A Method and System for Dynamically Updating a Geographic Information GIS Database

Abstract

This application relates to the technical field of dynamic update of geographic information, and specifically relates to a method and system for dynamically updating a geographic information GIS database. The method includes: determining a continuous change amount based on the difference between remote sensing grayscale images of the same area at each acquisition time and the previous acquisition time; determining a sensitivity index based on the overall distribution of the continuous change amounts of all acquisition times for each area; determining the degree of interest based on the distribution of the grayscale values of all pixel points within each area at each acquisition time; and updating the geographic information GIS database based on the degree of dispersion of the continuous change amounts of all acquisition times in the adjacent previous and subsequent cycle periods of any cycle period for each area, in combination with the sensitivity index and the degree of interest. This application aims to distinguish the sensitivities of different areas, achieve reasonable allocation of resources, and improve the resource utilization rate in the process of dynamically updating the geographic information GIS database.

Description

Technical Field

[0001] This application relates to the technical field of dynamic updating of geographic information, and specifically relates to a method and system for dynamically updating a geographic information GIS database. Background Art

[0002] With the continuous development of technology, the Geographic Information System (GIS) has become an indispensable important tool in all walks of life in modern society. The GIS database provides powerful technical support for fields such as urban planning, resource management, environmental protection, and disaster warning by efficiently storing, processing, and analyzing spatial data. Especially image geographic information, as an important data type in the GIS database, plays an irreplaceable role in fields such as navigation, remote sensing monitoring, and cartography. For example, satellite images and aerial images are widely used in surface change monitoring, precise agricultural planting, urban expansion analysis, etc. With the continuous increase in data sources and the improvement of update frequencies, the management and update of image geographic data have become particularly important.

[0003] Existing GIS database update methods face many technical challenges when processing image data. Current image data updates mostly rely on overall updates, that is, new image data completely replaces old data. This method results in extremely large computational amounts because it needs to process a large amount of image data. Especially in the case of high-resolution remote sensing image data, the data volume and computational complexity increase exponentially. In many cases, only the image data in some areas has changed, and overall updates will cause waste of resources during the update process, reducing the resource utilization rate during the dynamic update of the geographic information GIS database. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for dynamically updating a geographic information GIS database, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of this application provides a method for dynamically updating a geographic information GIS database, and the method includes the following steps:

[0006] Obtain remote sensing grayscale images at all acquisition times in the target area within a preset time period before the current time from the geographic information GIS database;

[0007] Based on the differences between the remote sensing grayscale images at each acquisition time and the previous acquisition time, construct an overlay image of the target area, divide the overlay image into multiple regions, and map the division form to the remote sensing grayscale images at each acquisition time;

[0008] Based on the differences between the remote sensing grayscale images of the same area at each acquisition time and its previous acquisition time, determine the continuous change amount of the same area at each acquisition time; based on the distribution of the continuous change amounts of all acquisition times of each area in the frequency domain, determine the period of each area, and based on the differences between the continuous change amounts of each acquisition time within any period of each area and the overall distribution of the continuous change amounts of all acquisition times, determine the sensitivity index of any period of each area;

[0009] Based on the distribution of the grayscale values of all pixel points within each area at each acquisition time, determine the degree of interest of each area; based on the degree of dispersion of the continuous change amounts of all acquisition times within the adjacent previous and subsequent period time intervals of any period of each area, determine the sensitivity weight of any period of each area, and combine the sensitivity index and the degree of interest to determine the comprehensive sensitivity coefficient of each area, and update the geographical information GIS database after the current time.

[0010] Preferably, the construction process of the superimposed image of the target area is as follows:

[0011] Take the remote sensing grayscale images of the target area at each acquisition time and its previous acquisition time as the input of the frame difference method, and output the frame difference images of the target area at each acquisition time;

[0012] Take the result of adding all the frame difference images of the target area as the superimposed image of the target area.

