CN116907282A - Unmanned target aircraft ultra-low altitude flight control method based on artificial intelligence algorithm - Google Patents
Unmanned target aircraft ultra-low altitude flight control method based on artificial intelligence algorithm Download PDFInfo
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Abstract
The invention discloses an artificial intelligence algorithm-based unmanned target aircraft ultra-low altitude flight control method, which comprises the steps of obtaining distance values of an unmanned target aircraft from sea surfaces at a plurality of preset time points in a preset time period; acquiring wind speed values, wind direction values and wave height values of the plurality of preset time points; performing joint analysis on the distance values of the unmanned target drones at the plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of preset time points to obtain a low-altitude flight parameter time domain feature map fused with the distance information; and determining a control strategy of the flying height value of the current time point based on the low-altitude flying parameter time domain feature map fused with the distance information. Therefore, the flying height of the unmanned target aircraft can be adaptively adjusted, so that the unmanned target aircraft can dynamically keep a preset distance from the sea level, and collision with the sea surface or washing away by sea waves is avoided.
Description
Technical Field
The invention relates to the technical field of intelligent flight control, in particular to an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm.
Background
The unmanned drone is an aircraft simulating an enemy plane or missile and is used for training and testing an air defense weapon system. The flight control method of the unmanned target aircraft is one of key technologies for influencing the flight performance and safety of the unmanned target aircraft.
When unmanned drone flight control is performed at sea, the influence of the marine environment on the unmanned drone needs to be considered. The flying height of the unmanned target aircraft should be changed along with the change of the marine environment so as to ensure the flying stability and the target simulation effect.
Therefore, how to keep the unmanned target drone at a preset distance from the sea surface so as to avoid collision with the sea surface or flushing by sea waves, and to realize stable flight in a complex marine environment, is an important technical problem.
Disclosure of Invention
The embodiment of the invention provides an artificial intelligence algorithm-based unmanned target aircraft ultra-low altitude flight control method, which comprises the steps of obtaining distance values of an unmanned target aircraft from sea surfaces at a plurality of preset time points in a preset time period; acquiring wind speed values, wind direction values and wave height values of the plurality of preset time points; performing joint analysis on the distance values of the unmanned target drones at the plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of preset time points to obtain a low-altitude flight parameter time domain feature map fused with the distance information; and determining a control strategy of the flying height value of the current time point based on the low-altitude flying parameter time domain feature map fused with the distance information. Therefore, the flying height of the unmanned target aircraft can be adaptively adjusted, so that the unmanned target aircraft can dynamically keep a preset distance from the sea level, and collision with the sea surface or washing away by sea waves is avoided.
The embodiment of the invention also provides an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm, which comprises the following steps: acquiring distance values of the unmanned target drone from the sea surface at a plurality of preset time points in a preset time period acquired by a laser sensor; acquiring wind speed values, wind direction values and wave height values of the plurality of preset time points acquired by a sensor group deployed on the unmanned target aircraft; performing joint analysis on the distance values of the unmanned target drones at the plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of preset time points to obtain a low-altitude flight parameter time domain feature map fused with the distance information; and determining a control strategy of the flying height value of the current time point based on the low-altitude flying parameter time domain feature map fused with the distance information.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
In the drawings: fig. 1 is a flowchart of an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm provided in an embodiment of the application.
Fig. 2 is a schematic diagram of a system architecture of an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm according to an embodiment of the present application.
Fig. 3 is a block diagram of an unmanned target aircraft ultra-low altitude flight control system based on an artificial intelligence algorithm according to an embodiment of the present application.
Fig. 4 is an application scenario diagram of an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm provided in an embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present application and their descriptions herein are for the purpose of explaining the present application, but are not to be construed as limiting the application.
Unless defined otherwise, all technical and scientific terms used in the embodiments of the application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In describing embodiments of the present application, unless otherwise indicated and limited thereto, the term "connected" should be construed broadly, for example, it may be an electrical connection, or may be a communication between two elements, or may be a direct connection, or may be an indirect connection via an intermediate medium, and it will be understood by those skilled in the art that the specific meaning of the term may be interpreted according to circumstances.
It should be noted that, the term "first\second\third" related to the embodiment of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that embodiments of the application described herein may be practiced in sequences other than those illustrated or described herein.
The unmanned drone is an unmanned aerial vehicle for simulating an enemy plane or missile to train and test the performance of an air defense weapon system, and is widely applied to the military field for improving the efficiency of the air defense weapon system and the training level of operators.
Unmanned drones generally have the following characteristics: unmanned aerial vehicle: unmanned drones are autonomous flying aircraft that do not require personnel to maneuver, and they can perform tasks through preset flight plans or remote instructions.
And (3) target simulation: unmanned drones are designed to simulate the flight characteristics and behaviour of an enemy plane or missile, which can simulate different flight speeds, maneuvers and radar features in order to test and evaluate the performance of the air defense weapon system.
Alternatively: the unmanned drone can replace a real enemy plane or missile for testing and training, thereby avoiding the use of expensive and precious aircrafts and reducing the control risk.
