Ice lake water area vector processing method and device, electronic equipment and storage medium

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for processing glacial lake water area vectors, comprising: acquiring glacial lake water area vectors from different periods and sorting them in descending order of area to obtain a vector sequence; constructing a regular grid covering the glacial lake water area to obtain multiple grid points; determining the target location index of the last glacial lake water area vector containing grid points in the vector sequence, and the target number of glacial lake water area vectors that do not contain grid points before the target location index; determining the elevation feature values ​​of the grid points based on the target location index and the target number; determining a feature threshold based on the multiple lower-ranked elevation feature values ​​corresponding to the glacial lake water area vectors, and correcting the glacial lake water area vectors based on the grid points whose elevation feature values ​​are greater than or equal to the feature thresholds. This invention can accurately correct and extract glacial lake water area vectors that conform to hydrological patterns under the interference of floating ice and glaciers.

Description

Technical Field

[0001] This invention relates to the interdisciplinary fields of remote sensing technology, hydrological monitoring and geographic information systems, and in particular to a vector processing method, device, electronic device and storage medium for glacial lake water areas. Background Technology

[0002] Glacial lakes, as important freshwater reserves in high-altitude regions, directly influence regional water security and flood risk early warning through periodic changes in their water levels and areas. However, glacial lake waters are frequently affected by seasonal floating ice, glacial debris, and cloud cover, leading to incomplete or blurred boundaries in the vector data extracted from optical remote sensing. This makes it difficult to accurately determine the true extent of glacial lake waters under disturbance, severely impacting the accuracy of glacial lake monitoring.

[0003] Existing technologies typically utilize spectral analysis methods such as the Normalized Difference Water Index (NDWI) to extract water bodies, or perform classification based on single-period imagery. To address the omissions caused by ice floes, some methods attempt to restore boundaries by filling in missing pixels, based on accurate water body classification. For example, they may use multi-year average reflectance to fill in water body pixels, or simply use morphological closing operations to fill in holes inside polygons.

[0004] However, traditional water index methods often incorrectly exclude areas with floating ice, and the fill method based on average reflectance is not suitable for glacial lakes with large water level fluctuations, resulting in boundary extraction results that deviate from reality and fail to reflect the hydrological continuity characteristics of glacial lakes. Therefore, how to accurately correct and extract glacial lake water area vectors that conform to hydrological patterns under the interference of floating ice and glaciers has become an urgent problem to be solved in this field. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for processing vector data of glacial lake water areas, in order to solve the technical problem of how to accurately correct and extract glacial lake water area vectors that conform to hydrological laws under the interference of floating ice and glaciers.

[0006] This invention provides a method for vector processing of glacial lake water areas, comprising: Obtain the water area vectors of the glacial lake at different times, and sort the water area vectors of the glacial lake in descending order of area to obtain a vector sequence; A regular grid covering the glacial lake area was constructed, resulting in multiple grid points; Determine the target location index of the last glacial lake water area vector in the vector sequence that contains the grid point, and the target number of glacial lake water area vectors that do not contain the grid point before the target location index; The elevation feature values ​​of the grid points are determined based on the target location index and the number of targets, and the elevation feature values ​​of each grid point contained in the ice lake water area vector are sorted in descending order of the elevation feature values; the elevation feature values ​​increase with the target location index. A feature threshold is determined based on the lower-ranked elevation feature values ​​corresponding to the ice lake water area vector, and the ice lake water area vector is corrected based on each grid point whose elevation feature value is greater than or equal to the feature threshold.

[0007] According to a method for processing glacial lake water area vectors provided by the present invention, the step of acquiring glacial lake water area vectors at different times and sorting the glacial lake water area vectors in descending order of area includes: Obtain the water area vectors of the glacial lake at different times; The preprocessing of the ice lake water area vector includes removing empty geometric regions in the ice lake water area vector, filling the internal holes of polygons in the ice lake water area vector, and repairing the geometric topology problems of the ice lake water area vector. The preprocessed vectors of the various glacial lake areas are sorted in descending order of area.

