Landing site-based geographic data collection and processing method and system

Abstract

The present invention relates to the field of geographic information technology, and discloses a method and system for geographic data collection and processing based on a landing site. The method comprises the following steps: collecting sea area condition data, shore condition data and land condition data of the landing site in real time, as well as coastline data, seabed terrain data and obstacle data through an Internet of Things sensor network, a drone and an unmanned ship; obtaining high-precision three-dimensional geographic data of the landing site based on satellite remote sensing data and lidar data; performing preliminary processing on the collected data through edge computing equipment, and uploading it to the cloud for aggregation analysis and processing, generating and constructing a three-dimensional geographic information model of the landing site based on multi-source data, and using the three-dimensional geographic information model for landing path planning and combat simulation, planning the best landing path, and calculating the risk value of the path, thereby realizing path planning and risk assessment, and dynamically adjusting the landing plan, thereby improving the combat success rate and flexibility.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a landing site-based geographic data collection and processing method and system. Background Art

[0002] In modern landing operations, the selection of landing sites and the planning of combat routes are extremely critical links. The complex geographical environment of the landing site, especially the variability of sea, beach and land conditions, has a direct impact on the success of the landing operation. These geographical conditions include tides, currents, wind and waves, terrain changes, and the distribution of obstacles, which will affect the speed of action and combat effectiveness of the landing troops. Therefore, accurately obtaining and processing the geographical information of the landing site, especially the dynamically changing sea and land environment information, is the basis for the formulation of combat plans.

[0003] The traditional method of collecting geographic data of landing sites mainly relies on historical remote sensing data, surveying and mapping data, and reconnaissance data. Although these data can provide basic environmental information, their limitations are gradually emerging. First, remote sensing data and surveying and mapping are mostly static information, which cannot capture the real-time dynamic changes of the landing area, especially under rapidly changing sea and meteorological conditions, and traditional methods are difficult to deal with effectively. During combat, changes in tides, currents, and wind and waves may have a significant impact on landing conditions in a short period of time, and static data cannot provide accurate real-time feedback. Secondly, the method of relying on historical data has a certain lag and cannot fully reflect the current actual situation of the landing site. Especially when the terrain conditions change or new obstacles are added, combat planning based on old data may lead to serious misjudgment. In addition, it is difficult for traditional data processing methods to effectively integrate the multi-dimensional factors involved in the landing process, especially when faced with the joint influence of multiple factors such as sea tides, terrain slopes and obstacles, the existing technology is difficult to provide accurate comprehensive evaluation. In this regard, the present invention proposes a method and system for collecting and processing geographic data based on landing sites. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a landing site-based geographic data collection and processing method and system, which solves the problem of being unable to obtain and comprehensively process dynamic geographic information of sea areas, beaches and land in real time during landing operations, and thus optimize landing path planning and risk assessment.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for collecting and processing geographic data based on a landing site comprises the following steps:

[0006] S1. Collecting sea area condition data, shore condition data and land condition data of the landing site in real time through an Internet of Things sensor network, wherein the Internet of Things sensors include ocean current sensors, meteorological sensors and geological sensors;

[0007] S2. Use drones and unmanned ships to autonomously survey the landing site to obtain coastline data, seabed terrain data, and obstacle data;

[0008] S3, based on satellite remote sensing data and lidar data, obtain high-precision three-dimensional geographic data of the landing site;

[0009] S4. Preliminary processing of the sea area condition data, shore condition data, land condition data and high-precision three-dimensional geographic data, as well as coastline data, seabed terrain data and obstacle data of the landing site is performed through edge computing equipment, and the preliminarily processed data is uploaded to the cloud for aggregation analysis and processing to generate multi-source data;

[0010] S5. Construct a three-dimensional geographic information model of the landing site based on the multi-source data, and use the three-dimensional geographic information model for landing path planning and combat simulation, plan the best landing path, and calculate the risk value of the path. .

[0011] Preferably, the step S1 is specifically:

[0012] S1.1. Using the ocean current sensor in the IoT sensor to monitor the speed and direction of the tidal current near the landing site, and collecting the sea condition data of the landing site, the sea condition data including the shoreward flow velocity data and lateral velocity data ;

[0013] S1.2. Use the meteorological sensor in the IoT sensor to monitor and collect beach condition data at the landing site in real time and generate wind and wave intensity parameters , wherein the beach condition data of the landing site includes wind speed, wind direction and precipitation data;

[0014] S1.3. Monitor the land conditions through the geological sensor in the Internet of Things sensor, collect and obtain the land condition data of the landing site, and the land condition data of the landing site includes soil moisture and geological structure data.

[0015] Preferably, the step S1 further includes calculating the degree of influence of the tidal current, and the calculation formula of the degree of influence of the tidal current is:

[0016] ,in, The degree of influence of the trend, represents the shoreward flow velocity data, represents the lateral flow velocity data, is the maximum acceptable velocity for onshore flow, is the maximum acceptable flow rate for cross flow.

