Atmospheric environment monitoring and early warning system and method

Abstract

The present application provides an atmospheric environment monitoring and early warning system and method. By performing differential analysis on the transmission spectrum of the atmosphere, the spectral distribution of atmospheric pollutants at each monitoring site is obtained. The coupling characteristics of the atmospheric pollutants at each monitoring site in the spectral band are determined from the spectral distribution. Based on the coupling characteristics, the absorption contribution of the atmospheric pollutants at each monitoring site in the transmission spectrum is determined. The influence coefficient of each physical state on the diffusion of atmospheric pollutants is determined, and the diffusion gradient of atmospheric pollutants in the target area is determined based on all the influence coefficients. The atmospheric pollutant concentration in the target area is analyzed by fusion of all the absorption contributions and diffusion gradients to obtain a composite index of atmospheric environmental pollution in the target area. The composite index of atmospheric environmental pollution is sent to a monitoring and early warning center. The solution of the present application can realize the composite pollution assessment of the atmosphere in a complex atmospheric pollution environment to reduce the monitoring error of atmospheric environmental pollution.

Description

Technical Field

[0001] The present application relates to the technical field of atmospheric environment monitoring, and more specifically, to an atmospheric environment monitoring and early warning system and method. Background Art

[0002] Atmospheric environmental monitoring is the process of continuously observing and analyzing the concentrations and changing trends of various substances in the atmosphere (such as pollutants, gases, and particulate matter) through scientific and technological means. Its goals are to assess air quality, understand environmental changes, and provide data support for pollution control and policy development.

[0003] Atmospheric environment monitoring and early warning is a comprehensive management method based on real-time monitoring data, data analysis models and meteorological condition forecasts. Its goal is to predict potential air pollution events in advance, take timely response measures, and reduce the harm of pollution to public health and the environment. However, in existing technologies, existing technologies usually rely on sparsely arranged fixed monitoring stations, which are difficult to fully cover the target atmospheric environment area, especially in areas with complex pollutant concentration distribution or drastic dynamic changes. It is impossible to accurately capture the spatial flow changes of atmospheric pollutants, resulting in one-sided monitoring data and difficulty in truly reflecting the overall pollution situation in the region, thereby affecting the accuracy of subsequent analysis and reducing the efficiency and effectiveness of atmospheric environmental pollution control. Therefore, how to realize the composite pollution assessment of the atmosphere under complex atmospheric pollution environment to reduce the monitoring error of atmospheric environmental pollution has become a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides an atmospheric environment monitoring and early warning system and method, which can realize the composite pollution assessment of the atmosphere in a complex atmospheric pollution environment, so as to reduce the monitoring error of atmospheric environmental pollution.

[0005] In a first aspect, the present application provides an atmospheric environment monitoring and early warning method, comprising the following steps:

[0006] Setting up multiple atmospheric environment monitoring stations in the target area, and setting up an optical intelligent sensor at each monitoring station, for each monitoring station, automatically collecting the transmission spectrum of the atmosphere at each monitoring station through the preset optical intelligent sensor;

[0007] Performing differential analysis on the transmission spectrum to obtain the spectral distribution of the atmospheric pollutants at each monitoring site, and then determining the coupling characteristics of the atmospheric pollutants at each monitoring site in the spectral band based on the spectral distribution; and determining the absorption contribution of the atmospheric pollutants at each monitoring site on the transmission spectrum based on the coupling characteristics and the spatial relationship between each monitoring site and each adjacent monitoring site in terms of position distribution;

[0008] The intelligent environmental sensors are used to monitor multiple physical states of the atmospheric environment in the target area, thereby determining the influence coefficient of each physical state on the diffusion of atmospheric pollutants, and determining the diffusion gradient of atmospheric pollutants in the target area through all the influence coefficients;

[0009] Performing a fusion analysis on the concentration of atmospheric pollutants in the target area through all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area;

[0010] The composite index of atmospheric environmental pollution is sent to a monitoring and early warning center.

[0011] In some embodiments, performing differential analysis on the transmission spectrum to obtain the spectral distribution of the atmospheric pollutants at each monitoring site specifically includes:

[0012] Obtaining a reference spectrum at each monitoring site;

[0013] Performing differential processing on the transmission spectrum using the reference spectrum to obtain characteristic absorption peaks of the atmospheric pollutants at each monitoring site;

[0014] The spectral distribution of the atmospheric pollutants at each monitoring site is determined by the characteristic absorption peaks.

[0015] In some embodiments, determining the coupling characteristics of the atmospheric pollutants in the spectral bands at each monitoring site from the spectral distribution specifically includes:

[0016] Determining an absorption correlation matrix of the atmospheric pollutants at each monitoring site in the spectral band according to the spectral distribution;

[0017] The coupling characteristics of the atmospheric pollutants at each monitoring site in the spectral band are determined through the absorption correlation matrix.

[0018] In some embodiments, determining the light absorption contribution of the atmospheric pollutants at each monitoring station on the transmission spectrum based on the coupling feature and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of location distribution specifically includes:

[0019] Determining the spatial impact factors of all adjacent monitoring stations on the spectral absorption of atmospheric pollutants at each monitoring station based on the spatial relationship between each monitoring station and each adjacent monitoring station in terms of location distribution;

[0020] Determining the absorption spectrum value of the atmospheric pollutants at each monitoring site through the transmission spectrum of the atmosphere at each monitoring site and the coupling characteristics;

[0021] The light absorption contribution of the atmospheric pollutants at each monitoring site on the transmission spectrum is determined according to the absorption spectrum value and the spatial influence factor.

