Method and device for determining drought degree of land, electronic equipment and storage medium

Abstract

The invention discloses a land drought degree determination method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence, the field of financial science and technology or other related technical fields, and the determination method comprises the steps: obtaining a remote sensing image set of a target land; extracting remote sensing data of different wavebands from the remote sensing image set to obtain remote sensing data sets corresponding to the different wavebands; performing discrete processing on the remote sensing data sets corresponding to different wavebands to obtain a target data set; and inputting the target data in the target data set into a drought evaluation model one by one, and outputting the drought degree of the target land, the drought evaluation model being a model which is pre-trained based on an inversion algorithm and is used for estimating the drought degree of the land. According to the method and the device, the technical problem that the evaluation accuracy of the land drought degree is relatively low because the land drought degree is often judged through manual visits and based on human experience in the related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, financial technology or other related technical fields, and specifically, to a method and device for determining the degree of land drought, an electronic device and a storage medium. Background Art

[0002] The degree of land drought, as an indicator for assessing the ecological conditions of the target land, can reflect the state and value of the land. At present, land drought assessment plays an important role in many fields. For example, in the field of agricultural production management, drought is one of the key factors affecting crop growth and yield. By monitoring the degree of land drought, managers can take timely measures, such as irrigation, planting drought-resistant crop varieties or adjusting planting plans, to reduce the impact of drought on agricultural production; in the field of disaster prevention, drought may lead to serious natural disasters such as forest fires, land degradation and desertification. By monitoring the degree of land drought, early warnings can be given and preventive measures can be taken to reduce disaster risks; or, in the financial or insurance fields, the degree of land drought is an indicator of land value. By monitoring the degree of land drought, risk assessments can be conducted on loan requests or insurance institutions can be assisted in assessing risks.

[0003] In related technologies, the degree of land drought is generally measured based on manual visits and sensor measurements. This method is costly, has low measurement efficiency and accuracy, and is difficult to meet business needs.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for determining the degree of land drought, an electronic device and a storage medium, so as to at least solve the technical problem in the related art that the degree of land drought is often judged through manual visits and based on human experience, resulting in low accuracy in the assessment of the degree of land drought.

[0006] According to one aspect of an embodiment of the present invention, a method for determining the degree of land drought is provided, comprising: acquiring a set of remote sensing images of target land; extracting remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to different bands; performing discrete processing on the remote sensing data sets corresponding to different bands to obtain a target data set; inputting the target data in the target data set into a drought assessment model one by one, and outputting the degree of drought of the target land, wherein the drought assessment model is a model for estimating the degree of land drought that is pre-trained based on an inversion algorithm.

[0007] Optionally, the step of extracting remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to different remote sensing bands includes: selecting a remote sensing band to be analyzed to obtain a first band and a second band, wherein the first band and the second band are two different remote sensing bands; for each remote sensing image in the remote sensing image set, extracting the remote sensing data of the first band in the remote sensing image to obtain a first remote sensing data set corresponding to the first band; for each remote sensing image in the remote sensing image set, extracting the remote sensing data of the second band in the remote sensing image to obtain a second remote sensing data set corresponding to the second band.

[0008] Optionally, the remote sensing data sets corresponding to different remote sensing bands are discretely processed to obtain a target data set, including: drawing a feature image based on the remote sensing data in the first remote sensing data set and the second remote sensing data set; and discretely processing the feature data in the feature image.

[0009] Optionally, the step of performing discrete processing on the feature data in the feature image includes: calculating the mean of the first remote sensing data set corresponding to the first band to obtain a first mean, and calculating the standard deviation of the first remote sensing data set corresponding to the first band to obtain a first standard deviation; calculating the mean of the second remote sensing data set corresponding to the second band to obtain a second mean, and calculating the standard deviation of the second remote sensing data set corresponding to the second band to obtain a second standard deviation; calculating a discrete range based on the first mean, the first standard deviation, the second mean and the second standard deviation; and eliminating the data outside the discrete range in the feature image as discrete data to obtain the target data set.

[0010] Optionally, after acquiring a set of remote sensing images of the target land, the method further includes: for each remote sensing image in the remote sensing image set, calculating the land flatness of the remote sensing image; when the land flatness of the remote sensing image is less than a preset flatness, correcting the remote sensing image based on a digital elevation algorithm to obtain a corrected remote sensing image.

[0011] Optionally, after acquiring the remote sensing image set of the target land, the method further includes: performing radiation correction and atmospheric correction on each remote sensing image in the remote sensing image set to obtain a corrected remote sensing image.

[0012] Optionally, after outputting the drought degree of the target land, it also includes: when the drought degree is greater than a preset drought degree threshold, matching a solution strategy for the target land based on the drought degree, wherein the solution strategy is used to provide information for solving the drought problem of the target land.

