Methods, devices, aircraft and storage media for monitoring airways in low-altitude areas

Abstract

This application relates to the field of monitoring technology, and in particular to a method, device, aircraft, and storage medium for monitoring airway areas of low-altitude aircraft. This application improves the efficiency of airway area monitoring by detecting when an aircraft is flying along a target flight path, collecting airway photographs of the target airway area at target time periods, identifying anomalies in the airway photographs, and issuing an alarm when abnormal data is detected in the airway photographs.

Description

Technical Field

[0001] This application relates to the field of monitoring technology, and in particular to a method, device, aircraft, and storage medium for monitoring airways in low-altitude areas. Background Technology

[0002] As an important component of airspace, low-altitude airspace plays an irreplaceable role in general aviation activities such as agricultural production, marine monitoring, remote sensing and mapping, meteorological detection, emergency rescue, disaster relief, education and training, culture and sports, and tourism.

[0003] Currently, waterway monitoring mostly relies on fixed camera surveillance or manual patrols, which suffers from low monitoring efficiency due to limitations in the work attitude and experience of patrol personnel. Summary of the Invention

[0004] In view of the above, this application provides a method, device, aircraft, and storage medium for monitoring airways in low-altitude areas, thereby improving monitoring efficiency and accuracy.

[0005] A first aspect of this application provides a method for monitoring airways in low-altitude aircraft areas, the method comprising:

[0006] When it is detected that the aircraft is flying according to the target flight path, the airway photos of the target airway area are collected according to the target time period;

[0007] Anomaly detection is performed on the waterway photos to determine whether there is abnormal data in the waterway photos;

[0008] When the abnormal data is detected in the waterway photograph, an alarm is triggered based on the abnormal data.

[0009] In an optional implementation, when the abnormal data indicates waterway congestion, the anomaly identification of the waterway photograph includes:

[0010] Determine the waterway traffic data of the target waterway area in the waterway photograph, and the clustering evaluation index of the waterway traffic data;

[0011] An evaluation sample matrix is ​​constructed based on the waterway traffic data and the clustering evaluation indicators;

[0012] Calculate the cluster evaluation value for each evaluation sample in the evaluation sample matrix;

[0013] Obtain the maximum evaluation value among the cluster evaluation values, and determine whether the waterway traffic data is congested based on the maximum evaluation value.

[0014] In an optional implementation, calculating the cluster evaluation value for each evaluation sample in the evaluation sample matrix based on the cluster evaluation index includes:

[0015] Determine multiple cluster centers of the evaluation sample matrix, and the membership value of each evaluation sample to each of the cluster centers;

[0016] The weight of each cluster center is determined, and the cluster evaluation value of each evaluation sample is determined based on the membership value and the weight.

[0017] In an optional implementation, when the abnormal data is an obstacle, the anomaly identification of the waterway photograph includes:

[0018] Determine the sea surface area and non-sea surface area in the waterway photograph based on the target waterway area;

[0019] Extract the first obstacle information of the sea surface area and the second obstacle information of the non-sea surface area from the preset obstacle database;

[0020] The presence of the obstacle in the sea surface area is determined based on the first obstacle information, and the presence of the obstacle in the non-sponge area is determined based on the second obstacle information.

[0021] In an optional implementation, when the abnormal data represents a potential threat from the external environment, the anomaly identification process for the waterway photographs includes:

[0022] Feature extraction is performed on the waterway photographs to obtain a feature set;

[0023] The target feature is compared with a preset environmental data set, where the target feature is any feature in the feature set; when it is determined that the target feature matches any environmental data in the preset environmental data set, it is determined that the waterway photograph has the external environmental hazard.

[0024] In an optional implementation, the method further includes:

[0025] The real-time flight trajectory, real-time flight speed, and real-time flight attitude of the aircraft are obtained.

[0026] Determine whether the aircraft is in an abnormal flight state based on the real-time flight trajectory, the real-time flight speed, and the real-time flight attitude.

[0027] When it is determined that the aircraft is in the abnormal flight state, the target flight data corresponding to the abnormal flight state is determined, and the target flight data is any one of the real-time flight trajectory, the real-time flight speed and the real-time flight attitude;

[0028] An alarm will be triggered based on the target flight data.

[0029] In an optional implementation, when it is determined that the anomalous data exists in the waterway photograph, the method further includes:

[0030] An alarm message is generated based on the abnormal data, and a set of waterway photos corresponding to the abnormal data is determined based on a preset time range;

[0031] The alarm information and the set of waterway photos are sent to the terminal monitoring device.

[0032] A second aspect of this application provides a low-altitude aircraft area airway monitoring device, the device comprising:

[0033] The data acquisition module is used to acquire photos of the target flight path area according to the target time period when the aircraft is detected to be flying along the target flight path.

