A method for managing remote sensing data of land and resources

Abstract

The present application discloses a method for managing remote sensing data of land and resources, which relates to the technical field of data processing. The method includes: obtaining remote sensing data of a stone forest area; obtaining characteristic data of the stone forest based on the remote sensing data; obtaining a plurality of differences based on the characteristic data; obtaining characteristic change data of the stone forest based on the plurality of differences; obtaining a dynamic division window based on the characteristic change data; and dividing and storing the characteristic data of the stone forest based on the dynamic division window. Through the remote sensing data of the stone forest area, the present application can extract the characteristic change data of the stone forest, and then, based on the characteristic change data of the stone forest, adaptively determine the size of the dynamic division window for subsequent division of the characteristic data of the stone forest. It can not only achieve efficient division of each data in the characteristic data of the stone forest, but also improve the data storage efficiency and facilitate subsequent research work.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and specifically to a method for managing remote sensing data of land and resources. Background Art

[0002] The recording and management of national land topography and geomorphology are indeed an important part of national land and resource protection, involving the detailed recording, monitoring, and management of natural geographical features. With the development of technology, especially the wide application of remote sensing technology, the acquisition, processing, and analysis of topographic and geomorphic data have become more efficient and accurate. In order to conduct data research more efficiently, accurate access to data needs to be achieved. However, due to the rugged and complex terrain of the stone forest, it is difficult to accurately divide the monitoring data of the stone forest, which in turn affects the effective classification and processing of the detection data. In the prior art, when dividing and storing the characteristic data of the stone forest, a fixed window is used to divide the characteristic data of the stone forest into multiple data segments for classification and storage. It should be noted that due to the rugged and changeable terrain of the stone forest, when using a fixed window to divide the characteristic data of the stone forest, it is easy to divide data with large differences into the same data segment, resulting in difficult effective matching and accurate classification, thus making data access difficult. Summary of the Invention

[0003] The purpose of this application is to provide a method for managing remote sensing data of land and resources to solve the technical problem that it is difficult to accurately divide and store the characteristic data of the stone forest in the prior art.

[0004] To achieve the above purpose, this application provides the following technical solutions:

[0005] A method for managing remote sensing data of land and resources, comprising:

[0006] Obtaining remote sensing data of the stone forest area; the remote sensing data at least includes the height of the stone forest and the horizontal spatial position of the stone forest;

[0007] Based on the remote sensing data, obtaining characteristic data of the stone forest; the characteristic data at least reflects the height of the stone forest in the horizontal direction;

[0008] Based on the characteristic data, obtaining a plurality of differences; the differences are the differences between any two adjacent height data values in the characteristic data;

[0009] Based on the plurality of differences, obtaining characteristic change data of the stone forest; the characteristic change data is at least used to reflect the height change of the stone forest in the horizontal direction;

[0010] Based on the characteristic change data, obtaining a dynamic division window;

[0011] Based on the dynamic division window, dividing and storing the characteristic data of the stone forest.

[0012] As a specific solution in the technical solution of the present application, obtaining the characteristic data of the stone forest based on the telemetry data includes:

[0013] Converting the telemetry data into an ordered point cloud according to the acquisition order;

[0014] Traversing all data points in the ordered point cloud along the order direction of the ordered point cloud to obtain a topographic data sequence of the stone forest;

[0015] Performing normalization processing on all data in the data sequence to obtain a normalized sequence;

[0016] Projecting the normalized sequence into a two-dimensional space to obtain the characteristic data of the stone forest; the two-dimensional space uses the height of the stone forest as the ordinate and the horizontal spatial position of the stone forest as the abscissa.

[0017] As a specific solution in the technical solution of the present application, obtaining the characteristic change data of the stone forest based on the multiple differences includes:

[0018] Based on the multiple differences, obtaining a first difference and a second difference; the first difference and the second difference are any two adjacent differences among the multiple differences

[0019] Based on the first difference and the second difference, obtaining a height change parameter; the height change parameter is at least used to reflect the magnitudes of the first difference and the second difference;

[0020] Based on the height change parameter, obtaining the characteristic change data of the stone forest.

