Sand storm information detection method and device, storage medium and electronic equipment
Through the combination of remote sensing images of stationary meteorological satellites and ground observation data, sandstorms and intensity are judged, and the problem of low detection efficiency in the existing technology is solved, and efficient sandstorm monitoring and forestry resource protection are achieved.
Patent Information
- Application Number
- CN202410862847.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-28
- Publication Date
- 2025-01-17
AI Technical Summary
In the prior art, the detection of sandstorms and their intensity and low efficiency through field investigations has failed to effectively solve the impact of forestry resources on extreme weather.
By obtaining remote sensing images collected by stationary meteorological satellites, light compensation and correction processing are performed, combined with ground observation data, algorithms are used to judge the occurrence of sandstorms and determine the intensity of sandstorms, which is used to formulate suggestions for afforestation.
It improves the efficiency of sandstorm information detection, avoids the shortcomings of field investigations, and achieves more accurate and efficient sandstorm monitoring and forestry resource protection.
Smart Images
Figure CN119323721A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of artificial intelligence technology, and more specifically, to a method and device for detecting sandstorm information, a storage medium, and an electronic device. Background Art
[0002] Dust storms are an important environmental problem that seriously affects forestry resources. In order to develop forestry, users often apply for relevant loans from financial institutions, and financial institutions need to conduct loan risk assessments on forestry customers based on the tree species value and carbon sink value of forestry resources. However, forestry resources are easily affected by extreme weather, so it is crucial to detect sandstorms in a timely manner and take corresponding measures according to the intensity of sandstorms. In existing technologies, sandstorms and their intensity are often detected through field investigations, which seriously affects the detection efficiency of sandstorm information.
[0003] With regard to the problem that the detection efficiency of sandstorm information is relatively low due to the on-site inspection method used in related technologies to detect sandstorms and their intensity, no effective solution has been proposed so far. Summary of the invention
[0004] The main purpose of this application is to provide a method and device for detecting sandstorm information, a storage medium and an electronic device, so as to solve the problem in the related art that sandstorms and their intensity are detected through field investigations, resulting in relatively low detection efficiency of sandstorm information.
[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for detecting sandstorm information is provided. The method comprises: acquiring target remote sensing images of multiple regions; judging whether a sandstorm occurs in the target region based on the target remote sensing images of each region; if a sandstorm occurs in the target region, determining the target sandstorm intensity of the target region based on the target remote sensing images of the target region and the ground observation data of the target region, wherein the target sandstorm intensity is used to determine afforestation recommendations for the target region.
[0006] Furthermore, obtaining target remote sensing images of multiple areas includes: collecting remote sensing images of each area through a geostationary meteorological satellite to obtain multiple initial remote sensing images; performing illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images; and performing correction processing on the processed initial remote sensing images to obtain the target remote sensing images.
[0007] Furthermore, performing illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images includes: acquiring acquisition information of each initial remote sensing image, wherein the acquisition information at least includes: acquisition time and geographic location information of the initial remote sensing image; processing the acquisition information by a first algorithm to obtain a compensation value when performing illumination compensation on each initial remote sensing image; performing illumination compensation processing on the multiple initial remote sensing images according to the compensation value to obtain the processed initial remote sensing images.
[0008] Furthermore, judging whether a sandstorm occurs in the target area based on the target remote sensing image of each area includes: performing calculations based on the target remote sensing image of each area to obtain brightness temperature data of each target remote sensing image; performing calculations based on the target remote sensing image of each area to obtain a dust aerosol optical thickness value of each target remote sensing image; judging whether a sandstorm occurs in the target area based on the brightness temperature data and the dust aerosol optical thickness value.
[0009] Furthermore, determining the target dust intensity of the target area based on the target remote sensing image of the target area and the ground observation data of the target area includes: calculating based on the target remote sensing image of the target area to obtain a dust concentration value of the target area; calculating based on the dust concentration value and the ground observation data to obtain a first dust intensity of the target area; processing the dust concentration value and the ground observation data by a second algorithm to obtain a second dust intensity of the target area; and calculating based on the first dust intensity and the second dust intensity to obtain the target dust intensity.
[0010] Furthermore, calculating based on the dust concentration value and the ground observation data to obtain the first dust intensity of the target area includes: determining a first influence value of the dust concentration value on the dust intensity and a second influence value of the ground observation data on the dust intensity through a third algorithm; and performing weighted calculation on the dust concentration value and the ground observation data based on the first influence value and the second influence value to obtain the first dust intensity.
