Reservoir upstream and downstream flight preview method, system, medium and equipment for multi-scale remote sensing data visualization scene

Abstract

The application relates to the field of three-dimensional simulation and reservoir monitoring, and discloses a reservoir upstream and downstream flight preview method and system for a multi-scale remote sensing data visualization scene, a medium and equipment, which comprises the following steps: constructing a topological relationship model of the reservoir upstream and downstream based on vector data associated with an input reservoir basin to extract a connected water system route; obtaining reservoir basin terrain data and automatically generating flight control parameters according to a preset parameter linkage rule; planning an optimal flight route through a path planning method based on the extracted connected water system route and the flight control parameters, and preferentially covering key monitoring nodes in the planning process; associating in-line stations, reservoirs, rivers and social and economic data through a space coordinate matching method by using the flight control parameters in the optimal flight route to realize water system connection and spatial association between the reservoirs and the stations; and counting flight key information after spatial association and outputting an automatic report containing charts and data tables.

Description

Methods, systems, media, and equipment for aerial preview of upstream and downstream reservoirs for multi-scale remote sensing data visualization scenarios. Technical Field

[0001] This invention relates to the field of three-dimensional simulation and reservoir monitoring technology, and in particular to a method, system, medium and equipment for upstream and downstream flight preview of reservoirs for multi-scale remote sensing data visualization scenarios. Background Technology

[0002] Existing technologies for aerial surveying of reservoir basins have the following four core shortcomings, which prevent them from meeting the requirements for precise flight:

[0003] 1. The flight path cannot be automatically generated based on the upstream and downstream topological relationships of the reservoir, such as the connection of the main stream and tributary water systems and the spatial relationship between the reservoir and the monitoring station. The path is prone to deviating from key monitoring areas such as the inlet river mouth and the downstream water intake, resulting in low practicality.

[0004] 2. Flight control parameters such as altitude, angle, and buffer distance are mostly set manually and independently, without being linked to the reservoir terrain. This can easily lead to problems such as "colliding with the terrain due to excessively low altitude" and "missing information due to insufficient buffer distance".

[0005] 3. In scenarios with many bends in the river, the flight path needs to be adjusted frequently with the river's turns, and the continuous rotation of the viewpoint can easily cause dizziness in users, making it impossible to guarantee a stable visual browsing experience.

[0006] 4. The flight path is disconnected from the data of stations, rivers, reservoirs and socio-economic data along the route, requiring manual matching of data statistics and generation of reports, which is inefficient and prone to errors. Summary of the Invention

[0007] To address the aforementioned issues, the present invention aims to provide a method, system, medium, and device for flight previewing upstream and downstream reservoirs in multi-scale remote sensing data visualization scenarios. This method effectively ensures the accuracy of flight paths and guarantees a stable visualization browsing experience.

[0008] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a method for flight preview of upstream and downstream reservoirs for multi-scale remote sensing data visualization scenarios, comprising: constructing a topological relationship model of upstream and downstream reservoirs based on vector data associated with the input reservoir basin to extract connected water system routes; wherein, the vector data includes water system, reservoir boundary, and station coordinates; acquiring reservoir basin topographic data, automatically generating flight control parameters according to preset parameter linkage rules, and supporting manual adjustment of parameters; the flight control parameters include flight altitude, viewing angle, and buffer distance; based on the extracted connected water system routes and flight control parameters, planning the optimal flight route through a path planning method, prioritizing coverage of key monitoring nodes and avoiding obstacles during the planning process; associating the flight control parameters in the optimal flight route with station, reservoir, river, and socio-economic data along the route through a spatial coordinate matching method to achieve water system connection and spatial association between reservoir and station; statistically analyzing key flight information after spatial association to output an automated report containing charts and data tables, thus completing the flight preview.

[0009] Furthermore, a topological relationship model of the upstream and downstream of the reservoir is constructed, including the following steps:

[0010] The vector data is preprocessed to unify the vector data coordinates and remove redundant and erroneous information;

[0011] Three types of rules are pre-set: water system connectivity, reservoir-station association, and upstream and downstream hierarchy. Based on these rules, spatial connectivity is verified, water system segments are connected into a continuous network, the spatial relationship between reservoirs and stations is bound, and the "reservoir-upstream tributary-downstream main stream" connectivity link is clarified. Then, the water system hierarchy is divided to structure the data storage element association relationship.

[0012] By verifying and optimizing the model through connectivity and key node coverage, a topological relationship model that accurately reflects the spatial relationship between reservoirs, water systems, and monitoring stations is finally formed.