[0008] According to the present invention, a vector processing method for glacial lake water areas includes constructing a regular grid covering the glacial lake water area, comprising: Based on the vector of the largest area of ​​the ice lake water body, extend outward by a preset distance to obtain the target area covering the ice lake water body; The target area is divided into grids based on a preset resolution to obtain the regular grid.

[0009] According to a vector processing method for glacial lake water areas provided by the present invention, the step of determining the elevation feature value of the grid points based on the target location index and the number of targets includes: Determine the ratio of the number of targets to the target location index; The elevation feature value is obtained by subtracting the ratio from the target location index.

[0010] According to a method for processing glacial lake water area vectors provided by the present invention, the step of correcting the glacial lake water area vector based on each of the grid points whose elevation feature value is greater than or equal to the feature threshold includes: Merge all grid points whose elevation feature values ​​are greater than or equal to the feature threshold into a polygon; The polygon is merged with the original ice lake water area vector to obtain the corrected ice lake water area vector.

[0011] According to a vector processing method for glacial lake water areas provided by the present invention, after determining the elevation feature values ​​of the grid points based on the target location index and the number of targets, the method further includes: Obtain the actual water level of the glacial lake at different times; Determine the minimum elevation characteristic value of each of the submerged grid points at each of the actual water levels; Based on the actual water level of each group and the corresponding minimum elevation feature value, establish a mapping relationship between the actual water level and the elevation feature value; Based on the mapping relationship, the underwater topography of the glacial lake can be inverted or the reservoir capacity curve of the glacial lake can be calculated.

[0012] The present invention also provides a vector processing device for glacial lake water areas, comprising: The vector sorting module is used to obtain the ice lake water area vectors at different times, and sort the ice lake water area vectors in descending order of area to obtain a vector sequence; The mesh building module is used to construct a regular mesh covering the glacial lake area, resulting in multiple mesh points; A grid encoding module is used to determine the target location index of the last glacial lake water area vector containing the grid point in the vector sequence, and the target number of glacial lake water area vectors that do not contain the grid point before the target location index; The feature determination module is used to determine the elevation feature value of the grid point according to the target location index and the number of targets, and sort the elevation feature values ​​of each grid point contained in the ice lake water area vector in descending order of the elevation feature value; the elevation feature value increases with the target location index; The vector correction module is used to determine a feature threshold based on the lower-ranked elevation feature values ​​corresponding to the ice lake water area vector, and to correct the ice lake water area vector based on each grid point whose elevation feature value is greater than or equal to the feature threshold.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for vector processing of glacial lake water areas.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vector processing method for glacial lake water areas as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the vector processing method for glacial lake water areas as described above.

[0016] The present invention provides a method, apparatus, electronic device, and storage medium for vector processing of glacial lake water areas. By constructing a vector sequence and introducing target location indices and target quantities to determine elevation feature values, it essentially encodes the "submergence history" of pixels in multi-temporal images into relative elevation information. Utilizing the complementarity of multi-period data, a dimensionless topographic model of the glacial lake's interior is established. By correcting the glacial lake water area vector through feature thresholding, it can intelligently identify and fill in areas that should be submerged at the current water level but were not identified due to floating ice interference. This invention does not rely on the quality of single-period images but rather performs physical-level logical correction based on hydrological continuity. It can accurately correct and extract glacial lake water area vectors that conform to hydrological patterns even under floating ice and glacier interference, significantly improving the accuracy and completeness of glacial lake water area boundary extraction under floating ice interference. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the vector processing method for glacial lake water areas provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the results of sorting elevation feature values ​​provided by the present invention.

[0020] Figure 3 This is a schematic diagram of the correction results of the ice lake water area vector provided by the present invention.

