Rainfall confluence path prediction method and system, electronic equipment and storage medium

Abstract

The invention provides a rainfall confluence path prediction method and system, electronic equipment and a storage medium, and relates to the technical field of data processing. A digital elevation model of a monitored area is gridded, a convergence starting point grid and a convergence terminal point grid are obtained by identifying the maximum elevation value and the minimum elevation value in the digital elevation model, and the grid with the minimum elevation value in adjacent grids of the convergence starting point grid is selected as a new central grid. And circularly selecting the grid with the minimum elevation value in the adjacent grids of the new central grids as the next new central grid until the selected new central grid is a confluence end point grid, and sequentially connecting the confluence starting point grid and all the selected central grids from the confluence starting point grid according to the sequence that all the central grids are selected to form a confluence end point grid. And obtaining a predicted confluence path. The method is based on the digital elevation model and is not influenced by other factors, so that the prediction result is more accurate, and the prediction stability is higher.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a rainfall catchment path prediction method and system, an electronic device and a storage medium. BACKGROUND

[0002] During a flood disaster or a short-term heavy rainfall, the catchment of the rainfall needs to be monitored so as to master the flooding situation that may be caused by the rainfall. The flow of the rainwater on the ground is not uniformly distributed, but is collected and flows along a specific path (catchment path) under the action of gravity and topography. Predicting the catchment path can accurately locate the main direction, intensity and final collection area of the water flow. Accordingly, it can more accurately predict which areas will be flooded, the depth, range and possible evolution speed of the flooding, and realize accurate positioning of the risk space.

[0003] The existing method uses historical rainfall, water level, flow and flooding monitoring data, etc. to train a model through a machine learning or deep learning algorithm, finds the relationship between the rainfall characteristics and the catchment route and the catchment result, and is used for predicting the future catchment route situation.

[0004] The model in the above method is usually pre-trained based on historical data. The prediction ability of extreme rainfall events beyond the historical data record range is doubtful, and if the historical data is insufficient or not representative, the model parameters will be distorted, so that the prediction result deviates greatly from the actual situation, and the prediction accuracy is low. SUMMARY

[0005] The problem to be solved by the present application is that the existing path prediction method using deep learning is based on historical data. If the historical data is insufficient, the prediction result deviates greatly, and the prediction accuracy is low.

[0006] To solve the above problems, in a first aspect, the present application provides a rainfall catchment path prediction method, comprising: obtaining a digital elevation model of a monitoring area, the digital elevation model comprising an elevation value of each terrain; gridding the digital elevation model of the monitoring area; identifying the maximum and minimum elevation values in the digital elevation model and marking them as a catchment starting point grid and a catchment ending point grid, respectively; selecting the grid with the minimum elevation value in the adjacent grid of the catchment starting point grid as a new center grid, and repeatedly selecting the grid with the minimum elevation value in the adjacent grid of the new center grid as the next new center grid until the selected new center grid is the catchment ending point grid; starting from the catchment starting point grid, sequentially connecting the catchment starting point grid and all selected center grids in the order of selection of all center grids to obtain a predicted catchment path.

[0007] Optionally, gridding the digital elevation model of the monitoring area includes: According to the properties of different surface covers, the digital elevation model of the monitored area is divided into different surface areas, wherein the surface areas include water-covered areas, vegetation-covered areas, bare land areas and artificial areas; Grid different surface areas using corresponding parameters; The area of ​​the incomplete grid to be merged at the junction of different surface areas is analyzed. When the area of ​​the grid to be merged is less than half of the area of ​​a complete grid in the same surface area, the incomplete grid is merged with a complete grid in the adjacent same surface area.

[0008] Optionally, the identifying the maximum elevation value and the minimum elevation value in the digital elevation model and marking them as a confluence starting point grid and a confluence ending point grid respectively includes: Identifying the maximum elevation value and the minimum elevation value in the digital elevation model, and recording the grid where the maximum elevation value is located as the confluence starting point grid; Combined with SAR images, determine whether there is water in the grid with the minimum elevation value; If there is water in the grid where the minimum elevation value is located, identify the coverage of the water body; Extract the boundary of the water body coverage area, record the grids that pass through the boundary as the confluence end grids, and eliminate the grids within the water body coverage area; If there is no water body in the grid where the minimum elevation value is located, the grid where the minimum elevation value is located will be directly recorded as the confluence end grid.