[0013] Furthermore, the parameter linkage rules include flight linkage and viewpoint linkage;

[0014] The flight linkage is as follows: the flight altitude is based on the topographic elevation data of the reservoir basin, and the flight altitude is adjusted in real time according to the ruggedness of the terrain. Combined with real-time water level data, when the water level exceeds the warning level by 10%, the flight altitude is increased by 50m; when the water level is lower than the dry season water level, the altitude is reduced by 15% of the baseline value to ensure clear presentation of key areas. A preset obstacle height threshold library is used. For fixed obstacles, the flight altitude is 50m higher than the highest point of the obstacle; for moving obstacles, the altitude is increased by 20-50m through real-time remote sensing image recognition.

[0015] The perspective linkage is as follows: the flight angle is determined based on the terrain slope analysis, so that the flight angle forms a 45° angle with the ground: when the terrain slope is >30°, the perspective angle is adjusted to 30°; when the terrain slope is <10°, the perspective angle can be adjusted to 60°; when flying to a key monitoring node, the perspective angle is switched to a vertical perspective.

[0016] Furthermore, it also includes a step of precisely smoothing the curvature of the optimal flight path: based on the river channel's bend radius and slope variation terrain features, data sampling, interpolation analysis, and visualization parameter methods are performed sequentially to precisely smooth the curvature of the flight path; the specific implementation process is as follows:

[0017] The measured data of the bending radius and slope of key points of the river channel topography were obtained by data sampling. The bending radius and slope changes of the river channel were used as the core indicators. High-density sampling points were set up in the topographically sensitive areas for sampling, and regular sampling points were set up at even intervals in the straight river channel sections for sampling. After sampling, invalid data with abnormal coordinates and numerical jumps were removed and verified by repeated sampling. Finally, the dataset of key points of the river channel was formed.

[0018] Based on the sampling points, the bending radius and slope data of the unsampled area are calculated by interpolation algorithm to fill the gaps in the terrain data and form a continuous terrain feature dataset covering the entire river basin. Using the interpolated continuous terrain data as a constraint, combined with the preset curvature change threshold, the initial flight route is smoothed and optimized: the broken line trajectory at the bend of the river is transformed into a continuous curve that conforms to the curvature law of the terrain, and the curvature change of any segment of the route is forced to not exceed the preset threshold.

[0019] The visualization frame rate is dynamically adjusted based on the smoothness of the route. The frame rate for smooth road sections is maintained at 30fps, while the frame rate for road sections with curvature higher than a set threshold is increased to 60fps.

[0020] Furthermore, the flight control parameters in the optimal flight route are correlated with data from stations, reservoirs, rivers, and socio-economic data along the route using a spatial coordinate matching method. Specifically, this includes:

[0021] Extract flight control parameters from the optimal flight path and standardize all data coordinates;

[0022] By using buffer analysis, the standardized flight path is spatially correlated with the precise parameters of stations, reservoirs, rivers, and socio-economic data along the route. A spatial topology consistency verification method is used to eliminate data with duplicate or abnormal coordinates. For data with missing coordinates, the nearest feature coordinate interpolation method is used to complete the data to ensure data integrity.

[0023] The optimal flight path after parameter association is discretized, and after verification and integration, a continuous integrated set of associated data coordinate points is generated.

[0024] Furthermore, the interconnected waterways cover key nodes of upstream tributaries flowing into the reservoir and downstream ecological flow control sections.

[0025] Furthermore, key flight information includes total mileage, number of covered stations, and flight duration in key areas; the automated report includes path points, statistical tables of data along the route, and annotations of abnormal areas.

[0026] Secondly, the technical solution adopted by this invention is as follows: a reservoir upstream and downstream flight preview system for multi-scale remote sensing data visualization scenarios, comprising: a topology relationship model construction module, which constructs a topology relationship model of the upstream and downstream of the reservoir based on vector data associated with the input reservoir basin to extract connected water system routes; wherein, the vector data includes water system, reservoir boundary and station coordinates; a flight control parameter generation module, which acquires reservoir basin topographic data, automatically generates flight control parameters according to preset parameter linkage rules, and supports manual adjustment of parameters; the flight control parameters include flight altitude, viewing angle and buffer distance; a flight route acquisition module, which plans the optimal flight route based on the extracted connected water system routes and flight control parameters, and prioritizes coverage of key monitoring nodes and avoids obstacles during the planning process; a spatial association module, which associates the flight control parameters in the optimal flight route with station, reservoir, river and socio-economic data along the route through spatial coordinate matching method to realize water system connection and spatial association between reservoir and station; and an output module, which statistically analyzes key flight information after spatial association to output an automated report containing charts and data tables to complete the flight preview.

[0027] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0028] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0029] The present invention has the following advantages due to the adoption of the above technical solutions:

[0030] 1. This invention can ensure the accuracy of flight routes and increase the coverage of key monitoring nodes from 60% in the existing technology to 100%, avoiding omission of core areas upstream and downstream of the reservoir.

