Unmanned aerial vehicles and their intelligent recognition methods for intelligent identification of polar ice fields

Abstract

This invention provides an unmanned aerial vehicle (UAV) for intelligent identification of polar ice rinks, comprising a main body, a solar panel, control wings, a propeller, and an observation cabin. The UAV is powered by solar energy, enabling a long endurance. The control wings are symmetrically mounted on both sides of the main body, enabling ascent, descent, and steering. The propeller provides propulsion, working in conjunction with the control wings to complete ascent, descent, and steering movements. The observation cabin incorporates multiple functional modules for ice rink identification, UAV and transport ship positioning, data transmission, and status monitoring. Illumination warning lights are installed on the leading edges of the wings to facilitate ice rink identification and landing during polar night conditions. A method for intelligent identification of polar ice rinks using this UAV is also provided. This invention enables detailed and accurate observation of sea ice morphology around shipping lanes, obtaining precise sea ice data to aid polar transport ships in navigation, assist captains in decision-making, and reduce the risk of distress.

Description

Technical Field

[0001] This invention relates to an unmanned aerial vehicle (UAV) in the field of polar equipment technology, specifically to an UAV for intelligent identification of polar ice rinks and its intelligent identification method. Background Technology

[0002] The Arctic region is rich in oil, natural gas, and mineral resources, and countries around the world are giving it unprecedented importance in formulating Arctic strategies, striving to gain an advantage in the development of Arctic shipping routes and resources. Unlike conventional open waters, the polar regions are covered by ice and snow year-round, with harsh weather conditions such as extremely low temperatures, polar days and nights, and blizzards occurring frequently. In such poor visibility conditions, polar transport ships are highly susceptible to losing their way, deviating from their course, or even colliding with thick ice, icebergs, or ice ridges if they cannot accurately identify the complex ice fields around the shipping lanes, resulting in significant economic losses. Ice field identification mainly relies on satellites and shipborne identification tools. Satellite identification has a large observation range, but it is greatly affected by weather conditions, cannot identify small-scale sea ice information, and its resolution is insufficient for high-precision identification requirements, so the accuracy and real-time performance of satellite identification cannot be guaranteed. Shipborne identification has higher accuracy and real-time performance, but transport ships have limited distances above the waterline and poor maneuverability, thus limiting the observation range of shipborne identification.

[0003] In summary, existing ice rink identification technologies typically cannot simultaneously guarantee high accuracy, real-time performance, and a large observation range. Currently, no descriptions or reports of technologies similar to this invention have been found, and no similar domestic or international materials have been collected. Summary of the Invention

[0004] To address the aforementioned shortcomings in the prior art, this invention provides an unmanned aerial vehicle for intelligent identification of polar ice rinks and its intelligent identification method.

[0005] According to one aspect of the present invention, an unmanned aerial vehicle (UAV) for intelligent identification of polar ice rinks is provided, comprising: a main body of the UAV, a solar panel, control wings, a propeller, and an observation cabin; wherein:

[0006] The main body of the aircraft is used to perform flight missions;

[0007] The solar panel is installed above the main body of the aircraft to absorb solar energy and convert it into electrical energy;

[0008] The control wings are symmetrically mounted on both sides of the aircraft body. By controlling the rotation angle of the control wings, the aircraft body can perform take-off, landing, and turning flight tasks.

[0009] The propeller is installed at the tail of the aircraft body. By controlling the rotation of the propeller around its axis, it provides thrust to the aircraft body to achieve the forward and backward flight missions of the aircraft body.

[0010] The observation cabin is installed below the main body of the aircraft and is used to perform identification tasks.

[0011] Preferably, the solar panel is mounted directly above the main body of the aircraft.

[0012] Preferably, the main body of the aircraft includes: a delta-wing-shaped airship, rudder wings, and lighting warning lights; wherein:

[0013] The delta-wing airship uses carbon fiber as its skeleton structure, which is covered with skin. The interior of the skeleton structure is filled with helium. By controlling the filling and releasing of the nitrogen gas, auxiliary control of the ascent and descent of the main body of the aircraft can be achieved.

[0014] The warning lights are symmetrically installed on the leading edges of the two wings of the delta-wing airship and connected to the power supply of the solar panel to provide lighting assistance for the detection of the observation cabin.

[0015] The rudder wings are symmetrically mounted on the trailing edges of the two wings of the delta-wing-shaped airship.

[0016] Preferably, the observation cabin is a pointed triangle shape that is narrower at the front and wider at the back.

[0017] Preferably, the observation cabin is installed at the center directly below the main body of the aircraft.

[0018] Preferably, the observation cabin is equipped with a windshield wiper at the rear.

[0019] Preferably, the observation cabin is equipped with a sea ice identification module, a positioning module, a data transmission module, and a status monitoring module; wherein:

[0020] The sea ice identification module is used to acquire on-site sea ice data and flight parameter data of the aircraft body, and feed them back to the positioning module;

[0021] The positioning module is used to acquire the navigation parameter data of the transport ship in real time, and obtain the motion control parameters of the aircraft body and the transport ship based on the navigation parameter data and the data fed back by the sea ice recognition module. The motion mode of the aircraft and the transport ship is obtained and fed back to the status monitoring module for flight control.

[0022] The data transmission module is used to realize data communication within the unmanned aerial vehicle and between the unmanned aerial vehicle and the transport ship;

[0023] The status monitoring module is used to monitor the flight parameter data and the operating parameter data inside and outside the aircraft body in real time during the flight process of the aircraft body, and: control the rotation direction of the propeller around the axis and / or the rotation angle of the rudder based on the motion mode of the aircraft and the transport ship fed back by the positioning module; and monitor the operating status of the aircraft body in real time based on the monitored operating parameter data.

[0024] Preferably, the sea ice identification module acquires on-site sea ice data and flight parameter data of the aircraft body, including:

[0025] Real-time images of the scene are captured using a high-definition camera.

[0026] The perspective transformation method is used to convert the scene image into a standard top view;

[0027] The standard top view is subjected to grayscale conversion and noise reduction processing to obtain a sea ice image of the shipping channel;

[0028] The thresholding method is used to segment the sea ice image of the waterway, where black represents seawater and white represents sea ice, resulting in a binarized image that distinguishes between seawater and sea ice.

[0029] The system acquires the internal and external parameters of the high-definition camera, matches the pixels of the scene image, calculates the parallax, and calculates the flight altitude of the aircraft body, the distance between the aircraft body and the transport ship and / or sea ice, and the size of the sea ice based on the binocular ranging principle.

