A surface remote sensing monitoring system and method

Abstract

This invention relates to the field of information processing and discloses a surface remote sensing monitoring system and method, including a remote sensing image acquisition module, an image feature processing module, a historical feature matching module, and a remote sensing result output module. By acquiring and analyzing remote sensing images, the system can assess the ground environment of large-area sites. Through feature comparison before and after, it can understand the distribution of garbage on the ground, thereby enabling efficient allocation and maintenance assistance for ground maintenance personnel and effectively improving the maintenance and management efficiency of large-area public places.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information processing, and particularly to a ground surface remote sensing monitoring system and method. BACKGROUND

[0002] In rural travel, farmland, forest land and other areas, daily people come and go, which will cause garbage, leftovers and other random existence in the area, seriously affecting the appearance of the site and the touring experience, so it is necessary to continuously maintain and manage it to ensure the cleanliness and tidiness of the place.

[0003] In the prior art, it is realized by the traversal reciprocating tour of maintenance personnel, which is low in efficiency, and in a larger place, limited maintenance personnel cannot effectively and quickly locate the distribution of garbage, resulting in more time in the search process, causing some places to be unable to be maintained in time. SUMMARY

[0004] The purpose of the present application is to provide a ground surface remote sensing monitoring system and method to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] A ground surface remote sensing monitoring system comprises:

[0007] A remote sensing image acquisition module is configured to generate a remote sensing execution request at a preset ground surface update time interval and respond, and to acquire remote sensing images of the ground surface of a supervision and scheduling area through a remote sensing device, wherein the ground surface update time interval is a dynamic time length, and the remote sensing images are used to represent the object image distribution of the ground surface;

[0008] An image feature processing module is configured to preprocess the remote sensing images to obtain a grayscale image, mark the pixel points of the grayscale image with grayscale values, calculate the grayscale difference values of adjacent pixel points in the grayscale image to generate a corresponding feature gradient matrix, and represent the distribution of image features by the feature gradient matrix;

[0009] A historical feature matching module is configured to acquire an initial gradient matrix of the supervision and scheduling area, difference the feature gradient matrix based on the initial gradient matrix to generate a supervision object matrix, and characterize the supervision object matrix based on the pixel matching relationship between the matrix and the image to generate a supervision object distribution image;

[0010] The remote sensing result output module is configured to generate a management scheduling instruction based on the supervision object distribution image, and forward the management scheduling instruction to a management mobile terminal through a preset communication channel. The management scheduling instruction is configured to perform supervision area redistribution on a management personnel of the supervision scheduling area and perform distribution marking on the supervision object based on an object distribution density of the supervision object distribution image, so as to output.

[0011] As a further scheme of the present application, the historical feature matching module comprises a feature unit, which specifically comprises:

[0012] The difference filtering subunit is configured to judge the elements of the rows and columns in the supervision object matrix one by one based on a preset difference threshold, generate a judgment result, and assign a value of zero to the element if the judgment result indicates that the element is less than the preset difference threshold.

[0013] The difference assignment subunit is configured to assign a value of one to the element if the judgment result indicates that the difference is greater than or equal to the preset difference threshold.

[0014] The matrix feature subunit is configured to feature the supervision object matrix after the assignment into a supervision object distribution image according to a pixel matching relationship between the matrix and the image, wherein the pixels assigned with one and the pixels assigned with zero are represented by different colors in the supervision object distribution image.

[0015] As a further scheme of the present application, the remote sensing image acquisition module comprises an image generation unit.

[0016] The image generation unit is configured to acquire a plurality of groups of basic remote sensing images through a remote sensing device, stack and match the plurality of groups of basic remote sensing images, identify motion feature objects in the plurality of groups of basic remote sensing images, eliminate the motion feature objects in the plurality of groups of basic remote sensing images, and generate a remote sensing image of a ground surface of the supervision scheduling area.

[0017] As a further scheme of the present application, the remote sensing image acquisition module further comprises a cycle interval determination unit.

