CN112949474B - Airport FOD monitoring method, equipment, storage medium and device - Google Patents
Airport FOD monitoring method, equipment, storage medium and device Download PDFInfo
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Abstract
The invention discloses an airport FOD monitoring method, equipment, a storage medium and a device, wherein a first preprocessing result and a second preprocessing result are determined according to first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar by a preset neural network model; determining the position information and the danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model; and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified. According to the invention, the image information is preprocessed according to the preset neural network model, and the position information and the danger level of the obstacle to be recognized are determined according to the preset obstacle model.
Description
Technical Field
The invention relates to the field of airport monitoring, in particular to an airport FOD monitoring method, equipment, a storage medium and a device.
Background
At present, when construction is carried out under the condition that an airport does not stop navigating, foreign matters can be placed on an airstrip, however, the foreign matters are very dangerous to the airplane, so that the monitoring of foreign matters (FOD) on the airstrip is very important for safe operation of the airport, but in the prior art, the foreign matters on the airstrip are manually inspected, the monitoring efficiency is low, the safety is poor, and great potential safety hazards exist.
The above is only for the purpose of assisting understanding of the technical solution of the present invention, and does not represent an admission that the above is the prior art.
Disclosure of Invention
The invention mainly aims to provide an airport FOD monitoring method, equipment, a storage medium and a device, and aims to solve the technical problem that the efficiency of monitoring foreign matters on a construction road surface in a non-stop airport by manual work in the prior art is low.
In order to achieve the above object, the present invention provides an airport FOD monitoring method, comprising the steps of:
acquiring first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar;
preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result;
preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result;
determining position information of the obstacle to be recognized and a danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model;
and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified.
Preferably, the step of preprocessing the image information of the runway of the first airport according to a preset neural network model to obtain a first preprocessing result includes:
extracting first characteristic point information from the first airport runway image information according to a preset neural network model;
taking the first feature point information as a first preprocessing result;
correspondingly, the step of preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result comprises the following steps:
extracting second characteristic point information from the second airport runway image information according to the preset neural network model;
and taking the second feature point information as a second preprocessing result.
Preferably, the step of determining the position information of the obstacle to be recognized and the danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model includes:
matching the second characteristic point information according to the first characteristic point information to obtain a matching result;
determining a security event information set according to the matching result;
and determining the position information of the barrier to be recognized and the danger level of the barrier to be recognized according to the safety event set and a preset barrier model.
Preferably, the step of determining the position information of the obstacle to be identified and the danger level of the obstacle to be identified according to the safety event set and the preset obstacle model includes:
extracting position information of an obstacle to be identified and extracting characteristic point information of the obstacle from the safety event information set;
matching the obstacle feature point information with a preset obstacle model to obtain a first matching result;
and determining the danger level of the obstacle to be identified according to the first matching result.
Preferably, after the step of generating the FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified, the method further includes:
acquiring airport flight operation information;
and determining the alarm level corresponding to the safety event information set according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information.
Preferably, after the step of determining the alarm level corresponding to the set of safety events according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified, and the airport flight operation information, the method further includes:
simulating the position information of the obstacle to be recognized, the danger level of the obstacle to be recognized and the flight operation information of the airport according to a preset digital twin model to obtain an expected obstacle clearing route;
acquiring the position information of constructors;
determining a target obstacle clearance route from the expected obstacle clearance routes according to the position information of the constructors;
and determining alarm information according to the target obstacle clearance route and the alarm level.
Preferably, after the step of determining the warning information according to the target obstacle clearance route and the warning level, the method further includes:
alarming according to the alarm level, and pushing the alarm information to mobile terminals of constructors and managers;
acquiring barrier information corresponding to the safety event information set;
and when the obstacle information meets the preset safety condition, sending return route information to a mobile terminal corresponding to an obstacle clearing person, and removing an alarm.
Furthermore, to achieve the above object, the present invention also provides an airport FOD monitoring device, which includes a memory, a processor and an airport FOD monitoring program stored on the memory and operable on the processor, the airport FOD monitoring program being configured to implement the steps of the airport FOD monitoring method as described above.
