Atmospheric environment particulate matter pollution detection method and device

Abstract

The invention provides an atmospheric environment particulate matter pollution detection method and device, and the method comprises the steps: carrying out the grading analysis of the scattered light intensity at each environment detection point in an environment pollution detection region, and obtaining the particle size distribution characteristics of air particulate matters at each environment detection point in a plurality of particle size intervals; determining a spatial distribution field of the particulate matter concentration in the air based on the particle size distribution characteristics; determining a dynamic correction factor of diffusion and aggregation of particulate matters in the air according to an atmospheric physical state, and further performing dynamic correction on the spatial distribution field based on the dynamic correction factor; performing spatial reconstruction on the spatial distribution field obtained by dynamic correction on a regular grid, and determining the distribution pattern of the air particulate matter concentration in the environmental pollution detection area according to a reconstruction result; and generating a particulate matter pollution index in the environmental pollution detection area according to the distribution pattern. By adopting the scheme of the invention, the reliability of atmospheric environment particulate matter pollution detection can be improved based on particulate matter concentration space reconstruction of multi-point dynamic correction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of particulate matter pollution detection, and more particularly to an atmospheric environmental particulate matter pollution detection method and device. BACKGROUND

[0003] With the development of environmental detection technology, atmospheric particulate matter detection methods are constantly evolving, mainly involving optical scattering method, beta-ray absorption method, gravimetric method, and sensor network technology, etc. However, in the prior art, atmospheric particulate matter detection usually relies on single-point measurement of fixed detection points, which is difficult to fully reflect the spatial distribution difference in the region, and the existing methods often ignore the details of the particulate matter size distribution, resulting in insufficient accuracy of the concentration measurement results due to the influence of sensor sampling rate and environment, which is difficult to meet the needs of fine environmental detection. In addition, meteorological conditions such as wind speed, humidity, temperature, etc. have a significant impact on the diffusion and aggregation of particulate matter, but the existing methods lack effective correction mechanisms and cannot adapt to changes in atmospheric physical state in real time, resulting in reduced reliability of pollution detection results in complex environments. Therefore, how to reconstruct the particulate matter concentration space based on multi-point dynamic correction to improve the reliability of atmospheric environmental particulate matter pollution detection has become a difficult problem in the industry. SUMMARY

[0004] The present application provides an atmospheric environmental particulate matter pollution detection method and device, which can reconstruct the particulate matter concentration space based on multi-point dynamic correction to improve the reliability of atmospheric environmental particulate matter pollution detection.

[0005] In a first aspect, the present application provides an atmospheric environmental particulate matter pollution detection method, comprising the following steps: Collecting the scattering light intensity of particulate matter in the air at each environmental detection point in the environmental pollution detection area at different particle sizes by a light scattering sensor; Analyzing the scattering light intensity by classification to obtain the particle size distribution characteristics of particulate matter in the air at each environmental detection point in multiple particle size intervals, and then determining the spatial distribution field of particulate matter concentration in the air at each environmental detection point based on the particle size distribution characteristics; Collecting the atmospheric physical state at each environmental detection point by a meteorological sensor, determining the dynamic correction factor of particulate matter diffusion and aggregation at each environmental detection point from the atmospheric physical state, and then dynamically correcting the spatial distribution field based on the dynamic correction factor; Establishing a regular grid division based on the geographical coordinate position relationship between each environmental detection point, performing spatial reconstruction of the spatial distribution field obtained by dynamic correction on the regular grid, and then determining the distribution pattern of air particulate matter concentration in the environmental pollution detection area according to the reconstruction result; According to the distribution pattern, a particulate matter pollution index in an environmental pollution detection area is generated.

[0006] In some embodiments, the scattered light intensity is hierarchically resolved to obtain particulate size distribution characteristics of particulate matter in the air at each environmental detection point in multiple particulate size intervals, specifically including: A particulate size distribution fitting curve is established according to the correspondence between the scattered light intensity and the particulate size; The particulate size distribution fitting curve is hierarchically divided according to a preset particulate size interval, and then a relative scattering contribution rate in each particulate size interval is obtained; The particulate size distribution characteristics of particulate matter in the air at each environmental detection point in multiple particulate size intervals are determined by the relative scattering contribution rate in each particulate size interval.

[0007] In some embodiments, the spatial distribution field of particulate matter concentration in the air at each environmental detection point is determined based on the particulate size distribution characteristics, specifically including: The relative number proportion in different particulate size intervals is extracted by the particulate size distribution characteristics; The total volume of sampled air at each environmental detection point is determined by the sampling rate at each environmental detection point; The particulate volume distribution amount of each particulate size interval at each environmental detection point is determined according to the relative number proportion in the different particulate size intervals and the total volume of sampled air at each environmental detection point; The spatial distribution field of particulate matter concentration in the air at each environmental detection point is converted from the particulate volume distribution amount of each particulate size interval at each environmental detection point.

[0008] In some embodiments, the dynamic correction factor of particulate matter diffusion and aggregation in the air at each environmental detection point is determined by the atmospheric physical state, specifically including: A plurality of single-factor correction factors are determined based on the atmospheric physical state; The dynamic correction factor of particulate matter diffusion and aggregation in the air at each environmental detection point is determined according to all single-factor correction factors.

[0009] In some embodiments, a regular grid division is established based on the geographical coordinate position relationship between the environmental detection points, specifically including: Geographical coordinate information of all environmental detection points is obtained; The environmental pollution detection area is divided into regular-shaped grid cells; The corresponding environmental detection points are mapped into the corresponding grid cells based on the geographical coordinate information of all environmental detection points, and a regular grid of the environmental pollution detection area is obtained.