[0021] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the height change parameter based on the first difference and the second difference is as follows:

[0022]

[0023] Wherein, represents the height change parameter corresponding to the data with sequence i; represents the height data value of the stone forest corresponding to the data with sequence i; represents the height data value of the stone forest corresponding to the data with sequence i + 1; represents the height data value of the stone forest corresponding to the data with sequence i - 1; represents the data difference corresponding to the data with sequence i; represents the data difference corresponding to the data with sequence i + 1; represents a normalization function for mapping the value within the parentheses to the interval (-1, 1).

[0024] As a specific solution in the technical solution of the present application, obtaining the characteristic change data of the stone forest based on the height change parameter includes:

[0025] Based on the ordered point cloud, obtaining a first vector and a second vector; the first vector is the direction vector from the first data point to the second data point in the ordered point cloud; the second vector is the direction vector from the second data point to the third data point in the ordered point cloud; the first data point and the second data point are adjacent; the second data point and the third data point are adjacent;

[0026] Based on the first vector, the second vector, and the height change parameter, obtaining a similarity parameter; the similarity parameter is at least used to characterize the similarity between the first difference and the second difference;

[0027] Based on the similarity parameter, obtaining the characteristic change data of the stone forest.

[0028] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the similarity parameter based on the first vector, the second vector, and the height change parameter is as follows:

[0029]

[0030] Wherein, represents the similarity parameter corresponding to the data with sequence number i; represents the height change parameter corresponding to the data with sequence number i; represents the first vector; represents the second vector; represents the first data point; represents the second data point; represents the third data point; represents the dot product of the first vector and the second vector; represents taking the absolute value; represents a normalization function for mapping the value within the brackets to the interval (0, 1); k is a preset constant.

[0031] As a specific solution in the technical solution of the present application, obtaining the dynamic division window based on the characteristic change data includes:

[0032] Based on the characteristic change data, obtaining an initial window;

[0033] Dividing the characteristic data of the stone forest into multiple data segments through the initial window;

[0034] Based on the multiple data segments, obtain a first data segment and a second data segment; the first data segment and the second data segment are any two adjacent data segments among the multiple data segments;

[0035] Based on the first data segment and the second data segment, obtain a data segment similarity; the data segment similarity is at least used to characterize the similarity between the first data segment and the second data segment;

[0036] Based on the data segment similarity, update the initial window to obtain a dynamically divided window.

[0037] As a specific solution in the technical solution of this application, the calculation formula for obtaining the data segment similarity based on the first data segment and the second data segment is as follows:

[0038]

[0039] Wherein, represents the data segment similarity between the mth data segment and the nth data segment, and m and n are adjacent positive integers; represents the minimum data value in the mth data segment; represents the minimum data value in the nth data segment; represents the average value of the similarity parameters of each data in the mth data segment and the nth data segment; represents taking the absolute value; represents taking the maximum value in the parentheses.

[0040] As a specific solution in the technical solution of this application, the method for updating the initial window based on the data segment similarity to obtain a dynamically divided window includes:

[0041] Based on the first data segment, obtain a first average height value; the first average height value is the average value of the height values of each data in the first data segment;

[0042] Based on the second data segment, obtain a second average height value; the second average height value is the average value of the height values of each data in the second data segment;

[0043] Based on the first average height value, the second average height value, and the data segment similarity, obtain a division coefficient;

[0044] If the division coefficient is greater than a first preset value, then merge the initial windows corresponding to the first data segment and the second data segment to obtain a dynamically divided window.

[0045] As a specific solution in the technical solution of this application, the calculation formula for obtaining the division coefficient based on the first average height value, the second average height value, and the data segment similarity is as follows:

[0046]

[0047] Among them, represents the data segment division coefficient between the m-th data segment and the n-th data segment, where m and n are adjacent positive integers; represents the average value of the data height values in the m-th data segment; represents the average value of the data height values in the n-th data segment; represents the data segment similarity between the m-th data segment and the n-th data segment; represents taking the maximum value in the parentheses; represents taking the minimum value in the parentheses; represents the normalization function.

