Aircraft positioning method, program product, electronic device and storage medium

Abstract

The present application relates to the field of navigation, guidance, and control technology, specifically providing an aircraft positioning method, program product, electronic device, and storage medium. The aircraft positioning method includes: inputting a target image captured by an aircraft into a trained image classification model and obtaining an image classification result; determining a target acquisition position of the target image based on the image classification result and inertial data provided by an inertial navigation unit; and then updating the aircraft's positioning information based on the target acquisition position. During the updating of the aircraft's positioning information, the aircraft positioning method simultaneously considers the classification result of the target image captured by the image acquisition device and the inertial data provided by the inertial navigation unit, thereby improving the accuracy of each updated positioning information and enabling accurate long-term positioning of the aircraft without relying on GPS information.

Description

Technical Field

[0001] The present application relates to the field of navigation, guidance and control technology, and in particular to an aircraft positioning method, a program product, an electronic device and a storage medium. Background Art

[0002] Traditional aircraft positioning solutions typically use GPS as their source of information. However, if GPS is interfered with or inaccurate, the aircraft's flight path can become disrupted. Therefore, research on positioning solutions that don't rely on GPS is crucial.

[0003] Common positioning methods that do not rely on GPS information include inertial navigation positioning methods. Inertial navigation positioning methods rely on the inertial measurement unit of the micro inertial navigation system. However, the inertial measurement unit has low accuracy and large cumulative errors, making it impossible to achieve accurate positioning for a long time. Summary of the Invention

[0004] In view of this, the purpose of the embodiments of the present application is to provide an aircraft positioning method, program product, electronic device and storage medium to solve the technical problem that existing positioning methods that do not rely on GPS information cannot achieve long-term accurate positioning.

[0005] In a first aspect, an embodiment of the present application provides an aircraft positioning method, the method comprising:

[0006] Inputting the target image captured by the image acquisition device of the aircraft into the trained image classification model to obtain the image classification result output by the image classification model;

[0007] determining a target acquisition position of the target image based on the image classification result and inertial data provided by an inertial navigation unit;

[0008] The positioning information of the aircraft is updated based on the target acquisition position.

[0009] In the above-mentioned implementation process, the aircraft positioning method inputs the target image captured by the aircraft's image acquisition device into a trained image classification model and obtains the image classification result output by the image classification model; determines the target acquisition position of the target image based on the image classification result and the inertial data provided by the inertial navigation unit; and then updates the aircraft's positioning information based on the target acquisition position. In the process of updating the aircraft's positioning information, the aircraft positioning method simultaneously considers the classification result of the target image captured by the image acquisition device and the inertial data provided by the inertial navigation unit, jointly determines the target acquisition position based on the inertial data based on the classification result of the target image, and locates the aircraft based on the target acquisition image, thereby improving the accuracy of each updated positioning information and thus being able to achieve long-term accurate positioning of the aircraft without relying on GPS information. This solves the technical problem that existing positioning methods that do not rely on GPS information cannot achieve long-term accurate positioning.

[0010] Optionally, in an embodiment of the present application, the image classification result includes: a plurality of category images and a classification similarity between the target image and each of the category images; each of the category images corresponds to an image classification position;

[0011] Determining the target acquisition position of the target image based on the image classification result and the inertial data provided by the inertial navigation unit includes: determining the inertial positioning position of the aircraft based on the inertial data; determining the weight processing result of the category image based on the positioning distance between the image classification position and the inertial positioning position corresponding to each category image and the classification similarity; and determining the target acquisition position based on the weight processing result.

[0012] In the above implementation process, by performing weight processing on the category images according to the positioning distance between the image classification position and the inertial positioning position of each category image and the classification similarity, a weight processing result that more accurately represents the target acquisition position information can be obtained; then the target acquisition position is determined based on the weight processing result, further improving the accuracy of the positioning information updated according to the target acquisition position.

[0013] Optionally, in an embodiment of the present application, the weight processing result of the category image is determined based on the positioning distance between the image classification position and the inertial positioning position corresponding to each category image and the classification similarity, including: determining the distance score of the category image based on the positioning distance; determining the weight processing result of each category image based on the distance score, a first weight ratio corresponding to the distance score, the classification similarity, and a second weight ratio corresponding to the classification similarity.

[0014] In the above implementation process, the positioning distance between the image classification position and the inertial positioning position can be used to determine the distance score reflecting the accuracy of the image processing result; then, based on the distance score, the first weight ratio, the classification similarity and the second weight ratio, the weight processing result that can characterize the target acquisition position information is determined.