[0034] The identification module is used to identify anomalies in the waterway photographs to determine whether there is abnormal data in the waterway photographs; the alarm module is used to issue an alarm based on the abnormal data when it is determined that there is abnormal data in the waterway photographs.

[0035] A third aspect of this application provides an aircraft, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low-altitude aircraft area airway monitoring method.

[0036] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for monitoring airways in low-altitude aircraft areas.

[0037] In summary, the low-altitude aircraft area airway monitoring method, device, aircraft, and storage medium provided in this application achieve real-time monitoring of the airway area by collecting airway photos of the target airway area according to the target time period. Combined with anomaly identification of the airway photos, abnormal data in the airway photos can be detected in a timely manner and an alarm mechanism can be triggered, effectively improving the efficiency and accuracy of airway area monitoring. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the structure of a low-altitude aircraft area airway monitoring system as shown in an embodiment of this application;

[0039] Figure 2 This is a flowchart illustrating a method for monitoring airways in low-altitude aircraft areas, as shown in an embodiment of this application.

[0040] Figure 3 This is a functional block diagram of a low-altitude aircraft area airway monitoring device shown in an embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the structure of a data processing unit shown in an embodiment of this application. Detailed Implementation

[0042] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0043] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0044] Reference Figure 1 The diagram shown is a structural schematic of a low-altitude aircraft area airway monitoring system according to an embodiment of this application.

[0045] The low-altitude aircraft area airway monitoring system 1 includes an aircraft 10 and a terminal monitoring device 20. The aircraft 10 includes a drone 11 and a data acquisition device 12. The drone 11 flies according to a preset route, and the data acquisition device 12 collects airway photographs of the monitored area according to a preset time period. The data acquisition device 12 can be an ultra-lightweight high-definition AI camera, etc.

[0046] The aircraft 10 also includes a data processing unit 13, which processes and analyzes the airway photos when it receives the airway photos collected by the acquisition device 12 according to a preset time period, in order to determine whether there is abnormal data in the airway photos, so as to further determine whether there is anomaly in the monitored airway area.

[0047] The aircraft 10 also includes an alarm device 14, which is used to sound an alarm when it receives an alarm signal generated by the data processing unit 13 based on abnormal data. The alarm device 14 can be a loudspeaker, a broadcasting system, or the like.

[0048] In some embodiments, the data processing unit 13 is further configured to send the corresponding waterway photos / videos to the terminal monitoring device 20 according to a preset time range (X minutes before and after the abnormal time, where X is greater than 0) when it is determined that there is an anomaly in the waterway area, so that the terminal monitoring device 20 can perform data processing and data analysis on the waterway photos / videos to determine the corresponding handling plan for the anomaly.

[0049] In some embodiments, the aircraft 10 also integrates an infrared sensor for auxiliary detection, enabling the aircraft 10 to continue to operate normally at night or in low light conditions, thereby improving the all-weather operation capability of the low-altitude aircraft area airway monitoring system 1.

[0050] Reference Figure 2 The diagram shown is a flowchart illustrating a method for monitoring airways in low-altitude airspace, according to an embodiment of this application. The method includes the following steps.

[0051] S21: When it is detected that the aircraft is flying according to the target flight path, the airway photos of the target airway area are collected according to the target time period.

[0052] In some embodiments, the aircraft can pre-set a flight path (target flight path) and a time period for collecting flight path photos (target time period) for the flight path area to be monitored. After determining the target flight path and target time period, the aircraft is controlled to fly according to the target flight path, and the flight trajectory of the UAV (real-time flight trajectory) is monitored in real time to see if it is consistent with the target flight path. When the real-time flight trajectory is found to be consistent with the target flight path, it can be determined that the aircraft has not experienced a flight deviation event. Therefore, the acquisition equipment in the aircraft can be controlled to collect flight path photos of the target flight path area according to the target time period. The flight path area being monitored is referred to as the target flight path area.

[0053] In some embodiments, the aircraft can automatically adjust its flight path and data collection period based on changes in the monitored waterway area. Secondly, the aircraft can monitor the flight path for obstacles in real time, and automatically adjust its flight path to avoid obstacles when they are detected. The aircraft can also shorten the data collection period based on the number of ships in the waterway area, when the number of ships reaches a preset threshold. In other embodiments, monitoring personnel can adjust the aircraft's flight path and data collection period through a terminal monitoring device according to their personal needs / experience. Specifically, they can generate corresponding adjustment instructions based on the adjusted flight path and data collection period, and send these instructions to the aircraft through the terminal monitoring device. Upon receiving the adjustment instructions, the aircraft can then adjust the pre-stored flight path and data collection period according to the flight path and data collection period specified in the instructions.