[0030] The intrinsic parameter K of the high-definition camera is obtained through camera calibration. In the geodetic coordinate system O-XYZ, the extrinsic parameters of the two high-definition cameras are determined by C. 10 and C 20 It means that C 10 and C 20 This can be expressed using the rotation matrix R and the translation vector t as follows:

[0031]

[0032] Transformation matrix C from the first high-definition camera to the second high-definition camera 21 Then it is:

[0033]

[0034] Among them, C 01 C 10 The reverse;

[0035] P h For a point on the sea ice image captured by a high-definition camera, satisfying n h For Ph The normal vector of the plane containing the point as seen by the high-definition camera, d is the normal vector of P. h The distance from the plane containing the point to the high-definition camera. Let P be the projection of a point onto the normal vector; for the first high-definition camera, there exists a point P on the plane. h1 satisfy:

[0036]

[0037] P h1 and P h2 P respectively h The coordinates n in the coordinate system of the first high-definition camera and the second high-definition camera h1 For P h1 The normal vector of the plane containing the point as seen by the high-definition camera; according to the projection relationship, Z C1 and Z C2 The parameters are obtained from the extrinsic parameters of the first and second high-definition cameras, respectively. K1 and K2 are the intrinsic parameters of the first and second high-definition cameras, respectively. p1 is the P... h1 The corresponding pixels on the acquired sea ice image; according to the coordinate transformation, we have:

[0038] P h2 =R 21 P h1 +t 21 (4)

[0039]

[0040] Where p2 is P h2 Collect the corresponding pixels on the sea ice image. The homography matrix is ​​used to calculate the distance data between the main body of the aircraft and the transport ship and / or sea ice.

[0041] The flight altitude of the main body of the aircraft and the distance between the main body of the aircraft and the transport ship and / or sea ice, which are updated in real time, are used as the flight parameter data of the main body of the aircraft.

[0042] A sea ice database is constructed using the real-time updated binarized result image and the sea ice size. Based on the sea ice database, the ice field is classified and its size is calculated. The types, sizes, distributions, and concentrations of sea ice are statistically analyzed to obtain on-site sea ice data.

[0043] Preferably, the positioning module acquires real-time navigation parameter data of the aircraft and the transport ship, and obtains the aircraft motion control parameters between the aircraft body and the transport ship based on the navigation parameter data and the data obtained by the sea ice recognition module, thereby obtaining the motion mode of the aircraft and the transport ship, including:

[0044] Real-time acquisition of navigation parameter data for aircraft and transport ships, including: the speed and trajectory of transport ships;

[0045] Based on the flight parameter data and sea ice data obtained from the sea ice identification module, the navigation hazard index k in a rectangular ice field centered on the transport ship and extending outward by m times the ship's length and n times its width is calculated:

[0046]

[0047] Among them, A total Let A represent the total area of ​​the rectangular ice rink. ice berg Indicates the area covered by icebergs in this region, A ice ridge Indicates the area occupied by the ice ridges in this region, A level ice Indicates the area occupied by layered ice in this region, A rubble ice Indicates the area occupied by floating ice fragments, A open water Indicates the area occupied by open water, A extra This represents the area occupied by other regions; α, β, γ, λ, μ, and τ are the risk coefficients corresponding to each ice condition, and satisfy the following:

[0048]

[0049] Based on the displacement and speed of the transport ship over a period of time 'a', the trajectory 's' is recorded. a Predict the trajectory s of the transport ship in the next time period b in the geodetic coordinate system. b :

[0050] s b =us a +s e (8)

[0051] Among them, s a =[s xa s ya v xa v ya In the formula, the first two terms represent the displacement and the last two terms represent the speed. For the transfer matrix, s b =[s xb s yb v xb v yb ], s e =[s xe s ye v xe v ye The additional movement of the transport ship due to avoiding icebergs and ice ridges is estimated based on the sea ice binarization result map;

[0052] The actual position of the transport ship sc With predicted trajectory s b The error E is:

[0053]

[0054]

[0055] RMSE(E i ) predicts the trajectory of the transport ship s b With actual motion position s c The mean square error between them; ||E i || indicates the error E i Take the determinant;

[0056] The error E represents the degree of overlap between the actual and predicted routes;

[0057] The distance between the aircraft body and the transport ship, the navigation hazard index of the transport ship, and the overlap between the actual and predicted routes in the flight parameter data are used as the motion control parameters of the aircraft to obtain the motion mode of the aircraft and the transport ship, and the aircraft is controlled as follows:

[0058] Based on the ice resistance rating of the transport ship, the maximum safe distance between the transport ship and the aircraft is set to s. safe and through The aircraft is controlled to achieve either Case 1 (full speed towards the transport ship to ensure safe navigation), Case 2 (moving away from the transport ship to capture and identify distant sea ice data), or Case 3 (moving closer to the transport ship to capture and identify sea ice data near the shipping channel), where ω, η, and ξ are constants whose magnitudes are determined by the ice resistance rating of the transport ship. Therefore:

[0059]

[0060] Preferably, the status monitoring module controls the rotation direction of the propeller around its axis and / or the rotation angle of the rudder based on the motion patterns of the aircraft and transport ship fed back by the positioning module, including:

[0061] Based on the control algorithm of motion control equations and the motion modes of the aircraft and transport ship, the rotation direction of the propeller around its axis and / or the rotation angle of the rudder are controlled as follows:

[0062] When the aircraft moves forward, the propeller rotation is defined as forward and recorded as 1; when the aircraft moves backward, the propeller rotation is reverse and recorded as -1; when the aircraft stops rotating, it is 0. When the aircraft climbs, the two rudders rotate in the positive direction and are recorded as 1; when the aircraft dives, the two rudders rotate in the negative direction and are recorded as -1; when the aircraft resets, it is 0. The aircraft uses a high-definition camera to determine whether it is traveling in the same direction as the transport ship and is recorded as 1 or traveling in the opposite direction and recorded as -1; other states are recorded as 0. The states of the propeller and the aircraft are controlled by STATE = [propeller 1 propeller 2 rudder 1 rudder 2 heading].

[0063] When the distance between the transport ship and the aircraft is greater than the maximum safe distance s safe If CONTROL < 0, activate case 1 motion mode: if STATE [0 0 0 0 1] T If ≠-1, then STATE[1 1 0 0 0] T =0 and STATE[0 0 1 1 0] T =0, the aircraft turns around using propellers and rudders; if STATE[0 0 0 0 1] T =-1, then STATE=[1100-1], the aircraft speeds toward the transport ship;

[0064] When the distance between the transport ship and the aircraft is less than the maximum safe distance s safe Furthermore, the hazard index k and the mean square error RMSE (E) of the transport ship navigation i When the value is relatively small (less than a set threshold), the transport ship travels along the predicted trajectory. If CONTROL > 1, the case2 motion mode is activated: If STATE[00 1 1 0] T =-1, then The aircraft moves forward and turns using rudders; if STATE[0 0 0 0 1] T If ≠-1, then STATE = [1 1 1 1 x], the aircraft moves forward and climbs, thus identifying a wider ice rink;

[0065] When the distance between the transport ship and the aircraft is less than the maximum safe distance s safe Furthermore, the hazard index k and the mean square error RMSE (E) of the transport ship navigation i When the value is large (greater than a set threshold), the transport ship deviates from the predicted trajectory, and the case3 motion mode is activated: if STATE[0 0 1 1 0] T = -1, then STATE = [1 1 -1 -1 x], the aircraft heads toward the transport ship and descends; if STATE [0 0 0 0 1] TIf the value is not equal to -1, then STATE = [-1 -1 -1 -1 x], and the aircraft moves backward and descends. In both cases, the aircraft moves close to the transport ship, thus identifying the transport ship and the ice rink around the waterway.

