Method and device for determining website address, storage medium and electronic equipment

Abstract

The invention discloses a website address determination method and device, a storage medium and electronic equipment. The method relates to the technical field of data processing, the field of satellite remote sensing and other related technical fields. The method comprises the following steps: acquiring first target remote sensing images and second target remote sensing images of a plurality of areas to be evaluated; obtaining first data information of the plurality of to-be-evaluated areas; processing the first target remote sensing image to obtain second data information of the plurality of to-be-evaluated areas; processing the second target remote sensing image to obtain urban population aggregation degrees of the plurality of to-be-evaluated areas; and according to the first data information, the second data information and the urban population aggregation degree, determining a target area in the plurality of to-be-evaluated areas. Through application of the method and the device, the problem that the determination accuracy of the website address is relatively low due to the fact that the website address of the financial institution is determined in a mode of evaluating a plurality of to-be-evaluated areas through artificial experience in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the fields of data processing technology, satellite remote sensing and other related technical fields, and more specifically, to a method and device for determining a network address, a storage medium and an electronic device. Background Art

[0002] With the development of social economy, customers' demand for handling financial services is also increasing. Therefore, it is very important for customers to reasonably determine the branch addresses of financial institutions. The traditional method mainly collects data such as the flow of people, the number of competing outlets, and traffic flow in multiple areas to be evaluated manually, and scores each area to be evaluated based on the opinions of experts, and then selects the best address of the financial institution. However, this method requires a lot of manpower and material resources in the data collection stage, resulting in a large workload and low work efficiency. At the same time, this method has a strong personal bias, resulting in non-objective decision-making results.

[0003] Currently, no effective solution has been proposed to address the problem that the addresses of financial institution outlets are determined by evaluating multiple areas to be evaluated through manual experience in related technologies, resulting in a relatively low accuracy rate in determining the outlet addresses. Summary of the invention

[0004] The main purpose of the present application is to provide a method and device for determining a branch address, a storage medium and an electronic device, so as to solve the problem that the addresses of financial institution branches are determined by evaluating multiple areas to be evaluated through manual experience in related technologies, resulting in a relatively low accuracy rate in determining the branch addresses.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for determining the address of a network point is provided. The method includes: obtaining a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of the multiple areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of the multiple areas to be evaluated at night; obtaining first data information of the multiple areas to be evaluated, wherein the first data information at least includes: the number of existing network points and traffic data of the multiple areas to be evaluated; processing the first target remote sensing image to obtain second data information of the multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of the multiple areas to be evaluated; processing the second target remote sensing image to obtain the urban population concentration of the multiple areas to be evaluated; determining a target area in the multiple areas to be evaluated based on the first data information, the second data information and the urban population concentration, wherein the target area is used to set a target network point.

[0006] Furthermore, processing the first target remote sensing image to obtain second data information of the multiple areas to be evaluated includes: performing edge extraction on the first target remote sensing image to obtain building information of the multiple areas to be evaluated, wherein the building information at least includes: building coverage, road network density and vegetation coverage of the multiple areas to be evaluated; performing land classification on the first target remote sensing image to obtain land types corresponding to the multiple areas to be evaluated; and determining the second data information of the multiple areas to be evaluated based on the building information and the land type.

[0007] Furthermore, processing the second target remote sensing image to obtain the urban population concentration of the multiple areas to be evaluated includes: performing projection conversion on the second target remote sensing image to obtain a converted second target remote sensing image; performing pixel adjustment and image cropping on the converted second target remote sensing image to obtain a processed second target remote sensing image; calculating the light radiation value in the processed second target remote sensing image; and determining the urban population concentration of the multiple areas to be evaluated based on the light radiation value.

[0008] Furthermore, calculating the light radiation value in the processed second target remote sensing image includes: performing image enhancement on the processed second target remote sensing image to obtain an enhanced second target remote sensing image; performing pixel extraction on the enhanced second target remote sensing image to obtain a light radiation value corresponding to each pixel; and performing calculation based on the light radiation value corresponding to each pixel to obtain the light radiation value in the processed second target remote sensing image.

[0009] Furthermore, determining the urban population concentration of the multiple areas to be evaluated based on the light radiation values ​​includes: obtaining population density information of the multiple areas to be evaluated; calculating based on the light radiation values ​​to obtain the light radiation intensity per unit area; and calculating based on the light radiation intensity per unit area and the population density information to obtain the urban population concentration of the multiple areas to be evaluated.

[0010] Furthermore, determining a target area among the multiple areas to be evaluated based on the first data information, the second data information and the urban population density includes: calculating the multiple areas to be evaluated based on the first data information, the second data information and the urban population density to obtain an evaluation score for each area to be evaluated; sorting the areas to be evaluated based on the evaluation score of each area to be evaluated to obtain a sorted evaluation list; obtaining a target evaluation score in the sorted evaluation list, wherein the order of the target evaluation score in the sorted evaluation list is less than the order of the remaining evaluation scores in the sorted evaluation list; and determining the area to be evaluated corresponding to the target evaluation score as the target area.