[0054] S22, perform anomaly identification on the waterway photos to determine whether there is abnormal data in the waterway photos.

[0055] In some embodiments, upon acquiring a waterway photograph, it is first preprocessed, such as through noise reduction, contrast enhancement, and color correction, to improve the image quality. This preprocessing may further include filtering (e.g., Gaussian filtering), noise reduction (removing isolated or outlier points), and thinning (reducing the number of points to speed up processing). Furthermore, anomaly detection algorithms are used to identify anomalies in the waterway photograph to determine if any anomalies exist in the target waterway area, such as waterway congestion, obstacles, and potential environmental hazards.

[0056] In an optional implementation, when the abnormal data indicates waterway congestion, the anomaly identification of the waterway photograph includes:

[0057] Determine the waterway traffic data of the target waterway area in the waterway photograph, and the clustering evaluation index of the waterway traffic data;

[0058] An evaluation sample matrix is ​​constructed based on the waterway traffic data and the clustering evaluation indicators;

[0059] Calculate the cluster evaluation value for each evaluation sample in the evaluation sample matrix;

[0060] Obtain the maximum evaluation value among the cluster evaluation values, and determine whether the waterway traffic data is congested based on the maximum evaluation value.

[0061] In some embodiments, the aircraft first needs to determine traffic state indicators and traffic state levels to assess the traffic conditions within the target waterway area. These traffic state indicators may include congestion, convergence, and equivalent traffic volume. Congestion represents the proportion of time ships spend stationary in the waterway, calculated based on ship speeds and positional markers. Convergence refers to the density of ships in the waterway, calculated based on the number of ships passing through the target waterway area per unit time and unit length. Traffic state levels may include unobstructed, stable, slightly congested, and congested. Each traffic state level defines a range of traffic state indicators; for example, unobstructed conditions correspond to low congestion, low convergence, and moderate equivalent traffic volume, while congested conditions correspond to high congestion, high convergence, and high equivalent traffic volume. It should be noted that corresponding thresholds are pre-set to assess the level of congestion, the magnitude of convergence, and the magnitude of equivalent traffic volume. Furthermore, the determined traffic state indicators and traffic state levels are integrated together as clustering assessment indicators.

[0062] Simultaneously, the aircraft can utilize image processing techniques (e.g., object detection algorithms and image segmentation algorithms) to extract waterway traffic data from acquired waterway photographs. This data can include information such as the number of vessels, vessel speeds, and vessel spacing. An evaluation sample matrix is ​​then constructed based on the waterway traffic data and clustering evaluation metrics, where rows represent evaluation samples (i.e., waterway traffic data) and columns represent clustering evaluation metrics. Next, clustering algorithms (e.g., K-means, hierarchical clustering) are used to perform cluster analysis on the evaluation sample matrix. Based on the clustering results and clustering evaluation metrics, a clustering evaluation value is calculated for each evaluation sample. This clustering evaluation value reflects the similarity between the evaluation sample and each cluster center, thus assessing the waterway's traffic status. Specifically, the aircraft can randomly determine multiple initial cluster centers. The number of initial cluster centers can be determined based on the expected number of classifications for the waterway traffic status (e.g., smooth, stable, slightly congested, congested). A membership matrix, identical to the evaluation sample matrix, is initialized to record the membership degree of each evaluation sample to each cluster center. For each evaluation sample in the evaluation sample matrix, calculate its distance to each cluster center (using Euclidean distance, Mahalanobis distance, or other distance metrics) to quantify the similarity between the evaluation sample and the cluster center. Then, based on the distance between each evaluation sample and the cluster center, use a membership function from fuzzy mathematics (such as Gaussian membership function, trapezoidal membership function, etc.) to calculate the membership degree of each evaluation sample to each cluster center, and fill the calculated membership degree value into the corresponding position to update the membership degree matrix. Based on the updated membership degree matrix, calculate the new position of each cluster center by weighted averaging or taking the median of all evaluation samples belonging to that cluster, thus obtaining the new cluster center. After updating the cluster centers, calculate the distance between each evaluation sample and the new cluster center and update the membership degree matrix according to the same implementation method described above, until the changes in the cluster centers and membership degree matrix reach a preset convergence condition or a preset number of iterations. Finally, based on the final membership matrix, the cluster evaluation value of each evaluation sample is calculated. The cluster evaluation value reflects the similarity between the evaluation sample and each cluster center, and can be used to determine the congestion level of the waterway traffic data.