[0010] In some embodiments, determining the distribution pattern of air particulate matter concentration in the environmental pollution detection area based on the reconstruction results specifically includes: Determine the neighborhood similarity between each environmental monitoring point; The stratified structure of air particulate matter concentration distribution within the environmental pollution detection area was determined by the reconstruction results; The distribution pattern of air particulate matter concentration in the environmental pollution detection area is generated based on the hierarchical structure and the neighborhood similarity.

[0011] In some embodiments, generating the particulate matter pollution index in the environmental pollution detection area based on the distribution pattern specifically includes: Based on the distribution pattern, the particulate matter concentration values ​​and corresponding pollution level ranges of each environmental monitoring point were extracted; The particulate matter concentration values ​​for each pollution level range are converted into the corresponding pollution index scale. The particulate matter pollution index in the environmental pollution detection area is determined by all pollution index scales.

[0012] Secondly, this application provides an atmospheric particulate matter pollution detection device, comprising: The acquisition module is used to acquire the intensity of scattered light from airborne particulate matter at different particle sizes at each environmental monitoring point in the environmental pollution detection area through a light scattering sensor. The processing module is used to perform hierarchical analysis of the intensity of the scattered light to obtain the particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental detection point, and then determine the spatial distribution field of airborne particulate matter concentration at each environmental detection point based on the particle size distribution characteristics. The processing module is also used to collect the atmospheric physical state at each environmental monitoring point through a meteorological sensor, determine the dynamic correction factor for the diffusion and aggregation of particulate matter in the air at each environmental monitoring point based on the atmospheric physical state, and then dynamically correct the spatial distribution field based on the dynamic correction factor. The processing module is also used to establish a regular grid division based on the geographical coordinate position relationship between each environmental monitoring point, to spatially reconstruct the spatial distribution field obtained by dynamic correction on the regular grid, and then to determine the distribution pattern of air particulate matter concentration in the environmental pollution monitoring area based on the reconstruction result. The execution module is used to generate the particulate matter pollution index in the environmental pollution detection area based on the distribution pattern.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device performs the above-described method for detecting particulate matter pollution in the atmospheric environment.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described method for detecting particulate matter pollution in the atmospheric environment.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In this application, the intensity of scattered light from airborne particulate matter at different particle sizes at each environmental monitoring point in an environmental pollution monitoring area is collected using a light scattering sensor. The scattered light intensity is then analyzed in a hierarchical manner to obtain the particle size distribution characteristics of airborne particulate matter at each environmental monitoring point across multiple particle size ranges. Based on these particle size distribution characteristics, the spatial distribution field of airborne particulate matter concentration at each environmental monitoring point is determined. The atmospheric physical state at each environmental monitoring point is collected using a meteorological sensor. The atmospheric physical state is used to determine a dynamic correction factor for the diffusion and aggregation of airborne particulate matter at each environmental monitoring point. Based on this dynamic correction factor, the spatial distribution field is dynamically corrected. A regular grid is established between the environmental monitoring points based on their geographical coordinates. The dynamically corrected spatial distribution field is then spatially reconstructed on this regular grid. Based on the reconstruction results, the distribution pattern of airborne particulate matter concentration in the environmental pollution monitoring area is determined. Finally, a particulate matter pollution index is generated in the environmental pollution monitoring area based on this distribution pattern.

[0016] Therefore, in this application, firstly, the spatial distribution field of particulate matter concentration in the air at each environmental monitoring point is determined based on the particle size distribution characteristics. This concentration calculation not only relies on static concentration values ​​but also reflects the actual influence of sampling conditions and particulate matter particle size structure, achieving a continuous characterization of particulate matter concentration changes with spatial location. This improves the accuracy and physical consistency of concentration measurement at the source, enhancing the comparability and stability of overall detection. Secondly, the spatial distribution field is dynamically corrected based on the dynamic correction factor, enabling the concentration data to reflect in real time the influence of meteorological conditions such as wind speed, humidity, and temperature on particulate matter diffusion and aggregation behavior. Through dynamic correction, the concentration data is no longer a static average value but a dynamic response value, adaptively reflecting the true trend of particulate matter spatial concentration changes under environmental disturbances. This ensures that the measurement results at different environmental monitoring points maintain consistency in time and environment, improving the authenticity of the spatial reconstruction input data. Then, the dynamically corrected spatial distribution... The field is spatially reconstructed on the regular grid, and the distribution pattern of air particulate matter concentration in the environmental pollution detection area is determined based on the reconstruction results. Spatial interpolation and reconstruction are performed on the dynamically corrected particulate matter concentration data, transforming discrete detection point data into a spatially continuous concentration distribution field. This achieves spatial visualization and distribution pattern analysis of particulate matter concentration. This step, through spatial reconstruction and similarity hierarchical structure generation, can reveal the diffusion direction of pollution sources and regional aggregation effects, thereby improving the spatial resolution and detection coverage of atmospheric particulate matter pollution status. Finally, a particulate matter pollution index is generated in the environmental pollution detection area based on the distribution pattern, enabling comprehensive evaluation with spatial information participation. This allows the pollution index to more accurately reflect the actual pollution situation, thereby improving the overall reliability of atmospheric particulate matter pollution detection results and its reference value for environmental decision-making. In summary, this scheme can improve the reliability of atmospheric particulate matter pollution detection based on multi-point dynamically corrected spatial reconstruction of particulate matter concentration. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary flowchart of an atmospheric particulate matter pollution detection method according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of particle size distribution characteristics according to some embodiments of this application; Figure 3This is an exemplary flowchart illustrating the determination of a distribution pattern according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of an atmospheric particulate matter pollution detection device according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a method for detecting particulate matter pollution in the atmospheric environment, according to some embodiments of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] refer to Figure 1 The figure is an exemplary flowchart of an atmospheric particulate matter pollution detection method according to some embodiments of this application. The atmospheric particulate matter pollution detection method mainly includes the following steps: In step 101, the intensity of scattered light from airborne particulate matter at different particle sizes at each environmental monitoring point in the environmental pollution detection area is collected by a light scattering sensor.