[0017] Preferably, the S2 step is specifically:

[0018] S2.1. The drone performs aerial scanning to obtain the coastline data, wherein the coastline data includes: coastline width and geographic contour data of the beach;

[0019] S2.2. The unmanned ship performs autonomous tasks in the designated sea area and collects the seabed topography data, which includes the seabed depth. and seafloor topographic structure data;

[0020] S2.3. Use radar and sonar equipment to detect underwater obstacles and collect and obtain the obstacle data, which includes the location and distribution of obstacles, as well as the width of obstacles. .

[0021] Preferably, the S2 further includes calculation of the degree of influence of obstacles, and the calculation formula of the degree of influence of obstacles is:

[0022] ,in, is the degree of influence of obstacles, is the obstacle width, The width of the shoreline.

[0023] Preferably, the S3 step is specifically as follows:

[0024] S3.1. Obtaining an optical remote sensing image of the landing site through a satellite or an unmanned aerial vehicle, and identifying and extracting satellite remote sensing data of the landing site based on the optical remote sensing image, wherein the satellite remote sensing data of the landing site includes terrain feature data and landform feature data;

[0025] S3.2, using a laser radar to scan the landing site, generate a high-precision three-dimensional terrain model, and record the laser radar data of the landing site, wherein the laser radar data of the landing site includes surface height and slope data;

[0026] S3.3. Generate a three-dimensional digital elevation model of the landing site based on the high-precision three-dimensional terrain model, and the terrain feature data, geomorphic feature data, surface height and slope data of the landing site by integrating remote sensing, and obtain the height difference and horizontal distance of each area of ​​the landing site;

[0027] S3.4. Use the slope calculation formula Calculate the slope value for each area , and generate high-precision three-dimensional geographic data of the landing site based on the three-dimensional digital elevation model of the landing site, where H is the height difference between each area of ​​the landing site, and D is the horizontal distance between each area of ​​the landing site.

[0028] Preferably, the S4 step is specifically:

[0029] S4.1. Preliminary filtering, denoising and compression of the sea area condition data, beach condition data, land condition data and high-precision three-dimensional geographic data, as well as the coastline data, seabed terrain data and obstacle data of the landing site are performed on the edge device to generate preliminary processed data;

[0030] S4.2. Upload the preliminary processed data to the cloud, and use a big data analysis algorithm to perform aggregate analysis and processing to generate the multi-source data.

[0031] Preferably, the risk value of the path is calculated in step S5 The specific formula is as follows:

[0032] + ,in, , , and are the weight coefficients of tidal current, obstacle, slope and wind and wave intensity, The degree of influence of the trend, is the degree of influence of obstacles on the beach, is the slope, is the wind and wave intensity parameter.

[0033] The present invention also provides a geographic data collection and processing system based on a landing site, the system comprising:

[0034] A data acquisition module is used to collect sea area condition data, shore condition data and land condition data of the landing site in real time through an Internet of Things sensor network, and to use drones and unmanned ships to conduct autonomous surveys of the landing site to obtain coastline data, seabed terrain data and obstacle data, wherein the Internet of Things sensors include ocean current sensors, meteorological sensors and geological sensors;

[0035] A data processing module is used to obtain high-precision three-dimensional geographic data of the landing site based on satellite remote sensing data and laser radar data, and to perform preliminary processing of the sea area condition data, shore condition data, land condition data and high-precision three-dimensional geographic data of the landing site, as well as coastline data, seabed terrain data and obstacle data through edge computing equipment, and upload the preliminarily processed data to the cloud for aggregation analysis and processing to generate multi-source data;

[0036] The simulation and decision-making module is used to construct a three-dimensional geographic information model of the landing site based on the multi-source data, and use the three-dimensional geographic information model for landing path planning and combat simulation, planning the best landing path, and calculating the risk value of the path. .

[0037] The present invention provides a method and system for collecting and processing geographic data based on a landing site, which has the following beneficial technical effects:

[0038] 1. The present invention generates a high-precision three-dimensional terrain model through satellite remote sensing data and laser radar, builds a three-dimensional geographic information model of the landing site in combination with multi-source data, and uses this model to perform path planning and simulation, calculate the optimal landing path and its risk value, thereby improving the intuitiveness and accuracy of geographic information, enabling commanders to better understand the terrain characteristics of the landing site and optimize the selection of the landing path.

[0039] 2. The present invention uses a simulation and decision-making module to simulate different landing paths based on a three-dimensional geographic information model, calculate the risk value of the path, and conduct a comprehensive assessment of factors such as tidal currents, obstacles, and terrain slopes. It can quantify various risk factors in the landing process, ensure that the combat plan is more robust, and improve the success rate of the operation.