[0013] Preferably, the expression of the continuous change amount of the same area at each acquisition time is: A i,j = exp(B i,j ); In the formula, A i,j represents the continuous change amount of area j at acquisition time i; B i,j represents the Bhattacharyya distance of the grayscale histogram of area j between acquisition time i and its previous acquisition time; exp() represents the exponential function with the natural constant as the base.

[0014] Preferably, the method for determining the period of each area is:

[0015] Take the continuous change amounts of all acquisition times of each area as the input of the time-frequency conversion algorithm, output the spectrogram of each area, and take the reciprocal of the frequency corresponding to the maximum peak value in the spectrogram as the period of each area.

[0016] Preferably, the method for determining the sensitivity index of any period of each area is:

[0017] Calculate the mean of the continuous change amounts at all acquisition moments within any cycle period for each region, and within this cycle period, take the acquisition moment corresponding to the continuous change amount with the smallest difference from the mean of the continuous change amounts as the approximate moment, and take the reciprocal of the time interval between the approximate moment and the middle moment of this cycle period as the sensitivity index for any cycle period of each region.

[0018] Preferably, the method for determining the degree of interest of each region is as follows:

[0019] Calculate the mean of the gray values of all pixel points within each region at each acquisition moment, denoted as the mean gray value of each region at each acquisition moment, and take the average of the mean gray values of each region at all acquisition moments as the degree of interest of each region.

[0020] Preferably, the sensitivity weight value for any cycle period of each region is the normalized value of the variance of the continuous change amounts at all acquisition moments within the adjacent previous and next cycle periods of any cycle period of each region.

[0021] Preferably, the expression for the comprehensive sensitivity coefficient of each region is: In the formula, C j represents the comprehensive sensitivity coefficient of region j; IN j represents the degree of interest of region j; represents the mean of the degrees of interest of all regions; D j,n represents the sensitivity index of region j in the nth cycle period; E j,n represents the sensitivity weight value of region j in the nth cycle period; N j represents all cycle periods of region j; norm() represents the normalization function; exp() represents the exponential function with the natural constant as the base.

[0022] Preferably, the update of the Geographic Information GIS database after the current moment includes:

[0023] The update period T′ of region j j has the following expression: In the formula, T j represents the cycle of region j; represents the ceiling function;

[0024] The process of updating the remote sensing image within the same preset time period after the current moment is as follows: Starting from the current moment, when it reaches an integer multiple of the update period of region j, obtain the new remote sensing image of the target area at the moment corresponding to the integer multiple of the update period, and replace the image of region j in the target area in the Geographic Information GIS database with the image of region j in the new remote sensing image.

[0025] In a second aspect, an embodiment of the present application further provides a dynamic update system for a geographic information GIS database, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned dynamic update methods for a geographic information GIS database are implemented.

[0026] The present application has at least the following beneficial effects:

[0027] By analyzing the differences between remote sensing grayscale images of the same area at different acquisition times, the present application constructs a continuous change quantity. The beneficial effect is that the continuous change quantity can quantify the information change degree of each area at different acquisition times, which helps to identify the change pattern of regional information, reduce the attention to areas insensitive to changes, and thus save resources in the process of updating the geographic information database. By analyzing the distribution of the continuous change quantity of each area within a period, the present application constructs a sensitivity index. The beneficial effect is that according to the sensitivity index, the update frequency of each area can be determined, ensuring that high-sensitivity areas are updated more timely, reducing the update frequency of low-sensitivity areas, and saving resources in the process of updating the geographic information database. By analyzing the distribution of the grayscale values of all pixel points within each area, the present application constructs an interest degree. The beneficial effect is that it helps to identify the importance of the area, thereby realizing the effective allocation of resources. By comprehensively considering the interest degree, the sensitivity index, and the dispersion degree of the continuous change quantity at all acquisition times in adjacent cycle periods, the present application constructs a comprehensive sensitivity coefficient. The beneficial effect is that according to the comprehensive sensitivity coefficient, resources can be reasonably allocated, and high-sensitivity areas can be preferentially processed, thereby improving the utilization rate of resources. The present application realizes the reasonable allocation of resources by distinguishing the sensitivities of different areas, and improves the resource utilization rate in the process of dynamically updating the geographic information GIS database. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is a flowchart of the steps of a dynamic update method for a geographic information GIS database provided by an embodiment of the present application;