[0031] 2. This invention effectively improves parameter configuration efficiency, reducing flight control parameter setting time from 30 minutes to 5 minutes, eliminating the need for repeated manual adjustments.

[0032] 3. This invention effectively improves data association efficiency, reducing the time for querying and matching multi-source data along the route from 30 minutes to 5 minutes.

[0033] 4. This invention effectively improves report generation efficiency, replacing manual writing with automated statistical report generation, reducing the time from 2 hours to 15 minutes, and lowering the data error rate to 0. Attached Figure Description

[0034] Figure 1 is a flowchart of the method for flight preview of upstream and downstream reservoirs in a multi-scale remote sensing data visualization scenario according to an embodiment of the present invention.

[0035] Figure 2 is a schematic diagram of the structure of the upstream and downstream flight preview system for multi-scale remote sensing data visualization in an embodiment of the present invention.

[0036] Figure 3 is a flowchart of the method for flight preview of upstream and downstream reservoirs in a multi-scale remote sensing data visualization scenario according to an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present 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 the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0038] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0039] In one embodiment of the present invention, a method for flight preview of upstream and downstream reservoirs is provided for multi-scale remote sensing data visualization scenarios. This method addresses multi-scale remote sensing data visualization scenarios involving space, time, and data accuracy. Based on the upstream and downstream topological relationships of the reservoir, it intelligently generates flight routes and acquires multi-source data along the route to complete visualization browsing and automatic report generation. This method is suitable for practical applications such as reservoir supervision, river patrol, and engineering monitoring. In this embodiment, as shown in Figure 1, the method includes the following steps:

[0040] 1) Based on the vector data associated with the input reservoir basin, a topological relationship model of the upstream and downstream of the reservoir is constructed to extract the connected water system lines to ensure that the flight route does not miss the core monitoring area of ​​the reservoir basin; among which, the vector data includes water system, reservoir boundary and station coordinates, etc.

[0041] 2) Acquire topographic data of the reservoir basin, generate flight control parameters such as flight altitude, viewing angle and buffer distance according to preset parameter linkage rules, and support manual adjustment of parameters;

[0042] 3) Based on the extracted connected waterways and flight control parameters, the optimal flight route is planned using path planning methods. During the planning process, key monitoring nodes are prioritized for coverage, while obstacles such as mountains, buildings, and dams are avoided.

[0043] 4) The flight control parameters in the optimal flight route are associated with the station, reservoir, river and socio-economic data on the water system route through the spatial coordinate matching method, so as to realize the spatial association between the water system and the reservoir and the station, and display it in the three-dimensional scene.

[0044] 5) Analyze the key flight information after spatial correlation to output an automated report containing charts and data tables, thus completing the flight preview. In this embodiment, the key flight information includes total mileage, number of covered stations, and flight time in key areas; the automated report includes path points, statistical tables of data along the route, and annotations of abnormal areas.

[0045] In step 1) above, the connected water system routes cover key nodes such as upstream tributaries flowing into the reservoir and downstream ecological flow control sections.

[0046] In this embodiment, based on the vector data associated with the reservoir basin, a topological relationship analysis method is used to construct an upstream-downstream topological relationship model by judging the spatial connectivity of vector elements. Specifically, the construction of the upstream-downstream topological relationship model of the reservoir based on vector data such as water system, reservoir boundary, and station coordinates includes the following steps:

[0047] 1.1) Preprocess vector data such as water system, reservoir boundary, and station coordinates to unify vector data coordinates and remove redundant and erroneous information;

[0048] 1.2) Pre-set three types of rules: water system connectivity, reservoir-station association, and upstream and downstream hierarchy. Based on these rules, verify spatial connectivity, connect water system segments into a continuous network, bind the spatial relationship between reservoirs and stations, and clarify the "reservoir-upstream tributary-downstream main stream" connectivity link. Then, divide the water system hierarchy to structure the data storage element association relationship.

[0049] 1.3) By verifying and optimizing the model through connectivity and key node coverage, a topological relationship model that accurately reflects the spatial connections between reservoirs, water systems, and monitoring stations is finally formed. Key nodes include monitoring stations, flood discharge outlets, and potential hazards in dikes.