[0040] 3. The present invention can dynamically update the three-dimensional geographic information model according to real-time data by adjusting the landing plan and path planning in real time, and adjust the path planning under different environmental conditions to simulate different combat scenarios. It has high flexibility and adaptability, and solves the problem that in a complex landing combat environment, the sea conditions, weather conditions and terrain conditions change rapidly, and the existing technology is difficult to flexibly respond to these uncertainties. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of the method of the present invention;

[0042] Figure 2 It is a framework diagram of the system of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Please see attached Figure 1 The embodiment of the present invention provides a method for collecting and processing geographic data based on a landing site, comprising the following steps:

[0045] S1. Collect the sea area condition data, shore condition data and land condition data of the landing site in real time through the Internet of Things sensor network. The Internet of Things sensors include ocean current sensors, meteorological sensors and geological sensors.

[0046] S2. Use drones and unmanned ships to conduct autonomous surveys of the landing site to obtain coastline data, seabed terrain data, and obstacle data.

[0047] S3. Based on satellite remote sensing data and lidar data, obtain high-precision three-dimensional geographic data of the landing site.

[0048] S4. Preliminary processing is performed on the sea condition data, shore condition data, land condition data and high-precision three-dimensional geographic data of the landing site, as well as the coastline data, seabed terrain data and obstacle data through edge computing equipment, and the preliminarily processed data is uploaded to the cloud for aggregation analysis and processing to generate multi-source data.

[0049] S5. Construct a three-dimensional geographic information model of the landing site based on the multi-source data, and use the three-dimensional geographic information model for landing path planning and combat simulation, plan the best landing path, and calculate the risk value of the path. .

[0050] In this embodiment, the step S1 is specifically as follows:

[0051] S1.1. Using the ocean current sensor in the IoT sensor to monitor the speed and direction of the tidal current near the landing site, and collecting the sea condition data of the landing site, the sea condition data including the shoreward flow velocity data and lateral velocity data ;

[0052] S1.2. Use the meteorological sensor in the IoT sensor to monitor and collect beach condition data at the landing site in real time and generate wind and wave intensity parameters , wherein the beach condition data of the landing site includes wind speed, wind direction and precipitation data;

[0053] S1.3. Monitor the land conditions through the geological sensor in the Internet of Things sensor, collect and obtain the land condition data of the landing site, and the land condition data of the landing site includes soil moisture and geological structure data.

[0054] Specifically, step S1 of the present invention mainly collects sea condition data, shore condition data and land condition data of the landing site through the Internet of Things sensor network, so as to provide basic data support for subsequent path planning and combat simulation.

[0055] The sea area monitoring system deploys multiple current sensors within the sea area of ​​the landing site and transmits real-time tidal current data to the command center through the network. The system automatically calculates the tidal current change trend based on the tidal current data, generates a tidal current prediction model, and provides tidal current impact analysis during the landing period.

[0056] The meteorological monitoring system collects wind speed, wind direction and precipitation data in the sea and on land in real time through meteorological sensors, automatically generates a wind and wave model, and predicts the impact of wind and waves on the navigation of landing craft. At the same time, the meteorological monitoring system will generate a precipitation forecast and combine geological data to determine the possible impact of precipitation on the landing point.

[0057] The geological sensors deployed by the land condition monitoring system can detect geological changes and compare the data with historical surveying and mapping data to determine the impact of the soil bearing capacity and slope of the current landing point on the operation of landing equipment.

[0058] The tidal current impact analysis module uses the above-mentioned sensor data in combination with the preset tidal current impact model to dynamically generate a tidal current impact assessment report and adjust the combat plan when necessary to ensure the safety and efficiency of the landing operation.

[0059] In this embodiment, the step S1 further includes calculating the degree of influence of the tidal current, and the calculation formula of the degree of influence of the tidal current is:

[0060] ,in, The degree of influence of the trend, represents the shoreward flow velocity data, represents the lateral flow velocity data, is the maximum acceptable velocity for onshore flow, is the maximum acceptable flow rate for cross flow.

[0061] In this embodiment, the step S2 is specifically as follows:

[0062] S2.1. The drone performs aerial scanning to obtain the coastline data, wherein the coastline data includes: coastline width and geographic contour data of the beach;

[0063] S2.2. The unmanned ship performs autonomous tasks in the designated sea area and collects the seabed topography data, which includes the seabed depth. and seafloor topographic structure data;

[0064] S2.3. Use radar and sonar equipment to detect underwater obstacles and collect and obtain the obstacle data, which includes the location and distribution of obstacles, as well as the width of obstacles. .

[0065] Specifically, the S2 step in the present invention aims to conduct a detailed survey of the coastline, beach and seabed environment of the landing site through unmanned equipment, and provide accurate geographic information support for combat path planning and landing site selection. This step mainly involves the coordinated use of drones, unmanned ships, radar and sonar equipment, and through multi-dimensional data collection and analysis, determines the feasibility of the landing site, especially the potential impact of underwater obstacles on the landing operation.