[0021] Figure 4 This is a schematic diagram illustrating the principle of the vector processing method for glacial lake water areas provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the structure of the vector processing device for glacial lake water areas provided by the present invention.

[0023] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] Glacial lakes, as vital freshwater reserves in high-altitude regions, hold significant indicative value in global climate change research. In particular, the dramatic fluctuations in water level and area of ​​periodically flowing glacial lakes directly impact water security in downstream areas. These lakes typically exhibit pronounced seasonal variations: summer meltwater inflow leads to lake surface expansion, while glacial melt and breakup create large amounts of floating ice; winter brings widespread freezing or dries up due to outflow. This cyclical change renders traditional water monitoring methods ineffective, making accurate understanding of water boundary and reservoir capacity changes crucial for predicting glacial lake outburst risk and assessing regional water resource conditions. Currently, global climate change is causing accelerated glacial retreat and a continuous increase in the number and size of glacial lakes, creating a clear demand for high-precision remote sensing monitoring of glacial lakes and necessitating the development of water boundary extraction technologies adapted to the unique environment of glacial lakes.

[0026] Existing techniques for extracting the boundaries of glacial lakes face three main challenges: First, optical remote sensing suffers from severe interference from floating ice and clouds, resulting in incomplete water body vector extraction. The spectral characteristics of floating ice in the visible and near-infrared bands are similar to those of water bodies, making accurate differentiation difficult using traditional classification methods. Second, single-image datasets struggle to distinguish between permanent water bodies and temporary ice surfaces. Glacial lake surfaces are often covered by seasonal floating ice, which overlaps with the spectral characteristics of water bodies in single-image datasets, further complicating boundary identification. Third, traditional water index methods have significant limitations in glacial lake boundary extraction. For example, methods based on the Normalized Difference Water Index (NDWI) often result in correct water body extraction but actually deduct water surfaces corresponding to floating ice, while indices like the Normalized Difference Water Index (MNDWI), which do not distinguish between ice and water, may incorrectly classify glaciers as water bodies. Therefore, most existing studies rely on filling in missing pixels to reconstruct the reasonable boundaries of glacial lakes based on accurate water body classification results. However, traditional methods for filling missing pixels are often based on multi-year average reflectance to fill water pixels, which is not suitable for glacial lakes and lakes where the water surface fluctuates significantly, resulting in boundary extraction results that deviate from the actual situation.

[0027] Current technologies lack effective solutions to the problems of blurred glacial lake boundaries caused by glacial ice debris and data loss due to cloud cover. Traditional methods mostly rely on spectral feature analysis of single-period images, which cannot fundamentally solve the problem of ice debris interference.

[0028] The following is combined with Figures 1 to 6 The present invention describes a method, apparatus, electronic device, and storage medium for vector processing of glacial lake water areas.

[0029] Figure 1 This is one of the flowcharts illustrating the vector processing method for glacial lake water areas provided by the present invention, such as... Figure 1 As shown, the method includes, but is not limited to, steps S1, S2, S3, S4 and S5.

[0030] Step S1: Obtain the vectors of the glacial lake water areas at different times, and sort the vectors of the glacial lake water areas in descending order of area to obtain a vector sequence.

[0031] Remote sensing can be used to acquire multiple images of the glacial lake area at different times, and conventional water body extraction methods (such as the NDWI water index method) can be used to obtain the glacial lake water area vectors for each period. Based on the hydrological principle that "generally, the higher the water level, the larger the water area," the area of ​​each glacial lake water area vector is calculated, and a vector sequence is constructed in descending order of area. G = [G1, G2, ..., G] n ]; Where n represents the total number of vectors representing the glacial lake area; G1 has the largest area and approximately corresponds to the highest water level; G n The smallest area corresponds approximately to the lowest water level; thus, an approximate correspondence between area and water level is established.

[0032] Because the area of ​​the glacial lake may be affected by factors such as floating ice, leading to observational errors, this correspondence is not a strict one.