[0013] Optionally, after outputting the drought degree of the target land, it also includes: when the drought degree is less than or equal to a preset drought degree threshold, matching a crop planting strategy for the target land based on the drought degree, wherein the crop planting strategy is used to provide information for determining crops that match the target land.

[0014] According to another aspect of an embodiment of the present invention, a device for determining the degree of land drought is also provided, including: an acquisition unit, used to acquire a set of remote sensing images of the target land; an extraction unit, used to extract remote sensing data of different bands from the remote sensing image set, and obtain remote sensing data sets corresponding to different bands; a processing unit, used to perform discrete processing on the remote sensing data sets corresponding to different bands, and obtain a target data set; an output unit, used to input the target data in the target data set into a drought assessment model one by one, and output the degree of drought of the target land, wherein the drought assessment model is a model for estimating the degree of land drought that is pre-trained based on an inversion algorithm.

[0015] Optionally, the extraction unit includes: a first selection module, used to select the remote sensing band to be analyzed, to obtain a first band and a second band, wherein the first band and the second band are two different remote sensing bands; a first extraction module, used to extract the remote sensing data of the first band in each remote sensing image in the remote sensing image set, to obtain a first remote sensing data set corresponding to the first band; and a second extraction module, used to extract the remote sensing data of the second band in each remote sensing image in the remote sensing image set, to obtain a second remote sensing data set corresponding to the second band.

[0016] Optionally, the processing unit includes: a first drawing module, used to draw a feature image based on the remote sensing data in the first remote sensing data set and the second remote sensing data set; and a first processing module, used to perform discrete processing on the feature data in the feature image.

[0017] Optionally, the first processing module includes: a first calculation submodule, used to calculate the mean of the first remote sensing data set corresponding to the first band to obtain a first mean, and calculate the standard deviation of the first remote sensing data set corresponding to the first band to obtain a first standard deviation; a second calculation submodule, used to calculate the mean of the second remote sensing data set corresponding to the second band to obtain a second mean, and calculate the standard deviation of the second remote sensing data set corresponding to the second band to obtain a second standard deviation; a third calculation submodule, used to calculate a discrete range based on the first mean, the first standard deviation, the second mean and the second standard deviation; a first elimination submodule, used to eliminate the data outside the discrete range in the feature image as discrete data to obtain the target data set.

[0018] Optionally, the device for determining the degree of land drought also includes: a first calculation module, used to calculate the land flatness of each remote sensing image in the remote sensing image set; a first correction module, used to correct the remote sensing image based on a digital elevation algorithm to obtain a corrected remote sensing image when the land flatness of the remote sensing image is less than a preset flatness.

[0019] Optionally, the device for determining the degree of land drought further includes: a second correction module, configured to perform radiation correction and atmospheric correction on each remote sensing image in the remote sensing image set to obtain a corrected remote sensing image.

[0020] Optionally, the device for determining the degree of land drought also includes: a first matching module, used to match a solution strategy for the target land based on the drought degree when the drought degree is greater than a preset drought degree threshold, wherein the solution strategy is used to provide information for solving the drought problem of the target land.

[0021] Optionally, the device for determining the degree of land drought also includes: a second matching module, used to match a crop planting strategy for the target land based on the drought degree when the drought degree is less than or equal to a preset drought degree threshold, wherein the crop planting strategy is used to provide information for determining crops that match the target land.

[0022] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any of the above-mentioned methods for determining the degree of land drought.

[0023] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising 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 any one of the above-described methods for determining the degree of land drought.

[0024] According to another aspect of an embodiment of the present invention, a computer program product is provided, the computer program product comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the degree of land drought described in any one of the above is implemented.

[0025] In the present application, the following steps are performed: first, a remote sensing image set of the target land is obtained; then, remote sensing data of different bands are extracted from the remote sensing image set to obtain remote sensing data sets corresponding to different bands; then, the remote sensing data sets corresponding to different bands are discretely processed to obtain a target data set; finally, the target data in the target data set are input one by one into a drought assessment model to output the degree of drought of the target land, wherein the drought assessment model is a model pre-trained based on an inversion algorithm for estimating the degree of land drought.

