Airport low-altitude operation situation awareness verification method, device, equipment and medium

Abstract

The application relates to the technical field of air traffic management, in particular to an airport low-altitude operation situation awareness verification method, device, equipment and medium, which comprises the following steps: constructing a situation awareness basic model, and deriving basic model parameters; constructing a complex low-altitude environment simulation model, generating pre-verification data and a targeted verification scene; based on a TCP / IP communication interface, linking the complex low-altitude environment simulation model and a real scene monitoring device to construct a virtual-real fusion verification model, and generating a virtual-real data comparison result; and generating a situation awareness performance evaluation result and optimization suggestions. The application solves the technical problems that in the prior art, there are no clear quantitative evaluation indexes in the verification process, and there is no standard verification process, which reduces the verification efficiency, affects the reliability of the verification result, and makes it difficult for the situation awareness technology to obtain accurate basis, thereby reducing the authenticity of the verification.

Description

Technical Field

[0001] This application relates to the field of air traffic management technology, and in particular to a method, device, equipment and medium for verifying airport low-altitude operational situational awareness. Background Technology

[0002] With the continued development of the low-altitude economy, the application of low-altitude aircraft such as drones in logistics delivery and emergency rescue scenarios is constantly increasing. At the same time, the operational situation in complex low-altitude environments such as airport terminal control areas is becoming more complex, placing higher demands on the reliability and environmental adaptability of low-altitude operational situational awareness technologies. The performance of situational awareness technology directly affects the level of safety management and control in low-altitude operations, and its verification process is also an important foundation for the engineering application of related technologies.

[0003] Currently, the verification methods for low-altitude operational situational awareness technology mainly rely on single numerical simulations or local real-world experiments. Single numerical simulations are insufficient in terms of scene fidelity, making it difficult to realistically reflect various disturbances in complex low-altitude environments and unable to match situations involving multiple target interactions. On the other hand, real-world experiments are easily limited by site, cost, and safety conditions, resulting in limited scene coverage and making it impossible to conduct verification under multiple operating conditions and multiple parameters. Furthermore, the verification process in existing technologies also suffers from the lack of clear quantitative evaluation indicators and standardized verification procedures, which reduces verification efficiency and affects the credibility of verification results. Consequently, situational awareness technology struggles to obtain accurate evidence, leading to poor verification authenticity.

[0004] Therefore, there is an urgent need to design a method, device, equipment, and medium for verifying airport low-altitude operational situational awareness. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, equipment, and medium for verifying airport low-altitude operational situational awareness in response to the above-mentioned technical problems. This method can solve the problem that existing technologies lack clear quantitative evaluation indicators and standard verification procedures, which leads to reduced verification efficiency, affects the credibility of verification results, and makes it difficult for situational awareness technology to obtain accurate evidence, resulting in poor verification authenticity.

[0006] The technical solution of this invention is as follows: A method for verifying airport low-altitude operational situational awareness, the method comprising: A basic situational awareness model is constructed based on the collected low-altitude target image data and location data, and basic model parameters are derived based on the basic situational awareness model. A complex low-altitude environment simulation model is constructed based on the geographical data of the target low-altitude operating area and the parameters of the basic model, and pre-verification data and targeted verification scenarios are generated based on the complex low-altitude environment simulation model. Based on the TCP / IP communication interface, the complex low-altitude environment simulation model and the real-scene monitoring equipment are linked to construct a virtual-real fusion verification model, and virtual-real data comparison results are generated based on the virtual-real fusion verification model. Based on the comparison results of the virtual and real data, a situational awareness performance evaluation result and optimization suggestions are generated.

[0007] Optionally, a situational awareness basic model is constructed based on the collected low-altitude target image data and location data, and basic model parameters are derived based on the situational awareness basic model, including: Collect low-altitude target image data and obtain low-altitude target recognition results based on the low-altitude target image data; Collect the location data of low-altitude targets, and associate and match the low-altitude target identification results with the location data to generate basic verification data; A basic situational awareness model is constructed based on the basic verification data, and basic model parameters are derived based on the basic situational awareness model.

[0008] Optionally, a situational awareness base model is constructed based on the basic verification data, and base model parameters are derived based on the situational awareness base model, including: The low-altitude target identification and location functions are debugged and calibrated based on the basic verification data, and the situational awareness basic model is constructed based on the debugged and calibrated basic verification data. The performance of the situational awareness base model is calibrated, and the parameters of the base model after performance calibration are exported.

[0009] Optionally, a complex low-altitude environment simulation model is constructed based on the geographical data of the target low-altitude operating area and the basic model parameters, including: Based on Unity3D modeling algorithms, a 3D scene model is created using geographic data of the target low-altitude operating area, and an initial simulation model of the low-altitude environment is generated. Add a target model and a sensor model to the initial simulation model of the low-altitude environment, and define the parameters of the target model and the sensor model; Meteorological interference scenarios and electromagnetic interference scenarios are integrated into the initial simulation model of the low-altitude environment, and the adjustable range of interference parameters is set. Import the basic model parameters and calibrate the target identification and positioning parameters in the initial simulation model of the low-altitude environment; Run the initial simulation model of the low-altitude environment to test the model stability and scene fidelity under different low-altitude operating scenarios. After the test is completed, the complex low-altitude environment simulation model is constructed.

[0010] Optionally, pre-verification data and targeted verification scenarios are generated based on the complex low-altitude environment simulation model, including: Based on the complex low-altitude environment simulation model, multi-scenario pre-verification is carried out, and verification data under different scenarios is collected to generate pre-verification data. Targeted verification scenarios are selected based on the pre-verification data.

[0011] Optionally, based on the TCP / IP communication interface, a virtual-real fusion verification model is constructed by linking the complex low-altitude environment simulation model and the real-world monitoring equipment, including: With the support of targeted verification scenarios and pre-verification data, the complex low-altitude environment simulation model and real-world monitoring equipment are respectively connected to the TCP / IP communication interface; Receive real-scene monitoring data collected by real-scene monitoring equipment, and construct a virtual-real fusion verification model based on the real-scene monitoring data; The virtual-real data comparison results are generated based on the virtual-real fusion verification model.

[0012] Optionally, based on the comparison results of the virtual and real data, situational awareness performance evaluation results and optimization suggestions are generated, including: Verification management is performed based on the comparison results of the virtual and real data, and verification data management results are generated. Based on the verification data management results, situational awareness performance evaluation results and optimization suggestions are generated.