[0022] In some embodiments, determining the influence coefficient of each physical state on the diffusion of atmospheric pollutants specifically includes:

[0023] Construct a simulation model of the physical state of the atmospheric environment and the diffusion of atmospheric pollutants;

[0024] Select a physical state as the selected physical state;

[0025] using the selected physical state as an input parameter of the diffusion simulation model;

[0026] Outputting the influence coefficient of the selected physical state on the diffusion of atmospheric pollutants through the diffusion simulation model;

[0027] Continue to determine the influence coefficient of the remaining physical state on the diffusion of atmospheric pollutants.

[0028] In some embodiments, the concentration of atmospheric pollutants in the target area is analyzed by fusing all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area, specifically including:

[0029] All absorption contributions are weighted and integrated to obtain the concentration distribution of atmospheric pollutants in the target area;

[0030] The composite index of atmospheric environmental pollution in the target area is determined by the concentration distribution of the atmospheric pollutants and the diffusion gradient.

[0031] In some embodiments, after sending the composite index of atmospheric pollution to a monitoring and early warning center, the method further includes:

[0032] When the composite index of the atmospheric environment pollution exceeds a preset threshold, a graded warning is issued for the atmospheric pollution in the target area according to the composite index of the atmospheric environment pollution.

[0033] In a second aspect, the present application provides an atmospheric environment monitoring and early warning system, comprising:

[0034] An acquisition module is used to set up multiple atmospheric environment monitoring sites in the target area and set up an optical intelligent sensor at each monitoring site. For each monitoring site, the preset optical intelligent sensor automatically acquires the transmission spectrum of the atmosphere at each monitoring site;

[0035] a processing module configured to perform differential analysis on the transmission spectrum to obtain a spectral distribution of the atmospheric pollutants at each monitoring site, further determine a coupling characteristic of the atmospheric pollutants at each monitoring site in a spectral band based on the spectral distribution, and determine an absorption contribution of the atmospheric pollutants at each monitoring site to the transmission spectrum based on the coupling characteristic and a spatial relationship between each monitoring site and each adjacent monitoring site in terms of position distribution;

[0036] The processing module is further configured to monitor multiple physical states of the atmospheric environment in the target area through intelligent environmental sensors, thereby determining the influence coefficient of each physical state on the diffusion of atmospheric pollutants, and determining the diffusion gradient of atmospheric pollutants in the target area based on all the influence coefficients;

[0037] The processing module is further configured to perform a fusion analysis on the concentration of atmospheric pollutants in the target area through all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area;

[0038] The execution module is used to send the composite index of atmospheric environmental pollution to a monitoring and early warning center.

[0039] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein 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 executes the above-mentioned atmospheric environment monitoring and early warning method.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned atmospheric environment monitoring and early warning method when executed.

[0041] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0042] In the present application, a plurality of atmospheric environment monitoring stations are set up in the target area, and an optical intelligent sensor is set up at each monitoring station. For each monitoring station, the transmission spectrum of the atmosphere at each monitoring station is automatically collected by a preset optical intelligent sensor; the transmission spectrum is differentially analyzed to obtain the spectral distribution of the atmospheric pollutants at each monitoring station, and then the coupling characteristics of the atmospheric pollutants at each monitoring station in the spectral band are determined from the spectral distribution, and the absorption contribution of the atmospheric pollutants at each monitoring station on the transmission spectrum is determined based on the coupling characteristics and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of position distribution; the multiple physical states of the atmospheric environment in the target area are monitored by intelligent environmental sensors, and then the influence coefficient of each physical state on the diffusion of atmospheric pollutants is determined, and the diffusion gradient of atmospheric pollutants in the target area is determined by all the influence coefficients; the concentration of atmospheric pollutants in the target area is fused and analyzed by all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area; the composite index of atmospheric environmental pollution is sent to a monitoring and early warning center.

[0043] It can be seen that in this application, firstly, by setting up multiple atmospheric environment monitoring stations in the target area, it is possible to obtain atmospheric pollutant data at different locations in the target area, reducing the limitations of single-site monitoring; secondly, based on the coupling characteristics and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of position distribution, the absorption contribution of the atmospheric pollutants at each monitoring station on the transmission spectrum is determined, so that the interaction relationship between various pollutants in the atmosphere in the spectral bands can be understood, thereby grasping the propagation and concentration distribution of atmospheric pollutants between different monitoring stations, and reducing monitoring errors caused by local pollution sources; then, the diffusion gradient of atmospheric pollutants in the target area is determined by all the influence coefficients, so that the diffusion trend and flow direction of atmospheric pollutants can be comprehensively evaluated, reducing the monitoring errors caused by ignoring meteorological changes and diffusion factors. Measurement errors are reduced to provide reliable data support and decision-making basis for atmospheric environment monitoring and early warning; further, the concentration of atmospheric pollutants in the target area is fused and analyzed through all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area. The composite index comprehensively considers the spatial distribution and the diffusion characteristics of atmospheric pollutants, which can reduce the errors caused by monitoring of a single pollution source and ensure the comprehensiveness and accuracy of atmospheric pollution assessment; finally, the composite index of atmospheric environmental pollution is sent to the monitoring and early warning center, and then the atmospheric pollution in the target area is graded and early warned based on the composite index, which can realize automated atmospheric pollution classification and early warning and reduce the impact of atmospheric pollution on people and the environment; in summary, the scheme can realize composite pollution assessment of the atmosphere under complex atmospheric pollution environment to reduce monitoring errors of atmospheric environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 is an exemplary flow chart of an atmospheric environment monitoring and early warning method according to some embodiments of the present application;