[0048] Compared with the prior art, the beneficial effects of the present application are:

[0049] Through the telemetry data of the stone forest area, the present application can extract the characteristic change data of the stone forest, and then adaptively determine the size of the dynamic division window for subsequent division of the stone forest characteristic data according to the characteristic change data of the stone forest. It can not only achieve efficient division of each data in the stone forest characteristic data, but also improve the data storage efficiency and facilitate subsequent research work. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a schematic flowchart of a method for managing telemetry data of land resources proposed in an embodiment of the present application;

[0051] Figure 2 is a schematic diagram of the characteristic data of a stone forest in a two-dimensional space proposed in an embodiment of the present application;

[0052] Figure 3 For the present application Figure 2 is a schematic diagram of dividing the characteristic data of the stone forest in by a dynamic division window. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0054] In the description of the embodiments of the present application and the above-mentioned accompanying drawings, terms such as "first", "second", etc. are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. For example, the first difference and the second difference proposed below belong to different values. It should be understood that such values can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules does not necessarily have to be limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. The division of modules in the embodiments of the present application is only a logical division, and there may be other division methods when implemented in actual applications. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, and the indirect coupling or communication connection between modules may be electrical or other similar forms, which are not limited in the embodiments of the present application. And the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed to multiple circuit modules, and some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0055] In order to solve the technical problem in the background art that it is difficult for the prior art to accurately divide and store the characteristic data of stone forests, an embodiment of a method for managing land and resources telemetry data is proposed in the present application. As Figure 1 shown, the method for managing land and resources telemetry data includes steps S100 to step S600.

[0056] Step S100: Obtain the telemetry data of the stone forest area.

[0057] It should be clear that in this embodiment, the telemetry data at least includes the height of the stone forest and the horizontal spatial position of the stone forest. Generally, in the prior art, based on unmanned aerial vehicle (UAV) remote sensing technology, a UAV equipped with a radar is used to obtain topographic telemetry data, monitor the terrain in real time, and transmit these data to an operation platform through signal transmission methods such as radio waves. Subsequently, technicians view and analyze these data information on the operation platform. Before using the UAV to obtain telemetry data, the staff will carefully design the aerial photography route according to the local terrain characteristics, aiming to ensure full coverage of the monitoring area while increasing the distance interval between different flight lines as much as possible to avoid the phenomenon of height overlap in the telemetry data, thus affecting subsequent data analysis work. When collecting data, a clear weather is selected to reduce the influence of environmental factors on the collected data, thereby improving the coincidence degree and accuracy between the data and the actual scene. It should be noted that since the above steps are all mature technologies, no further elaboration will be made.

[0058] Step S200: Based on the telemetry data, obtain the characteristic data of the stone forest.

[0059] It should be clear that the characteristic data at least reflects the height of the stone forest in the horizontal direction. That is to say, in this embodiment, the characteristic data of the stone forest is the spatial position and spatial height of the stone forest.

[0060] For the convenience of processing subsequent telemetry data, in this embodiment, the telemetry data can be preprocessed to obtain the characteristic data of the stone forest. Specifically, in this embodiment, step S200, based on the telemetry data, obtaining the characteristic data of the stone forest includes steps S210 to S240.

[0061] Step S210: Convert the telemetry data into an ordered point cloud according to the acquisition order.

[0062] As can be seen from the foregoing, the telemetry data of the stone forest area is obtained using remote sensing technology. This telemetry data contains spatial data information on the height and position of the stone forest. The detection data is extracted using telemetry data processing software to obtain each data of the stone forest in the monitoring area. These data are converted into an ordered point cloud according to the acquisition order. It should be noted that converting the data into an ordered point cloud according to the acquisition order is a mature technology and will not be elaborated here.

[0063] Step S220: Traverse all the data points in the ordered point cloud along the sequential direction of the ordered point cloud to obtain the topographic data sequence of the stone forest.

[0064] It should be clear that the point with the highest height in the ordered point cloud should be the vertex of the stone forest. Along the sequential direction of the ordered point cloud from the vertex of the stone forest (i.e., the horizontal direction in the following text), the topographic data sequence of the stone forest can be obtained. This is a mature technology and will not be elaborated here.

[0065] Step S230: Normalize all the data in the data sequence to obtain a normalized sequence.

[0066] It should be noted that normalizing data is a mature technology and will not be elaborated here.

[0067] Step S240: Project the normalized sequence into a two-dimensional space to obtain the characteristic data of the stone forest; in the two-dimensional space, the height of the stone forest is used as the ordinate and the horizontal spatial position of the stone forest is used as the abscissa.

