Railway electronic map generation method

Abstract

The invention relates to a railway electronic map generation method, which comprises the following steps: firstly, determining an aerial survey area and finding out a no-fly range, and then selecting an unmanned aerial vehicle take-off and landing point through field investigation and verifying image quality. In the course planning stage, it is ensured that the image overlapping rate meets the high-precision requirement. Performing aerial survey by using the unmanned aerial vehicle, and acquiring detailed geographic information of the railway yard; the accurate selection and collection of the control points further improve the accuracy of the map. And finally, based on the aerial survey data and the control point data, generating a high-quality railway electronic map. The method has the advantages of high efficiency, high precision, high flexibility and the like.

Description

Technical Field

[0001] The present invention relates to the field of geographic information systems, and in particular to a method for generating a railway electronic map. Background Art

[0002] With the rapid development of railway transportation, higher requirements are put forward for the management and planning of railway stations. Traditional paper railway maps have been difficult to meet the needs of modern railway operations, and there are problems such as difficult updates, limited information, and inconvenience for real-time updates. Therefore, developing an efficient and accurate method for generating railway electronic maps has become an urgent need for railway informatization construction.

[0003] Currently, the research and development of domestic railway geographic information systems are mostly based on mature geographic information systems for secondary development. However, the display of railway information in such systems is relatively limited, mainly representing natural and human geographical information, and the description of on-site railway equipment and line conditions is not clear enough. Summary of the Invention

[0004] In view of the above situation, it is necessary to provide a method for generating a railway electronic map that solves at least one of the above problems, including the following specific steps:

[0005] S1: Area determination, clarify the specific railway station area for aerial survey, and find out the no-fly and restricted-fly areas in this area and nearby areas;

[0006] S2: On-site investigation, conduct on-site investigation of the railway station, determine the take-off and landing locations of the unmanned aerial vehicle (UAV), and conduct at least one test flight to verify the clarity of the images and the accuracy of the shooting coordinates;

[0007] S3: Route planning, based on the results of the area determination step, plan the flight route of the UAV to ensure that the image overlap rate on the route is greater than 35%, and the overlap rate between routes is greater than 65%;

[0008] S4: Aerial survey, conduct UAV aerial survey according to the planned route, and collect the geographic information data of the railway station;

[0009] S5: Selection and collection of control points, select ground control points from the photos taken during the test flight, and measure them using a locator;

[0010] S6: Map generation, generate a railway electronic map based on the data collected in the aerial survey step and the control point data.

[0011] Preferably, in step S1, it includes judging the complexity of the regional terrain and landform, and adjusting the determination method of the no-fly and restricted-fly areas and the UAV flight parameters in the subsequent steps based on the complexity.

[0012] Preferably, in step S2, it includes the inspection steps of the hardware status before the UAV takes off, including battery power, propeller status, etc., and adds the evaluation of the UAV flight stability during the test flight.

[0013] Preferably, in step S3, when planning the flight route, further according to factors such as the length of the flight route, flight altitude, and wind speed, use an algorithm to automatically calculate and adjust the image overlap rate on the flight route and the overlap rate between flight routes.

[0014] Preferably, in step S4, add real-time image quality monitoring during the aerial survey process. When the image clarity is lower than the preset threshold, automatically trigger the logical judgment of reshooting or adjusting the UAV flight attitude.

[0015] Preferably, in step S5, when selecting control points, automatically screen out the optimal control points according to the clarity, quantity, and uniform distribution of the feature points in the taken photos.

[0016] Preferably, in step S5, further add the accuracy judgment of the control point measurement data. When the measurement data error exceeds the preset threshold, automatically trigger re-measurement or increase the number of control points.

[0017] Preferably, in step S6, add a data preprocessing step before map generation, including data integrity check and outlier removal.

[0018] Preferably, in step S6, add a map quality evaluation step after map generation, including the evaluation of map clarity, accuracy, and integrity indicators, and automatically adjust the map generation parameters or regenerate the map based on the evaluation results. Description of the Drawings

[0019] Figure 1 is a schematic flowchart of the method for generating a railway electronic map according to an embodiment of the present invention. Detailed Embodiments

[0020] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further elaborates on the method for generating a railway electronic map of the present invention in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] In the description of the present invention, unless otherwise specified, "a plurality of" means two or more; the terms "center", "longitudinal", "transverse", "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0022] In the description of the present utility model, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present utility model can be understood through specific situations.