[0011] Furthermore, calculating based on the dust concentration value and the ground observation data to obtain the first dust intensity of the target area includes: obtaining historical dust concentration values, historical ground observation data, and historical dust intensities corresponding to the historical dust concentration values and the historical ground observation data; performing multivariate regression analysis based on the historical dust concentration values, the historical ground observation data, and the historical dust intensities to obtain a regression curve; and calculating based on the regression curve, the dust concentration value, and the ground observation data to obtain the first dust intensity of the target area.
[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for detecting sandstorm information is provided. The device comprises: an acquisition unit, used to acquire target remote sensing images of multiple areas; a judgment unit, used to judge whether there is a sandstorm in the target area based on the target remote sensing image of each area; a determination unit, used to determine the target sandstorm intensity of the target area based on the target remote sensing image of the target area and the ground observation data of the target area if there is a sandstorm in the target area, wherein the target sandstorm intensity is used to determine the afforestation recommendation for the target area.
[0013] Furthermore, the acquisition unit includes: an acquisition module, which is used to collect remote sensing images of each area through a geostationary meteorological satellite to obtain multiple initial remote sensing images; a first processing module, which is used to perform illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images; and a correction module, which is used to perform correction processing on the processed initial remote sensing images to obtain the target remote sensing image.
[0014] Furthermore, the processing module includes: a first acquisition submodule, used to obtain acquisition information of each initial remote sensing image, wherein the acquisition information at least includes: the acquisition time and the geographical location information of the initial remote sensing image; a first processing submodule, used to process the acquisition information through a first algorithm to obtain a compensation value when performing illumination compensation on each initial remote sensing image; and a second processing submodule, used to perform illumination compensation processing on the multiple initial remote sensing images according to the compensation value to obtain the processed initial remote sensing images.
[0015] Furthermore, the judgment unit includes: a first calculation module, used to perform calculations based on the target remote sensing image of each area to obtain brightness temperature data of each target remote sensing image; a second calculation module, used to perform calculations based on the target remote sensing image of each area to obtain the dust aerosol optical thickness value of each target remote sensing image; and a judgment module, used to judge whether a sandstorm occurs in the target area based on the brightness temperature data and the dust aerosol optical thickness value.
[0016] Furthermore, the determination unit includes: a third calculation module, used to calculate based on the target remote sensing image of the target area to obtain a dust concentration value of the target area; a fourth calculation module, used to calculate based on the dust concentration value and the ground observation data to obtain a first dust intensity of the target area; a second processing module, used to process the dust concentration value and the ground observation data through a second algorithm to obtain a second dust intensity of the target area; and a fifth calculation module, used to calculate based on the first dust intensity and the second dust intensity to obtain the target dust intensity.
[0017] Furthermore, the fourth calculation module includes: a determination submodule, used to determine the first influence value of the dust concentration value on the dust intensity and the second influence value of the ground observation data on the dust intensity through a third algorithm; a first calculation submodule, used to perform weighted calculation on the dust concentration value and the ground observation data according to the first influence value and the second influence value to obtain the first dust intensity.
[0018] Furthermore, the fourth calculation module includes: a second acquisition submodule, used to obtain historical dust concentration values, historical ground observation data, and historical dust intensities corresponding to the historical dust concentration values and the historical ground observation data; an analysis submodule, used to perform multivariate regression analysis based on the historical dust concentration values, the historical ground observation data and the historical dust intensities to obtain a regression curve; and a second calculation submodule, used to perform calculations based on the regression curve, the dust concentration values and the ground observation data to obtain a first dust intensity in the target area.
[0019] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the storage medium stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned methods for detecting sandstorm information.
[0020] According to another aspect of an embodiment of the present invention, an electronic device is provided, including a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned methods for detecting sandstorm information when running.
[0021] Through this application, the following steps are adopted: acquiring target remote sensing images of multiple regions; judging whether there is a sandstorm in the target region based on the target remote sensing images of each region; if there is a sandstorm in the target region, determining the target sandstorm intensity of the target region based on the target remote sensing images of the target region and the ground observation data of the target region, wherein the target sandstorm intensity is used to determine the afforestation recommendations for the target region, solving the problem that the detection efficiency of sandstorm information is relatively low due to the detection of sandstorms and their intensity by field investigation in the related art. In this scheme, target remote sensing images of multiple regions are collected, and judging whether there is a sandstorm in the target region by the characteristic information contained in the target remote sensing images. In the case of a sandstorm in the target region, the sandstorm intensity is determined by the target remote sensing images and the ground observation data, avoiding the detection of sandstorms and their intensity by field investigation, thereby achieving the effect of improving the detection efficiency of sandstorm information. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0023] Figure 1 The process of the sandstorm information detection method provided in the embodiment of the present application is as follows Figure 1 ;
[0024] Figure 2 The process of the sandstorm information detection method provided in the embodiment of the present application is as follows Figure 2 ;
[0025] Figure 3 is a schematic diagram of a device for detecting sandstorm information provided according to an embodiment of the present application;
[0026] Figure 4 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION
[0027] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.