In addition, to achieve the above object, the present invention further provides a storage medium having an airport FOD monitoring program stored thereon, wherein the airport FOD monitoring program, when executed by a processor, implements the steps of the airport FOD monitoring method as described above.
In addition, in order to achieve the above object, the present invention further provides an airport FOD monitoring device, including:
the image acquisition module is used for acquiring first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar;
the preprocessing module is used for preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result;
the preprocessing module is further used for the preprocessing module and is further used for preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result;
the data processing module is used for determining the position information of the obstacle to be recognized and the danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model;
and the result generation module is used for generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified.
The method comprises the steps of acquiring first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar; preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result; preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result; determining position information of the obstacle to be recognized and a danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model; and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified. Compared with the prior art of manual inspection, the invention realizes the improvement of FOD monitoring efficiency and ensures the safety of airport personnel.
Drawings
FIG. 1 is a schematic structural diagram of an airport FOD monitoring device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a FOD monitoring method for an airport according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a FOD monitoring method in an airport according to a second embodiment of the present invention;
FIG. 4 is a schematic flow chart of a FOD monitoring method for an airport according to a third embodiment of the present invention;
FIG. 5 is a block diagram of a FOD monitoring device for an airport according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an airport FOD monitoring device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the airport FOD monitoring apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), and the optional user interface 1003 may further include a standard wired interface and a wireless interface, and the wired interface for the user interface 1003 may be a USB interface in the present invention. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or a Non-volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of airport FOD monitoring equipment and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in FIG. 1, a memory 1005, identified as one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and an airport FOD monitoring program.
In the airport FOD monitoring device shown in fig. 1, the network interface 1004 is mainly used for connecting with a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting user equipment; the airport FOD monitoring device calls an airport FOD monitoring program stored in the memory 1005 through the processor 1001 and executes the airport FOD monitoring method provided by the embodiment of the invention.
Based on the hardware structure, the embodiment of the airport FOD monitoring method is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the airport FOD monitoring method of the present invention, and proposes the first embodiment of the airport FOD monitoring method of the present invention.
In a first embodiment, the airport FOD monitoring method comprises the steps of:
step S10: and acquiring first airport runway image information acquired by the airport monitoring radar and second airport runway image information acquired by the satellite radar.
It should be noted that the execution subject of the present embodiment may be an airport FOD monitoring system, or may be a device including an airport FOD monitoring function. The device may be a computer, a notebook, a computer, a mobile phone, etc., which is not limited in this embodiment.
It should be understood that the airport monitoring radar can be a process search radar which is installed in an airport detection area with an airport as a center and is used together with the secondary monitoring radar in a preset distance radius space. For example: in the 100-150km radius airspace, the embodiment does not limit the scope, the airport monitoring radar can be a millimeter wave radar, the embodiment does not limit the scope, the monitoring radar can be a radar installed on a tower, a sidelight type radar or a patrol car, and the embodiment does not limit the scope. The satellite radar can enable the radar carrying the synthetic aperture radar corresponding to the earth observation remote sensing satellite to carry out high-resolution imaging investigation all the day and all the weather, thereby realizing more accurate surveying of airports.
It is understood that the first airport runway image information may refer to image information generated by radar detection of an aircraft taxiing area in an airport area, and the second airport runway image information may refer to image information generated by a satellite radar omni-directional survey of the airport.
In the specific implementation, the airport FOD monitoring system acquires image information of a first airport runway and image information of a second airport runway according to an airport monitoring radar and a satellite radar, wherein the airport runway is not limited to a runway for taking off and landing of an airplane, and the airport runway also comprises an area where the normal operation of the airplane is influenced by foreign matters in other areas.
Step S20: and preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result.
It should be noted that the preset neural network model may be a preset model for processing image information, or may be a model including a convolutional neural network algorithm.
It can be understood that the preset neural network model can perform category screening on image information, identify categories of images through deep learning, and perform target detection on the images.
It should be understood that the first airport runway image information may be image information collected by an airport monitoring radar, and the image information may include information such as people, cars, airplanes, animals, and foreign objects on the road surface in the airport.
In a specific implementation, the first processing result may be information obtained by preprocessing image information by using a preset neural network model, that is, the first processing result may be information obtained by performing category screening on the image information by using the preset neural network model, identifying a following category through deep learning, and performing target detection on the image.