[0009] Optionally, the step of selecting a grid with the smallest elevation value among the adjacent grids of the confluence starting point grid as a new center grid, and cyclically selecting a grid with the smallest elevation value among the adjacent grids of the new center grid as the next new center grid until the selected new center grid becomes the confluence ending point grid includes: If there are multiple confluence starting point grids, sort the multiple confluence starting point grids according to the distance between the confluence starting point grid and the confluence ending point grid from large to small to obtain a confluence starting point grid sequence; According to the order of the confluence starting point grids in the confluence starting point grid sequence, confluence path prediction is performed starting from each confluence starting point grid; Starting from the second confluence starting point grid, during the confluence path prediction process, if the grid with the smallest elevation value among the adjacent grids of the new central grid has been selected in the previous confluence path prediction, the elevation difference between the grid with the smallest elevation value and the grid with the second lowest elevation value is analyzed; If the elevation difference is less than or equal to the preset difference, the grid with the second lowest elevation value is selected as the next new center grid; If the elevation difference value is greater than the preset difference value, the grid with the minimum elevation value among the adjacent grids of the selected grid is still selected as the next new center grid.

[0010] Optionally, the grid with the minimum elevation value among the adjacent grids of the selected confluence starting grid is selected as the new center grid, and the grid with the minimum elevation value among the adjacent grids of the new center grid is selected as the next new center grid in a cycle until the selected new center grid is the confluence ending grid, comprising: When there are multiple grids with the minimum elevation value among the grids adjacent to the center grid, the multiple grids with the minimum elevation value are selected according to a preset priority, and the next new center grid is determined, wherein the preset priority order is a grid that maintains the same confluence trend, a grid that is not selected in the previous confluence path prediction, a grid that is closest to the confluence ending grid, and a grid in the lateral direction of the center grid.

[0011] Optionally, after the predicted confluence path is obtained by sequentially connecting the confluence starting grid and all the selected center grids in the order in which the center grids are selected, the method further comprises: After the confluence path prediction from the multiple confluence starting grids is completed, the grid with the maximum elevation value and the selected grid are excluded; The grid with the maximum elevation value is sequentially selected from the remaining grids in the digital elevation model as a new confluence starting grid for confluence path prediction until all the grids are selected.

[0012] Optionally, after the predicted confluence path is obtained by sequentially connecting the confluence starting grid and all the selected center grids in the order in which the center grids are selected, the method further comprises: A new SAR image is obtained, and a new water body coverage range is identified; According to the new water body coverage range, the confluence ending grid is updated, and the confluence path in the new water body coverage range is eliminated to simplify the predicted confluence path.

[0013] In a second aspect, the present application also provides a rainfall confluence path prediction system, comprising: A digital elevation model acquisition module is configured to acquire a digital elevation model of a monitoring area, wherein the digital elevation model comprises an elevation value of each terrain; A model gridding module is configured to grid the digital elevation model of the monitoring area; A model elevation value analysis module is configured to identify the maximum and minimum elevation values in the digital elevation model and mark the confluence starting grid and the confluence ending grid accordingly; The confluence path analysis module is configured to select a grid with the minimum elevation value in the adjacent grid of the confluence starting grid as a new center grid, and cyclically select a grid with the minimum elevation value in the adjacent grid of the new center grid as a next new center grid until the selected new center grid is the confluence ending grid. The confluence path prediction module is configured to sequentially connect the confluence starting grid and all the selected center grids in the order of selection of the center grids to obtain a predicted confluence path.

[0014] In a third aspect, the present application provides an electronic device comprising a memory and a processor. The memory is configured to store a computer program. The processor is configured to implement the rainfall confluence path prediction method according to the first aspect when executing the computer program.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program, when executed by a processor, implements the rainfall confluence path prediction method according to the first aspect.