[0050] In step 2) above, to achieve intelligent adaptation of flight control parameters for the reservoir basin, parameter linkage rules deeply bound to the basin's topography and hydrological characteristics are established to realize automated and precise configuration of flight control parameters. These parameter linkage rules include flight linkage and perspective linkage, as detailed below:

[0051] 2.1) Flight linkage: The flight altitude is based on the topographic elevation data of the reservoir basin and is adjusted in real time according to the ruggedness of the terrain to conform to the actual hydrological conditions and avoid obstacles such as dams and mountains;

[0052] Specifically, based on real-time water level data, the flight altitude automatically increases by 50m when the water level exceeds the warning level by 10%; when the water level is below the dry season level, the altitude decreases by 15% of the baseline value to ensure clear visibility of key areas such as shoals and estuaries; a preset obstacle height threshold database is used, and for fixed obstacles such as dams, mountains, and bridges, the flight altitude is 50m higher than the highest point of the obstacle; for mobile obstacles such as temporary construction facilities, the altitude is automatically increased by 20-50m through real-time remote sensing image recognition to avoid collision risks.

[0053] 2.2) Viewpoint linkage: The flight angle is determined based on terrain slope analysis, so that the flight angle forms a 45° angle with the ground, avoiding excessively steep dive due to large slope.

[0054] Based on terrain analysis, a threshold for terrain slope is set, and the tilt angle is adjusted. Specifically, the default flight angle forms a 45° angle with the ground to ensure a panoramic visualization of the watershed's topography and river system distribution. When the terrain slope is greater than 30°, the viewing angle is adjusted to 30° to avoid local information obstruction caused by excessively steep dives. When the terrain slope is less than 10°, the viewing angle can be adjusted to 60° to expand the horizontal field of view. When flying over key monitoring nodes such as stations, flood discharge outlets, and levee hazard points, the viewing angle is switched to a vertical view to avoid excessively steep dives due to large slopes.

[0055] The entire linkage process described above can quickly match the flight requirements along the river without requiring repeated manual adjustments to the parameters.

[0056] Step 3) above also includes a step of precisely smoothing the curvature of the optimal flight path: For flight requirements in the meandering river channels of the reservoir basin, based on the river's bend radius and slope variation terrain features, three steps are performed sequentially: data sampling, interpolation analysis, and parameter visualization. Specifically, this includes the following steps:

[0057] 3.1) Measured data on the curvature radius and slope of key river channel topographic points were obtained through data sampling. Using the curvature radius and slope variation as core indicators, high-density sampling points were set up in topographically sensitive areas, while conventional sampling points were set up at even intervals in straight river sections. After sampling, invalid data with abnormal coordinates and numerical jumps were removed, and the data were verified by repeated sampling. Finally, a dataset of key river channel points was formed, providing an accurate benchmark for subsequent interpolation analysis and parameter linkage. Topographically sensitive areas include river bends, boundaries between convex and concave banks, and sections with abrupt slope changes.

[0058] 3.2) Based on the sampling points, the bending radius and slope data of the unsampled areas are calculated by interpolation algorithm to fill the gaps in the terrain data and form a continuous terrain feature dataset covering the entire river basin. Using the interpolated continuous terrain data as a constraint, combined with the preset curvature change threshold, the initial flight route is smoothed and optimized—the broken line trajectory at the bend of the river is transformed into a continuous curve that conforms to the curvature law of the terrain. The curvature change of any segment of the route is forced to not exceed the preset threshold, eliminating the visual abruptness caused by sudden curvature changes from the source, while ensuring that the route conforms to the natural shape of the river and does not deviate from the core terrain features.

[0059] 3.3) The visualization frame rate is dynamically adjusted according to the smoothness of the route. The frame rate of smooth road sections is maintained at 30fps; the frame rate of road sections with curvature higher than the set threshold is increased to 60fps, reducing screen stuttering and jitter and optimizing the browsing experience.

[0060] In this embodiment, interpolation analysis is used to complete the global terrain feature data to form a continuous dataset. Then, the terrain feature data is bound to the flight path curvature parameters through parameter linkage. Based on the continuous dataset, the curvature of the flight path is dynamically adapted and adjusted to achieve smooth adjustment of the flight path curvature, thereby reducing the problem of dizziness caused by continuous rotation of the viewpoint due to sudden changes in curvature.

[0061] In step 4) above, the flight control parameters in the optimal flight route are correlated with data from stations, reservoirs, rivers, and socio-economic data along the route using a spatial coordinate matching method. This specifically includes the following steps:

[0062] 4.1) Extract flight control parameters from the optimal flight path and standardize all data coordinates;

[0063] Specifically, it supports multiple coordinate inputs, including Global Positioning System (WGS-84), Geodetic Coordinate System (CGCS2000), and local independent coordinate systems; through a seven-parameter transformation model, all data are uniformly converted to the CGCS2000 geodetic coordinate system, with the transformation error controlled within ±0.5m.