[0068] It should be clear that, in this embodiment, the obtained characteristic data of the stone forest is as Figure 2 shown, where the y-axis is the height of the stone forest; the x-axis is the horizontal spatial position of the stone forest, and the curve is the characteristic data of the stone forest. In this embodiment, the horizontal spatial position of the stone forest does not refer to the coordinates of the stone forest on the horizontal plane, but refers to the order of each data point in the ordered point cloud.

[0069] Step S300: Obtain a plurality of differences based on the characteristic data.

[0070] It should be noted that karst terrain is mainly composed of limestone, and these rocks are gradually dissolved during the long-term contact with water, thus forming various karst landforms including stone forests. In the stone forest, due to the long-term erosion of rainwater on the top of the stone pillars and the difficulty of the rainwater to drain away quickly, the dissolution reaction continues, resulting in a pitted and gully-ridden terrain. Therefore, when detecting the terrain of the stone forest, the elevation data change significantly between adjacent data points in the data point cloud. For example, when there are cracks in the top area of the stone pillar, the elevation data difference between adjacent data points will further increase. Therefore, in this embodiment, the difference is the difference between any two adjacent height data values in the characteristic data. The difference can reflect the height change of the stone forest. It is easy to understand that if the difference between two height data values is smaller, it means that the stone forest here is flatter; if the difference between two height data values is larger, it means that the stone forest here is steeper.

[0071] Step S400: Obtain the characteristic change data of the stone forest based on the plurality of differences.

[0072] In this embodiment, the characteristic change data is at least used to reflect the height change of the stone forest in the horizontal direction. In the embodiments of the present application, the difference can be directly used as the characteristic change data of the stone forest. In step S500, the characteristic data of the stone forest with relatively gentle changes is divided into the same dynamic window, and the characteristic data of the stone forest with relatively steep changes is also divided into the same dynamic window.

[0073] In order for the characteristic change data of the stone forest to accurately reflect the height change of the stone forest in the horizontal direction, in an embodiment of the present application, in step S400, obtaining the characteristic change data of the stone forest based on the multiple differences includes steps S410 to S430.

[0074] Step S410: Based on the multiple differences, obtain a first difference and a second difference.

[0075] It should be clear that the first difference and the second difference are any two adjacent differences among the multiple differences.

[0076] Step S420: Based on the first difference and the second difference, obtain a height change parameter.

[0077] It should be clear that in this embodiment, the height change parameter is at least used to reflect the magnitudes of the first difference and the second difference. As Figure 2 shown, if the first difference is the difference formed by coordinate point B and coordinate point A; the second difference is the difference formed by coordinate point C and coordinate point B, then since the first difference is negative and the second difference is positive, it is obvious that the data segment from coordinate point B to coordinate point A and the data segment from coordinate point C to coordinate point B cannot be placed in the same dynamic division window subsequently. As Figure 2 shown, if the first difference is the difference formed by coordinate point D and coordinate point E; the second difference is the difference formed by coordinate point F and coordinate point E, then since the first difference is negative and the second difference is also negative, the data segment from coordinate point E to coordinate point D and the data segment from coordinate point F to coordinate point E can be placed in the same dynamic division window subsequently.

[0078] It should be clear that in the embodiments of the present application, any suitable method can be adopted to obtain the height change parameter based on the first difference and the second difference. For example, in an embodiment of the present application, the height change parameter can be the absolute value of the difference between the first difference and the second difference; if the height change parameter is larger, it means the difference between the first difference and the second difference is larger, that is, the data segments corresponding to the first difference and the second difference are less likely to be merged subsequently; if the height change parameter is smaller, it means the difference between the first difference and the second difference is smaller, that is, the data segments corresponding to the first difference and the second difference are more likely to be merged subsequently.

[0079] In another embodiment of the present application, in step S420, the calculation formula for obtaining the height change parameter based on the first difference and the second difference is as follows:

[0080]

[0081] Wherein, Denote the height change parameter corresponding to the data with sequence number i; Denote the height data value of the stone forest corresponding to the data with sequence number i; Denote the height data value of the stone forest corresponding to the data with sequence number i + 1; Denote the height data value of the stone forest corresponding to the data with sequence number i - 1; Denote the data difference corresponding to the data with sequence number i; Denote the data difference corresponding to the data with sequence number i + 1; Denote the normalization function, which is used to map the value within the brackets to the interval (-1, 1).