[0016] The present application provides a rainfall confluence path prediction method, system, electronic device and storage medium. Compared with the prior art, the present application has the following beneficial effects: The digital elevation model of the monitoring area is gridded, the maximum and minimum elevation values in the digital elevation model are identified to obtain a confluence starting grid and a confluence ending grid, a grid with the minimum elevation value in the adjacent grid of the confluence starting grid is selected as a new center grid, a grid with the minimum elevation value in the adjacent grid of the new center grid is cyclically selected as a next new center grid until the selected new center grid is the confluence ending grid. Through this step-by-step analysis, the flow direction of the water flow in the next step can be theoretically and accurately located. Then, starting from the confluence starting grid, the confluence starting grid and all the selected center grids are sequentially connected in the order of selection of the center grids to obtain a predicted confluence path. Therefore, the confluence path of the water flow can be predicted in advance at the initial stage of precipitation, so that the rescue measures can be implemented in advance, for example, a drainage device or a roadblock can be arranged in advance on the confluence path to change the water flow collection direction, thereby avoiding the key protection area from being flooded. Since the method is based on the digital elevation model and is not affected by other factors, the prediction result is more accurate and the prediction stability is higher. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Figure 1 A flowchart of a rainfall runoff path prediction method provided by an embodiment of the present application is shown in the figure. Figure 2 A structural diagram of a rainfall runoff path prediction system provided by an embodiment of the present application is shown in the figure. Figure 3 A structural diagram of an electronic device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0019] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the drawings in the specification and specific embodiments.

[0020] As shown in the figure, the rainfall runoff path prediction method provided by an embodiment of the present application comprises: Figure 1 S1: obtaining a digital elevation model of a monitoring area, wherein the digital elevation model comprises an elevation value of each terrain. S2: gridding the digital elevation model of the monitoring area.

[0021] S3: identifying a maximum elevation value and a minimum elevation value in the digital elevation model, and marking the maximum elevation value and the minimum elevation value as a runoff starting point grid and a runoff ending point grid respectively.

[0022] S4: selecting a grid with the minimum elevation value in the adjacent grids of the runoff starting point grid as a new center grid, and selecting a grid with the minimum elevation value in the adjacent grids of the new center grid as a next new center grid, until the selected new center grid is the runoff ending point grid.

[0023] S5: starting from the runoff starting point grid, sequentially connecting the runoff starting point grid and all the selected center grids in the order of selection of the center grids, to obtain a predicted runoff path.

[0024] S5: starting from the runoff starting point grid, sequentially connecting the runoff starting point grid and all the selected center grids in the order of selection of the center grids, to obtain a predicted runoff path.

[0025] In this optional embodiment, the digital elevation model of the monitoring area is gridded, the flow convergence starting grid and the flow convergence ending grid are obtained by identifying the maximum elevation value and the minimum elevation value in the digital elevation model, the grid with the minimum elevation value in the adjacent grid of the flow convergence starting grid is selected as a new center grid, the grid with the minimum elevation value in the adjacent grid of the new center grid is selected as the next new center grid in a cycle, and the selection of the new center grid is stopped until the flow convergence ending grid is selected. Through this step-by-step analysis, the flow direction of the next step of the water flow can be theoretically and accurately located. Then, starting from the flow convergence starting grid, the flow convergence starting grid and all the selected center grids are connected in turn according to the order in which the center grids are selected, and the predicted flow convergence path is obtained. Therefore, the flow convergence path of the water flow can be predicted in advance at the initial stage of precipitation, so that the rescue measures can be implemented in advance, for example, the drainage device or roadblock can be arranged in advance on the flow convergence path to change the water flow convergence direction, so as to avoid the key protection area from being flooded. Since this method is based on the digital elevation model and is not affected by other factors, the prediction result is more accurate and the prediction stability is higher.

[0026] The following describes each step in detail.

[0027] S1: Obtain a digital elevation model of a monitoring area, wherein the digital elevation model comprises an elevation value of each terrain.

[0028] S2: Gridding the digital elevation model of the monitoring area. This step specifically includes the following contents.