[0021] In practical implementation, the collection of scattered light intensity from airborne particulate matter at different particle sizes at each environmental monitoring point within the environmental pollution detection area using a light scattering sensor can be achieved as follows: First, a light scattering sensor module is installed at multiple environmental monitoring points within the environmental pollution detection area. This module contains a directional laser emitting unit and a photoelectric receiving unit to detect the scattering of incident light by airborne particulate matter. Second, the laser emitting unit emits a collimated beam at a fixed wavelength into the sampling air area. When airborne particulate matter passes through the optical path, it generates scattered light signals with different angles and intensities due to differences in particle size. Then, the photoelectric receiving unit performs photoelectric conversion on the scattered light signals at each angle, converting the changes in scattered light intensity into electrical signals. Next, the built-in signal processing circuit performs amplitude calculation and time integration on the electrical signals, filtering out noise interference to obtain stable scattered light intensity data. Finally, this scattered light intensity data is categorized into particle size ranges (e.g., PM1, PM2.5, PM2.6, PM2.7, PM2.8, PM2.9 ... 10 The data is recorded in separate channels and uploaded to the data acquisition terminal. This allows the collection of the scattered light intensity of particulate matter in the air at different particle sizes at each environmental monitoring point in the environmental pollution monitoring area. Other methods can also be used for data collection in other embodiments, and no specific limitation is made here.

[0022] It should be noted that the light scattering sensor in this application is a sensing device for detecting the concentration of particulate matter in the air based on the principle of light scattering, which is used to realize non-contact, real-time optical detection of particulate matter; the environmental monitoring point in this application refers to the monitoring location deployed in the environmental pollution monitoring area for fixed-point sampling of air parameters, which is used to form a spatial particulate matter detection network; the scattered light intensity in this application refers to the light energy intensity in various angular directions after the incident light is scattered by particulate matter, which is used to reflect the particle size and quantity of air particulate matter.

[0023] In step 102, the intensity of the scattered light is analyzed in a hierarchical manner to obtain the particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental detection point, and then the spatial distribution field of airborne particulate matter concentration at each environmental detection point is determined based on the particle size distribution characteristics.

[0024] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flowchart for determining particle size distribution characteristics in some embodiments of this application. In this embodiment, the particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental detection point can be obtained by performing hierarchical analysis on the scattered light intensity using the following steps: First, in step 1021, a particle size distribution fitting curve is established based on the correspondence between the scattered light intensity and the particle size; Secondly, in step 1022, the particle size distribution fitting curve is divided into grades according to a preset particle size range, thereby obtaining the relative scattering contribution rate in each particle size range. Finally, in step 1023, the particle size distribution characteristics of airborne particulate matter at each environmental detection point in multiple particle size intervals are determined by the relative scattering contribution rate within each particle size interval.

[0025] In specific implementation, establishing a particle size distribution fitting curve based on the correspondence between the scattered light intensity and particle size can be achieved in the following way: A light scattering particle size inversion algorithm, such as a fitting method based on Mie scattering theory, can be used to calculate the particle size response corresponding to different scattering angles to establish the correspondence between scattered light intensity and particle size. This correspondence can then be used to mathematically fit the scattered light intensity of airborne particles at each environmental detection point under different particle sizes, generating a particle size distribution fitting curve, which serves as the basis for dividing each particle size interval. The particle size distribution fitting curve can be further divided into preset particle size intervals to obtain the relative scattering contribution rate within each interval. This can be achieved in the following way: First, a predefined particle size interval can be set based on national standards or experimental calibration results, such as 0.3–1 μm, 1–2 μm, etc. The particle size distribution fitting curves are then divided into 0.5 μm, 2.5–10 μm, and other particle size ranges. The proportion of light intensity within each particle size range to the total scattered light intensity is calculated using an integral summation method. This calculated proportion is then used as the relative scattering contribution rate within each particle size range. Determining the particle size distribution characteristics of airborne particles at each environmental monitoring point across multiple particle size ranges using the relative scattering contribution rate within each particle size range can be achieved by normalizing the relative scattering contribution rate within each particle size range and outputting the proportion distribution of particles within each range as a numerical vector. This proportion distribution is then used as the particle size distribution characteristics of airborne particles at each environmental monitoring point across multiple particle size ranges. Other methods can also be used in other embodiments, and are not limited here.

[0026] It should be noted that the correspondence between scattered light intensity and particle size in this application refers to a curve reflecting the trend of light intensity changing with particle size; the particle size distribution fitting curve in this application represents a continuous mathematical expression of light scattering intensity changing with particle size, which is used to realize particle size partitioning calculation; the hierarchical division in this application refers to the process of segmenting the particle size distribution fitting curve according to a preset particle size interval, which is used to structure the overall optical signal into multiple particle size levels; the relative scattering contribution rate in this application refers to the ratio of scattered light energy in each particle size interval to the total scattered energy, which is used to characterize the degree of optical influence of particles of each size; the particle size distribution feature in this application refers to a vector composed of the relative scattering contribution rates of multiple particle size intervals, which reflects the distribution information of particles of different sizes in the air sample at each environmental detection point.