[0082] It is easy to understand that if the value of the height change parameter is closer to 0, it means that the characteristics of the two data segments centered on the data with sequence number i (for example, the coordinate point E as shown Figure 2 ) are similar, and can be combined into one segment in the subsequent process; if the value of the height change parameter is farther away from 0, it means that the characteristics of the two data segments centered on the data with sequence number i (for example, the coordinate point E as shown Figure 2 ) are not similar, and cannot be combined into one segment in the subsequent process.

[0083] Step S430: Based on the height change parameter, obtain the characteristic change data of the stone forest.

[0084] It should be clear that in the embodiments of the present application, the height change parameter can be directly used as the characteristic change data of the stone forest. However, it should be clear that in order to more accurately determine whether the above-mentioned first difference and the second difference are similar, that is, to determine whether the two data segments centered on the data with sequence number i can be combined in the subsequent process. In an embodiment of the present application, step S430, based on the height change parameter, obtaining the characteristic change data of the stone forest includes steps S431 to S433.

[0085] Step S431: Based on the ordered point cloud, obtain a first vector and a second vector.

[0086] It should be clear that in this embodiment, the first vector is the direction vector from the first data point to the second data point in the ordered point cloud. The second vector is the direction vector from the second data point to the third data point in the ordered point cloud. The first data point and the second data point are adjacent, and the second data point and the third data point are adjacent.

[0087] Step S432: Based on the first vector, the second vector and the height change parameter, obtain a similarity parameter.

[0088] It should be clear that in this embodiment, the similarity parameter is at least used to characterize the similarity between the first difference and the second difference. The first vector represents the inclination direction between the first data point and the second data point (hereinafter referred to as the first inclination direction); the second vector represents the inclination direction between the second data point and the third data point (hereinafter referred to as the second inclination direction). If the dot product of the first vector and the second vector is closer to 1, it means that the first inclination direction and the second inclination direction are more similar; if the dot product of the first vector and the second vector is closer to -1, it means that the first inclination direction and the second inclination direction are more opposite. In the embodiments of the present application, the first vector and the second vector can be directly used as the similarity parameter.

[0089] In a specific embodiment of the present application, in step S432, based on the first vector, the second vector, and the height change parameter, the calculation formula for the similarity parameter is obtained as follows:

[0090]

[0091] Wherein, represents the similarity parameter corresponding to the data with sequence i; represents the height change parameter corresponding to the data with sequence i; represents the first vector; represents the second vector; represents the first data point; represents the second data point; represents the third data point; represents the dot product of the first vector and the second vector; represents taking the absolute value; represents a normalization function, which is used to map the value within the brackets to the interval range of (0, 1); k is a preset constant.

[0092] It should be noted that in the embodiments of the present application, the main function of the preset constant k is to avoid the denominator being 0. That is to say, in this embodiment, the preset constant k can be set to any positive integer according to requirements. For example, the preset constant k can be 0.5 or 1.0, etc. As can be seen from the foregoing, if the height change parameter is closer to 0, it means that the characteristics of the two data segments centered on the data with sequence i are similar. If the height change parameter is closer to 0, then is smaller, that is, is larger. As can be seen from the foregoing, if the inclination directions of the two data segments centered on the data with sequence i are more similar, the dot product of the first vector and the second vector is also larger. In other words, in this embodiment, if the similarity parameter The larger it is, the more similar the two data segments centered on the data with sequence i are. That is, in the subsequent process, the two data segments can be more easily combined into one data segment.

[0093] Step S433: Based on the similarity parameter, obtain the characteristic change data of the stone forest.

[0094] In the embodiments of the present application, the similarity parameter can be directly used as the characteristic change data of the stone forest.

[0095] Step S500: Based on the characteristic change data, obtain a dynamic division window.