[0029] S21: According to the properties of different land covers, the digital elevation model of the monitoring area is divided into different land surface regions, wherein the land surface regions include water cover regions, vegetation cover regions, bare land regions and artificial regions.

[0030] S22: The different land surface regions are gridded using corresponding parameters.

[0031] Specifically, for example, the water cover region can not be gridded, or the water cover region can be roughly gridded without refinement. The vegetation cover region (such as forest, grassland, farmland, park, etc.) and the bare land region (such as wasteland, farmland fallow land, etc.) are gridded and refined, and the artificial region (such as road or square, etc.) is finely gridded, so that different regions are gridded in a targeted manner.

[0032] S23: Analyze the to-be-merged grid area of each incomplete grid at the junction of different land surface regions, and when the to-be-merged grid area is less than half of the grid area of one complete grid in the same land surface region, the incomplete grid is merged with one complete grid in the same land surface region.

[0033] Specifically, since the shape of each region can be irregular, when meshing, the mesh formed inside the region is uniform, but at the position of the region boundary, the boundary shape is irregular, so the generated mesh is also incomplete, for example, there can be some incomplete meshes at the boundary of the artificial region, the area of these meshes can be half of a complete mesh area, or one third of a complete mesh area, or even smaller, for these smaller incomplete meshes, separate analysis as a mesh not only increases the number of meshes, but also has little meaning for separate analysis, because the elevation value of these small meshes is basically not much different from that of the surrounding meshes, so they can be merged with any one of the complete meshes adjacent to them, thereby reducing the number of meshes, improving the analysis speed, and also increasing the contact area of the boundary mesh with other region meshes, thereby reducing the difficulty of selecting the center point when predicting the confluence path, and improving the accuracy of path planning.

[0034] S3: Identify the maximum and minimum elevation values in the digital elevation model, and mark the corresponding grid as the confluence starting point grid and the confluence ending point grid.

[0035] S31: Identify the maximum and minimum elevation values in the digital elevation model, and mark the grid where the maximum elevation value is located as the confluence starting point grid.

[0036] S32: In combination with the SAR image, determine whether the grid where the minimum elevation value is located contains water.

[0037] S33: If the grid where the minimum elevation value is located contains water, identify the water coverage range.

[0038] S34: Extract the boundary of the water coverage range, mark the grid crossed by the boundary as the confluence ending point grid, and eliminate the grid within the water coverage range.

[0039] Specifically, the ground receiving end receiving the echo signal reflected by the ground object can generate a high-resolution image. Different objects or surfaces with different roughness have different backscattering intensities of microwaves. By backscattering intensity and a preset threshold, a threshold segmentation method is used to determine water bodies and non-water bodies. For example, the preset threshold is set to -20 dB. The area with backscattering intensity greater than -20 dB is determined as a non-water body, and the area with backscattering intensity less than or equal to -20 dB is determined as a water body. Accordingly, the water body area in the SAR image can be distinguished. The water body coverage area of the ground surface is identified through the SAR image. These water bodies may be long-existing natural water body areas. For such water body areas, they are naturally in low-lying areas and have water collection capacity. Therefore, for these naturally existing water body areas, the grids inside the water body area can be eliminated, and only the boundary grids of the water body area need to be focused on, so that from the relationship between the grids of the other areas adjacent to the water body coverage area and the grids at the boundary of the water body area, it can be analyzed whether the water flows to the water body area.

[0040] S35: If the grid where the minimum elevation value is located does not have a water body, the grid where the minimum elevation value is located is directly recorded as a confluence end grid.

[0041] Specifically, by combining the SAR image, the ground surface situation at the position of the minimum elevation value is further analyzed, and the natural water body coverage area is identified, so that the grid of the water body coverage area can be simplified, the number of grids is reduced, and the analysis timeliness is improved. The analysis of the water body coverage area is focused on the grids at the boundary, and then the confluence of the water flow coverage area can be monitored through the relationship between the boundary grids and the grids of the adjacent areas, which is convenient for subsequent monitoring of the water body coverage area.