[0066] According to another aspect of the present invention, a method for intelligent identification of polar ice fields using an unmanned aerial vehicle as described in any one of the above-mentioned invention is provided, comprising:

[0067] Using unmanned aerial vehicles to collect real-time images of the scene;

[0068] The perspective transformation method is used to convert the scene image into a standard top view;

[0069] The standard top view is subjected to grayscale conversion and noise reduction processing to obtain a sea ice image of the shipping channel;

[0070] The thresholding method is used to segment the sea ice image of the waterway, where black represents seawater and white represents sea ice, resulting in a binarized image that distinguishes between seawater and sea ice.

[0071] The internal and external parameters of the high-definition camera are obtained, the pixels of the on-site image are matched, the parallax is calculated, and the sea ice size is calculated based on the binocular ranging principle.

[0072] A sea ice database is constructed using the real-time updated binarized result image and the sea ice size. Based on the sea ice database, ice fields are classified and their sizes are calculated. The types, sizes, distributions, and concentrations of sea ice are statistically analyzed to obtain on-site sea ice data, thus completing the intelligent identification of polar ice fields.

[0073] Preferably, it further includes:

[0074] Based on the principle of binocular ranging, the flight altitude of the unmanned aerial vehicle and the distance between the unmanned aerial vehicle and the transport ship are calculated in real time and used as flight parameter data.

[0075] Calculate the relative motion displacement and velocity between the unmanned aerial vehicle and the transport ship based on the flight parameter data;

[0076] Based on the relative motion displacement and velocity, the rotation angle of the unmanned aerial vehicle's rudder wings and / or the rotation direction of the propeller around the axis are controlled to maintain a safe communication distance with the transport ship.

[0077] The acquired on-site sea ice data will be fed back to the transport ship in real time.

[0078] By adopting the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0079] This invention provides an unmanned aerial vehicle (UAV) and its intelligent identification method for intelligent identification of polar ice fields. The UAV can acquire real-time navigation parameter data of the UAV and transport ships and autonomously fly within an area surrounding the transport ships. A high-definition camera mounted on the monitoring cabin can photograph and record sea ice around the shipping lanes. A computer performs perspective transformation on the side views, converting the photos and videos into standard top-down views. Based on this, image grayscale conversion and noise reduction are performed, and feature extraction and target segmentation are conducted on the sea ice in the images. Combined with a sea ice database, ice blocks are identified, sea ice size is calculated, and the size distribution and concentration of sea ice are statistically analyzed. Simultaneously, the data is transmitted in real-time to the transport ships and polar service stations, ensuring the safe and efficient completion of transport missions and contributing to the development of Arctic shipping routes.

[0080] This invention provides an unmanned aerial vehicle (UAV) and its intelligent identification method for intelligent identification of polar ice rinks. The main body of the UAV is an inflatable airship, which can generate a certain amount of lift itself, without consuming excessive energy to offset its own weight. In the air, the UAV only requires a small thrust from its propeller, combined with the control wings on both sides, to freely complete its ascent, descent, and rotation. The overall shape of the UAV is delta-wing, which effectively reduces air resistance and further saves energy.

[0081] The unmanned aerial vehicle (UAV) and its intelligent identification method for intelligent identification of polar ice rinks provided by this invention are designed to avoid damaging the polar ecological environment. The main body of the UAV is powered primarily by solar panels on the upper part of its wings, making it environmentally friendly. During the polar day, solar energy is abundant and visibility is clear, making sea ice identification relatively easy. During the polar night, the warning lights on the leading edges of the UAV's wings will be turned on to facilitate observation of sea ice and also to facilitate an emergency landing on a transport ship when energy is insufficient.

[0082] The present invention provides an unmanned aerial vehicle and its intelligent identification method for intelligent identification of polar ice fields. The observation cabin is installed below the main body of the aircraft and has an overall pointed triangular shape that is narrow at the front and wide at the rear. The narrow front structure has less wind resistance and the sides are less prone to icing. A windshield wiper is installed at the rear of the observation cabin, where icing is most likely to occur, which can clear water vapor, dust and other debris and prevent icing.

[0083] The present invention provides an unmanned aerial vehicle and its intelligent identification method for intelligent identification of polar ice fields. The observation cabin is equipped with a status monitoring module, which can monitor in real time whether the aircraft's operating status is normal and whether its structure is damaged. For example, it can monitor the pressure inside an inflatable airship, control pressurization and depressurization, and ensure the safe operation of the aircraft.

[0084] The present invention provides an unmanned aerial vehicle and its intelligent identification method for intelligent identification of polar ice fields. The aircraft has a simple structure, low manufacturing and maintenance costs, and does not require a pilot.

[0085] The unmanned aerial vehicle and its intelligent identification method for intelligent identification of polar ice rinks provided by this invention are suitable for long-term observation operations in polar regions. Compared with ordinary unmanned aerial vehicles, it can autonomously fly above polar transport ships, identify ice rinks around the transport ship's shipping lanes, and share data with the transport ship in real time, helping polar transport ships to complete their transport missions safely and efficiently.

[0086] The unmanned aerial vehicle and its intelligent identification method for intelligent identification of polar ice fields provided by this invention can help polar transport ships navigate, assist captains in decision-making, and reduce the risk of distress; it can also observe the sea ice morphology around the shipping lanes in detail and accurately, obtain precise sea ice data, assist scientific research in the polar field, and provide a reference for the development of Arctic shipping routes. Attached Figure Description

[0087] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0088] Figure 1 This is a schematic diagram of the overall structural layout of an unmanned aerial vehicle in a preferred embodiment of the present invention;

[0089] Figure 2 This is an axonometric view of an unmanned aerial vehicle in a preferred embodiment of the present invention;

[0090] Figure 3 This is a rear view of an unmanned aerial vehicle in a preferred embodiment of the present invention;

[0091] Figure 4 This is an axonometric view of the main body of the aircraft in a preferred embodiment of the present invention;

[0092] Figure 5 This is a bottom view of the main body of the aircraft in a preferred embodiment of the present invention;

[0093] Figure 6 This is a schematic diagram of a solar cell array structure in a preferred embodiment of the present invention;

[0094] Figure 7 This is a schematic diagram of the splicing of two solar cell arrays (with gaps or transitions) in a preferred embodiment of the present invention;

[0095] Figure 8 This is a schematic diagram of the structural layout of the observation cabin in a preferred embodiment of the present invention;

[0096] Figure 9 This is a side view of the observation cabin in a preferred embodiment of the present invention;

[0097] Figure 10 This is a flowchart illustrating the intelligent identification of an ice rink by an unmanned aerial vehicle in a preferred embodiment of the present invention.

[0098] Wherein: 1 is the main body of the aircraft, 101 is the hull, 102 is the lighting warning light, and 103 is the rudder wing; 2 is the solar panel; 3 is the propeller; 4 is the observation cabin, 401 is the cabin, 402 is the windshield wiper, and 403 is the functional module located inside the observation cabin. Detailed Implementation

[0099] The embodiments of the present invention are described in detail below: These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0100] This invention provides a long-endurance unmanned aerial vehicle for intelligent identification of polar ice rinks. The vehicle can autonomously fly above a transport ship, identify ice rinks around the transport ship's shipping lanes, and share high-precision real-time observation data with the transport ship.

[0101] like Figure 1 As shown, the unmanned aerial vehicle (UAV) for intelligent identification of polar ice rinks provided in this embodiment may include: a main body of the UAV, a solar panel, control wings, a propeller, and an observation cabin; wherein:

[0102] The main body of the aircraft is used to achieve flight missions;

[0103] Solar panels are installed on top of the main body of the aircraft to absorb solar energy and convert it into electrical energy;

[0104] The control wings are symmetrically installed on both sides of the aircraft body. By controlling the rotation angle of the control wings, the aircraft body can be used to achieve take-off, landing and turning flight tasks.