[0026] In this application, remote sensing technology is combined with land drought assessment. Remote sensing images of the target land are collected through remote sensing technology, remote sensing data of specific bands are selected for discretization processing, and the target data obtained by discretization processing is input into a drought assessment model. The drought degree of the target land is calculated based on the inversion algorithm, thereby improving the measurement accuracy and the accuracy of the assessment results, thereby solving the technical problem in related technologies that the land drought degree is often judged through manual visits and based on human experience, resulting in low accuracy in land drought degree assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0028] Figure 1 is a flow chart of an optional method for determining the degree of land drought according to an embodiment of the present invention;

[0029] Figure 2 is a schematic diagram of an optional process for determining the degree of land drought according to an embodiment of the present invention;

[0030] Figure 3 is a schematic diagram of an optional device for determining the degree of land drought according to an embodiment of the present invention;

[0031] Figure 4 The present invention is a hardware structure block diagram of an electronic device (or mobile device) for determining a method for determining a degree of land drought according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] It should be noted that the method for determining the degree of land drought and the device thereof in the present application can be used in the field of artificial intelligence when the degree of land drought is estimated based on artificial intelligence, and can also be used in any field other than the field of artificial intelligence when the degree of land drought is estimated based on artificial intelligence. The application field of the method for determining the degree of land drought and the device thereof in the present application is not limited.

[0035] 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 used for analysis, stored data, displayed data, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or organizations. 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.

[0036] It should be noted that in this application, when collecting and analyzing customer information, corresponding operation entrances are provided for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0037] The following embodiments of the present invention can be applied to various systems / applications / devices for determining the degree of land drought. The present invention obtains remote sensing images of target land based on remote sensing technology, extracts remote sensing data of different bands based on the remote sensing images, and then draws images based on the remote sensing data, identifies discrete points in the images and removes them, making the data more accurate, improving the estimation accuracy and the calculation speed, and automatically outputs the degree of drought of the target land based on the drought assessment model, thereby realizing the automated assessment of the degree of drought of the target land.

[0038] The present invention is described in detail below in conjunction with various embodiments.

[0039] Embodiment 1

[0040] According to an embodiment of the present invention, an embodiment of a method for determining the degree of land drought is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] Figure 1 is a flow chart of an optional method for determining the degree of land drought according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0042] Step S101, obtaining a remote sensing image set of the target land;

[0043] Step S102, extracting remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to different bands;

[0044] Step S103, performing discrete processing on remote sensing data sets corresponding to different bands to obtain target data sets;

[0045] Step S104, inputting the target data in the target data set into the drought assessment model one by one, and outputting the drought degree of the target land, wherein the drought assessment model is a model pre-trained based on the inversion algorithm for estimating the drought degree of the land.

[0046] Through the above steps, a remote sensing image set of the target land is first obtained, and then remote sensing data of different bands are extracted from the remote sensing image set to obtain remote sensing data sets corresponding to different bands, and then the remote sensing data sets corresponding to different bands are discretely processed to obtain a target data set, and finally the target data in the target data set are input into the drought assessment model one by one to output the drought degree of the target land, wherein the drought assessment model is a model pre-trained based on the inversion algorithm for estimating the degree of land drought.

[0047] In this embodiment, remote sensing technology is combined with land drought assessment. Remote sensing images of the target land are collected through remote sensing technology, remote sensing data of specific bands are selected for discretization processing, and the target data obtained by discretization processing is input into a drought assessment model. The drought degree of the target land is calculated based on an inversion algorithm, thereby improving the measurement accuracy and the accuracy of the assessment results, thereby solving the technical problem in related technologies that the land drought degree is often judged through manual visits and based on human experience, resulting in low accuracy in land drought degree assessment.

[0048] The embodiment of the present invention is described in detail below in combination with the above steps.

[0049] The degree of drought of land will directly affect the ecological status of the land, thus affecting the production and value of the land. In many fields, the measurement of the degree of drought of land is of great significance. For example, in the field of agricultural production management, drought is one of the key factors affecting crop growth and yield. By monitoring the degree of drought of land, managers can take timely measures, such as irrigation, planting drought-resistant crop varieties or adjusting planting plans, to reduce the impact of drought on agricultural production. In the field of water resources management, understanding the drought conditions of land will help the rational allocation and utilization of water resources. Under drought conditions, water resources may become scarce, and key uses, such as drinking water supply and irrigation of key crops, need to be prioritized. In the field of disaster prevention and mitigation, drought may lead to serious natural disasters such as forest fires, land degradation and desertification. By monitoring the degree of land drought, early warnings can be given and preventive measures can be taken to reduce disaster risks. In the field of environmental protection, the degree of land drought has a profound impact on the ecosystem, which may lead to a decline in biodiversity and destruction of the ecological balance. Timely monitoring of land drought conditions will help assess environmental conditions and formulate protective measures. In the financial and insurance fields, drought has a direct impact on agricultural finance and the agricultural insurance industry. Accurate drought monitoring data can assist insurance companies in assessing risks and formulating reasonable insurance premiums. It can also assist financial institutions in conducting loan risk assessments.

[0050] In this embodiment, remote sensing technology is combined with the assessment of the degree of land drought. Remote sensing images of the land are collected through remote sensing technology. The information covered in the remote sensing images is very rich, and data extraction is required to obtain more accurate data that can reflect the land drought situation. At the same time, this embodiment discretizes the extracted remote sensing data in advance, identifies discrete data, and clears discrete data, thereby making the data more accurate and improving the accuracy of measurement.

