Airport diagram and approach chart superimposed positioning method, device, equipment, medium and product

Abstract

The application relates to an airport map and approach map superposition positioning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: obtaining an original image resource, performing image binarization processing on the original image resource, performing feature preprocessing on an airport map of obtained binary image data, and obtaining key point pixel coordinate information according to a check airport map; performing feature recognition on an approach map of the binary image data, and generating actual coordinate data according to obtained edge box feature data; performing proportion coefficient calculation according to the key point pixel coordinate and the actual coordinate data; performing four-corner coordinate backstepping on the check airport map according to obtained distance proportion coefficients, to obtain four-corner coordinate data; and performing map superposition positioning on the original image resource according to the four-corner coordinate data and the actual coordinate data, to obtain a map superposition positioning result. The method can efficiently perform high-precision superposition positioning on the airport map and the approach map.

Description

Technical Field

[0001] This application relates to the field of shipping positioning technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for positioning by overlaying airport charts and approach charts. Background Technology

[0002] With the development of aviation positioning technology, aircraft route monitoring technology has emerged. Currently, the civil aviation industry mainly relies on ADS-B (Automatic Dependent Surveillance-Broadcast) data for flight operation monitoring. This system broadcasts the aircraft's latitude, longitude, altitude, speed, and heading in real time through onboard equipment. The ADS-B data received on the ground can be visualized on ground monitoring map terminals, allowing dispatchers to intuitively monitor whether the aircraft deviates from the planned route. However, when the aircraft is taxiing on the ground, the background map lacks information about airport taxiways, making it impossible for users to intuitively confirm whether the aircraft is taxiing and on the runway correctly. In addition, during the aircraft approach phase, the background map lacks approach procedure references, resulting in an inability to monitor the crew's approach procedures. Furthermore, it is difficult to effectively combine the pilot's actions with the approach procedures during the post-incident investigation of pilot conduct, posing certain difficulties for post-incident investigation and evidence collection. On the other hand, the manual method of calculating the four corner coordinates of computer field maps and approach maps is time-consuming and has low accuracy. Summary of the Invention

[0003] Based on this, it is necessary to provide an airport map and approach map overlay positioning method, device, computer equipment, computer-readable storage medium, and computer program product that can efficiently calculate the four corner coordinates of the airport map and approach map, and use this information to overlay the key areas of the airport map and approach map onto the map for precise positioning and monitoring.

[0004] Firstly, this application provides a method for positioning by overlaying airport charts and approach charts, including:

[0005] The original image resources are acquired, and the original image resources are subjected to image binarization processing to obtain binary image data;

[0006] The airport map of the binary image data is preprocessed to obtain a verification airport map, and the key point pixel coordinate information is obtained based on the verification airport map;

[0007] Feature recognition is performed on the close-up image of the binary image data to obtain bounding box feature data, and actual coordinate data is generated based on the bounding box feature data;

[0008] The distance ratio coefficient is obtained by calculating the ratio coefficient based on the pixel coordinates of the key points and the actual coordinate data.

[0009] Based on the distance ratio coefficient, the four-corner coordinates of the test airport map are reversed to obtain the four-corner coordinate data;

[0010] Based on the four-corner coordinate data and the actual coordinate data, the original image resource is overlaid with a map for localization, resulting in a map overlay localization result.

[0011] In one embodiment, the step of performing feature preprocessing on the airport map of the binary image data to obtain a verified airport map includes:

[0012] The airport map is used for feature recognition to obtain the center runway number;

[0013] Obtain the magnetic azimuth information based on the center runway number;

[0014] The airport map is rotated based on the magnetic azimuth information to obtain a rotated airport map;

[0015] The rotated airport map is completed and verified to obtain the verified airport map.

[0016] In one embodiment, the step of completing and verifying the rotated airport map to obtain the verified airport map includes:

[0017] The four corner regions of the rotated airport map are filled in to obtain the completed airport map;

[0018] The proportion of the completed airport map is verified a second time to obtain the verified airport map.