[0011] Furthermore, obtaining a first target remote sensing image and a second target remote sensing image of a plurality of areas to be evaluated includes: obtaining target position information of the plurality of areas to be evaluated; determining a first initial remote sensing image and a second initial remote sensing image of the plurality of areas to be evaluated based on the target position information; and denoising and dimensionally converting the first initial remote sensing image and the second initial remote sensing image to obtain the first target remote sensing image and the second target remote sensing image.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for determining network point addresses is provided. The device includes: a first acquisition unit, used to acquire a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of the multiple areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of the multiple areas to be evaluated at night; a second acquisition unit, used to acquire first data information of the multiple areas to be evaluated, wherein the first data information at least includes: the number of existing network points and traffic data of the multiple areas to be evaluated; a first processing unit, used to process the first target remote sensing image to obtain second data information of the multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of the multiple areas to be evaluated; a second processing unit, used to process the second target remote sensing image to obtain the urban population concentration of the multiple areas to be evaluated; a determination unit, used to determine a target area in the multiple areas to be evaluated based on the first data information, the second data information and the urban population concentration, wherein the target area is used to set a target network point.

[0013] Furthermore, the first processing unit includes: an extraction module, used to perform edge extraction on the first target remote sensing image to obtain building information of the multiple areas to be evaluated, wherein the building information at least includes: building coverage, road network density and vegetation coverage of the multiple areas to be evaluated; a classification module, used to perform land classification on the first target remote sensing image to obtain land types corresponding to the multiple areas to be evaluated; a first determination module, used to determine second data information of the multiple areas to be evaluated based on the building information and the land type.

[0014] Furthermore, the second processing unit includes: a first conversion module, used to perform projection conversion on the second target remote sensing image to obtain a converted second target remote sensing image; a cropping module, used to perform pixel adjustment and image cropping on the converted second target remote sensing image to obtain a processed second target remote sensing image; a first calculation module, used to calculate the light radiation value in the processed second target remote sensing image; and a second determination module, used to determine the urban population concentration of the multiple areas to be evaluated based on the light radiation value.

[0015] Furthermore, the first calculation module includes: an enhancement submodule, used to perform image enhancement on the processed second target remote sensing image to obtain an enhanced second target remote sensing image; an extraction submodule, used to extract pixels from the enhanced second target remote sensing image to obtain the light radiation value corresponding to each pixel; and a first calculation submodule, used to perform calculations based on the light radiation value corresponding to each pixel to obtain the light radiation value in the processed second target remote sensing image.

[0016] Furthermore, the second determination module includes: an acquisition submodule, used to obtain the population density information of the multiple areas to be evaluated; a second calculation submodule, used to calculate based on the light radiation value to obtain the light radiation intensity per unit area; and a third calculation submodule, used to calculate based on the light radiation intensity per unit area and the population density information to obtain the urban population concentration of the multiple areas to be evaluated.

[0017] Furthermore, the determination unit includes: a second calculation module, used to calculate the multiple areas to be evaluated based on the first data information, the second data information and the urban population concentration, and obtain an evaluation score for each area to be evaluated; a sorting module, used to sort according to the evaluation score of each area to be evaluated, and obtain a sorted evaluation list; a first acquisition module, used to obtain a target evaluation score in the sorted evaluation list, wherein the order of the target evaluation score in the sorted evaluation list is less than the order of the remaining evaluation scores in the sorted evaluation list; a third determination module, used to determine the area to be evaluated corresponding to the target evaluation score as the target area.

[0018] Furthermore, the first acquisition unit includes: a second acquisition module, used to acquire the target position information of the multiple areas to be evaluated; a fourth determination module, used to determine the first initial remote sensing image and the second initial remote sensing image of the multiple areas to be evaluated based on the target position information; a second conversion module, used to denoise and dimensionally convert the first initial remote sensing image and the second initial remote sensing image to obtain the first target remote sensing image and the second target remote sensing image.

[0019] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned methods for determining a network address.

[0020] In order to achieve the above purpose, according to another aspect of the present application, an electronic device is also provided, the electronic device includes one or more processors and a memory, and the memory is used to store the method for determining the network point address described in any one of the above implementations of the one or more processors.

[0021] Through the present application, the following steps are adopted: obtaining a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of multiple areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of multiple areas to be evaluated at night; obtaining first data information of multiple areas to be evaluated, wherein the first data information at least includes: the number of existing outlets and traffic data of multiple areas to be evaluated; processing the first target remote sensing image to obtain second data information of multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of multiple areas to be evaluated; processing the second target remote sensing image to obtain urban population concentration of multiple areas to be evaluated; determining a target area in multiple areas to be evaluated based on the first data information, the second data information and the urban population concentration, wherein the target area is used to set a target outlet. Through the present application, the problem of determining the addresses of financial institution outlets by evaluating multiple areas to be evaluated through artificial experience in the related art, resulting in a relatively low accuracy rate in determining the outlet addresses, is solved. In this scheme, remote sensing images of multiple areas to be evaluated during the day and at night are obtained, namely, the first target remote sensing image and the second target remote sensing image. Then, the first target remote sensing image and the second target remote sensing image are processed respectively to obtain the development intensity information, compactness information and urban population concentration of multiple areas to be evaluated. Finally, the target area is determined in multiple areas to be evaluated through the above information and the existing number of outlets and traffic data of multiple areas to be evaluated. The development intensity information, compactness information and urban population concentration of multiple areas to be evaluated are determined by the first target remote sensing image and the second target remote sensing image, and the target area is comprehensively determined in combination with the existing number of outlets and traffic data to set the target outlet. It avoids the problems of large collection workload and strong subjectivity of evaluation caused by the traditional method that experts evaluate multiple areas to be evaluated by combining manually collected evaluation data and their own experience to determine the final outlet address, thereby achieving the effect of improving the accuracy of determining the outlet address. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 is a flow chart of a method for determining a network point address provided in an embodiment of the present application;

[0024] Figure 2 is a schematic diagram of a method for determining a network point address provided in an embodiment of the present application;

[0025] Figure 3 is a schematic diagram of a device for determining a network point address provided in an embodiment of the present application;

[0026] Figure 4 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0031] The present invention is described below in conjunction with preferred implementation steps. Figure 1 is a flow chart of a method for determining a network point address according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0032] Step S101, obtaining first target remote sensing images and second target remote sensing images of multiple areas to be evaluated, wherein the first target remote sensing images are remote sensing images of multiple areas to be evaluated during the day, and the second target remote sensing images are remote sensing images of multiple areas to be evaluated at night.