[0013] Optionally, an airport low-altitude operational situational awareness verification device is also provided, the device comprising: The basic model parameter export module is used to construct a situational awareness basic model based on the collected low-altitude target image data and location data, and to export basic model parameters based on the situational awareness basic model. The complex simulation model generation module is used to construct a complex low-altitude environment simulation model based on the geographical data of the target low-altitude operating area and the basic model parameters, and to generate pre-verification data and targeted verification scenarios based on the complex low-altitude environment simulation model. The virtual-real data comparison generation module is used to connect the complex low-altitude environment simulation model and the real-scene monitoring equipment based on the TCP / IP communication interface to build a virtual-real fusion verification model, and generate virtual-real data comparison results based on the virtual-real fusion verification model; The situation assessment optimization generation module is used to generate situation awareness performance assessment results and optimization suggestions based on the comparison results of virtual and real data.

[0014] Optionally, the basic model parameter export module is further configured to: collect low-altitude target image data and obtain low-altitude target recognition results based on the low-altitude target image data; collect low-altitude target location data and associate and match the low-altitude target recognition results with the location data to generate basic verification data; construct a situational awareness basic model based on the basic verification data and export basic model parameters based on the situational awareness basic model.

[0015] Optionally, the basic model parameter export module is further configured to: debug and calibrate the low-altitude target identification function and the location positioning function based on the basic verification data, and construct the situational awareness basic model based on the debugged and calibrated basic verification data; perform performance calibration on the situational awareness basic model, and export the performance-calibrated basic model parameters.

[0016] Optionally, the complex simulation model generation module is further configured to: perform 3D scene modeling based on the geographic data of the target low-altitude operating area using the Unity3D modeling algorithm, and generate an initial low-altitude environment simulation model; add a target model and a sensor model to the initial low-altitude environment simulation model, and define the parameters of the target model and the sensor model; integrate meteorological interference scenarios and electromagnetic interference scenarios into the initial low-altitude environment simulation model, and set the adjustable range of interference parameters; import the basic model parameters, and calibrate the target identification parameters and positioning parameters in the initial low-altitude environment simulation model; run the initial low-altitude environment simulation model, and test the model stability and scene fidelity under different low-altitude operating scenarios, and complete the construction of the complex low-altitude environment simulation model after the test is completed.

[0017] Optionally, the complex simulation model generation module is further configured to: conduct multi-scenario pre-verification based on the complex low-altitude environment simulation model, collect verification data under different scenarios, and generate pre-verification data; and select targeted verification scenarios based on the pre-verification data.

[0018] Optionally, the virtual-real data comparison generation module is further configured to: connect the complex low-altitude environment simulation model and the real-scene monitoring equipment to the TCP / IP communication interface respectively, under the support of targeted verification scenarios and pre-verification data; receive the real-scene monitoring data collected by the real-scene monitoring equipment, and construct a virtual-real fusion verification model based on the real-scene monitoring data; and generate virtual-real data comparison results according to the virtual-real fusion verification model.

[0019] Optionally, the situation assessment optimization generation module is further configured to: perform verification management based on the virtual-real data comparison results, and generate verification data management results; and generate situation awareness performance assessment results and optimization suggestions based on the verification data management results.

[0020] Optionally, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps described in the above-described airport low-altitude operational situational awareness verification method.

[0021] Optionally, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described airport low-altitude operational situational awareness verification method.

[0022] The technical effects achieved by this invention are as follows: The aforementioned airport low-altitude operation situational awareness verification method, device, equipment, and medium construct a basic situational awareness model and derive its parameters to provide a unified parameter basis for subsequent parameter calibration of complex low-altitude environment simulation models, thereby improving the consistency of the subsequent verification process. Then, based on the geographic data of the target low-altitude operation area and the basic model parameters, a complex low-altitude environment simulation model is constructed, and pre-verification data and targeted verification scenarios are generated. This improves the fit between the simulation scenario and the complex low-altitude actual operation environment of the airport terminal control area, enhancing the adaptability verification capability under complex scenarios. Next, based on the TCP / IP communication interface, the complex low-altitude environment simulation model and the real-scene monitoring equipment are linked to construct a virtual-real fusion verification model and generate virtual-real data comparison results. This achieves effective linkage and synchronous comparison between virtual scenarios and real-scene data, improving the authenticity of virtual-real fusion verification and also enhancing the reliability and comprehensiveness of the verification results. Finally, by generating situational awareness performance evaluation results and optimization suggestions based on the comparison results of virtual and real data, the verification results of the virtual-real fusion stage are transformed into quantifiable performance conclusions, which improves evaluation efficiency, reduces subjectivity in the evaluation process, and provides a clear basis for subsequent technical improvements. This solves the problem in the existing technology where there are no clear quantitative evaluation indicators or standard verification procedures, which leads to reduced verification efficiency, affects the credibility of verification results, and makes it difficult for situational awareness technology to obtain accurate evidence, resulting in poor verification authenticity. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an airport low-altitude operational situational awareness verification method in one embodiment. Figure 2 This is a structural block diagram of an airport low-altitude operational situational awareness verification device in one embodiment. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0025] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0026] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0028] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0030] In one embodiment, a terminal is provided, the terminal being used to: construct a situational awareness basic model based on collected low-altitude target image data and location data, and derive basic model parameters based on the situational awareness basic model; construct a complex low-altitude environment simulation model based on geographic data of the target's low-altitude operating area and the basic model parameters, and generate pre-verification data and targeted verification scenarios based on the complex low-altitude environment simulation model; construct a virtual-real fusion verification model by linking the complex low-altitude environment simulation model and real-scene monitoring equipment based on a TCP / IP communication interface, and generate virtual-real data comparison results based on the virtual-real data comparison results; and generate situational awareness performance evaluation results and optimization suggestions based on the virtual-real data comparison results.

[0031] The terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0032] In one embodiment, such as Figure 1 As shown, an airport low-altitude operational situational awareness verification method is provided, the method comprising: Step S100: Construct a basic situational awareness model based on the collected low-altitude target image data and location data, and derive the basic model parameters based on the basic situational awareness model. Step S200: Construct a complex low-altitude environment simulation model based on the geographical data of the target low-altitude operating area and the basic model parameters, and generate pre-verification data and targeted verification scenarios based on the complex low-altitude environment simulation model; Step S300: Based on the TCP / IP communication interface, link the complex low-altitude environment simulation model and the real-scene monitoring equipment to construct a virtual-real fusion verification model, and generate virtual-real data comparison results based on the virtual-real fusion verification model; Step S400: Generate situational awareness performance evaluation results and optimization suggestions based on the comparison results of virtual and real data.