[0046] Figure 2 is an exemplary flow chart of determining spectral distribution according to some embodiments of the present application;

[0047] Figure 3 This is an exemplary flow chart for implementing graded warning according to some embodiments of the present application;

[0048] Figure 4 is a structural diagram of an atmospheric environment monitoring and early warning system according to some embodiments of the present application;

[0049] Figure 5 It is a structural diagram of a computer device for implementing an atmospheric environment monitoring and early warning method according to some embodiments of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] refer to Figure 1 , which is an exemplary flow chart of an atmospheric environment monitoring and early warning method according to some embodiments of the present application. The atmospheric environment monitoring and early warning method 100 mainly includes the following steps:

[0052] In step 101, multiple atmospheric environment monitoring stations are set in the target area, and an optical intelligent sensor is set at each monitoring station. For each monitoring station, the transmission spectrum of the atmosphere at each monitoring station is automatically collected by the preset optical intelligent sensor.

[0053] In specific implementation, in the target area, a multi-objective optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) can be used to set multiple atmospheric environment monitoring stations based on the distribution of atmospheric pollution sources, population density and terrain characteristics with the goal of minimizing the number of stations and maximizing monitoring coverage. In other embodiments, other methods can also be used to set up atmospheric environment monitoring stations, which are not limited here.

[0054] In specific implementation, the automatic collection of the transmission spectrum of the atmosphere at each monitoring site by a preset optical intelligent sensor can be achieved in the following manner, namely: a light source beam can be projected to each monitoring site by a preset optical intelligent sensor, thereby obtaining the transmission spectrum of each monitoring site after the light beam passes through the atmosphere, wherein the optical intelligent sensor is, for example: a differential optical transmission spectrometer, a Fourier transform infrared spectrometer and an ultraviolet spectrometer, etc. In other embodiments, other methods can also be used for automatic collection, which is not limited here.

[0055] It should be noted that the transmission spectrum in this application includes the absorption information, background light intensity and optical noise of atmospheric pollutants at the monitoring site, among which common atmospheric pollutants include SO2, NO2, O3, etc.

[0056] In step 102, the transmission spectrum is differentially analyzed to obtain the spectral distribution of the atmospheric pollutants at each monitoring station, and then the coupling characteristics of the atmospheric pollutants at each monitoring station in the spectral band are determined based on the spectral distribution. Based on the coupling characteristics and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of position distribution, the absorption contribution of the atmospheric pollutants at each monitoring station in the transmission spectrum is determined.

[0057] In some embodiments, reference Figure 2 As shown in FIG, this figure is an exemplary flow chart for determining the spectral distribution in some embodiments of the present application. In this embodiment, differential analysis of the transmission spectrum is performed to obtain the spectral distribution of the atmospheric pollutants at each monitoring station, which can be achieved by the following steps:

[0058] First, in step 1021, a reference spectrum at each monitoring site is obtained;

[0059] Next, in step 1022, the transmission spectrum is differentially processed using the reference spectrum to obtain characteristic absorption peaks of the atmospheric pollutants at each monitoring site.

[0060] Finally, in step 1023, the spectral distribution of the atmospheric pollutants at each monitoring site is determined based on the characteristic absorption peaks.

[0061] In specific implementation, the reference spectrum refers to the transmission spectrum when there are no atmospheric pollutants in the air. Obtaining the reference spectrum that is not absorbed by atmospheric pollutants at each monitoring site can be achieved in the following manner, namely: obtaining a background spectrum that is not absorbed by atmospheric pollutants by using the reference light source provided in the preset optical intelligent sensor, and then using the background spectrum as the reference spectrum that is not absorbed by atmospheric pollutants at each monitoring site; the characteristic absorption peak indicates that the atmospheric pollutant molecules at a specific wavelength absorb light particularly strongly, reflecting the spectral absorption characteristics of atmospheric pollutants, and performing differential processing on the transmission spectrum through the reference spectrum to obtain the characteristic absorption peak of the atmospheric pollutants at each monitoring site can be achieved in the following manner, namely: obtaining the low-frequency background light in the reference spectrum spectrum, and then use a differential algorithm to remove the low-frequency background spectrum (i.e., low-frequency trend) in the transmission spectrum, thereby retaining the characteristic absorption peaks of high-frequency atmospheric pollutants; determining the spectral distribution of atmospheric pollutants at each monitoring station by the characteristic absorption peak can be achieved in the following manner, namely: first, identifying the position (wavelength) and intensity (absorbance) of the characteristic absorption peak by a peak detection algorithm, and secondly, determining the wavelength range corresponding to the characteristic absorption peak of each pollutant in the atmosphere, and then integrating the characteristic absorption peak intensity within the corresponding wavelength range to obtain a distribution map of atmospheric pollutants in the spectral band, and finally, using the distribution map as the spectral distribution of atmospheric pollutants at each monitoring station; in other embodiments, other methods can also be used for determination, which are not limited here.