[0018] The cycle interval determination unit is configured to mark and count a plurality of motion feature objects identified in the stacking and matching, calculate a motion object density of the supervision scheduling area, and generate a density range based on historical record statistics. The density range comprises a dynamic time interval range corresponding in inverse proportion thereto. When the motion object density increases, the dynamic time interval decreases. The dynamic time interval is used to control generation of a next remote sensing execution request.

[0019] As a further scheme of the present application: the initial gradient matrix is used to represent a feature gradient matrix generated based on processing of remote sensing images collected within a preset time period before the motion feature object is opened to the regulatory scheduling area, and the initial gradient matrix is used to perform basic inherent feature shielding on the feature gradient matrix in the regulatory scheduling area.

[0020] The embodiment of the present application aims to provide a ground surface remote sensing monitoring method, comprising the steps of:

[0021] A remote sensing execution request is generated at a preset ground surface update time interval and a response is obtained by a remote sensing device, the ground surface update time interval is a dynamic time length, and the remote sensing image is used to represent the object image distribution of the ground surface;

[0022] The remote sensing image is preprocessed to obtain a gray image, and the pixel points of the gray image are marked with gray values, the gray difference values of adjacent pixel points in the gray image are calculated to generate a corresponding feature gradient matrix, and the feature gradient matrix represents the distribution of image features;

[0023] An initial gradient matrix of the regulatory scheduling area is obtained, the feature gradient matrix is differentiated based on the initial gradient matrix to generate a regulatory object matrix, and the regulatory object matrix is characterized based on the pixel matching relationship between the matrix and the image to generate a regulatory object distribution image;

[0024] A management scheduling instruction is generated based on the regulatory object distribution image and is forwarded to a management mobile terminal through a preset communication channel, the management scheduling instruction is used to perform regulatory area redistribution on the management personnel of the regulatory scheduling area and distribution marking on the regulatory object based on the object distribution density of the regulatory object distribution image to output.

[0025] As a further scheme of the present application: the step of characterizing the regulatory object matrix based on the pixel matching relationship between the matrix and the image to generate a regulatory object distribution image comprises:

[0026] The elements of the regulatory object matrix are judged one by one based on a preset difference threshold value, a judgment result is generated, if it is less than the preset difference threshold value, the value of the element is assigned to zero;

[0027] When the judgment result represents that the difference is greater than or equal to the preset difference threshold value, the value of the element is assigned to one;

[0028] The regulatory object matrix after assignment is characterized into a regulatory object distribution image according to the pixel matching relationship between the matrix and the image, wherein the pixels assigned to one and the pixels assigned to zero are represented by different colors in the regulatory object distribution image.

[0029] As a further scheme of the present application: the step of acquiring the remote sensing image of the ground surface of the supervision and scheduling area by the remote sensing device specifically comprises:

[0030] A plurality of basic remote sensing images are acquired by the remote sensing device, and the plurality of basic remote sensing images are stacked and matched to identify the moving feature objects in the plurality of basic remote sensing images, to eliminate the moving feature objects in the plurality of basic remote sensing images, and to generate the remote sensing image of the ground surface of the supervision and scheduling area.

[0031] As a further scheme of the present application: further comprising the step of:

[0032] The number of moving feature objects identified in the stacked matching is labeled and counted to calculate the moving object density of the supervision and scheduling area, and the moving object density contains a density range generated based on historical record statistics, and the density range includes a dynamic time interval range corresponding in inverse proportion, and when the moving object density increases, the dynamic time interval decreases, and the dynamic time interval is used to control the generation of the next remote sensing execution request.

[0033] As a further scheme of the present application: the initial gradient matrix is used to represent a feature gradient matrix generated based on processing of the acquired remote sensing image within a preset time period before the supervision and scheduling area is opened to the moving feature object, and the initial gradient matrix is used to perform basic inherent feature shielding on the feature gradient matrix.

[0034] Compared with the prior art, the present application has the beneficial effects that: by acquiring and analyzing the remote sensing image, the ground environment of a large-area ground site is judged, the distribution of garbage on the ground is understood through comparison of the features before and after, and efficient allocation and maintenance assistance of ground maintenance personnel are realized, and the maintenance and management efficiency of a large-area public place can be effectively provided. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 It is a component block diagram of a ground remote sensing monitoring system.