Diversity of: the unmanned drone may be customized according to different needs and training goals. They may simulate different types of aircraft or missiles, including fighters, bombers, drones, etc.
Safety: unmanned drones are designed with safety factors in mind to ensure that they do not pose a hazard to personnel or equipment during flight, and are often equipped with safety systems and emergency measures to cope with possible faults or accidents.
The unmanned target drone plays an important role in military training and anti-air weapon system testing, and can provide highly controllable target simulation, so that the anti-air weapon system can perform effective testing and training in a real scene, and the combat capability and the response speed are improved.
The flight control method of the unmanned target aircraft is one of key technologies for influencing the flight performance and safety of the unmanned target aircraft. Unmanned drones are often equipped with autonomous navigation systems that use Global Positioning System (GPS) and Inertial Navigation Systems (INS) to determine their current position and attitude, and these systems can provide accurate position and navigation information to assist the drone in flight control. Before a mission is performed, the drone needs to preset a flight plan. The flight plan comprises information such as a route, a speed, a height, a mission target and the like, and the unmanned target can fly according to parameters set in the flight plan so as to simulate the behavior of an enemy plane or a missile. The unmanned drone can be controlled in real time by remote control instructions of a ground control station or an operator, and the operator can modify flight parameters such as altitude, speed and heading by the remote control instructions to adapt to different training and testing requirements. To ensure safe flight of unmanned drones, some advanced drones are equipped with autonomous obstacle avoidance systems that utilize sensors (e.g., radar, laser, infrared) and image processing algorithms to detect and avoid collisions with obstacles. The flight stability of the unmanned drone is crucial for accurately simulating the behavior of an enemy plane or a missile, and the flight stability control system can maintain a stable flight state by adjusting the attitude, thrust and the position of a control surface of the aircraft.
Further, in performing unmanned drone flight control at sea, the following factors need to be considered: offshore environmental conditions: offshore environmental conditions include sea wind, sea waves, ocean currents, and sea fog, among other factors that have an impact on the flight performance and stability of the unmanned aerial vehicle, and need to be considered and adjusted in flight control.
Lifting on water: taking off and landing of the unmanned target aircraft on the sea needs to consider the stability of the water surface and an area without obstacles, and the taking off and landing point and the flying height are reasonably selected so as to ensure the safe taking off and landing and flying of the unmanned target aircraft.
Electromagnetic interference: electromagnetic interference sources such as radars, communication equipment and the like can exist in the offshore environment, the interference sources can affect the communication and navigation system of the unmanned target aircraft, corresponding measures are needed to resist interference, and the reliability of flight control is ensured.
Avoiding voyage and collision: there may be other vessels, aircraft or offshore facilities at sea, and the risk of collision with it needs to be considered in flight control, with obstacle avoidance techniques and predictive algorithms, to ensure that unmanned targets can safely bypass obstacles.
Communication and data link: communication conditions in an offshore environment may be unstable and it may be desirable to ensure that communication between the drone and the ground control station is clear, with appropriate communication techniques and data links being selected to ensure reliability of transmission and reception of flight control commands.
Meteorological conditions: the weather conditions at sea can change, including wind speed, air temperature, precipitation and other factors, which have influence on the flight performance and stability of the unmanned target aircraft, and the weather conditions need to be considered in flight control and are correspondingly adjusted according to real-time conditions.
The flight control of the unmanned aerial vehicle at sea needs to comprehensively consider factors such as offshore environmental conditions, water take-off and landing, electromagnetic interference, avoidance and collision avoidance, communication and data links, meteorological conditions and the like so as to ensure the safe flight and task execution of the unmanned aerial vehicle. Therefore, the unmanned target aircraft should be equipped with a height control system, and the distance between the unmanned target aircraft and the sea level can be measured by using sensors such as barometers, laser height measuring instruments or radar height measuring instruments, and the predetermined flying height can be maintained by monitoring and adjusting the height of the unmanned target aircraft in real time, so that collision with the sea surface is avoided. The unmanned target aircraft is required to be provided with a flight stabilizing system, the gesture of the unmanned target aircraft can be detected and regulated in real time through equipment such as a gyroscope, an accelerometer and a gesture sensor, and the unmanned target aircraft can maintain a stable flight state through controlling the flight gesture, so that the flight swing influenced by sea waves is reduced, and the unmanned target aircraft is prevented from being washed away by the sea waves.
The autonomous obstacle avoidance system can help the unmanned target aircraft to detect obstacles on the sea surface, such as buoys, ships and the like, and take corresponding obstacle avoidance actions, and the autonomous obstacle avoidance system can sense the surrounding environment by using radars, lasers, infrared sensors and the like, avoid the obstacles by using a flight control algorithm, and ensure the safe flight of the unmanned target aircraft. The change of the wind speed and the wind direction at sea has an important influence on the flight stability of the unmanned drone. Therefore, the unmanned aerial vehicle should be equipped with wind speed and wind direction monitoring devices, such as anemometers and wind direction sensors, and the unmanned aerial vehicle can be better adapted to the change of the offshore wind power and maintain stable flying height by monitoring the wind speed and the wind direction in real time and adjusting the flying control parameters accordingly. In order to ensure the safety of unmanned drones flying at sea, it is necessary to establish a reliable real-time communication system. In this way, the operator can monitor the flight status of the drone in real time and intervene and adjust the flight control parameters as needed.