[0066] In step S2.1, a drone is deployed above the landing site, and the drone uses the high-definition camera and surveying equipment it carries to conduct aerial scanning to capture the overall shape of the coastline and the geographical contours of the beach, thereby obtaining coastline data including the width of the coastline and the geographical contours of the beach. The drone flight path covers the entire landing candidate area, and uses real-time imaging technology to generate high-resolution images of the beach.

[0067] The data obtained by drones can accurately describe the shape of the coastline, the slope and width of the beach, and other geographical features. These data will be used as a key reference in the subsequent landing path planning to ensure that the landing equipment can pass smoothly.

[0068] In step S2.2, the unmanned ship performs autonomous tasks within the sea area, uses multi-beam sonar equipment to scan the seabed, and collects and obtains data including the seabed depth. In addition, the unmanned ship can move along a predetermined route or automatically planned path to generate a three-dimensional seabed map of the area.

[0069] This data provides essential information for understanding possible routes for landing craft, water depths in landing areas, and the presence of undersea structures that would be detrimental to vessel operations.

[0070] Step S2.3: Use radar and sonar equipment deployed on unmanned vessels to conduct a detailed survey of the location and distribution of underwater obstacles. The purpose of this step is to determine the location and distribution of obstacles that landing craft or ships may encounter when sailing and docking, and record the width of these obstacles. , so as to accurately obtain obstacle data. These obstacles may include underwater rocks, artificial defense facilities or other objects that hinder the passage of landing craft.

[0071] Through sonar imaging, the command system can accurately locate obstacles and determine the extent of their impact on the landing path.

[0072] Step S2.4, in obtaining the width of the underwater obstacle and the width of the coastline Finally, the following formula can be used to quantify the impact of obstacles on beach landings: ,in, is the obstacle width, The width of the shoreline.

[0073] This formula quantifies the degree to which an obstacle would hinder landing operations by comparing the width of the obstacle to the total width of the coastline. Relative to If the obstacle is very small, the impact of the obstacle on landing will be small. On the contrary, if the obstacle occupies most of the coastline, the difficulty of landing will increase significantly.

[0074] In this embodiment, the step S2 further includes calculating the degree of influence of obstacles, and the calculation formula of the degree of influence of obstacles is:

[0075] ,in, is the degree of influence of obstacles, is the obstacle width, The width of the shoreline.

[0076] In this embodiment, the step S3 is specifically as follows:

[0077] S3.1. Obtaining an optical remote sensing image of the landing site through a satellite or an unmanned aerial vehicle, and identifying and extracting satellite remote sensing data of the landing site based on the optical remote sensing image, wherein the satellite remote sensing data of the landing site includes terrain feature data and landform feature data;

[0078] S3.2, using a laser radar to scan the landing site, generate a high-precision three-dimensional terrain model, and record the laser radar data of the landing site, wherein the laser radar data of the landing site includes surface height and slope data;

[0079] S3.3. Generate a three-dimensional digital elevation model of the landing site based on the high-precision three-dimensional terrain model, and the terrain feature data, geomorphic feature data, surface height and slope data of the landing site by integrating remote sensing, and obtain the height difference and horizontal distance of each area of ​​the landing site;

[0080] S3.4. Use the slope calculation formula Calculate the slope value for each area , and generate high-precision three-dimensional geographic data of the landing site based on the three-dimensional digital elevation model of the landing site, where H is the height difference between each area of ​​the landing site, and D is the horizontal distance between each area of ​​the landing site.

[0081] Specifically, step S3 in the present invention aims to utilize advanced remote sensing technology and LiDAR technology to obtain terrain feature data, landform feature data, surface height and slope data of the landing site, and then construct a three-dimensional digital elevation model through the fusion processing of remote sensing images and LiDAR data, and calculate the slope value of each landing area, so as to provide accurate geographic information for the planning of landing operations.

[0082] Step S3.1: First, obtain optical remote sensing images of the landing site through satellites or drones. These high-resolution images can accurately reflect the topographic and geomorphic features of the landing site, including coastline, vegetation cover, rock distribution, and other natural or man-made structures. Through image processing technology, extract key topographic and geographical features of optical remote sensing images, such as coastline contours, beach and shore area, obstacles, etc., to obtain satellite remote sensing data of the landing site. These data provide basic support for subsequent terrain modeling.

[0083] In step S3.2, based on the acquisition of optical remote sensing images, the landing site is scanned using LiDAR (laser radar) technology. The advantage of LiDAR data is its extremely high accuracy, which can generate a detailed, high-precision three-dimensional terrain model of the landing site, record the LiDAR data of the landing site, and accurately reflect the ups and downs of different areas.

[0084] The lidar data of the landing site includes surface height and slope data, which are used to further calculate the feasibility of each landing area.