[0015] Optionally, in an embodiment of the present application, the sum of the first weight ratio and the second weight ratio is a fixed value, and the first weight ratio is positively correlated with the positioning distance.

[0016] In the above implementation process, since the first weight ratio is positively correlated with the positioning distance, when the positioning distance between the image classification position and the inertial positioning position is relatively close, the first weight ratio is smaller and the second weight ratio is larger. At this time, more consideration is given to the impact of the image classification result represented by the classification similarity on the target acquisition position; and when the positioning distance between the image classification position and the inertial positioning position is relatively far, more consideration is given to the impact of the positioning distance represented by the distance score on the target acquisition position; so as to improve the accuracy of the updated positioning information.

[0017] Optionally, in an embodiment of the present application, the weight processing result includes a weight score for each of the category images; determining the target acquisition position based on the weight processing result includes: determining the weight category image with the highest weight score among the category images and satisfying the preset score condition based on the weight processing result; determining the image classification range of the target image based on the preset flight speed of the aircraft, the image acquisition interval of the image acquisition device, and the previous image acquisition position; wherein the previous image acquisition position includes the aircraft position information when the image acquisition device acquires the previous image of the target image; when the weight category image is within the image classification range, determining the target acquisition position based on the image classification position corresponding to the weight category image; when the weight category image is outside the image classification range, determining the target acquisition position based on the inertial data.

[0018] In the above implementation process, the image classification range of the target image is determined according to the preset flight speed of the aircraft, the image acquisition interval of the image acquisition device and the previous image acquisition position; and the determined weight category image is verified based on the determined image classification range. When the weight category image is within the image classification range, the target acquisition position is determined according to the image classification position corresponding to the weight category image; and when the weight category image is not within the image classification range, the target acquisition position is determined according to relatively accurate inertial data; so as to further improve the accuracy of the updated positioning information.

[0019] Optionally, in an embodiment of the present application, the target image captured by the image acquisition device of the aircraft is input into a trained image classification model to obtain an image classification result output by the image classification model, including: determining the image classification range of the target image based on a preset flight speed of the aircraft, the image acquisition interval of the image acquisition device, and the previous image acquisition position; wherein the previous image acquisition position includes the position information of the aircraft when the image acquisition device captured the previous image of the target image; and inputting the image classification range and the target image into the trained image classification model to obtain the image classification result output by the image classification model.

[0020] In the above implementation process, the image classification range and target image are input into the trained image classification model to narrow the range of image classification results, thereby reducing the computational complexity of the image classification model and improving the acquisition speed of image classification results. The target acquisition position can be determined more quickly based on the image classification results and inertial data, and the aircraft positioning information can be updated in a timely manner based on the target acquisition position.

[0021] Optionally, in an embodiment of the present application, the trained image classification model includes: a first-level classification model and a plurality of second-level classification models;

[0022] The target image captured by the image acquisition device of the aircraft is input into the trained image classification model to obtain the image classification result output by the image classification model, including: extracting features of the target image to obtain target image features; inputting the target image features into the first-level classification model to obtain the first-level classification result output by the first-level classification model; wherein the first-level classification result includes multiple hierarchical images and the classification similarity between the target image and each of the hierarchical images; each of the hierarchical images includes multiple category images; based on the first-level classification result, a second-level target classification model is determined from multiple second-level classification models; and the target image features are input into the second-level target classification model to obtain the image classification result output by the second-level target classification model.

[0023] In the above implementation process, the image classification model includes a first-level classification model and multiple second-level classification models. Compared with the method of directly using a single classification model for image classification, the multi-level classification method provided by this application (that is, first using the first-level classification model to perform feature processing on the target image features to obtain the first-level classification results, and then inputting the target image features into the second-level target classification model corresponding to the first-level classification results to obtain the image classification results output by the second-level target classification model) can greatly reduce the computational complexity of the classification model and improve the speed of obtaining image classification results; thereby, the target acquisition position can be determined more quickly based on the image classification results and inertial data, and the aircraft positioning information can be updated in a timely manner based on the target acquisition position.

[0024] In a second aspect, an embodiment of the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method described in any one of the first aspects above.

[0025] In a third aspect, an embodiment of the present application further provides an electronic device; the electronic device includes:

[0026] Memory;

[0027] processor;

[0028] The memory stores a computer program executable by the processor, and when the computer program is executed by the processor, the aircraft positioning method described in any one of the first aspects is performed.