Through measures such as reasonable altitude control, flight stability, autonomous obstacle avoidance, wind speed monitoring, real-time communication and monitoring, the unmanned target aircraft can be helped to fly stably in a complex marine environment, and a preset distance from the sea level is kept, so that safety and flight effect are ensured.
In one embodiment of the present invention, fig. 1 is a flowchart of an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm provided in the embodiment of the present invention. Fig. 2 is a schematic diagram of a system architecture of an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm according to an embodiment of the present invention. As shown in fig. 1 and 2, an artificial intelligence algorithm-based unmanned target aircraft ultra-low altitude flight control method according to an embodiment of the invention includes: 110, acquiring distance values of the unmanned target drone from the sea surface at a plurality of preset time points in a preset time period acquired by the laser sensor; 120, acquiring wind speed values, wind direction values and wave height values of the plurality of preset time points acquired by a sensor group deployed on the unmanned target aircraft; 130, performing joint analysis on the distance values of the unmanned drone at the plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of preset time points to obtain a low-altitude flight parameter time domain feature map fused with the distance information; and 140, determining a control strategy of the flying height value of the current time point based on the low-altitude flying parameter time domain feature map fused with the distance information.
In step 110, the accuracy and stability of the laser sensor is ensured to obtain reliable distance measurements. According to the time points within the preset time period, the proper data acquisition frequency is set so as to acquire enough data points for analysis. Thus, the distance value between the unmanned target drone and the sea surface can be obtained, and the altitude information required by the flight control system is provided. Through the distance values of a plurality of time points, the altitude change trend of the unmanned target drone can be analyzed so as to carry out subsequent fly altitude control strategy formulation.
In the step 120, the sensor group is deployed, taking into account the position and orientation of the sensor to ensure accurate measurement of wind speed, wind direction and wave height. The accuracy and stability of the sensor are ensured to obtain reliable environmental parameter data. In this way, wind speed values, wind direction values, and wave height values can be obtained, providing information on offshore environmental conditions. These environmental parameters have an important impact on the flight stability and altitude control of the unmanned drone and can be used for subsequent flight control strategy formulation.
In the step 130, data preprocessing and calibration are required for parameters such as distance, wind speed, wind direction, wave height, etc., so as to ensure consistency and reliability of data. The multiple parameters are jointly analyzed using appropriate data analysis and processing methods, such as statistical analysis, time domain feature extraction, and the like. The time domain features of different parameters are analyzed in a combined mode, and a low-altitude flight parameter time domain feature map integrating distance information can be obtained. The feature map can show the relation between the flying height of the unmanned target drone and the sea surface distance, wind speed, wind direction and wave height, and provides reference for subsequent flying height control.
In step 140, a fly height control strategy is formulated according to the analysis result of the feature map, and factors such as wind speed, wind direction, wave height, distance between the unmanned aerial vehicle and the sea surface and the like are considered. And (5) taking the flight safety and stability into consideration, and formulating a proper flight altitude range and a control algorithm. In this way, based on the time domain feature map fused with the distance information, a control strategy of the flying height value of the current time point can be determined. Through a reasonable flying height control strategy, the unmanned target aircraft can fly stably in a complex ocean environment, and collision with or washing away by sea waves is avoided.
Aiming at the technical problems, the technical concept of the application is to acquire the distance value of the unmanned target drone from the sea surface by using the laser sensor to represent real-time distance information, and acquire the wind speed, the wind direction and the wave height value by using the sensor group arranged on the unmanned target drone to represent marine environment information. More specifically, real-time distance information of the unmanned aerial vehicle from the sea surface and marine environment change information are comprehensively utilized, and the flying height of the unmanned aerial vehicle is adaptively adjusted, so that the unmanned aerial vehicle can dynamically keep a preset distance from the sea surface, and collision with the sea surface or washing away by sea waves is avoided.
Based on the above, in the technical scheme of the application, firstly, distance values of the unmanned drone from the sea surface at a plurality of preset time points in a preset time period acquired by a laser sensor are acquired; and acquiring wind speed values, wind direction values and wave height values at the plurality of predetermined time points acquired by a sensor group deployed at the unmanned aerial vehicle. In the marine environment, the wind speed is generally greatly changed, the height of sea waves is influenced, and the flight stability and the speed of the unmanned drone are obviously influenced; the change of the wind direction can cause the change of the direction and the intensity of the air flow, and is also an important factor to be considered in the unmanned target aircraft flight control; the wave height refers to the vertical height of the sea wave, the relative position and the height of the unmanned target drone and the sea surface can be influenced by the size and the strength of the sea wave, and the flying height can be adjusted according to the condition of the sea wave by acquiring the wave height value so as to prevent the unmanned target drone from being impacted by the sea wave or colliding with the sea surface.