[0064] 4.2) Through buffer analysis, the standardized flight path is spatially correlated with the precise parameters of stations, reservoirs, rivers, and socio-economic data along the route. A spatial topology consistency verification method is used to eliminate data with duplicate or abnormal coordinates. For data with missing coordinates, the coordinate interpolation method of neighboring elements is used to complete the data to ensure data integrity. Among them, buffer analysis refers to a spatial analysis method that uses the standardized flight path as the core and generates a continuous buffer zone according to a preset distance threshold. It can accurately delineate the influence range around the route and provide spatial constraint boundaries for the correlation of multi-source data.

[0065] 4.3) The optimal flight path after parameter association is discretized, and after verification and integration, a continuous integrated set of associated data coordinate points is generated to provide basic point support for accurate matching. Specifically, the optimal flight path is discretized at 10m intervals.

[0066] In this embodiment, by extracting flight control parameters and discrete coordinate point sets of the entire route from the optimal flight route, the coordinates of the multi-source data are standardized and unified. After buffer analysis, spatial correlation with the data of stations, reservoirs, rivers and socio-economic data along the route is achieved. After verification and integration, an integrated correlation dataset is formed.

[0067] In summary, as shown in Figures 2 and 3, the specific process of using this invention is as follows:

[0068] (1) Flight control parameters and information acquisition: Automatically retrieve historical flight route data from the flight route database, match key fields to see if there are reusable historical routes, if there are, match the results, automatically retrieve the basic parameters of the historical route, and display a route preview for operators to confirm or modify; if there are no historical routes, construct the flight route; key fields include route name, target area, execution time, etc.; basic parameters include origin and destination coordinates, flight altitude, path nodes, etc.

[0069] (1.1) Select the target reservoir from the "Reservoir Basic Information Database" by interactively selecting points on the map. The system triggers the "Water System Spatial Topology Analysis Engine" and calls the data in the "Reservoir Water System Spatial Topology Relationship Database", which includes the connectivity relationship between the reservoir and upstream and downstream rivers, tributaries, water conservancy projects, elevation difference, catchment area, etc. The system automatically generates and visualizes the upstream and downstream topology structure map of the target reservoir, including key node annotations, such as the location of control gates and hydrological stations.

[0070] (1.2) Set the start and end points of the flight route based on the topology map, support binding water conservancy element entities, such as the starting point of the reservoir dam and the ending point of the downstream reservoir, and configure core parameters. Set the flight altitude, cruising speed, and monitoring duration of key elements according to the accuracy requirements of the elements;

[0071] (1.3) Based on the set parameters, call the "Water Conservancy Element Spatial Library", which includes vector coordinates, spatial range, and type attributes, and automatically extract the topological relationships of water conservancy elements involved along the route, such as the spatial distance between bridges and rivers, the intersection of dikes and tributaries, and spatial information, to form a structured route basic dataset.

[0072] (2) Automatically generate flight routes and broadcast information: intelligent route planning is performed on the constructed flight routes, and the optimal flight routes are generated in combination with the set requirements, and information integration is carried out in conjunction with multiple source databases;

[0073] Specifically, the following requirements must be met simultaneously:

[0074] (2.1) The path should prioritize coverage of key monitoring elements such as reservoir water level stations, levee hazard points, and flood discharge outlets;

[0075] (2.2) Smooth curves are used to connect nodes of winding river routes to reduce the dizziness caused by repeated circling during flight.

[0076] (2.3) Simultaneously set the surrounding analysis range based on the total route length and element distribution density. The default range is centered on the route with a radius of 5000 meters, and manual adjustment is supported.

[0077] The specific steps for information integration through multi-source database collaboration are as follows:

[0078] a) Call the "Water Conservancy Element Attribute Database" to extract the static attributes and socio-economic data of elements such as sluice gates, pumping stations, rivers, reservoirs, and dikes along the route;

[0079] b) Connect to the "Real-time Monitoring Information Database" to obtain dynamic data, such as current water level, flow rate, and equipment operating status;

[0080] c) Clean and statistically analyze the data to generate structured broadcast information: organize the content according to the logic of "flight node → element type → status description → early warning prompt".

[0081] (3) Automatically generate flight reports: Generate flight reports based on the optimal flight route. The flight report includes the following content:

[0082] (3.1) Route details: coordinates of origin and destination, total length, flight time, coordinates of key nodes and stop plan;

[0083] (3.2) List of elements: Types, quantities, and spatial distribution of water conservancy elements involved along the route;

[0084] (3.3) Summary of monitoring data: Average value, extreme value and outlier record of dynamic monitoring indicators;

[0085] (3.4) Risk warning: Potential risks based on element status and topological relationship identification.