[0030] Figure 2 It is a flowchart of area division provided by an embodiment of the present application;

[0031] Figure 3Schematic diagram of the sensitive index acquisition process provided by an embodiment of the present application. Detailed implementation manners

[0032] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following describes in detail a method and system for dynamically updating a geographic information GIS database proposed according to the present application, its specific implementation manners, structures, features and effects in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs.

[0034] The following specifically describes the specific solutions of a method and system for dynamically updating a geographic information GIS database provided by the present application in conjunction with the accompanying drawings.

[0035] Please refer to Figure 1 , which shows a flowchart of the steps of a method for dynamically updating a geographic information GIS database provided by an embodiment of the present application. The method includes the following steps:

[0036] Step S1: Obtain remote sensing grayscale images at all acquisition times in the target area within a preset time period before the current time from the geographic information GIS database.

[0037] (1) In the geographic information GIS database, use a coordinate frame to obtain remote sensing images at all acquisition times in the target area within a preset time period before the current time, where the sampling interval of the remote sensing images is set to H.

[0038] It should be noted that the values of the preset time period and the sampling interval H of the remote sensing images are both set manually. In this embodiment, the value of the preset time period is 365 days, and the value of the sampling interval H of the remote sensing images is 1 day. Implementers can also set them according to specific situations, and this embodiment does not make special restrictions.

[0039] (2) Further, in order to eliminate the noise in the remote sensing images, the remote sensing images are used as the input of the filtering algorithm, and the remote sensing images after removing the noise influence are output.

[0040] It should be understood that there are many common filtering algorithms. In this embodiment, the Gaussian filtering algorithm is used to denoise the remote sensing images. Implementers can also use other filtering algorithms such as the median filtering algorithm or the mean filtering algorithm. This embodiment does not make special restrictions on the selection of the filtering algorithm.

[0041] Among them, the Gaussian filtering algorithm is a well-known technology in the field of image processing, and its specific denoising process is not repeated here.

[0042] (3) Furthermore, in order to simplify the analysis, the remote sensing images of the target area at all acquisition times are converted into remote sensing grayscale images.

[0043] Step S2: Based on the difference between the remote sensing grayscale image at each acquisition moment and the previous acquisition moment, an overlay image of the target area is constructed, the overlay image is segmented into multiple regions and the segmented form is mapped to the remote sensing grayscale image at each acquisition moment.

[0044] The purpose of updating geographic information is to ensure the timeliness of information changes and to record and update the changed information. In remote sensing images, because the area involved in each region is generally large, when updating the image, since the image corresponding to the area has only partially changed, only the information of the changed part is updated.

[0045] Perform local analysis on the remote sensing images of a certain area, analyze the interest level of different local areas and the sensitivity of image information changes, and then formulate targeted update strategies based on the interest level of different local areas and the sensitivity of image information changes, and perform local updates on remote sensing images, thereby reducing the computational loss of database updates. Specifically:

[0046] (1) The remote sensing grayscale image of the target area at each acquisition time and the previous acquisition time is used as the input of the frame difference method, and the frame difference image of the target area at each acquisition time is output;

[0047] It should be noted that the principle of the frame difference method is to use the absolute value of the difference between the grayscale value of the corresponding pixel in the remote sensing grayscale image of the target area at each acquisition time and the remote sensing grayscale image of the target area at the previous acquisition time as the new pixel value of the corresponding pixel, and form a so-called frame difference image.

[0048] Among them, the frame difference method is a well-known technology in the field of image processing, and its specific principle will not be described in detail.