[0096] It should be clear that in the embodiments of the present application, the two data segments centered on the data with sequence i can be determined whether they can be combined into one data segment according to the size of the similarity parameter in the above text. For example, if there are 20 consecutive data segments similar in the characteristic data of the stone forest, these 20 data segments are combined into one data segment, that is, the dynamic division window includes 20 data segments. In this embodiment, if the similarity parameter is greater than or equal to 0.8, two data segments can be combined into one data segment; if the similarity parameter is less than 0.8, two data segments cannot be combined. In other embodiments of the present application, other merging conditions can also be set according to requirements. For example: in an embodiment of the present application, if the similarity parameter is greater than or equal to 0.7, two data segments are combined, otherwise they are not combined. is greater than or equal to 0.7, then the two data segments are combined, otherwise they are not combined.

[0097] It should be clear that there are a large number of data in the characteristic data of a general stone forest. That is to say, there are many data segments formed by the characteristic data of the stone forest (here each data segment is composed of two adjacent data points). That is, the more times of calculation and comparison are required. In order to reduce the calculation amount, in an embodiment of the present application, step S500, based on the characteristic change data, obtain a dynamic division window, including steps S510 to S550.

[0098] Step S510: Based on the characteristic change data, obtain an initial window.

[0099] It should be clear that in the embodiments of the present application, the size of the initial window can be set according to requirements. For example: as Figure 2 shown, the initial window can be a window containing 3 data points. Of course, in other embodiments of the present application, the initial window can also be a window containing other numbers of data points, such as: including 10 data points, 100 data points or 500 data points, etc. It should be noted that the characteristic change data corresponding to the data points in the initial window can be similar. For example, if the data points If the corresponding feature change data is greater than or equal to 0.8, then the data points , data point and data point can all be put into the initial window.

[0100] Step S520: Divide the feature data of the stone forest into multiple data segments through the initial window.

[0101] In the embodiments of the present application, the feature data of the stone forest can be divided into multiple data segments according to the initial window in step S510. It is easy to understand that in this embodiment, the lengths of the respective data segments may not be equal.

[0102] Step S530: Based on the multiple data segments, obtain a first data segment and a second data segment.

[0103] In the embodiments of the present application, through the initial window, multiple data segments can be framed out from the feature data of the stone forest (the number of data points included in each data segment is the same as that in step S520), and each data segment is adjacent.

[0104] Step S540: Based on the first data segment and the second data segment, obtain the data segment similarity.

[0105] In the embodiments of the present application, the data segment similarity is at least used to characterize the similarity between the first data segment and the second data segment. It is easy to understand that if the similarity between the first data segment and the second data segment is relatively high, then the first data segment and the second data segment can be merged into a new data segment. Compared with separately calculating and merging each data segment based on a single data point multiple times in the previous text, in this embodiment, each data segment contains multiple data points, which can effectively reduce the calculation amount of the embodiments of the present application.

[0106] Specifically, in this embodiment, assume that as Figure 2 shown, Figure 2 the m-th data segment and the n-th data segment in are two adjacent data segments. Then in step S540, based on the first data segment and the second data segment, the calculation formula for obtaining the data segment similarity can be as follows:

[0107]

[0108] Among them, represents the data segment similarity between the m-th data segment and the n-th data segment, and m and n are adjacent positive integers; represents the minimum data value in the m-th data segment; represents the minimum data value in the n-th data segment; represents the average value of the similarity parameters of each data in the m-th data segment and the n-th data segment; Indicates taking the absolute value; Indicates taking the maximum value within the parentheses.

[0109] It is easy to understand that is the difference between the lowest points in the m-th data segment and the n-th data segment. The smaller this value is, the smaller the difference between the two data segments; then is also smaller, that is the larger it is, the greater the similarity between the m-th data segment and the n-th data segment, and then the two data segments can be merged.

[0110] Step S550: Update the initial window based on the data segment similarity to obtain a dynamically divided window.

[0111] It should be clear that in the embodiments of the present application, the data segments can be merged based on the data segment similarity.

[0112] In order to more accurately determine whether the adjacent data segments formed based on the initial window can be merged, in a specific embodiment of the present application, step S550, updating the initial window based on the data segment similarity to obtain a dynamically divided window, includes steps S551 to S554.

[0113] Step S551: Obtain a first height average value based on the first data segment.

[0114] In this embodiment, the first height average value is the average value of the data height values of the first data segment. Obtaining the average value based on multiple data is a mature technology and will not be elaborated here.

[0115] Step S552: Obtain a second height average value based on the second data segment.