[0023] Please refer to Figure 1 , the method for generating a railway electronic map according to the embodiment of the present invention includes the following specific steps:

[0024] S1: Area determination, clarify the specific railway station area for aerial survey, and find out the no-fly and restricted-fly areas in this area and the nearby areas;

[0025] S2: On-site investigation, conduct on-site investigation of the railway station, determine the take-off and landing locations of the unmanned aerial vehicle (UAV), and conduct at least one test flight to verify the clarity of the images and the accuracy of the shooting coordinates;

[0026] S3: Route planning, based on the results of the area determination step, plan the flight route of the UAV to ensure that the image overlap rate on the route is greater than 35%, and the overlap rate between routes is greater than 65%;

[0027] S4: Aerial survey, conduct UAV aerial survey according to the planned route, and collect the geographical information data of the railway station;

[0028] S5: Selection and collection of control points, select ground control points in the photos taken during the test flight, and measure them using a locator;

[0029] S6: Map generation, generate a railway electronic map based on the data collected in the aerial survey step and the control point data.

[0030] In the above embodiments, first, the workflow of the entire railway electronic map generation method starts with the area determination step (S1). In this step, the primary task is to clearly define the specific railway station yard area for aerial survey. This generally involves a comprehensive understanding of information such as the geographical location, scale, and structure of the railway station yard. By referring to relevant materials, maps, or satellite images, the scope of the aerial survey area can be initially determined. Next, it is necessary to identify the no-fly and restricted-fly areas in this area and the surrounding areas. The determination of no-fly and restricted-fly areas is to ensure the flight safety of the drone during the aerial survey and avoid conflicts with the flight routes of civil aviation aircraft or other flying vehicles. This process usually involves communication and coordination with local civil aviation management departments, military departments, etc. to ensure the legality and safety of the aerial survey activities. After completing the area determination, it enters the on-site investigation step (S2). On-site investigation is a crucial link to ensure the smooth progress of the drone aerial survey. In this step, it is necessary to conduct on-site investigations of the railway station yard to understand the detailed layout, building distribution, traffic conditions, etc. of the station yard. This information is crucial for determining the takeoff and landing locations of the drone, planning the flight route, and subsequent image processing. At the same time, at least one test flight is also required. The purpose of the test flight is to verify the clarity of the images and the accuracy of the shooting coordinates. Through the test flight, the shooting effect of the drone can be intuitively understood, such as the resolution, color restoration degree, distortion degree, etc. of the images. In addition, the accuracy of the sensors and positioning systems carried by the drone can also be verified to ensure the accuracy of the shooting coordinates. The success or failure of the test flight will directly affect the subsequent aerial survey effect and the accuracy of map generation. Immediately following is the flight route planning step (S3). Flight route planning is one of the important links in the drone aerial survey. In this step, it is necessary to plan the flight route of the drone according to the results of the area determination and on-site investigation. Flight route planning needs to comprehensively consider various factors, such as terrain and landform, meteorological conditions, and drone performance. Terrain and landform factors include natural obstacles such as hills, rivers, and lakes, as well as artificial obstacles such as buildings and roads. These factors will affect the flight altitude, speed, and heading of the drone. Meteorological condition factors include wind speed, wind direction, cloud cover, etc., which will affect the flight stability and shooting effect of the drone. Drone performance factors include the maximum flight