[0033] Optionally, high-resolution daytime remote sensing images (ie, first target remote sensing images) and nighttime remote sensing images (ie, second target remote sensing images) of a plurality of areas to be evaluated are acquired through satellite remote sensing technology.

[0034] Step S102: obtaining first data information of a plurality of areas to be evaluated, wherein the first data information at least includes: the number of existing outlets and traffic data of the plurality of areas to be evaluated.

[0035] Optionally, the number of existing outlets in multiple areas to be evaluated is obtained through statistical data or online maps provided by relevant departments, and traffic data such as traffic flow, public transportation services, and road networks are collected through relevant departments.

[0036] Step S103, processing the first target remote sensing image to obtain second data information of the multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of the multiple areas to be evaluated.

[0037] Optionally, the first target remote sensing image is processed to evaluate the development intensity of the multiple areas to be evaluated by calculating the area ratio of a specific land use type or the building density. At the same time, the compactness of the multiple areas to be evaluated is evaluated by measuring the distribution of building boundaries, road networks and open spaces.

[0038] Step S104, processing the second target remote sensing image to obtain the urban population concentration of multiple areas to be evaluated.

[0039] Optionally, the second target remote sensing image is used to calculate the population concentration of multiple areas to be evaluated, such as population density, uniformity or concentration of population distribution, etc.

[0040] Step S105, determining a target area from a plurality of areas to be evaluated based on the first data information, the second data information and the city population concentration, wherein the target area is used to set a target network point.

[0041] Optionally, comprehensive evaluation indicators are determined based on the first data information, the second data information and the urban population concentration data, and weights of various indicators are allocated based on the actual situation and the functional requirements of the target outlets. Comprehensive evaluation indicators are used to comprehensively score each area to be evaluated, and areas with high development potential are selected as target areas based on the scoring results.

[0042] In summary, multiple remote sensing images of the areas to be evaluated are obtained during the day and at night, namely, the first target remote sensing image and the second target remote sensing image. Then, the first target remote sensing image and the second target remote sensing image are processed respectively to obtain the development intensity information, compactness information and urban population concentration of the multiple areas to be evaluated. Finally, the target area is determined in the multiple areas to be evaluated through the above information and the existing number of outlets and traffic data of the multiple areas to be evaluated. The development intensity information, compactness information and urban population concentration of the multiple areas to be evaluated are determined by the first target remote sensing image and the second target remote sensing image, and the target area is comprehensively determined in combination with the existing number of outlets and traffic data to set the target outlet. It avoids the problems of large collection workload and strong subjectivity of evaluation caused by the traditional method that experts evaluate multiple areas to be evaluated by combining manually collected evaluation data and their own experience to determine the final outlet address, thereby achieving the effect of improving the accuracy of determining the outlet address.

[0043] Optionally, in the method for determining the address of a network point provided in an embodiment of the present application, processing the first target remote sensing image to obtain second data information of multiple areas to be evaluated includes: performing edge extraction on the first target remote sensing image to obtain building information of multiple areas to be evaluated, wherein the building information at least includes: building coverage rate, road network density and vegetation coverage rate of multiple areas to be evaluated; performing land classification on the first target remote sensing image to obtain land types corresponding to multiple areas to be evaluated; and determining the second data information of multiple areas to be evaluated based on the building information and the land type.

[0044] In an optional embodiment, first, edge extraction is performed on the first target remote sensing image by an edge detection algorithm (such as the Canny algorithm) to obtain the building outline information and building coverage of multiple areas to be evaluated; the road network information of multiple areas to be evaluated is identified by image segmentation technology and feature extraction technology, and the road network density is calculated; the normalized difference vegetation index (NDVI) of multiple areas to be evaluated is calculated to evaluate the vegetation coverage of multiple areas to be evaluated. Then, the first target remote sensing image is classified by a classification algorithm to obtain the land type information and construction land spatial distribution information of each network point to be evaluated. Finally, the development intensity information (for example: building density, building height, etc.) of multiple areas to be evaluated is obtained based on the building coverage and land type information, and the data such as building layout, road network density and vegetation coverage are analyzed and calculated to obtain the compactness information of multiple areas to be evaluated.

[0045] By performing edge extraction and land classification on the first target remote sensing image, the building coverage rate, road network density, vegetation coverage rate, land type information and spatial distribution information of construction land of multiple areas to be evaluated can be quickly determined, and then the development intensity information and compactness information of multiple areas to be evaluated can be accurately determined based on the above information, providing a scientific basis for urban planning and management.

[0046] Optionally, in the method for determining the address of the network point provided in the embodiment of the present application, the second target remote sensing image is processed to obtain the urban population density of multiple areas to be evaluated, including: performing projection conversion on the second target remote sensing image to obtain a converted second target remote sensing image; performing pixel adjustment and image cropping on the converted second target remote sensing image to obtain a processed second target remote sensing image; calculating the light radiation value in the processed second target remote sensing image; and determining the urban population density of multiple areas to be evaluated based on the light radiation value.