[0105] The propeller is installed at the tail of the aircraft body. By controlling the rotation of the propeller around its axis, it provides propulsion to the aircraft body, enabling the aircraft body to perform forward and backward flight missions.

[0106] The observation cabin is installed below the main body of the aircraft and is used to perform identification tasks.

[0107] In a preferred embodiment, the solar panel is mounted directly above the main body of the aircraft.

[0108] In a preferred embodiment, the main body of the aircraft includes: a delta-wing-shaped airship, rudder wings, and lighting warning lights; wherein:

[0109] The delta-wing airship uses carbon fiber as its skeleton structure, which is covered with skin. The interior of the skeleton structure is filled with helium. By controlling the filling and releasing of nitrogen, auxiliary control of the aircraft's ascent and descent can be achieved.

[0110] The warning lights are symmetrically installed on the leading edges of the two wings of the delta-wing airship and connected to the power supply of the solar panels to provide lighting assistance for the observation cabin's detection.

[0111] The rudders are symmetrically mounted on the trailing edges of the two wings of the delta-wing-shaped airship.

[0112] In a preferred embodiment, the observation cabin is generally triangular in shape.

[0113] In a preferred embodiment, the observation cabin is installed at the center directly below the main body of the aircraft.

[0114] In a preferred embodiment, the observation cabin is equipped with a windshield wiper at the rear.

[0115] In a preferred embodiment, the observation cabin is equipped with a sea ice identification module, a positioning module, a data transmission module, and a status monitoring module; as shown in the figure:

[0116] The sea ice identification module is used to acquire on-site sea ice data and flight parameter data of the aircraft body, and feed them back to the positioning module;

[0117] The positioning module is used to acquire real-time navigation parameter data of the aircraft and the transport ship. Based on the navigation parameter data and the data obtained by the sea ice recognition module, the motion control parameters of the aircraft body and the transport ship are obtained, the motion mode of the aircraft and the transport ship is obtained, and the status monitoring module is fed back to the flight control.

[0118] The data transmission module is used to enable data communication within the unmanned aerial vehicle (UAV) and between the UAV and the transport ship.

[0119] The status monitoring module is used to monitor flight parameter data and internal and external operating parameter data of the aircraft body in real time during flight. It also controls the rotation direction of the propeller around the axis and / or the rotation angle of the rudder based on the motion mode of the aircraft and the transport ship fed back by the positioning module. Based on the monitored operating parameter data, it monitors the operating status of the aircraft body in real time.

[0120] In a preferred embodiment, the sea ice identification module acquires on-site sea ice data and flight parameter data of the aircraft body, including:

[0121] Real-time images of the scene are captured using a high-definition camera.

[0122] The perspective transformation method is used to convert the on-site image into a standard top view;

[0123] The standard top view is converted to grayscale and denoised to obtain a sea ice image of the shipping channel;

[0124] The thresholding method was used to segment the sea ice image of the shipping channel, where black represents seawater and white represents sea ice, resulting in a binarized image that distinguishes between seawater and sea ice.

[0125] Acquire the internal and external parameters of the high-definition camera, match the pixel points of the on-site images, calculate the parallax, and based on the binocular ranging principle, calculate the flight altitude of the aircraft body, the distance between the aircraft body and the transport ship and / or sea ice, and the size of the sea ice, including:

[0126] The intrinsic parameter K of the high-definition camera is obtained through camera calibration. In the geodetic coordinate system O-XYZ, the extrinsic parameters of the two high-definition cameras are determined by C. 10 and C 20 It means that C 10 and C 20 This can be expressed using the rotation matrix R and the translation vector t as follows:

[0127]

[0128] Transformation matrix C from the first high-definition camera to the second high-definition camera 21 Then it is:

[0129]

[0130] Among them, C 01 C 10 The reverse;

[0131] P h For a point on the sea ice image captured by a high-definition camera, satisfying n h For P h The normal vector of the plane containing the point as seen by the high-definition camera, d is the normal vector of P. h The distance from the plane containing the point to the high-definition camera. Let P be the projection of a point onto the normal vector; for the first high-definition camera, there exists a point P on the plane. h1 satisfy:

[0132]

[0133] P h1 and P h2 P respectively h The coordinates n in the coordinate system of the first high-definition camera and the second high-definition camera h1 For P h1 The normal vector of the plane containing the point as seen by the high-definition camera; according to the projection relationship, Z C1 and Z C2 The parameters are obtained from the extrinsic parameters of the first and second high-definition cameras, respectively. K1 and K2 are the intrinsic parameters of the first and second high-definition cameras, respectively. p1 is the P...h1 The corresponding pixels on the acquired sea ice image; according to the coordinate transformation, we have:

[0134] P h2 =R 21 P h1 +t 21 (4)

[0135]

[0136] Where p2 is P h2 Collect the corresponding pixels on the sea ice image. This is a homography matrix, from which distance data between the main body of the aircraft and the transport ship and / or sea ice can be calculated;

[0137] The flight parameters of the aircraft body are the flight altitude of the main body and the distance between the main body of the aircraft body and the transport ship and / or sea ice, which are updated in real time.

[0138] A sea ice database was constructed using real-time updated binarized result images and sea ice dimensions. Based on the sea ice database, ice rinks were classified and their dimensions were calculated. The types, sizes, distributions, and concentrations of sea ice were statistically analyzed to obtain on-site sea ice data.

[0139] In a preferred embodiment, the positioning module acquires real-time navigation parameter data of the aircraft and the transport ship, and obtains aircraft motion control parameters between the aircraft body and the transport ship based on the navigation parameter data and data obtained by the sea ice recognition module, thereby obtaining the motion pattern of the aircraft and the transport ship for controlling the aircraft to navigate around the transport ship according to the motion pattern, including:

[0140] Real-time acquisition of navigation parameter data for aircraft and transport ships, including: the speed and trajectory of the transport ship; wherein, in a preferred embodiment, the speed can be directly obtained through a displacement sensor;

[0141] Based on flight parameter data and sea ice data obtained from the sea ice identification module, the navigation hazard index k in a rectangular ice field centered on the transport ship and extending outward by m times the ship's length and n times its width is calculated.