[0085] In step S3.3, in order to improve the accuracy of terrain modeling, the optical remote sensing image acquired in S3.1 is fused with the data obtained by the lidar scan in S3.2. Using the fused data and based on the high-precision three-dimensional terrain model, a three-dimensional digital elevation model (DEM) is generated. This model can more intuitively and comprehensively reflect the terrain characteristics of the landing site.

[0086] It should be noted that the optical remote sensing image provides the geomorphic features of the landing site, and the LiDAR data provides accurate surface elevation information. Through the data fusion of the optical remote sensing image and the data obtained by the LiDAR scan, the generated three-dimensional digital elevation model (DEM) can be displayed at different scales and provide data support for subsequent slope calculations.

[0087] Step S3.4: After generating the three-dimensional digital elevation model (DEM), the slope values ​​of different areas of the landing site need to be calculated. The slope value is one of the key factors in evaluating the landing path, especially in terms of the rapid passage of equipment and personnel. Excessive slope may affect the maneuverability of landing equipment.

[0088] Step S3.4: Calculate the slope value of each area , and generate high-precision three-dimensional geographic data of the landing site based on the three-dimensional digital elevation model of the landing site. The formula for calculating the slope is: .

[0089] in, : Indicates the slope value, usually expressed in angle or percentage;

[0090] H is the height difference between the various areas of the landing site, that is, the change in elevation between two points;

[0091] D is the horizontal distance between the various areas of the landing site, that is, the distance in the horizontal direction.

[0092] This formula can be used to calculate the slope value of each candidate landing area and further evaluate whether these areas are suitable as landing points. A smaller slope value means a flatter terrain, which is conducive to the smooth deployment of landing equipment, while a large slope may limit the equipment's ability to move.

[0093] In this embodiment, the step S4 is specifically as follows:

[0094] S4.1. Preliminary filtering, denoising and compression of the sea area condition data, beach condition data, land condition data and high-precision three-dimensional geographic data, as well as the coastline data, seabed terrain data and obstacle data of the landing site are performed on the edge device to generate preliminary processed data;

[0095] S4.2. Upload the preliminary processed data to the cloud, and use a big data analysis algorithm to perform aggregate analysis and processing to generate the multi-source data.

[0096] Specifically, the goal of step S4 is to process and transmit the data collected in real time in steps S1 to S3 in real time to complete preliminary processing. During the processing, the efficient processing and accurate analysis of data are ensured through the combination of edge computing and cloud computing. The core of this step is to use edge devices to perform preliminary processing on the sea condition data, shore condition data, land condition data and high-precision three-dimensional geographic data of the landing site, as well as coastline data, seabed terrain data and obstacle data collected, and use cloud computing platforms to aggregate and deeply analyze multi-source data to generate multi-source data and provide real-time support for landing operation decision-making.

[0097] On edge computing devices, the original multi-source data collected from IoT sensor networks, drones, unmanned ships and other devices are pre-processed to ensure data quality and transmission efficiency.

[0098] Step S4.1, preliminarily filtering, denoising and compressing the sea area condition data, beach condition data, land condition data and high-precision three-dimensional geographic data, as well as the coastline data, seabed terrain data and obstacle data of the landing site on the edge device, also includes the following steps:

[0099] 1) Ocean current data processing: tidal velocity data obtained from ocean current sensors and Perform preliminary noise filtering and correction.

[0100] 2) Meteorological data processing: wind speed , wind direction, precipitation and other data are obtained from meteorological sensors, denoised, and wind and wave intensity parameters are generated. .

[0101] 3) Geological data processing: Soil moisture, geological structure data and slope data obtained by geological sensors are smoothed to eliminate abnormal data points.

[0102] 4) Image data compression: compress the collected high-resolution satellite images and drone image data, using the JPEG compression algorithm or other image compression methods to reduce data volume and optimize transmission.

[0103] Step S4.2, uploading the preliminary processed data to the cloud, and using a big data analysis algorithm to perform aggregate analysis and processing to generate the multi-source data, specifically:

[0104] 1) Sensor data optimization: For preliminary processing of data, compress data packets and prioritize critical data to ensure fast transmission to the cloud.

[0105] 2) Real-time processing: Edge devices process preliminary processing data locally in real time, perform preliminary data integration, and generate temporary risk assessment results to support commanders' real-time decision-making.

[0106] 3) Data transmission: The data after preliminary processing is compressed and uploaded to the cloud via high-speed network for more in-depth analysis and model training.

[0107] 4) In the cloud, the system will use big data analysis algorithms to aggregate and analyze the uploaded preliminary processed data.

[0108] Aggregate analytical processing is a data processing method that integrates data from multiple different data sources (such as multiple databases, applications, etc.) by combining information from different data sources and analyzing data correlations. It provides data with deep insights, can quickly obtain accurate data, simplify the data analysis process, and reduce the complexity of the entire data processing process.