[0116] In this embodiment, the second height average value is the average value of the data height values of the second data segment. Obtaining the average value based on multiple data is a mature technology and will not be elaborated here.

[0117] Step S553: Obtain a division coefficient based on the first height average value, the second height average value, and the data segment similarity.

[0118] It should be clear that if the first height average value and the second height average value are relatively close, and the data segment similarity is also relatively close, it indicates that the two data segments can be merged into one data segment.

[0119] Specifically, in this embodiment, step S553, obtaining the division coefficient based on the first height average value, the second height average value, and the data segment similarity, the calculation formula is as follows:

[0120]

[0121] Among them, represents the data segment division coefficient between the m-th data segment and the n-th data segment, where m and n are adjacent positive integers; represents the average value of the data height values in the m-th data segment; represents the average value of the data height values in the n-th data segment; represents the data segment similarity between the m-th data segment and the n-th data segment; represents taking the maximum value in the parentheses; represents taking the minimum value in the parentheses; represents the normalization function.

[0122] It is easy to understand that if the data heights in the n-th data segment are more similar to the data heights in the m-th data segment, then is closer to 1. Otherwise, it is closer to 0. That is to say, if the data heights in the n-th data segment are more similar to the data heights in the m-th data segment, then the data segment division coefficient is larger. Otherwise, it is smaller. In other words, in the embodiments of the present application, it is possible to determine whether two data segments can be merged according to the size of the data segment division coefficient .

[0123] Step S554: If the division coefficient is greater than the first preset value, then merge the initial windows corresponding to the first data segment and the second data segment to obtain a dynamic division window.

[0124] In the embodiments of the present application, the first preset value can be set according to requirements. For example, the first preset value can be 0.7, 0.8, or 0.9, etc. It is easy to understand that if the first preset value is larger, then as Figure 3 shown, more data segments are obtained; if the first preset value is smaller, then as Figure 3 shown, fewer data segments are obtained.

[0125] It should be clear that in this embodiment, steps S530 to S540 need to be executed multiple times until all data segments cannot be merged again.

[0126] Step S600: Based on the dynamic division window, divide and save the characteristic data of the stone forest.

[0127] It should be clear that in this embodiment, the dynamic division window is as Figure 3As shown, based on the dynamic partitioning window, the characteristic data of the stone forest can be divided into multiple data segments according to the morphological characteristics of the stone forest itself. Each data in each data segment is similar. That is to say, in the embodiment of the land resource telemetry data management method proposed in this application, through the telemetry data of the stone forest area, the characteristic change data of the stone forest can be extracted, and then based on the characteristic change data of the stone forest, the size of the dynamic partitioning window for subsequent partitioning of the stone forest characteristic data can be adaptively determined. It can not only achieve efficient partitioning of each data in the stone forest characteristic data, but also improve the data storage efficiency and facilitate subsequent research work.

[0128] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described methods, devices, and equipment can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0129] In several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0130] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, in each embodiment of the embodiments of the present application, each functional module can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0132] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product.

[0133] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, it generates, wholly or partly, a process or a function as described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or by wireless means (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server or a data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a digital video disc), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0134] Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles of the present application.