[0036] Figure 2 It is a component block diagram of a feature unit in a ground remote sensing monitoring system.

[0037] Figure 3 It is a flow block diagram of a ground remote sensing monitoring method. EMBODIMENT

[0038] In order to make the objects, technical solutions and advantages of the present application clearer, further detailed description will be given to the present application in combination with the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0039] The specific implementation of the present application will be described in detail in combination with specific examples.

[0040] As Figure 1 The ground remote sensing monitoring system provided by one embodiment of the present application comprises the following steps:

[0041] The remote sensing image acquisition module 100 is configured to generate a remote sensing execution request at a preset ground surface update time interval and respond, and acquire remote sensing images of the ground surface of the supervision and scheduling area through a remote sensing device, wherein the ground surface update time interval is a dynamic time length, and the remote sensing images are used to represent the object image distribution of the ground surface.

[0042] The image feature processing module 300 is configured to pre-process the remote sensing images to obtain a gray image, mark the pixel points of the gray image with gray values, calculate the gray difference values of adjacent pixel points in the gray image to generate a corresponding feature gradient matrix, and represent the distribution of image features by the feature gradient matrix.

[0043] The historical feature matching module 500 is configured to acquire an initial gradient matrix of the supervision and scheduling area, perform difference calculation on the feature gradient matrix based on the initial gradient matrix to generate a supervision object matrix, and perform feature extraction on the supervision object matrix based on the pixel matching relationship between the matrix and the image to generate a supervision object distribution image.

[0044] The remote sensing result output module 700 is configured to generate a management and scheduling instruction based on the supervision object distribution image, and forward the management and scheduling instruction to a management mobile terminal through a preset communication channel, wherein the management and scheduling instruction is used to perform supervision area redistribution on the management personnel of the supervision and scheduling area and perform distribution marking on the supervision object based on the object distribution density of the supervision object distribution image to output.

[0045] In this embodiment, a ground surface remote sensing monitoring system is given, through remote sensing image acquisition and analysis, the ground environment of a large area ground surface place is judged, through the comparison of the characteristics before and after, the ground garbage distribution is understood, and then the efficient distribution and maintenance assistance of the ground maintenance personnel are realized, the maintenance and management efficiency of the large area public place can be effectively provided; When in use, the environment here can be specifically understood as, for example, a large area of public grassland, rural tourism, farmland, forest land and the like, in the daily coming and going, garbage, leftovers and the like will exist randomly in the area, which seriously affects the appearance and touring experience of the site, therefore, it needs to be maintained and managed constantly to ensure its cleanliness, here, the corresponding remote sensing image is obtained by high-altitude remote sensing technology (which can be realized by regular cruising of a drone), the shape and distribution image of the garbage is obtained by feature processing of the remote sensing image (the feature object is identified by gray scale processing), so that the maintenance and management personnel can be dispatched, and the maintenance and management personnel can be assisted by image marking to maintain and clean.

[0046] As another preferred embodiment of the present application, the historical feature matching module 500 comprises a feature unit 510, which specifically comprises:

[0047] A difference filtering sub-unit 511 is configured to judge the elements of the rows and columns in the supervision object matrix one by one based on a preset difference threshold, generate a judgment result, and assign a value of zero to the element if the result is less than the preset difference threshold.

[0048] A difference assignment sub-unit 512 is configured to assign a value of one to the element when the judgment result indicates that the difference is greater than or equal to the preset difference threshold.

[0049] A matrix feature sub-unit 513 is configured to feature the supervision object matrix after assignment into a supervision object distribution image according to the pixel matching relationship between the matrix and the image, wherein the pixels assigned with one and the pixels assigned with zero are represented by different colors in the supervision object distribution image.

[0050] In this embodiment, the process of obtaining the characteristics of the object matrix is further described. In actual remote sensing images, changes in environmental lighting and other conditions will cause changes in the overall or large-scale tone of the remote sensing image. Therefore, direct difference calculation of color features with historical remote sensing images without personnel entering cannot effectively determine the feature conditions (environmental tone changes will cause large difference values). Therefore, first, the gray scale change matrix is generated through the gray scale change of the image itself, and then the difference calculation is performed with the initial matrix to realize the positioning of the garbage. By assigning 0 and 1, the position of the garbage and other objects in the output image can be more obvious and easy to identify (the existence of the preset difference threshold value is to eliminate the influence of some non-feature objects, such as grass dumped on the lawn, which will also produce small difference changes, so it needs to be removed).