[0051] Step S101, obtaining a set of remote sensing images of target land.

[0052] It should be noted that the embodiment of the present invention combines remote sensing technology to evaluate the drought degree of the target land. First, the target area and target features to be monitored are determined, and an appropriate remote sensing satellite or aircraft is selected. For example, a satellite equipped with an infrared or multi-spectral sensor, or a drone equipped with a specific sensor, is used. These sensors can capture radiation of a specific wavelength related to a heat source, and then accurate remote sensing images containing multiple features can be obtained. The surface of the target land is photographed by a remote sensing satellite to obtain multiple remote sensing images of the target land surface. The multiple remote sensing images can be remote sensing images taken at different angles, or remote sensing images taken from different areas of the target land.

[0053] Optionally, after acquiring the remote sensing image set of the target land, the method further includes: performing radiation correction and atmospheric correction on each remote sensing image in the remote sensing image set to obtain a corrected remote sensing image.

[0054] It should be noted that after collecting the remote sensing images of the target land, the remote sensing images need to be preprocessed to remove the noise in the images and then obtain the corrected remote sensing images. The specific preprocessing operations may include: radiation correction, eliminating the errors of the sensor itself and the impact of external factors such as changes in the atmosphere and solar radiation on the image, to ensure that the image data reflects the true situation of the surface; atmospheric correction, correcting the scattering and absorption of radiation by the atmosphere, and converting the image data into surface reflectivity or radiation brightness, so that images acquired at different times and with different sensors can be compared.

[0055] Optionally, after acquiring a set of remote sensing images of the target land, the method further includes: for each remote sensing image in the remote sensing image set, calculating the land flatness of the remote sensing image; when the land flatness of the remote sensing image is less than a preset flatness, correcting the remote sensing image based on a digital elevation algorithm to obtain a corrected remote sensing image.

[0056] It should be noted that in addition to basic corrections such as atmospheric correction and radiation correction of remote sensing images, based on the terrain and other features of the land, the remote sensing images of the target land also need to be corrected for flatness correction and geometric correction. Geometric correction is used to adjust the geometric distortion of the image, such as distortion caused by terrain, changes in the sensor's field of view, etc., to ensure that the image matches the actual geographical location; image enhancement, by adjusting the image's contrast, brightness, color, etc., makes specific features in the image more obvious, making it easier to identify and analyze; cloud removal is used to identify and remove clouds and cloud shadows in the image, because clouds will affect the observation of surface features; image segmentation is used to divide an image into multiple regions or objects, each region containing pixels with similar attributes. Image fusion is used to combine image data from different sensors or different bands to obtain richer surface information; noise removal uses filters to remove random noise in images and improve the signal-to-noise ratio of images. Preprocessing operations help improve the availability of remote sensing images and the accuracy of analysis results; flatness correction is used for each remote sensing image. The high-altitude height variance algorithm can be used to calculate the flatness of the land, and remote sensing images with a land flatness less than the flatness threshold can be adjusted for flatness. For example, remote sensing images can be corrected using a digital elevation algorithm to eliminate or reduce image deformation caused by terrain undulations.

[0057] Step S102: extract remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to different bands.

[0058] It should be noted that remote sensing images contain remote sensing data of many bands, but not every band can obtain data related to land drought conditions. Therefore, it is necessary to extract remote sensing data of specific bands from remote sensing images, and then analyze them to calculate the degree of drought of the target land.

[0059] Optionally, the step of extracting remote sensing data of different bands from a remote sensing image set to obtain remote sensing data sets corresponding to different remote sensing bands includes: selecting a remote sensing band to be analyzed to obtain a first band and a second band, the first band and the second band being two different remote sensing bands; for each remote sensing image in the remote sensing image set, extracting remote sensing data of a first band in the remote sensing image to obtain a first remote sensing data set corresponding to the first band; for each remote sensing image in the remote sensing image set, extracting remote sensing data of a second band in the remote sensing image to obtain a second remote sensing data set corresponding to the second band.

[0060] It should be noted that the embodiment of the present invention extracts remote sensing data from multiple bands. When extracting remote sensing data, the remote sensing band to be analyzed is first selected. When analyzing the degree of land drought, data of two different bands, the red light band and the infrared light band, are generally selected for analysis. This is because the data of the red band and the infrared band have unique sensitivity to the reflective characteristics of surface vegetation and soil. The red band is usually located in the red part of visible light, with a wavelength of approximately 620-750 nanometers. The reflectivity of vegetation in the red band is low because chlorophyll strongly absorbs red light for photosynthesis. Therefore, the data of the red band can be used to identify vegetation coverage. The infrared band is sensitive to soil moisture. The reflectivity of moist soil in the infrared band is high, while the reflectivity of dry soil is low. This makes the infrared band an effective tool for monitoring soil moisture conditions. For each remote sensing image, this embodiment can select remote sensing data of the first band (red band or infrared band) and the second band (red band or infrared band) to obtain a first remote sensing data set and a second remote sensing data set, respectively.