[0019] In one embodiment, the key point pixel coordinate information includes: center position pixel coordinates and runway nose pixel coordinates; obtaining the key point pixel coordinate information based on the verified airport map includes:

[0020] Obtain the center location information and runway nose coordinate data based on the verified airport map;

[0021] The pixel coordinates of the center position are obtained based on the center position information;

[0022] The runway head pixel coordinates are obtained based on the runway head coordinate data.

[0023] In one embodiment, the step of performing a four-corner coordinate inverse calculation on the test airport map based on the distance scaling factor to obtain four-corner coordinate data includes:

[0024] The axial distance is calculated based on the four-corner features of the verified airport map to obtain the four-corner axial distance data;

[0025] The four-corner coordinate data are obtained by reverse calculation of the four-corner axis distance data based on the distance ratio coefficient.

[0026] In one embodiment, after performing map overlay positioning on the original image resource based on the four-corner coordinate data and the actual coordinate data to obtain the map overlay positioning result, the method further includes:

[0027] Based on the map overlay positioning results and the original image resources, a dynamic overlay design is performed to obtain a dynamic overlay effect image;

[0028] The map overlay positioning results are dynamically displayed based on the dynamic overlay effect diagram.

[0029] Secondly, this application also provides an airport map and approach map overlay positioning device, comprising:

[0030] The acquisition module is used to acquire the original image resources and perform image binarization processing on the original image resources to obtain binary image data;

[0031] The feature preprocessing module is used to perform feature preprocessing on the airport map of the binary image data to obtain a verification airport map, and to obtain key point pixel coordinate information based on the verification airport map;

[0032] The feature recognition module is used to perform feature recognition on the close-up image of the binary image data to obtain border feature data, and generate actual coordinate data based on the border feature data;

[0033] The scaling module is used to calculate the scaling factor based on the key point pixel coordinates and the actual coordinate data to obtain the distance scaling factor.

[0034] The coordinate inversion module is used to invert the four-corner coordinates of the test airport map based on the distance ratio coefficient to obtain the four-corner coordinate data.

[0035] The overlay positioning module is used to perform map overlay positioning on the original image resource based on the four corner coordinate data and the actual coordinate data to obtain the map overlay positioning result.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] The original image resources are acquired, and the original image resources are subjected to image binarization processing to obtain binary image data;

[0038] The airport map of the binary image data is preprocessed to obtain a verification airport map, and the key point pixel coordinate information is obtained based on the verification airport map;

[0039] Feature recognition is performed on the close-up image of the binary image data to obtain bounding box feature data, and actual coordinate data is generated based on the bounding box feature data;

[0040] The distance ratio coefficient is obtained by calculating the ratio coefficient based on the pixel coordinates of the key points and the actual coordinate data.

[0041] Based on the distance ratio coefficient, the four-corner coordinates of the test airport map are reversed to obtain the four-corner coordinate data;

[0042] Based on the four-corner coordinate data and the actual coordinate data, the original image resource is overlaid with a map for localization, resulting in a map overlay localization result.

[0043] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0044] The original image resources are acquired, and the original image resources are subjected to image binarization processing to obtain binary image data;

[0045] The airport map of the binary image data is preprocessed to obtain a verification airport map, and the key point pixel coordinate information is obtained based on the verification airport map;

[0046] Feature recognition is performed on the close-up image of the binary image data to obtain bounding box feature data, and actual coordinate data is generated based on the bounding box feature data;

[0047] The distance ratio coefficient is obtained by calculating the ratio coefficient based on the pixel coordinates of the key points and the actual coordinate data.

[0048] Based on the distance ratio coefficient, the four-corner coordinates of the test airport map are reversed to obtain the four-corner coordinate data;

[0049] Based on the four-corner coordinate data and the actual coordinate data, the original image resource is overlaid with a map for localization, resulting in a map overlay localization result.

[0050] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0051] The original image resources are acquired, and the original image resources are subjected to image binarization processing to obtain binary image data;

[0052] The airport map of the binary image data is preprocessed to obtain a verification airport map, and the key point pixel coordinate information is obtained based on the verification airport map;

[0053] Feature recognition is performed on the close-up image of the binary image data to obtain bounding box feature data, and actual coordinate data is generated based on the bounding box feature data;

[0054] The distance ratio coefficient is obtained by calculating the ratio coefficient based on the pixel coordinates of the key points and the actual coordinate data.