[0042] S4: Confluence path prediction: selecting the grid with the minimum elevation value in the adjacent grid of the confluence starting grid as a new center grid, and repeatedly selecting the grid with the minimum elevation value in the adjacent grid of the new center grid as the next new center grid until the selected new center grid is the confluence end grid.

[0043] Specifically, by continuously selecting a new center grid, the path of water flow confluence can be gradually explored until the confluence end grid in the low-lying area is reached. However, the following points need to be noted during the confluence path prediction process: S411: If there are multiple confluence starting grids, the multiple confluence starting grids are sorted in descending order according to the distance between the confluence starting grid and the confluence end grid to obtain a confluence starting grid sequence.

[0044] S412: According to the order of the confluence starting grids in the confluence starting grid sequence, the confluence path prediction is started from each confluence starting grid.

[0045] Specifically, for example, if there are three grids with maximum elevation values, there are three starting grids of the flow convergence, and it is a potential problem to start from which grid to explore the flow convergence path. According to common sense, path exploration can be simultaneously started from the three starting grids of the flow convergence, but there may be a situation that the flow convergence paths obtained from the three starting grids of the flow convergence are highly overlapped, which causes repeated analysis, some grids are repeatedly selected, and the probability of other grids being selected is reduced, the whole analysis speed is reduced, and the effective analysis content is also reduced. In order to avoid the above problems, the multiple starting grids of the flow convergence can be analyzed in sequence. When there are multiple grids with minimum elevation values in the grids adjacent to the center grid during the flow convergence path prediction, the multiple grids with minimum elevation values are selected according to a preset priority, and a next new center grid is determined, wherein the preset priority order is a grid keeping the flow convergence trend unchanged, a grid not selected in the previous flow convergence path prediction, a grid with the shortest distance to the flow convergence end grid, and a grid in the lateral direction of the center grid. The flow convergence direction is the direction from the previous center grid to the new center grid. Since the center grid is updated constantly, the flow convergence direction may also change after each selection of a new center grid. Keeping the flow convergence trend unchanged means that the newly generated flow convergence direction does not project a flow convergence direction component with opposite direction on the previous flow convergence direction; that is, keeping the flow convergence trend unchanged means that the new flow convergence direction is completely the same as the previous flow convergence direction, the new flow convergence direction projects a flow convergence direction component with the same direction on the previous flow convergence direction, or the new flow convergence direction projects a zero component (i.e., the previous and next flow convergence directions are perpendicular) on the previous flow convergence direction.

[0046] The grid keeping the flow convergence trend unchanged in the preset priority can reduce the prediction error of the flow convergence path. The grid not selected in the previous flow convergence path prediction in the preset priority can avoid repeated selection of the grid and improve the analysis efficiency. The grid with the shortest distance to the flow convergence end grid and the grid in the lateral direction of the center grid in the preset priority can indicate the direction for the exploration of the flow convergence path, reduce the analysis dead point, and continuously promote the exploration of the path in order.

[0047] S413: Starting from the second starting grid of the flow convergence, if the grid with the minimum elevation value in the adjacent grids of the new center grid has been selected in the previous flow convergence path prediction, the elevation difference between the grid with the minimum elevation value and the grid with the second minimum elevation value is analyzed in the flow convergence path prediction process.

[0048] S414: If the elevation difference is less than or equal to a preset difference value, the grid with the second minimum elevation value is selected as the next new center grid.

[0049] S415: If the elevation difference is greater than the preset difference value, the grid with the minimum elevation value is still selected as the next new center grid.

[0050] Specifically, if the grid with the minimum elevation value has been selected in the previous path analysis process, in order to reduce the number of times the grid is repeatedly selected, when the elevation difference between the grid with the minimum elevation value and the grid with the second minimum elevation value is less than or equal to the preset difference, we can choose the grid with the second minimum elevation value as a substitute, because when the elevation difference between the two adjacent grids is small, in the case that there is already water flow through the grid with the minimum elevation value, other water flows will be pushed to the surrounding grids under the influence of the previous water flow, therefore, in the case of small elevation difference, it is reasonable to choose the grid with the second minimum elevation value, which can reduce the number of times the grid is repeatedly selected, improve the efficiency of traversing all grids, and also keep the trend of water flow convergence in line with the actual situation.