[0033] Step S2: Construct a regular grid covering the glacial lake area to obtain multiple grid points.

[0034] By constructing a regular grid covering the glacial lake region, a spatial framework for boundary correction and elevation sorting is provided.

[0035] Step S3: Determine the target location index of the last glacial lake water area vector containing grid points in the vector sequence, and the number of glacial lake water area vectors that do not contain grid points before the target location index.

[0036] Spatial analysis is performed on each grid point to determine its inclusion relationship in the vector sequence. Specifically, for each grid point, the target location index refers to the index of the last glacial lake area vector (i.e., the smallest glacial lake area vector) in the vector sequence that contains that grid point. The target location index is denoted as q, and q is used as the baseline for relative elevation. The target quantity refers to the number of glacial lake area vectors in the vector sequence that are located before the target location index and do not contain that grid point. The target quantity is denoted as p.

[0037] For the center P of each grid point j It is possible to traverse the vector sequence G1 to G n Judge P one by one j Is it in G? i If it is present, record it as 1; otherwise, record it as 0. Generate the binary sequence corresponding to the j-th grid point: S j =[s j1 s j2 , ..., s ji , ..., s jn ]; In the formula s ji =1 indicates that P j Located in G i In the middle, s ji =0 indicates that P j Not located in G i middle.

[0038] Then search for S j The position of the rightmost 1 gives q; the smaller q is, the better P is. j The higher the elevation, the better. Then calculate S. j The number of zeros to the left of q gives p. With the same q value, a larger p indicates higher efficiency. j The higher the relative elevation, the lower the elevation; conversely, the lower the relative elevation, the lower the elevation. Clearly, p ≤ q.

[0039] Step S4: Determine the elevation feature values ​​of the grid points based on the target location index and the number of targets, and sort the elevation feature values ​​of each grid point contained in the ice lake water area vector in descending order of elevation feature values; the elevation feature values ​​increase with the target location index.

[0040] The elevation feature value represents the relative elevation of the grid point. Since the elevation feature value increases with the target location index, the smaller the target location index, the smaller the elevation feature value, which means the higher the relative elevation of the grid point.

[0041] Step S5: Determine the feature threshold based on the lower-ranked elevation feature values ​​corresponding to the ice lake water area vector, and correct the ice lake water area vector based on each grid point whose elevation feature value is greater than or equal to the feature threshold.

[0042] For a specific period's glacial lake water area vector, multiple values ​​with lower elevation feature values ​​(i.e., smaller elevation feature values ​​and relatively higher elevations) among the grid points included can be selected as references to determine a feature threshold. For example, the bottom 1% of elevation feature values ​​can be selected; if there are fewer than 3, the bottom 3 elevation feature values ​​can be selected, and the average value can be taken as the feature threshold.

[0043] Since the feature threshold is determined based on the smallest multiple elevation feature values, the feature threshold is equivalent to the water boundary. Grid points with elevation feature values ​​greater than or equal to the feature threshold are regarded as areas that should be submerged at that water level. Thus, these grid points can be used to correct the original glacial lake water vector and fill in the areas missing due to the obstruction of floating ice.

[0044] Existing technologies for extracting glacial lake boundaries are often hampered by floating ice and cloud cover, resulting in fragmented, voided, or missing boundary vectors. Single-period images struggle to distinguish between permanent water bodies and temporary floating ice, while traditional spectral infilling methods ignore the dynamic changes in glacial lake water levels, making it difficult to ensure that the filled area conforms to the actual water level distribution patterns. This leads to low accuracy and poor reliability of the extracted boundaries.

[0045] This invention determines elevation feature values ​​by constructing a vector sequence and introducing target location indices and target quantities. Essentially, it encodes the "submergence history" of pixels in multi-temporal imagery into relative elevation information. Utilizing the complementarity of multi-period data, a dimensionless topographic model of the glacial lake's interior is established. By correcting the glacial lake's water area vector through feature thresholding, it can intelligently identify and fill in areas that should be submerged at the current water level but were not identified due to ice floes. This invention does not rely on the quality of single-period imagery but rather performs physical-level logical correction based on hydrological continuity. It can accurately correct and extract glacial lake water area vectors that conform to hydrological patterns even under ice floes and glacier interference, significantly improving the accuracy and completeness of glacial lake boundary extraction under ice floe interference.