[0142]

[0143] Among them, A total Let A represent the total area of ​​the rectangular ice rink. ice berg Indicates the area covered by icebergs in this region, A ice ridge Indicates the area occupied by the ice ridges in this region, A level ice Indicates the area occupied by layered ice in this region, A rubble ice Indicates the area occupied by floating ice fragments, A open water Indicates the area occupied by open water, A extraThis represents the area occupied by other regions; α, β, γ, λ, μ, and τ are the risk coefficients corresponding to each ice condition, and satisfy the following:

[0144]

[0145] Based on the displacement and speed of the transport ship over a period of time 'a', the trajectory 's' is recorded. a Predict the trajectory s of the transport ship in the next time period b in the geodetic coordinate system. b :

[0146] s b =us a +s e (8)

[0147] Among them, s a =[s xa s ya v xa v ya In the formula, the first two terms represent the displacement and the last two terms represent the speed. For the transfer matrix, s b =[s xb s yb v xb v yb ], s e =[s xe s ye v xe v ye The additional movement of the transport ship due to avoiding icebergs and ice ridges is estimated based on the sea ice binarization result map;

[0148] The actual position of the transport ship s c With predicted trajectory s b The error E is:

[0149]

[0150]

[0151] RMSE(E i ) predicts the trajectory of the transport ship s b With actual motion position s c The mean square error between them; ||E i || indicates the error E i Take the determinant;

[0152] Error E represents the degree of overlap between the actual and predicted routes;

[0153] Using the distance between the aircraft and the transport ship, the transport ship's navigation hazard index, and the overlap between the actual and predicted routes from the flight parameter data as aircraft motion control parameters, the motion patterns of the aircraft and the transport ship are obtained, and the aircraft is controlled as follows:

[0154] Based on the ice resistance rating of the transport ship, the maximum safe distance between the transport ship and the aircraft is set to s. safe and through The aircraft is controlled to achieve either Case 1 (full speed towards the transport ship to ensure safe navigation), Case 2 (moving away from the transport ship to capture and identify distant sea ice data), or Case 3 (moving closer to the transport ship to capture and identify sea ice data near the shipping channel), where ω, η, and ξ are constants whose magnitudes are determined by the ice resistance rating of the transport ship. Therefore:

[0155]

[0156] In a preferred embodiment, the status monitoring module controls the rotation direction of the propeller around its axis and / or the rotation angle of the rudder based on the motion patterns of the aircraft and transport ship fed back by the positioning module, including:

[0157] Based on the control algorithm of motion control equations and the motion modes of the aircraft and transport ship, the rotation direction of the propeller around the axis and / or the rotation angle of the rudder are controlled as follows:

[0158] When the aircraft moves forward, the propeller rotation is defined as forward and recorded as 1; when the aircraft moves backward, the propeller rotation is reverse and recorded as -1; stopping rotation is recorded as 0. When the aircraft climbs, the two rudders rotate in the positive direction and recorded as 1; when the aircraft dives, the two rudders rotate in the negative direction and recorded as -1; resetting is recorded as 0. The aircraft's direction of travel relative to the transport ship is determined using a high-definition camera's binocular ranging principle and recorded as 1; or its direction of travel relative to the transport ship is determined as -1; other states are recorded as 0. The states of the propeller and the aircraft are controlled by STATE = [propeller 1 propeller 2 rudder 1 rudder 2 heading].

[0159] When the distance between the transport ship and the aircraft is greater than the maximum safe distance s safe If CONTROL < 0, activate case 1 motion mode: if STATE [0 0 0 0 1] T If ≠-1, then STATE[1 1 0 0 0] T =0 and STATE[0 0 1 1 0] T =0, the aircraft turns around using propellers and rudders; if STATE[0 0 0 0 1] T =-1, then STATE=[1 1 0 0 -1], the aircraft speeds toward the transport ship;

[0160] When the distance between the transport ship and the aircraft is less than the maximum safe distance s safe Furthermore, the hazard index k and the mean square error RMSE (E) of the transport ship navigation i When the value is relatively small (which can be determined by a set threshold), i.e., when the transport ship is traveling along the predicted trajectory, CONTROL > 1, activate case 2 motion mode: if STATE[0 0 1 1 0] T =-1, then The aircraft moves forward and turns using rudders; if STATE[0 0 0 0 1] T If ≠-1, then STATE = [1 1 1 1x], the aircraft moves forward and climbs, thus identifying a wider ice rink;

[0161] When the distance between the transport ship and the aircraft is less than the maximum safe distance s safe Furthermore, the hazard index k and the mean square error RMSE (E) of the transport ship navigation i When the value is large (which can be determined by a set threshold), that is, when the transport ship deviates from the predicted trajectory, the case3 motion mode is activated: if STATE[0 0 1 1 0] T = -1, then STATE = [1 1 -1 -1 x], the aircraft heads toward the transport ship and descends; if STATE [0 0 0 0 1] T If ≠-1, then STATE = [-1 -1 -1 -1 x], and the aircraft moves backward and descends; in both cases, the aircraft moves close to the transport ship, thereby identifying the transport ship and the ice rink around the waterway.

[0162] An embodiment of the present invention also provides a method for intelligent identification of polar ice fields using unmanned aerial vehicles (UAVs). The UAV used in this method can be any of the UAVs used for intelligent identification of polar ice fields in the above embodiments of the present invention.

[0163] The intelligent identification method for polar ice fields provided in this embodiment may include:

[0164] Using unmanned aerial vehicles to collect real-time images of the scene;

[0165] The perspective transformation method is used to convert the on-site image into a standard top view;

[0166] The standard top view is converted to grayscale and denoised to obtain a sea ice image of the shipping channel;

[0167] The thresholding method was used to segment the sea ice image of the shipping channel, where black represents seawater and white represents sea ice, resulting in a binarized image that distinguishes between seawater and sea ice.

[0168] Obtain the internal and external parameters of the high-definition camera, match the pixels of the on-site images, calculate the parallax, and calculate the sea ice size based on the binocular ranging principle;

[0169] A sea ice database is constructed using real-time updated binarized result images and sea ice dimensions. Based on the sea ice database, ice fields are classified and their dimensions are calculated. The types, sizes, distributions, and concentrations of sea ice are statistically analyzed to obtain on-site sea ice data and complete the intelligent identification of polar ice fields.

[0170] In a preferred embodiment, the method may further include:

[0171] Based on the principle of binocular ranging, the flight altitude of the unmanned aerial vehicle and the distance between the unmanned aerial vehicle and the transport ship are calculated in real time and used as flight parameter data.

[0172] Calculate the relative motion displacement and velocity between the unmanned aerial vehicle and the transport ship based on flight parameter data;

[0173] Based on relative motion displacement and velocity, the rotation angle of the unmanned aerial vehicle's rudder wings and / or the rotation direction of the propeller around the axis are controlled to maintain a safe communication distance with the transport ship.

[0174] The acquired on-site sea ice data will be fed back to the transport ship in real time.

[0175] In some embodiments of the present invention:

[0176] The main body of the aircraft is a combination of a delta-wing-shaped airship, several lighting warning lights, and two rudders.

[0177] The delta-wing airship has a carbon fiber frame, covered with skins such as linen, silk, and rubber, and is filled with helium.

[0178] The lighting warning lights consist of two warning lights and eight illumination lights (the number can be adjusted as needed), symmetrically installed on the leading edges of the aircraft's two wings, powered by solar panels. The lighting warning lights are activated when the observation cabin detects polar night phenomena or emergencies.

[0179] The control wings are symmetrically mounted on the trailing edges of the aircraft's wings, and their rotation angle controls the aircraft's ascent, descent, and steering.

[0180] Solar panels are the core energy source for aircraft. They are installed on top of the aircraft and can fully absorb solar energy and convert it into electrical energy for the aircraft's use.

[0181] The propeller is the core of an aircraft's propulsion system. It is installed at the tail of the aircraft and can rotate around an axis to provide propulsion.

[0182] The observation cabin is located below the main body of the aircraft. It is triangular in shape and contains multiple modules such as ice rink identification, positioning, data transmission and status monitoring. It also has a windshield wiper installed at the tail.

