Three-dimensional space positioning and path planning method of unmanned aerial vehicle pole tower

Abstract

The invention relates to the technical field of space positioning and path planning, in particular to a three-dimensional space positioning and path planning method for an unmanned aerial vehicle tower. The method comprises the following steps of performing accurate longitude and latitude height data acquisition based on a laser range finder to generate accurate longitude and latitude height data; obtaining tower equipment parameters; performing inspection point three-dimensional coordinate calculation according to the tower equipment parameters and the tower three-dimensional coordinate system to generate inspection point three-dimensional coordinate data; cruise point comprehensive constraint range data are obtained according to the unmanned aerial vehicle flight association data and the polling area terrain data, and cruise point comprehensive constraint range data are generated; performing cruise node three-dimensional coordinate design on the three-dimensional coordinate data of the inspection point according to the cruise point comprehensive constraint range data to generate cruise point three-dimensional coordinate data; and performing optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data. According to the invention, more accurate unmanned aerial vehicle tower space positioning and path planning are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of space positioning and path planning, and in particular to a three-dimensional space positioning and path planning method for an unmanned aerial vehicle tower. Background Art

[0002] With the rapid development of drone technology, drones are increasingly used in the field of power inspection. These applications usually require precise positioning and path planning in three-dimensional space to achieve tower inspection. Reasonable path planning can minimize the time and energy consumption required for inspection, ensure that drones can cover all areas that need to be inspected, and automatically avoid obstacles to ensure safe flight. However, the three-dimensional spatial positioning and path planning methods of traditional drone towers are limited in positioning accuracy and path planning efficiency by a single technical means, making it difficult to cope with complex and changing environmental conditions. Visual recognition is not effective when light is insufficient or the line of sight is blocked; LiDAR may be affected by multipath effects in dense environments; and high-precision GPS may be blocked or interfered by signals, making it difficult for the technology to achieve efficient path planning while ensuring positioning accuracy. Summary of the invention

[0003] Based on this, the present invention provides a three-dimensional spatial positioning and path planning method for an unmanned aerial vehicle tower to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a three-dimensional spatial positioning and path planning method for a drone tower comprises the following steps: Step S1: collecting test longitude and latitude data of the tower head according to the preset laser ranging conditions and the first laser rangefinder to generate test longitude and latitude data; performing effective ranging range data analysis based on the test longitude and latitude data to generate effective ranging range data; Step S2: Collect effective pole tower recognition image data according to the effective ranging range data and the monitoring equipment built into the drone device to generate effective pole tower recognition image data; Step S3: Based on the effective tower recognition image data and the second laser rangefinder built into the UAV equipment, accurate longitude and latitude data are collected to generate accurate longitude and latitude data; Step S4: Obtain tower equipment parameters; construct a tower three-dimensional coordinate system based on precise longitude and latitude data; perform correction tower structure coordinate data analysis based on tower equipment parameters and tower three-dimensional coordinate system to generate correction tower structure coordinate data; perform inspection point three-dimensional coordinate calculation based on correction tower structure coordinate data to generate inspection point three-dimensional coordinate data; Step S5: Acquire the drone flight related data; collect the inspection area terrain data based on GIS technology and the inspection point three-dimensional coordinate data to generate the inspection area terrain data; perform cruise point comprehensive constraint range data based on the drone flight related data and the inspection area terrain data to generate the cruise point comprehensive constraint range data; perform cruise node three-dimensional coordinate design on the inspection point three-dimensional coordinate data based on the cruise point comprehensive constraint range data to generate the cruise point three-dimensional coordinate data; Step S6: Perform optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data.