[0033] In this embodiment, a situational awareness basic model is first constructed and its parameters are exported to provide a unified parameter basis for the parameter calibration of the subsequent complex low-altitude environment simulation model, thereby improving the consistency of the subsequent verification process. Then, a complex low-altitude environment simulation model is constructed based on the geographic data of the target low-altitude operating area and the basic model parameters, and pre-verification data and targeted verification scenarios are generated. This improves the fit between the simulation scenario and the complex low-altitude actual operating environment of the airport terminal control area, and enhances the adaptability verification capability under complex scenarios. Next, the complex low-altitude environment simulation model and the real-world monitoring equipment are linked based on the TCP / IP communication interface to construct a virtual-real fusion verification model and generate virtual-real data comparison results. This achieves effective linkage and synchronous comparison between the virtual scenario and the real-world data, improving the authenticity of the virtual-real fusion verification and also improving the reliability and comprehensiveness of the verification results. Finally, by generating situational awareness performance evaluation results and optimization suggestions based on the comparison results of virtual and real data, the verification results of the virtual-real fusion stage are transformed into quantifiable performance conclusions, which improves evaluation efficiency, reduces subjectivity in the evaluation process, and provides a clear basis for subsequent technical improvements. This solves the problem in the existing technology where there are no clear quantitative evaluation indicators or standard verification procedures, which leads to reduced verification efficiency, affects the credibility of verification results, and makes it difficult for situational awareness technology to obtain accurate evidence, resulting in poor verification authenticity.

[0034] In one embodiment, step S100: constructing a situational awareness basic model based on the acquired low-altitude target image data and location data, and deriving basic model parameters based on the situational awareness basic model, including: Step S110: Collect low-altitude target image data and obtain low-altitude target recognition results based on the low-altitude target image data; Step S120: Collect the location data of low-altitude targets, and associate and match the low-altitude target identification results with the location data to generate basic verification data; Step S130: Construct a situational awareness basic model based on the basic verification data, and derive the basic model parameters based on the situational awareness basic model.

[0035] In this embodiment, in step S110, a YOLOv8 basic network model adapted to low-altitude scenes is selected, and the model initialization configuration is completed based on the PyTorch framework. The model input size is adjusted to 640×640 pixels, and the initial learning rate of the model is set to 1e-4 and the batch processing size is 32.

[0036] Collect low-altitude target sample data including different models of manned and unmanned aircraft, different flight attitudes, and different lighting conditions, with a total sample size of no less than 5,000 sets. Perform data augmentation processing such as normalization, random cropping, and flipping on the sample data, remove fuzzy and invalid samples, and divide the training set and validation set in an 8:2 ratio.

[0037] The preprocessed sample data was input into the initialized YOLOv8 model, and the SGD optimizer was used for model training. The number of training iterations was set to 100 rounds, and a validation was performed every 10 rounds. The model confidence threshold and anchor box parameters were adjusted according to the recognition accuracy of the validation set, and finally the confidence threshold was optimized to 0.6.

[0038] Next, the trained and optimized YOLOv8 algorithm was deployed to a pre-defined situational awareness prototype system. A PyTorch 1.13.1 runtime environment and a CUDA 11.7 acceleration environment were installed on the prototype system's hardware terminal. The trained and optimized YOLOv8 model weight file was imported into the prototype system, model runtime parameters were configured, and a model call interface was established. An adaptation program for the YOLOv8 algorithm and the prototype system's data acquisition interface was developed, enabling the algorithm to receive low-altitude target image data collected by sensors in real time and output low-altitude target recognition results such as target category, confidence level, and bounding box coordinates. Finally, local testing was conducted by accessing a single set of low-altitude target image samples to identify and troubleshoot model import and interface adaptation anomalies, ensuring stable algorithm operation and output of effective low-altitude target recognition results.

[0039] It should be noted that the preset situational awareness prototype system is a basic verification carrier that integrates the core functions of low-altitude target recognition and location positioning. It is used for the initial debugging of the core situational awareness module and the export of parameter benchmarks. Its core components include the YOLOv8 target recognition algorithm, the GPS / BeiDou dual-mode positioning module, and a data acquisition interface and a simple debugging platform. It realizes the initial verification and parameter calibration of the low-altitude target recognition and positioning functions, and provides a unified parameter benchmark and performance reference for subsequent numerical simulation and virtual-real fusion verification, which is in line with the core positioning of the prototype system module of this invention.

[0040] In step S120, location data of low-altitude targets is collected, and the low-altitude target identification results are correlated and matched with the location data to generate basic verification data. Specifically, the GPS / BeiDou dual-mode positioning module is connected to the prototype system's industrial computer via an RS232 interface, and the positioning module driver is installed to ensure normal module communication. Then, a linkage program between the YOLOv8 algorithm identification results and the positioning data is developed, and data linkage rules are set. When the algorithm identifies a low-altitude target, the positioning module is triggered to collect the target's real-time location coordinates, and the target category, identification confidence level, and location coordinates are correlated and matched to generate basic verification data. Subsequently, a simple test scenario is built, simulating low-altitude targets are placed and moved to different positions, and multiple sets of basic verification data are collected. The linkage response speed and the matching accuracy between the identification results and location data are debugged and calibrated to eliminate data transmission delay. Further, 500 sets of linkage data are continuously tested to ensure that the linkage response time is ≤50ms, the matching accuracy between positioning data and identification results is ≥99%, and there is no data loss or mismatch.

[0041] In step S130, specifically, based on the deployed and debugged YOLOv8 target recognition algorithm, a GPS / BeiDou dual-mode positioning module is further integrated to construct an integrated situational awareness basic model architecture that combines recognition and positioning. The model input is defined as low-altitude target image data and location data collected by sensors, and the output is the target category and precise location coordinates. Next, the linkage performance of the YOLOv8 target recognition module and positioning module is debugged to ensure that the target category information output by the recognition module accurately matches the location coordinates output by the positioning module, eliminating data transmission delay between modules and ensuring that the data linkage delay is ≤50ms. Then, low-altitude target simulation data is imported to perform overall model performance calibration and optimize model parameters, ensuring that the model's target recognition accuracy is ≥90% and positioning accuracy is ≤3m. Finally, the calibrated model parameters are exported as basic model parameters for subsequent numerical simulation verification and virtual-real fusion verification parameter benchmarks.