[0046] In one embodiment, step S1, obtaining the ice lake water area vectors from different periods and sorting the ice lake water area vectors in descending order of area, may further include: Obtain vectors of glacial lake water areas at different times; Preprocessing of the ice lake water area vector includes removing empty geometric regions in the ice lake water area vector, filling the internal holes of polygons in the ice lake water area vector, and repairing the geometric topology problems of the ice lake water area vector; The vectors of each preprocessed glacial lake area are sorted in descending order of area.

[0047] This invention provides a detailed description of the acquisition and sorting process of glacial lake water area vectors. After acquiring glacial lake water area vectors from different periods, a preprocessing operation is first performed. Preprocessing includes: removing empty geometric regions from the vector files, which are typically noise points generated during processing; filling the internal voids of polygons in the glacial lake water area vectors to eliminate internal water voids caused by local noise or small pieces of floating ice; and correcting geometric topological issues in the vectors to ensure that polygons are closed and free from self-intersections. After preprocessing, the geometric area of ​​each purified glacial lake water area vector is calculated, and the vectors are strictly sorted from largest to smallest area value to generate an ordered vector sequence for subsequent analysis.

[0048] Through systematic preprocessing steps, geometric noise and topological anomalies in the original glacial lake water vectors can be effectively removed, ensuring the quality of the vector data used in the analysis. By filling internal voids, obvious spectral identification errors are initially corrected. More importantly, area sorting based on the purified data enables a more accurate approximate mapping relationship between "area" and "water level," providing a solid and reliable data foundation for subsequent calculations of elevation feature values ​​based on sequence locations, thereby improving the overall robustness of the algorithm.

[0049] In one embodiment, step S2, constructing a regular grid covering the glacial lake water area, may further include: Based on the vector of the largest area of ​​the glacial lake, extend outward by a preset distance to obtain the target area covering the glacial lake area; The target area is divided into grids based on a preset resolution to obtain a regular grid.

[0050] This invention details the process of constructing a regular grid covering the glacial lake area. First, based on the largest glacial lake area vector G1, a preset distance is set for outward expansion, resulting in a target region that completely covers the historically largest water area and its surrounding potential change zones. Next, this target region is spatially meshed based on a preset resolution, generating a regular grid. This grid contains two core attributes: one is the vector range corresponding to the grid cell, used for subsequent generation of repaired polygons; the other is the grid's center point, serving as the spatial carrier for subsequent calculation of elevation feature values ​​and sorting.

[0051] This invention constructs a regular grid by expanding the buffer zone to the maximum extent of the water area, ensuring that the analysis scope covers all possible inundated areas of the glacial lake and avoiding omissions. By gridding at a preset resolution, the continuous geographic space is discretized into computable units, providing a standard spatial benchmark for subsequently transforming complex vector containment relationships into quantifiable target location indices and target quantities, making spatial overlay analysis of multi-time series data possible.

[0052] In one embodiment, step S4, determining the elevation feature values ​​of the grid points based on the target location index and the number of targets, may further include: Determine the ratio of the number of targets to their location indices; Subtract the ratio from the target location index to obtain the elevation feature value.

[0053] This invention provides a specific mathematical model for calculating elevation characteristic values. After determining the target location index q and the target quantity p of the grid points, the ratio of the target quantity to the target location index, i.e., p / q, is first determined. Then, the target location index is subtracted from this ratio to obtain the elevation characteristic value Y. j The calculation formula can be expressed as: Y j =qp / q; q represents the approximate water level at which the grid point is submerged, while p represents the unsubmerged anomaly above that level (usually caused by floating ice).