[0047] In an optional embodiment, in order to make the data more accurate, avoid the influence of grid deformation and facilitate the calculation of the area of ​​the light brightness pixel in the remote sensing image, the second target remote sensing image is usually subjected to Lambert projection to obtain the converted second target remote sensing image, and the image size is set to 10×10 to obtain the processed second target remote sensing image. The total amount of nighttime light radiation within the construction land of the area to be evaluated in the processed second target remote sensing image is calculated, and the urban population concentration of each area to be evaluated is calculated by the total amount of nighttime light radiation.

[0048] It should be noted that there are two types of Lambert projection: Lambert conformal conic projection and Lambert equal-area azimuthal projection.

[0049] By performing Lambert projection on the second target remote sensing image, the urban population concentration information of multiple areas to be evaluated can be quickly and accurately extracted from the converted second target remote sensing image.

[0050] Optionally, in the method for determining the network point address provided in the embodiment of the present application, calculating the light radiation value in the processed second target remote sensing image includes: performing image enhancement on the processed second target remote sensing image to obtain an enhanced second target remote sensing image; performing pixel extraction on the enhanced second target remote sensing image to obtain the light radiation value corresponding to each pixel; and performing calculation based on the light radiation value corresponding to each pixel to obtain the light radiation value in the processed second target remote sensing image.

[0051] In an optional embodiment, first, according to the specific characteristics and analysis requirements of the second target remote sensing image, a suitable image enhancement technology (e.g., contrast enhancement, histogram equalization, edge enhancement, etc.) is selected, and a linear stretching or nonlinear stretching method is used to improve the image contrast to better distinguish areas of different brightness, and a sharpening filter (e.g., Laplace filter) is applied to enhance the details of the image to obtain an enhanced second target remote sensing image. Then, the enhanced second target remote sensing image is subjected to pixel extraction, and the brightness value of each pixel is extracted to obtain the light radiation value corresponding to each pixel. Finally, the light radiation value corresponding to each pixel is accumulated and normalized to obtain the light radiation value in the processed second target remote sensing image.

[0052] By performing image enhancement and pixel extraction on the processed second target remote sensing image, the details in the image can be better enhanced and areas of different brightness can be distinguished, thereby accurately obtaining the light radiation value corresponding to each pixel.

[0053] Optionally, in the method for determining the address of a network point provided in an embodiment of the present application, determining the urban population concentration of multiple areas to be evaluated based on the light radiation value includes: obtaining population density information of multiple areas to be evaluated; calculating based on the light radiation value to obtain the light radiation intensity per unit area; calculating based on the light radiation intensity per unit area and the population density information to obtain the urban population concentration of multiple areas to be evaluated.

[0054] In an optional embodiment, first, population data of multiple areas to be evaluated are obtained through statistical departments, census data or third-party data providers, and the population data is associated with the geographical location to ensure the spatial accuracy of the data. The population density information is obtained by calculating the ratio of the total population in multiple areas to be evaluated to the area of ​​the area. Then, the ratio of the light radiation value and the area of ​​the area of ​​multiple areas to be evaluated is calculated to obtain the light radiation intensity per unit area. Finally, based on historical data or related research, a correlation model between light radiation intensity and population density is established. The light radiation intensity per unit area is combined with the population density information, and the urban population concentration of each area to be evaluated is calculated through the correlation model.

[0055] Through the population density information and light radiation values ​​of multiple areas to be evaluated, the urban population concentration of each area to be evaluated can be obtained quickly and accurately, and the development of each area to be evaluated based on the urban population density.

[0056] Optionally, in the method for determining the address of a network point provided in an embodiment of the present application, determining a target area from multiple areas to be evaluated based on the first data information, the second data information and the urban population density includes: calculating the multiple areas to be evaluated based on the first data information, the second data information and the urban population density to obtain an evaluation score for each area to be evaluated; sorting the areas to be evaluated based on the evaluation score of each area to be evaluated to obtain a sorted evaluation list; obtaining a target evaluation score in the sorted evaluation list, wherein the order of the target evaluation score in the sorted evaluation list is less than the order of the remaining evaluation scores in the sorted evaluation list; and determining the area to be evaluated corresponding to the target evaluation score as the target area.

[0057] In an optional embodiment, first, according to the importance of the first data information, the second data information, and the urban population concentration index and its impact on the target area to be evaluated, weights are reasonably allocated, and a comprehensive evaluation model is established by combining the indexes and weights. Then, the evaluation model is applied to each area to be evaluated to calculate the evaluation score of each area to be evaluated. According to the calculated evaluation scores, all areas to be evaluated are sorted in descending order. Finally, the target evaluation score with the highest score is selected from the sorted evaluation list, and the area to be evaluated corresponding to the target evaluation score is determined as the target area.

[0058] By assigning weights to the first data information, the second data information, and indicators such as urban population concentration, the evaluation score of each area to be evaluated can be quickly calculated, and then the target area can be accurately determined among multiple areas to be evaluated based on the evaluation score.

[0059] Optionally, in the method for determining the address of a network point provided in an embodiment of the present application, obtaining a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated includes: obtaining target position information of multiple areas to be evaluated; determining a first initial remote sensing image and a second initial remote sensing image of multiple areas to be evaluated based on the target position information; and denoising and dimensionalizing the first initial remote sensing image and the second initial remote sensing image to obtain a first target remote sensing image and a second target remote sensing image.