[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0030] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.
[0031] The present invention is described below in conjunction with preferred implementation steps. Figure 1 The process of the sandstorm information detection method provided in the embodiment of the present application is as follows Figure 1 ,like Figure 1 As shown, the method comprises the following steps:
[0032] Step S101, acquiring target remote sensing images of multiple areas;
[0033] Optionally, remote sensing images of multiple areas that need to be detected for dust storms can be collected through geostationary meteorological satellites. It should be noted that after obtaining remote sensing images of multiple areas, in order to improve the accuracy of subsequent judgment of sandstorm information, the remote sensing images can be pre-processed by radiation calibration, atmospheric correction, geometric correction, etc.
[0034] Step S102, judging whether a sandstorm occurs in the target area based on the target remote sensing image of each area;
[0035] Optionally, after obtaining the above-mentioned target remote sensing image, it is determined whether a sandstorm occurs in the target area by using the information contained in the target remote sensing image. For example, it is determined whether a sandstorm occurs based on the brightness temperature feature of infrared channel data in the target remote sensing image. For another example, a feature of the target remote sensing image is extracted by a deep learning algorithm, and whether a sandstorm occurs is predicted based on the extracted features.
[0036] Step S103, if a sandstorm occurs in the target area, a target sandstorm intensity of the target area is determined based on the target remote sensing image of the target area and the ground observation data of the target area, wherein the target sandstorm intensity is used to determine afforestation recommendations for the target area.
[0037] Optionally, if a sandstorm is found in a target area, the sandstorm intensity of the target area is calculated based on the target remote sensing image of the target area and the ground observation data of the target area (e.g., wind speed) to obtain the above-mentioned target sandstorm intensity. The sandstorm intensity can be used to more reasonably formulate afforestation plans and more effectively protect trees in the target area.
[0038] To summarize, target remote sensing images of multiple areas are collected, and the characteristic information contained in the target remote sensing images is used to determine whether a sandstorm has occurred in the target area. When a sandstorm has occurred in the target area, the intensity of the sandstorm is determined through the target remote sensing images and ground observation data, thereby avoiding the detection of sandstorms and their intensity through field investigations, thereby achieving the effect of improving the detection efficiency of sandstorm information.
[0039] Optionally, in the method for detecting sandstorm information provided in an embodiment of the present application, obtaining target remote sensing images of multiple areas includes: collecting remote sensing images of each area through a geostationary meteorological satellite to obtain multiple initial remote sensing images; performing illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images; and performing correction processing on the processed initial remote sensing images to obtain target remote sensing images.
[0040] In an optional embodiment, in order to improve the accuracy of subsequent detection of sandstorm information, obtaining target remote sensing images of multiple areas includes the following steps: collecting remote sensing images of each area through a geostationary meteorological satellite, then performing illumination compensation processing on multiple initial remote sensing images to obtain processed initial remote sensing images, and finally performing correction processing on the processed initial remote sensing images to obtain the above-mentioned target remote sensing images.
[0041] By supplementing the illumination and correcting the remote sensing images, the brightness of the remote sensing images can be ensured to be the same, thereby improving the accuracy of subsequent detection of sandstorm information.
[0042] Optionally, in the sandstorm information detection method provided in the embodiment of the present application, multiple initial remote sensing images are subjected to illumination compensation processing to obtain the processed initial remote sensing images, including: obtaining acquisition information of each initial remote sensing image, wherein the acquisition information includes at least: the acquisition time and the geographic location information of the initial remote sensing image; processing the acquisition information by a first algorithm to obtain a compensation value when performing illumination compensation on each initial remote sensing image; performing illumination compensation processing on multiple initial remote sensing images according to the compensation value to obtain the processed initial remote sensing images.
[0043] In an optional embodiment, the following steps are used to implement illumination compensation processing for multiple initial remote sensing images: acquisition information of each initial remote sensing image, such as acquisition time and geographic location information, is obtained, and then the acquisition information is input into a first algorithm. It should be noted that the first algorithm may be a deep learning algorithm, which is trained by a training set constructed by acquisition time, acquisition location, and corresponding illumination supplement values.