[0109] Among them, the big data analysis algorithm used is the random forest algorithm. The specific operation process is as follows:

[0110] 1) Input data and obtain feature data:

[0111] The system uses the preprocessed preliminary processing data as the feature vector X, which includes the feature vectors corresponding to the sea condition data, the shore condition data, the land condition data and the high-precision three-dimensional geographic data, as well as the coastline data, the seabed terrain data and the obstacle data.

[0112] 2) Target output: The generated output value is the risk score of the landing path , which is the result predicted by the model.

[0113] 3) Feature Selection:

[0114] The random forest algorithm evaluates the input features through a built-in feature selection mechanism to determine which features have higher importance for path risk assessment.

[0115] For example, the current speed It may have a greater impact on navigation, and the slope value It may be more critical to the feasibility of land landing.

[0116] Training and generation of random forest models: The system creates multiple decision trees, each of which randomly selects a portion of the features from the input feature set for model training. For example, one decision tree may mainly use wind and wave strength and slope data for judgment, while another decision tree combines tidal and obstacle data.

[0117] Model training: Each decision tree is trained to predict new data by learning the path results in historical data or simulated data.

[0118] Voting mechanism: 1) Ensemble decision: In the model prediction stage, all decision trees in the random forest evaluate the given landing path, and each tree gives its risk score. 2) Voting result: the final path risk value It is determined by the voting results of each decision tree. During the voting process, the algorithm will make a majority vote based on the outputs of all decision trees to obtain the final risk prediction result.

[0119] Risk assessment and output: output risk value A comprehensive risk score can be generated for each landing path based on characteristics such as tidal currents, wind and waves, obstacles, and slope. These scores will be used in subsequent path planning and simulation systems to help commanders select the optimal landing path.

[0120] In this embodiment, the risk value of the path calculated in step S5 is The specific formula is as follows:

[0121] + ,in, , , and are the weight coefficients of tidal current, obstacle, slope and wind and wave intensity, The degree of influence of the trend, is the degree of influence of obstacles on the beach, is the slope, is the wind and wave intensity parameter.

[0122] Specifically, the S5 step aims to build a three-dimensional digital geographic information model based on multi-source data after aggregation analysis and processing, and use the model to plan and assess the risk of landing paths. Through the path simulation system, commanders can evaluate the feasibility of different landing paths to ensure the safety and success of combat operations.

[0123] In the previous steps (S1 to S4), the multi-source data after aggregation analysis and processing already includes complete information on sea area, shore and land conditions, such as sea area condition data, shore condition data, land condition data and high-precision three-dimensional geographic data, as well as coastline data, seabed terrain data and obstacle data, etc. These data are input into the three-dimensional modeling engine to generate a three-dimensional digital geographic information model covering all key areas of the landing site.

[0124] Sea condition data including shoreward current velocity data and lateral velocity data , which reflects the dynamics of ocean current speed, tidal changes, etc., and can simulate various conditions that landing craft may encounter when sailing on the sea.

[0125] Beach condition data, in addition to wind speed, wind direction and precipitation data, also includes the slope, width, obstacle distribution, etc. of the beach, which are used to assess the difficulty of landing craft docking.

[0126] Land condition data includes soil moisture and geological structure data, covering the terrain and road information required for equipment and troop movements after landing, which helps plan subsequent advancement paths.

[0127] Through these data, the constructed three-dimensional digital geographic information model can accurately reflect the terrain, ocean environment and obstacle distribution of the landing site in three-dimensional space, providing detailed geographic information support for subsequent path planning.

[0128] Based on the constructed three-dimensional digital geographic information model, the best path for the landing craft from the sea to the beach and then to the land combat area is planned. The path planning mainly depends on the comprehensive evaluation of key factors such as tides, obstacles, and slopes.

[0129] The calculation formula of path risk value is as follows:

[0130] + .

[0131] in, is the comprehensive risk value of the path, which is used to evaluate the feasibility of the path. The higher the value, the greater the risk. , , and They are the weight coefficients for tide, obstacle, slope and wind and wave intensity, and these weights can be adjusted according to combat requirements. It is the degree of influence of tidal current, used to quantify the impact of tidal current on the navigation of landing craft. It is the degree of influence of obstacles on the beach, reflecting the degree of obstruction of obstacles to the landing craft from docking or advancing. It is the slope, which reflects the impact of terrain and slope on the actions of landing equipment or personnel. is the wind and wave intensity parameter.

[0132] These factors together determine the risk value of each landing path. By adjusting the weight, the influence of a certain factor can be given priority. For example, in the case of strong winds and waves, the weight of the tide may need to be increased, and the weight of obstacles or slopes may need to be reduced.

[0133] After calculating the risk value of each path, the landing path simulation system can simulate different landing paths to evaluate their feasibility. The simulation system will dynamically test each path based on different combat scenarios, such as tidal changes, wind and wave intensity, and obstacle distribution.

[0134] Functions of the simulation system: Based on the three-dimensional digital geographic information model, the system can simulate various situations that landing craft may encounter on different paths, such as the resistance of the tide, the impact of obstacles, the difficulty of landing equipment moving on a steep beach, etc.