[0055] Based on the distance ratio coefficient, the four-corner coordinates of the test airport map are reversed to obtain the four-corner coordinate data;

[0056] Based on the four-corner coordinate data and the actual coordinate data, the original image resource is overlaid with a map for localization, resulting in a map overlay localization result.

[0057] The aforementioned airport map and approach map overlay positioning method, apparatus, computer equipment, computer-readable storage medium, and computer program product acquire original image resources and perform image binarization processing on the original image resources to obtain binary image data; perform feature preprocessing on the airport map of the binary image data to obtain a verification airport map, and obtain key point pixel coordinate information based on the verification airport map; perform feature recognition on the approach map of the binary image data to obtain border feature data, and generate actual coordinate data based on the border feature data; calculate a scale coefficient based on the key point pixel coordinates and actual coordinate data to obtain a distance scale coefficient; perform four-corner coordinate back-calculation on the verification airport map based on the distance scale coefficient to obtain four-corner coordinate data; and perform map overlay positioning on the original image resources based on the four-corner coordinate data and actual coordinate data to obtain the map overlay positioning result. Therefore, by binarizing the original image resources to simplify the complexity of subsequent feature recognition and analysis, feature preprocessing is performed on the airport map of the binary image data to make the image features more compliant with standards. Key point pixel coordinate information is obtained from the verified airport map to provide a basis for subsequent mapping between geographic coordinates and pixel coordinates. Feature recognition is performed on the approach map of the binary image data to obtain border feature data, which can be used for subsequent coordinate calculation and boundary matching for map overlay. Actual coordinate data is generated based on the border feature data. The scaling factor is calculated based on the key point pixel coordinates and the actual coordinate data to provide a quantitative basis for subsequent geographic coordinate back-inference. The four corner coordinates are back-inferred from the verified airport map based on the distance scaling factor to obtain the four corner coordinate data, thereby completing the association between the image and geographic coordinates. Finally, map overlay positioning is performed to achieve accurate matching and display of the airport map / approach map and the actual geographic space. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is an application environment diagram of the airport map and approach map overlay positioning method in one embodiment;

[0060] Figure 2 This is a flowchart illustrating a method for overlaying airport maps and approach maps for localization in one embodiment.

[0061] Figure 3 This is a schematic diagram illustrating the dynamic process of a method for overlaying airport maps and approach maps for localization in one embodiment.

[0062] Figure 4 An airport map is the original image resource of the airport map and approach map overlay positioning method in one embodiment;

[0063] Figure 5 An approach map is the original image resource of the airport map and approach map overlay positioning method in one embodiment;

[0064] Figure 6 This is a schematic diagram illustrating the center runway number identification in an embodiment of an airport map and approach map overlay positioning method.

[0065] Figure 7 This is a rotated airport map obtained from an airport map and approach map overlay positioning method in one embodiment;

[0066] Figure 8 This is a schematic diagram of the approach map border feature recognition in an airport map and approach map overlay localization method in one embodiment;

[0067] Figure 9 This is a schematic diagram illustrating the effect of overlaying the approach map onto a map in one embodiment of the airport map and approach map overlay positioning method.

[0068] Figure 10 This is a schematic diagram illustrating the effect of overlaying an airport onto a map using an airport map and approach map overlay positioning method in one embodiment.