[0183] The sea ice identification module activates high-definition cameras to capture on-site images of polar shipping lanes and ice rinks, transmitting the data to the module's computing unit. The computer uses perspective transformation technology to convert the images captured by the UAV into a standard top-down view, and performs grayscale conversion and noise reduction on the photos and videos to obtain highly reliable sea ice images of the shipping lanes. A thresholding method is then used to segment the images, using black to represent seawater and white to represent sea ice, resulting in a binary image that accurately distinguishes between seawater and sea ice. Simultaneously, the computer reads the internal parameters (such as focal length and principal point) and external parameters (such as relative position and orientation) of the two high-definition cameras, matches the pixels of the captured images to calculate parallax, and calculates the UAV altitude, the distance between the UAV and the transport ship, and the size of the sea ice based on the binocular ranging principle. A sea ice database is constructed to assist in the identification, classification, and size calculation of ice blocks, and to statistically analyze the size distribution and concentration of sea ice. The sea ice data includes: 1. Category (iceberg / ice ridge / layer ice / floating ice, annual ice / multi-year ice, morphological classification such as pancake ice, lotus leaf ice, etc.); 2. Size (size, etc.); 3. Ice break channel data (distribution); 4. Sea ice concentration, etc.

[0184] The positioning module reads data such as the aircraft's altitude and the distance between the aircraft and the transport ship from the computer, calculates the relative motion displacement and velocity in real time, and uses a control algorithm based on motion control equations to process and analyze the data and feed it back to the propeller and rudder, thereby controlling the aircraft to move relatively stably above the transport ship.

[0185] The data transmission module reads high-precision sea ice data from the computer and synchronizes it to the transport ship in real time to guide the ship's navigation and assist the captain's decision-making; it also transmits the captured photos, videos, and calculated sea ice size distribution and density data to polar scientific research service stations to support scientific research in the polar field and provide reference for the development of the Arctic shipping route.

[0186] The status monitoring module can monitor various key parameters of the unmanned aerial vehicle (UAV) in real time, such as altitude, speed, tilt angle, and temperature. It also monitors critical parameters like air pressure, temperature, and humidity inside and outside the UAV, ensuring long-term safe operation in high-risk polar environments. This module can also monitor for structural damage, such as monitoring internal pressure and controlling pressurization and depressurization. Methods for monitoring structural damage include: 1. Monitoring internal pressure through pressure sensors. Normal pressure is dynamically balanced within a certain range; a damaged hull will exhibit abnormal pressure, such as a continuous drop in pressure due to leakage or an increase in pressure when a large area is under pressure but not damaged; 2. Monitoring for abnormalities in flight altitude, speed, tilt angle, internal temperature, and humidity.

[0187] It should be noted that the steps in the method provided by the present invention can be implemented using the corresponding components of the aircraft. Those skilled in the art can refer to the technical solution of the aircraft to implement the steps of the method, and can also refer to the technical solution of the method to implement the composition of the aircraft. That is, the embodiments in the aircraft and the embodiments in the method can be understood as preferred examples of each other, which will not be elaborated here.

[0188] The technical solutions provided by the above embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0189] like Figure 1 , Figure 2 , Figure 3 As shown, the unmanned aerial vehicle provided in the above embodiments of the present invention includes an aircraft body 1, a solar panel 2, a rudder 103, a propeller 3, and an observation cabin 4. Multiple solar panels 2 are symmetrically mounted on the upper part of the aircraft body 1, the propeller 3 is installed at the center of the tail of the aircraft body 1, and the observation cabin 4 is installed at the center directly below the aircraft body 1.

[0190] like Figure 4 , Figure 5 As shown, the main body 1 of the aircraft consists of a hull 101 and lighting warning lights 102, with control wings 103 mounted on both sides of the hull 101. Helium is injected into the hull 101 to provide lift. The main body 1 is generally delta-wing shaped, which effectively reduces forward drag and facilitates in-flight turning and acceleration. Multiple lighting warning lights 102 are mounted on the leading edges of the wings of the main body 1 for illumination and warning during polar nights or in case of emergencies; two control wings 103 are mounted on the trailing edges of the wings of the main body 1 for controlling the aircraft's ascent, descent, and turning.

[0191] like Figure 6As shown, the solar cell array 2 is a rectangular solar panel with three blind holes on one side. Three shafts that can mate with the blind holes (gap or transition) are welded to the opposite side. Four vertical shafts are welded to the bottom of the panel. The entire solar cell array is connected to the main body 1 of the aircraft through these four shafts. Figure 7 This is a schematic diagram showing the connection between a blind aperture of one solar cell array and the shaft of another solar cell array. Multiple solar cell arrays are connected by their shafts and holes. Figure 2 The large solar panel shown.

[0192] like Figure 8 , Figure 9 As shown, the observation cabin 4 consists of a compartment 401, a windshield wiper 402, and functional modules 403, including a sea ice identification module, a positioning module, a data transmission module, and a status monitoring module, all housed within the compartment 401. The observation cabin 4 and its compartment 401 are triangular in shape, narrow at the front and wide at the rear. This structure experiences less air resistance and is less prone to icing on the sides. The windshield wiper 402 is installed at the rear of the compartment 401, and its periodic reciprocating motion removes moisture, dust, and other debris adhering to the rear of the observation cabin, preventing icing. The various functional modules 403 are installed inside the compartment 401 and, during aircraft operation, perform functions such as ice rink identification around the transport ship's route, real-time positioning of the aircraft and the transport ship, real-time transmission of monitoring data, and monitoring of the aircraft's operational status.

[0193] like Figure 10 The diagram shows the operational flow of each functional module inside compartment 401. The sea ice recognition module activates a high-definition camera to capture sea ice images. These images are then processed by a computer to obtain a binarized seawater and sea ice image, and data such as sea ice size, concentration, and the relative motion between the spacecraft and the transport ship are calculated. The positioning module controls the spacecraft's stable movement around the transport ship, and the data transmission module transmits precise sea ice data to the transport ship and research stations. The status monitoring module monitors the air pressure, temperature, humidity, and structural damage levels inside and outside the spacecraft to ensure long-term safe operation. Figure 10 In this system, perspective transformation, image grayscale conversion, denoising, thresholding image segmentation, binarization of sea ice images, calculation of relative motion based on binocular ranging principle, calculation and processing of ice block size, distribution characteristics, and ice density can all be completed by a single computer.

[0194] Furthermore:

[0195] The main body 1 of the aircraft is filled with helium, and the whole body is subjected to a certain lift. Then the propeller 3 starts to rotate to provide thrust, and the rear ends of the two control wings 103 at the trailing edge of the main body 1 are tilted upward to provide lift. After the aircraft takes off and stabilizes in the air, it uses the control wings 103 at the trailing edge of the main body 1 and the propeller 3 to adjust its motion.

[0196] During the polar day, the solar panel 2 receives solar energy and converts it into electricity to power the aircraft. In the event of polar night or an emergency, the warning lights 102 on the leading edges of the wings of the main body 1 are turned on, the hull 101 of the aircraft releases some helium gas to depressurize, and the propeller 3 and the two rudders 103 on the trailing edge of the main body 1 are used to land.