[0054] According to the elevation characteristic value Y j The elevation eigenvalues ​​Y of each grid point contained in the glacial lake water area vector are arranged in descending order. j Sort the results as follows Figure 2 As shown. Elevation characteristic value Y j The sorting can reflect the relative topography inside the glacial lake.

[0055] The calculation formula proposed in this invention has a clear physical meaning: the smaller the q value, the more likely the grid point is included in the larger area of ​​the glacial lake's water vector, i.e., the higher the relative elevation; the p value reflects the degree of disturbance from floating ice. Through Y... j =qp / q constructs a continuous variable that increases with the target location index. This elevation feature not only uses q to determine the basic elevation level, but also uses the p / q term to perform probabilistic correction (fine-tuning) for ice floe disturbance, making the calculated relative elevation more refined and smooth. This allows for the high-precision inversion of the relative terrain distribution through multi-time-series logic, even in the absence of measured water depth data.

[0056] In one embodiment, step S5, correcting the glacial lake water area vector based on each grid point whose elevation feature value is greater than or equal to a feature threshold, may further include: Merge all grid points whose elevation feature values ​​are greater than or equal to the feature threshold into polygons; The polygon is merged with the original ice lake water area vector to obtain the corrected ice lake water area vector.

[0057] This invention details the specific steps for correcting glacial lake water area vectors using elevation feature values. For each glacial lake water area vector, firstly, all grid points with elevation feature values ​​greater than or equal to a determined feature threshold are selected. These grid points represent areas that, according to topographic logic, should be submerged at that specific water level (including areas obscured by floating ice). The grid cells corresponding to these selected grid points are geometrically merged to generate a completed polygon. Finally, this completed polygon is spatially merged with the original glacial lake water area vector before correction to obtain the final corrected glacial lake water area vector. Figure 3 As shown, green represents the original ice lake water level extraction result, and blue represents the restored ice lake water area vector, successfully solving the problem that floating ice could not be correctly removed.

[0058] The correction method of this invention is based on relative topography. By merging grid points with compliant elevation feature values, it effectively reconstructs the theoretical water area below the water level. Merging this area with the original glacial lake water area vector accurately fills in voids obscured by floating ice and clouds, while preserving the correctly identified water boundary details in the original glacial lake water area vector. This method achieves intelligent filling of the glacial lake water area, ensuring that the corrected glacial lake water area vector is hydrologically reasonable and continuous, effectively solving the problem of boundary incompleteness caused by floating ice interference.

[0059] In one embodiment, after determining the elevation feature values ​​of the grid points based on the target location index and the number of targets, the method of the present invention may further include: Obtain the actual water level of the glacial lake at different times; Determine the minimum elevation characteristic value of each submerged grid point at each actual water level; Based on the actual water level and the corresponding minimum elevation characteristic value of each group, establish the mapping relationship between the actual water level and the elevation characteristic value; Based on the mapping relationship, the underwater topography of the glacial lake can be inverted or the reservoir capacity curve of the glacial lake can be calculated.

[0060] This invention describes an extended application of inversion based on elevation characteristic values. After calculating the elevation characteristic values ​​of the grid points, the actual water level data of the glacial lake at different periods are obtained (which can be obtained through field measurements, altimetry satellites, or digital elevation models (DEMs)). The actual water level h of each point is then determined. k Below, the minimum elevation characteristic value (or a certain proportion of elevation characteristic values ​​representing the water level) corresponding to the grid points within the flooded area. k This yields multiple sets of correspondences between "actual water level - elevation characteristic values" (Y1, h1), (Y2, h2), ..., (Y...). k h k (Y1) <Y2<...<Y kBased on these data, a mapping relationship (such as linear regression or piecewise interpolation) can be established to convert dimensionless elevation characteristic values ​​into actual altitudes. Based on this mapping relationship, the underwater topography model of the entire glacial lake can be inverted, or the water volume at different water levels can be calculated through integration, thereby deriving the reservoir capacity curve of the glacial lake.