[0060] In an optional embodiment, first, the target location information such as the geographic coordinates and boundaries of multiple areas to be evaluated is determined. The first initial remote sensing image and the second initial remote sensing image of multiple areas to be evaluated are determined based on the target location information by satellite remote sensing technology. Then, the noise type in the remote sensing image (for example: salt and pepper noise, Gaussian noise, etc.) is identified, and a suitable denoising algorithm (for example: median filtering, Gaussian filtering, wavelet transform, etc.) is selected according to the noise type to reduce the noise in the image. Finally, in order to avoid problems such as incomplete numerical display and facilitate subsequent operations, the first initial remote sensing image and the second initial remote sensing image are dimensionally converted to obtain the first target remote sensing image and the second target remote sensing image.

[0061] By using the location information of multiple areas to be evaluated, the first initial remote sensing image and the second initial remote sensing image can be quickly obtained, and the above remote sensing images can be denoised and dimensionally converted to accurately obtain the first target remote sensing images and the second target remote sensing images of multiple areas to be evaluated.

[0062] It should be noted that Figure 2 Schematic diagram of a method for determining a network point address according to an embodiment of the present application. Figure 2 As shown, first, first data information such as first target remote sensing images, second target remote sensing images, and the number of existing network points and traffic data corresponding to the multiple areas to be evaluated are determined according to the location information of the multiple areas to be evaluated.

[0063] Then, edge extraction and land classification are performed on the first target remote sensing image to obtain building information and land types such as building coverage, road network density and vegetation coverage corresponding to multiple areas to be evaluated. At the same time, projection conversion, pixel adjustment, image cropping and image enhancement are performed on the second target remote sensing image to obtain an enhanced second target remote sensing image, and pixel extraction is performed on the enhanced second target remote sensing image to obtain the light radiation value corresponding to each pixel, and the light radiation value corresponding to each pixel is accumulated to obtain the light radiation value. The light radiation value and the population density information of multiple areas to be evaluated are used for calculation to obtain the urban population concentration of multiple areas to be evaluated.

[0064] Finally, multiple areas to be evaluated are calculated based on the first data information, the second data information and the urban population concentration to obtain an evaluation score for each area to be evaluated, the evaluation score of each area to be evaluated is sorted in descending order, the target evaluation score with the highest evaluation score is selected, and the area to be evaluated corresponding to the target evaluation score is determined as the target area.

[0065] The method for determining the address of a branch provided in an embodiment of the present application comprises obtaining a first target remote sensing image and a second target remote sensing image of a plurality of areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of the plurality of areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of the plurality of areas to be evaluated at night; obtaining first data information of the plurality of areas to be evaluated, wherein the first data information at least includes: the number of existing branches and traffic data of the plurality of areas to be evaluated; processing the first target remote sensing image to obtain second data information of the plurality of areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of the plurality of areas to be evaluated; processing the second target remote sensing image to obtain the urban population concentration of the plurality of areas to be evaluated; determining a target area in the plurality of areas to be evaluated based on the first data information, the second data information and the urban population concentration, wherein the target area is used to set a target branch, thereby solving the problem that the address of a financial institution branch is determined by evaluating the plurality of areas to be evaluated by artificial experience in the related art, resulting in a relatively low accuracy rate in determining the address of the branch. In summary, in this scheme, remote sensing images of multiple areas to be evaluated during the day and at night are obtained, namely, the first target remote sensing image and the second target remote sensing image. Then, the first target remote sensing image and the second target remote sensing image are processed respectively to obtain the development intensity information, compactness information and urban population concentration of multiple areas to be evaluated. Finally, the target area is determined in multiple areas to be evaluated through the above information and the existing number of outlets and traffic data of multiple areas to be evaluated. The development intensity information, compactness information and urban population concentration of multiple areas to be evaluated are determined by the first target remote sensing image and the second target remote sensing image, and the target area is comprehensively determined in combination with the existing number of outlets and traffic data to set the target outlet. It avoids the problems of large collection workload and strong subjectivity of evaluation caused by the traditional method that experts evaluate multiple areas to be evaluated by combining manually collected evaluation data and their own experience to determine the final outlet address, thereby achieving the effect of improving the accuracy of determining the outlet address.

[0066] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0067] The embodiment of the present application also provides a device for determining a network point address. It should be noted that the device for determining a network point address in the embodiment of the present application can be used to execute the method for determining a network point address provided in the embodiment of the present application. The device for determining a network point address provided in the embodiment of the present application is introduced below.

[0068] Figure 3 is a schematic diagram of a device for determining a network point address according to an embodiment of the present application. Figure 3 As shown, the device includes: a first acquisition unit 301, a second acquisition unit 302, a first processing unit 303, a second processing unit 304, and a determination unit 305.

[0069] The first acquisition unit 301 is used to acquire a first target remote sensing image and a second target remote sensing image of a plurality of areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of the plurality of areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of the plurality of areas to be evaluated at night;

[0070] The second acquisition unit 302 is used to acquire first data information of multiple areas to be evaluated, wherein the first data information at least includes: the number of existing network points and traffic data of the multiple areas to be evaluated;

[0071] The first processing unit 303 is used to process the first target remote sensing image to obtain second data information of the multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of the multiple areas to be evaluated;

[0072] The second processing unit 304 is used to process the second target remote sensing image to obtain the urban population concentration of multiple areas to be evaluated;

[0073] The determination unit 305 is used to determine a target area among a plurality of areas to be evaluated according to the first data information, the second data information and the urban population concentration, wherein the target area is used to set a target network point.