[0027] In some embodiments, determining the spatial distribution field of airborne particulate matter concentration at each environmental monitoring point based on the particle size distribution characteristics can be achieved using the following steps: The relative quantity ratio within different particle size ranges is extracted based on the particle size distribution characteristics. The total volume of sampled air at each environmental monitoring point is determined by the sampling rate at each environmental monitoring point. The volume distribution of particulate matter in each particle size range at each environmental monitoring point is determined based on the relative proportions within the different particle size ranges and the total volume of sampled air at each environmental monitoring point. The volume distribution of particulate matter in each particle size range at each environmental monitoring point is converted into the spatial distribution field of particulate matter concentration in the air at each environmental monitoring point.

[0028] In specific implementation, extracting the relative quantity ratio within different particle size intervals based on the particle size distribution characteristics can be achieved in the following way: extracting the proportion of each particle size interval in the air sample from the particle size distribution characteristics, and using it as the relative quantity ratio within different particle size intervals; determining the total volume of sampled air at each environmental monitoring point based on the sampling rate at each environmental monitoring point can be achieved in the following way: obtaining the sampling rate and sampling duration of each environmental monitoring point within the monitoring period, and then multiplying the sampling rate and sampling duration corresponding to each environmental monitoring point, using the calculated product as the total volume of sampled air at each environmental monitoring point within the measurement period; determining the particulate matter volume distribution of each particle size interval at each environmental monitoring point based on the relative quantity ratio within different particle size intervals and the total volume of sampled air at each environmental monitoring point can be achieved in the following way: combining the relative quantity ratio within different particle size intervals with the total volume of sampled air at each environmental monitoring point, specifically, multiplying the relative quantity ratio of each particle size interval by the total volume of sampled air to obtain the volume distribution of each environmental monitoring point. The volume distribution of particulate matter in each particle size range at each detection point; the conversion of the volume distribution of particulate matter in each particle size range at each environmental detection point into the spatial distribution field of particulate matter concentration in the air at each environmental detection point can be achieved in the following way: based on the calibration curve between light scattering intensity and particulate matter mass concentration, where the calibration curve is an empirical model that establishes a quantitative relationship between the scattered light signal and the actual particulate matter concentration, which can be obtained by experiment or preset by reference instruments, the volume distribution of particulate matter in each particle size range at each environmental detection point can be converted into the corresponding particulate matter mass concentration value. This conversion is usually achieved through the conversion factor between particulate matter material density and volume, so as to obtain the mass concentration value of particulate matter in each particle size range in the air sample at each environmental detection point. Finally, the spatial difference calculation of the mass concentration values ​​of adjacent environmental detection points can be performed using the geographical location relationship of multiple environmental detection points to obtain the rate distribution of particulate matter concentration in the air with geographical location, and this is used as the spatial distribution field of particulate matter concentration in the air at each environmental detection point; other methods can also be used to determine this in other embodiments, which are not limited here.

[0029] It should be noted that the relative quantity ratio in this application refers to the proportion of particulate matter in each particle size range in the total sample, which is used to convert the optical measurement signal into a particulate matter quantity distribution; the sampling rate in this application represents the volume rate at which the light scattering sensor collects air per unit time; the total volume of sampled air in this application refers to the total volume of air collected during the detection period; the particulate matter volume distribution in this application refers to the total volume of particulate matter in each particle size range, which is a physical quantity determined by the relative quantity ratio and the total volume of sampled air; the spatial distribution field in this application represents the rate at which the particulate matter mass concentration changes with spatial location between different environmental detection points, which is used to reflect changes in the direction and degree of pollution diffusion.

[0030] In step 103, atmospheric physical states at each environmental monitoring point are collected by meteorological sensors. Dynamic correction factors for the diffusion and aggregation of particulate matter in the air at each environmental monitoring point are determined from the atmospheric physical states. Then, the spatial distribution field is dynamically corrected based on the dynamic correction factors.

[0031] In specific implementation, the atmospheric physical state at each environmental monitoring point can be collected using meteorological sensors in the following manner: meteorological sensors can be set at each environmental monitoring point to collect atmospheric physical state data, such as temperature, humidity, air pressure, and wind speed and direction. The meteorological sensors may include temperature sensors, humidity sensors, air pressure sensors, and wind speed and direction sensors. The temperature sensor detects the thermal radiation generated by the thermal motion of air molecules at the environmental monitoring point to obtain the air temperature value. The humidity sensor performs capacitive measurement of the water vapor content in the air at the environmental monitoring point to obtain the relative humidity value. The air pressure sensor measures the pressure of the air column per unit area at the environmental monitoring point to obtain the air pressure value. The wind speed and direction sensor uses rotating blades or ultrasonic principles to detect the air flow direction and velocity at the environmental monitoring point. Other methods can also be used for data collection in other embodiments, which are not limited here.

[0032] It should be noted that the meteorological sensor in this application refers to a device used to sense the physical quantities of air at an environmental monitoring point and convert them into collectable electrical signals; the atmospheric physical state in this application refers to a set of parameters characterizing the local air thermal and flow characteristics at an environmental monitoring point, which is used to reflect the temperature, humidity, pressure and flow of the air.