[0042] In one embodiment, step S130: constructing a situational awareness basic model based on the basic verification data, and deriving basic model parameters based on the situational awareness basic model, includes: Step S131: Debug and calibrate the low-altitude target identification function and the location positioning function according to the basic verification data, and construct the situational awareness basic model based on the debugged and calibrated basic verification data; Step S132: Perform performance calibration on the situational awareness base model and export the calibrated base model parameters.

[0043] In this embodiment, the YOLOv8 target recognition algorithm, which has been deployed and debugged, is used as the core. It integrates a GPS / BeiDou dual-mode positioning module to build an integrated situational awareness basic model architecture of "recognition-positioning". The model input is defined as low-altitude target images and position data collected by sensors, and the output is target category and precise position coordinates.

[0044] Then, module linkage debugging is performed. Specifically, the linkage performance of the YOLOv8 target recognition module and the positioning module is debugged to ensure that the target category information output by the recognition module and the position coordinates output by the positioning module are accurately matched, eliminate data transmission delay between modules, and ensure that the data linkage delay is ≤50ms.

[0045] Next, model performance calibration is performed. Specifically, by importing low-altitude target simulation data, the overall performance of the model is calibrated, the model parameters are optimized, and the target recognition accuracy of the model is ≥90% and the positioning accuracy is ≤3m, so as to meet the basic performance requirements of subsequent verification work.

[0046] Finally, the model parameters are exported. The calibrated model parameters (including the YOLOv8 algorithm confidence threshold, positioning module accuracy parameters, etc.) are exported and used as the parameter benchmark for the numerical simulation module and the virtual-real fusion test platform module to ensure the consistency of the verification parameters throughout the process.

[0047] In summary, by leveraging the synergy between the YOLOv8 algorithm and the basic situational awareness model, the initial debugging of the core performance of situational awareness was completed, and standardized parameter benchmarks were derived. This provides unified parameter support for subsequent numerical simulation pre-verification and comprehensive verification through virtual-real fusion, ensuring the continuity of the entire verification process and the accuracy of the verification results.

[0048] In one embodiment, step S200, constructing a complex low-altitude environment simulation model based on the geographical data of the target low-altitude operating area and the basic model parameters, includes: Step S211: Based on the Unity3D modeling algorithm, perform 3D scene modeling according to the geographic data of the target low-altitude operating area, and generate an initial simulation model of the low-altitude environment; Step S212: Add a target model and a sensor model to the initial simulation model of the low-altitude environment, and specify the parameters of the target model and the sensor model; Step S213: Integrate the meteorological interference scenario and the electromagnetic interference scenario into the initial simulation model of the low-altitude environment, and set the adjustable range of the interference parameters; Step S214: Import the basic model parameters and calibrate the target identification parameters and positioning parameters in the initial simulation model of the low-altitude environment; Step S215: Run the initial simulation model of the low-altitude environment to test the model stability and scene fidelity under different low-altitude operating scenarios. After the test is completed, the complex low-altitude environment simulation model is constructed.

[0049] In this embodiment, the target low-altitude operating area refers to the airport terminal control area, and the geographic data refers to the measured GIS data within the airport terminal control area. The core of 3D scene modeling is the Unity3D modeling algorithm. First, a 3D geographic scene is constructed based on the Unity3D modeling algorithm, adding target models (manned aircraft, unmanned aircraft) and sensor models (radar, ADS-B), clarifying the geometric and dynamic parameters of the models, and ensuring that the models are consistent with the actual aircraft and equipment parameters (error ≤ 3%).

[0050] Then, interference scenarios are integrated, specifically by integrating meteorological interference and electromagnetic interference simulation modules into the scenario model, setting the adjustable range of interference parameters, supporting scenario simulation under different interference intensities, and meeting the pre-verification requirements of multiple operating conditions.

[0051] Next, parameter benchmark matching is performed, the basic model parameters are imported, and the parameters related to virtual target recognition and positioning are calibrated to ensure that the situational awareness simulation effect in the virtual scene is consistent with the prototype system, thus ensuring the validity of the pre-verification data.

[0052] Finally, model performance testing was conducted. By running the scene model and simulating different low-altitude operating scenarios, the stability and fidelity of the model were tested to ensure that the model's frame rate was ≥30fps and the scene parameter error was ≤1m. The modeling was then completed and put into pre-validation use.

[0053] To further explain, the first step is to configure the algorithm environment. First, install the Unity3D engine (version ≥ 2021.3), configure core plugins such as terrain rendering, lighting simulation, and physics engine, and set the scene rendering resolution to 1920×1080 and the frame rate to ≥ 30fps to ensure the smoothness and realism of scene rendering.

[0054] Then, geographic data import and preprocessing are performed to obtain measured GIS data of the airport terminal control area (using the WGS84 coordinate system, with an accuracy of ≤0.5m), including core geographic information such as terrain elevation, building coordinates, and runway layout. The data is then converted to a Unity3D compatible format, and redundant data is removed to ensure the accuracy and completeness of the geographic data.

[0055] Next, scene modeling is implemented. The Unity3D terrain rendering algorithm is called to construct a 1:1 three-dimensional geographic scene model based on the preprocessed GIS data. The modeling of key elements such as terrain undulation, runway, terminal, and core obstacles is completed in sequence. Texture mapping, lighting simulation and other effects are added to improve the scene fidelity. Specifically, it utilizes Unity3D's built-in Terrain Renderer algorithm. This algorithm employs texture stitching and LOD (Level of Detail) layer rendering technology, achieving high-fidelity terrain rendering through three core steps: multi-resolution texture fusion, terrain normal baking, and lightmap generation. The texture stitching uses a bilinear interpolation algorithm, which achieves smooth transitions between textures of different resolutions by calculating a weighted average of the grayscale values ​​of four adjacent pixels, effectively solving the problems of jagged edges and layering that occur during texture stitching.

[0056] Subsequently, meteorological interference and electromagnetic interference simulation parameters were integrated to simplify the construction of interference scenario sub-models, which were then integrated into the three-dimensional geographic scene model. The meteorological interference parameters were set to wind speed of 0-20m / s and the electromagnetic interference parameters were set to 0-100dB, which are highly consistent with the characteristics of the complex low-altitude actual operating environment of the airport terminal control area. During the modeling process, a coordinate calibration algorithm is also used to accurately match the GIS data coordinates with the Unity3D scene coordinates, with a calibration error of ≤0.1m, to ensure the accuracy of scene modeling.