[0062] It should be noted that the spectral distribution in this application represents the absorption intensity distribution of atmospheric pollutants in different wavelength ranges.

[0063] In some embodiments, determining the coupling characteristics of the atmospheric pollutants in the spectral bands at each monitoring site from the spectral distribution can be achieved by using the following steps:

[0064] Determining an absorption correlation matrix of the atmospheric pollutants at each monitoring site in the spectral band according to the spectral distribution;

[0065] The coupling characteristics of the atmospheric pollutants at each monitoring site in the spectral band are determined through the absorption correlation matrix.

[0066] It should be noted that when multiple pollutants exist in the atmosphere at the same time, their characteristic absorption peaks will overlap and influence each other in the spectral bands, resulting in the inability to independently identify the absorption intensity of a single pollutant; as a preferred embodiment, determining the absorption correlation matrix of atmospheric pollutants in the spectral bands at each monitoring station based on the spectral distribution can be achieved in the following manner, namely: through a deep learning algorithm, using the known standard absorption spectra of various pollutants in the atmosphere, identifying from the spectral distribution the spectral bands where multiple pollutants have common absorption (for example, overlapping characteristic absorption peak bands of multiple pollutants), and then quantifying the degree of correlation between the multiple pollutants with overlapping absorption in the identified spectral bands, and constructing a correlation matrix for each monitoring station. The absorption correlation matrix of atmospheric pollutants at each monitoring station in the spectral band is obtained, wherein the deep learning algorithm is, for example, a convolutional neural network or a recursive neural network. In other embodiments, other methods may be used for determination, which is not limited here. Determining the coupling characteristics of atmospheric pollutants at each monitoring station in the spectral band through the absorption correlation matrix can be achieved in the following manner, namely, through a deep learning model (such as a convolutional long short-term memory network), calculating the degree of superposition of multiple pollutants in the atmosphere when overlapping absorption exists in the same spectral band based on the absorption correlation matrix, and then using the superposition degree as the coupling characteristics of atmospheric pollutants at each monitoring station in the spectral band. In other embodiments, other methods may be used for determination, which is not limited here.

[0067] It should be noted that the absorption correlation matrix in the present application is a matrix that represents the degree of absorption correlation between various pollutants in the atmosphere in the spectral band. The larger the value corresponding to the element in the absorption correlation matrix, the greater the degree of absorption correlation between the corresponding pollutants in the spectral band, and the smaller the value corresponding to the element in the absorption correlation matrix, the smaller the degree of absorption correlation between the corresponding pollutants in the spectral band. In addition, the coupling characteristics represent the characteristics of mutual influence between different pollutants in the atmosphere in the same spectral band. Therefore, the coupling characteristics can be used to understand the mutual influence relationship between multiple pollutants in the atmosphere in the spectral band.

[0068] In some embodiments, determining the absorption contribution of the atmospheric pollutants at each monitoring station on the transmission spectrum based on the coupling characteristics and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of location distribution can be achieved by using the following steps:

[0069] Determining the spatial impact factors of all adjacent monitoring stations on the spectral absorption of atmospheric pollutants at each monitoring station based on the spatial relationship between each monitoring station and each adjacent monitoring station in terms of location distribution;

[0070] Determining the absorption spectrum value of the atmospheric pollutants at each monitoring site through the transmission spectrum of the atmosphere at each monitoring site and the coupling characteristics;

[0071] The light absorption contribution of the atmospheric pollutants at each monitoring site on the transmission spectrum is determined according to the absorption spectrum value and the spatial influence factor.

[0072] It should be noted that the spatial impact factor indicates the degree of spatial influence of the neighboring monitoring stations when the atmospheric pollutants at the monitoring station are subjected to spectral absorption. The larger the spatial impact factor, the greater the degree of spatial influence of the neighboring monitoring stations when the atmospheric pollutants at the monitoring station are subjected to spectral absorption. The smaller the spatial impact factor, the smaller the degree of spatial influence of the neighboring monitoring stations when the atmospheric pollutants at the monitoring station are subjected to spectral absorption. As a preferred embodiment, the spatial relationship between each monitoring station and each neighboring monitoring station in terms of position distribution determines the influence of all neighboring monitoring stations on the atmospheric pollutants at each monitoring station. The spatial influence factor on spectral absorption can be achieved in the following manner, namely: first, the first three monitoring stations with the closest Euclidean distance to each monitoring station are used as the neighboring monitoring stations of each monitoring station, and then the Euclidean distances corresponding to each monitoring station and each neighboring monitoring station are used as the spatial relationship between each monitoring station and each neighboring monitoring station in position distribution. Finally, the spatial influence factors of the atmospheric pollutants of each monitoring station on the spectral absorption of all neighboring monitoring stations are calculated based on all the spatial relationships obtained above by the inverse distance weighted method. In other embodiments, other methods can also be used for determination, which is not limited here.