[0044] After the acquisition situation information is input into the first algorithm, the first algorithm predicts the compensation value corresponding to the initial remote sensing image based on the acquisition time and the geographical location information of the initial remote sensing image to obtain the compensation value during the above-mentioned illumination compensation.
[0045] Finally, illumination compensation processing is performed on each initial remote sensing image based on the compensation value corresponding to the initial remote sensing image to obtain the above-mentioned processed initial remote sensing image.
[0046] The first algorithm determines the corresponding illumination compensation according to the acquisition situation of each remote sensing image, thereby improving the accuracy of illumination compensation.
[0047] Optionally, in the method for detecting sandstorm information provided in the embodiment of the present application, judging whether a sandstorm occurs in the target area based on the target remote sensing image of each area includes: performing calculations based on the target remote sensing image of each area to obtain brightness temperature data of each target remote sensing image; performing calculations based on the target remote sensing image of each area to obtain a dust aerosol optical thickness value of each target remote sensing image; and judging whether a sandstorm occurs in the target area based on the brightness temperature data and the dust aerosol optical thickness value.
[0048] In an optional embodiment, the following steps are used to determine whether a sandstorm has occurred in the target area: first, calculations are performed based on the brightness temperature data in the target remote sensing image of each area to obtain the above-mentioned brightness temperature data. For example, the brightness temperature data of the 11μm and 12μm channels in the target remote sensing image are extracted, and the difference calculation is performed, and the obtained difference is used as the above-mentioned brightness temperature data.
[0049] Then, the dust aerosol optical thickness value at a wavelength of 550nm is calculated based on the target remote sensing image of each area. Finally, based on the brightness temperature data and the dust aerosol optical thickness value, it is determined whether a sandstorm occurs in the target area. In an optional embodiment, a brightness temperature threshold and a dust aerosol optical thickness threshold can be set. If the brightness temperature data or the dust aerosol optical thickness value of a certain area exceeds the threshold, it is determined that a sandstorm occurs in the area.
[0050] Brightness temperature data and dust aerosol optical thickness values can be used to accurately assess the current dust conditions and improve the accuracy of judging whether a sandstorm exists.
[0051] Optionally, in the sandstorm information detection method provided in the embodiment of the present application, determining the target sand and dust intensity of the target area based on the target remote sensing image of the target area and the ground observation data of the target area includes: calculating based on the target remote sensing image of the target area to obtain a sand and dust concentration value of the target area; calculating based on the sand and dust concentration value and the ground observation data to obtain a first sand and dust intensity of the target area; processing the sand and dust concentration value and the ground observation data by a second algorithm to obtain a second sand and dust intensity of the target area; and calculating based on the first sand and dust intensity and the second sand and dust intensity to obtain the target sand and dust intensity.
[0052] In an optional embodiment, the dust concentration value of the target area is determined by the following steps: first, the spectral information contained in the target remote sensing image of the target area is calculated to obtain the dust concentration value of the target area. Then, a fitting calculation is performed based on the dust concentration value and the ground observation data to obtain the above-mentioned first dust intensity. And the dust concentration value and the ground observation data are input into the second algorithm. It should be noted that the second algorithm can be a neural network model.
[0053] Then, the second algorithm is used to extract features from the dust concentration value and the ground observation data, and prediction is performed based on the extracted features to obtain the second dust intensity of the target area.
[0054] Finally, a final target dust intensity is obtained by calculation according to the first dust intensity and the second dust intensity.
[0055] The dust intensity is calculated by data fitting calculation and the second algorithm, which improves the accuracy of calculating the dust intensity.
[0056] Optionally, in the method for detecting sandstorm information provided in an embodiment of the present application, calculation is performed based on the dust concentration value and ground observation data to obtain the first dust intensity of the target area, including: determining a first influence value of the dust concentration value on the dust intensity and a second influence value of the ground observation data on the dust intensity through a third algorithm; and performing weighted calculation on the dust concentration value and the ground observation data based on the first influence value and the second influence value to obtain the first dust intensity.
[0057] In an optional embodiment, a deep learning algorithm (i.e., the third algorithm mentioned above) can be used to determine the first influence value of dust concentration on dust intensity and the second influence value of ground observation data on dust intensity. The first influence value and the second influence value mentioned above can also be set directly based on expert experience.
[0058] Then, the dust concentration value and the ground observation data are normalized to unify the dimensions. Finally, the dust concentration value and the ground observation data are weighted according to the first impact value and the second impact value to obtain the first dust intensity.
