , expressed as:

[0089]

[0090] in, It represents the relative pollution contribution index after correction of air pollution sources. Represents the relative pollution contribution index of air pollution sources, represents the wind speed weight factor, Represents the wind direction weight factor.

[0091] This embodiment corrects the relative pollution contribution index by analyzing the relationship between the wind speed and direction between the pollution source and the pollution point, so as to obtain a more accurate and scientific pollution contribution index.

[0092] S6. Classify the pollution levels according to the corrected relative pollution contribution index of air pollution sources.

[0093] In an optional embodiment of the present invention, step S6 classifies the pollution level according to the corrected relative pollution contribution index of the air pollution source, including the following steps:

[0094] Normalize the corrected relative pollution contribution index of air pollution sources;

[0095] Based on the preset pollution classification strategy, the pollution level of air pollution sources is determined according to the normalized relative pollution contribution index.

[0096] Exemplarily, this embodiment uses data normalization to map the pollution data to between [0, 1], and then classifies the pollution levels according to the percentage division strategy.

[0097] The normalization formula is as follows:

[0098]

[0099] in, represents the normalized result, It represents the relative pollution contribution index of air pollution sources after correction, Min represents the minimum function, and Max represents the maximum function.

[0100] The preset pollution classification strategy is as follows:

[0101]

[0102] in, Indicates slight pollution. Indicates low pollution. Indicates moderate pollution. Indicates high pollution.

[0103] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0106] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0107] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for tracing the source of high-risk gas-related enterprises based on an urban VOCs vehicle, characterized in that: The following steps are involved: Obtain the VOCs cruise monitoring data of the cruise vehicle; Screen high-risk monitoring points based on cruise monitoring data; Match air pollution sources according to high-risk monitoring points; The relative pollution contribution index of air pollution sources is calculated using the inverse distance weighted method, specifically: in, Represents the relative pollution contribution index of air pollution sources, Indicates l The distance coefficient, k represents the power coefficient, h represents the number of distance coefficients, Indicates the TVOC monitoring value of the high-risk monitoring point; The weight factor is determined by wind speed and wind direction to correct the relative pollution contribution index of air pollution sources, including the following steps: Calculate the angle between each high-risk monitoring point and the pollution source, and determine the position of each high-risk monitoring point in the pollution source based on the corresponding relationship between wind direction and angle; According to the wind speed data at the pollution source, the wind speed weight factor is calculated as follows: in, represents the wind speed weight factor, Represents the wind speed data of pollution source s; According to the wind direction data at the pollution source and the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is determined, including: If the wind direction data at the pollution source is consistent with the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is set to the first weight; If the wind direction data at the pollution source is inconsistent with the location of the high-risk monitoring point at the pollution source, the wind direction weight factor is set to the second weight; The first weight is greater than the second weight; The relative pollution contribution index of air pollution sources is corrected according to the wind speed weight factor and wind direction weight factor, specifically: in, It represents the relative pollution contribution index after correction of air pollution sources. Represents the relative pollution contribution index of air pollution sources, represents the wind speed weight factor, represents the wind direction weight factor; Pollution levels are classified according to the corrected relative pollution contribution index of air pollution sources.

2. According to claim 1, a method for tracing the source of high-risk gas-related enterprises based on an urban VOCs vehicle is characterized in that: The cruise monitoring data includes: Monitoring values ​​of volatile organic compounds, monitoring longitude and latitude, and release time.

3. According to claim 1, a method for tracing the source of high-risk gas-related enterprises based on an urban VOCs vehicle is characterized in that: High-risk monitoring points screened based on cruise monitoring data include: Set the TVOC artificial threshold to determine whether the TVOC monitoring value in the cruise monitoring data of each monitoring point is greater than or equal to the TVOC artificial threshold; if so, the monitoring point is determined to be a high-risk monitoring point; otherwise, it is a non-high-risk monitoring point and the corresponding monitoring point is deleted.

4. According to claim 1, a method for tracing the source of high-risk gas-related enterprises based on a city VOCs mobile vehicle is characterized in that: Matching air pollution sources according to high-risk monitoring points includes the following steps: Calculate the distance coefficient between each pollution source and the high-risk monitoring point respectively; The radius coefficient is set, and pollution sources with a distance coefficient less than or equal to the radius coefficient are screened for each high-risk monitoring point to match the air pollution sources.

5. According to claim 1, a method for tracing the source of high-risk gas-related enterprises based on an urban VOCs vehicle is characterized in that: The pollution level classification based on the corrected relative pollution contribution index of air pollution sources includes the following steps: Normalize the corrected relative pollution contribution index of air pollution sources; Based on the preset pollution classification strategy, the pollution level of air pollution sources is determined according to the normalized relative pollution contribution index.

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