[0063] In some embodiments, when calculating the cluster evaluation value for each evaluation sample, for each evaluation sample, the importance of each cluster center is first determined, and a weight is assigned to each cluster center according to its importance. If the importance of each cluster center is equal, an equal-weight method is used, that is, each cluster center is assigned the same weight, and the weight vector is [1, 1, ..., 1], where the length of the weight vector is equal to the number of cluster centers. If the importance of each cluster center is inconsistent, that is, a "crowded" cluster center is more important than other cluster centers, a higher weight is assigned to that cluster center. For each evaluation sample, its membership values ​​to each cluster center are multiplied by the corresponding weights, and then summed to obtain the cluster evaluation value of that evaluation sample.

[0064] The cluster evaluation value for each evaluation sample is determined using the following formula:

[0065]

[0066] Among them, cluster evaluation value i Let C be the cluster evaluation value of the i-th evaluation sample, and C be the number of cluster centers. It is the membership value of the i-th evaluation sample to the j-th cluster center, and the weight is... j It is the weight of the j-th cluster center.

[0067] A higher clustering evaluation value indicates a greater similarity between the evaluated sample and the "congested" cluster center, thus suggesting more congested waterway traffic. Therefore, the maximum evaluation value is determined from the clustering evaluation values ​​of each evaluated sample, and this maximum evaluation value is compared with a preset threshold to determine whether the evaluated sample represents waterway congestion. If the maximum evaluation value meets the preset threshold, it indicates that the evaluated sample is congested; otherwise, it indicates that there is no waterway congestion, meaning that waterway traffic is normal.

[0068] The above-mentioned optional implementation methods can effectively identify anomalies in traffic data in waterway photographs, especially in cases of waterway congestion. This not only improves the accuracy and efficiency of anomaly identification but also provides strong technical support for the intelligent monitoring and management of waterway traffic.

[0069] In an optional implementation, when the abnormal data is an obstacle, the anomaly identification of the waterway photograph includes:

[0070] Determine the sea surface area and non-sea surface area in the waterway photograph based on the target waterway area;

[0071] Extract the first obstacle information of the sea surface area and the second obstacle information of the non-sea surface area from the preset obstacle database;

[0072] The presence of the obstacle in the sea surface area is determined based on the first obstacle information, and the presence of the obstacle in the non-sponge area is determined based on the second obstacle information.

[0073] In some embodiments, the aircraft can extract raw point cloud data from flight path photographs to identify anomalous data, particularly obstacles, in the flight path photographs. Further point cloud segmentation is performed to determine sea surface and non-sea surface areas. To identify different types of obstacles, features are extracted from the raw point cloud data of different areas. These features may include normal features (representing the direction of the surface), curvature features (representing the degree of surface curvature), and shape features (such as the overall shape of the obstacle). Alternatively, first obstacle information for the sea surface area and second obstacle information for the non-sea surface area can be determined based on the extracted features. The first obstacle information is obtained by comparing the first output of a pre-trained obstacle recognition model with the features of the raw point cloud data corresponding to the sea surface area, and a preset obstacle database. The second obstacle information is obtained by comparing the second output of a pre-trained obstacle recognition model with the features of the raw point cloud data corresponding to the non-sea surface area, and a preset obstacle database. The presence of an obstacle in the flight path photograph is confirmed when either the first obstacle information or the second obstacle information contains any data from the preset obstacle database. When any data point from the preset obstacle database is present in the first obstacle information, it indicates the presence of an obstacle in the sea area, requiring vessels in the normal navigation channel to take note. Conversely, when any data point from the preset obstacle database is present in the second obstacle information, it indicates the presence of an obstacle outside the sea area. In this case, the aircraft must be notified to take note of the obstacle and avoid a collision.

[0074] The obstacle recognition model is pre-trained using a deep learning network based on historical raw point cloud data, with each historical raw point cloud data set including annotation information. The pre-defined obstacle database stores information on various obstacles in the waterway, and may include obstacle images (sample images of various obstacles for matching with waterway photographs), feature descriptions (detailed feature descriptions of each obstacle, such as color, shape, texture, and size), obstacle types (categorization of obstacles, such as floating objects, shipwrecks, bridges, docks, etc.), and location information (known obstacles frequently appearing in specific locations within the waterway). The pre-defined obstacle database can be subsequently constructed and updated through manual annotation, automatic annotation using machine learning algorithms, or a combination of both, to ensure its accuracy and completeness.

[0075] By employing the aforementioned optional implementation methods, and accurately distinguishing between sea surface and non-sea surface areas, and extracting obstacle information from each separately, the presence of obstacles within and around the waterway can be accurately determined, improving the accuracy and reliability of obstacle identification. Furthermore, this helps ships avoid obstacles in a timely manner during navigation, ensuring the safe flight of aircraft and significantly enhancing the intelligence level of waterway monitoring.