[0005] The present invention uses a laser rangefinder to collect the latitude and longitude data of the tower head, which can greatly improve the accuracy of the geographic location data. Compared with traditional GPS measurement, laser ranging technology can provide higher-precision geographic coordinate data, especially in terms of measurement accuracy in vertical height. The effective ranging range analysis of the test latitude and longitude data and the accurate determination of the optimal working range and conditions of the laser rangefinder are crucial for subsequent precise positioning and data collection, ensuring that the ranging activities are carried out under the most effective conditions, thereby minimizing data errors. The generated test latitude and longitude data provide a basis for subsequent data calibration and analysis, ensuring that the drone can perform accurate flight and inspection tasks in the correct position. The built-in monitoring equipment of the drone equipment is used to effectively identify the tower, significantly improving the recognition rate and accuracy of the tower. Especially in complex environments, high-definition image data is extremely critical for the identification and distinction of the tower. Image data collection based on the effective ranging range data ensures the efficiency of the data collection process. By pre-analyzing the ranging range, the drone can directly locate the best shooting position, reducing invalid flight and adjustment time. The precise longitude and latitude data collected by the second laser rangefinder further improves the accuracy of tower positioning compared to the preliminary laser ranging results, which is crucial for inspection tasks that require high-precision positioning. It not only improves the accuracy of positioning data, but also enhances the reliability of data, so that the UAV can accurately plan the flight path and inspection points according to the actual geographical location and tower height, ensuring the efficiency and safety of the inspection process. By obtaining the tower equipment parameters and combining the precise longitude and latitude data to build the tower's three-dimensional coordinate system, the accuracy of the inspection model is ensured to be consistent with the real world, providing an accurate reference model for inspection. The tower's three-dimensional coordinate system is used to correct and analyze the structural coordinate data, further improving the accuracy of the tower model, especially the accurate description of the tower's fine structure, laying the foundation for refined inspection. The three-dimensional coordinate calculation of the inspection point based on the corrected tower structure coordinate data ensures the precise positioning of the inspection point, enabling the UAV to conduct detailed inspections on specific structural components, improving the coverage and efficiency of the inspection. The terrain data of the inspection area collected based on GIS technology provides detailed terrain information for UAV flight, ensuring the safety and rationality of flight planning, especially in areas with complex terrain. The cruise point comprehensive constraint range data generated by combining the UAV flight association data and terrain data provides a clear operating boundary for the UAV flight and reduces the safety risks during the flight. Based on the cruise point comprehensive constraint range data, the cruise node design of the inspection point three-dimensional coordinates is carried out to optimize the UAV flight path, ensure that each inspection point can be effectively inspected, and improve flight efficiency.Optimizing the cruise path planning based on the three-dimensional coordinate data of the cruise point can minimize the flight distance and time of the drone, reduce energy consumption, and improve the efficiency of task execution. The optimized cruise path takes into account factors such as terrain, obstacles, and flight constraints to ensure the flight safety of the drone when performing inspection tasks and reduce potential collision risks. By accurately calculating and planning the cruise path, it is ensured that the drone can take pictures and collect data at the inspection point at the best position and angle.

[0006] Preferably, step S1 comprises the following steps: Step S11: using the preset laser ranging conditions to adjust the ranging parameters of the first laser rangefinder to generate an adjusted first laser rangefinder; Step S12: collecting test longitude, latitude and height data of the tower head according to the adjusted first laser rangefinder to generate test longitude, latitude and height data; Step S13: performing data cleaning processing on the test longitude and latitude height data to generate cleaned test longitude and latitude height data; Step S14: Calculate the test accuracy according to the cleaning test latitude and longitude data corresponding to the preset real latitude and longitude data to generate test accuracy evaluation data; Step S15: Perform effective distance measurement range analysis based on the test accuracy evaluation data to generate effective distance measurement range data.

[0007] The present invention can ensure that the equipment achieves optimal performance in a specific operating environment and improves the accuracy and stability of ranging by adjusting the ranging parameters of the first laser rangefinder under preset conditions. The adjusted laser rangefinder can optimize the data acquisition quality according to the needs of the actual operating environment and reduce the impact of environmental interference on the measurement results. The adjusted and optimized laser rangefinder is used to collect the test longitude and latitude data of the tower head to obtain high-precision geographical location data, providing reliable support for the accurate positioning of the tower. The optimized ranging parameters can improve the speed and efficiency of data acquisition, shorten the operation time, and improve the efficiency of the entire inspection process. The collected test longitude and latitude data are cleaned and processed to remove noise and outliers, which can ensure the data quality of subsequent analysis and application and improve the accuracy and reliability of the data. The cleaned data is closer to the actual situation, providing a solid foundation for subsequent accuracy evaluation and effective ranging range analysis. By comparing with the preset real longitude and latitude data, the test data is calculated for accuracy, the measurement accuracy of the laser rangefinder is intuitively evaluated, and the effectiveness of data acquisition is verified. The test accuracy evaluation data provides a basis for further parameter adjustment and equipment calibration of the laser rangefinder, which helps to ensure the long-term stable operation of the equipment. Based on the effective range analysis of the accuracy evaluation data, the optimal operating range of the laser rangefinder under different environmental conditions is determined, and the guiding principles for optimizing the ranging operation are optimized. The clear effective range data provides an accurate reference for the flight altitude and position of the drone, making the flight planning more in line with the actual ranging requirements, ensuring the efficiency and accuracy of the inspection activities.