[0061] Step S103, performing discrete processing on remote sensing data sets corresponding to different bands to obtain target data sets.

[0062] It should be noted that the embodiment of the present invention performs discrete processing on the extracted remote sensing data set, identifies discrete data and removes it, so as to obtain more accurate data, speed up the calculation efficiency and improve the accuracy of the calculation results.

[0063] Optionally, the step of performing discrete processing on remote sensing data sets corresponding to different remote sensing bands to obtain a target data set includes: drawing a feature image based on the remote sensing data in the first remote sensing data set and the second remote sensing data set; and performing discrete processing on feature data in the feature image.

[0064] It should be noted that when performing discrete processing on remote sensing data sets corresponding to different bands, it is necessary to first establish a coordinate system, use either the first band or the second band as the horizontal coordinate, and use the other band as the vertical coordinate to draw the feature image, and determine the discrete points in the feature image and remove them from the feature image.

[0065] Optionally, the step of performing discrete processing on the feature data in the feature image includes: calculating the mean of the first remote sensing data set corresponding to the first band to obtain a first mean, and calculating the standard deviation of the first remote sensing data set corresponding to the first band to obtain a first standard deviation; calculating the mean of the second remote sensing data set corresponding to the second band to obtain a second mean, and calculating the standard deviation of the second remote sensing data set corresponding to the second band to obtain a second standard deviation; calculating a discrete range based on the first mean, the first standard deviation, the second mean and the second standard deviation; and eliminating the data outside the discrete range in the feature image as discrete data to obtain a target data set.

[0066] Specifically, when determining discrete points in a feature image, first calculate the mean of a first remote sensing data set corresponding to a first band to obtain a first mean, then calculate the standard deviation of the first remote sensing data set corresponding to the first band to obtain a first standard deviation, and at the same time calculate the mean and standard deviation of a second remote sensing data set corresponding to a second band to obtain a second mean and a second standard deviation, respectively, and then calculate a discrete range based on the first mean, the first standard deviation, and the second mean and the second standard deviation. Usually, data points falling within the range of ±2 times the standard deviation of the mean are considered "normal", while points falling outside this range may be discrete or abnormal, so the calculated discrete range is marked in the feature image, thereby eliminating data outside the discrete range as discrete data, and retaining the remaining data as target data, thereby obtaining a target data set.

[0067] Step S104, inputting the target data in the target data set into the drought assessment model one by one, and outputting the drought degree of the target land, wherein the drought assessment model is a model pre-trained based on the inversion algorithm for estimating the drought degree of the land.

[0068] It should be noted that the embodiment of the present invention uses an inversion algorithm-based drought assessment model to analyze the screened target data set, and outputs the drought degree of the target land through the model. The drought degree of the target land has a very wide range of applications. It can be used as a basis for risk assessment of loan requests by financial institutions to evaluate loan requests based on the target land, and can also be used as a basis for land management to configure management strategies for the target land.

[0069] Optionally, after outputting the drought degree of the target land, it also includes: when the drought degree is greater than a preset drought degree threshold, matching a solution strategy for the target land based on the drought degree, wherein the solution strategy is used to provide information for solving the drought problem of the target land.

[0070] It should be noted that after determining the degree of drought of the target land, different solution strategies can be matched for the target land based on the degree of drought. If the output degree of drought of the target land is greater than the preset drought threshold, it means that the degree of drought of the target land is more serious. The primary goal is to carry out land management, and the solution strategy is matched for the target land to solve the drought problem of the target land. The solution strategy may include irrigation strategy, soil deep loosening strategy, etc.

[0071] Optionally, after outputting the drought degree of the target land, it also includes: when the drought degree is less than or equal to a preset drought degree threshold, matching a crop planting strategy for the target land based on the drought degree, wherein the crop planting strategy is used to provide information for determining crops that match the target land.

[0072] It should be noted that if the output drought degree of the target land is less than or equal to the preset drought degree threshold, it means that the drought degree of the target land is not very serious, and the target land can be matched with a crop planting strategy to maximize the yield of the target land. For example, drought-resistant crop varieties can be planted, crop layout can be optimized, and dependence on water resources can be reduced.

[0073] Another optional specific implementation is described in detail below.

