Real-time geographic information analysis method and system fusing large model and remote sensing technology

Abstract

The invention discloses a real-time geographic information analysis method and system fusing a large model and a remote sensing technology, which are used for identifying hydrological information on a remote sensing image, and the method comprises the following steps: obtaining image information of the remote sensing image, the image information comprising a wave band set, a reflectivity, an elevation value, a hydrological parameter, a meteorological parameter, a vegetation index and soil humidity; setting a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculating a feature value and a comprehensive gradient at each position according to the image information; calculating a new characteristic value of each position according to the characteristic value and the comprehensive gradient of each position, calculating a final characteristic value of each position according to the new characteristic value of each position, comparing the final characteristic value with a preset threshold value, and when the final characteristic value exceeds the preset threshold value, determining that the position of the target object is the position of the target object. The position corresponding to the final characteristic value is the water area position.

Description

Technical Field

[0001] The present invention belongs to the field of geographic information analysis technology, and more specifically, relates to a real-time geographic information analysis method and system integrating a large model and remote sensing technology. Background Art

[0002] Remote sensing images are image data of the earth's surface or atmosphere obtained by sensors on satellites or aircraft. These images can be used in many fields such as environmental monitoring, land use analysis, disaster assessment, resource exploration, etc. Remote sensing images usually contain multi-spectral (multi-band) information, which can provide rich spatial and spectral information and help to analyze the characteristics of the surface and atmosphere in detail.

[0003] The main features of remote sensing images are: Multispectral information: Remote sensing images can capture data in multiple bands from visible light to infrared light, which helps to distinguish different land features.

[0004] High spatial resolution: Some remote sensing images have very high spatial resolution and can show surface features such as buildings, roads, vegetation, etc. in detail.

[0005] Large-scale coverage: Satellite remote sensing images can cover a large area of ​​the Earth's surface and are suitable for regional and global monitoring and analysis.

[0006] Time series data: By acquiring images of the same area at regular intervals, dynamic changes in the surface and environment can be analyzed, such as vegetation growth, land use changes, disaster occurrences, etc.

[0007] However, there is no technical solution in the prior art that can accurately identify the hydrological information on the remote sensing image. Summary of the invention

[0008] In order to solve the above technical problems, the present invention proposes a real-time geographic information analysis method integrating a large model and remote sensing technology, which is used to identify hydrological information on remote sensing images, including: Acquiring image information of a remote sensing image, wherein the image information includes: band set, reflectivity, elevation value, hydrological parameters, meteorological parameters, vegetation index and soil moisture; Setting a remote sensing image feature extraction function and a comprehensive gradient calculation function to calculate the feature value and comprehensive gradient at each position according to the image information; According to the characteristic value and the comprehensive gradient at each position, a new characteristic value at each position is calculated, and according to the new characteristic value at each position, a final characteristic value at each position is calculated, and the final characteristic value is compared with a preset threshold. When the final characteristic value exceeds the preset threshold, the position corresponding to the final characteristic value is a water area position.

[0009] Furthermore, the remote sensing image feature extraction function includes: , in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

[0010] Furthermore, the comprehensive gradient calculation function includes: , , in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

[0011] Furthermore, the position of the remote sensing image is calculated The new eigenvalue at include: , in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

[0012] Furthermore, the position of the remote sensing image is calculated The final eigenvalue at include: , in, is the position in the remote sensing image The final eigenvalue at is the position in the remote sensing image The vegetation index at is the position in the remote sensing image The soil moisture of the place.

[0013] Furthermore, the hydrological parameter is flow velocity or flow, the meteorological parameter is rainfall, temperature or air humidity, and the vegetation index is NDVI.

[0014] The present invention also proposes a real-time geographic information analysis system integrating a large model and remote sensing technology, which is used to identify hydrological information on remote sensing images, including: An information acquisition module is used to acquire image information of remote sensing images, wherein the image information includes: band set, reflectivity, elevation value, hydrological parameters, meteorological parameters, vegetation index and soil moisture; The module for calculating eigenvalues ​​and gradients is used to set a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculate the eigenvalues ​​and comprehensive gradients at each position according to the image information; The identification module is used to calculate a new feature value at each position according to the feature value and the comprehensive gradient at each position, and calculate a final feature value at each position according to the new feature value at each position, and compare the final feature value with a preset threshold. When the final feature value exceeds the preset threshold, the position corresponding to the final feature value is a water area position.

[0015] Furthermore, the remote sensing image feature extraction function includes: , in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

[0016] Furthermore, the comprehensive gradient calculation function includes: , , in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

[0017] Furthermore, the position of the remote sensing image is calculated The new eigenvalue at include: , in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

[0018] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art: The present invention sets a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculates the feature value and comprehensive gradient at each position according to the image information; calculates the new feature value at each position according to the feature value and comprehensive gradient at each position, and calculates the final feature value at each position according to the new feature value at each position, and compares the final feature value with a preset threshold value. When the final feature value exceeds the preset threshold value, the position corresponding to the final feature value is a water area position. Through the above technical scheme, the present invention can identify hydrological information on remote sensing images, thereby providing support for geographic data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 It is a structural diagram of the system of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0022] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0023] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0024] The display screen is used to display the user interface of each application.