[0008] Preferably, step S2 comprises the following steps: Step S21: using the built-in monitoring equipment of the drone device to collect pole tower recognition image data and generate pole tower recognition image data; Step S22: Analyze the distance between the drone and the tower according to the tower recognition image data to generate drone-tower distance data; Step S23: performing tower recognition image update processing on the tower recognition image data according to the drone-tower distance data, and obtaining updated tower recognition image data and conventional tower recognition image data respectively; Step S24: Integrate the updated pole tower recognition image data and the conventional pole tower recognition image data to generate effective pole tower recognition image data.

[0009] The present invention uses the built-in monitoring equipment of the drone device to collect high-definition images, which greatly enhances the recognition ability of the pole tower. Especially in complex environments, the real-time collected pole tower image data provides the drone with instant environmental information, which helps to quickly respond and adjust the flight path or strategy. The pole tower recognition image data is analyzed to calculate the precise distance between the drone and the pole tower to ensure that the drone can operate within a safe distance. The generated drone-tower spacing data is crucial to avoid collisions and ensure the safety of drones and equipment, especially in automatic flight mode. According to the actual distance between the drone and the pole tower, the collected image data is optimized to improve image quality and recognition accuracy. The image data obtained after the update processing is distinguished from the conventional data to select the most suitable image set for analysis according to actual needs. The updated pole tower recognition image data is integrated with the conventional image data to form a comprehensive and detailed pole tower image data set, which provides a rich source of information for subsequent recognition and analysis. The integrated effective pole tower recognition image data set improves the availability and reliability of the data, ensuring that the image processing and analysis of the inspection task are based on the most accurate and comprehensive information.

[0010] Preferably, step S23 includes the following steps: When the UAV-tower distance data is greater than the effective distance measurement range data, the UAV approach operation is performed on the UAV equipment based on the preset approach measurement strategy, and the pole tower recognition image data is updated according to the UAV approach operation to obtain updated pole tower recognition image data; When the drone-tower distance data is not greater than the effective ranging range data, the tower recognition image data is marked as regular tower recognition image data.

[0011] When the present invention executes the approach measurement strategy, the UAV automatically adjusts the flight path according to the preset strategy to ensure that it approaches the target pole tower in the safest and most effective way. This dynamic path adjustment improves the flexibility and efficiency of flight operations. The images collected by the UAV after the approach operation can obtain higher-definition pole tower images due to the closer distance, thereby greatly improving the accuracy of pole tower identification and analysis. The automated execution of this process reduces the need for manual operation, improves the automation level of inspection tasks, and also reduces the complexity of operations. The pole tower identification image data is directly marked as conventional pole tower identification image data, which simplifies the data processing process, allowing subsequent analysis to be directly based on the confirmed valid data, avoiding unnecessary approach operations, saving flight time and energy consumption, and allowing the UAV to complete more inspection tasks in the shortest time. By preliminarily screening and classifying the data at the data collection stage, the subsequent data processing steps are simplified, and the overall inspection efficiency is improved.

[0012] Preferably, step S3 comprises the following steps: Step S31: Perform dynamic visual calibration processing on the UAV equipment based on the effective tower recognition image data to generate a calibrated UAV equipment; Step S32: using the calibrated second laser rangefinder built into the UAV device to collect the target longitude and latitude data of the tower head, generating the target longitude and latitude data, and performing time series matching processing on the target longitude and latitude data to generate time series longitude and latitude data; Step S33: Analyze the available frame longitude and latitude data on the time series longitude and latitude data to generate available frame longitude and latitude data; Step S34: Analyze the precise longitude and latitude data of the tower head based on the available frame longitude and latitude data to generate precise longitude and latitude data.