[0074] The device for determining the address of a network point provided in the embodiment of the present application comprises a first acquisition unit 301, which is used to acquire a first target remote sensing image and a second target remote sensing image of a plurality of areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of the plurality of areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of the plurality of areas to be evaluated at night; a second acquisition unit 302, which is used to acquire first data information of the plurality of areas to be evaluated, wherein the first data information at least includes: the number of existing network points and traffic data of the plurality of areas to be evaluated; a first processing unit 303, which is used to process the first target remote sensing image, and obtain first data information of the plurality of areas to be evaluated. Second data information, wherein the second data information at least includes: development intensity information and compactness information of multiple areas to be evaluated; a second processing unit 304, used to process the second target remote sensing image to obtain the urban population concentration of multiple areas to be evaluated; a determination unit 305, used to determine the target area in multiple areas to be evaluated based on the first data information, the second data information and the urban population concentration, wherein the target area is used to set the target outlets, which solves the problem that the address of the financial institution outlets is determined by evaluating multiple areas to be evaluated by artificial experience in the related art, resulting in a relatively low accuracy rate of determining the outlet address. In this solution, remote sensing images of multiple areas to be evaluated during the day and at night are obtained, namely, the first target remote sensing image and the second target remote sensing image. Then, the first target remote sensing image and the second target remote sensing image are processed respectively to obtain the development intensity information, compactness information and urban population concentration of multiple areas to be evaluated. Finally, the target area is determined in multiple areas to be evaluated through the above information and the existing number of outlets and traffic data of multiple areas to be evaluated. The development intensity information, compactness information and urban population concentration of multiple areas to be evaluated are determined through the first target remote sensing image and the second target remote sensing image, and the target area is comprehensively determined in combination with the number of existing outlets and traffic data to set the target outlets. This avoids the problems of large collection workload and strong subjectivity of evaluation caused by the traditional method in which experts evaluate multiple areas to be evaluated by combining manually collected evaluation data and their own experience to determine the final outlet address, thereby achieving the effect of improving the accuracy of determining the outlet address.

[0075] Optionally, in the device for determining the network point address provided in the embodiment of the present application, the first processing unit includes: an extraction module, which is used to perform edge extraction on the first target remote sensing image to obtain building information of multiple areas to be evaluated, wherein the building information at least includes: building coverage, road network density and vegetation coverage of multiple areas to be evaluated; a classification module, which is used to perform land classification on the first target remote sensing image to obtain land types corresponding to multiple areas to be evaluated; and a first determination module, which is used to determine second data information of multiple areas to be evaluated based on the building information and the land type.

[0076] Optionally, in the device for determining the network point address provided in the embodiment of the present application, the second processing unit includes: a first conversion module, used to perform projection conversion on the second target remote sensing image to obtain a converted second target remote sensing image; a cropping module, used to perform pixel adjustment and image cropping on the converted second target remote sensing image to obtain a processed second target remote sensing image; a first calculation module, used to calculate the light radiation value in the processed second target remote sensing image; and a second determination module, used to determine the urban population concentration of multiple areas to be evaluated based on the light radiation value.

[0077] Optionally, in the device for determining the network point address provided in the embodiment of the present application, the first calculation module includes: an enhancement submodule, used to perform image enhancement on the processed second target remote sensing image to obtain an enhanced second target remote sensing image; an extraction submodule, used to extract pixels from the enhanced second target remote sensing image to obtain the light radiation value corresponding to each pixel; and a first calculation submodule, used to perform calculations based on the light radiation value corresponding to each pixel to obtain the light radiation value in the processed second target remote sensing image.

[0078] Optionally, in the device for determining the network address provided in the embodiment of the present application, the second determination module includes: an acquisition submodule, used to obtain population density information of multiple areas to be evaluated; a second calculation submodule, used to calculate based on the light radiation value to obtain the light radiation intensity per unit area; a third calculation submodule, used to calculate based on the light radiation intensity per unit area and the population density information to obtain the urban population concentration of multiple areas to be evaluated.

[0079] Optionally, in the device for determining the address of a network point provided in an embodiment of the present application, the determination unit includes: a second calculation module, used to calculate multiple areas to be evaluated based on the first data information, the second data information and the urban population density, to obtain an evaluation score for each area to be evaluated; a sorting module, used to sort according to the evaluation score of each area to be evaluated, to obtain a sorted evaluation list; a first acquisition module, used to obtain a target evaluation score in the sorted evaluation list, wherein the order of the target evaluation score in the sorted evaluation list is less than the order of the remaining evaluation scores in the sorted evaluation list; and a third determination module, used to determine the area to be evaluated corresponding to the target evaluation score as the target area.

[0080] Optionally, in the device for determining the network point address provided in the embodiment of the present application, the first acquisition unit includes: a second acquisition module, used to obtain target position information of multiple areas to be evaluated; a fourth determination module, used to determine the first initial remote sensing image and the second initial remote sensing image of the multiple areas to be evaluated based on the target position information; a second conversion module, used to denoise and dimensionally convert the first initial remote sensing image and the second initial remote sensing image to obtain a first target remote sensing image and a second target remote sensing image.

[0081] The device for determining the network point address includes a processor and a memory. The above-mentioned first acquisition unit 301, second acquisition unit 302, first processing unit 303, second processing unit 304, determination unit 305, 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 accurate determination of the network point address.

[0082] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the network node address can be accurately determined by adjusting the kernel parameters.

[0083] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0084] An embodiment of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, a method for determining a network node address is implemented.

[0085] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for determining a network node address is executed when the program is running.