[0069] Figure 11 This is a complete flowchart of the airport map and approach map overlay positioning method in another embodiment;

[0070] Figure 12 This is a structural block diagram of an airport map and approach map overlay positioning device in one embodiment;

[0071] Figure 13 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0073] The airport map and approach map overlay positioning method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Server 104 acquires the original image resources and performs image binarization processing on them to obtain binary image data; it performs feature preprocessing on the airport map of the binary image data to obtain a verification airport map, and obtains key point pixel coordinate information based on the verification airport map; it performs feature recognition on the approach map of the binary image data to obtain border feature data, and generates actual coordinate data based on the border feature data; it calculates a scale coefficient based on the key point pixel coordinates and actual coordinate data to obtain a distance scale coefficient; it performs four-corner coordinate inversion on the verification airport map based on the distance scale coefficient to obtain four-corner coordinate data; and it performs map overlay positioning on the original image resources based on the four-corner coordinate data and actual coordinate data to obtain the map overlay positioning result. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0074] In one exemplary embodiment, such as Figure 2 As shown, a method for overlaying airport maps and approach maps for localization is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 202 to 212. Wherein:

[0075] Step 202: Obtain the original image resources and perform image binarization processing on the original image resources to obtain binary image data.

[0076] The original image resources can include airport maps and approach maps. The airport maps and approach maps can be in PDF format or other formats, and are not limited to these.

[0077] In some embodiments, PDF files of airport maps and approach maps issued by civil aviation authorities can be obtained as raw image resources, or pre-stored airport maps and approach maps can be directly obtained from a raw image resource library, and this is not limited to these methods.

[0078] In some embodiments, such as Figure 4 As shown, Figure 4 This shows airport images from the acquired raw image resources, such as... Figure 5 As shown, Figure 5 The image shows a close-up view of the original image resource.

[0079] In some embodiments, the airport map or approach map image of the original image resource is binarized to convert the image into binary image data containing only black and white pixels, thereby simplifying the complexity of subsequent feature recognition and analysis.

[0080] Step 204: Perform feature preprocessing on the airport map of the binary image data to obtain the verification airport map, and obtain the key point pixel coordinate information based on the verification airport map.

[0081] Feature preprocessing involves multiple image processing steps to process airport images in three aspects: orientation alignment, boundary integrity, and image scale, so that the preprocessed airport images meet the preset conditions in these three aspects.

[0082] In some embodiments, feature preprocessing is performed on the airport map of binary image data to obtain a verification airport map, including: performing feature recognition on the airport map to obtain the center runway number; obtaining magnetic azimuth information based on the center runway number; performing a rotation operation on the airport map based on the magnetic azimuth information to obtain a rotated airport map; and performing completion verification on the rotated airport map to obtain a verification airport map.

[0083] In some embodiments, feature recognition is performed on the binarized airport map to extract and determine the location information of the airport center, and the center runway number corresponding to the airport center is identified. The specific feature recognition process is as follows: Figure 6 As shown, Figure 6 A partial view of key points for identifying the runway center and its number is shown.

[0084] In some embodiments, the magnetic azimuth information of the center runway is retrieved based on its number. Then, the airport map is rotated according to the magnetic azimuth information to align the image's orientation with the actual geographic orientation. The resulting rotated airport map is shown below. Figure 7 As shown, Figure 7 The image shows a rotated map of the airport.

[0085] In some embodiments, performing a completion verification on a rotating airport map to obtain a verified airport map includes: completing the four corner regions of the rotating airport map to obtain a completed airport map; and performing a secondary verification on the scale of the completed airport map to obtain a verified airport map.

[0086] In some embodiments, the four corner regions of the rotated airport map are filled to obtain a filled airport map to ensure the integrity of the image boundaries. At the same time, the scale of the filled airport map is checked a second time to obtain a checked airport map, thereby verifying the accuracy of the scale after the image is scaled.

[0087] In some embodiments, the key point pixel coordinate information includes: center position pixel coordinates and runway nose pixel coordinates; obtaining key point pixel coordinate information based on the verified airport map includes: obtaining center position information and runway nose coordinate data based on the verified airport map; obtaining center position pixel coordinates based on the center position information; and obtaining runway nose pixel coordinates based on the runway nose coordinate data.

[0088] In some embodiments, the center location information of the airport center and the latitude and longitude coordinates of the corresponding runway head are extracted from the airport map to obtain runway head coordinate data, which provides basic data for subsequent geographic coordinate association.

[0089] In some embodiments, pixel coordinate information corresponding to key elements such as the airport center and runway head is extracted from the airport verification map based on the center location information and runway head coordinate data. That is, the center location pixel coordinates are obtained based on the center location information, and the runway head pixel coordinates are obtained based on the runway head coordinate data, so as to provide a basis for subsequent mapping of geographic coordinates and pixel coordinates.