[0013] The present invention performs dynamic visual calibration on the drone based on effective pole tower recognition image data to ensure that the drone's photography equipment maintains optimal performance in the pole tower inspection mission, especially in terms of focus and image stability. The dynamic visual calibration process enhances the image acquisition accuracy of the drone under various flight conditions, as well as the accuracy of the subsequent longitude and latitude data acquisition using the second seasonal light detector, especially the operational performance in complex environments or severe weather conditions. The calibrated drone uses the built-in second laser rangefinder to collect the target longitude and latitude data of the pole tower head, quickly obtains high-precision position data, improves the efficiency and accuracy of data acquisition, and after the time series matching process, the generated time series longitude and latitude data ensures the consistency of the data in the time series, provides accurate time tags for subsequent data analysis, and enhances the application value of the data. By performing available frame analysis on the time series longitude and latitude data, high-quality and reliable data frames are screened out, and abnormal data caused by flight jitter, environmental interference and other factors are excluded, ensuring the basic data quality of data analysis. The selected available frame longitude and latitude data simplifies the data processing process, reduces the calculation burden of subsequent analysis, and improves the overall processing efficiency and speed. Accurate longitude and latitude data analysis based on available frame longitude and latitude data ensures the most accurate location information of the tower head, greatly improves positioning accuracy, and provides strong support for the subsequent inspection task execution. Accurate longitude and latitude data is not only crucial for the execution of inspection tasks, but also provides key support for the analysis and decision-making of inspection results, especially in inspection activities that require delicate operations, such as close-up shooting, observation at specific angles, etc.

[0014] Preferably, step S4 comprises the following steps: Step S41: Obtain tower equipment parameters; Step S42: Using three-dimensional simulation technology to establish a three-dimensional digital model of the tower based on the tower equipment parameters to generate a three-dimensional model of the tower; Step S43: constructing a three-dimensional coordinate system of the tower according to the precise longitude, latitude and height data; Step S44: performing a pole tower rotation parameter analysis based on the pole tower three-dimensional model to generate pole tower rotation parameters, and performing a pole tower translation parameter analysis based on the pole tower three-dimensional model to generate pole tower translation parameters; Step S45: performing tower transfer parameter fitting analysis processing according to the tower rotation parameters and the tower translation parameters to generate fitting tower transfer parameters, and constructing a tower transfer matrix according to the fitting tower transfer parameters; Step S46: according to the tower transfer matrix, the three-dimensional tower model is transferred to the three-dimensional tower coordinate system for tower structure coordinate mapping processing to generate tower structure coordinate data, and defect node correction processing is performed on the tower structure coordinate data to generate corrected tower structure coordinate data; Step S47: Calculate the three-dimensional coordinates of the inspection point based on the corrected tower structure coordinate data to generate the three-dimensional coordinate data of the inspection point.

[0015] The present invention obtains the detailed parameters of the pole tower equipment to provide accurate basic information for the subsequent three-dimensional model construction, ensuring the accuracy and practicality of the model. The detailed equipment parameters help to deeply understand the design characteristics and structural complexity of the pole tower, and provide support for accurate inspection and model analysis. The three-dimensional model of the pole tower established by three-dimensional simulation technology provides an intuitive basis for the visual analysis and inspection of the pole tower. The three-dimensional model can be used for various simulation tests, including structural stress analysis and environmental impact simulation, which improves the efficiency of pole tower maintenance and improvement. The three-dimensional coordinate system of the pole tower constructed according to the precise longitude and latitude data provides an accurate reference for the spatial positioning of the pole tower, which is an important basis for subsequent analysis and correction processing, ensuring the correct position of the three-dimensional model in the actual geographic space, and improving the accuracy and practicality of the model application. The generated pole tower rotation parameters and translation parameters provide a basis for the fine adjustment of the pole tower model in three-dimensional space, ensuring the accurate alignment of the model with the actual pole tower. These parameters help to accurately calibrate the pole tower model to reflect its exact position and direction in the actual environment. Through the fitting analysis and processing of the tower transfer parameters, the position and direction of the tower model in three-dimensional space are further optimized, and the accuracy and practicality of the model are improved. The generated fitting tower transfer parameters simplify the model adjustment process and improve the efficiency and accuracy of parameter application. Using the tower transfer matrix, the tower three-dimensional model is transferred to the tower three-dimensional coordinate system for structural coordinate mapping, ensuring that every detail of the model is accurately reflected in the three-dimensional coordinate system. The defect node correction processing of the tower structure coordinate data is carried out to optimize the tower defect identification process and improve the accuracy and efficiency of inspection. The three-dimensional coordinates of the inspection point are calculated based on the corrected tower structure coordinate data, ensuring that the inspection covers all key parts of the tower without omission. The generated three-dimensional coordinate data of the inspection point provides accurate navigation for the flight planning and inspection operations of the drone, improving the inspection efficiency and the quality of data collection.

