CN114943288A - Method for monitoring spatial distribution of atmospheric particulate pollution source - Google Patents
Method for monitoring spatial distribution of atmospheric particulate pollution source Download PDFInfo
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- CN114943288A CN114943288A CN202210557555.0A CN202210557555A CN114943288A CN 114943288 A CN114943288 A CN 114943288A CN 202210557555 A CN202210557555 A CN 202210557555A CN 114943288 A CN114943288 A CN 114943288A
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Abstract
The invention relates to a method for monitoring the spatial distribution of an atmospheric particulate pollution source, which comprises the following steps: s1, obtaining remote sensing image data, generating a DOM data product after image registration and orthorectification of the remote sensing image data, and establishing a remote sensing model; s2, distributing the DOM data products to operators for interpretation according to different frames, and extracting the raised dust surface pattern spots; s3, acquiring important industry pollution source information, and sorting and checking enterprise legal data according to pollution types; s4, extracting enterprise information according to the enterprise legal data after arrangement and investigation and according to the industry types, the invention uses big data thinking and method to efficiently collect, process and analyze the multisource isomerism and mass remote sensing data, and can converge, fuse and share the environment monitoring data resources of various observation means, so that multiple isolated and one-sided environment elements are organically associated, and the accuracy, universality, intelligence and automation level of the environment remote sensing monitoring are improved.
Description
Technical Field
The invention relates to the technical field of monitoring of atmospheric pollution sources, in particular to a method for monitoring spatial distribution of atmospheric particulate pollution sources.
Background
The environmental air quality is becoming the focus of people's concern day by day, under the quiet weather condition, because atmospheric pollutants especially fine particulate matter accumulates gradually, can form atmospheric pollution usually, the haze weather appears, and phenomenon such as low visibility accompanies, the appearance of haze weather not only makes urban road, highway, water route and airport visibility descend, influences the normal development of transportation etc. simultaneously, when the human body carries out work in dense fog and atmosphere polluted environment, along with inhaling the increase of the internal pollutant quantity, cause the adverse effect to the health of human body, therefore it is very important to the monitoring of atmospheric pollution source.
The atmospheric pollution source monitoring method has the advantages of huge data volume, various types, large format difference, inconsistent time, space and attribute scales, extremely high updating speed, obvious new characteristics of all-around, all-weather, all-time, all-factor, all-cycle, high precision and time-space continuity, and capability of emphasizing integration, fusion, association, collaborative inversion and joint extraction of different types of data, so that the existing analysis and processing mode of the environmental remote sensing monitoring data is difficult to adapt.
Disclosure of Invention
In view of the above problems in the prior art, the main object of the present invention is to provide a method for monitoring the spatial distribution of atmospheric particulate pollution sources.
The technical scheme of the invention is as follows: the method for monitoring the spatial distribution of the atmospheric particulate pollution source comprises the following steps:
s1, obtaining remote sensing image data, generating a DOM data product after image registration and orthorectification of the remote sensing image data, and establishing a remote sensing model;
s2, distributing the DOM data products to operators for interpretation according to different frames, and extracting the raised dust surface pattern spots;
s3, acquiring important industry pollution source information, and sorting and checking enterprise legal data according to pollution types;
s4, extracting enterprise information according to the enterprise legal person data after arrangement and investigation and according to the industry type;
s5, acquiring positioning information of the enterprise according to the enterprise information, and extracting position information of the enterprise from the remote sensing image data;
s6, performing field check, and correcting the extracted raise dust surface pattern spots one by one according to the field check condition;
and S7, carrying out quantitative statistics and analysis according to the statistical data to form a monitoring information statistical analysis report and a picture result.
In step S1, coordinate conversion is required to be performed when remote sensing image data from different sources are obtained.
The step of generating the DOM data product from the remote sensing image data in step S1 includes:
s11, adopting a 1A/1B registration fusion module of EasyOrtho software to perform batch automatic registration work;
s12, compiling corresponding band combination batch script statements for each remote sensing image data by adopting a batch processing module of Erdas software, and carrying out batch band combination and data batch fusion by operating a batch processing command;
s13, adopting an easy ortho software true color output module and an image fusion module to carry out batch waveband combination of mass data;
s14, performing manual rough correction first, and then performing batch and automatic orthorectification by using an automatic fine correction method;
and S15, performing automatic mosaic by using a semiautomatic mosaic method based on PixelEngine software.