[0090] Step 206: Perform feature recognition on the close-up image of the binary image data to obtain bounding box feature data, and generate actual coordinate data based on the bounding box feature data.

[0091] In some embodiments, feature recognition is performed on the border region of the approach image of binary image data to extract feature information such as the shape and position of the border, obtaining border feature data for subsequent coordinate calculation and boundary matching for map overlay. The specific feature recognition process of the approach image border is as follows: Figure 8 As shown, Figure 8 A schematic diagram of the approach image bounding box feature recognition is shown.

[0092] In some embodiments, based on the border feature data, actual coordinate data corresponding to the border feature data is generated, and a mapping relationship between features and spatial location is established. Assuming that the latitude and longitude of key point 1 (x1,y1) and key point 2 (x2,y2) are identified, the actual distance between them, Distance_real, is calculated, and the pixel distance, Distance_image, is calculated at the same time.

[0093] Step 208: Calculate the scaling factor based on the key point pixel coordinates and actual coordinate data to obtain the distance scaling factor.

[0094] The scaling factor calculation is used to calculate the scaling factor between pixel distance and actual distance.

[0095] In some embodiments, by comparing the pixel distance of key point pixel coordinates in the image with the actual geographic distance of the actual coordinate data, the ratio coefficient of pixel distance to actual distance = Distance_real / Distance_image is calculated, providing a quantitative basis for subsequent geographic coordinate back-calculation.

[0096] Step 210: Based on the distance ratio coefficient, perform a reverse calculation of the four-corner coordinates of the verification airport map to obtain the four-corner coordinate data.

[0097] Among them, the four-corner coordinate data refers to the actual coordinate positions of the four corners of the airport's outline on the map.

[0098] In some embodiments, the four-corner coordinates are reversed based on the distance scaling factor to obtain the four-corner coordinate data, including: calculating the axial distance based on the four-corner features of the test airport map to obtain the four-corner axial distance data; and reversing the four-corner coordinates based on the four-corner axial distance data to obtain the four-corner coordinate data.

[0099] In some embodiments, the geographical coordinates of the four corners are calculated by using a distance scaling factor and combining the four-corner features of the airport map to establish the association between the image and geographical coordinates. Specifically, assuming that there is a key point 1 in the airport map with a shortest distance of (y1) to the image X-axis and the airport center with a shortest distance of (x1) to the image Y-axis, the axial distances are calculated based on the four-corner features of the airport map to obtain the four-corner axial distance data. Then, the four-corner coordinates are deduced by using the distance scaling factor to obtain the latitude and longitude coordinates of the upper left corner of the airport map or approach map. The latitude and longitude coordinates of the lower right corner can be obtained in the same way, thus obtaining the four-corner coordinate data.

[0100] Step 212: Perform map overlay positioning on the original image resources based on the four corner coordinate data and the actual coordinate data to obtain the map overlay positioning result.

[0101] Map overlay positioning refers to overlaying airport map / approach map images with corresponding geographic base maps based on the four corner coordinate data and actual coordinate data.

[0102] In some embodiments, an airport map / approach map image with geographic coordinate association is generated based on the four-corner coordinate data and the actual coordinate data, and then overlaid with the corresponding geographic base map to achieve accurate matching and display of the airport map / approach map and the actual geographic space. The effect after overlay is as follows: Figure 8 and Figure 9 As shown, Figure 8 The image shows the effect of overlaying the close-up view onto the map. Figure 9 The image shows the effect of overlaying an airport map onto a map.