[0016] Preferably, step S5 comprises the following steps: Step S51: Acquire UAV flight related data, wherein the UAV flight related data includes flight performance specification data and measured flight data; Step S52: performing cruise point basic constraint range analysis according to the flight performance specification data and the measured flight data to generate cruise point basic constraint range data; Step S53: collecting terrain data of the inspection area based on GIS technology and the three-dimensional coordinate data of the inspection points to generate terrain data of the inspection area; Step S54: performing terrain obstacle node identification processing based on the inspection area terrain data to generate terrain obstacle node data; Step S55: performing cruise point comprehensive constraint range data according to cruise point basic constraint range data and terrain obstacle node data to generate cruise point comprehensive constraint range data; Step S56: Design the three-dimensional coordinates of the cruise node for the three-dimensional coordinate data of the inspection point according to the comprehensive constraint range data of the cruise point to generate the three-dimensional coordinate data of the cruise point.

[0017] The present invention optimizes the flight plan of the UAV by obtaining flight performance specification data and measured flight data, ensuring that the execution of the flight mission not only meets the performance characteristics of the UAV, but also takes into account the actual flight experience. Accurate flight correlation data helps to evaluate the flight safety boundary of the UAV, prevent potential flight risks, and ensure the safe execution of the inspection mission. The basic constraint range analysis of the cruise point combined with the flight performance specification data and the measured flight data can ensure that the planned flight path and cruise point setting meet the actual flight capability of the UAV, increase the rationality and feasibility of the flight path, and through accurate basic constraint range analysis, effectively avoid the potential safety hazards encountered in the flight of the UAV, optimize the flight path, and thus improve the efficiency of task execution. The terrain data of the inspection area collected by GIS technology can accurately identify obstacles in the environment, such as mountains, buildings, etc., and provide important environmental information for flight planning. Based on accurate terrain data, the flight path planning of the UAV can be more accurate, avoid unnecessary detours and flight risks, and ensure flight efficiency and safety. Obstacle node identification processing of terrain data can provide early warning for UAV flight and avoid collision with obstacles during flight. By identifying and marking terrain obstacle nodes, the location of the cruise point can be determined more accurately, ensuring that the cruise point avoids these obstacle areas and improving inspection efficiency and safety. The cruise point comprehensive constraint range data generated by combining the basic constraint range data of the cruise point with the terrain obstacle node data can fully consider various constraints in the UAV flight and provide a comprehensive reference for the setting of the cruise point. The determination of the comprehensive constraint range helps to avoid potential flight risks and ensure that the UAV flies within a safe range while achieving the purpose of inspection. The three-dimensional coordinate design of the cruise node based on the comprehensive constraint range data ensures that each inspection point can be accurately covered by the UAV, greatly improving the accuracy and quality of the inspection, reducing energy consumption, improving flight efficiency, and ensuring the efficient completion of the inspection task.

[0018] Preferably, step S52 includes the following steps: Perform measured flight capability data analysis based on flight performance specification data and measured flight data to generate measured flight capability data; The basic constraint range of the cruise point is designed according to the measured flight capability data, and the basic constraint range data of the cruise point is generated.

[0019] The present invention analyzes the flight performance specification data and the measured flight data to generate measured flight capability data that can accurately confirm the current flight performance of the UAV, including key performance indicators such as maximum flight distance, altitude, and speed. This ensures that flight planning is based on the most accurate and up-to-date performance data. The analysis process helps to identify the difference between the manufacturer's specifications and the actual flight performance, and provides a basis for adjusting the flight plan and improving the configuration of the UAV. The cruise point basic constraint range designed according to the measured flight capability data takes into account the actual flight capability of the UAV and ensures that the cruise point is set within the safe flight range of the UAV. This reduces safety risks during flight. Through the accurately designed cruise point basic constraint range, the flight path of the UAV can be optimized to ensure that the flight mission is both efficient and energy-saving. For example, it prevents the UAV from flying to areas beyond its flight capability range, reducing unnecessary energy consumption and flight time.