[0073] In specific implementation, the absorption spectrum value represents the absorption degree of the atmospheric pollutants at the monitoring site on the spectral wavelength. The absorption spectrum value can be used to understand the coupling result of the atmospheric pollutant concentration at the monitoring site and its spectral absorption characteristics. Therefore, the absorption spectrum value of the atmospheric pollutants at each monitoring site is determined by the transmission spectrum of the atmosphere at each monitoring site and the coupling characteristics. It can be implemented in the following way, namely: according to the Lambert-Beer law, the absorbance of the atmospheric pollutants at each monitoring site is determined based on the transmission spectrum of the atmosphere at each monitoring site, and then the absorbance is multiplied by the natural logarithm of the coupling characteristics to obtain the product result as the absorption spectrum value of the atmospheric pollutants at each monitoring site. In other embodiments, other methods can also be used for determination, which is not limited here; according to the absorption spectrum value and the spatial influence factor, the absorption contribution of the atmospheric pollutants at each monitoring site on the transmission spectrum can be determined in the following way, namely: the product value of the absorption spectrum value and the spatial influence factor can be used as the absorption contribution of the atmospheric pollutants at each monitoring site on the transmission spectrum. In other embodiments, other methods can also be used for determination, which is not limited here.

[0074] It should be noted that the absorption contribution in this application indicates the degree of contribution of atmospheric pollutants at the monitoring site to the absorption of the transmission spectrum. The larger the absorption contribution, the greater the contribution of atmospheric pollutants at the monitoring site to the absorption of the transmission spectrum, and the smaller the absorption contribution, the smaller the contribution of atmospheric pollutants at the monitoring site to the absorption of the transmission spectrum.

[0075] In step 103, multiple physical states of the atmospheric environment in the target area are monitored by intelligent environmental sensors to determine the influence coefficient of each physical state on the diffusion of atmospheric pollutants, and the diffusion gradient of atmospheric pollutants in the target area is determined by all the influence coefficients.

[0076] In specific implementation, intelligent environmental sensors can be used to monitor multiple physical states of the atmospheric environment in the target area and obtain parameter values ​​of multiple physical states in the atmospheric environment, including temperature, humidity, wind speed, wind direction, and air pressure. It should be noted that these physical state parameters directly affect the diffusion, propagation, and deposition of atmospheric pollutants in the target area.

[0077] In some embodiments, determining the influence coefficient of each physical state on the diffusion of atmospheric pollutants can be achieved by using the following steps:

[0078] Construct a simulation model of the physical state of the atmospheric environment and the diffusion of atmospheric pollutants;

[0079] Select a physical state as the selected physical state;

[0080] using the selected physical state as an input parameter of the diffusion simulation model;

[0081] Outputting the influence coefficient of the selected physical state on the diffusion of atmospheric pollutants through the diffusion simulation model;

[0082] Continue to determine the influence coefficient of the remaining physical state on the diffusion of atmospheric pollutants.

[0083] It should be noted that the diffusion simulation model in this application is a model used to describe how atmospheric pollutants in the air are affected by the physical state of the atmospheric environment (such as temperature, humidity, wind speed, wind direction and air pressure, etc.) and then diffuse into the surrounding areas. The diffusion simulation model can determine the degree of influence of the physical state of the atmospheric environment on the diffusion of atmospheric pollutants. As a preferred embodiment, the diffusion simulation model of atmospheric pollutants can be constructed based on machine learning or deep learning technology through a large amount of historical experimental experience and data. In other embodiments, other methods can also be used to construct the diffusion simulation model, which is not specifically limited here.

[0084] In specific implementation, the selected physical state can be used as the input parameter of the diffusion simulation model in the following manner, that is, the parameter value corresponding to the selected physical state can be input into the diffusion simulation model; the influence coefficient of the selected physical state on the diffusion of atmospheric pollutants output by the diffusion simulation model can be implemented in the following manner, that is, the degree of influence of the selected physical state on the diffusion of atmospheric pollutants calculated by the diffusion simulation model is output as the influence coefficient of the selected physical state on the diffusion of atmospheric pollutants; other methods can also be used to implement it in other embodiments, which are not limited here.

[0085] It should be noted that the impact coefficient in this application indicates the degree of influence of the physical state of the atmospheric environment in the target area (such as temperature, humidity, wind speed, wind direction and air pressure, etc.) on the diffusion of atmospheric pollutants. The larger the impact coefficient, the greater the influence of the corresponding physical state of the atmospheric environment on the diffusion of atmospheric pollutants, and the smaller the impact coefficient, the smaller the influence of the corresponding physical state of the atmospheric environment on the diffusion of atmospheric pollutants. It will not be repeated here.

[0086] In some embodiments, determining the diffusion gradient of atmospheric pollutants in the target area using all influence coefficients can be achieved by using the following steps:

[0087] Determine the diffusion field of atmospheric pollutants in the target area based on all impact coefficients;

[0088] The diffusion gradient of the atmospheric pollutants in the target area is determined from the diffusion field.