[0033] In some embodiments, determining the dynamic correction factor for the diffusion and aggregation of particulate matter in the air at each environmental monitoring point based on the atmospheric physical state can be achieved through the following steps: Multiple single-factor correction factors were determined based on the atmospheric physical state. The dynamic correction factor for the diffusion and aggregation of airborne particulate matter at each environmental monitoring point is determined based on all single-factor correction factors.

[0034] In specific implementation, determining multiple single-factor correction factors based on the atmospheric physical state can be achieved in the following way: Atmospheric physical state data such as temperature, humidity, air pressure, wind speed, and wind direction at each environmental monitoring point are used for correction factor calculation. For each atmospheric parameter, the corresponding single-factor correction factor can be calculated using empirical formulas or physical models. For example, correction factors for air density and particulate matter hygroscopic or condensation effects can be calculated using temperature and humidity; the influence of air column pressure on particulate matter settling velocity can be calculated using air pressure; and correction factors for horizontal and vertical diffusion capacity can be calculated using wind speed and wind direction. Based on all the single-factor correction factors, the dynamic correction factor for particulate matter diffusion and aggregation at each environmental monitoring point can be determined using... The following method is used to achieve this: all single-factor correction factors can be combined and calculated according to a preset weighting rule or a comprehensive physical model to obtain a comprehensive correction factor, which serves as the dynamic correction factor for the diffusion and aggregation of particulate matter in the air at each environmental monitoring point. The weighting rule can linearly combine the single-factor correction factors by assigning different influence weights to each single factor, and the weights in the weighting rule can be obtained through statistical analysis of historical monitoring data, experimental calibration, or by referring to existing aerodynamic models. In addition, the comprehensive physical model can nonlinearly couple the single-factor correction factors through physical formulas or diffusion equations to obtain a correction factor that reflects the combined effects of multiple atmospheric conditions. Other methods can also be used to determine the correction factor in other embodiments, which are not limited here.

[0035] It should be noted that the single-factor correction factor in this application refers to the adjustment coefficient calculated from a single atmospheric physical parameter, which is used to quantify the influence of the corresponding single atmospheric factor on particulate matter diffusion or accumulation; the dynamic correction factor for particulate matter diffusion and accumulation in this application refers to the particulate matter concentration adjustment coefficient calculated based on local atmospheric conditions, which is used to correct the spatial distribution field to reflect the actual diffusion and deposition situation.

[0036] In specific implementation, the dynamic correction of the spatial distribution field based on the dynamic correction factor can be achieved in the following way: First, the dynamic correction factor can be used as an adjustment parameter and applied to the original concentration of particulate matter concentration in the spatial distribution field of air at each environmental monitoring point by multiplication or weighting coefficient to correct the original concentration. Then, for the particulate matter concentration of adjacent environmental monitoring points, it can be further smoothed by interpolation or neighborhood weighted averaging to ensure the spatial continuity of the corrected concentration. Finally, the corrected particulate matter concentration is remapped to the geographical coordinates of the corresponding environmental monitoring point by bilinear interpolation to form the dynamically corrected particulate matter concentration corresponding to each environmental monitoring point. Other methods can also be used in other embodiments, which are not limited here.

[0037] It should be noted that the dynamic correction in this application refers to the process of applying a dynamic correction factor to the original particulate matter concentration so that the concentration is adjusted to truly reflect the effect of environmental conditions on particulate matter distribution; the particulate matter concentration after dynamic correction in this application refers to the corrected concentration result, which characterizes the particulate matter concentration at the environmental monitoring point after atmospheric conditions are corrected.

[0038] In step 104, a regular grid is established between each environmental monitoring point based on the geographical coordinate relationship. The spatial distribution field obtained by dynamic correction is spatially reconstructed on the regular grid, and then the distribution pattern of air particulate matter concentration in the environmental pollution monitoring area is determined based on the reconstruction result.

[0039] In some embodiments, establishing a regular grid division based on the geographic coordinate relationship between environmental monitoring points can be achieved through the following steps: Obtain the geographic coordinates of all environmental monitoring points; The environmental pollution detection area is divided into regularly shaped grid units; Based on the geographic coordinates of all environmental monitoring points, the corresponding environmental monitoring points are mapped to the corresponding grid cells to obtain a regular grid of the environmental pollution monitoring area.

[0040] In practical implementation, obtaining the geographic coordinates of all environmental monitoring points can be achieved in the following way: The geographic coordinates of all environmental monitoring points, including longitude, latitude, and, if necessary, altitude, can be obtained to accurately locate the position of each environmental monitoring point within the environmental pollution monitoring area. Dividing the environmental pollution monitoring area into regularly shaped grid cells can be achieved in the following way: Based on the boundary range and required spatial resolution of the environmental pollution monitoring area, the area can be divided into regularly shaped grid cells, such as square or rectangular grids, where each grid cell represents a spatial segment of the environmental pollution monitoring area; based on the geographic coordinates of all environmental monitoring points... The coordinate information maps the corresponding environmental monitoring points to the corresponding grid cells, and the regular grid of the environmental pollution monitoring area can be obtained in the following way: each environmental monitoring point can be mapped to the corresponding grid cell according to its geographical coordinate information to establish the correspondence between environmental monitoring points and grid cells. In addition, for grid cells that do not directly cover environmental monitoring points, null value markers can be set or neighbor interpolation methods can be used for subsequent spatial reconstruction. Finally, the division structure of the regular grid of the environmental pollution monitoring area is output to form a unified spatial framework, providing a basis for the distribution and reconstruction of particulate matter concentration data on the grid. Other methods can also be used in other embodiments, which are not limited here.