[0059] The dust concentration value and ground observation data are calculated through the influence value, which improves the accuracy of calculating the first dust intensity.
[0060] Optionally, in the method for detecting sandstorm information provided in the embodiment of the present application, calculation is performed based on the sand dust concentration value and ground observation data to obtain the first sand dust intensity of the target area, including: obtaining historical sand dust concentration values, historical ground observation data, and historical sand dust intensities corresponding to the historical sand dust concentration values and historical ground observation data; performing a multivariate regression analysis based on the historical sand dust concentration values, historical ground observation data, and historical sand dust intensities to obtain a regression curve; and calculating based on the regression curve, sand dust concentration values, and ground observation data to obtain the first sand dust intensity of the target area.
[0061] In an optional embodiment, the following steps can also be used to calculate the above-mentioned first dust intensity: collect historical dust concentration values, historical ground observation data, and historical dust intensities corresponding to historical dust concentration values and historical ground observation data, and perform multivariate regression analysis on these data to obtain a regression curve, and finally calculate based on the regression curve and the current dust concentration value and ground observation data to obtain the first dust intensity of the target area.
[0062] Through multiple regression analysis, the regression curve can be accurately fitted and the first dust intensity in the target area can be calculated more efficiently.
[0063] In an optional embodiment, the Figure 2 The flowchart shown realizes the detection of sandstorm information: remote sensing images of cities prone to sandstorms are monitored through satellite remote sensing, remote sensing data are obtained and pre-processed: infrared channel data of geostationary meteorological satellites are obtained, and pre-processing such as radiation calibration, atmospheric correction, and geometric correction is performed. According to the brightness temperature characteristics of infrared channel data, the threshold range of the sandstorm area is determined, and it is judged whether a sandstorm has occurred. Machine learning methods (such as support vector machines, random forests, etc.) are used to quantitatively evaluate and grade the sandstorm intensity in combination with ground observation data and remote sensing images, so as to formulate afforestation recommendations later.
[0064] The method for detecting sandstorm information provided by the embodiment of the present application is to obtain target remote sensing images of multiple areas; determine whether there is a sandstorm in the target area based on the target remote sensing images of each area; if there is a sandstorm in the target area, determine the target sandstorm intensity of the target area based on the target remote sensing image of the target area and the ground observation data of the target area, wherein the target sandstorm intensity is used to determine the afforestation suggestion for the target area, solving the problem of low efficiency of detecting sandstorm information by detecting sandstorms and their intensity through field investigation in the related art. In this scheme, target remote sensing images of multiple areas are collected, and it is determined whether there is a sandstorm in the target area based on the characteristic information contained in the target remote sensing images. In the case of a sandstorm in the target area, the sandstorm intensity is determined through the target remote sensing image and the ground observation data, avoiding the detection of sandstorms and their intensity through field investigation, thereby achieving the effect of improving the detection efficiency of sandstorm information.
[0065] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.
[0066] The present application also provides a device for detecting sandstorm information. It should be noted that the device for detecting sandstorm information in the present application can be used to execute the method for detecting sandstorm information provided in the present application. The following introduces the device for detecting sandstorm information provided in the present application.
[0067] Figure 3 Schematic diagram of a device for detecting sandstorm information according to an embodiment of the present application. Figure 3 As shown, the device includes: an acquisition unit 301, a judgment unit 302 and a determination unit 303.
[0068] An acquisition unit 301 is used to acquire target remote sensing images of multiple regions;
[0069] A determination unit 302 is used to determine whether a sandstorm occurs in the target area based on the target remote sensing image of each area;
[0070] The determination unit 303 is used to determine the target dust intensity of the target area based on the target remote sensing image of the target area and the ground observation data of the target area if a sandstorm occurs in the target area, wherein the target sand intensity is used to determine afforestation recommendations for the target area.
[0071] The detection device for sandstorm information provided by the embodiment of the present application obtains target remote sensing images of multiple regions through an acquisition unit 301; the judgment unit 302 judges whether there is a sandstorm in the target region based on the target remote sensing image of each region; the determination unit 303 determines the target sandstorm intensity of the target region based on the target remote sensing image of the target region and the ground observation data of the target region if there is a sandstorm in the target region, wherein the target sandstorm intensity is used to determine the afforestation suggestion for the target region, solving the problem that the detection efficiency of sandstorm information is relatively low due to the detection of sandstorm and its intensity by field investigation in the related art. In this scheme, target remote sensing images of multiple regions are collected, and it is judged whether there is a sandstorm in the target region by the feature information contained in the target remote sensing images. In the case of a sandstorm in the target region, the sandstorm intensity is determined by the target remote sensing image and the ground observation data, avoiding the detection of sandstorm and its intensity by field investigation, thereby achieving the effect of improving the detection efficiency of sandstorm information.