Claims

1. A method for managing remote sensing data of land and resources, characterized in that, Including: Obtaining the telemetry data of the stone forest area; the telemetry data at least includes the height of the stone forest and the horizontal spatial position of the stone forest; Based on the telemetry data, obtaining the characteristic data of the stone forest; the characteristic data at least reflects the height of the stone forest in the horizontal direction; Based on the characteristic data, obtaining a plurality of differences; the differences are the differences between any two adjacent height data values in the characteristic data; Based on the plurality of differences, obtaining the characteristic change data of the stone forest; The characteristic change data is at least used to reflect the height change of the stone forest in the horizontal direction; Based on the characteristic change data, obtaining a dynamically divided window; Based on the dynamically divided window, dividing and storing the characteristic data of the stone forest; Wherein, obtaining the characteristic data of the stone forest includes: converting the telemetry data into an ordered point cloud according to the acquisition order; traversing all data points in the ordered point cloud along the order direction of the ordered point cloud to obtain the terrain data sequence of the stone forest; performing normalization processing on all data in the data sequence to obtain a normalized sequence; projecting the normalized sequence onto a two-dimensional space to obtain the characteristic data of the stone forest; the two-dimensional space uses the height of the stone forest as the ordinate and the horizontal spatial position of the stone forest as the abscissa; Wherein, obtaining the characteristic change data of the stone forest includes: based on the plurality of differences, obtaining a first difference and a second difference; the first difference and the second difference are any two adjacent differences among the plurality of differences; based on the first difference and the second difference, obtaining a height change parameter; the height change parameter is at least used to reflect the magnitudes of the first difference and the second difference; based on the height change parameter, obtaining the characteristic change data of the stone forest; Wherein, obtaining the characteristic change data of the stone forest includes: based on the ordered point cloud, obtaining a first vector and a second vector; the first vector is the direction vector from the first data point to the second data point in the ordered point cloud; the second vector is the direction vector from the second data point to the third data point in the ordered point cloud; the first data point and the second data point are adjacent; the second data point and the third data point are adjacent; based on the first vector, the second vector and the height change parameter, obtaining a similarity parameter; the similarity parameter is at least used to characterize the similarity between the first difference and the second difference; based on the similarity parameter, obtaining the characteristic change data of the stone forest; Wherein, the calculation formula for obtaining the similarity parameter based on the first vector, the second vector and the height change parameter is as follows: Among them, represents the similarity parameter corresponding to the data with sequence i; represents the height change parameter corresponding to the data with sequence i; represents the first vector; represents the second vector; represents the first data point; represents the second data point; represents the third data point; represents the dot product of the first vector and the second vector; represents taking the absolute value; represents a normalization function used to map the value within the brackets to the range of (0, 1); k is a preset constant; Among them, obtaining a dynamic division window based on the feature change data includes: obtaining an initial window based on the feature change data; dividing the feature data of the stone forest into multiple data segments through the initial window; obtaining a first data segment and a second data segment based on the multiple data segments; the first data segment and the second data segment are any two adjacent data segments among the multiple data segments; obtaining a data segment similarity based on the first data segment and the second data segment; the data segment similarity is at least used to characterize the similarity between the first data segment and the second data segment; updating the initial window based on the data segment similarity to obtain a dynamic division window; Among them, the updating the initial window based on the data segment similarity to obtain a dynamic division window includes: obtaining a first height average value based on the first data segment; the first height average value is the average value of the data height values of the first data segment; obtaining a second height average value based on the second data segment; the second height average value is the average value of the data height values of the second data segment; obtaining a division coefficient based on the first height average value, the second height average value and the data segment similarity; If the division coefficient is greater than a first preset value, the initial windows corresponding to the first data segment and the second data segment are merged to obtain a dynamic division window.

2. The method for managing land and resources telemetry data according to claim 1, wherein The calculation formula for obtaining the height change parameter based on the first difference and the second difference is as follows: Among them, represents the height change parameter corresponding to the data with sequence number i; represents the height data value of the stone forest corresponding to the data with sequence number i; represents the height data value of the stone forest corresponding to the data with sequence number i + 1; represents the height data value of the stone forest corresponding to the data with sequence number i - 1; represents the data difference corresponding to the data with sequence number i; represents the data difference corresponding to the data with sequence number i + 1; represents a normalization function used to map the value within the brackets to the interval (-1, 1).

3. The method for managing remote sensing data of land and resources according to claim 1, characterized in that, The calculation formula for obtaining the data segment similarity based on the first data segment and the second data segment is as follows: Among them, represents the data segment similarity between the m-th data segment and the n-th data segment, where m and n are adjacent positive integers; represents the minimum data value in the m-th data segment; represents the minimum data value in the n-th data segment; represents the average value of the similarity parameters of each data in the m-th data segment and the n-th data segment; represents taking the absolute value; represents taking the maximum value in the parentheses.

4. The method for managing remote sensing data of land and resources according to claim 1, characterized in that The calculation formula for obtaining the division coefficient based on the first height average value, the second height average value and the data segment similarity is as follows: Among them, represents the data segment division coefficient between the m-th data segment and the n-th data segment, where m and n are adjacent positive integers; represents the average value of the data height values in the m-th data segment; represents the average value of the data height values in the n-th data segment; represents the data segment similarity between the m-th data segment and the n-th data segment; represents taking the maximum value in the parentheses; represents taking the minimum value in the parentheses; represents the normalization function.

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