[0029] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the aircraft positioning method as described in any one of the first aspects is executed.

[0030] In a fifth aspect, an embodiment of the present application further provides an aircraft positioning device, the device comprising:

[0031] a classification result acquisition module, configured to input a target image acquired by an image acquisition device of the aircraft into a trained image classification model and acquire an image classification result output by the image classification model;

[0032] a target acquisition position determination module, configured to determine a target acquisition position of the target image based on the image classification result and inertial data provided by an inertial navigation unit;

[0033] A positioning information updating module is used to update the positioning information of the aircraft based on the target acquisition position.

[0034] The beneficial effects of this application are as follows: in the process of updating the aircraft positioning information, the aircraft positioning method simultaneously considers the classification results of the target image captured by the image acquisition device and the inertial data provided by the inertial navigation unit, jointly determines the target acquisition position based on the classification results of the target image and the inertial data, and determines the positioning information of the aircraft based on the target acquisition image, thereby improving the accuracy of each updated positioning information, thereby achieving long-term accurate positioning of the aircraft without relying on GPS information. This solves the technical problem that existing positioning methods that do not rely on GPS information cannot achieve long-term accurate positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 A schematic diagram of a flow chart of an aircraft positioning method provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a process for determining a target acquisition location provided in an embodiment of the present application;

[0038] Figure 3 A flowchart of a method for obtaining image classification results provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0042] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is two or more, unless otherwise specifically defined.

[0043] See Figure 1 The flowchart of an aircraft positioning method provided by an embodiment of the present application is shown. The aircraft positioning method may include the following steps:

[0044] S101, inputting a target image captured by an image acquisition device of an aircraft into a trained image classification model, and obtaining an image classification result output by the image classification model;

[0045] S102, determining a target acquisition position of the target image according to the image classification result and inertial data provided by an inertial navigation unit;

[0046] S103: Update the positioning information of the aircraft based on the target acquisition position.

[0047] Among them, in step S101, the aircraft can be controlled to leave the ground and fly in space, such as an airplane, a drone or an aircraft. The image acquisition device can be specifically implemented by a visible light camera; the visible light camera can use the visible light band to monitor the ground in real time and capture images from a high altitude. The image acquisition device can also be implemented by other devices that can realize target image acquisition, such as a fisheye camera or a macro camera. The image classification model can be implemented by a support vector machine, a convolutional neural network or a recurrent neural network, etc., and this application does not make specific limitations on this. The image classification result may include the image type to which the target image belongs; it may also include multiple category images and the classification similarity between the target image and each category image. According to the classification similarity, the category image that is most similar to the target image can be determined.

[0048] In step S102, the inertial navigation unit can measure the acceleration data and three-axis attitude data of the aircraft. The inertial data may include acceleration data, three-axis attitude data, and other data that can determine the position of the aircraft. The target acquisition position refers to "the position of the aircraft when the image acquisition device acquires the target image" determined based on the image classification results and inertial data. Specifically, when the image classification results include multiple category images and the classification similarity between the target image and each category image, the optimal category image that best matches the actual flight situation can be determined based on the image classification results and inertial data. If the image position corresponding to the optimal category image is within a reasonable position range, the image position corresponding to the optimal category image is determined as the target acquisition position. If the image position corresponding to the optimal category image is not within a reasonable position range, the target acquisition position is determined based on the inertial position corresponding to the inertial data.

[0049] In step S103, the target acquisition position may be used as the position of the aircraft when acquiring the target image, and the aircraft's positioning information may be updated based on the target acquisition position. The frequency of updating the aircraft's positioning information may be determined based on the target image acquisition interval, which may be determined based on the actual image acquisition device model or set shooting parameters.

[0050] Thus, the aircraft positioning method provided by the embodiment of the present application, in the process of updating the aircraft positioning information, simultaneously considers the classification results of the target image captured by the image acquisition device and the inertial data provided by the inertial navigation unit, jointly determines the target acquisition position based on the classification results of the target image and the inertial data, and determines the positioning information of the aircraft based on the target acquisition image, thereby improving the accuracy of each updated positioning information, thereby achieving long-term accurate positioning of the aircraft without relying on GPS information. This solves the technical problem that existing positioning methods that do not rely on GPS information cannot achieve long-term accurate positioning.