[0074] Figure 2 is a schematic diagram of an optional process for determining the degree of land drought according to an embodiment of the present invention. Figure 2 As shown, the process of determining the degree of land drought includes:

[0075] Step 1, start;

[0076] Step 2: Obtain remote sensing images of the target land through satellite remote sensing technology;

[0077] Step 3, extracting remote sensing data in different bands from the remote sensing image and drawing it into a feature image;

[0078] Take the data of the red band and near-infrared band in the remote sensing image and plot them on the coordinate axis. The x-axis can be the red band data and the y-axis can be the near-infrared band data.

[0079] Step 4: Based on the Gaussian distribution principle, the discrete data in the image is eliminated to obtain the target data;

[0080] Calculate the mean and standard deviation of the remote sensing data corresponding to the red band, and calculate the mean and standard deviation of the remote sensing data corresponding to the infrared band at the same time. Calculate the discrete range based on the mean and standard deviation of the remote sensing data in each band. Usually, data points falling within the range of ±2 times the standard deviation of the mean are considered "normal", and points falling outside this range may be discrete. Mark the discrete range in the feature image and remove the data outside the discrete range as discrete data.

[0081] Step 5: Calculate the drought degree corresponding to the target data based on the inversion algorithm to obtain the drought degree of the target land;

[0082] Step six, end.

[0083] In an embodiment of the present invention, a remote sensing image of the target land is acquired based on remote sensing technology, and remote sensing data of different bands are extracted based on the remote sensing image, and an image is drawn based on the remote sensing data, and discrete points in the image are identified and removed, so that the data is more accurate, the estimation accuracy is improved, and the measurement speed is increased, and the drought degree of the target land is automatically output based on the drought assessment model, thereby realizing an automated assessment of the drought degree of the target land.

[0084] The following is a detailed description in conjunction with another embodiment.

[0085] Embodiment 2

[0086] A device for determining the degree of land drought provided in this embodiment includes multiple implementation units, each of which corresponds to each implementation step in the above-mentioned embodiment 1. Its specific implementation method and beneficial effects can refer to the above-mentioned method embodiment and will not be repeated here.

[0087] Figure 3 is a schematic diagram of an optional device for determining the degree of land drought according to an embodiment of the present invention, such as Figure 3 As shown, the device for determining the degree of land drought may include: an acquisition unit 31, an extraction unit 32, a processing unit 33, and an output unit 34, wherein:

[0088] An acquisition unit 31 is used to acquire a remote sensing image set of the target land;

[0089] An extraction unit 32 is used to extract remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to different bands;

[0090] The processing unit 33 is used to perform discrete processing on remote sensing data sets corresponding to different bands to obtain a target data set;

[0091] The output unit 34 is used to input the target data in the target data set into the drought assessment model one by one, and output the drought degree of the target land, wherein the drought assessment model is a model pre-trained based on the inversion algorithm for estimating the drought degree of the land.

[0092] The above-mentioned device for determining the degree of land drought obtains a set of remote sensing images of the target land through an acquisition unit 31; extracts remote sensing data of different bands from the remote sensing image set through an extraction unit 32 to obtain remote sensing data sets corresponding to different bands; performs discrete processing on the remote sensing data sets corresponding to different bands through a processing unit 33 to obtain a target data set; and inputs the target data in the target data set into a drought assessment model one by one through an output unit 34 to output the degree of drought of the target land, wherein the drought assessment model is a model pre-trained based on an inversion algorithm for estimating the degree of land drought.

[0093] In this embodiment, remote sensing technology is combined with land drought assessment. Remote sensing images of the target land are collected through remote sensing technology, remote sensing data of specific bands are selected for discretization processing, and the target data obtained by discretization processing is input into a drought assessment model. The drought degree of the target land is calculated based on an inversion algorithm, thereby improving the measurement accuracy and the accuracy of the assessment results, thereby solving the technical problem in related technologies that the land drought degree is often judged through manual visits and based on human experience, resulting in low accuracy in land drought degree assessment.

[0094] Optionally, the extraction unit includes: a first selection module, used to select the remote sensing band to be analyzed, and obtain the first band and the second band, wherein the first band and the second band are two different remote sensing bands; a first extraction module, used to extract the remote sensing data of the first band in the remote sensing image for each remote sensing image in the remote sensing image set, and obtain a first remote sensing data set corresponding to the first band; a second extraction module, used to extract the remote sensing data of the second band in the remote sensing image for each remote sensing image in the remote sensing image set, and obtain a second remote sensing data set corresponding to the second band.

[0095] Optionally, the processing unit includes: a first drawing module, used to draw a feature image based on the remote sensing data in the first remote sensing data set and the second remote sensing data set; and a first processing module, used to perform discrete processing on the feature data in the feature image.