Wherein the distance value of the unmanned target aircraft from the sea surface, which is acquired by the laser sensor, represents the vertical distance between the unmanned target aircraft and the sea surface, and can be used for determining the flying height of the unmanned target aircraft. The distance values of a plurality of preset time points are obtained, so that the change trend of the unmanned target drone height can be analyzed, and a basis is provided for a flying height control strategy.
The wind speed value acquired by the sensor group, wherein the wind speed represents the flow speed of air in unit time, and is one of important environmental parameters to be considered in the flight process. The magnitude and direction of the wind speed have important influence on the flight stability and control of the unmanned aerial vehicle, and the intensity and the change trend of the current wind power can be known by acquiring wind speed values at a plurality of preset time points.
Wind direction values collected by the sensor group. The wind direction represents the direction of the wind, i.e. the direction from which the wind comes, and has an important influence on the navigation and flight path selection of the drone. By acquiring wind direction values at a plurality of preset time points, the current wind direction can be determined, and the unmanned target drone is helped to carry out reasonable course adjustment.
Wave height values acquired by the sensor group. The wave height represents the vertical distance between the wave crest and the wave trough of the sea wave, is an important parameter in the marine environment, and the size and the change of the wave height have important influence on the flight stability and the safety of the unmanned drone. The current sea wave state can be known by acquiring wave height values at a plurality of preset time points, and the unmanned target drone is helped to carry out adaptive flight adjustment. In the technical scheme of the application, the wave height value can be obtained by the following methods: 1. ocean buoy: marine buoys are used that are equipped with wave height measuring means, which usually carry wave height sensors. In the vicinity of the fly-to buoy, data from the sensor is received, thereby obtaining a wave height value. 2. Remote sensing technology: the sea level height and morphology can be measured using remote sensing techniques, such as radar or lidar. The device may be equipped with such remote sensing devices to obtain wave height values by measuring the change in altitude of the sea surface. 3. A camera head: the equipment can be provided with a camera for visual observation. By analyzing sea surface images captured by the camera, wave height can be estimated. This can be achieved by calculating the distance between the peaks and valleys on the sea surface. 4. Weather data: local weather data, including wave height forecasts, are acquired. Such data is typically provided by the weather department and may be obtained via the internet or other communication means. The drone may receive these data and record wave height values.
It should be noted that the acquisition of wave height values may be affected by a number of factors, including the accuracy of the sensor, weather conditions, and the flying height of the drone. Therefore, in making wave height measurements, multiple methods should be considered in combination and the results verified and corrected.
The acquisition of the distance values of the unmanned drone from the sea surface at a plurality of preset time points within a preset time period acquired by the laser sensor and the wind speed value, the wind direction value and the wave height value acquired by the sensor group plays an important role in determining the control strategy of the flight height value at the current time point.
By analyzing the distance value of the unmanned target drone collected by the laser sensor from the sea surface, the height change trend of the unmanned target drone can be obtained, and the wind power and wave conditions of the current marine environment can be known by combining the wind speed value, the wind direction value and the wave height value collected by the sensor group, wherein the data are vital to a control strategy for determining the flying height value of the current time point. The wind speed value and the wind direction value acquired by the sensor group are acquired, so that the current wind power size and direction can be known in real time, and the information is critical to the flight stability and safety of the unmanned target aircraft. According to the change of wind speed and wind direction, the flying height value can be adjusted to maintain the preset distance between the unmanned target drone and the sea level. Wave height is an important parameter in marine environment, has influence on the flight stability and safety of unmanned drone, and can know the current sea wave size by acquiring wave height values acquired by a sensor group. According to the change of wave height, the flying height value can be adjusted so as to keep the unmanned target aircraft stably flying in the marine environment.
The control strategy of the flight height value at the current time point can be adjusted by acquiring the distance value of the unmanned target drone from the sea surface and the environmental parameters (wind speed, wind direction and wave height) so as to ensure that the unmanned target drone stably flies in a complex marine environment and keeps a preset distance from the sea surface, thereby improving the safety and effect of flight. In one embodiment of the present application, performing joint analysis on distance values of the unmanned drone at the plurality of predetermined time points from the sea surface, wind speed values, wind direction values and wave height values at the plurality of predetermined time points to obtain a low-altitude flight parameter time domain feature map fused with distance information, including: performing data preprocessing on the distance values of the unmanned target drone at the plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of preset time points to obtain a distance time sequence input vector and a low-altitude flight parameter time domain matrix; and fusing the distance time sequence input vector and the low-altitude flight parameter time domain matrix to obtain a low-altitude flight parameter time domain feature map fused with the distance information.
And smoothing the distance value to remove abnormal values or noise so as to obtain a stable altitude change trend, thereby being beneficial to reducing the interference of unstable factors on the altitude control strategy. And the wind speed, the wind direction and the wave height value are subjected to averaging, filtering or interpolation processing to obtain a smooth environment parameter change trend, so that the influence of abrupt change or noise on flight stability analysis is reduced.