The step of establishing the remote sensing model in the step S1 includes extracting satellite remote sensing, ground monitoring, wireless sensors, crowdsourcing, network media data samples and their corresponding relations, eliminating data with low reliability, confusion and noise, screening out reliable, accurate and representative data as samples, and automatically extracting and establishing a sample library from existing environmental remote sensing big data, thereby establishing the remote sensing model.
The step of interpreting by the operator in step S2 includes performing internal interpretation and interpretation by using the collected DOM data product and combining automatic classification and manual interpretation based on the ortho image, and performing earth surface coverage classification.
Wherein, the important industry pollution source in the step S3 refers to pollution enterprises generating emission of the atmospheric particulate matter PM10 and PM2.5, including ferrous metal smelting and calendaring industry, electric power and heat power production and supply industry, non-metallic mineral product industry, steel plant, thermal power plant, building material plant, cement plant, oil refinery and chemical plant.
When field inspection is performed in step S6, the range of the pattern spots on the dust surface is difficult to inspect, and the current inspection state is based on the image range and the pattern spots with large field differences, and when various types of accurately positioned typical contaminated enterprises are selected for field inspection, the factory address needs to be found, and the updated information is inspected.
The invention has the following advantages and beneficial effects: according to the invention, the big data thinking and the method are used for efficiently acquiring, processing and analyzing the multi-source heterogeneous mass remote sensing data, and the environmental monitoring data resources of various observation means can be gathered, fused and shared, so that multiple isolated and one-sided environmental elements are organically associated, and the accuracy, universality, intelligence and automation levels of the environmental remote sensing monitoring are improved.
Drawings
Fig. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The invention will be further described with reference to the drawings and specific examples.
As shown in fig. 1: the method for monitoring the spatial distribution of the atmospheric particulate pollution source comprises the following steps:
s1, obtaining remote sensing image data, generating a DOM data product after image registration and orthorectification of the remote sensing image data, and establishing a remote sensing model;
s2, distributing the DOM data products to operators according to different frames for interpretation, and extracting raised dust surface pattern spots;
s3, acquiring important industry pollution source information, and sorting and checking enterprise legal data according to pollution types;
s4, extracting enterprise information according to the enterprise legal person data after arrangement and investigation and according to the industry type;
s5, acquiring positioning information of the enterprise according to the enterprise information, and extracting position information of the enterprise from the remote sensing image data;
s6, performing field check, and correcting the extracted raise dust surface pattern spots one by one according to the field check condition;
and S7, carrying out quantitative statistics and analysis according to the statistical data to form a monitoring information statistical analysis report and a diagram result.
Understandably, the processing process of the remote sensing image data relates to the cooperative work of a plurality of modules, is expressed as definite task flow, has logic programmable control flow aiming at the input and the output of each module, and compiles and adopts a task management tool based on the data inspection and processing technology of the workflow to realize the automatic flow control of the modules according to the actual situation The dust surface refers to various natural open ground and artificial excavation construction sites which are easy to generate atmospheric particulate matter PM10 and have the vegetation coverage of less than 10%, and is mainly interpreted by remote sensing image data with the resolution of 0.2 meter and 0.5 meter.
Further, when the remote sensing image data from different sources are obtained in step S1, coordinate conversion is required to be performed, so that it is possible to ensure that coordinate systems of all data sources are uniform.
Further, the step of generating a DOM data product from the remote sensing image data in step S1 includes:
s11, adopting a 1A/1B registration fusion module of EasyOrtho software to perform batch automatic registration work;
s12, compiling corresponding band combination batch script statements for each remote sensing image data by adopting a batch processing module of Erdas software, and carrying out batch band combination and data batch fusion by operating a batch processing command;
s13, adopting a true color output module and an image fusion module of EasyOrtho software to carry out batch waveband combination of mass data;
s14, performing manual rough correction first, and then performing batch and automatic orthorectification by using an automatic fine correction method;
and S15, performing automatic mosaic by using a semiautomatic mosaic method based on PixelEngine software.
Further, the step of establishing the remote sensing model in step S1 includes extracting satellite remote sensing, ground monitoring, wireless sensors, crowdsourcing, network media data samples and their corresponding relationships, and excluding data with low reliability, confusion and noise, screening out reliable, accurate and representative data as samples, and automatically extracting and establishing a sample library from existing environmental remote sensing big data, thereby establishing the remote sensing model.