[0086] In one embodiment of the present invention, a flight preview system for upstream and downstream reservoirs for multi-scale remote sensing data visualization scenarios is provided, comprising:

[0087] The topology model construction module constructs a topology model of the upstream and downstream of the reservoir based on vector data associated with the input reservoir basin, in order to extract connected water system routes; the vector data includes water system, reservoir boundary and station coordinates;

[0088] The flight control parameter generation module acquires topographic data of the reservoir basin, automatically generates flight control parameters according to preset parameter linkage rules, and supports manual adjustment of parameters; the flight control parameters include flight altitude, viewing angle, and buffer distance;

[0089] The flight route acquisition module, based on the extracted connected waterways and flight control parameters, plans the optimal flight route using path planning methods. During the planning process, key monitoring nodes are prioritized and obstacles are avoided.

[0090] The spatial association module uses a spatial coordinate matching method to associate the flight control parameters in the optimal flight route with data from stations, reservoirs, rivers, and socio-economic data along the route, so as to achieve spatial association between water systems and reservoirs and stations.

[0091] The output module collects key flight information after spatial correlation to generate an automated report containing charts and data tables, thus completing the flight preview.

[0092] In the above embodiments, constructing a topological relationship model of the upstream and downstream of the reservoir includes the following steps:

[0093] The vector data is preprocessed to unify the vector data coordinates and remove redundant and erroneous information;

[0094] Three types of rules are pre-set: water system connectivity, reservoir-station association, and upstream and downstream hierarchy. Based on these rules, spatial connectivity is verified, water system segments are connected into a continuous network, the spatial relationship between reservoirs and stations is bound, and the "reservoir-upstream tributary-downstream main stream" connectivity link is clarified. Then, the water system hierarchy is divided to structure the data storage element association relationship.

[0095] By verifying and optimizing the model through connectivity and key node coverage, a topological relationship model that accurately reflects the spatial relationship between reservoirs, water systems, and monitoring stations is finally formed.

[0096] In the above embodiments, the parameter linkage rules include flight linkage and perspective linkage. Flight linkage is as follows: the flight altitude is based on the topographic elevation data of the reservoir basin, and the flight altitude is adjusted in real time according to the ruggedness of the terrain. Combined with real-time water level data, when the water level exceeds the warning water level line by 10%, the flight altitude is increased by 50m; when the water level is lower than the dry season water level line, the altitude is reduced by 15% of the baseline value to ensure clear presentation of key areas. A preset obstacle height threshold library is used. For fixed obstacles, the flight altitude is 50m higher than the highest point of the obstacle. For moving obstacles, the altitude is increased by 20-50m through real-time remote sensing image recognition. Perspective linkage is as follows: the flight angle is determined based on the terrain slope analysis so that the flight angle forms a 45° angle with the ground. When the terrain slope is >30°, the perspective angle is adjusted to 30°. When the terrain slope is <10°, the perspective angle can be adjusted to 60°. When flying to a key monitoring node, the perspective angle is switched to a vertical perspective.

[0097] The above embodiments also include a step of precisely and smoothly adjusting the curvature of the optimal flight path: based on the river channel bend radius and slope variation terrain features, data sampling, interpolation analysis, and parameter linkage methods are performed sequentially to precisely and smoothly adjust the curvature of the flight path; the specific implementation process is as follows:

[0098] The measured data of the bending radius and slope of key points of the river channel topography were obtained by data sampling. The bending radius and slope changes of the river channel were used as the core indicators. High-density sampling points were set up in the topographically sensitive areas for sampling, and regular sampling points were set up at even intervals in the straight river channel sections for sampling. After sampling, invalid data with abnormal coordinates and numerical jumps were removed and verified by repeated sampling. Finally, the dataset of key points of the river channel was formed.

[0099] Based on the sampling points, the bending radius and slope data of the unsampled area are calculated by interpolation algorithm to fill the gaps in the terrain data and form a continuous terrain feature dataset covering the entire river basin. Using the interpolated continuous terrain data as a constraint, combined with the preset curvature change threshold, the initial flight route is smoothed and optimized: the broken line trajectory at the bend of the river is transformed into a continuous curve that conforms to the curvature law of the terrain, and the curvature change of any segment of the route is forced to not exceed the preset threshold.

[0100] The visualization frame rate is dynamically adjusted based on the smoothness of the route. The frame rate for smooth road sections is maintained at 30fps, while the frame rate for road sections with curvature higher than a set threshold is increased to 60fps.

[0101] In the above embodiments, the flight control parameters in the optimal flight route are correlated with data from stations, reservoirs, rivers, and socio-economic data along the route through a spatial coordinate matching method, specifically including:

[0102] Extract flight control parameters from the optimal flight path and standardize all data coordinates;

[0103] By using buffer analysis, the standardized flight path is spatially correlated with the precise parameters of stations, reservoirs, rivers, and socio-economic data along the route. A spatial topology consistency verification method is used to eliminate data with duplicate or abnormal coordinates. For data with missing coordinates, the nearest feature coordinate interpolation method is used to complete the data to ensure data integrity.