The preprocessed distance values are arranged in time sequence to form a time sequence vector, and the time sequence vector can be used for analyzing and predicting the altitude change trend of the unmanned aerial vehicle and providing input for a flight altitude control strategy. The preprocessed wind speed value, wind direction value and wave height value are combined into a matrix according to time sequence, and the matrix can be used for analyzing the change trend of environmental parameters and providing input for flight stability analysis and flight path planning.
The distance time sequence input vector and the low-altitude flight parameter time domain matrix are fused, and more comprehensive flight state description can be obtained by combining the distance information and the environment parameter information, so that the comprehensive analysis capability of the flight height and the flight stability is improved. The fused data is converted into a time domain feature map, time sequence data can be converted into a two-dimensional image representation, and the representation mode is helpful for intuitively observing the flight state and environmental parameter changes at different time points, so that convenience is provided for further analysis and decision.
Through data preprocessing, generation of a distance time sequence input vector and a low-altitude flight parameter time domain matrix, and generation of a low-altitude flight parameter time domain feature map fused with distance information, understanding and analysis capability of unmanned target aircraft flight state and environmental parameters can be improved, and beneficial information is provided for flight control and decision.
The method for preprocessing the data of the distance values of the unmanned drone at a plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at a plurality of preset time points to obtain a distance time sequence input vector and a low-altitude flight parameter time domain matrix comprises the following steps: arranging distance values of the unmanned target drones at a plurality of preset time points from the sea surface into the distance time sequence input vector according to a time dimension; and arranging the wind speed values, the wind direction values and the wave height values of the plurality of preset time points into the low-altitude flight parameter time domain matrix according to the time dimension and the sample dimension.
Then, arranging distance values of the unmanned target drones at a plurality of preset time points from the sea surface into distance time sequence input vectors according to a time dimension; and arranging the wind speed values, the wind direction values and the wave height values of the plurality of preset time points into a low-altitude flight parameter time domain matrix according to the time dimension and the sample dimension. That is, the time sequence discrete distribution of the distance value of the unmanned target drone from the sea surface is converted into a distance time sequence input vector, so that the time sequence change information is reserved when the unmanned target drone is subjected to data structuring processing; and integrating the wind speed values, the wind direction values and the wave height values at a plurality of preset time points into a low-altitude flight parameter time domain matrix so as to represent the overall change mode of the marine environment information.
In one embodiment of the present application, fusing the distance timing input vector and the low-altitude flight parameter time domain matrix to obtain the low-altitude flight parameter time domain feature map fused with the distance information includes: inputting the distance time sequence input vector and the low-altitude flight parameter time domain matrix into a MetaNet module to obtain a low-altitude flight parameter time domain feature map fused with the distance information; the method for obtaining the low-altitude flight parameter time domain feature map of the fusion distance information comprises the steps of: passing the distance time sequence input vector through a point convolution layer to obtain a first convolution feature vector; passing the first convolution feature vector through a modified linear unit based on a ReLU function to obtain a first modified convolution feature vector; passing the first modified convolution feature vector through a point convolution layer to obtain a second convolution feature vector; passing the second convolution feature vector through a correction linear unit based on a Sigmoid function to obtain a second correction convolution feature vector; the low-altitude flight parameter time domain matrix is processed through a CNN model to obtain a high-dimensional implicit feature map; and fusing the second modified convolution feature vector and the high-dimensional implicit feature map to obtain a low-altitude flight parameter time domain feature map of the fused distance information.
And then, inputting the distance time sequence input vector and the low-altitude flight parameter time domain matrix into a MetaNet module to obtain a low-altitude flight parameter time domain feature map fused with the distance information. The MetaNet module can fuse the distance time sequence input vector and the low-altitude flight parameter time domain matrix, and utilizes the relevance and interaction between the distance time sequence input vector and the low-altitude flight parameter time domain matrix to extract richer characteristic information. The MetaNet module can learn and extract the time domain characteristics of the distance information and the low-altitude flight parameters, and the time domain characteristics can capture the dynamic change and the key mode in the flight process, so that the unmanned aerial vehicle is helpful to understand and analyze the flight behaviors of the unmanned aerial vehicle in the complex marine environment. The low-altitude flight parameter time domain feature map fused with the distance information can be used for comprehensive decision and prediction, and risk and challenge in the flight process can be identified and corresponding adjustment and decision can be made through analysis of the feature map. For example, future wind speed variation trend can be predicted according to the characteristic diagram so as to adjust the flying speed and the course and ensure the safety and the stability of flying.
The MetaNet module is used for fusing the distance time sequence input vector and the low-altitude flight parameter time domain matrix, so that the understanding and analysis capability of the unmanned target aircraft on the flight state and environment can be improved, and the accuracy and effect of flight control and decision are improved.