[0086] like Figure 4As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: obtaining a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of multiple areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of multiple areas to be evaluated at night; obtaining first data information of multiple areas to be evaluated, wherein the first data information at least includes: the number of existing outlets and traffic data of multiple areas to be evaluated; processing the first target remote sensing image to obtain second data information of multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of multiple areas to be evaluated; processing the second target remote sensing image to obtain urban population concentration of multiple areas to be evaluated; determining a target area in multiple areas to be evaluated based on the first data information, the second data information and the urban population concentration, wherein the target area is used to set a target outlet.

[0087] Optionally, processing the first target remote sensing image to obtain second data information of multiple areas to be evaluated includes: performing edge extraction on the first target remote sensing image to obtain building information of multiple areas to be evaluated, wherein the building information at least includes: building coverage, road network density and vegetation coverage of multiple areas to be evaluated; performing land classification on the first target remote sensing image to obtain land types corresponding to multiple areas to be evaluated; and determining the second data information of multiple areas to be evaluated based on the building information and the land type.

[0088] Optionally, processing the second target remote sensing image to obtain the urban population concentration of multiple areas to be evaluated includes: performing projection conversion on the second target remote sensing image to obtain a converted second target remote sensing image; performing pixel adjustment and image cropping on the converted second target remote sensing image to obtain a processed second target remote sensing image; calculating the light radiation value in the processed second target remote sensing image; and determining the urban population concentration of multiple areas to be evaluated based on the light radiation value.

[0089] Optionally, calculating the light radiation value in the processed second target remote sensing image includes: performing image enhancement on the processed second target remote sensing image to obtain an enhanced second target remote sensing image; performing pixel extraction on the enhanced second target remote sensing image to obtain the light radiation value corresponding to each pixel; and performing calculation based on the light radiation value corresponding to each pixel to obtain the light radiation value in the processed second target remote sensing image.

[0090] Optionally, determining the urban population concentration of multiple areas to be evaluated based on the light radiation values ​​includes: obtaining population density information of multiple areas to be evaluated; calculating based on the light radiation values ​​to obtain the light radiation intensity per unit area; calculating based on the light radiation intensity per unit area and population density information to obtain the urban population concentration of multiple areas to be evaluated.

[0091] Optionally, determining a target area among multiple areas to be evaluated based on the first data information, the second data information and the urban population density includes: calculating the multiple areas to be evaluated based on the first data information, the second data information and the urban population density to obtain an evaluation score for each area to be evaluated; sorting the areas to be evaluated based on the evaluation scores of each area to be evaluated to obtain a sorted evaluation list; obtaining a target evaluation score in the sorted evaluation list, wherein the order of the target evaluation score in the sorted evaluation list is less than the order of the remaining evaluation scores in the sorted evaluation list; and determining the area to be evaluated corresponding to the target evaluation score as the target area.

[0092] Optionally, obtaining a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated includes: obtaining target position information of multiple areas to be evaluated; determining a first initial remote sensing image and a second initial remote sensing image of the multiple areas to be evaluated based on the target position information; and denoising and dimensionality conversion of the first initial remote sensing image and the second initial remote sensing image to obtain a first target remote sensing image and a second target remote sensing image.

[0093] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0094] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: obtaining a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of multiple areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of multiple areas to be evaluated at night; obtaining first data information of multiple areas to be evaluated, wherein the first data information at least includes: the number of existing network points and traffic data of multiple areas to be evaluated; processing the first target remote sensing image to obtain second data information of multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of multiple areas to be evaluated; processing the second target remote sensing image to obtain urban population concentration of multiple areas to be evaluated; determining a target area in multiple areas to be evaluated based on the first data information, the second data information and the urban population concentration, wherein the target area is used to set a target network point.

[0095] Optionally, processing the first target remote sensing image to obtain second data information of multiple areas to be evaluated includes: performing edge extraction on the first target remote sensing image to obtain building information of multiple areas to be evaluated, wherein the building information at least includes: building coverage, road network density and vegetation coverage of multiple areas to be evaluated; performing land classification on the first target remote sensing image to obtain land types corresponding to multiple areas to be evaluated; and determining the second data information of multiple areas to be evaluated based on the building information and the land type.

[0096] Optionally, processing the second target remote sensing image to obtain the urban population concentration of multiple areas to be evaluated includes: performing projection conversion on the second target remote sensing image to obtain a converted second target remote sensing image; performing pixel adjustment and image cropping on the converted second target remote sensing image to obtain a processed second target remote sensing image; calculating the light radiation value in the processed second target remote sensing image; and determining the urban population concentration of multiple areas to be evaluated based on the light radiation value.

[0097] Optionally, calculating the light radiation value in the processed second target remote sensing image includes: performing image enhancement on the processed second target remote sensing image to obtain an enhanced second target remote sensing image; performing pixel extraction on the enhanced second target remote sensing image to obtain the light radiation value corresponding to each pixel; and performing calculation based on the light radiation value corresponding to each pixel to obtain the light radiation value in the processed second target remote sensing image.

[0098] Optionally, determining the urban population concentration of multiple areas to be evaluated based on the light radiation values ​​includes: obtaining population density information of multiple areas to be evaluated; calculating based on the light radiation values ​​to obtain the light radiation intensity per unit area; calculating based on the light radiation intensity per unit area and population density information to obtain the urban population concentration of multiple areas to be evaluated.