[0135] Landing path feasibility assessment: Based on simulation results, the system automatically generates a feasibility report for each path, listing the advantages and potential risks of each path to help commanders choose the optimal landing path.

[0136] By applying the above technical solutions provided by the present invention, 1. A high-precision three-dimensional terrain model is generated through satellite remote sensing data and laser radar, and a three-dimensional geographic information model of the landing site is constructed in combination with multi-source data. This model is used for path planning and simulation, and the optimal landing path and its risk value are calculated, thereby improving the intuitiveness and accuracy of geographic information, enabling commanders to better understand the terrain characteristics of the landing site and optimize the choice of landing paths. 2. Through the simulation and decision-making module, different landing paths are simulated based on the three-dimensional geographic information model, the risk value of the path is calculated, and factors such as tides, obstacles and terrain slopes are comprehensively evaluated, which can quantify various risk factors in the landing process, ensure that the combat plan is more robust, and improve the combat success rate. 3. By adjusting the landing plan and path planning in real time, the three-dimensional geographic information model can be dynamically updated according to real-time data, and the path planning adjustment can be performed under different environmental conditions to simulate different combat scenarios, with high flexibility and adaptability, solving the problem that in a complex landing combat environment, the sea conditions, meteorological conditions and terrain conditions change rapidly, and the existing technology is difficult to flexibly respond to these uncertainties.

[0137] Please see attached Figure 2 , a geographic data collection and processing system based on a landing site, the system comprising:

[0138] A data acquisition module is used to collect sea area condition data, shore condition data and land condition data of the landing site in real time through an Internet of Things sensor network, and to use drones and unmanned ships to conduct autonomous surveys of the landing site to obtain coastline data, seabed terrain data and obstacle data, wherein the Internet of Things sensors include ocean current sensors, meteorological sensors and geological sensors;

[0139] A data processing module is used to obtain high-precision three-dimensional geographic data of the landing site based on satellite remote sensing data and laser radar data, and to perform preliminary processing of the sea area condition data, shore condition data, land condition data and high-precision three-dimensional geographic data of the landing site, as well as coastline data, seabed terrain data and obstacle data through edge computing equipment, and upload the preliminarily processed data to the cloud for aggregation analysis and processing to generate multi-source data;

[0140] The simulation and decision-making module is used to construct a three-dimensional geographic information model of the landing site based on the multi-source data, and use the three-dimensional geographic information model for landing path planning and combat simulation, planning the best landing path, and calculating the risk value of the path. .

[0141] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for collecting and processing geographic data based on a landing site, characterized in that: The following steps are involved: S1. Collecting sea area condition data, shore condition data and land condition data of the landing site in real time through an Internet of Things sensor network, wherein the Internet of Things sensors include ocean current sensors, meteorological sensors and geological sensors; S2. Use drones and unmanned ships to autonomously survey the landing site to obtain coastline data, seabed terrain data, and obstacle data; S3, based on satellite remote sensing data and lidar data, obtain high-precision three-dimensional geographic data of the landing site; S4. Preliminary processing of the sea area condition data, shore condition data, land condition data and high-precision three-dimensional geographic data, as well as coastline data, seabed terrain data and obstacle data of the landing site is performed through edge computing equipment, and the preliminarily processed data is uploaded to the cloud for aggregation analysis and processing to generate multi-source data; S5. Construct a three-dimensional geographic information model of the landing site based on the multi-source data, and use the three-dimensional geographic information model for landing path planning and combat simulation, plan the best landing path, and calculate the risk value R of the path. path ; The S1 step is specifically as follows: S1.

1. Use the ocean current sensor in the IoT sensor to monitor the speed and direction of the tidal current near the landing site, and collect the sea condition data of the landing site, wherein the sea condition data includes the shoreward flow velocity data v shore and lateral velocity data v cross ; S1.2, using the meteorological sensor in the IoT sensor to monitor and collect beach condition data of the landing site in real time, and generate a wind and wave intensity parameter v wave , wherein the beach condition data of the landing site includes wind speed, wind direction and precipitation data; S1.

3. Monitor the land conditions through the geological sensor in the Internet of Things sensor, collect and obtain the land condition data of the landing site, and the land condition data of the landing site includes soil moisture and geological structure data.

2. The method for collecting and processing geographic data based on a landing site according to claim 1, characterized in that: The step S1 also includes the calculation of the degree of influence of the tidal current. The calculation formula of the degree of influence of the tidal current is: Among them, C current is the influence degree of tidal current, v shore represents the shoreward flow velocity data, v cross Represents the lateral flow velocity data, a3 is the maximum acceptable flow velocity for onshore flow, and a4 is the maximum acceptable flow velocity for lateral flow.

3. The method for collecting and processing geographic data based on a landing site according to claim 1, characterized in that: The S2 step is specifically as follows: S2.