[0197] Under normal operating conditions, the status monitoring module inside observation cabin 4 monitors the structural damage of the aircraft and the pressure inside hull 101 in real time, controlling pressurization and depressurization. The positioning module acquires navigation parameter data of the aircraft and the transport ship, calculates motion control parameters such as the distance between the aircraft body and the transport ship, and controls the aircraft to move within an area above the transport ship. The sea ice identification module intelligently identifies the sea ice around the channel and highlights dangerous points (such as multi-year-old thick ice, icebergs, ice ridges, etc. that the transport ship cannot break through). When it is necessary to identify a large area of ​​ice, the status monitoring module can control the pressurization inside the aircraft hull 101, which, together with the two rudders 103 at the trailing edge of the main body 1 and the propeller 3, controls the aircraft to ascend; conversely, when it is necessary to obtain precise ice field data for a small area, the pressure inside the aircraft hull 101 is depressurized, which, together with the two rudders 103 at the trailing edge of the main body 1 and the propeller 3, controls the aircraft to descend. The data transmission module transmits the identified sea ice data and aircraft status monitoring data to the transport ship and polar service station in real time, ensuring that the transport ship completes its transport mission safely and efficiently, and contributing to the development of the Arctic shipping route.

[0198] The unmanned aerial vehicle (UAV) for intelligent identification of polar ice fields provided in the above embodiments of the present invention has a certain lift and flies autonomously above polar ships, using solar power to save energy and ensure long endurance. At the same time, the various functional modules loaded in the observation cabin can take pictures and record the ice field around the ice channel. The computer performs side view perspective transformation, image grayscale conversion, image denoising and image segmentation, etc. Based on the principle of binocular ranging combined with sea ice database and binarized images, the ice blocks are identified, the sea ice size is calculated, and the size distribution and concentration of sea ice are statistically analyzed. Finally, the high-precision sea ice data is synchronized to the transport ship in real time using the data transmission module to guide the ship's navigation and assist the captain's decision-making. The captured photos, videos and the calculated sea ice size distribution and concentration data are transmitted to polar service stations to help scientific research in the polar field and provide reference for the development of Arctic shipping routes.

[0199] Any matters not covered in the above embodiments of the present invention are well-known in the art.

[0200] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. An unmanned aerial vehicle for polar ice field intelligent identification, characterized in that, The unmanned aerial vehicle comprises an aircraft body, a solar cell group, a rudder wing, a screw propeller and an observation cabin. The aircraft body is used to realize flight tasks. The solar cell group is installed above the aircraft body and is used to absorb solar energy and convert it into electric energy. The rudder wing is symmetrically installed on both sides of the aircraft body and is used to realize the lifting and turning flight tasks of the aircraft body by controlling the rotation angle of the rudder wing. The screw propeller is installed at the tail of the aircraft body and is used to provide propulsion for the aircraft body by controlling the rotation of the screw propeller around the shaft, thereby realizing the forward and backward flight tasks of the aircraft body. The observation cabin is installed below the aircraft body and is used to realize the identification task. The observation cabin is internally provided with a sea ice identification module, a positioning module, a data transmission module and a state monitoring module. The sea ice identification module is used to obtain the field sea ice data and the flight parameter data of the aircraft body and feed back to the positioning module. The positioning module is used to obtain the navigation parameter data of the transport ship in real time, obtain the aircraft motion control parameters between the aircraft body and the transport ship according to the navigation parameter data and the data fed back by the sea ice identification module, obtain the aircraft and transport ship motion mode and feed back to the state monitoring module for flight control. The data transmission module is used to realize the data communication between the unmanned aerial vehicle and the transport ship. The state monitoring module is used to monitor the flight parameter data in the flight process of the aircraft body and the operation parameter data inside and outside the aircraft body in real time, and control the rotation direction of the screw propeller around the shaft and / or the rotation angle of the rudder wing according to the aircraft and transport ship motion mode fed back by the positioning module, and monitor the operation state of the aircraft body in real time according to the monitored operation parameter data. The sea ice identification module obtains the field sea ice data and the flight parameter data of the aircraft body, which comprises: real-time collection of field pictures by a high-definition camera; conversion of the field pictures into a standard overhead view by using a perspective transformation method; graying and denoising of the standard overhead view to obtain a channel ice image; segmentation of the channel ice image by using a threshold method, wherein black is used to represent seawater and white is used to represent sea ice, thereby obtaining a binary result image distinguishing seawater and sea ice; matching of the pixel points of the field pictures by using the internal and external parameters of the high-definition camera, calculation of the parallax, calculation of the flight height of the aircraft body, the distance between the aircraft body and the transport ship and / or sea ice and the size of the sea ice according to the binocular distance measurement principle, and The intrinsic parameter K of the high-definition camera is obtained by camera calibration, and the extrinsic parameters of the two high-definition cameras are represented by C 10 and C 20 , and C 10 and C 20 are represented by a rotation matrix R and a translation vector t. The transformation matrix C from the first high definition camera to the second high definition camera 21 is then: wherein C 01 is the inverse of C 10 ; P h A point on the sea ice image collected by the high-definition camera satisfies n h The normal vector of the plane where the point P h is located under the high-definition camera, and d is the distance from the plane where the point P h is located to the high-definition camera, is the projection of the point to the normal vector; for the first high-definition camera, a point P h1 on the plane satisfies: P h1 and P h2 P respectively h The coordinates n in the coordinate system of the first high-definition camera and the second high-definition camera h1 For P h1 The normal vector of the plane containing the point as seen by the high-definition camera; according to the projection relationship, Z C1 and Z C2 The parameters are obtained from the extrinsic parameters of the first and second high-definition cameras, respectively. K1 and K2 are the intrinsic parameters of the first and second high-definition cameras, respectively. p1 is the P... h1 The corresponding pixels on the acquired sea ice image; according to the coordinate transformation, we have: P h2 = R 21 P h1 + t 21 (4) where p2 is P h2 on the collected sea ice image, is a homography matrix, thereby calculating distance data between the aircraft body and the transport ship and / or sea ice; updating of the flight height of the aircraft body and the distance between the aircraft body and the transport ship and / or sea ice as the flight parameter data of the aircraft body in real time. A sea ice database is constructed using the real-time updated binarized result image and the sea ice size. Based on the sea ice database, the ice field is classified and its size is calculated. The types, sizes, distributions, and concentrations of sea ice are statistically analyzed to obtain on-site sea ice data.

2. The unmanned vehicle for polar ice sheet intelligent identification of claim 1, wherein, The solar panel is mounted directly above the main body of the aircraft.

3. The unmanned vehicle for polar ice sheet intelligent identification of claim 1, wherein, The main body of the aircraft includes: a delta-wing-shaped airship and a lighting warning light; wherein: The delta-wing airship uses carbon fiber as its skeleton structure, which is covered with skin. The interior of the skeleton structure is filled with helium. By controlling the filling and releasing of the helium, auxiliary control of the ascent and descent of the main body of the aircraft can be achieved. The warning lights are symmetrically installed on the leading edges of the two wings of the delta-wing airship and connected to the power supply of the solar panel to provide lighting assistance for the detection of the observation cabin. The rudder wings are symmetrically mounted on the trailing edges of the two wings of the delta-wing-shaped airship.