Step S30: and preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result.
It should be noted that the preset neural network model may be a preset model for processing image information, or may be a model including a convolutional neural network algorithm.
It can be understood that the preset neural network model can perform category screening on image information, identify categories of images through deep learning, and perform target detection on the images.
It should be understood that the second airport runway image information may be image information collected by an airport monitoring radar, and the image information may include information such as people, cars, airplanes, animals, and foreign objects on the road surface in the airport.
In a specific implementation, the second processing result may be information obtained by preprocessing the image information by the preset neural network model, that is, the second processing result may be information obtained by performing category screening on the image information by the preset neural network model, identifying the following categories through deep learning, and performing target detection on the image.
Step S40: and determining the position information and the danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model.
It should be noted that, the preset obstacle model may refer to a preset model, and the obstacle model may be based on a point cloud data 3D obstacle model.
It can be understood that the obstacle position information to be recognized may refer to obstacle position coordinate information, and the danger level of the obstacle to be recognized may be a level classified according to the type of the obstacle.
It will be appreciated that the wide variety of sources available outside the airport area can be roughly classified into three categories according to the size of the hazard to the safety of aircraft operation: high-risk foreign matter, medium-risk foreign matter and low-risk foreign matter, especially to under the construction condition, F is especially important to FOD monitoring.
In specific implementation, the position information of the obstacle to be recognized and the danger level of the obstacle to be recognized are determined according to the first preprocessing result, the second preprocessing result and a preset obstacle model.
Step S50: and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified.
It should be noted that FOD (Foreign Object Debris) is a Foreign substance, debris or Object that may damage an aircraft.
It is understood that the FOD monitoring result may be information determined according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified.
In the specific implementation, the FOD monitoring system of the airport generates an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified.
In the embodiment, first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar are acquired; preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result; preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result; determining position information of the obstacle to be recognized and a danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model; and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified. Because this embodiment carries out the preliminary treatment to image information according to predetermineeing neural network model to according to predetermineeing the position letter and the danger level of barrier of waiting to discern of barrier model determination, this embodiment realizes promoting FOD monitoring efficiency for prior art manual inspection, and has guaranteed airport personnel's safety.
Referring to fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the airport FOD monitoring method of the present invention, and the second embodiment of the airport FOD monitoring method of the present invention is proposed based on the first embodiment shown in fig. 2.
In this embodiment, the step S20 includes:
step S201: and extracting first characteristic point information from the first airport runway image information according to a preset neural network model.
The first feature point information may be based on feature point information included in the first airport runway image information, for example: and extracting the characteristic point information of each object from the image according to a preset neural network model from the information of people, vehicles, airplanes, animals, road foreign matters and the like contained in the image information.
It can be understood that different objects correspond to different feature point sets, for example: and extracting the characteristic points of the vehicle from the image information, namely taking the image characteristic points corresponding to the vehicle as a set, thereby improving the subsequent data processing efficiency.
It should be understood that, when the preset neural network model extracts the first feature point information from the first airport runway image information, the noise reduction processing may be performed on the image, and the target detection may be performed on the noise-reduced image information, so as to extract the feature point information. The feature point information may be size, contrast, texture, edge feature point, and the like, which is not limited in this embodiment.
In specific implementation, the airport FOD monitoring system can extract first feature point information from the first airport runway image information according to a preset neural network model.
Step S202: and taking the first characteristic point information as a first preprocessing result.
It should be noted that the first preprocessing result may be a result generated after the collected image information is identified and classified and the target thereof is detected.
In this embodiment, the step S30 includes:
step S301: and extracting second characteristic point information from the second airport runway image information according to the preset neural network model.
The second feature point information may be based on feature point information included in the second airport runway image information, for example: and extracting the characteristic point information of each object from the image according to a preset neural network model from the information of people, vehicles, airplanes, animals, road foreign matters and the like contained in the image information.
It can be understood that different objects correspond to different feature point sets, for example: and extracting the characteristic points of the vehicle from the image information, namely taking the image characteristic points corresponding to the vehicle as a set, thereby improving the subsequent data processing efficiency.