[0051] In some optional embodiments, the image classification result includes: a plurality of category images and a classification similarity between the target image and each of the category images; each of the category images corresponds to an image classification position;

[0052] S102. Determine the target acquisition position of the target image based on the image classification result and the inertial data provided by the inertial navigation unit, including: determining the inertial positioning position of the aircraft based on the inertial data; determining the weight processing result of the category image based on the positioning distance between the image classification position and the inertial positioning position corresponding to each category image and the classification similarity; and determining the target acquisition position based on the weight processing result.

[0053] The aircraft's designated flight area can be divided into multiple partitioned areas, each corresponding to a category image. A category image can be an image captured by the aircraft in any of the partitioned areas within the designated flight area, assuming accurate GPS information. Since each partitioned area corresponds to a category image, the "position of the aircraft at the time the image acquisition device captured the target image," i.e., the target acquisition location, can be determined based on the image classification position corresponding to the category image. The aircraft's real-time flight speed can be determined based on the aircraft's initial velocity and acceleration data, its real-time flight attitude can be determined based on its three-axis attitude data, and its inertial positioning position can be determined based on its real-time flight attitude and flight speed. A weighted processing result can be determined based on the positioning distance, a preset distance range, classification similarity, and a preset similarity threshold. Specifically, the weighted processing result can be determined based on category images whose positioning distance is within the preset distance range and whose classification similarity exceeds the preset similarity threshold. The target acquisition location is determined based on the image classification position corresponding to the category image with the greatest classification similarity in the weighted processing result. Alternatively, a distance score corresponding to the positioning distance may be determined first, and then a weight processing result may be determined based on the distance score, a first weight ratio corresponding to the distance score, the classification similarity, and a second weight ratio corresponding to the classification similarity.

[0054] In some optional embodiments, the weight processing result of the category image is determined based on the positioning distance between the image classification position and the inertial positioning position corresponding to each category image and the classification similarity, including: determining the distance score of the category image based on the positioning distance; determining the weight processing result of each category image based on the distance score, a first weight ratio corresponding to the distance score, the classification similarity and a second weight ratio corresponding to the classification similarity.

[0055] Among them, the positioning distance can be divided into multiple distance levels, and different distance levels correspond to different distance scores. According to the distance level corresponding to the positioning distance between the image classification position and the inertial positioning position, the distance score of the category image can be determined. The positioning distance ranges corresponding to different distance levels can be the same or not completely different. The range of the distance score can be greater than or equal to 0 and less than or equal to 100; it can also be greater than or equal to 50 and less than or equal to 100. The first weight ratio and the second weight ratio can be determined according to the distance level corresponding to the positioning distance, or they can be fixed ratio values. This application does not make specific restrictions on this. The weight processing result can include a weight processing score corresponding to the category image, and the weight processing score can be determined according to the product of the distance score and the first weight ratio and the sum of the product of the classification similarity and the second weight ratio; and the target acquisition position is determined according to the image classification position corresponding to the category image with the highest weight processing score.

[0056] In some optional embodiments, the sum of the first weight ratio and the second weight ratio is a fixed value, and the first weight ratio is positively correlated with the positioning distance.

[0057] The weight ratio can be expressed as a decimal or a percentage, and the sum of the first weight ratio and the second weight ratio can be 1 or 100%. Since the first weight ratio is directly proportional to the positioning distance, when the positioning distance between the image classification position and the inertial positioning position is relatively close, the first weight ratio is smaller and the second weight ratio is larger. In this case, the impact of the image classification result represented by the classification similarity on the target acquisition position is given more consideration; when the positioning distance between the image classification position and the inertial positioning position is relatively far, the impact of the positioning distance represented by the distance score on the target acquisition position is given more consideration, thereby improving the accuracy of the updated positioning information.

[0058] Please refer to Figure 2 , Figure 2A flow chart for determining a target acquisition position is provided for an embodiment of the present application. In some optional embodiments, the weight processing result includes a weight score for each of the category images; the determining the target acquisition position according to the weight processing result includes: S201, determining the weight category image with the highest weight score and satisfying the preset score condition in the category images according to the weight processing result; S202, determining the image classification range of the target image according to the preset flight speed of the aircraft, the image acquisition interval of the image acquisition device, and the previous image acquisition position; wherein the previous image acquisition position includes the aircraft position information when the image acquisition device acquired the previous image of the target image; S203, when the weight category image is within the image classification range, determining the target acquisition position according to the image classification position corresponding to the weight category image; S204, when the weight category image is outside the image classification range, determining the target acquisition position according to the inertial data.