[0061] This invention achieves low-cost reconstruction of the underwater topography of glacial lakes by combining relative elevations (elevation characteristic values) derived from multi-time-series remote sensing with a small amount of actual water level data. This not only verifies the physical validity of the elevation characteristic values ​​but also provides crucial reservoir capacity curves and topographic data for glacial lake outburst flood simulation and quantitative water resource assessment, realizing a leap from two-dimensional area monitoring to three-dimensional water volume estimation.

[0062] Based on the preceding text, we can conclude that... Figure 4 The complete technical solution is shown below. It can be understood that this invention, through temporal analysis of multiple image periods, utilizes the continuous characteristics of glacial lake water level changes and employs a pixel-ordering approach to analyze the relative height between pixels. Specifically, by constructing a multi-period glacial lake water area vector sequence, the inclusion relationship of each pixel in different time phases is encoded into a binary sequence. Based on the sequence features, the relative elevation of the pixels is inferred, i.e., the dimensionless glacial lake topography, thereby correcting the original glacial lake water area vector sequence. Finally, by combining a small amount of measured water level or topographic data, the dimensionless glacial lake topography can be transformed into actual underwater topography. This method does not rely on the quality of a single image but rather restores the true glacial lake boundary through the complementarity of multiple data periods, providing an innovative solution to the problem of glacial lake monitoring under the interference of floating ice.

[0063] This invention utilizes the area of ​​multiple image periods to represent water levels, constructing a vector hierarchy and transforming the challenge of spectral recognition into modeling the continuity of hydrological patterns. It pioneers a binary sequence encoding and relative sorting algorithm, retrieving dimensionless lakebed topography through pixel "submergence history" to logically correct for floating ice interference. Based on the retrieved relative topography, it intelligently fills in missing boundaries, ensuring the results conform to the physical continuity of water levels, rather than relying on local spectral filling. By calibrating the dimensionless topography with a small amount of measured data, it can quickly obtain actual underwater topography and reservoir capacity curves, achieving low-cost, high-precision three-dimensional monitoring.

[0064] The following describes the ice lake water vector processing device provided by the present invention. The ice lake water vector processing device described below can be referred to in correspondence with the ice lake water vector processing method described above.

[0065] like Figure 5 As shown, the present invention also provides a vector processing device for glacial lake water areas, comprising: The vector sorting module is used to obtain the vectors of glacial lake water areas at different times, and sort the vectors of each glacial lake water area in descending order of area to obtain a vector sequence; The mesh building module is used to construct a regular mesh covering the glacial lake area, resulting in multiple mesh points; The grid encoding module is used to determine the target location index of the last glacial lake water area vector containing grid points in the vector sequence, and the number of glacial lake water area vectors that do not contain grid points before the target location index. The feature determination module is used to determine the elevation feature values ​​of grid points based on the target location index and the number of targets, and sort the elevation feature values ​​of each grid point contained in the ice lake water area vector in descending order of elevation feature values; the elevation feature values ​​increase with the target location index; The vector correction module is used to determine the feature threshold based on the lower-ranked elevation feature values ​​corresponding to the ice lake water area vector, and to correct the ice lake water area vector based on each grid point whose elevation feature value is greater than or equal to the feature threshold.

[0066] Figure 6 A schematic diagram of the physical structure of an electronic device is provided. This device may include a processor, a communications interface, memory, and a communication bus. The processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions from the memory to execute a vector processing method for glacial lake water areas.

[0067] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0068] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the ice lake water area vector processing method provided by the above methods.