speed, maximum climb rate, maximum hover time, etc., which will limit the flight ability and shooting range of the drone. By comprehensively considering these factors, a reasonable flight route can be planned to ensure that the image overlap rate on the flight route is greater than 35%, and the overlap rate between flight routes is greater than 65%. This can ensure that in subsequent data processing, a continuous and complete railway electronic map can be generated. After completing the flight route planning, it enters the aerial survey step (S4). Aerial survey is the core link of the entire railway electronic map generation method. In this step, it is necessary to conduct drone aerial survey according to the planned flight route to collect geographical information data of the railway station yard. During the aerial survey process, the drone will fly according to the preset flight route and capture a large amount of high-definition image data. This image data will serve as the basic data for subsequent map generation.To ensure the accuracy and integrity of aerial survey, it is also necessary to monitor and control the UAV in real time. Through the sensors and positioning systems carried by the UAV, information such as the position, speed, and altitude of the UAV can be obtained in real time, and corresponding adjustments and controls can be carried out. At the same time, it is also necessary to set and optimize the parameters of the camera carried by the UAV to ensure clear and accurate image data is captured. After the aerial survey is completed, it enters the control point selection and collection step (S5). Control point selection and collection is an important link to ensure the accuracy of the railway electronic map. In this step, ground control points need to be selected from the photos taken during the test flight and measured using a locator. The selection of control points needs to follow certain principles, such as the control points should be evenly distributed, sufficient in quantity, and easy to identify. By selecting reasonable control points and conducting precise measurements, accurate ground control information can be provided for subsequent map generation. These control information will be used to correct and enhance the accuracy and reliability of the image data. Finally, it enters the map generation step (S6). Map generation is the final link of the entire railway electronic map generation method. In this step, based on the data collected in the aerial survey step and the control point data, professional geographic information system (GIS) software is used for processing and analysis. First, it is necessary to preprocess the aerial survey data, including operations such as image stitching, correction, and enhancement. Through preprocessing, interference factors such as noise and distortion in the image can be eliminated, and the quality and clarity of the image can be improved. Next, the GIS software is used to perform coordinate transformation and georeferencing on the preprocessed image. Through coordinate transformation and georeferencing, the image data can be converted from the pixel coordinate system to the geographic coordinate system, realizing the fusion of the image and geographic information. Then, using the data processing and analysis functions of the GIS software, operations such as classification, extraction, and calculation are performed on the image data to generate various thematic layers and statistical reports. Finally, the generated thematic layers and statistical reports are integrated and overlaid to generate the final railway electronic map. The generated map will contain detailed geographic information data of railway stations and yards, such as railway tracks, bridges, tunnels, buildings, etc. These map data will provide important support and reference for railway planning, construction, and management. In summary, the working principle of the railway electronic map generation method includes steps such as area determination, on-site investigation, flight route planning, aerial survey, control point selection and collection, and map generation. These steps are interrelated and interdependent, jointly constituting a complete railway electronic map generation process. Through this process, the geographic information data of railway stations and yards can be efficiently and accurately collected, and a high-precision and high-quality railway electronic map can be generated.