[0049] (2) Further, the result of adding all the frame difference images of the target area is used as the superimposed image of the target area.

[0050] It should be understood that the adding of the frame difference images at all acquisition moments specifically refers to adding the pixel values ​​of corresponding pixel points in the frame difference images at all acquisition moments to obtain a final superimposed image.

[0051] (3) Further, in the target area, generally, the same structure such as a forest, a road, a crop field, etc. has the same change trend. Therefore, in this embodiment, the superimposed image of the target area is used as the input of the tile segmentation algorithm, and multiple regions in the superimposed image are output, that is, the superimposed image is segmented into multiple image blocks, and each image block can also be called a region. Therefore, the superimposed image is segmented into multiple regions here.

[0052] According to the segmentation method of the superimposed image, the segmentation forms of multiple regions in the superimposed image are mapped to the remote sensing grayscale images at all acquisition times. Thus, there are also corresponding multiple regions in the remote sensing grayscale images at each acquisition time.

[0053] Among them, the tile segmentation algorithm is a well-known technology in the field of image processing, and its specific segmentation principle will not be elaborated in this embodiment.

[0054] Preferably, the region division flow chart provided in this embodiment is as Figure 2 shown.

[0055] S3: Based on the difference between the remote sensing grayscale images of the same region at each acquisition time and its previous acquisition time, determine the continuous change amount of the same region at each acquisition time; based on the distribution of the continuous change amounts of all acquisition times of each region in the frequency domain, determine the period of each region, and based on the difference between the continuous change amounts of each acquisition time in any period of each region and the overall distribution of the continuous change amounts of all acquisition times, determine the sensitivity index of any period of each region.

[0056] In different regions of the target area, the information changes in the remote sensing images do not all have significant periodic change characteristics. Generally speaking, there are two types of information changes in different regions. One is that the information within the region changes completely randomly, and the other is that the information within the region changes regularly within a fixed period of time. The more regular the time of the regional information change is, the more sensitive the region is to the external information change, and the more attention should be paid to it during the update of geographical information. On the contrary, if the regional information change is more random, it means that the region is less sensitive to the external information change and less attention can be given.

[0057] Therefore, in this embodiment, by analyzing the difference between the remote sensing grayscale images of the same region at different acquisition times, the continuous change amount of the same region at each acquisition time is determined. Based on the distribution of the continuous change amounts of all acquisition times of each region in the frequency domain, the period of each region is determined. Based on the difference between the continuous change amounts of each acquisition time in any period of each region and the overall distribution of the continuous change amounts of all acquisition times, the sensitivity index of any period of each region is determined to judge the sensitivity degree of the corresponding region to the external information. Specifically:

[0058] (1) Based on the difference between the remote sensing grayscale images of the same area at each acquisition time and its previous acquisition time, determine the continuous change amount of the same area at each acquisition time, specifically:

[0059] The expression for the continuous change amount of the same area at each acquisition time is: A i,j = exp(B i,j ); where A i,j represents the continuous change amount of area j at acquisition time i; B i,j represents the Bhattacharyya distance between the grayscale histograms of area j between acquisition time i and its previous acquisition time; exp() represents the exponential function with the natural constant as the base.

[0060] It should be noted that the process of obtaining the grayscale histogram and the calculation steps of the Bhattacharyya distance are both well-known technologies, and the specific process of obtaining the grayscale histogram and the specific calculation process of the Bhattacharyya distance will not be elaborated here.

[0061] From the continuous change amount of the same area at each acquisition time, it can be understood that if the difference between the corresponding parts of the area between the current acquisition time and its previous acquisition time in the remote sensing grayscale image is larger, that is, the Bhattacharyya distance between the grayscale histograms of the area between the current acquisition time and its previous acquisition time is larger, then the continuous change amount is larger, indicating that the area is more sensitive to changes in external information; conversely, if the difference between the corresponding parts of the area between the current acquisition time and its previous acquisition time in the remote sensing grayscale image is smaller, that is, the Bhattacharyya distance between the grayscale histograms of the area between the current acquisition time and its previous acquisition time is smaller, then the continuous change amount is smaller, indicating that the area is less sensitive to changes in external information.