In a specific example of the present application, the encoding process of inputting the distance time sequence input vector and the low-altitude flight parameter time domain matrix into a MetaNet module to obtain a low-altitude flight parameter time domain feature map with fused distance information includes: firstly, the distance time sequence input vector passes through a point convolution layer to obtain a first convolution characteristic vector; subsequently, the first convolution eigenvector is passed through a modified linear unit based on a ReLU function to obtain a first modified convolution eigenvector; then, the first modified convolution eigenvector passes through a point convolution layer to obtain a second convolution eigenvector; then, the second convolution feature vector passes through a correction linear unit based on a Sigmoid function to obtain a second correction convolution feature vector; meanwhile, the low-altitude flight parameter time domain matrix is processed through a CNN model to obtain a high-dimensional implicit feature map; and fusing the second modified convolution feature vector and the high-dimensional implicit feature map to obtain a low-altitude flight parameter time domain feature map fused with the distance information. That is, the MetaNet module enables one-dimensional time sequence data to directly interact with the two-dimensional parameter matrix, directly controls the relevant characteristics of each characteristic channel, helps the network concentrate on the specific part of each characteristic channel, and improves the expression capacity of the characteristics.
Specifically, through point convolution processing, the time sequence local characteristics in the distance time sequence input vector can be extracted, and the time sequence relevance between the distance time sequence change mode and the time sequence can be captured; and enhancing the expressive power of the feature representation by modifying the linear element. Meanwhile, extracting features by using a CNN model, and extracting inter-parameter high-dimensional implicit feature distribution contained in the low-altitude flight parameter time domain matrix; and fusing the distance time sequence characteristic information and the flight parameter implicit characteristic information containing the marine environment change information to obtain a low-altitude flight parameter time domain characteristic map fusing the distance information, so that the low-altitude flight parameter time domain characteristic map fusing the distance information has excellent expression capability and characteristic diversity.
In one embodiment of the present application, based on the low-altitude flight parameter time domain feature map fused with the distance information, a control strategy for determining a flight altitude value at a current time point includes: performing feature distribution optimization on the low-altitude flight parameter time domain feature map fused with the distance information to obtain an optimized low-altitude flight parameter time domain feature map fused with the distance information; the optimized low-altitude flight parameter time domain feature map fused with the distance information is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the flight height value of the current time point should be increased, maintained or decreased; and taking the classification result as a control strategy of the flying height value of the current time point.
In the technical scheme of the application, when the distance time sequence input vector and the low-altitude flight parameter time domain matrix are input into the MetaNet module to obtain the low-altitude flight parameter time domain feature map of the fusion distance information, each feature matrix of the low-altitude flight parameter time domain feature map of the fusion distance information expresses time sequence-sample cross local correlation features of a wind speed value, a wind direction value and a wave height value, and the time sequence distribution of the distance values is followed among all feature matrices of the low-altitude flight parameter time domain feature map of the fusion distance information, therefore, when the time sequence-sample cross local correlation feature representation in each feature matrix of the low-altitude flight parameter time domain feature map of the fusion distance information is used as a foreground object feature representation, background distribution noise is introduced while the feature matrix distribution representation of the time sequence distribution based on the distance value is carried out, and because the time sequence vector-cross dimensional matrix high-sequence distribution representation of the MetaNet module also introduces time sequence vector-cross dimensional space feature matrix high-dimension feature-based time sequence cross local correlation feature of each feature matrix of the low-altitude feature map of the fusion distance information, thereby obtaining the accurate probability of the fusion distance information in the low-altitude flight parameter time domain feature map of the fusion distance information by the low-altitude information has the effect on the time domain feature map of the fusion distance information.
Based on this, the applicant of the present application refers to each feature matrix of the low-altitude flight parameter time domain feature map fused with distance information, for example, written asThe characteristic scale is used as the rank arrangement distribution soft matching of the imitation mask, and the method is specifically expressed as follows: performing characteristic scale on each characteristic matrix of the low-altitude flight parameter time domain characteristic map fused with the distance information by using the following optimization formula as a rank arrangement distribution soft match of a simulated mask; wherein, the optimization formula is:
wherein (1)>Is the feature matrixIs>Characteristic value of the location->Is the feature matrix->Is of the scale of (a)I.e. width multiplied by height +.>Representing the feature matrix->Is the square of the Frobenius norm, < >>Representing the feature matrix->And (2) is two norms ofIs a weighted superparameter,/->Is the +.th of the optimized feature matrix of the optimized low-altitude flight parameter time domain feature map>Characteristic value of the location->Representing the calculation of a value of a natural exponent function that is a power of a value.
Here, when the rank-ordered distribution soft matching of the feature scale as an imitation mask can map high-dimensional features into a probability density space, focusing the feature scale as an imitation mask for mapping on foreground object features while ignoring background distribution noise, and passing through the feature matrix The distribution soft matching of pyramid rank arrangement distribution by different norms of the probability density distribution is used for effectively capturing the correlation between the central area and the tail area of the probability density distribution, and the characteristic matrix is avoided>Probability density mapping bias caused by parameter-dependent spatial heterogeneous distribution of high-dimensional features of (a)Thereby improving the accuracy of the classification result obtained by the classifier through the low-altitude flight parameter time domain feature map fused with the distance information.