[0041] It should be noted that the geographic coordinate information in this application refers to the location parameters of each environmental monitoring point on the Earth's surface, which is used to accurately locate the environmental monitoring points; the grid cell in this application refers to the smallest spatial unit in a regular grid, which is used to carry the data of one or more monitoring points; the regular grid in this application refers to the spatial unit that divides the environmental pollution monitoring area according to a regular shape, which is used to uniformly organize and analyze spatial data.

[0042] In practical implementation, the spatial reconstruction of the dynamically corrected spatial distribution field on the regular grid can be achieved in the following way: First, the dynamically corrected spatial distribution field is matched with its corresponding grid cell on the regular grid to identify the effective measurement points and their concentration information within each grid cell. Second, for cases where multiple environmental monitoring points exist within the same grid cell, multiple concentration values ​​are fused into a representative concentration of the grid cell using methods such as weighted averaging or local least squares fitting to eliminate noise caused by local measurement fluctuations. Then, for empty grid cells without direct environmental monitoring point coverage, spatial interpolation methods can be used, such as... Inverse distance weighting, Kriging interpolation, or polynomial surface fitting are used to extrapolate the concentration information of surrounding grid cells to the corresponding empty grid cells to obtain a continuous concentration distribution. Then, the concentration values ​​of all grid cells are integrated to form a continuous spatially reconstructed concentration field of the environmental pollution detection area, which is used as the reconstruction result. This can be represented by a two-dimensional or three-dimensional matrix while maintaining the spatial positional relationship with geographic coordinates. Finally, the reconstruction result is smoothed or the boundary is corrected to reduce outliers and edge effects, so that the concentration distribution is continuous on a regular grid and can be used for subsequent pollution pattern analysis. Other methods can also be used in other embodiments, which are not limited here.

[0043] It should be noted that the spatial reconstruction in this application refers to the process of generating a continuous concentration distribution on the entire regular grid structure by mapping and interpolation methods from the concentration values ​​of dispersed environmental monitoring points; the spatially reconstructed concentration field in this application refers to the spatial distribution of particulate matter concentration represented on the regular grid after reconstruction, which can reflect the concentration change trend within the environmental pollution monitoring area.

[0044] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the distribution pattern in some embodiments of this application. In this embodiment, determining the distribution pattern of air particulate matter concentration in the environmental pollution detection area based on the reconstruction results can be achieved by the following steps: First, in step 1041, the neighborhood similarity between each environmental detection point is determined; Secondly, in step 1042, the hierarchical structure of the air particulate matter concentration distribution within the environmental pollution detection area is determined by the reconstruction results; Finally, in step 1043, the distribution pattern of air particulate matter concentration in the environmental pollution detection area is generated based on the hierarchical structure and the neighborhood similarity.

[0045] In specific implementation, determining the neighborhood similarity between environmental monitoring points can be achieved in the following way: Based on the spatially reconstructed concentration field obtained from the reconstruction results, the similarity in concentration values ​​between each environmental monitoring point and its neighboring monitoring points can be calculated. This calculation process can use the correlation coefficient method, and the final calculated result is used as the neighborhood similarity between each environmental monitoring point and its neighboring monitoring points, thus obtaining the neighborhood similarity between each environmental monitoring point to characterize the spatial consistency of concentration changes at each environmental monitoring point. Determining the hierarchical structure of air particulate matter concentration distribution within the environmental pollution monitoring area through the reconstruction results can be achieved in the following way: The concentration values ​​of each environmental monitoring point in the reconstruction results can be divided into layers according to similar numerical ranges or trends, and the final concentration stratification structure is used as the basis for determining the environmental pollution monitoring area. The hierarchical structure of air particulate matter concentration distribution within a region reflects the relative levels of particulate matter concentration in different regions. The distribution pattern of air particulate matter concentration in the environmental pollution detection area, based on the hierarchical structure and neighborhood similarity, can be generated in the following way: spatial clustering or region interpolation can be used to fuse similar concentration layers with spatially continuous environmental monitoring points based on the hierarchical structure and neighborhood similarity, generating the overall spatial distribution pattern and gradient change of particulate matter concentration within the entire environmental pollution detection area. This pattern is then used as the distribution pattern of air particulate matter concentration in the environmental pollution detection area, effectively revealing the spatial diffusion and aggregation patterns of air particulate matter, making the determination of the pollution distribution pattern more continuous and realistic. Other methods can also be used in other embodiments, which are not limited here.

[0046] It should be noted that, in this application, neighborhood similarity refers to the degree of consistency in the changes of air particulate matter concentration values ​​between adjacent environmental monitoring points, which is used to determine the spatial continuity of concentration changes; the hierarchical structure of concentration distribution in this application refers to the hierarchical relationship divided according to numerical intervals based on the reconstructed concentration results, which is used to distinguish between high-concentration areas and low-concentration areas; the distribution pattern of air particulate matter concentration in this application refers to the overall description of the spatial distribution of particulate matter concentration within the environmental pollution monitoring area, which is used to reflect the pollution range, intensity, and trend of change.

[0047] In step 105, the particulate matter pollution index in the environmental pollution detection area is generated based on the distribution pattern.

[0048] In some embodiments, generating the particulate matter pollution index in the environmental pollution detection area based on the distribution pattern can be achieved by the following steps: Based on the distribution pattern, the particulate matter concentration values ​​and corresponding pollution level ranges of each environmental monitoring point were extracted; The particulate matter concentration values ​​for each pollution level range are converted into the corresponding pollution index scale. The particulate matter pollution index in the environmental pollution detection area is determined by all pollution index scales.