[0103] In the aforementioned airport map and approach map overlay positioning method, the original image resources are acquired and binarized to obtain binary image data. Feature preprocessing is performed on the airport map in the binary image data to obtain a verification airport map, and key point pixel coordinate information is obtained from the verification airport map. Feature recognition is performed on the approach map in the binary image data to obtain border feature data, and actual coordinate data is generated based on the border feature data. A scaling factor is calculated based on the key point pixel coordinates and the actual coordinate data to obtain a distance scaling factor. The four-corner coordinates of the verification airport map are inversely derived based on the distance scaling factor to obtain four-corner coordinate data. Finally, the original image resources are overlaid and positioned using the four-corner coordinate data and the actual coordinate data to obtain the map overlay positioning result. Therefore, by binarizing the original image resources to simplify the complexity of subsequent feature recognition and analysis, feature preprocessing is performed on the airport map of the binary image data to make the image features more compliant with standards. Key point pixel coordinate information is obtained from the verified airport map to provide a basis for subsequent mapping between geographic coordinates and pixel coordinates. Feature recognition is performed on the approach map of the binary image data to obtain border feature data, which can be used for subsequent coordinate calculation and boundary matching for map overlay. Actual coordinate data is generated based on the border feature data. The scaling factor is calculated based on the key point pixel coordinates and the actual coordinate data to provide a quantitative basis for subsequent geographic coordinate back-inference. The four corner coordinates are back-inferred from the verified airport map based on the distance scaling factor to obtain the four corner coordinate data, thereby completing the association between the image and geographic coordinates. Finally, map overlay positioning is performed to achieve accurate matching and display of the airport map / approach map and the actual geographic space.

[0104] In one exemplary embodiment, such as Figure 3 As shown, the dynamic overlay display steps include steps 302 to 304. Wherein:

[0105] Step 302: Perform dynamic overlay design based on map overlay positioning results and original image resources to obtain dynamic overlay effect image.

[0106] Among them, dynamic overlay design refers to a dynamic animation design process that involves transforming original image resources into map overlay positioning results.

[0107] In some embodiments, key areas of the airport map and approach map of the original image resources are overlaid on the map to obtain the map overlay positioning result. Combined with the visualized flight monitoring map and aircraft ADS-B data, as well as the dynamic overlay effect diagram, the aircraft's taxiing trajectory on the airport map, runway position, and whether the aircraft's approach procedures are compliant can be seen intuitively on the map, thereby improving the company's overall visualization monitoring capabilities.

[0108] Step 304: Dynamically display the map overlay positioning results based on the dynamic overlay effect diagram.

[0109] In some embodiments, the map overlay positioning results are dynamically displayed based on the dynamic overlay effect diagram. That is, the dynamic overlay effect diagram can effectively improve the ground signing party's monitoring of the crew's approach compliance, and also provide a visual interface for post-event review, thereby improving review efficiency.

[0110] In some embodiments, to better understand the solution of this application, the following are combined with Figure 11 The explanation is as follows:

[0111] like Figure 11 As shown, the process involves acquiring airport map and approach map PDFs, converting them into images to obtain raw image resources, and then performing image binarization to obtain binary image data, including airport map and approach map data. The airport map undergoes feature preprocessing to identify the airport center and corresponding runway numbers. Based on the identified runway numbers, the magnetic azimuth angle is obtained and rotated. Then, the four-corner image is completed and a secondary verification of the scaling ratio is performed to obtain the latitude and longitude information of the airport center and runway nose. The corresponding key point pixel information is extracted. The approach map's border features are identified, generating corresponding coordinates for the feature information. Then, pixel distance is converted to actual distance to generate a scaling factor. Based on the distance scaling factor, the four-corner coordinates of the verified airport map are inversely derived to obtain the four-corner coordinate data. Finally, the original image resources are overlaid and positioned using the four-corner coordinate data and the actual coordinate data to obtain the map overlay positioning result.

[0112] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0113] Based on the same inventive concept, this application also provides an airport map and approach map overlay positioning device for implementing the airport map and approach map overlay positioning method described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more airport map and approach map overlay positioning device embodiments provided below can be found in the limitations of the airport map and approach map overlay positioning method above, and will not be repeated here.