[0072] Optionally, in the detection device for sandstorm information provided in the embodiment of the present application, the acquisition unit includes: an acquisition module, used to collect remote sensing images of each area through a geostationary meteorological satellite to obtain multiple initial remote sensing images; a first processing module, used to perform illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images; and a correction module, used to perform correction processing on the processed initial remote sensing images to obtain a target remote sensing image.
[0073] Optionally, in the sandstorm information detection device provided in the embodiment of the present application, the processing module includes: a first acquisition submodule, used to acquire acquisition information of each initial remote sensing image, wherein the acquisition information includes at least: the acquisition time and the geographical location information of the initial remote sensing image; a first processing submodule, used to process the acquisition information through a first algorithm to obtain a compensation value when performing illumination compensation on each initial remote sensing image; and a second processing submodule, used to perform illumination compensation processing on multiple initial remote sensing images according to the compensation value to obtain processed initial remote sensing images.
[0074] Optionally, in the detection device for sandstorm information provided in the embodiment of the present application, the judgment unit includes: a first calculation module, used to perform calculations based on the target remote sensing image of each area to obtain brightness temperature data of each target remote sensing image; a second calculation module, used to perform calculations based on the target remote sensing image of each area to obtain the dust aerosol optical thickness value of each target remote sensing image; and a judgment module, used to judge whether a sandstorm occurs in the target area based on the brightness temperature data and the dust aerosol optical thickness value.
[0075] Optionally, in the detection device for sandstorm information provided in the embodiment of the present application, the determination unit includes: a third calculation module, used to calculate based on the target remote sensing image of the target area to obtain the sand and dust concentration value of the target area; a fourth calculation module, used to calculate based on the sand and dust concentration value and ground observation data to obtain a first sand and dust intensity of the target area; a second processing module, used to process the sand and dust concentration value and ground observation data by a second algorithm to obtain a second sand and dust intensity of the target area; a fifth calculation module, used to calculate based on the first sand and dust intensity and the second sand and dust intensity to obtain the target sand and dust intensity.
[0076] Optionally, in the sandstorm information detection device provided in the embodiment of the present application, the fourth calculation module includes: a determination submodule, used to determine a first influence value of the dust concentration value on the dust intensity and a second influence value of the ground observation data on the dust intensity through a third algorithm; a first calculation submodule, used to perform weighted calculation on the dust concentration value and the ground observation data according to the first influence value and the second influence value to obtain a first dust intensity.
[0077] Optionally, in the detection device for sandstorm information provided in the embodiment of the present application, the fourth calculation module includes: a second acquisition submodule, used to obtain historical sand and dust concentration values, historical ground observation data, and historical sand and dust intensities corresponding to the historical sand and dust concentration values and historical ground observation data; an analysis submodule, used to perform multivariate regression analysis based on the historical sand and dust concentration values, historical ground observation data, and historical sand and dust intensities to obtain a regression curve; and a second calculation submodule, used to perform calculations based on the regression curve, sand and dust concentration values, and ground observation data to obtain a first sand and dust intensity in the target area.
[0078] The device for detecting sandstorm information includes a processor and a memory. The above-mentioned acquisition unit 301, judgment unit 302 and determination unit 303 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.
[0079] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the sandstorm information can be accurately detected by adjusting the kernel parameters.
[0080] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.
[0081] An embodiment of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, a method for detecting sandstorm information is implemented.
[0082] An embodiment of the present invention provides a processor, which is used to run a program, wherein a method for detecting sandstorm information is executed when the program is running.
[0083] like Figure 4 As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: acquiring target remote sensing images of multiple areas; judging whether a sandstorm occurs in the target area based on the target remote sensing image of each area; if a sandstorm occurs in the target area, determining a target sandstorm intensity of the target area based on the target remote sensing image of the target area and ground observation data of the target area, wherein the target sandstorm intensity is used to determine afforestation recommendations for the target area.
[0084] Optionally, acquiring target remote sensing images of multiple areas includes: collecting remote sensing images of each area through a geostationary meteorological satellite to obtain multiple initial remote sensing images; performing illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images; and performing correction processing on the processed initial remote sensing images to obtain target remote sensing images.