[0062] (2) Further, based on the distribution of the continuous change amounts of all acquisition times of each area in the frequency domain, determine the period of each area, specifically:

[0063] Take the continuous change amounts of all acquisition times of each area as the input of the time-frequency conversion algorithm, output the spectrogram of each area, and take the reciprocal of the frequency corresponding to the maximum peak in the spectrogram as the period of each area.

[0064] It should be noted that there are many commonly used time-frequency conversion algorithms. In this embodiment, the discrete Fourier transform is used. Implementers can also use other time-frequency conversion algorithms such as wavelet transform. There is no special limitation on the selection of the time-frequency conversion algorithm in this embodiment.

[0065] Among them, the discrete Fourier transform is a well-known technology in the field of signal processing, and the specific process of converting the time-domain signal to the frequency domain will not be elaborated here.

[0066] (3) Further, based on the difference between the continuous change amount at each acquisition moment within any cycle period of each region and the overall distribution of the continuous change amounts at all acquisition moments, determine the sensitivity index for any cycle period of each region to judge the sensitivity degree of the region to changes in external information. Specifically:

[0067] Calculate the mean value of the continuous change amounts at all acquisition moments within any cycle period of each region, and within this cycle period, take the acquisition moment corresponding to the continuous change amount with the smallest difference from the mean value of the continuous change amounts as the similar moment, and take the reciprocal of the time interval between the similar moment and the middle moment of this cycle period as the sensitivity index for any cycle period of each region.

[0068] Among them, the cycle period refers to the time period occupied by the cycle. If a signal changes periodically, there will be many time periods with the same regular changes.

[0069] It can be understood from the sensitivity index of any cycle period of each region that when the cycle is shorter, and the difference between the continuous change amounts at each acquisition moment within the cycle period and the mean value of the continuous change amounts at all acquisition moments within this cycle period is smaller, and the similar moment is closer to the middle moment of the cycle period, the sensitivity index is larger, indicating that the region is more sensitive to changes in external information; on the contrary, when the cycle is longer, and the difference between the continuous change amounts at each acquisition moment within the cycle period and the mean value of the continuous change amounts at all acquisition moments within this cycle period is larger, and the similar moment is farther from the middle moment of the cycle period, the sensitivity index is smaller, indicating that the region is less sensitive to changes in external information.

[0070] Step S4: Based on the distribution of the gray values of all pixel points within each region at each acquisition moment, determine the degree of interest of each region; based on the dispersion degree of the continuous change amounts at all acquisition moments within the adjacent previous and next cycle periods of any cycle period of each region, determine the sensitivity weight value of any cycle period of each region, and combine the sensitivity index and the degree of interest to determine the comprehensive sensitivity coefficient of each region.

[0071] Based on step S2, the remote sensing grayscale images at each acquisition moment are divided into multiple regions. However, not all regions need to be equally concerned. For regions with a long change time interval and a small change degree, that is, regions insensitive to external information changes, the analysis of such regions can be appropriately reduced. Instead, regions with a large change degree and a fast change speed, that is, regions sensitive to external information changes, should be analyzed emphatically. Therefore, in this embodiment, by analyzing the distribution of the grayscale values of all pixel points in each region at each acquisition moment, the degree of interest of each region is determined. Based on the degree of dispersion of the continuous change amounts of all acquisition moments in the adjacent front and rear cycle periods of any cycle period of each region, the sensitivity weight value of any cycle period of each region is determined, and by combining the sensitivity index and the degree of interest, the comprehensive sensitivity coefficient of each region is determined to identify the regions sensitive to external information, and emphatically analyze such regions to reduce waste in the resource update process. Specifically:

[0072] (1) Based on the distribution of the grayscale values of all pixel points in each region at each acquisition moment, determine the degree of interest of each region, specifically:

[0073] Calculate the mean value of the grayscale values of all pixel points in each region at each acquisition moment, denoted as the grayscale value mean of each region at each acquisition moment, and take the average value of the grayscale value means of all acquisition moments of each region as the degree of interest of each region.