[0031] Please refer to Figure 1 , in another embodiment, in step S1, it includes judging the complexity of the regional topography and adjusting the determination method of the no-fly and restricted-fly ranges based on the complexity, as well as the UAV flight parameters in subsequent steps.

[0032] In the above embodiments, the system immediately conducts comprehensive data collection and analysis on the terrain and landforms of the target area. This includes using various means such as satellite remote sensing, ground survey, or drone reconnaissance to obtain information such as elevation data, terrain features, and landform types of the area. These data will be input into a dedicated geographic information system for further processing and analysis. In the data analysis stage, the system will use complex algorithms to evaluate the complexity of the terrain and landforms. Here, the complexity not only refers to the undulation degree of the terrain, but also includes the diversity of landforms, the types of surface coverings (such as water areas, forests, cities, etc.), and potential natural disaster risks (such as flash floods, debris flows, etc.). These factors will all have an important impact on the flight safety of the drone. Next, based on the evaluation results of the terrain and landform complexity, the system will start to determine the no-fly and restricted-fly areas. The no-fly areas usually include those areas with extremely complex terrain and extremely high flight risks, such as high mountain canyons, deep lakes, or volcanic craters. Due to the extreme terrain conditions in these areas, it is difficult for drones to ensure safe flight, so they are set as no-fly areas. The restricted-fly areas refer to those areas with relatively complex terrain but relatively low flight risks. In these areas, drones can fly under certain conditions, but more cautious flight strategies and parameter settings are required. During the determination of the no-fly and restricted-fly areas, the system will also fully consider the flight performance of the drone and the requirements of the flight mission. For example, if the drone has strong vertical takeoff and landing capabilities and high wind resistance, then in some areas with relatively complex terrain but small wind speeds, the restricted-fly conditions can be appropriately relaxed. Similarly, if the flight mission requires the drone to conduct detailed reconnaissance or shooting in a specific area, then the restricted-fly area also needs to be flexibly adjusted. With the determination of the no-fly and restricted-fly areas, step S1 will also set the flight parameters of the drone in the subsequent steps according to this information. These parameters include flight altitude, speed, heading, attitude, etc., and they will directly affect the flight trajectory and flight effect of the drone. When setting the flight parameters, the system will comprehensively consider factors such as terrain and landform complexity, flight safety rules, and drone flight performance to ensure that the drone can maximize flight safety while meeting the mission requirements. In addition, step S1 also has the ability of dynamic adjustment. During the flight of the drone, if sudden situations or weather changes and other factors cause changes in the flight environment, the system can re-evaluate the terrain and landform complexity in real time and accordingly adjust the no-fly, restricted-fly areas, and flight parameters. This ability of dynamic adjustment enables the drone to maintain a high degree of flexibility and adaptability in a complex and changeable flight environment.

[0033] Please refer to Figure 1 , in another embodiment, in step S2, it includes the inspection steps of the hardware state before the drone takes off, including battery power, propeller state, etc., and adds the evaluation of the flight stability of the drone during the test flight.

[0034] In the above embodiments, the endurance of the drone directly depends on the battery power. Therefore, the system will use a dedicated power detection module to read the remaining battery power in real time and evaluate whether it meets the flight requirements. If the power is insufficient, the system will issue an alarm and prevent the drone from taking off to avoid a crash caused by power exhaustion. In addition to the battery power, the condition of the propellers is also a key point for inspection. The propellers are the key components for generating lift of the drone, and their integrity directly affects the flight performance of the drone. The system will conduct a detailed inspection of the integrity, installation position, rotational balance, etc. of the propellers through visual detection or sensor detection. Once any problems such as damage or improper installation of the propellers are found, the system will immediately give a prompt and require the user to replace or adjust them. After completing the hardware status inspection, the system will enter the test flight phase to evaluate the flight stability of the drone. The test flight is an important step before the drone takes off. It can help the user intuitively understand the flight performance of the drone and discover potential problems. During the test flight, the system will use the built-in sensors and flight control system to monitor the flight status of the drone in real time, including flight altitude, speed, attitude, etc. In the flight stability evaluation, the system mainly focuses on the attitude stability and flight trajectory stability of the drone. Attitude stability means that the drone can maintain a stable attitude during flight and is not affected by external interference. The system will use attitude sensors to monitor the attitude changes of the drone in real time and make dynamic adjustments through the flight control system to ensure that the drone can maintain a stable attitude. Flight trajectory stability means that the drone can fly along a predetermined route without deviation or shaking. The system will use the GPS positioning module and flight control system to monitor the flight trajectory of the drone in real time and make necessary adjustments to ensure that the drone can fly stably along the predetermined route.

[0035] Please refer to Figure 1 , in another embodiment, in step S3, when planning the route, further according to factors such as the length of the route, flight altitude, and wind speed, an algorithm is used to automatically calculate and adjust the image overlap rate on the route and the overlap rate between routes.