[0025] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0026] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a real-time geographic information analysis method integrating a large model and remote sensing technology, which is used to identify hydrological information on remote sensing images, including: Step 101, obtaining image information of a remote sensing image, wherein the image information includes: band set, reflectivity, elevation value, hydrological parameters, meteorological parameters, vegetation index and soil moisture; Step 102, setting a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculating the feature value and comprehensive gradient at each position according to the image information; Specifically, the remote sensing image feature extraction function includes: , in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

[0027] Specifically, the comprehensive gradient calculation function includes: , , in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

[0028] Step 103, calculate the new characteristic value at each position according to the characteristic value and the comprehensive gradient at each position, and calculate the final characteristic value at each position according to the new characteristic value at each position, compare the final characteristic value with a preset threshold, and when the final characteristic value exceeds the preset threshold, the position corresponding to the final characteristic value is a water area position.

[0029] Specifically, calculate the position of the remote sensing image The new eigenvalue at include: , in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

[0030] Specifically, calculate the position of the remote sensing image The final eigenvalue at include: , in, is the position in the remote sensing image The final eigenvalue at is the position in the remote sensing image The vegetation index at is the position in the remote sensing image The soil moisture of the place.

[0031] Specifically, the hydrological parameter is flow velocity or flow, the meteorological parameter is rainfall, temperature or air humidity, and the vegetation index is NDVI.

[0032] Example 2 like Figure 2 As shown, an embodiment of the present invention further provides a real-time geographic information analysis system integrating a large model and remote sensing technology, which is used to identify hydrological information on remote sensing images, including: An information acquisition module is used to acquire image information of remote sensing images, wherein the image information includes: band set, reflectivity, elevation value, hydrological parameters, meteorological parameters, vegetation index and soil moisture; The module for calculating eigenvalues ​​and gradients is used to set a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculate the eigenvalues ​​and comprehensive gradients at each position according to the image information; Specifically, the remote sensing image feature extraction function includes: , in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

[0033] Specifically, the comprehensive gradient calculation function includes: , , in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

[0034] The identification module is used to calculate a new feature value at each position according to the feature value and the comprehensive gradient at each position, and calculate a final feature value at each position according to the new feature value at each position, and compare the final feature value with a preset threshold. When the final feature value exceeds the preset threshold, the position corresponding to the final feature value is a water area position.

[0035] Specifically, calculate the position of the remote sensing image The new eigenvalue at include: , in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

[0036] Specifically, calculate the position of the remote sensing image The final eigenvalue at include: , in, is the position in the remote sensing image The final eigenvalue at is the position in the remote sensing image The vegetation index at is the position in the remote sensing image The soil moisture of the place.

[0037] Specifically, the hydrological parameter is flow velocity or flow, the meteorological parameter is rainfall, temperature or air humidity, and the vegetation index is NDVI.

[0038] Example 3 The embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the real-time geographic information analysis method integrating a large model with remote sensing technology.

[0039] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0040] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, acquiring image information of a remote sensing image, wherein the image information includes: a band set, reflectivity, elevation value, hydrological parameters, meteorological parameters, vegetation index, and soil moisture; Step 102, setting a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculating the feature value and comprehensive gradient at each position according to the image information; Specifically, the remote sensing image feature extraction function includes: , in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

[0041] Specifically, the comprehensive gradient calculation function includes: , , in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

[0042] Step 103, calculate the new characteristic value at each position according to the characteristic value and the comprehensive gradient at each position, and calculate the final characteristic value at each position according to the new characteristic value at each position, compare the final characteristic value with a preset threshold, and when the final characteristic value exceeds the preset threshold, the position corresponding to the final characteristic value is a water area position.

[0043] Specifically, calculate the position of the remote sensing image The new eigenvalue at include:

[0044] in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

[0045] Specifically, calculate the position of the remote sensing image The final eigenvalue at include: , in, is the position in the remote sensing image The final eigenvalue at is the position in the remote sensing image The vegetation index at is the position in the remote sensing image The soil moisture of the place.

[0046] Specifically, the hydrological parameter is flow velocity or flow, the meteorological parameter is rainfall, temperature or air humidity, and the vegetation index is NDVI.

[0047] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the real-time geographic information analysis method that integrates a large model and remote sensing technology.

[0048] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0049] Among them, the storage medium can be used to store software programs and modules, such as a real-time geographic information analysis method that integrates a large model and remote sensing technology in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned real-time geographic information analysis method that integrates a large model and remote sensing technology. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0050] The processor can call the information and application program stored in the storage medium through the transmission system to perform the following steps: Step 101, obtaining image information of the remote sensing image, wherein the image information includes: band set, reflectivity, elevation value, hydrological parameter, meteorological parameter, vegetation index and soil moisture; Step 102, setting a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculating the feature value and comprehensive gradient at each position according to the image information; Specifically, the remote sensing image feature extraction function includes: , in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

[0051] Specifically, the comprehensive gradient calculation function includes: , , in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

[0052] Step 103, calculate the new characteristic value at each position according to the characteristic value and the comprehensive gradient at each position, and calculate the final characteristic value at each position according to the new characteristic value at each position, compare the final characteristic value with a preset threshold, and when the final characteristic value exceeds the preset threshold, the position corresponding to the final characteristic value is a water area position.