[0020] Preferably, step S54 includes the following steps: Based on the preset convolutional neural network algorithm, terrain obstacle recognition and classification processing are performed on the terrain data of the inspection area to generate terrain obstacle data; The terrain data of the inspection area is processed for terrain obstacle node identification according to the terrain obstacle data to generate terrain obstacle node data.

[0021] The present invention uses a preset convolutional neural network (CNN) algorithm to automatically identify and classify terrain obstacles in the inspection area. This automated processing greatly improves the speed and efficiency of data processing. Compared with traditional manual identification methods, it can process more data in a shorter time. Convolutional neural networks are widely used due to their high accuracy in image recognition and classification. By processing the terrain data of the inspection area through this advanced algorithm, various terrain obstacles, such as mountains, buildings, trees, etc., can be more accurately identified, thereby improving the accuracy of obstacle identification. The terrain obstacle node identification processing performed according to the terrain obstacle data provides accurate terrain obstacle information for the planning of the UAV flight path, ensuring that the flight planning can effectively avoid these obstacles and reduce the risk of collision during the flight. The generated terrain obstacle node data makes the UAV flight safer, reduces potential flight accidents by avoiding obstacles in flight, and improves the safety of the UAV in performing inspection tasks. The clear identification of terrain obstacle nodes also helps to optimize the flight route of the UAV, bypass obstacles by selecting the best flight path, reduce flight time, and improve the overall efficiency and effect of the inspection.

[0022] Preferably, step S6 comprises the following steps: Step S61: using a path planning algorithm to perform cruise path node sequence planning processing on the cruise point three-dimensional coordinate data to generate cruise path node sequence data; Step S62: performing cruise path planning processing on the cruise point three-dimensional coordinate data according to the cruise path node sequence data to generate cruise path data; Step S63: Acquire real-time environmental impact data; Step S64: performing a weighted influencing factor analysis on the cruise path data according to the real-time environmental impact data to generate weighted influencing cruise path data; Step S65: Utilize a genetic algorithm to perform cruise path optimization and adjustment processing on the weighted cruise path data to generate optimized cruise path data.

[0023] The present invention uses a path planning algorithm to process the three-dimensional coordinate data of the cruise point. The generated cruise path node sequence data ensures that the route of the drone inspection is optimized, and each inspection point is visited in the most effective order. By optimizing the cruise path node sequence, the drone can complete all inspection tasks in the shortest time, thereby improving the inspection efficiency and reducing energy consumption. The cruise path is planned for the three-dimensional coordinate data of the cruise point according to the cruise path node sequence data. The generated cruise path data ensures the continuity and logic of the drone flight, avoids invalid flight and repeated coverage, and the carefully planned cruise path reduces safety hazards during flight, such as avoiding flying into a no-fly zone or a dangerous area, ensuring the safe execution of the drone inspection task. The acquired real-time environmental impact data (such as weather conditions, wind speed, etc.) enables the cruise path planning to adapt to real-time environmental changes, improves the adaptability and flexibility of the inspection task, and the application of real-time environmental data enables the flight plan to be adjusted in real time according to the actual situation to cope with sudden environmental changes. By analyzing the weighted influencing factors of the cruise path data, it is possible to identify and manage the risks that may be encountered during the flight, such as adjusting the flight altitude to avoid strong wind areas. The weighted impact analysis makes the cruise path planning more personalized and tailors the most suitable flight strategy according to the actual environmental conditions. By optimizing and adjusting the weighted influencing cruise path data using genetic algorithms, it is possible to find the most efficient cruise path under the current environmental conditions, ensuring the maximum efficiency of the UAV flight. The optimized cruise path data not only takes into account flight safety and efficiency, but also adapts to the complex and changeable actual environmental conditions, improving the success rate and reliability of the inspection mission.