[0074] (2) Further, based on the degree of dispersion of the continuous change amounts of all acquisition moments in the adjacent front and rear cycle periods of any cycle period of each region, determine the sensitivity weight value of any cycle period of each region, specifically:

[0075] Take the normalized value of the variance of the continuous change amounts of all acquisition moments in the adjacent front and rear cycle periods of any cycle period of each region as the sensitivity weight value of any cycle period of each region.

[0076] (3) Further, based on the sensitivity weight value of any cycle period of each region, and by combining the sensitivity index and the degree of interest, determine the comprehensive sensitivity coefficient of each region, specifically:

[0077] The comprehensive sensitivity coefficient C j of region j has the following expression: In the formula, IN j represents the degree of interest of region j; represents the mean value of the degrees of interest of all regions; D j,n represents the sensitivity index of the nth cycle period of region j; E j,n represents the sensitivity weight value of the nth cycle period of region j; N jAll periodic time periods of area j are represented; norm() represents the normalization function; exp() represents the exponential function with the natural constant as the base.

[0078] It can be understood from the comprehensive sensitivity coefficient of each area that if the information change within the area is more regular, the area is more sensitive to the change of external information. Therefore, the greater the sensitivity index, the greater the sensitivity weight, and the greater the difference between the degree of interest of this area and the average degree of interest of all areas, the greater the comprehensive sensitivity coefficient of this area, and the more urgent it is to adjust the geographical information of this area in a timely manner; on the contrary, if the information change within the area is more random, the area is less sensitive to the change of external information. Therefore, the smaller the sensitivity index, the smaller the sensitivity weight, and the smaller the difference between the degree of interest of this area and the average degree of interest of all areas, the smaller the comprehensive sensitivity coefficient of this area, and the less need to adjust the geographical information of this area.

[0079] Preferably, the schematic diagram of the process for obtaining the sensitivity index provided in this embodiment is as Figure 3 shown.

[0080] Step S5: Update the geographical information GIS database after the current moment based on the comprehensive sensitivity coefficient of each area.

[0081] Based on the comprehensive sensitivity coefficient of each area obtained in step S4, the geographical information GIS database is updated based on the period and comprehensive sensitivity coefficient of each area. Specifically:

[0082] The update period T′ of area j j has the following expression: In the formula, T j represents the period of area j; represents the ceiling function;

[0083] Starting from the current moment, the remote sensing images within the same preset time period after the current moment are updated. Specifically: starting from the current moment, when it reaches an integer multiple of the update period of area j, new remote sensing images of the target area at the corresponding moment of the integer multiple of the update period are obtained, and the images of area j in the corresponding target area in the geographical information GIS database are replaced with the images of area j in the new remote sensing images.

[0084] It should be noted that since geographic information usually needs to be collected through satellite images, aerial photography, ground measurements, etc., it often takes a long time, and geographic information does not change in real time. For example, topographic maps may only need to be updated every few years because the changes are very slow, and the changes in geographic information have a certain periodicity, and the periods in different regions are different. Therefore, this embodiment obtains remote sensing images within a preset time period before the current moment, and uses historical data to predict the information update period of different regions in each region within the same preset time period after the current moment, and updates the geographic information at times that are integer multiples of the period, thereby improving resource utilization and reducing resource waste during the update process.

[0085] Based on the same inventive concept as the above method, an embodiment of the present application also provides a geographic information GIS database dynamic update system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned geographic information GIS database dynamic update methods.