[0051] S5: From the convergence starting point grid, connect the convergence starting point grid and all selected center grids in the order of selection to obtain the predicted convergence path.

[0052] Specifically, connecting the selected center grids between each convergence starting point grid and its corresponding convergence ending point grid can obtain a predicted convergence path corresponding to the convergence starting point grid.

[0053] S6: After the convergence path prediction from multiple convergence starting point grids is completed, the grid with the maximum elevation value and the selected grid are excluded.

[0054] S7: From the remaining grids in the digital elevation model, select the grid with the maximum elevation value as a new convergence starting point grid for convergence path prediction in sequence until all grids are selected.

[0055] Specifically, since the convergence path prediction from the initial grid with the maximum elevation value cannot traverse all grids, it is necessary to continue analyzing the remaining grids. The grid with the maximum elevation value and the selected grid are excluded, and the grid with the maximum elevation value is selected as a new convergence starting point grid from the remaining grids. Repeat the same convergence path prediction steps to continuously select new convergence starting point grids until all grids are selected. After analyzing all convergence paths from the gridded model, the concentrated location of water flow convergence can be observed as a whole. If the location is a region that needs to be protected, flood prevention measures such as installing temporary water diversion and water retention facilities can be taken in advance.

[0056] S8: Obtain a new SAR image and identify a new water body coverage range.

[0057] S9: Update the convergence ending point grid according to the new water body coverage range, and eliminate the convergence paths within the new water body coverage range to simplify the predicted convergence paths.

[0058] Specifically, as the precipitation continues, water flow begins to converge, and when it converges to a certain extent, the ground surface begins to accumulate water or the water level of the original water body begins to rise, thus increasing the water body coverage area of the monitoring area or the original water body coverage area. Therefore, the water body coverage area needs to be updated in a timely manner, and the grid boundary crossed by the water body coverage area (converging end grid) is further updated, and the grid within the water body coverage area is further eliminated in a timely manner, thereby reducing the number of grids, eliminating the converging path within the new water body coverage area, simplifying the predicted converging path, making the converging trend easier to identify, increasing the readability of the converging path, and improving the accuracy of the prediction.

[0059] In addition to predicting the converging path, the flow volume can be further predicted, and the specific analysis process is as follows.

[0060] Due to the extremely complex geographical space features, the slope length and average slope of a converging path can be calculated according to the elevation values of the grids crossed by the converging path, and then the converging time of each grid to the converging end grid can be calculated as follows: In the formula, is the converging time, with the unit of s; Ne is the surface type parameter of the converging path, and when there are multiple surface types, a weighted average method is used for calculation; L is the slope length of the converging path, with the unit of m; S is the average slope of the converging path. The slope length between two adjacent grids on the converging path can be calculated using a trigonometric function according to the elevation values, and then L is obtained by accumulation; similarly, the slope between two adjacent grids can be calculated according to the elevation values, and then the average slope of the entire converging path is obtained.

[0061] According to the above formula, the converging time of each grid can be calculated, and thus the area sum of the grids flowing to the water body coverage area or a certain converging end grid in each period, i.e., the isochronous area, can be calculated according to the converging time: In the formula, is the isochronous area, with the unit of ; is the period length, with the unit of s; is the area sum of the grids flowing to the converging end grid in the i th period, with the unit of .

[0062] The average runoff depth of the grids flowing to the water body coverage area or a certain converging end grid in each period corresponding to the isochronous area, i.e., the isochronous runoff depth, in the period is: wherein, is the runoff depth at the same time, and the unit is mm; is the grid average runoff depth of the i th time period corresponding to the same time area and flowing to the confluence end grid, and the unit is mm.

[0063] The yield of each grid to the confluence end grid in the time period is: wherein, is the yield of each grid to the confluence end grid, and the unit is .

[0064] The yield of each grid to the confluence end grid in each time period is: Through the above calculation, the water flow reaching the confluence end grid or the water body coverage area can be predicted.