[0057] Then, algorithm parameter matching and debugging are performed. By importing the basic model parameters, the parameters of the target (manned aircraft, unmanned aircraft) model and sensor model in the virtual scene are matched to ensure that the motion characteristics of the virtual target and the measurement accuracy of the sensor are consistent with the prototype system.

[0058] When importing the basic model parameters, the parameter baselines exported from the prototype system (including YOLOv8 algorithm confidence threshold, positioning accuracy parameters, target recognition accuracy standards, etc.) are first converted into an XML format compatible with the Unity3D engine, and then imported into the virtual scene project through the Unity3D AssetDatabase interface to create a parameter configuration file, in accordance with the steps of "parameter baseline export". Then, virtual target model parameter matching is performed. By retrieving manned and unmanned aircraft models in the virtual scene and comparing them with the prototype system parameter benchmark, the geometric parameters (body size, wingspan, etc.) and dynamic parameters (flight speed, turning radius, etc.) of the virtual target are adjusted to ensure that the deviation from the target parameters in the prototype system is ≤3% (consistent with the error requirements for the model base construction in the following text). Next, sensor model parameter matching is performed: for the radar and ADS-B sensor models in the virtual scene, their measurement accuracy, data acquisition frequency and other parameters are adjusted to make them consistent with the parameters of the sensors in the prototype system, so as to ensure that the output characteristics of the virtual sensors match the real scene sensors and correspond to the sensor configuration of the prototype system. Next, a virtual scenario is run to simulate low-altitude target flight, collecting virtual target identification and positioning data. This data is then compared with the prototype system's debugging data to ensure that the deviation between the virtual data and the prototype system data is ≤5%. Parameter matching and debugging are completed to ensure the feasibility of the parameter matching step, providing reliable scenario support for subsequent numerical simulation pre-verification. The operational stability of the scenario model is then debugged to eliminate problems such as scenario stuttering and parameter anomalies, ensuring that the scenario model can stably support multi-scenario pre-verification work.

[0059] In one embodiment, step S200, generating pre-verification data and targeted verification scenarios based on the complex low-altitude environment simulation model, includes: Step S221: Conduct multi-scenario pre-verification based on the complex low-altitude environment simulation model, collect verification data under different scenarios, and generate pre-verification data; Step S222: Select targeted verification scenarios based on the pre-verification data.

[0060] In this embodiment, a high-fidelity simulation model of a complex low-altitude environment is constructed using Unity3D modeling algorithms. Multi-scenario pre-verification is then conducted, and verification data from different scenarios is collected. This pre-verification data is then used to identify scenarios with weaker performance and more prominent problems. These selected scenarios are the targeted verification scenarios. This setup provides targeted scenarios and data support for subsequent comprehensive virtual-real fusion verification, improving the efficiency and focus of the verification work.

[0061] In one embodiment, step S300 involves constructing a virtual-real fusion verification model by linking the complex low-altitude environment simulation model and the real-world monitoring equipment based on a TCP / IP communication interface, including: Step S310: With the support of targeted verification scenarios and pre-verification data, connect the complex low-altitude environment simulation model and the real-world monitoring equipment to the TCP / IP communication interface respectively; Step S320: Receive the real-scene monitoring data collected by the real-scene monitoring device, and construct a virtual-real fusion verification model based on the real-scene monitoring data; Step S330: Generate virtual-real data comparison results based on the virtual-real fusion verification model.

[0062] In this embodiment, firstly, the virtual-real fusion verification model architecture is established with the TCP / IP communication interface as the core, clarifying that the input of the model is real-scene monitoring data and the output is the comparison result of virtual and real data.

[0063] Next, module integration is carried out by connecting the complex low-altitude environment simulation model of the numerical simulation module and the real-scene monitoring equipment (radar, ADS-B) to the TCP / IP communication interface to ensure that real-scene data can be imported into the virtual scene in real time and virtual scene data can be exported to the data analysis module in real time.

[0064] In addition, data synchronization calibration is performed, specifically by calibrating the timestamps of virtual and real data through the BeiDou time synchronization module, optimizing the data mapping algorithm, and ensuring that the synchronization error between the motion trajectory of the real target and the virtual target is ≤30ms, and the comparison error between virtual and real data is ≤8%.

[0065] Next, model performance is debugged. By running the virtual-real fusion verification model, a semi-physical verification scenario is simulated to test the model's data interaction stability and synchronization accuracy, ensuring that the model can run continuously without failure for ≥24 hours, and then the modeling is completed and put into full-scale verification.

[0066] Therefore, this embodiment uses TCP / IP communication algorithms to achieve real-time interaction and synchronization of virtual and real data, relies on the virtual-real fusion verification model to carry out semi-physical verification, compares virtual simulation data with real-world monitoring data, comprehensively verifies the performance of situational awareness technology, and ensures the authenticity and reliability of the verification results.

[0067] In step S330, the virtual-real fusion verification model is used to realize real-time interaction and synchronous comparison between real-scene monitoring data and virtual scene data, and output the virtual-real data comparison results.

[0068] In this embodiment, the TCP / IPv4 protocol is specifically adopted. The TCP / IPv4 protocol includes two core layers: the IP layer and the TCP layer. The IP layer uses the IPv4 address encoding format to realize the routing and forwarding of data packets, and uses the ARP protocol to resolve the correspondence between IP addresses and physical addresses to ensure accurate data transmission. The TCP layer uses a three-way handshake to establish a connection and a four-way handshake to release the connection. It uses the sliding window protocol to implement flow control, and uses timeout retransmission and checksum mechanisms to ensure the reliability of data transmission. The data segment size is set to 1460 bytes to adapt to the verification data transmission requirements.

[0069] When configuring data communication parameters, the communication port is set to 5000-5010, the transmission rate is ≥200Mbps, and the data transmission format adopts the standardized JSON format to ensure the standardization and compatibility of data transmission. At the same time, a data encryption mechanism is configured, using the AES-256 encryption algorithm, specifically the AES-256-ECB encryption mode. This algorithm uses a block cipher system with a block length of 128 bits and a key length of 256 bits. Data encryption is achieved through round encryption operations, which specifically include four core steps: byte substitution, row shifting, column mixing, and round key addition. The key adopts a random generation mechanism, and the key update cycle is 24 hours to ensure encryption security.