It should be understood that, when the preset neural network model extracts the second feature point information from the second airport runway image information, the noise reduction processing may be performed on the image, and the target detection may be performed on the noise-reduced image information, so as to extract the feature point information. The feature point information may be size, contrast, texture, edge feature point, and the like, which is not limited in this embodiment.
In a specific implementation, the airport FOD monitoring system may extract second feature point information from the second airport runway image information according to a preset neural network model.
Step S302: and taking the second characteristic point information as a second preprocessing result.
It should be noted that the second preprocessing result may be a result generated after the collected image information is identified and classified and the target thereof is detected.
Further, the step of determining the position information and the danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model includes: matching the second characteristic point information according to the first characteristic point information to obtain a matching result; determining a security event information set according to the matching result; and determining the position information of the barrier to be recognized and the danger level of the barrier to be recognized according to the safety event information set and a preset barrier model.
It should be noted that the matching result may be a result generated after the first feature point information and the second feature point information are matched, and the matching is performed according to image information acquired by the airport radar and image information acquired by the satellite radar, so that the accuracy of identifying the obstacle is improved.
It is understood that the safety event information set may refer to a safety event information set corresponding to the determined to-be-identified obstacle in the airport area after the first characteristic point information is matched with the second characteristic point information.
It should be understood that the preset obstacle model may be a model including target sample data, target features, simulation data, and a priori data.
In a specific implementation, the airport FOD monitoring system may match the second feature point information according to the first feature point information to obtain a matching result; determining a security event information set according to the matching result; and determining the position information of the barrier to be recognized and the danger level of the barrier to be recognized according to the safety event information set and a preset barrier model.
Further, the step of determining the position information and the danger level of the obstacle to be recognized according to the safety event set and the preset obstacle model comprises the following steps: extracting position information of the obstacles to be identified and extracting characteristic point information of the obstacles from the safety event information set; matching the obstacle feature point information with a preset obstacle model to obtain a first matching result; and determining the danger level of the obstacle to be identified according to the first matching result.
It should be noted that the first matching result may be a result generated after the obstacle feature point information is matched with a preset obstacle model, that is, the obstacle type is determined according to the obstacle feature point, and the obstacle risk level is determined according to the type.
In the specific implementation, an FOD monitoring system for airport non-stop construction extracts position information of an obstacle to be identified and characteristic point information of the obstacle from a safety event information set; matching the obstacle feature point information with a preset obstacle model to obtain a first matching result; and determining the danger level of the obstacle to be recognized according to the first matching result. That is, the FOD monitoring system for airport non-stop construction extracts the position information of the obstacle to be identified from the safety event information set, and extracts the feature point information of the obstacle to be identified, that is, the obstacle type is obtained by comparing and matching the feature point information of the obstacle with the feature point set contained in the data, and the danger level of the obstacle is determined according to the obstacle type, for example: high-risk foreign matter: metal parts and heavy foreign objects such as: the trailer is hung, and high-risk foreign objects are extremely hard, so that the high-risk foreign objects can be greatly damaged when hitting the aircraft; medium-risk foreign matters: foreign matters such as broken stones, newspapers and packing cases which have certain influence on flight safety; low risk foreign objects: and foreign matters such as non-metal fragmentary garbage, paper scraps, leaves and the like which have small threat to flight safety.
In the embodiment, first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar are acquired; extracting first characteristic point information from the first airport runway image information according to a preset neural network model; taking the first feature point information as a first preprocessing result; extracting second characteristic point information from the second airport runway image information according to the preset neural network model; taking the second feature point information as a second preprocessing result; determining position information of the obstacle to be recognized and a danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model; and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified. Because this embodiment carries out the preliminary treatment to image information according to predetermineeing neural network model to according to characteristic point information and predetermine the position letter and the danger level of barrier that wait to discern the barrier, this embodiment has realized promoting FOD monitoring efficiency for the artifical inspection of prior art, and has guaranteed airport personnel's safety, avoids loss of property.
Referring to fig. 4, fig. 4 is a schematic flow chart of a third embodiment of the airport FOD monitoring method of the present invention, and the third embodiment of the airport FOD monitoring method of the present invention is proposed based on the second embodiment shown in fig. 3.
In this embodiment, the step S50 includes:
step S60: acquiring airport flight operation information.