1. The drone performs aerial scanning to obtain the coastline data, wherein the coastline data includes: coastline width and geographic contour data of the beach; S2.

2. The unmanned ship performs autonomous tasks in the designated sea area and collects the seabed topography data, which includes the seabed depth D depth and seafloor topographic structure data; S2.

3. Use radar and sonar equipment to detect underwater obstacles and collect and obtain the obstacle data, which includes the location and distribution of obstacles, as well as the width L of the obstacles. obstacle .

4. The method for collecting and processing geographic data based on a landing site according to claim 1, characterized in that: The S2 also includes the calculation of the degree of influence of obstacles, and the calculation formula of the degree of influence of obstacles is: Among them, C obstacle is the degree of influence of obstacles, L obstacle is the obstacle width, L shore The width of the shoreline.

5. The method for collecting and processing geographic data based on a landing site according to claim 1, characterized in that: The S3 step is specifically as follows: S3.

1. Obtaining an optical remote sensing image of the landing site through a satellite or an unmanned aerial vehicle, and identifying and extracting satellite remote sensing data of the landing site based on the optical remote sensing image, wherein the satellite remote sensing data of the landing site includes terrain feature data and landform feature data; S3.2, using a laser radar to scan the landing site, generate a high-precision three-dimensional terrain model, and record the laser radar data of the landing site, wherein the laser radar data of the landing site includes surface height and slope data; S3.

3. Generate a three-dimensional digital elevation model of the landing site based on the high-precision three-dimensional terrain model, and the terrain feature data, geomorphic feature data, surface height and slope data of the landing site by integrating remote sensing, and obtain the height difference and horizontal distance of each area of ​​the landing site; S3.

4. Use the slope calculation formula Calculate the slope value P of each area slope , and generate high-precision three-dimensional geographic data of the landing site based on the three-dimensional digital elevation model of the landing site, where H is the height difference between each area of ​​the landing site, and D is the horizontal distance between each area of ​​the landing site.

6. The method for collecting and processing geographic data based on a landing site according to claim 1, characterized in that: The S4 step is specifically as follows: S4.

1. Preliminary filtering, denoising and compression of the sea area condition data, beach condition data, land condition data and high-precision three-dimensional geographic data, as well as the coastline data, seabed terrain data and obstacle data of the landing site are performed on the edge device to generate preliminary processed data; S4.

2. Upload the preliminary processed data to the cloud, and use a big data analysis algorithm to perform aggregate analysis and processing to generate the multi-source data.

7. The method for collecting and processing geographic data based on a landing site according to claim 1, characterized in that: In step S5, the risk value R of the path is calculated. path The specific formula is as follows: R path =w1·C current +w2·C obstacle +w3·P slope +w4·v wave , where w1, w2, w3 and w4 are weight coefficients of tidal current, obstacle, slope and wind and wave intensity respectively, C current is the influence degree of the tidal current, C obstacle is the degree of influence of obstacles on the beach, P slope is the slope value, v wave is the wind and wave intensity parameter.

8. The geographical data collection and processing system based on the landing site is characterized by: The system comprises: A data acquisition module is used to collect sea area condition data, shore condition data and land condition data of the landing site in real time through an Internet of Things sensor network, and to use drones and unmanned ships to conduct autonomous surveys of the landing site to obtain coastline data, seabed terrain data and obstacle data, wherein the Internet of Things sensors include ocean current sensors, meteorological sensors and geological sensors; A data processing module is used to obtain high-precision three-dimensional geographic data of the landing site based on satellite remote sensing data and laser radar data, and to perform preliminary processing of the sea area condition data, shore condition data, land condition data and high-precision three-dimensional geographic data of the landing site, as well as coastline data, seabed terrain data and obstacle data through edge computing equipment, and upload the preliminarily processed data to the cloud for aggregation analysis and processing to generate multi-source data; The simulation and decision-making module is used to construct a three-dimensional geographic information model of the landing site based on the multi-source data, and use the three-dimensional geographic information model for landing path planning and combat simulation, plan the best landing path, and calculate the risk value R of the path. path ; The specific steps of collecting the sea area condition data, shore condition data and land condition data of the landing site in real time through the Internet of Things sensor network are as follows: S1.

1. Use the ocean current sensor in the IoT sensor to monitor the speed and direction of the tidal current near the landing site, and collect the sea condition data of the landing site, wherein the sea condition data includes the shoreward flow velocity data v shore and lateral velocity data v cross ; S1.2, using the meteorological sensor in the IoT sensor to monitor and collect beach condition data of the landing site in real time, and generate a wind and wave intensity parameter v wave , wherein the beach condition data of the landing site includes wind speed, wind direction and precipitation data; S1.

3. Monitor the land conditions through the geological sensor in the Internet of Things sensor, collect and obtain the land condition data of the landing site, and the land condition data of the landing site includes soil moisture and geological structure data.

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