[0076] In some embodiments, once obstacles are identified in a waterway photograph, the aircraft can further classify the obstacles into different categories (such as ships, buoys, buildings, etc.) and identify the specific type of each obstacle. Secondly, based on the results of target detection or semantic segmentation, the aircraft can use pixel coordinates or position coordinates relative to the waterway to determine the location of each obstacle in the waterway photograph, improving the intelligence level of waterway monitoring and providing strong support for the safe and efficient use of waterways through accurate obstacle classification and localization.

[0077] In other embodiments, the aircraft can also directly acquire raw point cloud data related to the target flight path area from the acquisition equipment.

[0078] In an optional implementation, when the abnormal data represents a potential threat from the external environment, the anomaly identification process for the waterway photographs includes:

[0079] Feature extraction is performed on the waterway photographs to obtain a feature set;

[0080] The target feature is compared with a preset environmental data set, where the target feature is any feature in the feature set; when it is determined that the target feature matches any environmental data in the preset environmental data set, it is determined that the waterway photograph has the external environmental hazard.

[0081] In some embodiments, the aircraft can use image analysis technology and a preset environmental data set to make a comprehensive judgment to determine whether there are potential external environmental hazards in the flight path photographs. The preset environmental data set is a database that stores information on various potential external environmental hazards in the flight path.

[0082] Specifically, computer vision technologies such as image segmentation, edge detection, and target recognition are used to extract features related to external hazards from flight path photographs. These features include weather characteristics (such as clouds, heavy rain, and blizzards), water surface characteristics (such as wave height, water flow speed, oil spills, and algal blooms), and shoreline characteristics (such as landslides, riverbank erosion, and fallen trees). These features are then integrated to obtain a feature set. Furthermore, any feature extracted from the flight path photograph is compared with a preset environmental data set. If at least one feature matches any environmental data in the preset environmental data set, it is determined that there is an external environmental hazard in the flight path photograph, meaning that the target flight path area monitored by the aircraft has an external environmental hazard.

[0083] In other embodiments, the aircraft also integrates multiple sensor modules for collecting external environmental data, including meteorological data, water surface data, etc.

[0084] By employing the aforementioned optional implementation methods and accurately identifying potential environmental hazards in waterway photographs, the system can promptly detect and determine potential risks in the waterway, thereby improving the efficiency and accuracy of waterway monitoring. This helps prevent safety accidents caused by environmental factors such as severe weather and water pollution, and provides strong support for the safe operation of the waterway.

[0085] S23, when it is determined that the abnormal data exists in the waterway photograph, an alarm is triggered based on the abnormal data.

[0086] When an anomaly is detected, an alarm is issued via the aircraft's loudspeaker, and the alarm information, along with flight path photos / video data collected within a preset time range, is sent to the terminal monitoring equipment.

[0087] In some embodiments, when an anomaly is determined to exist in a target waterway area, the aircraft can determine the severity of the anomaly and issue different levels of alarms based on the severity. Specifically, when waterway congestion is determined to exist in the waterway area, the degree of congestion is determined based on clustering evaluation values, such as mild, moderate, or severe. Mild corresponds to the first level of alarm, moderate to the second level, and severe to the third level. For example, when the waterway congestion is mild, the aircraft can use the first level of alarm, such as recording anomaly information; when the waterway congestion is moderate, the aircraft can use the second level of alarm, such as flashing warning lights and sending alarm information to ground personnel's electronic devices (e.g., mobile phones); when the waterway congestion is severe, the aircraft can use the third level of alarm, such as emergency broadcasting, automatic obstacle avoidance, and notifying the ground control center. In other embodiments, the aircraft can also obtain the number of ships in the waterway area and set different levels of alarms based on the number of ships.

[0088] In some embodiments, a 5G network can be pre-deployed in the airway. Through the 5G network, the aircraft can transmit airway photos and anomaly identification results to the terminal monitoring equipment in real time, which makes it easier for monitoring personnel to understand the airway situation and take action in a timely manner, thus improving the efficiency and accuracy of data transmission.

[0089] Through the aforementioned optional implementation methods, real-time monitoring of airways by aircraft enables rapid response to anomalies such as airway congestion and obstacles. Simultaneously, the real-time transmission of airway photographs and anomaly identification results via 5G networks ensures that monitoring personnel can quickly obtain accurate information and make timely decisions, further enhancing the intelligence and real-time performance of airway monitoring.

[0090] In an optional implementation, the method further includes:

[0091] The real-time flight trajectory, real-time flight speed, and real-time flight attitude of the aircraft are obtained.