[0049] In specific implementation, extracting the particulate matter concentration values ​​and corresponding pollution level intervals of each environmental monitoring point based on the distribution pattern can be achieved in the following way: The particulate matter concentration values ​​of each environmental monitoring point can be extracted based on the distribution pattern of air particulate matter concentration in the environmental pollution monitoring area, and their respective pollution level intervals can be divided according to national or regional air quality standards, thereby classifying the air quality levels of different environmental monitoring points. Converting the particulate matter concentration values ​​of each pollution level interval into corresponding pollution index scales can be achieved in the following way: The particulate matter concentration values ​​within each pollution level interval can be converted into a unified pollution index scale through a set index mapping function. This index mapping function can be linear or piecewise to ensure the continuity and comparability of pollution levels between different concentration intervals. Determining the particulate matter pollution index in the environmental pollution monitoring area from all pollution index scales can be achieved in the following way: All pollution index scales can be weighted and summarized according to the geographical location and spatial distribution weights of each environmental monitoring point, where the spatial distribution weights reflect the importance of the environmental monitoring points within the region. To determine the degree of representativeness, for example, spatial weighted averaging or interpolation fusion methods can be used to fuse the pollution index scales of different environmental monitoring points according to spatial proximity, thereby smoothing local outliers and forming a continuous pollution index distribution surface. This surface reflects the continuous trend of pollution intensity changes at different geographical locations in the environmental pollution monitoring area. Finally, a representative value reflecting the overall air particulate matter pollution level of the environmental pollution monitoring area is calculated based on this pollution index distribution surface. For example, the calculation process involves calculating the weighted pollution contribution value of each grid unit based on the pollution index scale corresponding to each grid unit on the surface and the area weight of the corresponding grid unit in the overall area. This weighted pollution contribution value refers to the relative influence of the pollution intensity of each local area on the overall pollution level. Then, the weighted pollution contribution values ​​of all grid units are integrated or summed to obtain the comprehensive particulate matter pollution index of the environmental pollution monitoring area, which is the representative value of the air particulate matter pollution level in the environmental pollution monitoring area. This representative value is then used as the particulate matter pollution index in the environmental pollution monitoring area. Other methods can also be used to determine this in other embodiments, which are not limited here.

[0050] It should be noted that the particulate matter concentration value in this application is used to reflect the local air pollution level at the environmental monitoring point; the pollution level range in this application refers to the range of quality levels divided according to concentration, which is used to classify the degree of pollution; the pollution index scale in this application refers to the standardized value mapped from the particulate matter concentration value, which is used to unify the quantitative standard of local pollution level at different environmental monitoring points; the particulate matter pollution index in this application refers to a comprehensive quantitative indicator characterizing the overall pollution level of particulate matter in the air within the environmental pollution monitoring area, which can be used for environmental quality assessment and early warning decision-making.

[0051] In another aspect, in some embodiments, this application provides an atmospheric particulate matter pollution detection device, with reference to... Figure 4 The figure is a schematic diagram of the structure of an atmospheric particulate matter pollution detection device according to some embodiments of this application. The atmospheric particulate matter pollution detection device 400 includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: Acquisition module 401, in this application, is mainly used to acquire the intensity of scattered light of air particles of different sizes at each environmental monitoring point in the environmental pollution detection area through a light scattering sensor; Processing module 402, in this application, is mainly used to perform hierarchical analysis on the intensity of the scattered light to obtain the particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental detection point, and then determine the spatial distribution field of airborne particulate matter concentration at each environmental detection point based on the particle size distribution characteristics. The processing module 402 described in this application is further configured to collect the atmospheric physical state at each environmental monitoring point through a meteorological sensor, determine the dynamic correction factor for the diffusion and aggregation of particulate matter in the air at each environmental monitoring point based on the atmospheric physical state, and then dynamically correct the spatial distribution field based on the dynamic correction factor. The processing module 402 described in this application is also used to establish a regular grid division based on the geographical coordinate position relationship between each environmental monitoring point, to spatially reconstruct the spatial distribution field obtained by dynamic correction on the regular grid, and then to determine the distribution pattern of air particulate matter concentration in the environmental pollution monitoring area based on the reconstruction result. The execution module 403 in this application is mainly used to generate the particulate matter pollution index in the environmental pollution detection area based on the distribution pattern.

[0052] The foregoing has detailed examples of atmospheric particulate matter pollution detection methods and apparatus provided in the embodiments of this application. It is understood that, in order to achieve the aforementioned functions, the corresponding apparatus includes hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0053] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described method for detecting particulate matter pollution in the atmospheric environment.

[0054] In some embodiments, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device implementing the atmospheric particulate matter pollution detection method of this application. The atmospheric particulate matter pollution detection method in the above embodiments can be achieved through… Figure 5 The computer device 500 shown is used to implement this, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 may be a terminal device, a server or a chip.

[0055] The processor 501 can be a general-purpose processor or a special-purpose processor. For example, the processor 501 can be a central processing unit (CPU). The CPU can be used to control the computer device 500, execute software programs, and process data from the software programs. The computer device 500 may also include a communication unit 505 for inputting (receiving) and outputting (transmitting) signals.

[0056] For example, computer device 500 may be a chip, communication unit 505 may be the input and / or output circuit of the chip, or communication unit 505 may be the communication interface of the chip, and the chip may be a component of terminal device, network device or other device.

[0057] For example, computer device 500 may be a terminal device or a server, and communication unit 505 may be a transceiver of the terminal device or the server, or communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0058] The computer device 500 may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0059] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0060] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0061] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to perform the above-described method for detecting particulate matter pollution in the atmospheric environment.