Further, the step of interpreting by the operator in step S2 includes using the orthographic image as a basis, using the collected DOM data product, combining automatic classification and manual interpretation to develop domestic interpretation and interpretation, performing surface coverage classification, and producing a corresponding data layer meeting the general check requirement of the geographic national conditions, wherein the interpretation work adopts a human-computer interaction work mode, and the geographic national condition element extraction and interpretation system includes two subsystems of geographic national condition basic element extraction and geographic national condition surface coverage classification, and provides the multi-scale rapid image segmentation and classification of the facing object, and the geographic national condition general check data production function of the collection, compilation and quality inspection series of the basic element information.
Further, the important industrial pollution source in the step S3 refers to pollution enterprises that generate emission of PM10 and PM2.5, including ferrous metal smelting and rolling processing industry, electric power and heat power production and supply industry, non-metallic mineral product industry, steel plant, thermal power plant, building material plant, cement plant, oil refinery and chemical plant.
Further, when field inspection is performed in step S6, the range of the pattern spots on the dust surface is difficult to inspect, and if the image range is used as the reference, and the pattern spots have a large difference with the field, the current situation is inspected, and when field inspection is performed by selecting various types of accurately positioned typical pollution enterprises, the factory address needs to be found, and the update information is inspected.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The method for monitoring the spatial distribution of the atmospheric particulate pollution source is characterized by comprising the following steps:
s1, obtaining remote sensing image data, generating a DOM data product after image registration and orthorectification of the remote sensing image data, and establishing a remote sensing model;
s2, distributing the DOM data products to operators according to different frames for interpretation, and extracting raised dust surface pattern spots;
s3, acquiring important industry pollution source information, and sorting and checking enterprise legal data according to pollution types;
s4, extracting enterprise information according to the enterprise legal data after arrangement and investigation and according to the industry type;
s5, acquiring positioning information of the enterprise according to the enterprise information, and extracting the position information of the enterprise from the remote sensing image data;
s6, performing field check, and correcting the extracted raise dust surface pattern spots one by one according to the field check condition;
and S7, carrying out quantitative statistics and analysis according to the statistical data to form a monitoring information statistical analysis report and a diagram result.
2. The method for monitoring the spatial distribution of the atmospheric particulate pollution source as claimed in claim 1, wherein in the step S1, coordinate transformation is required when the remote sensing image data of different sources are acquired.
3. The method for monitoring the spatial distribution of the atmospheric particulate pollution source as claimed in claim 1, wherein the step of generating the DOM data product from the remotely sensed image data in the step S1 comprises:
s11, adopting a 1A/1B registration fusion module of EasyOrtho software to perform batch automatic registration work;
s12, compiling corresponding band combination batch script statements for each remote sensing image data by adopting a batch processing module of Erdas software, and carrying out batch band combination and data batch fusion by operating a batch processing command;
s13, adopting an easy ortho software true color output module and an image fusion module to carry out batch waveband combination of mass data;
s14, performing manual rough correction first, and then performing batch and automatic orthorectification by using an automatic fine correction method;
and S15, performing automatic mosaic by using a semiautomatic mosaic method based on PixelEngine software.
4. The atmospheric particulate pollution source spatial distribution monitoring method according to claim 1, wherein the step of establishing a remote sensing model in step S1 includes extracting satellite remote sensing, ground monitoring, wireless sensors, crowdsourcing, network media data samples and their corresponding relationships, excluding low-reliability, confusing and noisy data, screening reliable, accurate and representative data as samples, and automatically extracting and establishing a sample library from existing environmental remote sensing big data, thereby establishing the remote sensing model.
5. The method as claimed in claim 1, wherein the step of interpreting by the operator in step S2 includes performing earth surface coverage classification by using collected DOM data products and combining automatic classification and manual interpretation based on the orthographic images.
6. The method for monitoring the spatial distribution of the atmospheric particulate pollution source as claimed in claim 1, wherein the important industry pollution source in the step S3 refers to pollution enterprises generating emission of the atmospheric particulate matter PM10 and PM2.5, including ferrous metal smelting and calendaring industry, electric power and heat power production and supply industry, non-metal mineral product industry, steel plant, thermal power plant, building material plant, cement plant, oil refinery and chemical plant.
7. The method for monitoring the spatial distribution of the atmospheric particulate pollution source according to claim 1, wherein when the field inspection is performed in step S6, the range of the pattern spots on the dust surface is difficult to inspect, and the current inspection is based on the image range and the pattern spots with larger field differences, and when various types of accurately positioned typical pollution enterprises are selected for the field inspection, the sites need to be found to inspect the updated information.
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