[0092] Determine whether the aircraft is in an abnormal flight state based on the real-time flight trajectory, the real-time flight speed, and the real-time flight attitude.

[0093] When it is determined that the aircraft is in the abnormal flight state, the target flight data corresponding to the abnormal flight state is determined, and the target flight data is any one of the real-time flight trajectory, the real-time flight speed and the real-time flight attitude;

[0094] An alarm will be triggered based on the target flight data.

[0095] In some embodiments, sensors or other data acquisition devices mounted on the aircraft can collect flight data in real time, including flight path (real-time flight trajectory), flight speed (real-time flight speed), and flight attitude (real-time flight attitude). The flight data is then transmitted to the aircraft's data processing unit via the aircraft's internal communication bus or wireless transmission technology. Upon receiving the flight data, the data processing unit can preprocess the data, such as data cleaning and format conversion. It can further compare the real-time flight trajectory with the target flight path, the real-time flight speed with a preset speed threshold, and the real-time flight attitude with a preset flight attitude. If any abnormality is detected in any flight data, i.e., the real-time flight trajectory does not conform to the target flight path, and / or the real-time flight speed exceeds the preset speed threshold, and / or the real-time flight attitude does not conform to the preset flight attitude, the aircraft is in an abnormal flight state. The system can then determine which one or more flight data are causing the abnormal flight state and identify the abnormal flight data as the target flight data. Once the target flight data is identified, the aircraft will trigger the corresponding alarm mechanism according to the preset alarm strategy. For example, the aircraft may trigger an internal audible or visual alarm to notify ground personnel, or the alarm information may be sent to terminal monitoring equipment or the electronic devices of monitoring personnel via wireless communication technology. The alarm information may include a detailed description of the abnormal flight status, including the type of abnormality, the target flight data and its abnormal value, and the time of occurrence.

[0096] Upon receiving an alarm message, the terminal monitoring equipment can take corresponding measures, such as adjusting the flight plan or activating emergency procedures. Simultaneously, the aircraft can also automatically adjust according to preset automatic response strategies, such as automatically correcting its flight attitude and decelerating.

[0097] In some embodiments, if the real-time flight trajectory is detected to be inconsistent with the target flight path, and / or the real-time flight speed exceeds a preset speed threshold, and / or the real-time flight attitude is inconsistent with the preset flight attitude, the flight path photo acquisition work of the aircraft shall be stopped.

[0098] By employing the aforementioned optional implementation methods, and through real-time acquisition, monitoring, and comparison of aircraft flight data, abnormal flight states can be quickly detected. Once an anomaly is detected, not only can the type and data of the anomaly be accurately determined, but an alarm mechanism can also be triggered promptly to effectively notify ground personnel and take appropriate countermeasures. This not only improves flight safety and reduces the risk of accidents, but also enhances the aircraft's autonomy and response capabilities through the application of automatic response strategies, providing strong technical support for safe flight.

[0099] In an optional implementation, when it is determined that the anomalous data exists in the waterway photograph, the method further includes:

[0100] An alarm message is generated based on the abnormal data, and a set of waterway photos corresponding to the abnormal data is determined based on a preset time range;

[0101] The alarm information and the set of waterway photos are sent to the terminal monitoring device.

[0102] In some embodiments, once abnormal data is detected, the aircraft immediately generates an alarm message based on the abnormal data. This alarm message may include key information such as the type, location, and time of the anomaly, enabling monitoring personnel at the terminal monitoring equipment to quickly understand and respond. Simultaneously, the aircraft can determine a set of flight path photos related to the abnormal data within a preset time range. This set of flight path photos includes multiple photos taken before and after the anomaly occurred, allowing monitoring personnel to understand the anomaly's occurrence and surrounding environment. Furthermore, the aircraft integrates the alarm message and the corresponding set of flight path photos and sends them to the terminal monitoring equipment.

[0103] By implementing the above-mentioned alternative methods, real-time monitoring of anomalies in the waterway and the provision of intuitive and comprehensive information support to monitoring personnel through alarm information and waterway photo collections can help improve the efficiency and safety of waterway management.

[0104] Reference Figure 3 The diagram shown is a functional block diagram of a low-altitude aircraft area airway monitoring device according to an embodiment of this application.

[0105] In some embodiments, the low-altitude aircraft area airway monitoring device 30 may include multiple functional modules composed of computer program segments. The computer programs for each program segment of the low-altitude aircraft area airway monitoring device 30 may be stored in the memory of an electronic device and executed by at least one processor to perform (see details). Figure 2 (Description) Function of monitoring airways in low-altitude areas for aircraft.