[0114] In one exemplary embodiment, such as Figure 12 As shown, an airport map and approach map overlay positioning device is provided, including: an acquisition module 1201, a feature preprocessing module 1202, a feature recognition module 1203, a scale calculation module 1204, a coordinate inversion module 1205, and an overlay positioning module 1206, wherein:

[0115] The acquisition module 1201 is used to acquire the original image resources and perform image binarization processing on the original image resources to obtain binary image data;

[0116] The feature preprocessing module 1202 is used to perform feature preprocessing on the airport map of the binary image data to obtain the verification airport map, and to obtain the key point pixel coordinate information based on the verification airport map;

[0117] The feature recognition module 1203 is used to perform feature recognition on the close-up image of the binary image data, obtain the bounding box feature data, and generate actual coordinate data based on the bounding box feature data.

[0118] The scaling calculation module 1204 is used to calculate the scaling coefficient based on the key point pixel coordinates and actual coordinate data to obtain the distance scaling coefficient.

[0119] The coordinate inversion module 1205 is used to invert the four-corner coordinates of the test airport map based on the distance ratio coefficient to obtain the four-corner coordinate data.

[0120] The overlay positioning module 1206 is used to perform map overlay positioning on the original image resources based on the four corner coordinate data and the actual coordinate data, so as to obtain the map overlay positioning result.

[0121] In some embodiments, the feature preprocessing module 1202 is further configured to perform feature recognition on the airport map to obtain the center runway number; obtain magnetic azimuth information based on the center runway number; perform a rotation operation on the airport map based on the magnetic azimuth information to obtain a rotated airport map; and perform completion verification on the rotated airport map to obtain a verified airport map.

[0122] In some embodiments, the feature preprocessing module 1202 is further configured to perform completion verification on the rotating airport map to obtain a verified airport map, including: performing completion processing on the four corner regions of the rotating airport map to obtain a completed airport map; and performing secondary verification on the proportion of the completed airport map to obtain a verified airport map.

[0123] In some embodiments, the key point pixel coordinate information includes: center position pixel coordinates and runway nose pixel coordinates; the feature preprocessing module 1202 is further configured to obtain center position information and runway nose coordinate data based on the verified airport map; obtain center position pixel coordinates based on the center position information; and obtain runway nose pixel coordinates based on the runway nose coordinate data.

[0124] In some embodiments, the coordinate back-reasoning module 1205 is further configured to calculate the axial distance based on the four-corner features of the verification airport map to obtain the four-corner axial distance data; and to perform four-corner coordinate back-reasoning on the four-corner axial distance data based on the distance ratio coefficient to obtain the four-corner coordinate data.

[0125] In some embodiments, the device further includes: a dynamic overlay display module, used to perform dynamic overlay design based on map overlay positioning results and original image resources to obtain a dynamic overlay effect diagram; and to dynamically display the map overlay positioning results based on the dynamic overlay effect diagram.

[0126] In the aforementioned airport map and approach map overlay positioning device, the original image resources are acquired and binarized to obtain binary image data; the airport map in the binary image data undergoes feature preprocessing to obtain a verification airport map, and key point pixel coordinate information is obtained based on the verification airport map; feature recognition is performed on the approach map in the binary image data to obtain border feature data, and actual coordinate data is generated based on the border feature data; a scale factor is calculated based on the key point pixel coordinates and actual coordinate data to obtain a distance scale factor; the four corner coordinates of the verification airport map are reversed based on the distance scale factor to obtain four corner coordinate data; and the original image resources are overlaid and positioned based on the four corner coordinate data and the actual coordinate data to obtain the map overlay positioning result. Therefore, by binarizing the original image resources to simplify the complexity of subsequent feature recognition and analysis, feature preprocessing is performed on the airport map of the binary image data to make the image features more compliant with standards. Key point pixel coordinate information is obtained from the verified airport map to provide a basis for subsequent mapping between geographic coordinates and pixel coordinates. Feature recognition is performed on the approach map of the binary image data to obtain border feature data, which can be used for subsequent coordinate calculation and boundary matching for map overlay. Actual coordinate data is generated based on the border feature data. The scaling factor is calculated based on the key point pixel coordinates and the actual coordinate data to provide a quantitative basis for subsequent geographic coordinate back-inference. The four corner coordinates are back-inferred from the verified airport map based on the distance scaling factor to obtain the four corner coordinate data, thereby completing the association between the image and geographic coordinates. Finally, map overlay positioning is performed to achieve accurate matching and display of the airport map / approach map and the actual geographic space.