It should be noted that the flight operation information of the airport may include information of the takeoff time, landing time, takeoff location, landing location, and the like of the airplane. This embodiment is not limited in this regard.
In the specific implementation, the FOD monitoring system in the airport can acquire the operation information of the airplane, the ferry vehicle and the like through the airplane management platform.
And S70, determining an alarm level corresponding to the safety event information set according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information.
It should be noted that the alarm level may be determined according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified, and the airport flight operation information, where the safety event information set corresponds to the degree of urgency.
It can be understood that different flight areas of the airport correspond to different levels, and the flight areas refer to the fields for taking off, landing, sliding and parking of airplanes, and comprise runways, lifting belts, runway end safety areas, taxiways, aprons and areas with limited requirements on obstacles around the airport. The grade of the flight area is classified by the indexes I and II of the flight area, wherein the length of the longest reference flight field in various airplanes of the runway of the flight area is divided into four grades (represented by numbers 1 to 4); the maximum span or the spacing of the outer side of the outer wheel of the largest main landing gear in each type of aircraft on the runway in the flight area is classified into six classes (indicated by letters a to F).
It should be appreciated that the alert level may alert based on the degree of emergency of the threat aircraft.
In a specific implementation, the airport FOD monitoring system may determine the alarm level corresponding to the safety event information set according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified, and the airport flight operation information, that is, the airport FOD monitoring system may comprehensively determine the alarm level corresponding to the safety event information set according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified, and the airport flight operation information.
Further, after the step S70, the method further includes: simulating the position information of the obstacle to be recognized, the danger level of the obstacle to be recognized and the flight operation information of the airport according to a preset digital twin model to obtain an expected obstacle clearing route; acquiring the position information of constructors; determining a target obstacle clearance route from the expected obstacle clearance routes according to the position information of the constructors; and determining alarm information according to the target obstacle clearance route and the alarm level.
It should be noted that the preset digital twin model may be a preset model, the digital twin model is a model integrating a multi-scale and multi-probability simulation process by using data such as a physical model, sensor update, operation history, and the like, and the digital twin model may perform simulation based on connection data and information between a virtual entity and a real entity and obtain a corresponding simulation result.
It can be understood that the expected obstacle clearance route can be an obstacle clearance route mapped in the virtual space according to the preset digital twin model, and is not limited to one obstacle clearance route. The target obstacle clearance route can be an optimal obstacle clearance route determined according to the position information of the constructors, namely, the coordinates of the constructors are determined according to the position information of the constructors, and an optimal obstacle clearance route is selected from the expected obstacle clearance routes according to the coordinates of the constructors.
In the specific implementation, in order to save manpower and material resources and improve the obstacle clearing efficiency, the airport FOD monitoring system simulates the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information according to a preset digital twin model to obtain an expected obstacle clearing route; acquiring the position information of constructors; determining a target obstacle clearance route from the expected obstacle clearance routes according to the position information of the constructors; and determining alarm information according to the target obstacle clearance route and the alarm level.
Further, after the step of determining alarm information according to the target obstacle clearance route and the alarm level, the method further comprises the following steps of: alarming according to the alarm level, and pushing the alarm information to mobile terminals of constructors and managers; acquiring barrier information corresponding to the safety event information set; and when the obstacle information meets the preset safety condition, sending return route information to a mobile terminal corresponding to the obstacle clearing personnel, and removing the alarm.
It should be noted that the preset safety condition may refer to that the obstacle is cleared to reach the airport safety condition.
In the specific implementation, in order to ensure the safety of airport personnel and improve the obstacle clearing efficiency, the airport FOD monitoring system gives an alarm according to the alarm level and pushes the alarm information to mobile terminals of constructors and managers; acquiring barrier information corresponding to the safety event information set; and when the obstacle information meets the preset safety condition, sending return route information to a mobile terminal corresponding to the obstacle clearing personnel, and removing the alarm.
In the embodiment, first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar are acquired; preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result; preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result; determining position information of the obstacle to be recognized and a danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model; and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified. Acquiring airport flight operation information; and determining the alarm level corresponding to the safety event information set according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information. Because the alarm level corresponding to the safety event information set is determined according to the airport flight operation information, the position information of the obstacle to be identified and the danger level of the obstacle to be identified, compared with the prior art, the obstacle clearing efficiency is improved and the obstacle clearing safety is improved by manually checking the obstacle in the embodiment.