[0106] In this embodiment, the low-altitude aircraft area airway monitoring device 30 can be divided into multiple functional modules according to its functions. These functional modules may include: a data acquisition module 301, an identification module 302, an alarm module 303, and a transmission module 304. The term "module" in this application refers to a series of computer program segments that can be executed by at least one processor and perform a fixed function, stored in memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0107] The acquisition module 301 is used to acquire airway photos of the target airway area according to the target time period when it is detected that the aircraft is flying according to the target flight route.

[0108] The identification module 302 is used to identify anomalies in the waterway photos to determine whether there is abnormal data in the waterway photos.

[0109] The alarm module 303 is used to issue an alarm based on the abnormal data when it is determined that the abnormal data exists in the waterway photo.

[0110] The identification module 302 is further specifically used for: determining the waterway traffic data of the target waterway area in the waterway photograph, and the clustering evaluation index of the waterway traffic data; constructing an evaluation sample matrix based on the waterway traffic data and the clustering evaluation index; calculating the clustering evaluation value of each evaluation sample in the evaluation sample matrix; obtaining the maximum evaluation value among the clustering evaluation values, and determining whether the waterway traffic data is congested based on the maximum evaluation value.

[0111] The identification module 302 is further specifically used for: determining multiple cluster centers of the evaluation sample matrix, and the membership degree value between each evaluation sample and each cluster center; determining the weight of each cluster center, and determining the cluster evaluation value of each evaluation sample based on the membership degree value and the weight.

[0112] The identification module 302 is further specifically configured to: determine the sea surface area and non-sea surface area in the navigation photo based on the target navigation area; extract first obstacle information of the sea surface area and second obstacle information of the non-sea surface area based on a preset obstacle database; determine whether the obstacle exists in the sea surface area based on the first obstacle information and determine whether the obstacle exists in the non-sponge area based on the second obstacle information.

[0113] The identification module 302 is further specifically used for: extracting features from the waterway photograph to obtain a feature set; comparing the target feature with a preset environmental data set, wherein the target feature is any one feature in the feature set; and determining that the waterway photograph has the external environmental hazard when it is determined that the target feature matches any one environmental data in the preset environmental data set.

[0114] The alarm module 303 is further configured to: acquire the real-time flight trajectory, real-time flight speed, and real-time flight attitude of the aircraft; determine whether the aircraft is in an abnormal flight state based on the real-time flight trajectory, real-time flight speed, and real-time flight attitude; when it is determined that the aircraft is in the abnormal flight state, determine the target flight data corresponding to the abnormal flight state, wherein the target flight data is any one of the real-time flight trajectory, real-time flight speed, and real-time flight attitude; and issue an alarm based on the target flight data.

[0115] The sending module 304 is used to: generate alarm information based on the abnormal data, and determine the set of waterway photos corresponding to the abnormal data according to a preset time range; and send the alarm information and the set of waterway photos to the terminal monitoring device.

[0116] It should be understood that the various variations and specific embodiments of the low-altitude aircraft area airway monitoring method provided in the above embodiments are also applicable to the low-altitude aircraft area airway monitoring device of this embodiment. Through the foregoing detailed description of the low-altitude aircraft area airway monitoring method, those skilled in the art can clearly understand the implementation method of the low-altitude aircraft area airway monitoring device in this embodiment. For the sake of brevity, it will not be described in detail here.

[0117] Reference Figure 4 The diagram shown is a structural schematic of a data processing unit according to an embodiment of this application. In a preferred embodiment of this application, the data processing unit 13 includes a memory 131, at least one processor 132, and at least one communication bus 133.

[0118] Those skilled in the art should understand that Figure 4 The structure of the data processing unit shown does not constitute a limitation of the embodiments of this application. It can be a bus structure or a star structure. The data processing unit 13 may also include more or fewer other hardware or software than shown, or different component arrangements.

[0119] In some embodiments, the data processing unit 13 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices.

[0120] It should be noted that the data processing unit 13 is only an example. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0121] In some embodiments, the memory 131 stores a computer program that, when executed by the at least one processor 132, implements all or part of the steps in the low-altitude aircraft area airway monitoring method described above.

[0122] In some embodiments, the at least one processor 132 is the control unit of the data processing unit 13, connecting various components of the data processing unit 13 via various interfaces and lines. It executes programs or modules stored in the memory 131 and calls data stored in the memory 131 to perform various functions and process data. For example, when the at least one processor 132 executes a computer program stored in the memory, it implements all or part of the steps of the low-altitude aircraft area airway monitoring method described above in this application embodiment; or it implements all or part of the functions of the low-altitude aircraft area airway monitoring device. The at least one processor 132 may be composed of integrated circuits, such as a single-packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0123] In some embodiments, the at least one communication bus 133 is configured to enable communication between the memory 131 and the at least one processor 132, etc.