[0089] In specific implementation, the diffusion field represents the distribution of the concentration of atmospheric pollutants in the target area. The diffusion field of atmospheric pollutants in the target area can be determined based on all the influence coefficients in the following way, namely: geographic information system tools or numerical calculation software (such as Python, MATLAB) can be used to generalize all the influence coefficients to each position in the target area through spatial interpolation (such as Kriging interpolation method, inverse distance weighted method IDW), and then the concentration gradient distribution of atmospheric pollutants in the target area is obtained by numerical difference or gradient calculation method. Finally, the concentration gradient distribution of atmospheric pollutants in the target area is used as the concentration gradient distribution of atmospheric pollutants in the target area. The diffusion field in the diffusion field may be determined by other methods in other embodiments, which is not limited here. The diffusion gradient of atmospheric pollutants in the target area determined by the diffusion field may be achieved in the following manner, namely: an atmospheric diffusion equation (such as a Gaussian diffusion model) may be used to determine the evolution trend of atmospheric pollutant concentrations based on the diffusion field, and then the obtained evolution trend is quantitatively evaluated by an existing evaluation algorithm, and finally the value obtained by the quantitative evaluation is used as the diffusion gradient of atmospheric pollutants in the target area, wherein the evaluation algorithm, for example, cross-validation, genetic algorithm, and ensemble learning, may be determined by other methods in other embodiments, which is not limited here.

[0090] It should be noted that the diffusion gradient in this application represents the rate of change of concentration of atmospheric pollutants in the target area. The larger the diffusion gradient, the greater the rate of change of concentration of atmospheric pollutants in the target area, and the smaller the diffusion gradient, the smaller the rate of change of concentration of atmospheric pollutants in the target area; therefore, the distribution changes of atmospheric pollutants in the target area can be dynamically predicted through the diffusion gradient.

[0091] In step 104, the concentration of atmospheric pollutants in the target area is fused and analyzed by all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area.

[0092] In some embodiments, the following steps may be used to perform a fusion analysis of the atmospheric pollutant concentration in the target area by combining all the light absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area:

[0093] All absorption contributions are weighted and integrated to obtain the concentration distribution of atmospheric pollutants in the target area;

[0094] The composite index of atmospheric environmental pollution in the target area is determined by the concentration distribution of the atmospheric pollutants and the diffusion gradient.

[0095] In specific implementation, all absorption contributions are weighted and integrated to obtain the concentration distribution of atmospheric pollutants in the target area. This can be achieved in the following manner: first, through the existing weight distribution model, based on the spatial relationship between each monitoring station and its corresponding adjacent monitoring stations in terms of location distribution and the degree of impact of atmospheric pollutants at each monitoring station on human health and the environment, a weight coefficient of atmospheric pollutants is allocated to each monitoring station. Then, the weight coefficient of each monitoring station is weightedly summed with the corresponding absorption contribution, and the value obtained by the weighted sum is used as the result of the concentration distribution of atmospheric pollutants in the target area. The existing weight distribution model is, for example, a BP neural network model. In other embodiments, other methods can also be used for determination, which is not limited here. Determining the composite index of atmospheric environmental pollution in the target area through the concentration distribution of atmospheric pollutants and the diffusion gradient can be achieved in the following manner: first, calculating the result of the base e of the natural logarithm negative the power of the diffusion gradient, and then multiplying the calculated result by the result corresponding to the concentration distribution of atmospheric pollutants. Finally, the obtained product value is used as the composite index of atmospheric environmental pollution in the target area. In other embodiments, other methods can also be used for determination, which is not limited here.

[0096] It should be noted that the composite index of atmospheric environmental pollution in this application represents the comprehensive parameter value of the pollution degree of atmospheric environmental pollutants in the target area. The larger the composite index, the greater the pollution degree of atmospheric environmental pollutants in the target area, and the smaller the composite index, the smaller the pollution degree of atmospheric environmental pollutants in the target area. Therefore, the atmospheric environmental quality of the target area can be quantified by the composite index, which will not be repeated here.

[0097] In step 105, the composite index of atmospheric environmental pollution is sent to a monitoring and early warning center.

[0098] In specific implementation, the composite index of atmospheric environmental pollution can be sent to a monitoring and early warning center via an Internet of Things platform. In other embodiments, other methods can also be used for implementation, which is not limited here.

[0099] In some embodiments, after sending the composite index of atmospheric pollution to a monitoring and early warning center, the method further includes:

[0100] When the composite index of the atmospheric environment pollution exceeds a preset threshold, a graded warning is issued for the atmospheric pollution in the target area according to the composite index of the atmospheric environment pollution.

[0101] It should be noted that the preset threshold in this application is a threshold used to judge the atmospheric environment quality in the target area. As a preferred embodiment, the preset threshold can be set through expert review based on the air quality standards issued by the country or region and the environmental carrying capacity, pollutant diffusion capacity and historical pollution data of the target area to ensure that the concentration limit of atmospheric pollutants in the target area is reasonably set. In other embodiments, other methods can also be used to set the threshold of atmospheric environment quality, which is not specifically limited here.

[0102] For specific implementation, refer to Figure 3 As shown, this figure is an exemplary flow chart for graded warning in some embodiments of the present application. The graded warning of atmospheric pollution in the target area based on the composite index of atmospheric pollution can be implemented in the following manner, namely: when the composite index of atmospheric pollution exceeds a preset threshold, if the excess range of the composite index is within the range of 10%-30% of the preset threshold, the monitoring and warning center issues a light pollution warning for atmospheric pollution in the target area; if the excess range of the composite index is within the range of 30%-60% of the preset threshold, the monitoring and warning center issues a moderate pollution warning for atmospheric pollution in the target area; if the excess range of the composite index is within the range of 60%-100% of the preset threshold, the monitoring and warning center issues a heavy pollution warning for atmospheric pollution in the target area; if the excess range of the composite index is above 100% of the preset threshold, the monitoring and warning center issues a severe pollution warning for atmospheric pollution in the target area; thereby achieving graded warning of atmospheric pollution in the target area. In other embodiments, other methods may also be used for implementation, which is not limited here.