[0104] The optimal flight path after parameter association is discretized, and after verification and integration, a continuous integrated set of associated data coordinate points is generated.

[0105] In the above embodiments, the connected water system routes cover key nodes of upstream tributaries flowing into the reservoir and downstream ecological flow control sections.

[0106] In the above embodiments, key flight information includes total mileage, number of covered stations, and flight time in key areas; the automated report includes path points, statistical tables of data along the route, and annotations of abnormal areas.

[0107] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0108] In this embodiment, the workflow during use is as follows:

[0109] (1) Import basic data such as the reservoir basin water system vector map, station coordinates, and reservoir parameters;

[0110] (2) The topology modeling module constructs and obtains the topology relationship diagram of "reservoir-tributary-monitoring station" and marks key monitoring nodes, such as reservoirs and monitoring stations;

[0111] (3) The parameter configuration module reads the reservoir topographic height data and automatically calculates the flight altitude and buffer distance;

[0112] (4) The path generation module aims to "cover all key nodes" and uses the basic path planning algorithm to generate flight routes to avoid obstacles such as mountains and buildings;

[0113] (5) The data association module matches real-time data from monitoring stations, river data, and socio-economic data within a 1km radius along the route;

[0114] (6) The report generation module calculates the flight mileage, number of covered stations, and flight time, and generates a PDF report containing route points and data tables.

[0115] In one embodiment of the present invention, a computing device is provided. This computing device can be a terminal and may include a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. When the computer programs are executed by the processor, they implement the methods described in the above embodiments. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.

[0116] 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.

[0117] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0118] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0119] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0120] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0123] 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 aerial preview of upstream and downstream reservoirs in multi-scale remote sensing data visualization scenarios, characterized in that, include: Based on vector data associated with the input reservoir basin, a topological relationship model of the upstream and downstream of the reservoir is constructed to extract connected waterway routes. The vector data includes waterways, reservoir boundaries, and station coordinates. Topographic data of the reservoir basin is acquired, and flight control parameters are automatically generated according to preset parameter linkage rules, with manual adjustment supported. Flight control parameters include flight altitude, viewing angle, and buffer distance. Based on the extracted connected waterway routes and flight control parameters, an optimal flight route is planned using path planning methods, prioritizing coverage of key monitoring nodes and avoiding obstacles. The flight control parameters in the optimal flight route are then linked to station data, reservoir data, river data, and socio-economic data along the route using spatial coordinate matching methods to achieve spatial association between the waterway, reservoir, and station data. Key flight information after spatial association is statistically analyzed to output an automated report containing charts and data tables, completing the flight pre-flight preparation. The parameters are linked, including flight linkage and perspective linkage. Flight linkage is as follows: Flight altitude is based on the topographic elevation data of the reservoir basin and is adjusted in real time according to the terrain ruggedness. Combined with real-time water level data, when the water level exceeds the warning level by 10%, the flight altitude is increased by 50m; when the water level is lower than the dry season water level, the altitude is reduced by 15% of the baseline value to ensure clear presentation of key areas. A preset obstacle height threshold library is used. For fixed obstacles, the flight altitude is 50m higher than the highest point of the obstacle. For moving obstacles, the altitude is increased by 20-50m through real-time remote sensing image recognition. Perspective linkage is as follows: The flight angle is determined based on terrain slope analysis to form a 45° angle with the ground. When the terrain slope is >30°, the perspective angle is adjusted to 30°; when the terrain slope is <10°, the perspective angle is adjusted to 60°; when flying to a key monitoring node, the perspective angle is switched to a vertical perspective.

2. The method for flight preview of upstream and downstream reservoirs for multi-scale remote sensing data visualization scenarios as described in claim 1, characterized in that, The construction of a topological relationship model for the upstream and downstream of a reservoir includes the following steps: preprocessing vector data to unify coordinates and remove redundant and erroneous information; pre-setting three types of rules—water system connectivity, reservoir-station association, and upstream-downstream hierarchy—to verify spatial connectivity, connecting water system segments into a continuous network, binding the spatial relationship between the reservoir and the station, and clarifying the "reservoir-upstream tributary-downstream main stream" connectivity link; then dividing the water system hierarchy to structure the data storage element association relationship; and optimizing the model through connectivity and key node coverage verification, ultimately forming a topological relationship model that accurately reflects the spatial association between the reservoir, water system, and station.