[0070] Data transmission security is ensured by encrypting the transmitted data. Specifically, JSON format data uses a standardized key-value pair definition, with keys uniformly named "Device ID, Data Type, Timestamp, Data Value, Checksum". The checksum is calculated using the MD5 algorithm, with the formula MD5(Device ID + Data Type + Timestamp + Data Value + Key), ensuring the integrity of data transmission.

[0071] In addition, during the development of the data interaction interface, a standardized data interaction interface is developed based on the configured TCP / IP communication parameters. It is divided into a real-scene data import interface and a virtual data export interface. The real-scene data import interface is used to receive data collected by real-scene monitoring equipment such as radar and ADS-B, while the virtual data export interface is used to output simulation data in the virtual scene. After the interface is developed, it is debugged to ensure that there is no data loss or transmission anomaly.

[0072] During data synchronization debugging, the BeiDou timing module was integrated, and the TCP / IP communication algorithm was used to achieve time synchronization between virtual and real data. The time synchronization accuracy was set to be better than 0.5ms. The data transmission delay was debugged to ensure that the delay of real data import and virtual data export were both ≤50ms, which was adapted to the subsequent requirement of virtual and real data synchronization error ≤30ms, thus realizing real-time synchronization of virtual and real data.

[0073] In addition, during algorithm linkage debugging, the TCP / IP communication algorithm is linked with the complex low-altitude environment simulation model and real-scene monitoring equipment to simulate the interaction process between virtual and real data. This ensures that real-scene data can accurately drive the operation of the virtual scene, and that virtual data can be accurately compared with real-scene data. The specific implementation steps are as follows: ①Equipment docking: Connect the real-scene monitoring equipment (radar, ADS-B, with sensor configuration consistent with the prototype system and virtual-real fusion module) to the virtual-real fusion test platform host via Ethernet interface, configure the device IP address (in the same network segment as the platform host IP), and ensure normal communication between the device and the platform host; integrate the Unity3D virtual scene project (a complex low-altitude environment simulation model built by the numerical simulation module) with the TCP / IP communication algorithm module, configure the virtual scene data export interface and communication parameters, and ensure that the virtual scene can output simulation data through the TCP / IP protocol, meeting the linkage requirements of the core module; ② Connection establishment: The TCP / IP communication service is initiated, with the platform host acting as the server and the real-scene monitoring devices and virtual scene projects acting as clients. TCP connections are established through preset communication ports (5000-5010), and a three-way handshake mechanism is used to confirm connection stability, troubleshoot connection timeouts and connection failures, and comply with the TCP / IP protocol connection mechanism.

[0074] ③ Data interaction debugging: The real-scene monitoring equipment is activated to collect low-altitude real-scene data (target position, flight attitude, etc.) in the airport terminal control area. The data is then transmitted to the platform host through the developed real-scene data import interface. The platform host forwards the real-scene data to the virtual scene through the TCP / IP communication algorithm. The driving effect of the real-scene data on the virtual scene is tested to ensure that the virtual target can synchronously simulate the movement trajectory of the real-scene target. Meanwhile, the virtual scene simulation data is transmitted to the platform host through the virtual data export interface and compared with the real scene data in real time to debug the data transmission delay and synchronization accuracy, so as to meet the requirements of "data transmission delay ≤50ms and synchronization error ≤30ms".

[0075] ④ Anomaly detection and optimization: The system was continuously run for 24 hours, recording issues such as packet loss rate and latency during data transmission. Communication parameters were optimized to ensure that the data packet loss rate was ≤0.1%, the transmission latency was ≤50ms, and the virtual-real data synchronization error was ≤30ms. The linkage debugging was completed to ensure the integrity of algorithm deployment and debugging, and to ensure that virtual-real fusion verification could be carried out normally.

[0076] In one embodiment, step S400: generating situational awareness performance evaluation results and optimization suggestions based on the virtual-real data comparison results, including: Step S410: Perform verification management based on the virtual-real data comparison results, and generate verification data management results; Step S420: Generate situational awareness performance evaluation results and optimization suggestions based on the verification data management results.

[0077] In this embodiment, in step S410, verification management is performed, which is mainly used to verify the secure storage, classification management and traceability of the virtual and real data comparison results.

[0078] Verification management is based on a verification data management model, which stores verification data from each module. This model, with data storage and management algorithms at its core, is the central carrier of verification data management. The modeling steps are as follows: 1. Model Foundation Construction: Establish a data storage framework, dividing the system into a prototype system data area, a numerical simulation data area, and a virtual-real fusion data area, and clarifying the storage rules and access permissions for each data area. The virtual-real data comparison results are stored in the virtual-real fusion data area.

[0079] 2. Functional module integration: Integrates functional modules such as data query, export, backup, and encryption to ensure that data can be categorized for query, quickly exported, and securely backed up, meeting the core needs of verification data management.

[0080] 3. Model debugging and optimization: Import a small amount of validation data to test the model's storage performance, query efficiency, and data security. Optimize model parameters to ensure stable model operation, no data loss, and no query delay, thus completing the modeling process.

[0081] Therefore, the verification data management model enables secure storage, categorized management, and traceability of data throughout the verification process, supporting the orderly implementation of verification work in each module and providing complete data support for subsequent intelligent evaluation and technology optimization.

[0082] In one embodiment, the verification data management model is implemented as follows: 1. Architecture Design: By clearly defining data classification rules, the verification data is divided into three categories: prototype system data, numerical simulation data, and virtual-real fusion data. Each category of data is further subdivided according to timestamp and scenario type.

[0083] 2. Storage Parameter Configuration: Configure data storage parameters, set the storage capacity to ≥1000GB, adopt a distributed storage method to ensure the security and scalability of data storage; set the data retention period to ≥1 year, configure a data backup mechanism, and perform a full backup once a day to prevent data loss.

[0084] 3. Data Encryption and Filtering: An integrated data encryption module is used, employing AES-256-ECB encryption mode (consistent with the encryption mode of the virtual-physical fusion module, ensuring data encryption security throughout the entire process), to encrypt stored data and ensure data security; at the same time, invalid and abnormal data are filtered out through algorithms.