[0103] In addition, in another aspect of the present application, in some embodiments, the present application provides an atmospheric environment monitoring and early warning system, referring to Figure 4 , which is a schematic diagram of the structure of an atmospheric environment monitoring and early warning system according to some embodiments of the present application. The atmospheric environment monitoring and early warning system 400 includes: a collection module 401, a processing module 402 and an execution module 403, which are described as follows:

[0104] Acquisition module 401, in this application, is mainly used to set up multiple atmospheric environment monitoring sites in the target area, and set up an optical intelligent sensor at each monitoring site. For each monitoring site, the preset optical intelligent sensor automatically collects the transmission spectrum of the atmosphere at each monitoring site;

[0105] Processing module 402, in the present application, is mainly used to perform differential analysis on the transmission spectrum to obtain the spectral distribution of the atmospheric pollutants at each monitoring station, and then determine the coupling characteristics of the atmospheric pollutants at each monitoring station in the spectral band based on the spectral distribution, and determine the absorption contribution of the atmospheric pollutants at each monitoring station to the transmission spectrum based on the coupling characteristics and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of position distribution;

[0106] The processing module 402 in the present application is further configured to monitor multiple physical states of the atmospheric environment in the target area through intelligent environmental sensors, thereby determining the influence coefficient of each physical state on the diffusion of atmospheric pollutants, and determining the diffusion gradient of atmospheric pollutants in the target area through all the influence coefficients;

[0107] The processing module 402 in the present application is further configured to perform a fusion analysis on the concentration of atmospheric pollutants in the target area through all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area;

[0108] Execution module 403, in this application, execution module 403 is mainly used to send the composite index of atmospheric environmental pollution to the monitoring and early warning center.

[0109] The above describes in detail the examples of the atmospheric environment monitoring and early warning system and method provided by the embodiments of the present application. It is understandable that the corresponding device includes a hardware structure and / or software module for performing each function in order to realize the above functions. Those skilled in the art should easily appreciate that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to realize the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0110] In some embodiments, the present application also provides a computer device, which 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 executes the above-mentioned atmospheric environment monitoring and early warning method.

[0111] In some embodiments, reference Figure 5 The dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device that implements the atmospheric environment monitoring and early warning method of the present application. The atmospheric environment monitoring and early warning method in the above embodiment can be Figure 5The computer device 500 is implemented as shown, 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 can be a terminal device, a server or a chip.

[0112] The processor 501 may be a general-purpose processor or a special-purpose processor. For example, the processor 501 may be a central processing unit (CPU), which may 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.

[0113] For example, the computer device 500 may be a chip, the communication unit 505 may be an input and / or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0114] For another example, the computer device 500 may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0115] The computer device 500 may include one or more memories 502, on which a program 504 is stored. The program 504 can be executed by the processor 501 to generate instructions 503, so that the processor 501 executes the method described in the above method embodiment according to the instructions 503. Optionally, data (such as a target audit model) can also be stored in the memory 502. Optionally, the processor 501 can also read data stored in the memory 502. The data can be stored at the same storage address as the program 504, or at a different storage address from the program 504.

[0116] The processor 501 and the memory 502 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0117] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based 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.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] For example, in some embodiments, the present application also provides a computer-readable storage medium, which stores instructions or codes. When the instructions or codes are run on a computer, the computer implements the above-mentioned atmospheric environment monitoring and early warning method when executed.

[0120] In summary, in the atmospheric environment monitoring and early warning system and method disclosed in the embodiments of the present application, multiple atmospheric environment monitoring stations are set up in the target area, and an optical intelligent sensor is set up at each monitoring station. For each monitoring station, the transmission spectrum of the atmosphere at each monitoring station is automatically collected by a preset optical intelligent sensor; the transmission spectrum is differentially analyzed to obtain the spectral distribution of the atmospheric pollutants at each monitoring station, and then the coupling characteristics of the atmospheric pollutants at the monitoring station in the spectral band are determined from the spectral distribution, and the absorption contribution of the atmospheric pollutants at each monitoring station on the transmission spectrum is determined based on the coupling characteristics and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of position distribution; multiple physical states of the atmospheric environment in the target area are monitored by intelligent environmental sensors, and the influence coefficient of each physical state on the diffusion of atmospheric pollutants is determined, and the diffusion gradient of atmospheric pollutants in the target area is determined by all the influence coefficients; the concentration of atmospheric pollutants in the target area is fused and analyzed by all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area; the composite index of atmospheric environmental pollution is sent to the monitoring and early warning center; composite pollution assessment of the atmosphere can be realized in a complex atmospheric pollution environment to reduce the monitoring error of atmospheric environmental pollution.