[0085] Optionally, performing illumination compensation processing on multiple initial remote sensing images to obtain processed initial remote sensing images includes: obtaining acquisition information of each initial remote sensing image, wherein the acquisition information includes at least: acquisition time and geographic location information of the initial remote sensing image; processing the acquisition information by a first algorithm to obtain a compensation value when performing illumination compensation on each initial remote sensing image; performing illumination compensation processing on multiple initial remote sensing images according to the compensation value to obtain processed initial remote sensing images.
[0086] Optionally, judging whether a sandstorm occurs in the target area based on the target remote sensing images of each area includes: performing calculations based on the target remote sensing images of each area to obtain brightness temperature data of each target remote sensing image; performing calculations based on the target remote sensing images of each area to obtain dust aerosol optical thickness values of each target remote sensing image; judging whether a sandstorm occurs in the target area based on the brightness temperature data and the dust aerosol optical thickness values.
[0087] Optionally, determining the target dust intensity of the target area based on the target remote sensing image of the target area and the ground observation data of the target area includes: calculating based on the target remote sensing image of the target area to obtain a dust concentration value of the target area; calculating based on the dust concentration value and the ground observation data to obtain a first dust intensity of the target area; processing the dust concentration value and the ground observation data by a second algorithm to obtain a second dust intensity of the target area; and calculating based on the first dust intensity and the second dust intensity to obtain the target dust intensity.
[0088] Optionally, calculating based on the dust concentration value and ground observation data to obtain the first dust intensity of the target area includes: determining a first influence value of the dust concentration value on the dust intensity and a second influence value of the ground observation data on the dust intensity through a third algorithm; and performing weighted calculation on the dust concentration value and the ground observation data based on the first influence value and the second influence value to obtain the first dust intensity.
[0089] Optionally, calculating based on dust concentration values and ground observation data to obtain a first dust intensity in the target area includes: obtaining historical dust concentration values, historical ground observation data, and historical dust intensities corresponding to the historical dust concentration values and historical ground observation data; performing a multivariate regression analysis based on the historical dust concentration values, historical ground observation data, and historical dust intensities to obtain a regression curve; and calculating based on the regression curve, dust concentration values, and ground observation data to obtain the first dust intensity in the target area.
[0090] The devices in this article can be servers, PCs, PADs, mobile phones, etc.
[0091] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: acquiring target remote sensing images of multiple areas; judging whether a sandstorm has occurred in the target area based on the target remote sensing images of each area; if a sandstorm has occurred in the target area, determining a target sandstorm intensity of the target area based on the target remote sensing images of the target area and ground observation data of the target area, wherein the target sandstorm intensity is used to determine afforestation recommendations for the target area.
[0092] Optionally, acquiring target remote sensing images of multiple areas includes: collecting remote sensing images of each area through a geostationary meteorological satellite to obtain multiple initial remote sensing images; performing illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images; and performing correction processing on the processed initial remote sensing images to obtain target remote sensing images.
[0093] Optionally, performing illumination compensation processing on multiple initial remote sensing images to obtain processed initial remote sensing images includes: obtaining acquisition information of each initial remote sensing image, wherein the acquisition information includes at least: acquisition time and geographic location information of the initial remote sensing image; processing the acquisition information by a first algorithm to obtain a compensation value when performing illumination compensation on each initial remote sensing image; performing illumination compensation processing on multiple initial remote sensing images according to the compensation value to obtain processed initial remote sensing images.
[0094] Optionally, judging whether a sandstorm occurs in the target area based on the target remote sensing images of each area includes: performing calculations based on the target remote sensing images of each area to obtain brightness temperature data of each target remote sensing image; performing calculations based on the target remote sensing images of each area to obtain dust aerosol optical thickness values of each target remote sensing image; judging whether a sandstorm occurs in the target area based on the brightness temperature data and the dust aerosol optical thickness values.
[0095] Optionally, determining the target dust intensity of the target area based on the target remote sensing image of the target area and the ground observation data of the target area includes: calculating based on the target remote sensing image of the target area to obtain a dust concentration value of the target area; calculating based on the dust concentration value and the ground observation data to obtain a first dust intensity of the target area; processing the dust concentration value and the ground observation data by a second algorithm to obtain a second dust intensity of the target area; and calculating based on the first dust intensity and the second dust intensity to obtain the target dust intensity.
[0096] Optionally, calculating based on the dust concentration value and ground observation data to obtain the first dust intensity of the target area includes: determining a first influence value of the dust concentration value on the dust intensity and a second influence value of the ground observation data on the dust intensity through a third algorithm; and performing weighted calculation on the dust concentration value and the ground observation data based on the first influence value and the second influence value to obtain the first dust intensity.