[0051] As another preferred embodiment of the present application, the remote sensing image acquisition module 100 includes an image generation unit.

[0052] The image generation unit is configured to acquire a plurality of basic remote sensing images through a remote sensing device, and stack and match the plurality of basic remote sensing images to identify motion feature objects in the plurality of basic remote sensing images, eliminate the motion feature objects in the plurality of basic remote sensing images, and generate a remote sensing image of the ground surface of the monitoring and scheduling area.

[0053] In this embodiment, in the process of generating a remote sensing image, it is necessary to exclude personnel, animals and other objects to realize the positioning of garbage and other objects. Therefore, such objects are motion objects, which can be eliminated by matching and eliminating multiple images. In addition, long-time cumulative exposure can be performed by using a low-sensitivity long shutter to eliminate moving objects, but the implementation effect of the former is more excellent.

[0054] As another preferred embodiment of the present application, the remote sensing image acquisition module 100 further includes a cycle interval determination unit.

[0055] The cycle interval determination unit is configured to mark and count a plurality of motion feature objects identified in the stack matching to calculate a motion object density of the monitoring and scheduling area, the motion object density including a density range generated based on historical record statistics, the density range including a dynamic time interval range corresponding thereto in inverse proportion, the dynamic time interval decreasing as the motion object density increases, and the dynamic time interval being used to control generation of a next remote sensing execution request.

[0056] In this embodiment, the dynamic time interval is defined because the more the flow of people such as tourists in the place, the higher the probability of generating garbage or leaving, and in the same time period, more garbage may accumulate on the ground, so more frequent adjustments and management of maintenance personnel are needed, and therefore a lower dynamic time interval is set to achieve more frequent analysis and scheduling.

[0057] As another preferred embodiment of the present application, the initial gradient matrix is used to represent a feature gradient matrix generated by processing the collected remote sensing images within a preset time period before the motion feature object is opened to the regulated scheduling area, and the initial gradient matrix is used to mask the basic inherent features of the regulated scheduling area.

[0058] In this embodiment, it refers to the initial state of the place, for example, data collection is performed five minutes before the tourists are opened every day to obtain the initial gradient matrix, because there are different changes in the place every day or within a certain time.

[0059] As Figure 3 shown, the present application also provides a ground remote sensing monitoring method, which includes the following steps:

[0060] S200, a remote sensing execution request is generated at a preset ground update time interval and a response is generated, and a remote sensing image of the ground of the regulated scheduling area is obtained through a remote sensing device, the ground update time interval is a dynamic time length, and the remote sensing image is used to represent the object image distribution of the ground.

[0061] S400, the remote sensing image is preprocessed to obtain a gray image, and the pixel points of the gray image are marked with gray values, the gray difference values of adjacent pixel points in the gray image are calculated to generate a corresponding feature gradient matrix, and the feature gradient matrix represents the distribution of image features.

[0062] S600, an initial gradient matrix of the regulated scheduling area is obtained, the feature gradient matrix is subtracted based on the initial gradient matrix to generate a regulated object matrix, and the regulated object matrix is characterized based on the pixel matching relationship between the matrix and the image to generate a regulated object distribution image.

[0063] S800, a management scheduling instruction is generated based on the regulated object distribution image, and is forwarded to a management mobile terminal through a preset communication channel, the management scheduling instruction is used to redistribute the management personnel of the regulated scheduling area based on the object distribution density of the regulated object distribution image and to mark the distribution of the regulated object for output.