[0096] Optionally, the first processing module includes: a first calculation submodule, used to calculate the mean of the first remote sensing data set corresponding to the first band to obtain a first mean, and calculate the standard deviation of the first remote sensing data set corresponding to the first band to obtain a first standard deviation; a second calculation submodule, used to calculate the mean of the second remote sensing data set corresponding to the second band to obtain a second mean, and calculate the standard deviation of the second remote sensing data set corresponding to the second band to obtain a second standard deviation; a third calculation submodule, used to calculate a discrete range based on the first mean, the first standard deviation, the second mean and the second standard deviation; a first elimination submodule, used to eliminate data in the feature image that is outside the discrete range as discrete data to obtain a target data set.

[0097] Optionally, the device for determining the degree of land drought also includes: a first calculation module, used to calculate the land flatness of the remote sensing image for each remote sensing image in the remote sensing image set; a first correction module, used to correct the remote sensing image based on a digital elevation algorithm to obtain a corrected remote sensing image when the land flatness of the remote sensing image is less than a preset flatness.

[0098] Optionally, the device for determining the degree of land drought further includes: a second correction module, configured to perform radiation correction and atmospheric correction on each remote sensing image in the remote sensing image set to obtain a corrected remote sensing image.

[0099] Optionally, the device for determining the degree of land drought also includes: a first matching module, which is used to match a solution strategy for the target land based on the degree of drought when the degree of drought is greater than a preset drought degree threshold, wherein the solution strategy is used to provide information for solving the drought problem of the target land.

[0100] Optionally, the device for determining the degree of land drought also includes: a second matching module, used to match a crop planting strategy for the target land based on the degree of drought when the degree of drought is less than or equal to a preset drought degree threshold, wherein the crop planting strategy is used to provide information for determining crops that match the target land.

[0101] The above-mentioned device for determining the degree of land drought may also include a processor and a memory. The above-mentioned acquisition unit 31, extraction unit 32, processing unit 33, output unit 34, etc. 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.

[0102] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the drought degree of the target land can be estimated by adjusting the kernel parameters.

[0103] The above-mentioned 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 memory (fl ash RAM), and the memory includes at least one storage chip.

[0104] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any of the above-mentioned methods for determining the degree of land drought.

[0105] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising 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 one or more processors, the one or more processors implement any one of the above-mentioned methods for determining the degree of land drought.

[0106] According to another aspect of an embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program, wherein when the computer program is executed by a processor, any one of the above-mentioned methods for determining the degree of land drought is implemented.

[0107] 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: obtaining a set of remote sensing images of the target land; extracting remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to the different bands; performing discrete processing on the remote sensing data sets corresponding to the different bands to obtain a target data set; inputting the target data in the target data set into a drought assessment model one by one, and outputting the degree of drought of the target land, wherein the drought assessment model is a model pre-trained based on an inversion algorithm for estimating the degree of land drought.

[0108] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that is initialized with the following method steps: extracting remote sensing data of different bands from a remote sensing image set, and obtaining remote sensing data sets corresponding to different remote sensing bands, including the steps of: selecting a remote sensing band to be analyzed to obtain a first band and a second band, the first band and the second band being two different remote sensing bands; for each remote sensing image in the remote sensing image set, extracting remote sensing data of a first band in the remote sensing image, and obtaining a first remote sensing data set corresponding to the first band; for each remote sensing image in the remote sensing image set, extracting remote sensing data of a second band in the remote sensing image, and obtaining a second remote sensing data set corresponding to the second band.

[0109] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: discrete processing is performed on remote sensing data sets corresponding to different remote sensing bands, and the steps of obtaining a target data set include: drawing a feature image based on the remote sensing data in the first remote sensing data set and the second remote sensing data set; and discrete processing is performed on the feature data in the feature image.

[0110] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program initialized with the following method steps: the step of discretely processing the feature data in the feature image includes: calculating the mean of a first remote sensing data set corresponding to a first band to obtain a first mean, and calculating the standard deviation of the first remote sensing data set corresponding to the first band to obtain a first standard deviation; calculating the mean of a second remote sensing data set corresponding to a second band to obtain a second mean, and calculating the standard deviation of the second remote sensing data set corresponding to the second band to obtain a second standard deviation; calculating a discrete range based on the first mean, the first standard deviation, the second mean and the second standard deviation; eliminating data in the feature image that is outside the discrete range as discrete data to obtain a target data set.

[0111] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: after obtaining a set of remote sensing images of the target land, it also includes: for each remote sensing image in the remote sensing image set, calculating the land flatness of the remote sensing image; when the land flatness of the remote sensing image is less than a preset flatness, correcting the remote sensing image based on a digital elevation algorithm to obtain a corrected remote sensing image.

[0112] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: after obtaining a set of remote sensing images of the target land, it also includes: performing radiation correction and atmospheric correction on each remote sensing image in the remote sensing image set to obtain a corrected remote sensing image.