[0099] Optionally, determining a target area among multiple areas to be evaluated based on the first data information, the second data information and the urban population density includes: calculating the multiple areas to be evaluated based on the first data information, the second data information and the urban population density to obtain an evaluation score for each area to be evaluated; sorting the areas to be evaluated based on the evaluation scores of each area to be evaluated to obtain a sorted evaluation list; obtaining a target evaluation score in the sorted evaluation list, wherein the order of the target evaluation score in the sorted evaluation list is less than the order of the remaining evaluation scores in the sorted evaluation list; and determining the area to be evaluated corresponding to the target evaluation score as the target area.

[0100] Optionally, obtaining a first target remote sensing image and a second target remote sensing image of multiple areas to be evaluated includes: obtaining target position information of multiple areas to be evaluated; determining a first initial remote sensing image and a second initial remote sensing image of the multiple areas to be evaluated based on the target position information; and denoising and dimensionality conversion of the first initial remote sensing image and the second initial remote sensing image to obtain a first target remote sensing image and a second target remote sensing image.

[0101] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0106] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0107] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0108] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0110] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining a network point address, characterized in that: include: Acquire a first target remote sensing image and a second target remote sensing image of a plurality of areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of the plurality of areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of the plurality of areas to be evaluated at night; Acquire first data information of the multiple areas to be evaluated, wherein the first data information at least includes: the number of existing outlets and traffic data of the multiple areas to be evaluated; Processing the first target remote sensing image to obtain second data information of the multiple areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of the multiple areas to be evaluated; Processing the second target remote sensing image to obtain the urban population concentration of the multiple areas to be evaluated; A target area is determined among the multiple areas to be evaluated based on the first data information, the second data information and the city population concentration, wherein the target area is used to set a target network point.

2. The method according to claim 1, characterized in that Processing the first target remote sensing image to obtain second data information of the plurality of areas to be evaluated includes: Performing edge extraction on the first target remote sensing image to obtain building information of the multiple areas to be evaluated, wherein the building information at least includes: building coverage, road network density and vegetation coverage of the multiple areas to be evaluated; Performing land classification on the first target remote sensing image to obtain land types corresponding to the multiple areas to be evaluated; According to the building information and the land type, second data information of the plurality of areas to be evaluated is determined.

3. The method according to claim 1, characterized in that Processing the second target remote sensing image to obtain the urban population concentration of the plurality of areas to be evaluated includes: Performing a projection transformation on the second target remote sensing image to obtain a transformed second target remote sensing image; Performing pixel adjustment and image cropping on the converted second target remote sensing image to obtain a processed second target remote sensing image; Calculating the light radiation value in the processed second target remote sensing image; The urban population concentration of the multiple areas to be evaluated is determined based on the light radiation value.

4. The method according to claim 3, characterized in that: Calculating the light radiation value in the processed second target remote sensing image includes: Performing image enhancement on the processed second target remote sensing image to obtain an enhanced second target remote sensing image; Extracting pixels from the enhanced remote sensing image of the second target to obtain a light radiation value corresponding to each pixel; Calculation is performed based on the light radiation value corresponding to each pixel to obtain the light radiation value in the processed second target remote sensing image.

5. The method according to claim 3, characterized in that: Determining the urban population concentration of the plurality of areas to be evaluated based on the light radiation value includes: Obtaining population density information of the multiple areas to be evaluated; Calculating according to the light radiation value to obtain the light radiation intensity per unit area; The urban population concentration of the multiple areas to be evaluated is obtained by calculation based on the light radiation intensity per unit area and the population density information.

6. The method according to claim 1, characterized in that Determining a target area in the plurality of areas to be evaluated according to the first data information, the second data information and the city population concentration includes: Calculating the plurality of areas to be evaluated based on the first data information, the second data information and the urban population concentration to obtain an evaluation score for each area to be evaluated; Sort by the assessment score of each area to be assessed to obtain a sorted assessment list; Obtaining a target evaluation score in the sorted evaluation list, wherein the order of the target evaluation score in the sorted evaluation list is less than the order of the remaining evaluation scores in the sorted evaluation list; The area to be evaluated corresponding to the target evaluation score is determined as the target area.

7. The method according to claim 1, characterized in that Acquiring a first target remote sensing image and a second target remote sensing image of a plurality of areas to be evaluated comprises: Acquiring target location information of the multiple areas to be evaluated; Determining, based on the target location information, a first initial remote sensing image and a second initial remote sensing image of the plurality of areas to be evaluated; Denoising and dimension conversion are performed on the first initial remote sensing image and the second initial remote sensing image to obtain the first target remote sensing image and the second target remote sensing image.

8. A device for determining a network point address, characterized in that: include: A first acquisition unit is used to acquire a first target remote sensing image and a second target remote sensing image of a plurality of areas to be evaluated, wherein the first target remote sensing image is a remote sensing image of the plurality of areas to be evaluated during the day, and the second target remote sensing image is a remote sensing image of the plurality of areas to be evaluated at night; A second acquisition unit is used to acquire first data information of the plurality of areas to be evaluated, wherein the first data information at least includes: the number of existing outlets and traffic data of the plurality of areas to be evaluated; A first processing unit is configured to process the first target remote sensing image to obtain second data information of the plurality of areas to be evaluated, wherein the second data information at least includes: development intensity information and compactness information of the plurality of areas to be evaluated; A second processing unit is used to process the second target remote sensing image to obtain the urban population concentration of the multiple areas to be evaluated; A determination unit is used to determine a target area among the multiple areas to be evaluated based on the first data information, the second data information and the city population concentration, wherein the target area is used to set a target network point.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the storage medium is controlled to execute the method for determining a network point address according to any one of claims 1 to 7 on a device.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the network point address as described in any one of claims 1 to 7.

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