[0085] During the screening process, a data threshold range is set (e.g., positioning error ≤ 10m, data acquisition delay ≤ 100ms), and the 3σ criterion (normal distribution outlier identification method) is adopted. The specific steps are as follows: 1. Calculate the mean μ and standard deviation σ of the data sample; 2. Set the outlier detection threshold to μ±3σ; 3. Remove data that exceeds the threshold range, and combine this with manually set invalid data labels (such as null values ​​and duplicate data) to complete the filtering of invalid and abnormal data, ensuring the accuracy of the stored data until the filtering accuracy rate is ≥99.5%.

[0086] 4. Deployment and Debugging: Deploy the data storage, develop data query and export interfaces, debug the running stability, ensure that the data query response time is ≤50ms, support PDF and Excel data export formats, and complete the algorithm deployment and debugging.

[0087] In one embodiment, step S420 is implemented as follows: 1. Algorithm initialization: Build a quantitative evaluation algorithm architecture, clarify that the input of the algorithm is virtual-real fusion verification data, the output is situational awareness performance evaluation results, and configure the algorithm calculation precision to 0.01 to ensure the accuracy of the evaluation results.

[0088] 2. Evaluation Indicator Setting: Based on the core requirements of situational awareness verification, four core evaluation indicators (accuracy, real-time performance, robustness, and reliability) are constructed, and the quantitative standards and calculation logic of each indicator are clarified. Accuracy indicators include target recognition accuracy (accuracy indicator calculation formula: target recognition accuracy = number of correctly identified targets / total number of targets × 100%, standard ≥ 95%) and positioning error (standard ≤ 3m); Real-time performance metrics include data acquisition latency (standard ≤ 30ms) and recognition response time (standard ≤ 0.3s). Robustness metrics include performance fluctuations under complex environments (standard ≤5%). Reliability metrics include the system’s continuous trouble-free operation time (standard ≥ 1000 hours).

[0089] 3. Algorithm calculation and optimization: The integrated indicator calculation logic specifically involves modularly integrating the calculation logic of the four core evaluation indicators into the quantitative evaluation algorithm. An object-oriented programming approach is used to construct an independent calculation function for each of the aforementioned evaluation indicators.

[0090] The accuracy index calculation function calls the data statistics interface to extract core parameters such as the total number of targets, the number of correctly identified targets, and the location coordinates from the verification data, and completes the calculation according to the above accuracy index calculation formula; The real-time performance indicator calculation function calculates the time difference between data acquisition and identification response by calling the timestamp interface; The robustness index calculation function uses the analysis of variance algorithm to calculate the fluctuation values ​​of various performance parameters under complex environments. The specific steps are as follows: 1. Extract the same performance parameter data under different interference scenarios; 2. Calculate the mean of each group of data and the overall mean; 3. Calculate the between-group variance and the within-group variance; 4. Calculate the F-statistic (F = between-group variance / within-group variance) and use the F-value to determine the degree of performance fluctuation.

[0091] The reliability index calculation function counts the continuous running time and number of failures of the system. All calculation functions are called in tandem to achieve synchronous calculation and summary of index data. The verification data management results corresponding to the comparison results of virtual and real data are imported into the algorithm to automatically calculate the actual value of each evaluation index and compare it with the preset quantitative standard. An error analysis algorithm (specifically the absolute error method, the calculation formula is absolute error = |actual value - standard value|, relative error = (absolute error / standard value) × 100%) is used to calculate the index deviation. The algorithm calculation efficiency is optimized and multi-threaded parallel computing is used to ensure that the index calculation response time is ≤1 second.

[0092] In summary, the analysis of validation data, the calculation and comparison of evaluation indicators, and the generation of standardized evaluation results are all part of the process. Specifically, the evaluation model automatically summarizes the actual values, standard values, absolute errors, and relative errors of each indicator, thus generating a situational awareness performance evaluation result. Finally, an evaluation report is generated that includes indicator compliance status, overall performance conclusions, and optimization suggestions. This provides accurate and reliable data support for the optimization and iteration of situational awareness technology, improving the efficiency and objectivity of the validation evaluation.

[0093] In one embodiment, such as Figure 2 As shown, an airport low-altitude operational situational awareness verification device is also provided, the device comprising: The basic model parameter export module is used to construct a situational awareness basic model based on the collected low-altitude target image data and location data, and to export basic model parameters based on the situational awareness basic model. The complex simulation model generation module is used to construct a complex low-altitude environment simulation model based on the geographical data of the target low-altitude operating area and the basic model parameters, and to generate pre-verification data and targeted verification scenarios based on the complex low-altitude environment simulation model. The virtual-real data comparison generation module is used to connect the complex low-altitude environment simulation model and the real-scene monitoring equipment based on the TCP / IP communication interface to build a virtual-real fusion verification model, and generate virtual-real data comparison results based on the virtual-real fusion verification model; The situation assessment optimization generation module is used to generate situation awareness performance assessment results and optimization suggestions based on the comparison results of virtual and real data.

[0094] In one embodiment, the basic model parameter export module is further configured to: acquire low-altitude target image data and obtain low-altitude target recognition results based on the low-altitude target image data; acquire low-altitude target location data and associate and match the low-altitude target recognition results with the location data to generate basic verification data; construct a situational awareness basic model based on the basic verification data and export basic model parameters based on the situational awareness basic model.

[0095] In one embodiment, the basic model parameter export module is further configured to: debug and calibrate the low-altitude target identification function and the location positioning function based on the basic verification data, and construct the situational awareness basic model based on the debugged and calibrated basic verification data; perform performance calibration on the situational awareness basic model, and export the performance-calibrated basic model parameters.

[0096] In one embodiment, the complex simulation model generation module is further configured to: perform 3D scene modeling based on the geographic data of the target low-altitude operating area using the Unity3D modeling algorithm, and generate an initial low-altitude environment simulation model; add a target model and a sensor model to the initial low-altitude environment simulation model, and define the parameters of the target model and the sensor model; integrate meteorological interference scenarios and electromagnetic interference scenarios into the initial low-altitude environment simulation model, and set the adjustable range of interference parameters; import the basic model parameters, and calibrate the target identification parameters and positioning parameters in the initial low-altitude environment simulation model; run the initial low-altitude environment simulation model, and test the model stability and scene fidelity under different low-altitude operating scenarios, and complete the construction of the complex low-altitude environment simulation model after the test is completed.

[0097] In one embodiment, the complex simulation model generation module is further configured to: conduct multi-scenario pre-verification based on the complex low-altitude environment simulation model, collect verification data under different scenarios, and generate pre-verification data; and select targeted verification scenarios based on the pre-verification data.