[0036] In the above embodiments, at the beginning of route planning, it is necessary to determine the length and flight altitude of the route. The length of the route usually depends on the size of the shooting area and shooting requirements, while the flight altitude is affected by the performance of the UAV, safety regulations, and shooting effects. When determining these basic parameters, the system comprehensively considers factors such as the endurance of the UAV, flight speed, and shooting range to ensure that the UAV can complete the predetermined shooting task while ensuring safety. Next, wind speed, as an important factor affecting the flight stability and shooting effects of the UAV, must be taken into account in route planning. Wind speed not only affects the flight speed and stability of the UAV but also has an impact on the clarity and overlap rate of the captured images. Therefore, when planning the route, the system collects real-time wind speed data and adjusts the flight trajectory and flight speed of the route accordingly based on the magnitude and direction of the wind speed. After determining the basic parameters of the route and considering the wind speed factor, the system starts to use a special algorithm to calculate and adjust the image overlap rate on the route and the overlap rate between routes. The image overlap rate is an important parameter in the UAV shooting task, which determines the stitching effect of the images and the resolution of the final mapped image. By automatically calculating and adjusting the image overlap rate through the algorithm, it can be ensured that the images captured by the UAV during flight can be seamlessly stitched to form high-quality aerial images. The overlap rate between routes is also an important parameter that needs to be concerned about during route planning. It determines whether there are overlapping areas in the images captured between different routes and the size of these overlapping areas. By automatically adjusting the overlap rate between routes through the algorithm, it can be ensured that the UAV can cover the entire shooting area during continuous shooting, avoiding missing any important information. During the process of calculating and adjusting the image overlap rate and the overlap rate between routes, the system comprehensively considers multiple factors such as the flight speed of the UAV, shooting speed, and lens focal length. Through precise algorithm calculation and flexible parameter adjustment, the system can ensure that the UAV can fly stably along the predetermined route during flight while capturing high-quality and high-clarity aerial images.

[0037] Please refer to Figure 1 , in another embodiment, in step S4, during the aerial survey process, real-time image quality monitoring is added. When the image clarity is lower than the preset threshold, a logical judgment to automatically trigger reshooting or adjusting the flight attitude of the UAV is made.

[0038] In the above embodiments, during the aerial survey process, the imaging system continuously captures images of the target area and transmits these images to the ground station or remote monitoring center in real time. The ground station or monitoring center uses image processing techniques to quickly analyze these images and evaluate whether their clarity meets the preset requirements. To accurately evaluate the clarity of the images, the system usually adopts a series of image quality metrics, such as contrast, sharpness, noise level, etc. These metrics can be automatically calculated through image processing algorithms and compared with the preset thresholds. When the clarity of the image is lower than the preset threshold, the system immediately issues an alarm and starts the corresponding logical judgment program. In the logical judgment program, the system automatically determines whether to retake the image or adjust the flight attitude of the drone according to the current flight state and shooting conditions. If the decrease in image clarity is caused by environmental factors (such as haze, insufficient light, etc.), the system will choose to adjust the flight altitude or shooting angle of the drone to improve the shooting conditions. If the image quality problem is caused by a hardware failure of the drone or a problem with the imaging system, the system will trigger the retake mechanism to retake the images of the area. During the process of automatically triggering a retake or adjusting the flight attitude, the system considers various factors to ensure the rationality of the decision. For example, when deciding to retake the image, the system will evaluate whether the remaining battery power and flight time are sufficient to complete the retake task; when adjusting the flight attitude, the system will consider factors such as the maneuverability and flight safety of the drone.

[0039] Please refer to Figure 1 , in another embodiment, in step S5, when selecting control points, the optimal control points are automatically selected according to the clarity, quantity, and uniform distribution of the feature points in the captured photos.