[0113] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: after outputting the drought degree of the target land, it also includes: when the drought degree is greater than a preset drought degree threshold, matching a solution strategy for the target land based on the drought degree, wherein the solution strategy is used to provide information for solving the drought problem of the target land.

[0114] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: after outputting the drought degree of the target land, it also includes: when the drought degree is less than or equal to a preset drought degree threshold, matching a crop planting strategy for the target land based on the drought degree, wherein the crop planting strategy is used to provide information for determining crops that match the target land.

[0115] Figure 4 1 is a hardware structure block diagram of an electronic device (or mobile device) according to a method for determining the degree of land drought in an embodiment of the present invention. Figure 4 As shown, the electronic device may include one or more ( Figure 4 402a, 402b, ..., 402n are used to illustrate) processor 402 (processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), memory 404 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components as shown, or with Figure 4 Different configurations are shown.

[0116] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0117] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0120] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0121] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0122] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the degree of land drought, characterized in that: include: Obtain a collection of remote sensing images of the target land; Extracting remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to the different bands; Discretely process the remote sensing data sets corresponding to different bands to obtain target data sets; The target data in the target data set are input into the drought assessment model one by one, and the drought degree of the target land is output, wherein the drought assessment model is a model pre-trained based on an inversion algorithm for estimating the drought degree of the land.

2. The determination method according to claim 1, characterized in that: The steps of extracting remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to different remote sensing bands include: Selecting a remote sensing band to be analyzed to obtain a first band and a second band, wherein the first band and the second band are two different remote sensing bands; For each remote sensing image in the remote sensing image set, extract remote sensing data of the first band in the remote sensing image to obtain a first remote sensing data set corresponding to the first band; For each remote sensing image in the remote sensing image set, remote sensing data of the second band in the remote sensing image is extracted to obtain a second remote sensing data set corresponding to the second band.

3. The determination method according to claim 2, characterized in that: The step of performing discrete processing on the remote sensing data sets corresponding to different remote sensing bands to obtain the target data set comprises: Drawing a feature image based on the remote sensing data in the first remote sensing data set and the second remote sensing data set; The feature data in the feature image is discretized.

4. The determination method according to claim 3, characterized in that: The step of performing discrete processing on the feature data in the feature image comprises: Calculating the mean of the first remote sensing data set corresponding to the first band to obtain a first mean, and calculating the standard deviation of the first remote sensing data set corresponding to the first band to obtain a first standard deviation; Calculating the mean of the second remote sensing data set corresponding to the second band to obtain a second mean, and calculating the standard deviation of the second remote sensing data set corresponding to the second band to obtain a second standard deviation; Calculating a discrete range based on the first mean, the first standard deviation, the second mean, and the second standard deviation; The data outside the discrete range in the feature image is removed as discrete data to obtain the target data set.

5. The determination method according to claim 1, characterized in that: After acquiring a collection of remote sensing images of the target land, it also includes: For each remote sensing image in the remote sensing image set, calculating the land flatness of the remote sensing image; When the land flatness of the remote sensing image is less than a preset flatness, the remote sensing image is corrected based on a digital elevation algorithm to obtain a corrected remote sensing image.

6. The determination method according to claim 1, characterized in that: After acquiring a collection of remote sensing images of the target land, it also includes: Radiation correction and atmospheric correction are performed on each remote sensing image in the remote sensing image set to obtain a corrected remote sensing image.

7. The determination method according to claim 1, characterized in that: After outputting the drought degree of the target land, it also includes: In the case that the drought degree is greater than a preset drought degree threshold, a solution strategy is matched for the target land based on the drought degree, wherein the solution strategy is used to provide information for solving the drought problem of the target land.

8. The determination method according to claim 1, characterized in that: After outputting the drought degree of the target land, it also includes: When the drought degree is less than or equal to a preset drought degree threshold, a crop planting strategy is matched for the target land based on the drought degree, wherein the crop planting strategy is used to provide information for determining crops that match the target land.

9. A device for determining the degree of land drought, characterized in that: include: An acquisition unit, used for acquiring a remote sensing image set of the target land; An extraction unit, used to extract remote sensing data of different bands from the remote sensing image set to obtain remote sensing data sets corresponding to different bands; A processing unit, used for performing discrete processing on the remote sensing data sets corresponding to different bands to obtain a target data set; An output unit is used to input the target data in the target data set into the drought assessment model one by one, and output the drought degree of the target land, wherein the drought assessment model is a model pre-trained based on an inversion algorithm for estimating the drought degree of the land.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for determining the degree of land drought according to any one of claims 1 to 8.

11. An electronic device, characterized in that: It comprises 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 method for determining the degree of land drought as described in any one of claims 1 to 8.

12. A computer program product, characterized in that The computer program product comprises a computer program, wherein the computer program, when executed by a processor, implements the method for determining the degree of land drought according to any one of claims 1 to 8.

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