[0059] Among them, the preset score condition may include a preset score threshold; accordingly, the category image with the highest weight score and a weight score greater than the preset score threshold can be determined as a weight category image. The preset score condition may also include a distance score threshold and a similarity threshold; accordingly, the category image with the highest weight score and "the distance score is greater than the distance score threshold, and the classification similarity is greater than the similarity threshold" can be determined as a weight category image. The preset flight speed may include the maximum flight speed of the aircraft or the flight speed range corresponding to each speed level. The maximum flight distance of the aircraft within the image acquisition interval can be determined based on the maximum flight speed of the aircraft or the currently set speed level (different speeds correspond to inconsistent allowable flight speed ranges). And based on the previous image acquisition position and the maximum flight distance, the possible flight range of the aircraft is determined; the image classification range of the target image is determined based on the possible flight range. The previous image acquisition position can be determined based on GPS positioning information, or it can be determined using the aircraft positioning method provided in this application. Exemplarily, when the acquisition frequency of the image acquisition device is 50Hz, the maximum flight speed of the aircraft is Max (unit: m / s), and the previous image acquisition position is (x0, y0), the maximum flight distance of the aircraft is L=0.02*Max (unit: m); the corresponding image classification range can be a circular range with (x0, y0) as the center and L as the radius; or it can be a square range with (x0, y0) as the center and 2L as the side length. It should be noted that this application mainly considers the positioning information of the aircraft in longitude and latitude, and the flight altitude information of the aircraft can be determined by methods such as barometric altimetry, ultrasonic altimetry or lidar altimetry. The determined weight category image is verified based on the image classification range. When the weight category image is within the image classification range, the target acquisition position is determined according to the image classification position corresponding to the weight category image; and when the weight category image is not within the image classification range, the target acquisition position is determined based on relatively accurate inertial data; the accuracy of the updated positioning information can be further improved.

[0060] In some optional embodiments, S101, inputting the target image captured by the image acquisition device of the aircraft into the trained image classification model to obtain the image classification result output by the image classification model, including: determining the image classification range of the target image according to the preset flight speed of the aircraft, the image acquisition interval of the image acquisition device and the previous image acquisition position; wherein the previous image acquisition position includes the position information of the aircraft when the image acquisition device captured the previous image of the target image; inputting the image classification range and the target image into the trained image classification model to obtain the image classification result output by the image classification model.

[0061] Among them, by inputting the image classification range and target image into the trained image classification model, the range of image classification results can be narrowed, thereby reducing the computational complexity of the image classification model and improving the acquisition speed of image classification results; so as to more quickly determine the target acquisition position based on the image classification results and inertial data, and realize timely update of the aircraft positioning information based on the target acquisition position.

[0062] Please refer to Figure 3 , Figure 3 A flowchart of a method for obtaining image classification results provided in an embodiment of the present application.

[0063] In some optional embodiments, the trained image classification model includes: a first-level classification model and multiple second-level classification models; S101, inputting the target image captured by the image acquisition device of the aircraft into the trained image classification model, and obtaining the image classification result output by the image classification model, including: S1011, performing feature extraction on the target image to obtain target image features; S1012, inputting the target image features into the first-level classification model, and obtaining the first-level classification result output by the first-level classification model; wherein the first-level classification result includes multiple hierarchical images and the classification similarity between the target image and each of the hierarchical images; each of the hierarchical images includes multiple category images; S1013, determining a second-level target classification model from multiple second-level classification models based on the first-level classification result; S1014, inputting the target image features into the second-level target classification model, and obtaining the image classification result output by the second-level target classification model.

[0064] In particular, when the specified flight area of ​​the aircraft is determined, the specified flight area can be divided into multiple hierarchical images based on the actual positioning accuracy requirements, and each hierarchical image can be divided into multiple category images; to ensure that the target acquisition position determined based on the category image can meet the actual positioning accuracy requirements. The first-level classification model can be implemented by ResNet or VGGNet model, and the second-level classification model can also be implemented by ResNet or VGGNet model. The first-level classification model and the second-level classification model can be the same or different. When the first-level classification model or the second-level classification model is implemented by ResNet, Bottleneck Block can be used to replace the basic residual block to further reduce the model calculation amount and parameter amount and optimize the model's computational efficiency. The size of each hierarchical image can be n*g*g, where n represents the number of category images that can be divided into each hierarchical image, and g*g represents the size of each category image. Image acquisition can be performed within the specified flight area according to the actual positioning accuracy requirements. Multiple n*g*g images can be input into the first-level classification model to be trained, and multiple g*g images can be input into the second-level classification model to be trained. The specified flight area can be divided into multiple n*g*g areas, and image acquisition can be performed within any n*g*g area. The second-level classification model can be trained using the multiple g*g images collected.