[0124] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause an electronic device (which may be a personal computer, electronic device, or network device, etc.) or processor to execute portions of the methods described in the various embodiments of this application.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0126] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for monitoring airways in low-altitude airspace, characterized in that, The method includes: When it is detected that the aircraft is flying according to the target flight path, the airway photos of the target airway area are collected according to the target time period; Anomaly detection is performed on the waterway photos to determine whether there is abnormal data in the waterway photos; When it is determined that the abnormal data exists in the waterway photograph, an alarm is triggered based on the abnormal data. Wherein, when the abnormal data is waterway congestion, the anomaly identification of the waterway photograph includes: Determine the waterway traffic data of the target waterway area in the waterway photograph, and the clustering evaluation index of the waterway traffic data; An evaluation sample matrix is ​​constructed based on the waterway traffic data and the clustering evaluation indicators; Calculate the cluster evaluation value for each evaluation sample in the evaluation sample matrix; Obtain the maximum evaluation value among the cluster evaluation values, and determine whether the waterway traffic data is congested based on the maximum evaluation value; The calculation of the cluster evaluation value for each evaluation sample in the evaluation sample matrix includes: Determine multiple cluster centers of the evaluation sample matrix, and the membership value of each evaluation sample to each of the cluster centers; The weight of each cluster center is determined, and the cluster evaluation value of each evaluation sample is determined based on the membership value and the weight.

2. The method for monitoring airways in low-altitude aircraft areas according to claim 1, characterized in that, When the abnormal data is an obstacle, the anomaly identification of the waterway photograph includes: Determine the sea surface area and non-sea surface area in the waterway photograph based on the target waterway area; Extract the first obstacle information of the sea surface area and the second obstacle information of the non-sea surface area from the preset obstacle database; The system determines whether the obstacle exists in the sea surface area based on the first obstacle information, and determines whether the obstacle exists in the non-sea surface area based on the second obstacle information.

3. The method for monitoring airways in low-altitude aircraft areas according to claim 1, characterized in that, When the abnormal data indicates a potential hazard from the external environment, the anomaly identification process for the waterway photographs includes: Feature extraction is performed on the waterway photographs to obtain a feature set; The target feature is compared with a preset environmental data set, wherein the target feature is any one of the features in the feature set; When the target feature is determined to match any environmental data in the preset environmental data set, it is determined that the waterway photograph has the external environmental hazard.

4. The method for monitoring airways in low-altitude aircraft areas according to any one of claims 1 to 3, characterized in that, The method further includes: The real-time flight trajectory, real-time flight speed, and real-time flight attitude of the aircraft are obtained. Determine whether the aircraft is in an abnormal flight state based on the real-time flight trajectory, the real-time flight speed, and the real-time flight attitude. When it is determined that the aircraft is in the abnormal flight state, the target flight data corresponding to the abnormal flight state is determined, and the target flight data is any one of the real-time flight trajectory, the real-time flight speed and the real-time flight attitude; An alarm will be triggered based on the target flight data.

5. The method for monitoring airways in low-altitude aircraft areas according to any one of claims 1 to 3, characterized in that, When it is determined that the abnormal data exists in the waterway photograph, the method further includes: An alarm message is generated based on the abnormal data, and a set of waterway photos corresponding to the abnormal data is determined based on a preset time range; The alarm information and the set of waterway photos are sent to the terminal monitoring device.

6. A low-altitude aircraft area airway monitoring device, characterized in that, The device includes: The data acquisition module is used to acquire photos of the target flight path area according to the target time period when the aircraft is detected to be flying along the target flight path. The identification module is used to identify anomalies in the waterway photographs to determine whether there is abnormal data. It is used to determine waterway traffic data for the target waterway area in the waterway photographs, and clustering evaluation indicators for the waterway traffic data. Based on the waterway traffic data and the clustering evaluation indicators, it constructs an evaluation sample matrix, calculates the clustering evaluation value of each evaluation sample in the evaluation sample matrix, obtains the maximum evaluation value among the clustering evaluation values, and determines whether there is waterway congestion based on the maximum evaluation value. It is also used to determine multiple cluster centers of the evaluation sample matrix, and the membership degree value between each evaluation sample and each cluster center; determine the weight of each cluster center; and determine the clustering evaluation value of each evaluation sample based on the membership degree value and the weight. An alarm module is used to issue an alarm based on the abnormal data when it is determined that the abnormal data exists in the waterway photograph.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low-altitude aircraft area airway monitoring method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-altitude aircraft area airway monitoring method according to any one of claims 1 to 5.

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