[0065] As shown in Figure 2 , the rainfall confluence path prediction system provided by the embodiment of the present application comprises: A digital elevation model acquisition module 100 is configured to acquire a digital elevation model of a monitoring area, wherein the digital elevation model comprises an elevation value of each terrain.

[0066] A model gridding module 200 is configured to grid the digital elevation model of the monitoring area.

[0067] A model elevation value analysis module 300 is configured to identify a maximum elevation value and a minimum elevation value in the digital elevation model, and mark the maximum elevation value and the minimum elevation value as a confluence start grid and a confluence end grid, respectively.

[0068] A confluence path analysis module 400 is configured to select a grid with the minimum elevation value in the adjacent grids of the confluence start grid as a new center grid, and cyclically select a grid with the minimum elevation value in the adjacent grids of the new center grid as a next new center grid, until the selected new center grid is the confluence end grid.

[0069] A confluence path prediction module 500 is configured to sequentially connect the confluence start grid and all the selected center grids in the order of selection of all the center grids, to obtain a predicted confluence path.

[0070] In the embodiment, the rainfall confluence path prediction system has similar beneficial effects to the rainfall confluence path prediction method, and thus the description is omitted here.

[0071] As shown in Figure 3As shown, the electronic device provided by the embodiment of the present application comprises a memory 610 and a processor 620; the memory 610 is configured to store a computer program; the processor 620 is configured to, when executing the computer program, implement the rainfall catchment path prediction method as described above.

[0072] The computer readable storage medium provided by the embodiment of the present application has the computer program stored thereon, and when the computer program is executed by the processor, the rainfall catchment path prediction method as described above is implemented.

[0073] In the embodiment, the electronic device and the computer readable storage medium have similar beneficial effects to the rainfall catchment path prediction method, which will not be described herein again.

[0074] Now, an electronic device that can be a server or a client of the present application will be described, which is an example of a hardware device that can be applied to various aspects of the present application. The electronic device is intended to represent a variety of forms of digital electronic computing devices, such as a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent a variety of forms of mobile devices, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections, and relationships, and their functions, are merely examples and are not intended to limit implementations of the present application described and / or claimed herein.

[0075] The electronic device comprises a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The computing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0076] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer readable storage medium. When the program is executed, the processes of the above-mentioned embodiment methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the modules described separately can or can not be physically separated. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application. In addition, the functional modules in each embodiment of the present application can be integrated in one processing unit, or each module can be physically present separately, or two or more modules can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0077] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0078] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones. The modification or replacement does not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting rainfall runoff paths, characterized in that: include: Obtaining a digital elevation model of the monitored area, wherein the digital elevation model includes elevation values ​​of each terrain location; Gridding the digital elevation model of the monitoring area; Identify the maximum elevation value and the minimum elevation value in the digital elevation model, and mark them as the confluence starting point grid and the confluence ending point grid respectively; The grid with the smallest elevation value among the adjacent grids of the confluence starting point grid is selected as the new center grid, and the grid with the smallest elevation value among the adjacent grids of the new center grid is cyclically selected as the next new center grid, until the new center grid selected is the confluence end grid; Starting from the confluence starting point grid, the confluence starting point grid and all selected center grids are connected in sequence according to the order in which all center grids are selected to obtain the predicted confluence path.

2. The rainfall runoff path prediction method according to claim 1, wherein: The gridding of the digital elevation model of the monitoring area includes: According to the properties of different surface covers, the digital elevation model of the monitored area is divided into different surface areas, wherein the surface areas include water-covered areas, vegetation-covered areas, bare land areas and artificial areas; Grid different surface areas using corresponding parameters; The area of ​​the incomplete grid to be merged at the junction of different surface areas is analyzed. When the area of ​​the grid to be merged is less than half of the area of ​​a complete grid in the same surface area, the incomplete grid is merged with a complete grid in the adjacent same surface area.