Furthermore, an embodiment of the present invention further provides a storage medium, where an airport FOD monitoring program is stored, and when being executed by a processor, the airport FOD monitoring program implements the steps of the airport FOD monitoring method as described above.
Referring to fig. 5, fig. 5 is a block diagram illustrating a first embodiment of an airport FOD monitoring apparatus according to the present invention.
As shown in fig. 5, an airport FOD monitoring apparatus provided by an embodiment of the present invention includes:
the image acquisition module 10 is used for acquiring first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar;
the preprocessing module 20 is configured to preprocess the first airport runway image information according to a preset neural network model to obtain a first preprocessing result;
the preprocessing module 20 is further configured to perform preprocessing on the second airport runway image information according to the preset neural network model, and obtain a second preprocessing result;
the data processing module 30 is configured to determine position information of the obstacle to be identified and a danger level of the obstacle to be identified according to the first preprocessing result, the second preprocessing result and a preset obstacle model;
and the result generating module 40 is used for generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified.
In the embodiment, first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar are acquired; preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result; preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result; determining position information of the obstacle to be recognized and a danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model; and generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified. Because this embodiment carries out the preliminary treatment to image information according to predetermineeing neural network model to according to predetermineeing the position letter and the danger level of barrier of waiting to discern of barrier model determination, this embodiment realizes promoting FOD monitoring efficiency for prior art manual inspection, and has guaranteed airport personnel's safety.
Further, the preprocessing module 20 is further configured to extract first feature point information from the first airport runway image information according to a preset neural network model; taking the first feature point information as a first preprocessing result; correspondingly, the step of preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result comprises the following steps: extracting second characteristic point information from the second airport runway image information according to the preset neural network model; and taking the second characteristic point information as a second preprocessing result.
Further, the data processing module 30 is further configured to match the second feature point information according to the first feature point information, so as to obtain a matching result; determining a security event information set according to the matching result; and determining the position information and the danger level of the barrier to be identified according to the safety event set and a preset barrier model.
Further, the data processing module 30 is further configured to extract obstacle position information to be identified and obstacle feature point information from the safety event information set; matching the obstacle feature point information with a preset obstacle model to obtain a first matching result; and determining the danger level of the obstacle to be identified according to the first matching result.
Further, the airport FOD monitoring device further comprises an alarm determination module, wherein the alarm determination module acquires airport flight operation information; and determining the alarm level corresponding to the safety event information set according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information.
Furthermore, the alarm determination module is further used for simulating the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the flight operation information of the airport according to a preset digital twin model to obtain an expected obstacle clearance route; acquiring position information of constructors; determining a target obstacle clearance route from the expected obstacle clearance routes according to the position information of the constructors; and determining alarm information according to the target obstacle clearance route and the alarm level.
Further, the alarm determining module is also used for alarming according to the alarm level and pushing the alarm information to mobile terminals of constructors and managers; acquiring barrier information corresponding to the safety event information set; and when the obstacle information meets the preset safety condition, sending return route information to a mobile terminal corresponding to the obstacle clearing personnel, and removing the alarm.
Furthermore, an embodiment of the present invention further provides a storage medium, where an airport FOD monitoring program is stored, and when being executed by a processor, the airport FOD monitoring program implements the steps of the airport FOD monitoring method as described above.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not elaborated in this embodiment may refer to the airport FOD monitoring method provided in any embodiment of the present invention, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order, but rather the words first, second, third, etc. are to be interpreted as names.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., a Read Only Memory (ROM)/Random Access Memory (RAM), a magnetic disk, an optical disk), and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An airport FOD monitoring method is characterized by comprising the following steps:
acquiring first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar;
preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result;
preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result;
determining the position information and the danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model;
generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified;
simulating the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information according to a preset digital twin model to obtain an expected obstacle clearing route;
acquiring the position information of constructors;
determining a target obstacle clearance route from the expected obstacle clearance routes according to the position information of the constructors, wherein the expected obstacle clearance route can be an obstacle clearance route mapped in a virtual space according to a preset digital twin model;
and determining alarm information according to the target obstacle clearance route and the alarm level.