[0063] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0064] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for detecting particulate matter pollution in the atmospheric environment, characterized in that, Includes the following steps: The intensity of scattered light from airborne particulate matter of different sizes is collected at each environmental monitoring point in the environmental pollution monitoring area using a light scattering sensor. The intensity of the scattered light is analyzed in a hierarchical manner to obtain the particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental monitoring point, and then the spatial distribution field of airborne particulate matter concentration at each environmental monitoring point is determined based on the particle size distribution characteristics. The atmospheric physical state at each environmental monitoring point is collected by meteorological sensors. The atmospheric physical state is used to determine the dynamic correction factor for the diffusion and accumulation of particulate matter in the air at each environmental monitoring point. The spatial distribution field is then dynamically corrected based on the dynamic correction factor. A regular grid is established between environmental monitoring points based on their geographical coordinates. The spatial distribution field obtained by dynamic correction is then reconstructed on the regular grid. Based on the reconstruction results, the distribution pattern of air particulate matter concentration in the environmental pollution monitoring area is determined. The particulate matter pollution index in the environmental pollution detection area is generated based on the distribution pattern.

2. The method as described in claim 1, characterized in that, The scattered light intensity is analyzed in a hierarchical manner to obtain the particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental detection point. Specifically, this includes: A particle size distribution fitting curve is established based on the correspondence between the scattered light intensity and the particle size; The particle size distribution fitting curve is divided into preset particle size intervals to obtain the relative scattering contribution rate within each particle size interval. The particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental monitoring point are determined by the relative scattering contribution rate within each particle size range.

3. The method as described in claim 1, characterized in that, Determining the spatial distribution field of air particulate matter concentration at each environmental monitoring point based on the aforementioned particle size distribution characteristics specifically includes: The relative quantity ratio within different particle size ranges is extracted based on the particle size distribution characteristics. The total volume of sampled air at each environmental monitoring point is determined by the sampling rate at each environmental monitoring point. The volume distribution of particulate matter in each particle size range at each environmental monitoring point is determined based on the relative proportions within the different particle size ranges and the total volume of sampled air at each environmental monitoring point. The volume distribution of particulate matter in each particle size range at each environmental monitoring point is converted into the spatial distribution field of particulate matter concentration in the air at each environmental monitoring point.

4. The method as described in claim 1, characterized in that, The dynamic correction factor for the diffusion and accumulation of particulate matter in the air at each environmental monitoring point, determined by the atmospheric physical state, specifically includes: Multiple single-factor correction factors were determined based on the atmospheric physical state. The dynamic correction factor for the diffusion and aggregation of airborne particulate matter at each environmental monitoring point is determined based on all single-factor correction factors.

5. The method as described in claim 1, characterized in that, The establishment of a regular grid division based on the geographical coordinate relationships among various environmental monitoring points specifically includes: Obtain the geographic coordinates of all environmental monitoring points; The environmental pollution detection area is divided into regularly shaped grid units; Based on the geographic coordinates of all environmental monitoring points, the corresponding environmental monitoring points are mapped to the corresponding grid cells to obtain a regular grid of the environmental pollution monitoring area.

6. The method as described in claim 1, characterized in that, The distribution pattern of air particulate matter concentration in the environmental pollution monitoring area, determined based on the reconstruction results, specifically includes: Determine the neighborhood similarity between each environmental monitoring point; The stratified structure of air particulate matter concentration distribution within the environmental pollution detection area was determined by the reconstruction results; The distribution pattern of air particulate matter concentration in the environmental pollution detection area is generated based on the hierarchical structure and the neighborhood similarity.

7. The method as described in claim 1, characterized in that, The generation of particulate matter pollution index in the environmental pollution detection area based on the aforementioned distribution pattern specifically includes: Based on the distribution pattern, the particulate matter concentration values ​​and corresponding pollution level ranges of each environmental monitoring point were extracted; The particulate matter concentration values ​​for each pollution level range are converted into the corresponding pollution index scale. The particulate matter pollution index in the environmental pollution detection area is determined by all pollution index scales.

8. An atmospheric particulate matter pollution detection device, characterized in that, include: The acquisition module is used to acquire the intensity of scattered light from airborne particulate matter at different particle sizes at each environmental monitoring point in the environmental pollution detection area through a light scattering sensor. The processing module is used to perform hierarchical analysis of the intensity of the scattered light to obtain the particle size distribution characteristics of airborne particulate matter in multiple particle size ranges at each environmental detection point, and then determine the spatial distribution field of airborne particulate matter concentration at each environmental detection point based on the particle size distribution characteristics. The processing module is also used to collect the atmospheric physical state at each environmental monitoring point through a meteorological sensor, determine the dynamic correction factor for the diffusion and aggregation of particulate matter in the air at each environmental monitoring point based on the atmospheric physical state, and then dynamically correct the spatial distribution field based on the dynamic correction factor. The processing module is also used to establish a regular grid division based on the geographical coordinate position relationship between each environmental monitoring point, to spatially reconstruct the spatial distribution field obtained by dynamic correction on the regular grid, and then to determine the distribution pattern of air particulate matter concentration in the environmental pollution monitoring area based on the reconstruction result. The execution module is used to generate the particulate matter pollution index in the environmental pollution detection area based on the distribution pattern.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the atmospheric particulate matter pollution detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to perform the atmospheric particulate matter pollution detection method as described in any one of claims 1 to 7.

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