[0127] The modules in the aforementioned airport map and approach map overlay positioning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0128] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 13As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores raw image resources. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an airport map and approach map overlay positioning method.

[0129] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0130] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described airport map and approach map overlay positioning method.

[0131] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described airport map and approach map overlay positioning method.

[0132] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described airport map and approach map overlay positioning method.

[0133] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0135] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0136] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for positioning by overlaying airport charts and approach charts, characterized in that, The method includes: The original image resources are acquired, and the original image resources are subjected to image binarization processing to obtain binary image data; The airport map of the binary image data is preprocessed to obtain a verification airport map, and the key point pixel coordinate information is obtained based on the verification airport map; Feature recognition is performed on the close-up image of the binary image data to obtain bounding box feature data, and actual coordinate data is generated based on the bounding box feature data; The distance ratio coefficient is obtained by calculating the ratio coefficient based on the pixel coordinates of the key points and the actual coordinate data. Based on the distance ratio coefficient, the four-corner coordinates of the test airport map are reversed to obtain the four-corner coordinate data; Based on the four-corner coordinate data and the actual coordinate data, the original image resource is overlaid with a map for localization, resulting in a map overlay localization result.

2. The method according to claim 1, characterized in that, The step of performing feature preprocessing on the airport map of the binary image data to obtain the verified airport map includes: The airport map is used for feature recognition to obtain the center runway number; Obtain the magnetic azimuth information based on the center runway number; The airport map is rotated based on the magnetic azimuth information to obtain a rotated airport map; The rotated airport map is completed and verified to obtain the verified airport map.

3. The method according to claim 2, characterized in that, The step of completing and verifying the rotated airport map to obtain the verified airport map includes: The four corner regions of the rotated airport map are filled in to obtain the completed airport map; The proportion of the completed airport map is verified a second time to obtain the verified airport map.

4. The method according to claim 1, characterized in that, The key point pixel coordinate information includes: center position pixel coordinates and runway nose pixel coordinates; obtaining the key point pixel coordinate information based on the verified airport map includes: Obtain the center location information and runway nose coordinate data based on the verified airport map; The pixel coordinates of the center position are obtained based on the center position information; The runway head pixel coordinates are obtained based on the runway head coordinate data.

5. The method according to claim 1, characterized in that, The step of performing a four-corner coordinate inversion on the test airport map based on the distance ratio coefficient to obtain four-corner coordinate data includes: The axial distance is calculated based on the four-corner features of the verified airport map to obtain the four-corner axial distance data; The four-corner coordinate data are obtained by reverse calculation of the four-corner axis distance data based on the distance ratio coefficient.

6. The method according to claim 1, characterized in that, After performing map overlay positioning on the original image resource based on the four corner coordinate data and the actual coordinate data to obtain the map overlay positioning result, the method further includes: Based on the map overlay positioning results and the original image resources, a dynamic overlay design is performed to obtain a dynamic overlay effect image; The map overlay positioning results are dynamically displayed based on the dynamic overlay effect diagram.

7. A device for overlaying and positioning airport charts and approach charts, characterized in that, The device includes: The acquisition module is used to acquire the original image resources and perform image binarization processing on the original image resources to obtain binary image data; The feature preprocessing module is used to perform feature preprocessing on the airport map of the binary image data to obtain a verification airport map, and to obtain key point pixel coordinate information based on the verification airport map; The feature recognition module is used to perform feature recognition on the close-up image of the binary image data to obtain border feature data, and generate actual coordinate data based on the border feature data; The scaling module is used to calculate the scaling factor based on the key point pixel coordinates and the actual coordinate data to obtain the distance scaling factor. The coordinate inversion module is used to invert the four-corner coordinates of the test airport map based on the distance ratio coefficient to obtain the four-corner coordinate data. The overlay positioning module is used to perform map overlay positioning on the original image resource based on the four corner coordinate data and the actual coordinate data to obtain the map overlay positioning result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images