[0097] Optionally, calculating based on dust concentration values and ground observation data to obtain a first dust intensity in the target area includes: obtaining historical dust concentration values, historical ground observation data, and historical dust intensities corresponding to the historical dust concentration values and historical ground observation data; performing a multivariate regression analysis based on the historical dust concentration values, historical ground observation data, and historical dust intensities to obtain a regression curve; and calculating based on the regression curve, dust concentration values, and ground observation data to obtain the first dust intensity in the target area.
[0098] 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0103] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0104] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0105] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0106] 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 adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0107] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A method for detecting sandstorm information, characterized in that: include: Acquire target remote sensing images of multiple areas; Based on the target remote sensing images of each area, determine whether there is a sandstorm in the target area; If a sandstorm occurs in the target area, a target sandstorm intensity of the target area is determined based on the target remote sensing image of the target area and the ground observation data of the target area, wherein the target sandstorm intensity is used to determine afforestation recommendations for the target area.
2. The method according to claim 1, characterized in that Acquiring target remote sensing images of multiple areas includes: Remote sensing images of each area are collected by geostationary meteorological satellites to obtain multiple initial remote sensing images; Performing illumination compensation processing on the multiple initial remote sensing images to obtain processed initial remote sensing images; The processed initial remote sensing image is corrected to obtain the target remote sensing image.
3. The method according to claim 2, characterized in that The illumination compensation processing is performed on the multiple initial remote sensing images to obtain the processed initial remote sensing images, including: Acquiring acquisition information of each initial remote sensing image, wherein the acquisition information at least includes: acquisition time and geographic location information of the initial remote sensing image; Processing the acquisition information by a first algorithm to obtain a compensation value for illumination compensation of each initial remote sensing image; The illumination compensation processing is performed on the multiple initial remote sensing images according to the compensation value to obtain the processed initial remote sensing images.
4. The method according to claim 1, characterized in that: Based on the target remote sensing images of each area, judging whether there is a sandstorm in the target area includes: Calculate based on the target remote sensing image of each area to obtain the brightness temperature data of each target remote sensing image; Calculate based on the target remote sensing image of each area to obtain the dust aerosol optical thickness value of each target remote sensing image; Based on the brightness temperature data and the dust aerosol optical thickness value, it is determined whether a sandstorm occurs in the target area.
5. The method according to claim 1, characterized in that Determining the target dust intensity of the target area according to the target remote sensing image of the target area and the ground observation data of the target area includes: Calculating based on the target remote sensing image of the target area to obtain the dust concentration value of the target area; Calculating according to the dust concentration value and the ground observation data to obtain a first dust intensity of the target area; Processing the dust concentration value and the ground observation data by a second algorithm to obtain a second dust intensity of the target area; The target dust intensity is obtained by performing calculation according to the first dust intensity and the second dust intensity.
6. The method according to claim 5, characterized in that Calculating according to the dust concentration value and the ground observation data to obtain the first dust intensity of the target area includes: Determine, by a third algorithm, a first influence value of the dust concentration value on the dust intensity and a second influence value of the ground observation data on the dust intensity; The first dust intensity is obtained by performing weighted calculation on the dust concentration value and the ground observation data according to the first impact value and the second impact value.
7. The method according to claim 5, characterized in that Calculating according to the dust concentration value and the ground observation data to obtain the first dust intensity of the target area includes: Obtaining historical dust concentration values, historical ground observation data, and historical dust intensities corresponding to the historical dust concentration values and the historical ground observation data; Performing a multivariate regression analysis based on the historical dust concentration values, the historical ground observation data and the historical dust intensity to obtain a regression curve; A first dust intensity of the target area is obtained by performing calculations based on the regression curve, the dust concentration value and the ground observation data.
8. A device for detecting sandstorm information, characterized in that: include: An acquisition unit, used for acquiring target remote sensing images of multiple areas; A judgment unit, used for judging whether a sandstorm occurs in the target area according to the target remote sensing image of each area; A determination unit is used to determine a target dust intensity of the target area based on a target remote sensing image of the target area and ground observation data of the target area if a sandstorm occurs in the target area, wherein the target sand intensity is used to determine afforestation recommendations for the target area.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the storage medium is controlled to execute the method for detecting sandstorm information according to any one of claims 1 to 7 on a device.
10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the sandstorm information detection method described in any one of claims 1 to 7.