3. The method for upstream and downstream flight preview of reservoirs for multi-scale remote sensing data visualization scenarios as described in claim 1, characterized in that, It also includes a step of precisely smoothing the curvature of the optimal flight path: based on the terrain features of river bend radius and slope changes, data sampling, interpolation analysis, and visualization of parameters are performed sequentially to precisely smooth the curvature of the flight path; the specific implementation process is as follows: by obtaining measured data of the bend radius and slope of key points of the river terrain through data sampling, using the river bend radius and slope changes as core indicators, high-density sampling points are set up in terrain-sensitive areas for sampling, and conventional sampling points are set up at even intervals in straight river sections for sampling. After sampling, invalid data with abnormal coordinates and numerical jumps are removed and verified by repeated sampling, finally forming the river channel key. Key point dataset; based on the sampling points, the bending radius and slope data of unsampled areas are calculated through interpolation algorithms to fill the gaps in terrain data, forming a continuous terrain feature dataset covering the entire river basin. Using the interpolated continuous terrain data as constraints, combined with a preset curvature change threshold, the initial flight route is smoothed and optimized: the broken line trajectory at the river bend is transformed into a continuous curve that conforms to the curvature law of the terrain, and the curvature change of any segment of the route is forced to not exceed the preset threshold; the visualization frame rate is dynamically adjusted according to the smoothness of the route, with the frame rate of smooth sections maintained at 30fps; the frame rate of sections with curvature higher than the set threshold is increased to 60fps.

4. The method for upstream and downstream flight preview of reservoirs for multi-scale remote sensing data visualization scenarios as described in claim 1, characterized in that, The flight control parameters in the optimal flight route are associated with data from stations, reservoirs, rivers, and socio-economic data along the route through a spatial coordinate matching method. Specifically, this includes: extracting flight control parameters from the optimal flight route and standardizing all data coordinates; using buffer analysis to accurately associate the standardized flight route with the spatial parameters of stations, reservoirs, rivers, and socio-economic data along the route, and using a spatial topology consistency verification method to remove data with duplicate or abnormal coordinates; for data with missing coordinates, using the neighboring element coordinate interpolation method to complete the data and ensure data integrity; and discretizing the optimal flight route after parameter association, and generating a continuous integrated set of associated data coordinate points after verification and integration.

5. The method for upstream and downstream flight preview of reservoirs for multi-scale remote sensing data visualization scenarios as described in claim 1, characterized in that, The interconnected waterways cover key nodes of upstream tributaries flowing into the reservoir and downstream ecological flow control sections.

6. The method for flight preview of upstream and downstream reservoirs for multi-scale remote sensing data visualization scenarios as described in claim 1, characterized in that, Key flight information includes total mileage, number of covered stations, and flight duration in key areas; the automated report includes path points, statistical tables of data along the route, and annotations of abnormal areas.

7. A reservoir upstream and downstream flight preview system for multi-scale remote sensing data visualization scenarios, characterized in that, include: The topology model construction module constructs a topology model of the upstream and downstream of the reservoir based on vector data associated with the input reservoir basin to extract connected waterway routes. The vector data includes waterways, reservoir boundaries, and station coordinates. The flight control parameter generation module acquires reservoir basin topographic data and automatically generates flight control parameters according to preset parameter linkage rules, while also supporting manual parameter adjustment. Flight control parameters include flight altitude, viewing angle, and buffer distance. The flight route acquisition module, based on the extracted connected waterway routes and flight control parameters, plans the optimal flight route using path planning methods, prioritizing coverage of key monitoring nodes and avoiding obstacles. The spatial association module associates the flight control parameters from the optimal flight route with station, reservoir, river, and socio-economic data along the route using spatial coordinate matching methods to achieve waterway connectivity and spatial association between the reservoir and monitoring stations. The output module statistically analyzes key flight information after spatial association to output... The system includes automated reports with charts and data tables, and provides flight previews. Parameter linkage rules include flight linkage and perspective linkage. Flight linkage is as follows: Flight altitude is based on the reservoir basin's topographic elevation data and adjusted in real time according to the terrain's ruggedness. Combining real-time water level data, when the water level exceeds the warning level by 10%, the flight altitude increases by 50m; when the water level is below the dry season level, the altitude decreases by 15% of the baseline value to ensure clear presentation of key areas. A preset obstacle height threshold library is used; for fixed obstacles, the flight altitude is 50m higher than the highest point of the obstacle; for moving obstacles, the altitude is increased by 20-50m through real-time remote sensing image recognition. Perspective linkage is as follows: The flight angle is determined based on terrain slope analysis, forming a 45° angle with the ground. When the terrain slope is >30°, the perspective angle is adjusted to 30°; when the terrain slope is <10°, the perspective angle is adjusted to 60°; when flying to key monitoring nodes, the perspective angle is switched to a vertical view.

8. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 6.

9. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 6.

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