[0064] As another preferred embodiment of the present application, the step of characterizing the supervision object matrix based on the matching relationship between the matrix and the pixels of the image to generate a supervision object distribution image comprises:

[0065] The elements of the rows and columns of the supervision object matrix are judged one by one based on a preset difference threshold to generate a judgment result, and if the value of the element is less than the preset difference threshold, the value of the element is assigned to zero;

[0066] When the judgment result indicates that the difference is greater than or equal to the preset difference threshold, the value of the element is assigned to one;

[0067] The supervision object matrix after assignment is characterized as a supervision object distribution image based on the matching relationship between the matrix and the pixels of the image, wherein the pixels assigned to one and the pixels assigned to zero are represented by different colors in the supervision object distribution image.

[0068] As another preferred embodiment of the present application, the step of obtaining a remote sensing image of the ground surface of the supervision and scheduling area by a remote sensing device specifically comprises:

[0069] A plurality of basic remote sensing images are obtained by a remote sensing device, and the plurality of basic remote sensing images are stacked and matched to identify motion feature objects in the plurality of basic remote sensing images, to eliminate the motion feature objects in the plurality of basic remote sensing images, and to generate a remote sensing image of the ground surface of the supervision and scheduling area.

[0070] As another preferred embodiment of the present application, it further comprises the steps of:

[0071] The number of motion feature objects identified in the stacked matching is labeled and counted to calculate the motion object density of the supervision and scheduling area, and the motion object density includes a density range generated based on historical record statistics, and the density range includes a dynamic time interval range corresponding in inverse proportion, wherein the dynamic time interval decreases as the motion object density increases, and the dynamic time interval is used to control the generation of the next remote sensing execution request.

[0072] As another preferred embodiment of the present application, the initial gradient matrix is used to represent a feature gradient matrix generated based on processing of the collected remote sensing images within a preset time period before the supervision and scheduling area is opened to the motion feature objects, and the initial gradient matrix is used to mask the basic inherent features of the supervision and scheduling area from the feature gradient matrix.

[0073] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0074] Other embodiments of the present disclosure will be apparent to those skilled in the art with the accomplishment of the present disclosure as reflected in the specification and embodiments. The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure following the general principles of the present disclosure and including common knowledge or conventional technical means in the art not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0075] It should be understood that the present disclosure is not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A ground surface remote sensing monitoring system, characterized by, The remote sensing image acquisition module is configured to generate a remote sensing execution request at a preset ground surface update time interval and acquire a remote sensing image of a ground surface of a supervision and dispatch region through a remote sensing device in response. The image feature processing module is configured to preprocess the remote sensing image to obtain a grayscale image, mark pixel points of the grayscale image, calculate a grayscale difference value of adjacent pixel points in the grayscale image to generate a corresponding feature gradient matrix, and represent a distribution of image features by the feature gradient matrix. The historical feature matching module is configured to acquire an initial gradient matrix of the supervision and dispatch region, perform a difference operation on the feature gradient matrix based on the initial gradient matrix to generate a supervision object matrix, and perform featureization on the supervision object matrix based on a pixel matching relationship between a matrix and the image to generate a supervision object distribution image. The remote sensing result output module is configured to generate a management and dispatch instruction based on the supervision object distribution image, and forward the management and dispatch instruction to a management mobile terminal through a preset communication channel. The historical feature matching module includes a featureization unit, which specifically includes:

2. The system according to claim 1, wherein, The difference filtering subunit is configured to judge elements of rows and columns in the supervision object matrix one by one based on a preset difference threshold value, generate a judgment result, and assign a value of zero to the element if the judgment result indicates that the element is less than the preset difference threshold value. The difference assignment subunit is configured to assign a value of one to the element if the judgment result indicates that the difference is greater than or equal to the preset difference threshold value. The matrix featureization subunit is configured to feature the supervision object matrix after the assignment into a supervision object distribution image according to a pixel matching relationship between a matrix and the image, wherein pixels with a value of one and pixels with a value of zero are represented by different colors in the supervision object distribution image. The remote sensing image acquisition module includes an image generation unit.

3. The ground remote sensing monitoring system according to claim 1, wherein, The image generation unit is configured to acquire a plurality of basic remote sensing images through a remote sensing device, stack and match the plurality of basic remote sensing images, identify moving feature objects in the plurality of basic remote sensing images, eliminate the moving feature objects in the plurality of basic remote sensing images, and generate a remote sensing image of a ground surface of a supervision and dispatch region. The remote sensing image acquisition module further includes a cycle interval determination unit.