2. The method of claim 1, wherein said step of preprocessing said first airport runway image information according to a predetermined neural network model to obtain a first preprocessing result comprises:
extracting first characteristic point information from the first airport runway image information according to a preset neural network model;
taking the first characteristic point information as a first preprocessing result;
correspondingly, the step of preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result includes:
extracting second characteristic point information from the second airport runway image information according to the preset neural network model;
and taking the second characteristic point information as a second preprocessing result.
3. The method of claim 2, wherein said step of determining obstacle location information and obstacle risk level of the obstacle to be identified based on the first pre-processing result, the second pre-processing result and a pre-determined obstacle model comprises:
matching the second characteristic point information according to the first characteristic point information to obtain a matching result;
determining a security event information set according to the matching result;
and determining the position information of the barrier to be recognized and the danger level of the barrier to be recognized according to the safety event set and a preset barrier model.
4. The airport FOD monitoring method of claim 2, wherein said step of determining obstacle location information and obstacle hazard level to be identified based on the set of security events and a predetermined obstacle model comprises:
extracting position information of an obstacle to be identified and extracting characteristic point information of the obstacle from the safety event information set;
matching the obstacle feature point information with a preset obstacle model to obtain a first matching result;
and determining the danger level of the obstacle to be identified according to the first matching result.
5. The method for airport FOD monitoring of claim 4, wherein after said step of generating FOD monitoring results based on said obstacle location information and obstacle risk rating to be identified, further comprising:
acquiring airport flight operation information;
and determining the alarm level corresponding to the safety event information set according to the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information.
6. The airport FOD monitoring method of claim 1, wherein said step of determining alert information based on said target cleared route and alert level further comprises, after said step of:
alarming according to the alarm level, and pushing the alarm information to mobile terminals of constructors and managers;
acquiring barrier information corresponding to the safety event information set;
and when the obstacle information meets the preset safety condition, sending return route information to a mobile terminal corresponding to the obstacle clearing personnel, and removing the alarm.
7. An airport FOD monitoring device, comprising: a memory, a processor and an airport FOD monitoring program stored on the memory and executable on the processor, the airport FOD monitoring program when executed by the processor implementing the steps of the airport FOD monitoring method of any of claims 1 to 6.
8. A storage medium having stored thereon an airport FOD monitoring program which when executed by a processor implements the steps of the airport FOD monitoring method of any of claims 1 to 6.
9. An airport FOD monitoring device, comprising:
the image acquisition module is used for acquiring first airport runway image information acquired by an airport monitoring radar and second airport runway image information acquired by a satellite radar;
the preprocessing module is used for preprocessing the image information of the first airport runway according to a preset neural network model to obtain a first preprocessing result;
the preprocessing module is further used for the preprocessing module and is further used for preprocessing the second airport runway image information according to the preset neural network model to obtain a second preprocessing result;
the data processing module is used for determining the position information of the obstacle to be recognized and the danger level of the obstacle to be recognized according to the first preprocessing result, the second preprocessing result and a preset obstacle model;
the result generation module is used for generating an FOD monitoring result according to the position information of the obstacle to be identified and the danger level of the obstacle to be identified;
the alarm determination module is used for simulating the position information of the obstacle to be identified, the danger level of the obstacle to be identified and the airport flight operation information according to a preset digital twin model to obtain an expected obstacle clearance route;
the alarm determining module is used for acquiring the position information of the constructors;
the alarm determining module is used for determining a target obstacle clearing route from the expected obstacle clearing routes according to the position information of the constructors, wherein the expected obstacle clearing route can be an obstacle clearing route mapped in a virtual space according to a preset digital twin model;
and the alarm determining module is used for determining alarm information according to the target obstacle clearing route and the alarm level.
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| CN114399498B (en) * | 2022-01-19 | 2022-08-30 | 浙大网新系统工程有限公司 | Airport runway surface condition assessment method and system |
| CN117150632B (en) * | 2023-10-27 | 2024-01-19 | 北京云庐科技有限公司 | Airport runway health situation sensing system and method based on digital twin technology |
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