[0069] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the glacial lake water area vector processing methods provided by the above methods.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for vector processing of glacial lake water areas, characterized in that, include: Obtain the water area vectors of the glacial lake at different times, and sort the water area vectors of the glacial lake in descending order of area to obtain a vector sequence; A regular grid covering the glacial lake area was constructed, resulting in multiple grid points; Determine the target location index of the last glacial lake water area vector in the vector sequence that contains the grid point, and the target number of glacial lake water area vectors that do not contain the grid point before the target location index; The elevation feature values ​​of the grid points are determined based on the target location index and the number of targets, and the elevation feature values ​​of each grid point contained in the ice lake water area vector are sorted in descending order of the elevation feature values; the elevation feature values ​​increase with the target location index. A feature threshold is determined based on the lower-ranked elevation feature values ​​corresponding to the ice lake water area vector, and the ice lake water area vector is corrected based on each grid point whose elevation feature value is greater than or equal to the feature threshold.

2. The vector processing method for glacial lake water areas according to claim 1, characterized in that, The process of obtaining glacial lake water area vectors from different periods and sorting the glacial lake water area vectors in descending order of area includes: Obtain the water area vectors of the glacial lake at different times; The preprocessing of the ice lake water area vector includes removing empty geometric regions in the ice lake water area vector, filling the internal holes of polygons in the ice lake water area vector, and repairing the geometric topology problems of the ice lake water area vector. The preprocessed vectors of the various glacial lake areas are sorted in descending order of area.

3. The vector processing method for glacial lake water areas according to claim 1, characterized in that, The construction of a regular grid covering the glacial lake water area includes: Based on the vector of the largest area of ​​the ice lake water body, extend outward by a preset distance to obtain the target area covering the ice lake water body; The target area is divided into grids based on a preset resolution to obtain the regular grid.

4. The vector processing method for glacial lake water areas according to claim 1, characterized in that, Determining the elevation feature value of the grid point based on the target location index and the target quantity includes: Determine the ratio of the number of targets to the target location index; The elevation feature value is obtained by subtracting the ratio from the target location index.

5. The vector processing method for glacial lake water areas according to claim 1, characterized in that, The step of correcting the glacial lake water area vector based on each of the grid points whose elevation feature value is greater than or equal to the feature threshold includes: Merge all grid points whose elevation feature values ​​are greater than or equal to the feature threshold into a polygon; The polygon is merged with the original ice lake water area vector to obtain the corrected ice lake water area vector.

6. The vector processing method for glacial lake water areas according to claim 1, characterized in that, After determining the elevation feature value of the grid point based on the target location index and the target quantity, the method further includes: Obtain the actual water level of the glacial lake at different times; Determine the minimum elevation characteristic value of each of the submerged grid points at each of the actual water levels; Based on the actual water level of each group and the corresponding minimum elevation feature value, establish a mapping relationship between the actual water level and the elevation feature value; Based on the mapping relationship, the underwater topography of the glacial lake can be inverted or the reservoir capacity curve of the glacial lake can be calculated.

7. A vector processing device for glacial lake water areas, characterized in that, include: The vector sorting module is used to obtain the ice lake water area vectors at different times, and sort the ice lake water area vectors in descending order of area to obtain a vector sequence; The mesh building module is used to construct a regular mesh covering the glacial lake area, resulting in multiple mesh points; A grid encoding module is used to determine the target location index of the last glacial lake water area vector containing the grid point in the vector sequence, and the target number of glacial lake water area vectors that do not contain the grid point before the target location index; The feature determination module is used to determine the elevation feature value of the grid point according to the target location index and the number of targets, and sort the elevation feature values ​​of each grid point contained in the ice lake water area vector in descending order of the elevation feature value; the elevation feature value increases with the target location index; The vector correction module is used to determine a feature threshold based on the lower-ranked elevation feature values ​​corresponding to the ice lake water area vector, and to correct the ice lake water area vector based on each grid point whose elevation feature value is greater than or equal to the feature threshold.

8. An electronic device 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, it implements the vector processing method for glacial lake water areas as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the vector processing method for glacial lake water areas as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vector processing method for glacial lake water areas as described in any one of claims 1 to 6.

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