4. The ground-based remote sensing monitoring system of claim 3, wherein, The cycle interval determination unit is configured to mark and count a plurality of moving feature objects identified in the stacking and matching to calculate a moving object density of the supervision and dispatch region, wherein the moving object density includes a density range generated based on historical record statistics, the density range includes a dynamic time interval range corresponding in inverse proportion thereto, the dynamic time interval decreases as the moving object density increases, and the dynamic time interval is used to control generation of a next remote sensing execution request. ​ 5. A ground-based remote sensing monitoring system according to claim 4, wherein, The initial gradient matrix is used to represent a feature gradient matrix generated by processing remote sensing images collected within a preset time period before the motion feature object is opened to the supervision scheduling area, and the initial gradient matrix is used to perform basic inherent feature shielding on the feature gradient matrix in the supervision scheduling area.

6. A method of ground remote sensing monitoring, characterized in that, The method comprises the steps of: Generating a remote sensing execution request and responding by acquiring a remote sensing image of the ground surface of the supervision scheduling area through a remote sensing device at a preset ground surface update time interval, wherein the ground surface update time interval is a dynamic time length, and the remote sensing image is used to represent the object image distribution of the ground surface; Pretreating the remote sensing image to obtain a gray image, and marking the pixel points of the gray image with gray values, calculating the gray difference between adjacent pixel points in the gray image to generate a corresponding feature gradient matrix, and the feature gradient matrix represents the distribution of image features; Obtaining an initial gradient matrix of the supervision scheduling area, performing difference calculation on the feature gradient matrix based on the initial gradient matrix to generate a supervision object matrix, and performing feature extraction on the supervision object matrix based on the pixel matching relationship between the matrix and the image to generate a supervision object distribution image; Generating a management scheduling instruction based on the supervision object distribution image, and forwarding the management scheduling instruction to a management mobile terminal through a preset communication channel, wherein the management scheduling instruction is used to perform supervision area redistribution on the management personnel of the supervision scheduling area and to perform distribution marking on the supervision object based on the object distribution density of the supervision object distribution image to output.

7. The method according to claim 6, wherein, The step of performing feature extraction on the supervision object matrix based on the pixel matching relationship between the matrix and the image to generate a supervision object distribution image comprises: Judging the elements of the supervision object matrix row by row based on a preset difference threshold to generate a judgment result, and assigning a value of zero to the element if it is less than the preset difference threshold; When the judgment result indicates that the difference is greater than or equal to the preset difference threshold, the value of the element is assigned as one; According to the pixel matching relationship between the matrix and the image, the supervision object matrix after assignment is characterized as a supervision object distribution image, wherein the pixels with assigned values of one and zero are represented by different colors in the supervision object distribution image.

8. The method of claim 6, wherein, The step of acquiring a remote sensing image of the ground surface of the supervision scheduling area through a remote sensing device comprises: Acquiring a plurality of basic remote sensing images through a remote sensing device, and performing stack matching on the plurality of basic remote sensing images to identify motion feature objects in the plurality of basic remote sensing images, to eliminate the motion feature objects in the plurality of basic remote sensing images, and to generate a remote sensing image of the ground surface of the supervision scheduling area.

9. The method according to claim 8, wherein, Further comprising the steps of: Labeling and counting a plurality of motion feature objects identified in the stack matching to calculate a motion object density of the supervision scheduling area, wherein the motion object density comprises a density range generated based on historical record statistics, and the density range comprises a dynamic time interval range corresponding in inverse proportion, wherein the dynamic time interval decreases as the motion object density increases, and the dynamic time interval is used to control the generation of the next remote sensing execution request.

10. The method of remote sensing of the Earth's surface according to claim 9, characterized in that, The initial gradient matrix is used to represent a feature gradient matrix generated by processing remote sensing images collected within a preset time period before the motion feature object is opened to the regulatory scheduling area, and the initial gradient matrix is used to perform basic inherent feature shielding on the feature gradient matrix in the regulatory scheduling area.

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