[0086] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0088] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for dynamically updating a geographic information GIS database, characterized in that: The method comprises the following steps: Obtain remote sensing grayscale images of all acquisition times of the target area within a preset time period before the current time from the geographic information GIS database; Based on the difference between the remote sensing grayscale image at each acquisition moment and the previous acquisition moment, an overlay image of the target area is constructed, the overlay image is segmented into multiple regions and the segmented form is mapped to the remote sensing grayscale image at each acquisition moment; Based on the difference between the remote sensing grayscale images of the same area at each acquisition time and the previous acquisition time, the continuous change amount of the same area at each acquisition time is determined; based on the distribution of the continuous change amount of all acquisition times in each area in the frequency domain, the period of each area is determined; based on the difference between the continuous change amount of each acquisition time in any period of each area and the overall distribution of the continuous change amount of all acquisition times, the sensitivity index of any period of each area is determined; Based on the distribution of the grayscale values ​​of all pixels in each area at each acquisition moment, the interest level of each area is determined; based on the discrete degree of the continuous change of all acquisition moments in the adjacent previous and next cycle periods of each area, the sensitivity weight of any cycle period of each area is determined, and the comprehensive sensitivity coefficient of each area is determined in combination with the sensitivity index and the interest level, and the geographic information GIS database after the current moment is updated.

2. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The construction process of the superimposed image of the target area is as follows: The remote sensing grayscale image of the target area at each acquisition time and the previous acquisition time is used as the input of the frame difference method, and the frame difference image of the target area at each acquisition time is output; The result of adding all the frame difference images of the target area is used as the superimposed image of the target area.

3. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The expression of the continuous change amount of the same area at each acquisition time is: i,j =exp(B i,j );where A i,j represents the continuous change of area j at the acquisition time i; B i,j It represents the Bhattacharyya distance of the grayscale histogram of area j between the acquisition time i and its previous acquisition time; exp() represents an exponential function with a natural constant as the base.

4. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The method for determining the period of each area is: The continuous changes of all acquisition moments in each region are used as the input of the time-frequency conversion algorithm, and the spectrum of each region is output. The inverse of the frequency corresponding to the maximum peak in the spectrum is used as the period of each region.

5. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The method for determining the sensitivity index of each region in any period is as follows: The mean of the continuous changes of all the acquisition moments in any periodic period of each region is calculated, and the acquisition moment corresponding to the continuous change with the smallest difference from the mean of the continuous change in the periodic period is taken as the close moment, and the reciprocal of the time interval between the close moment and the middle moment of the periodic period is taken as the sensitivity index of any periodic period of each region.

6. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The method for determining the interest level of each region is as follows: The mean grayscale value of all pixels in each region at each acquisition time is calculated, recorded as the mean grayscale value of each region at each acquisition time, and the average of the mean grayscale values ​​of each region at all acquisition times is taken as the interest level of each region.

7. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The sensitivity weight of any periodic period of each region is a normalized value of the variance of the continuous changes of all acquisition moments in the periodic periods before and after any periodic period of each region.

8. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The expression of the comprehensive sensitivity coefficient of each area is: In the formula, C j Indicates the comprehensive sensitivity coefficient of region j; IN j represents the interest level of region j; Indicates the mean value of the interest level of all regions; D j，n represents the sensitivity index of the nth period of region j; E j，n represents the sensitivity weight of the nth period in region j; Nj represents all periodic periods in region j; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base.

9. A method for dynamically updating a geographic information GIS database as claimed in claim 8, characterized in that: The geographic information GIS database after the current moment is updated, including: Update period T′ of region j j The expression is: Where, T j represents the period of region j; represents the ceiling function; The process of updating the remote sensing image within the same preset time after the current moment is: starting from the current moment, when the integer multiple of the update period of area j is reached, a new remote sensing image of the target area at the moment corresponding to the integer multiple of the update period is obtained, and the image of area j in the corresponding target area in the geographic information GIS database is replaced with the image of area j in the new remote sensing image.

10. A geographic information GIS database dynamic update system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for dynamically updating a geographic information GIS database as described in any one of claims 1 to 9 are implemented.

Images