[0024] The beneficial effect of the present application is that the three-dimensional spatial positioning and path planning method of a drone pole tower of the present invention aims to improve the accuracy and efficiency of drones in three-dimensional spatial positioning and path planning of pole tower structures by integrating multiple sensing technologies. By integrating multiple sensor data such as visual recognition, laser ranging, GPS, etc., accurate positioning can be achieved in complex and changeable environments to avoid accidents such as collisions caused by drones. Comprehensive consideration of multiple sensing technologies enables accurate positioning of the three-dimensional positioning information of drone pole towers, and path planning is performed based on these data, thereby significantly improving the efficiency and safety of drone inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the steps of a three-dimensional spatial positioning and path planning method for a drone tower according to the present invention; Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S6 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0028] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0029] To achieve this, please refer to Figures 1 to 3 The present invention provides a three-dimensional spatial positioning and path planning method for a drone tower, comprising the following steps: Step S1: collecting test longitude and latitude data of the tower head according to the preset laser ranging conditions and the first laser rangefinder to generate test longitude and latitude data; performing effective ranging range data analysis based on the test longitude and latitude data to generate effective ranging range data; Step S2: Collect effective pole tower recognition image data according to the effective ranging range data and the monitoring equipment built into the drone device to generate effective pole tower recognition image data; Step S3: Based on the effective tower recognition image data and the second laser rangefinder built into the UAV equipment, accurate longitude and latitude data are collected to generate accurate longitude and latitude data; Step S4: Obtain tower equipment parameters; construct a tower three-dimensional coordinate system based on precise longitude and latitude data; perform correction tower structure coordinate data analysis based on tower equipment parameters and tower three-dimensional coordinate system to generate correction tower structure coordinate data; perform inspection point three-dimensional coordinate calculation based on correction tower structure coordinate data to generate inspection point three-dimensional coordinate data; Step S5: Acquire the drone flight related data; collect the inspection area terrain data based on GIS technology and the inspection point three-dimensional coordinate data to generate the inspection area terrain data; perform cruise point comprehensive constraint range data based on the drone flight related data and the inspection area terrain data to generate the cruise point comprehensive constraint range data; perform cruise node three-dimensional coordinate design on the inspection point three-dimensional coordinate data based on the cruise point comprehensive constraint range data to generate the cruise point three-dimensional coordinate data; Step S6: Perform optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data.

[0030] The present invention uses a laser rangefinder to collect the latitude and longitude data of the tower head, which can greatly improve the accuracy of the geographic location data. Compared with traditional GPS measurement, laser ranging technology can provide higher-precision geographic coordinate data, especially in terms of measurement accuracy in vertical height. The effective ranging range analysis of the test latitude and longitude data and the accurate determination of the optimal working range and conditions of the laser rangefinder are crucial for subsequent precise positioning and data collection, ensuring that the ranging activities are carried out under the most effective conditions, thereby minimizing data errors. The generated test latitude and longitude data provide a basis for subsequent data calibration and analysis, ensuring that the drone can perform accurate flight and inspection tasks in the correct position. The built-in monitoring equipment of the drone equipment is used to effectively identify the tower, significantly improving the recognition rate and accuracy of the tower. Especially in complex environments, high-definition image data is extremely critical for the identification and distinction of the tower. Image data collection based on the effective ranging range data ensures the efficiency of the data collection process. By pre-analyzing the ranging range, the drone can directly locate the best shooting position, reducing invalid flight and adjustment time. The precise longitude and latitude data collected by the second laser rangefinder further improves the accuracy of tower positioning compared to the preliminary laser ranging results, which is crucial for inspection tasks that require high-precision positioning. It not only improves the accuracy of positioning data, but also enhances the reliability of data, so that the UAV can accurately plan the flight path and inspection points according to the actual geographical location and tower height, ensuring the efficiency and safety of the inspection process. By obtaining the tower equipment parameters and combining the precise longitude and latitude data to build the tower's three-dimensional coordinate system, the accuracy of the inspection model is ensured to be consistent with the real world, providing an accurate reference model for inspection. The tower's three-dimensional coordinate system is used to correct and analyze the structural coordinate data, further improving the accuracy of the tower model, especially the accurate description of the tower's fine structure, laying the foundation for refined inspection. The three-dimensional coordinate calculation of the inspection point based on the corrected tower structure coordinate data ensures the precise positioning of the inspection point, enabling the UAV to conduct detailed inspections on specific structural components, improving the coverage and efficiency of the inspection. The terrain data of the inspection area collected based on GIS technology provides detailed terrain information for UAV flight, ensuring the safety and rationality of flight planning, especially in areas with complex terrain. The cruise point comprehensive constraint range data generated by combining the UAV flight association data and terrain data provides a clear operating boundary for the UAV flight and reduces the safety risks during the flight. Based on the cruise point comprehensive constraint range data, the cruise node design of the inspection point three-dimensional coordinates is carried out to optimize the UAV flight path, ensure that each inspection point can be effectively inspected, and improve flight efficiency.Optimizing the cruise path planning based on the three-dimensional coordinate data of the cruise point can minimize the flight distance and time of the drone, reduce energy consumption, and improve the efficiency of task execution. The optimized cruise path takes into account factors such as terrain, obstacles, and flight constraints to ensure the flight safety of the drone when performing inspection tasks and reduce potential collision risks. By accurately calculating and planning the cruise path, it is ensured that the drone can take pictures and collect data at the inspection point at the best position and angle.