4. The unmanned vehicle for polar ice sheet intelligent identification of claim 1, wherein, The observation cabin includes any one or more of the following: -The observation cabin is generally triangular in shape, narrower at the front and wider at the back; - The observation cabin is installed at the center directly below the main body of the aircraft; - The observation cabin is equipped with a windshield wiper at the rear.

5. The unmanned vehicle for polar ice sheet intelligent identification of claim 1, wherein, The positioning module acquires real-time navigation parameter data of the aircraft and the transport ship, and obtains the aircraft motion control parameters between the aircraft body and the transport ship based on the navigation parameter data and the data obtained by the sea ice recognition module, thus obtaining the motion mode of the aircraft and the transport ship, including: Real-time acquisition of navigation parameter data for aircraft and transport ships, including: the speed and trajectory of transport ships; Based on the flight parameter data and sea ice data obtained from the sea ice identification module, the navigation hazard index k in a rectangular ice field centered on the transport ship and extending outward by m times the ship's length and n times its width is calculated: wherein, A total represents the total area of the rectangular ice field, A iceberg represents the area occupied by the ice mountain of the rectangular ice field, A iceridge represents the area occupied by the ice ridge of the rectangular ice field, A levelice represents the area occupied by the layer ice of the rectangular ice field, A rubbleice represents the area occupied by the floating broken ice of the rectangular ice field, A openwater represents the area occupied by the open water area of the rectangular ice field, A extra represents the area occupied by other regions; α, β, γ, λ, μ, τ are risk coefficients corresponding to each ice condition, and satisfy: According to the running displacement and the running speed of the transport ship in a previous period of time a, denoted as a running trajectory s a predicting the running trajectory s of the transport ship in a next period of time b in the geodetic coordinate system b : s b = us a + s e (8) wherein s a = [s xa s ya v xa v ya ], in which the first two terms are the travel displacement, and the last two terms are the travel speed, is the transfer matrix, s b = [s xb s yb v xb v yb ], s e = [s xe s ye v xe v ye ] is the additional motion of the transport ship caused by the icebergs and ice ridges estimated according to the binary result map of the sea ice; Actual movement position s of the transport ship c Error E of the predicted trajectory s b ​ RMSE(E i ) is the root mean square error between the predicted trajectory s b and the actual motion position s c ; ||E i || denotes taking the determinant of the error E i ; The error E represents the degree of overlap between the actual and predicted routes; The distance between the aircraft body and the transport ship, the navigation hazard index of the transport ship, and the overlap between the actual and predicted routes in the flight parameter data are used as the motion control parameters of the aircraft to obtain the motion mode of the aircraft and the transport ship, and the aircraft is controlled as follows: According to the ice resistance level of the transport ship, the maximum safe distance between the transport ship and the aircraft is set as s safe , and by controlling the aircraft to realize case 1 motion mode full-speed sailing to the transport ship to ensure safe navigation, case 2 motion mode moving away from the transport ship to shoot and identify the data of the sea ice far away, or case 3 motion mode moving close to the transport ship to shoot and identify the data of the sea ice near the channel, where ω, η, ξ are constants, and their sizes are determined by the ice resistance level of the transport ship, then:

6. The unmanned vehicle for polar ice sheet intelligent identification of claim 5, wherein, The status monitoring module controls the rotation direction of the propeller around its axis and / or the rotation angle of the rudder based on the motion patterns of the aircraft and transport ship fed back by the positioning module, including: Based on the control algorithm of motion control equations and the motion modes of the aircraft and transport ship, the rotation direction of the propeller around its axis and / or the rotation angle of the rudder are controlled as follows: Define the propeller rotation direction as forward when the aircraft moves forward and record it as 1; the propeller rotation direction as reverse when the aircraft moves backward and record it as -1; stopping rotation is 0. When the aircraft climbs, the two rudders rotate in the positive direction and record it as 1; when the aircraft dives, the two rudders rotate in the negative direction and record it as -1; resetting is 0. The aircraft is determined by the binocular ranging principle of the high-definition camera to determine whether it is traveling in the same direction as the transport ship and record it as 1 or traveling in the opposite direction and record it as -1; other states are recorded as 0. The state of the propeller and the aircraft is controlled by STATE = [propeller 1 propeller 2 rudder 1 rudder 2 heading]. When the distance between the transport ship and the aircraft is greater than the maximum safe distance s safe , CONTROL < 0, start case1 movement mode: if STATE[0 0 0 0 1] T ≠ -1, then STATE[1 1 0 0 0] T = 0 and STATE[0 0 1 1 0] T = 0, the aircraft turns around by propeller and rudder wings; if STATE[0 0 0 0 1] T = -1, then STATE = [1 1 0 0 -1], the aircraft drives at full speed towards the transport ship; When the distance between the transport ship and the aircraft is less than the maximum safe distance s safe and the transport ship navigation danger index k and the root mean square error RMSE(E i ) are small, at which time the transport ship travels according to the predicted trajectory, CONTROL > 1, the case 2 movement mode is started: if STATE[0 0 1 1 0] T = -1, then the aircraft advances and turns through ailerons; if STATE[0 0 0 0 1] T ≠ -1, then STATE = [1 1 1 1 x], the aircraft advances and climbs, thereby identifying a wider ice field; When the distance between the transport ship and the aircraft is less than the maximum safe distance s safe and the transport ship navigation danger index k and the root mean square error RMSE(E i ) are large, at this time the transport ship deviates from the predicted trajectory and case3 motion mode is started: if STATE[0 0 1 10] T =-1, then STATE=[1 1 -1 -1 x], the aircraft moves towards the transport ship and descends; if STATE[0 0 0 0 1] T ≠-1, then STATE=[-1 -1 -1 -1 x], the aircraft retreats and descends; in both cases the aircraft moves close to the transport ship, thereby identifying the transport ship and the ice field around the channel.

7. The polar ice rink intelligent recognition method of the unmanned aerial vehicle according to any one of claims 1-6, characterized in that, include: Using unmanned aerial vehicles to collect real-time images of the scene; The perspective transformation method is used to convert the on-site picture into a standard overhead view; The standard overhead view is subjected to grayscale and denoising processing to obtain a channel ice image; The channel ice image is segmented by using a threshold method, wherein black is used to represent seawater and white is used to represent sea ice, and a binary result image distinguishing seawater from sea ice is obtained; The internal and external parameters of the high-definition camera are acquired, the pixel points of the on-site picture are matched, the parallax is calculated, the sea ice size is calculated according to the binocular distance measuring principle; The real-time updated binary result image and the sea ice size are used to construct a sea ice database, the ice field is classified and the size is calculated based on the sea ice database, the category, size, distribution and density of the sea ice are counted, the on-site sea ice data is obtained, and the intelligent identification of the polar ice field is completed. 8.The polar ice rink intelligent identification method of the unmanned aerial vehicle according to claim 7, characterized in that, Further comprising: The flight height of the unmanned aerial vehicle and the distance between the unmanned aerial vehicle and the transport ship are simultaneously and real-timely calculated as flight parameter data according to the binocular distance measuring principle; The relative motion displacement and speed between the unmanned aerial vehicle and the transport ship are calculated according to the flight parameter data; The rotation angle of the rudder wing and / or the rotation direction of the helical propeller of the unmanned aerial vehicle are controlled according to the relative motion displacement and speed, so as to maintain a safe communication distance with the transport ship; The obtained on-site sea ice data is real-timely fed back to the transport ship.

Images