[0121] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0122] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. An atmospheric environment monitoring and early warning method, characterized in that: The steps include: Setting up multiple atmospheric environment monitoring stations in the target area, and setting up an optical intelligent sensor at each monitoring station, for each monitoring station, automatically collecting the transmission spectrum of the atmosphere at each monitoring station through the preset optical intelligent sensor; Performing differential analysis on the transmission spectrum to obtain the spectral distribution of the atmospheric pollutants at each monitoring site, and then determining the coupling characteristics of the atmospheric pollutants at each monitoring site in the spectral band based on the spectral distribution; and determining the absorption contribution of the atmospheric pollutants at each monitoring site on the transmission spectrum based on the coupling characteristics and the spatial relationship between each monitoring site and each adjacent monitoring site in terms of position distribution; The intelligent environmental sensors are used to monitor multiple physical states of the atmospheric environment in the target area, thereby determining the influence coefficient of each physical state on the diffusion of atmospheric pollutants, and determining the diffusion gradient of atmospheric pollutants in the target area through all the influence coefficients; Performing a fusion analysis on the concentration of atmospheric pollutants in the target area through all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area; The composite index of atmospheric environmental pollution is sent to a monitoring and early warning center.

2. The method according to claim 1, wherein Performing differential analysis on the transmission spectrum to obtain the spectral distribution of the atmospheric pollutants at each monitoring site specifically includes: Obtaining a reference spectrum at each monitoring site; Performing differential processing on the transmission spectrum using the reference spectrum to obtain characteristic absorption peaks of the atmospheric pollutants at each monitoring site; The spectral distribution of the atmospheric pollutants at each monitoring site is determined by the characteristic absorption peaks.

3. The method according to claim 1, wherein Determining the coupling characteristics of the atmospheric pollutants in the spectral bands at each monitoring site from the spectral distribution specifically includes: Determining an absorption correlation matrix of the atmospheric pollutants at each monitoring site in the spectral band according to the spectral distribution; The coupling characteristics of the atmospheric pollutants at each monitoring site in the spectral band are determined through the absorption correlation matrix.

4. The method according to claim 1, wherein Determining the light absorption contribution of the atmospheric pollutants at each monitoring station on the transmission spectrum based on the coupling characteristics and the spatial relationship between each monitoring station and each adjacent monitoring station in terms of position distribution specifically includes: Determining the spatial impact factors of all adjacent monitoring stations on the spectral absorption of atmospheric pollutants at each monitoring station based on the spatial relationship between each monitoring station and each adjacent monitoring station in terms of location distribution; Determining the absorption spectrum value of the atmospheric pollutants at each monitoring site through the transmission spectrum of the atmosphere at each monitoring site and the coupling characteristics; The light absorption contribution of the atmospheric pollutants at each monitoring site on the transmission spectrum is determined according to the absorption spectrum value and the spatial influence factor.

5. The method according to claim 1, wherein The specific factors for determining the influence of various physical states on the diffusion of atmospheric pollutants include: Construct a simulation model of the physical state of the atmospheric environment and the diffusion of atmospheric pollutants; Select a physical state as the selected physical state; using the selected physical state as an input parameter of the diffusion simulation model; Outputting the influence coefficient of the selected physical state on the diffusion of atmospheric pollutants through the diffusion simulation model; Continue to determine the influence coefficient of the remaining physical state on the diffusion of atmospheric pollutants.

6. The method according to claim 1, wherein The concentration of atmospheric pollutants in the target area is analyzed by integrating all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area, which specifically includes: All absorption contributions are weighted and integrated to obtain the concentration distribution of atmospheric pollutants in the target area; The composite index of atmospheric environmental pollution in the target area is determined by the concentration distribution of the atmospheric pollutants and the diffusion gradient.

7. The method according to claim 1, wherein After the composite index of atmospheric pollution is sent to the monitoring and early warning center, the following steps are also included: When the composite index of the atmospheric environment pollution exceeds a preset threshold, a graded warning is issued for the atmospheric pollution in the target area according to the composite index of the atmospheric environment pollution.

8. An atmospheric environment monitoring and early warning system, characterized in that: include: An acquisition module is used to set up multiple atmospheric environment monitoring sites in the target area and set up an optical intelligent sensor at each monitoring site. For each monitoring site, the preset optical intelligent sensor automatically acquires the transmission spectrum of the atmosphere at each monitoring site; a processing module configured to perform differential analysis on the transmission spectrum to obtain a spectral distribution of the atmospheric pollutants at each monitoring site, further determine a coupling characteristic of the atmospheric pollutants at each monitoring site in a spectral band based on the spectral distribution, and determine an absorption contribution of the atmospheric pollutants at each monitoring site to the transmission spectrum based on the coupling characteristic and a spatial relationship between each monitoring site and each adjacent monitoring site in terms of position distribution; The processing module is further configured to monitor multiple physical states of the atmospheric environment in the target area through intelligent environmental sensors, thereby determining the influence coefficient of each physical state on the diffusion of atmospheric pollutants, and determining the diffusion gradient of atmospheric pollutants in the target area based on all the influence coefficients; The processing module is further configured to perform a fusion analysis on the concentration of atmospheric pollutants in the target area through all the absorption contributions and the diffusion gradient to obtain a composite index of atmospheric environmental pollution in the target area; The execution module is used to send the composite index of atmospheric environmental pollution to a monitoring and early warning center.

9. A computer device, characterized in that: The computer device 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 executes the atmospheric environment monitoring and early warning 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 codes, and when the instructions or codes are executed on a computer, the computer implements the atmospheric environment monitoring and early warning method according to any one of claims 1 to 7.

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