[0040] In the above embodiments, the system will perform preliminary processing and analysis on the photos taken by the drone. These photos contain a large amount of geographical information and feature points, such as road intersections, building corners, obvious landmarks, etc. These feature points are the basis for subsequent selection of control points, and they will be recognized and extracted by the system. After extracting the feature points, the system will evaluate the clarity of these points. Clarity is one of the important indicators for evaluating the quality of feature points, which reflects the recognizability and accuracy of feature points in the photos. The system will use image processing algorithms to analyze the pixels around the feature points and calculate parameters such as contrast and sharpness to evaluate their clarity. Feature points with high clarity usually have more obvious edges and textures, are easier to be recognized and located, and thus are more suitable as control points. Next, the system will screen according to the number of feature points. The number of feature points is also an important factor to consider when selecting control points. In aerial photos, the number of feature points varies due to factors such as the size of the shooting area and the terrain complexity. The system will determine the required minimum number of feature points according to preset criteria or actual needs. Then, the system will count the number of extracted feature points to ensure that the number of selected control points meets the requirements. In addition to the number and clarity, the distribution uniformity of feature points is also an important factor to consider when selecting control points. Feature points with uniform distribution can more comprehensively reflect the geographical information of the shooting area and improve the accuracy and reliability of measurement. The system will perform spatial analysis on the extracted feature points, calculate the spacing and distribution density between them to evaluate their distribution uniformity. If the distribution of feature points is too concentrated or sparse, the system will automatically adjust the selection strategy to ensure that the control points are more evenly distributed. After comprehensively considering the clarity, number and distribution uniformity of feature points, the system will automatically screen out the optimal control points. This screening process is based on certain algorithms and rules, aiming to ensure that the selected control points have the characteristics of high quality, sufficient quantity and uniform distribution. Specifically, the system will sort and screen the feature points according to preset weights or priorities, and finally select the control points that meet the requirements. It should be noted that in special cases, such as areas with complex terrain or few feature points, the system cannot automatically screen out a sufficient number of high-quality control points. At this time, the system will issue a prompt or warning, asking the user to perform manual assistance in selection or adjust the shooting parameters. At the same time, the user can also manually adjust and optimize the control points screened by the system according to the actual situation and needs.

[0041] Please refer to Figure 1 , in another embodiment, in step S5, further increase the accuracy judgment of the measurement data of the control points. When the measurement data error exceeds the preset threshold, automatically trigger re-measurement or increase the number of control points.

[0042] In the above-mentioned upper embodiment, after the optimal control points are screened out, the system will immediately perform initial measurements on these control points. The initial measurements are usually based on high-definition images captured by drones and advanced image processing technologies. Through precise matching and calculation, the accurate position information of each control point in the geographical coordinate system is obtained. These position information will serve as the benchmark for subsequent data processing. Next, the system will conduct strict quality control and accuracy evaluation on the initial measurement data. The evaluation process mainly focuses on two key indicators: the accuracy and stability of the measurement data. Accuracy reflects the deviation degree between the measurement data and the actual ground control point positions, while stability reflects the consistency of the data between multiple measurements. To evaluate the accuracy of the measurement data, the system will adopt a series of statistical methods and mathematical models, such as the least squares method, maximum likelihood method, etc., to process and analyze the measurement data. Through these methods, the system can calculate the measurement error of each control point and compare it with the preset accuracy threshold. If the measurement error of a certain control point exceeds the accuracy threshold, then the system will consider the measurement data of this control point to be inaccurate. At the same time, to evaluate the stability of the measurement data, the system will consider the data fluctuation situation between multiple measurements. If there are significant differences in the position information of a certain control point in multiple measurements, then the system will consider the measurement data of this control point to be unstable. When the system detects that the measurement data error of a certain control point exceeds the preset threshold, or the measurement data is not stable, it will immediately trigger the corresponding processing mechanism. For the control points with excessive errors, the system will automatically select to re-measure or increase the number of control points according to the type and degree of the errors. If the error is small and within the acceptable range, the system will choose to re-measure this control point. When re-measuring, the system will adjust parameters such as the flight attitude, shooting angle, or exposure time of the drone to improve the shooting conditions and obtain more accurate measurement data. At the same time, the system will also re-evaluate the data obtained from the re-measurement to ensure the accuracy and stability of the data. If the error is large or the problem cannot be solved by re-measuring, the system will choose to increase the number of control points. Increasing the number of control points can improve the redundancy and reliability of the measurement and reduce the impact of the error of a single control point on the entire measurement result. When selecting new control points, the system will comprehensively consider other high-quality feature points in the image and screen them according to factors such as the clarity, quantity, and uniform distribution of these points. The newly added control points will participate in the subsequent measurement and data processing processes together with the original control points.

[0043] Please refer to Figure 1 , in another embodiment, in step S6, a data preprocessing step is added before map generation, including data integrity check and outlier removal.