3. The rainfall runoff path prediction method according to claim 1, wherein: The identifying the maximum elevation value and the minimum elevation value in the digital elevation model and marking them as the confluence starting point grid and the confluence ending point grid respectively includes: Identifying the maximum elevation value and the minimum elevation value in the digital elevation model, and recording the grid where the maximum elevation value is located as the confluence starting point grid; Combined with SAR images, determine whether there is water in the grid with the minimum elevation value; If there is water in the grid where the minimum elevation value is located, identify the coverage of the water body; Extract the boundary of the water body coverage area, record the grids that pass through the boundary as the confluence end grids, and eliminate the grids within the water body coverage area; If there is no water body in the grid where the minimum elevation value is located, the grid where the minimum elevation value is located will be directly recorded as the confluence end grid.

4. The rainfall runoff path prediction method according to claim 1, wherein: The step of selecting the grid with the smallest elevation value among the adjacent grids of the confluence starting point grid as the new center grid, and cyclically selecting the grid with the smallest elevation value among the adjacent grids of the new center grid as the next new center grid until the selected new center grid becomes the confluence end point grid includes: If there are multiple confluence starting point grids, sort the multiple confluence starting point grids according to the distance between the confluence starting point grid and the confluence ending point grid from large to small to obtain a confluence starting point grid sequence; According to the order of the confluence starting point grids in the confluence starting point grid sequence, confluence path prediction is performed starting from each confluence starting point grid; Starting from the second confluence starting point grid, during the confluence path prediction process, if the grid with the smallest elevation value among the adjacent grids of the new central grid has been selected in the previous confluence path prediction, the elevation difference between the grid with the smallest elevation value and the grid with the second lowest elevation value is analyzed; If the elevation difference is less than or equal to the preset difference, the grid with the second lowest elevation value is selected as the next new center grid; If the elevation difference is greater than the preset difference, the grid with the smallest elevation value will be selected as the next new center grid.

5. The rainfall runoff path prediction method according to claim 4, wherein: Also includes: When there are multiple grids with the smallest elevation values ​​among the grids adjacent to the central grid, the grids with the smallest elevation values ​​are selected according to the preset priority to determine the next new central grid. The preset priority order is the grid that maintains the convergence trend unchanged, the grid that was not selected in the previous convergence path prediction, the grid with the shortest distance to the convergence end grid, and the grid horizontally to the central grid.

6. The rainfall runoff path prediction method according to claim 4, wherein: After the method starts from the confluence starting point grid and sequentially connects the confluence starting point grid and all selected center grids in the order in which all center grids are selected to obtain a predicted confluence path, the method further includes: After the confluence path prediction is completed from multiple confluence starting point grids, the grid with the maximum elevation value and the selected grid are excluded; The grid with the maximum elevation value is selected from the remaining grids in the digital elevation model as the new confluence starting point grid for confluence path prediction until all grids are selected.

7. The rainfall runoff path prediction method according to claim 1, wherein: After the method starts from the confluence starting point grid and sequentially connects the confluence starting point grid and all selected center grids in the order in which all center grids are selected to obtain a predicted confluence path, the method further includes: Acquire new SAR images and identify new water coverage areas; According to the new water body coverage, the confluence destination grid is updated, and the confluence path within the new water body coverage is eliminated to simplify the predicted confluence path.

8. A rainfall runoff path prediction system, characterized in that: include: A digital elevation model acquisition module is used to obtain a digital elevation model of the monitored area, wherein the digital elevation model includes the elevation value of each terrain; Model gridding module, used to grid the digital elevation model of the monitoring area; A model elevation value analysis module is used to identify the maximum elevation value and the minimum elevation value in the digital elevation model, and mark them as a confluence starting point grid and a confluence end point grid respectively; The confluence path analysis module is used to select the grid with the smallest elevation value among the adjacent grids of the confluence starting point grid as the new center grid, and cyclically select the grid with the smallest elevation value among the adjacent grids of the new center grid as the next new center grid, until the new center grid is selected as the confluence end point grid; The confluence path prediction module is used to start from the confluence starting point grid and connect the confluence starting point grid and all selected center grids in sequence according to the order in which all center grids are selected to obtain the predicted confluence path.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the rainfall runoff path prediction method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the rainfall runoff path prediction method according to any one of claims 1 to 7 is implemented.

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