Further, the low-altitude flight parameter time domain feature map fused with the distance information is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the flight height value of the current time point should be increased, maintained or decreased. According to the classification result of the classifier, guidance and suggestion can be provided for the flight height of the unmanned target aircraft, and if the classification result indicates that the flight height at the current time point should be increased, the flight control system can be correspondingly adjusted to enable the unmanned target aircraft to be lifted; if the classification result indicates that the current altitude should be maintained, the current flying altitude can be maintained; if the classification result indicates that the altitude should be reduced, the flight control system may be adjusted accordingly to lower the drone. Such guidance helps to achieve accurate fly height control, improving the safety and efficiency of the flight.
By inputting the low-altitude flight parameter time domain feature map fused with the distance information into the classifier, the real-time analysis and judgment of the current flight state can be realized, the classifier can rapidly classify the current state, and rapid feedback and decision are provided, so that the flight control system can timely respond to the changed environmental conditions and flight demands. The flight strategy can be optimized through the classification result of the classifier, and different flight control strategies can be adopted according to different classification results, such as adjusting the flight speed, changing the course or changing the altitude change rate, and the like. Such optimization helps to improve the stability, efficiency and adaptability of the flight, enabling the unmanned target to better adapt to changes and demands of the marine environment.
In summary, the unmanned aerial vehicle ultra-low altitude flight control method based on the artificial intelligence algorithm according to the embodiment of the invention is explained, a laser sensor is used for obtaining the distance value of the unmanned aerial vehicle from the sea surface to represent real-time distance information, and a sensor group arranged on the unmanned aerial vehicle is used for collecting the wind speed, the wind direction and the wave height value to represent marine environment information. More specifically, real-time distance information of the unmanned aerial vehicle from the sea surface and marine environment change information are comprehensively utilized, and the flying height of the unmanned aerial vehicle is adaptively adjusted, so that the unmanned aerial vehicle can dynamically keep a preset distance from the sea surface, and collision with the sea surface or washing away by sea waves is avoided.
Fig. 3 is a block diagram of an unmanned target aircraft ultra-low altitude flight control system based on an artificial intelligence algorithm according to an embodiment of the present invention. As shown in fig. 3, the unmanned drone ultra-low altitude flight control system 200 based on the artificial intelligence algorithm includes: a distance value acquisition module 210, configured to acquire distance values of the unmanned drone from the sea surface at a plurality of predetermined time points within a predetermined time period acquired by the laser sensor; an environmental data acquisition module 220 for acquiring wind speed values, wind direction values, and wave height values at the plurality of predetermined time points acquired by a sensor group deployed at the unmanned aerial vehicle; the joint analysis module 230 is configured to perform joint analysis on the distance values of the unmanned drone at the plurality of predetermined time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of predetermined time points to obtain a low-altitude flight parameter time domain feature map with fused distance information; and a control strategy determining module 240, configured to determine a control strategy of the flying height value at the current time point based on the low-altitude flying parameter time domain feature map fused with the distance information.
It will be appreciated by those skilled in the art that the specific operation of the steps in the above-described unmanned aerial vehicle ultra-low altitude flight control system based on the artificial intelligence algorithm has been described in detail in the above description of the unmanned aerial vehicle ultra-low altitude flight control method based on the artificial intelligence algorithm with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
As described above, the unmanned aerial vehicle ultra-low altitude flight control system 200 based on the artificial intelligence algorithm according to the embodiment of the present invention may be implemented in various terminal devices, for example, a server for unmanned aerial vehicle ultra-low altitude flight control based on the artificial intelligence algorithm, or the like. In one example, the unmanned drone ultra low altitude flight control system 200 based on the artificial intelligence algorithm according to an embodiment of the present invention may be integrated into the terminal device as a software module and/or hardware module. For example, the unmanned drone ultra-low altitude flight control system 200 based on the artificial intelligence algorithm may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the unmanned drone ultra-low altitude flight control system 200 based on the artificial intelligence algorithm may also be one of the numerous hardware modules of the terminal device.
Alternatively, in another example, the unmanned aerial vehicle ultra-low altitude flight control system 200 based on the artificial intelligence algorithm and the terminal device may be separate devices, and the unmanned aerial vehicle ultra-low altitude flight control system 200 based on the artificial intelligence algorithm may be connected to the terminal device through a wired and/or wireless network and transmit the interactive information in a agreed data format.
Fig. 4 is an application scenario diagram of an unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm provided in an embodiment of the invention. As shown in fig. 4, in this application scenario, first, distance values (e.g., C1 as illustrated in fig. 4) of an unmanned target from the sea surface at a plurality of predetermined time points within a predetermined period of time acquired by a laser sensor are acquired, and wind speed values (e.g., C2 as illustrated in fig. 4), wind direction values (e.g., C3 as illustrated in fig. 4), and wave height values (e.g., C4 as illustrated in fig. 4) at the plurality of predetermined time points acquired by a sensor group deployed to the unmanned target are acquired; the obtained distance value, wind speed value, wind direction value and wave height value are then input into a server (e.g. S as illustrated in fig. 4) deployed with an artificial intelligence algorithm based unmanned aerial vehicle ultra low altitude flight control algorithm, wherein the server is capable of processing the distance value, the wind speed value, the wind direction value and the wave height value based on the artificial intelligence algorithm based unmanned aerial vehicle ultra low altitude flight control algorithm to determine a control strategy for the flight height value at the current point in time.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. An unmanned target aircraft ultra-low altitude flight control method based on an artificial intelligence algorithm is characterized by comprising the following steps: acquiring distance values of the unmanned target drone from the sea surface at a plurality of preset time points in a preset time period acquired by a laser sensor; acquiring wind speed values, wind direction values and wave height values of the plurality of preset time points acquired by a sensor group deployed on the unmanned target aircraft; performing joint analysis on the distance values of the unmanned target drones at the plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of preset time points to obtain a low-altitude flight parameter time domain feature map fused with the distance information; and determining a control strategy of the flying height value of the current time point based on the low-altitude flying parameter time domain feature map fused with the distance information.