[0053] Specifically, calculate the position of the remote sensing image The new eigenvalue at include: , in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

[0054] Specifically, calculate the position of the remote sensing image The final eigenvalue at include: , in, is the position in the remote sensing image The final eigenvalue at is the position in the remote sensing image The vegetation index at is the position in the remote sensing image The soil moisture of the place.

[0055] Specifically, the hydrological parameter is flow velocity or flow, the meteorological parameter is rainfall, temperature or air humidity, and the vegetation index is NDVI.

[0056] 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.

[0057] 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.

[0058] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic, for example, the division of the units is only a logical function division, and 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.

[0059] 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 network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] 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.

[0061] 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, 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 storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0062] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A real-time geographic information analysis method integrating large models and remote sensing technology for identifying hydrological information on remote sensing images, characterized in that: include: Acquiring image information of a remote sensing image, wherein the image information includes: band set, reflectivity, elevation value, hydrological parameters, meteorological parameters, vegetation index and soil moisture; Setting a remote sensing image feature extraction function and a comprehensive gradient calculation function to calculate the feature value and comprehensive gradient at each position according to the image information; According to the characteristic value and the comprehensive gradient at each position, a new characteristic value at each position is calculated, and according to the new characteristic value at each position, a final characteristic value at each position is calculated, and the final characteristic value is compared with a preset threshold. When the final characteristic value exceeds the preset threshold, the position corresponding to the final characteristic value is a water area position.

2. A real-time geographic information analysis method integrating a large model and remote sensing technology as claimed in claim 1, characterized in that: The remote sensing image feature extraction function includes: ， in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

3. A real-time geographic information analysis method integrating a large model and remote sensing technology as claimed in claim 2, characterized in that: The comprehensive gradient calculation function includes: ， ， in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

4. A real-time geographic information analysis method integrating a large model and remote sensing technology as claimed in claim 3, characterized in that: Calculate positions in remote sensing images The new eigenvalue at include: ， in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

5. A real-time geographic information analysis method integrating a large model and remote sensing technology as claimed in claim 4, characterized in that: Calculate positions in remote sensing images The final eigenvalue at include: ， in, is the position in the remote sensing image The final eigenvalue at is the position in the remote sensing image The vegetation index at is the position in the remote sensing image The soil moisture of the place.

6. A real-time geographic information analysis method integrating a large model and remote sensing technology as claimed in claim 5, characterized in that: The hydrological parameter is flow velocity or flow, the meteorological parameter is rainfall, temperature or air humidity, and the vegetation index is NDVI.

7. A real-time geographic information analysis system integrating large models and remote sensing technology for identifying hydrological information on remote sensing images, characterized in that: include: An information acquisition module is used to acquire image information of remote sensing images, wherein the image information includes: band set, reflectivity, elevation value, hydrological parameters, meteorological parameters, vegetation index and soil moisture; The module for calculating eigenvalues ​​and gradients is used to set a remote sensing image feature extraction function and a comprehensive gradient calculation function, and calculate the eigenvalues ​​and comprehensive gradients at each position according to the image information; The identification module is used to calculate a new feature value at each position according to the feature value and the comprehensive gradient at each position, and calculate a final feature value at each position according to the new feature value at each position, and compare the final feature value with a preset threshold. When the final feature value exceeds the preset threshold, the position corresponding to the final feature value is a water area position.

8. A real-time geographic information analysis system integrating large models and remote sensing technology as claimed in claim 7, characterized in that: The remote sensing image feature extraction function includes: ， in, is the position in the remote sensing image The eigenvalue at For location The horizontal axis of For location The vertical coordinate of is the band set, When the wavelength is The weight of is the first adjustment factor of the eigenvalue, is the second adjustment factor of the eigenvalue, is the position in the remote sensing image The wavelength is The reflectivity, is the third adjustment factor of the eigenvalue, is the fourth adjustment factor of the eigenvalue, For The value after the convolution operation, is the fifth adjustment factor of the eigenvalue, is the position in the remote sensing image The elevation value at .

9. A real-time geographic information analysis system integrating large models and remote sensing technology as claimed in claim 8, characterized in that: The comprehensive gradient calculation function includes: ， ， in, is the position in the remote sensing image Along the horizontal axis The gradient of For the position of the remote sensing image The eigenvalue at The eigenvalue after Gaussian filtering, is the position in the remote sensing image Along the vertical axis The gradient of is the position in the remote sensing image The comprehensive gradient of .

10. A real-time geographic information analysis system integrating large models and remote sensing technology as claimed in claim 9, characterized in that: Calculate positions in remote sensing images The new eigenvalue at include: ， in, is the position in the remote sensing image The new eigenvalue at For the The weight of the scale, After wavelet transformation, The eigenvalues ​​of the scale, For time The meteorological parameters at that time, is the position in the remote sensing image The hydrological parameters of the place.

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