[0098] In one embodiment, the virtual-real data comparison generation module is further configured to: connect the complex low-altitude environment simulation model and the real-scene monitoring device to the TCP / IP communication interface respectively, under the support of targeted verification scenarios and pre-verification data; receive the real-scene monitoring data collected by the real-scene monitoring device, and construct a virtual-real fusion verification model based on the real-scene monitoring data; and generate virtual-real data comparison results according to the virtual-real fusion verification model.

[0099] In one embodiment, the situation assessment optimization generation module is further configured to: perform verification management based on the virtual-real data comparison results and generate verification data management results; and generate situation awareness performance assessment results and optimization suggestions based on the verification data management results.

[0100] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0101] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0102] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0103] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0104] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to execute the steps described in the various method embodiments above.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0107] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0109] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such 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 this application, and should all be included within the protection scope of this application.

[0111] One embodiment of this application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above-described methods.

[0112] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above description is an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than described above, or a combination of certain components, or different components, such as input / output devices, network access devices, etc.

[0113] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0114] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0115] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for verifying airport low-altitude operational situational awareness, characterized in that, The method includes: A basic situational awareness model is constructed based on the collected low-altitude target image data and location data, and basic model parameters are derived based on the basic situational awareness model. A complex low-altitude environment simulation model is constructed based on the geographical data of the target low-altitude operating area and the parameters of the basic model, and pre-verification data and targeted verification scenarios are generated based on the complex low-altitude environment simulation model. Based on the TCP / IP communication interface, the complex low-altitude environment simulation model and the real-scene monitoring equipment are linked to construct a virtual-real fusion verification model, and virtual-real data comparison results are generated based on the virtual-real fusion verification model. Based on the comparison results of the virtual and real data, a situational awareness performance evaluation result and optimization suggestions are generated. A basic situational awareness model is constructed based on the collected low-altitude target image data and location data, and basic model parameters are derived from the basic situational awareness model, including: Collect low-altitude target image data and obtain low-altitude target recognition results based on the low-altitude target image data; Collect the location data of low-altitude targets, and associate and match the low-altitude target identification results with the location data to generate basic verification data; A basic situational awareness model is constructed based on the basic verification data, and basic model parameters are derived based on the basic situational awareness model. The basic model parameters include the YOLOv8 algorithm confidence threshold and the positioning module accuracy parameters; When constructing the complex low-altitude environment simulation model, the basic model parameters are imported to calibrate the target identification parameters and positioning parameters in the initial low-altitude environment simulation model; Develop a program to link the YOLOv8 algorithm recognition results with the positioning data, set data linkage rules, trigger the positioning module to collect the real-time position coordinates of the target when the algorithm recognizes a low-altitude target, and associate and match the target category, recognition confidence and position coordinates to generate basic verification data.

2. The airport low-altitude operational situational awareness verification method according to claim 1, characterized in that, A basic situational awareness model is constructed based on the aforementioned basic verification data, and basic model parameters are derived from the aforementioned basic situational awareness model, including: The low-altitude target identification and location functions are debugged and calibrated based on the basic verification data, and the situational awareness basic model is constructed based on the debugged and calibrated basic verification data. The performance of the situational awareness base model is calibrated, and the parameters of the base model after performance calibration are exported.

3. The airport low-altitude operational situational awareness verification method according to claim 1, characterized in that, A complex low-altitude environment simulation model is constructed based on the geographical data of the target low-altitude operating area and the parameters of the basic model, including: Based on Unity3D modeling algorithms, a 3D scene model is created using geographic data of the target low-altitude operating area, and an initial simulation model of the low-altitude environment is generated. Add a target model and a sensor model to the initial simulation model of the low-altitude environment, and define the parameters of the target model and the sensor model; Meteorological interference scenarios and electromagnetic interference scenarios are integrated into the initial simulation model of the low-altitude environment, and the adjustable range of interference parameters is set. Import the basic model parameters and calibrate the target identification and positioning parameters in the initial simulation model of the low-altitude environment; Run the initial simulation model of the low-altitude environment to test the model stability and scene fidelity under different low-altitude operating scenarios. After the test is completed, the complex low-altitude environment simulation model is constructed.

4. The airport low-altitude operational situational awareness verification method according to claim 3, characterized in that, Based on the aforementioned complex low-altitude environment simulation model, pre-verification data and targeted verification scenarios are generated, including: Based on the complex low-altitude environment simulation model, multi-scenario pre-verification is carried out, and verification data under different scenarios is collected to generate pre-verification data. Targeted verification scenarios are selected based on the pre-verification data.

5. The airport low-altitude operational situational awareness verification method according to claim 1, characterized in that, Based on the TCP / IP communication interface, a virtual-real fusion verification model is constructed by linking the complex low-altitude environment simulation model and the real-world monitoring equipment, including: With the support of targeted verification scenarios and pre-verification data, the complex low-altitude environment simulation model and real-world monitoring equipment are respectively connected to the TCP / IP communication interface; Receive real-scene monitoring data collected by real-scene monitoring equipment, and construct a virtual-real fusion verification model based on the real-scene monitoring data; The virtual-real data comparison results are generated based on the virtual-real fusion verification model.

6. The airport low-altitude operational situational awareness verification method according to claim 1, characterized in that, Based on the comparison results of the virtual and real data, situational awareness performance evaluation results and optimization suggestions are generated, including: Verification management is performed based on the comparison results of the virtual and real data, and verification data management results are generated. Based on the verification data management results, situational awareness performance evaluation results and optimization suggestions are generated.

7. An airport low-altitude operation situational awareness verification device based on the airport low-altitude operation situational awareness verification method according to any one of claims 1-6, characterized in that, The device includes: The basic model parameter export module is used to construct a situational awareness basic model based on the collected low-altitude target image data and location data, and to export basic model parameters based on the situational awareness basic model. The complex simulation model generation module is used to construct a complex low-altitude environment simulation model based on the geographical data of the target low-altitude operating area and the basic model parameters, and to generate pre-verification data and targeted verification scenarios based on the complex low-altitude environment simulation model. The virtual-real data comparison generation module is used to connect the complex low-altitude environment simulation model and the real-scene monitoring equipment based on the TCP / IP communication interface to build a virtual-real fusion verification model, and generate virtual-real data comparison results based on the virtual-real fusion verification model; The situation assessment optimization generation module is used to generate situation awareness performance assessment results and optimization suggestions based on the comparison results of virtual and real data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images