[0044] In the above embodiments, a data preprocessing step is added before map generation, which is a key link to ensure the quality of the map. Data preprocessing mainly includes data integrity check and outlier removal. The system will first perform an integrity check on all the collected aerial survey data to ensure that the data of each control point and each area is complete. Subsequently, the system uses advanced statistical analysis methods to identify and remove outliers in the data. These outliers are caused by equipment failures, environmental factors or operational errors, and their existence will seriously affect the accuracy and reliability of the map. Through data preprocessing, the system can ensure that the data input into the map generation step is accurate, complete and reliable, laying a solid foundation for subsequent map generation.

[0045] Please refer to Figure 1 , in another embodiment, in step S6, after the map is generated, a map quality assessment step is added, including the assessment of map clarity, accuracy and integrity indicators, and automatically adjusting the map generation parameters or regenerating the map based on the assessment results.

[0046] In the above embodiments, after the map is generated, the system will immediately perform a map quality assessment. This step aims to ensure that the generated map meets the preset quality standards. The system will first evaluate the clarity of the map and check whether the image details are clearly distinguishable; then evaluate the accuracy of the map by comparing the actual ground information with the display on the map to determine whether there are significant errors; finally, evaluate the integrity of the map to ensure that the map covers all the required areas and there are no omissions or missing parts. Based on these assessment results, the system will automatically adjust the map generation parameters, such as resolution, projection method, etc., to optimize the map quality. If the problem is serious, the system will automatically trigger the process of regenerating the map until the generated map meets the preset quality standards.

[0047] The above are only preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the equivalent embodiments by using the above-disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for generating a railway electronic map, characterized in that: The specific steps include: S1: Area determination: clarify the specific railway station area to be surveyed, and find out the no-fly and restricted-fly areas in the area and nearby areas; S2: On-site survey: conduct an on-site survey of the railway yard, determine the take-off and landing locations of the drone, and conduct at least one test flight to verify the clarity of the image and the accuracy of the shooting coordinates; S3: Route planning, based on the results of the area determination step, the flight route of the UAV is planned to ensure that the image overlap rate on the route is greater than 35% and the overlap rate between routes is greater than 65%; S4: Aerial survey: Use drones to conduct aerial surveys according to the planned routes and collect geographic information data of railway stations; S5: Control point selection and collection: ground control points are selected from the photos taken during the test flight and measured using a locator; S6: Map generation, generating a railway electronic map based on the data collected in the aerial survey step and the control point data.

2. The method for generating a railway electronic map according to claim 1, wherein: In step S1, it includes judging the complexity of the regional topography and adjusting the method of determining the no-fly and restricted-fly areas based on the complexity, as well as the UAV flight parameters in subsequent steps.

3. The method for generating a railway electronic map according to claim 1, wherein: In step S2, the hardware status of the drone is checked before takeoff, including battery power, propeller status, etc., and an assessment of the drone's flight stability is added during the test flight.

4. The method for generating a railway electronic map according to claim 1, wherein: In step S3, when planning the route, an algorithm is used to automatically calculate and adjust the image overlap rate on the route and the overlap rate between routes based on the route length, flight altitude, and wind speed factors.

5. The method for generating a railway electronic map according to claim 1, wherein: In step S4, real-time image quality monitoring is added during the aerial survey process. When the image clarity is lower than the preset threshold, the logic judgment of retaking the image or adjusting the flight attitude of the drone is automatically triggered.

6. The method for generating a railway electronic map according to claim 1, wherein: In step S5, when the control points are selected, the optimal control points are automatically selected according to the clarity, quantity and distribution uniformity of the feature points in the captured photos.

7. The method for generating a railway electronic map according to claim 1, wherein: In step S5, the accuracy of the control point measurement data is further judged. When the measurement data error exceeds a preset threshold, re-measurement or increase in the number of control points is automatically triggered.

8. The method for generating an electronic railway map as claimed in claim 1, wherein: In step S6, a data preprocessing step is added before map generation, including data integrity check and outlier removal.

9. The method for generating a railway electronic map according to claim 1, wherein: In step S6, a map quality assessment step is added after the map is generated, including the assessment of map clarity, accuracy, and completeness indicators, and the map generation parameters are automatically adjusted or the map is regenerated based on the assessment results.

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