[0065] An embodiment of the present application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the aircraft positioning method as described in any one of the first aspects above.

[0066] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present application. The electronic device 300 includes: a memory 302 and a processor 301; the memory 302 stores a computer program executable by the processor 301, and when the computer program is executed by the processor 301, it performs the aircraft positioning method described in any one of the first aspects.

[0067] Memory 302 and processor 301 may be interconnected and communicate with each other via a communication bus 303 and / or other connection mechanisms (not shown). Memory 302 stores a computer program executable by processor 301. When executed by processor 301, the computer program performs the aircraft positioning method described in the first aspect above.

[0068] An embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by the processor 301 , the aircraft positioning method described in the first aspect above is executed.

[0069] Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0070] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices / systems and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0071] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0072] The above description is only an optional implementation method of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited to this. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in the embodiment of the present application, and they should all be covered by the protection scope of the embodiment of the present application.

Claims

1. A method for positioning an aircraft, characterized in that: The method comprises: Inputting the target image captured by the image acquisition device of the aircraft into the trained image classification model to obtain the image classification result output by the image classification model; determining a target acquisition position of the target image based on the image classification result and inertial data provided by an inertial navigation unit; updating the positioning information of the aircraft based on the target acquisition position; The image classification result includes: multiple category images and the classification similarity between the target image and each category image; each category image corresponds to an image classification position; Determining a target acquisition position of the target image according to the image classification result and inertial data provided by an inertial navigation unit includes: determining an inertial positioning position of the aircraft based on the inertial data; determining a weight processing result of the category image according to a positioning distance between the image classification position and the inertial positioning position corresponding to each category image and the classification similarity; Determining the target acquisition position according to the weight processing result; The step of determining the weight processing result of the category image according to the positioning distance between the image classification position and the inertial positioning position corresponding to each category image and the classification similarity includes: Determining a distance score of the category image according to the positioning distance; The weight processing result of each of the category images is determined according to the distance score, the first weight ratio corresponding to the distance score, the classification similarity, and the second weight ratio corresponding to the classification similarity.

2. The method according to claim 1, characterized in that in, The sum of the first weight ratio and the second weight ratio is a fixed value, and the first weight ratio is positively correlated with the positioning distance.

3. The method according to claim 1, characterized in that in, The weight processing result includes a weight score of each category image; and determining the target acquisition position according to the weight processing result includes: Determining, based on the weight processing result, a weighted category image having the highest weight score among the category images and meeting a preset score condition; determining an image classification range of the target image based on a preset flight speed of the aircraft, an image acquisition interval of the image acquisition device, and a previous image acquisition position; wherein the previous image acquisition position includes aircraft position information when the image acquisition device acquired the previous image of the target image; When the weighted category image is within the image classification range, determining the target acquisition position according to the image classification position corresponding to the weighted category image; In a case where the weighted category image is outside the image classification range, the target acquisition position is determined according to the inertial data.

4. The method according to claim 1, wherein The step of inputting the target image captured by the image acquisition device of the aircraft into a trained image classification model and obtaining the image classification result output by the image classification model comprises: determining an image classification range of the target image based on a preset flight speed of the aircraft, an image acquisition interval of the image acquisition device, and a previous image acquisition position; wherein the previous image acquisition position includes aircraft position information when the image acquisition device acquired the previous image of the target image; The image classification range and the target image are input into the trained image classification model to obtain the image classification result output by the image classification model.

5. The method according to any one of claims 1 to 3, characterized in that: in, The trained image classification model includes: a first-level classification model and multiple second-level classification models; The step of inputting the target image captured by the image acquisition device of the aircraft into a trained image classification model and obtaining the image classification result output by the image classification model comprises: Performing feature extraction on the target image to obtain target image features; Inputting the target image features into the first-level classification model to obtain a first-level classification result output by the first-level classification model; wherein the first-level classification result includes a plurality of hierarchical images and a classification similarity between the target image and each of the hierarchical images; each of the hierarchical images includes a plurality of the category images; Determining a second-level target classification model from a plurality of second-level classification models according to the first-level classification results; The target image features are input into the second-level target classification model to obtain the image classification result output by the second-level target classification model.

6. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

7. An electronic device, characterized in that: The electronic device comprises: Memory; processor; The memory stores a computer program executable by the processor, and when the computer program is executed by the processor, the method according to any one of claims 1 to 5 is performed.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is executed.

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