2. The artificial intelligence algorithm-based unmanned aerial vehicle ultra-low altitude flight control method according to claim 1, wherein performing joint analysis on the distance values of the unmanned aerial vehicle from the sea surface at the plurality of predetermined time points, the wind speed values, the wind direction values and the wave height values at the plurality of predetermined time points to obtain a low altitude flight parameter time domain feature map with fused distance information comprises: performing data preprocessing on the distance values of the unmanned target drone at the plurality of preset time points from the sea surface, the wind speed values, the wind direction values and the wave height values at the plurality of preset time points to obtain a distance time sequence input vector and a low-altitude flight parameter time domain matrix; and fusing the distance time sequence input vector and the low-altitude flight parameter time domain matrix to obtain a low-altitude flight parameter time domain feature map fused with the distance information.
3. The unmanned aerial vehicle ultra-low altitude flight control method based on the artificial intelligence algorithm according to claim 2, wherein the data preprocessing is performed on the distance values of the unmanned aerial vehicle from the sea surface at the plurality of predetermined time points, the wind speed values, the wind direction values and the wave height values at the plurality of predetermined time points to obtain a distance time sequence input vector and a low altitude flight parameter time domain matrix, and the method comprises the following steps: arranging distance values of the unmanned target drones at a plurality of preset time points from the sea surface into the distance time sequence input vector according to a time dimension; and arranging the wind speed values, the wind direction values and the wave height values of the plurality of preset time points into the low-altitude flight parameter time domain matrix according to the time dimension and the sample dimension.
4. The unmanned aerial vehicle ultra-low altitude flight control method based on an artificial intelligence algorithm according to claim 3, wherein fusing the distance timing input vector and the low altitude flight parameter time domain matrix to obtain the low altitude flight parameter time domain feature map fused with the distance information comprises: inputting the distance time sequence input vector and the low-altitude flight parameter time domain matrix into a MetaNet module to obtain a low-altitude flight parameter time domain feature map fused with the distance information; the method for obtaining the low-altitude flight parameter time domain feature map of the fusion distance information comprises the steps of: passing the distance time sequence input vector through a point convolution layer to obtain a first convolution feature vector; passing the first convolution feature vector through a modified linear unit based on a ReLU function to obtain a first modified convolution feature vector; passing the first modified convolution feature vector through a point convolution layer to obtain a second convolution feature vector; passing the second convolution feature vector through a correction linear unit based on a Sigmoid function to obtain a second correction convolution feature vector; the low-altitude flight parameter time domain matrix is processed through a CNN model to obtain a high-dimensional implicit feature map; and fusing the second modified convolution feature vector and the high-dimensional implicit feature map to obtain a low-altitude flight parameter time domain feature map of the fused distance information.
5. The unmanned aerial vehicle ultra-low altitude flight control method based on the artificial intelligence algorithm of claim 4, wherein determining a control strategy of a flight altitude value at a current time point based on the low altitude flight parameter time domain feature map fused with distance information comprises: performing feature distribution optimization on the low-altitude flight parameter time domain feature map fused with the distance information to obtain an optimized low-altitude flight parameter time domain feature map fused with the distance information; the optimized low-altitude flight parameter time domain feature map fused with the distance information is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the flight height value of the current time point should be increased, maintained or decreased; and taking the classification result as a control strategy of the flying height value of the current time point.
6. The unmanned aerial vehicle ultra-low altitude flight control method based on an artificial intelligence algorithm according to claim 5, wherein performing feature distribution optimization on the low-altitude flight parameter time domain feature map of the fusion distance information to obtain an optimized low-altitude flight parameter time domain feature map of the fusion distance information comprises: performing characteristic scale on each characteristic matrix of the low-altitude flight parameter time domain characteristic map fused with the distance information by using the following optimization formula as a rank arrangement distribution soft match of a simulated mask; wherein, the optimization formula is: Wherein (1)>Is the feature matrix->Is>Characteristic value of the location->Is the feature matrix->I.e., width times height,representing the feature matrix->Is the square of the Frobenius norm, < >>Representing the feature matrix->Is equal to or greater than the second norm of (2)>Is a weighted superparameter,/->Is the +.th of the optimized feature matrix of the optimized low-altitude flight parameter time domain feature map>Characteristic value of the location->Representing the calculation of a value of a natural exponent function that is a power of a value.
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