[0031] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a three-dimensional spatial positioning and path planning method of a drone tower of the present invention. In this embodiment, the three-dimensional spatial positioning and path planning method of the drone tower includes the following steps: A three-dimensional spatial positioning and path planning method for a drone tower comprises the following steps: Step S1: collecting test longitude and latitude data of the tower head according to the preset laser ranging conditions and the first laser rangefinder to generate test longitude and latitude data; performing effective ranging range data analysis based on the test longitude and latitude data to generate effective ranging range data; In the embodiment of the present invention, according to the environmental conditions and the characteristics of the pole tower, the ranging conditions of the first laser rangefinder are preset, such as the ranging distance, angle and ranging accuracy, etc., and the test longitude and latitude data of the pole tower head are collected according to the preset laser ranging conditions and the first laser rangefinder, and the longitude and latitude data of the pole tower head are collected at different distances and angles by using laser ranging, and the collected data are cleaned, abnormal values ​​are removed, and effective measurement points are screened to generate test longitude and latitude data, and all test longitude and latitude data are compared with the actual known longitude and latitude data of the pole tower, and the measurement accuracy is evaluated, and a reasonable measurement range is set according to the statistical results of the ranging data. And combined with the specific needs of the application, such as the minimum and maximum distance restrictions, the measurement range is finally determined to generate effective ranging range data.

[0032] Step S2: Collect effective pole tower recognition image data according to the effective ranging range data and the monitoring equipment built into the drone device to generate effective pole tower recognition image data; In the embodiment of the present invention, according to the effective ranging range data, the parameters (such as focal length, exposure, ISO, etc.) of the built-in monitoring equipment of the UAV device are adjusted to adapt to different ambient light and distances to ensure image quality, so that the effective ranging range data is set in advance to meet the threshold value of the measurement condition, such as the maximum allowable distance between the measurement point and the UAV. When the distance between the laser ranging measurement point and the current position of the UAV is greater than the threshold, the approach measurement strategy is started, and the coordinates of the measurement point are used as the approach flight target point to command the UAV to fly to the target point. The above-mentioned laser ranging method is repeated during the flight until the measurement is stopped when the measurement condition is met, and effective tower recognition image data is generated.

[0033] Step S3: Based on the effective tower recognition image data and the second laser rangefinder built into the UAV equipment, accurate longitude and latitude data are collected to generate accurate longitude and latitude data; In the embodiment of the present invention, based on the image recognition technology, the tower head target is identified in the real-time video stream shot by the monitoring device built into the drone device. Combined with the attitude information of the drone and the pan-tilt control algorithm, the drone pan-tilt and the drone yaw angle are automatically adjusted so that the target tower head is always in the middle of the frame. At this time, the laser rangefinder is started to measure the distance to the tower head. Based on the shooting frame rate (33ms / time), multiple measurement point data are continuously acquired, and the collected longitude and latitude data are processed in time series matching to ensure the consistency and integrity of the data. The time series matching process helps to identify and remove abnormal data points that may appear during the measurement process, and the data obtained from multiple measurements are comprehensively analyzed, the abnormal values ​​are removed, the average value is calculated, and accurate longitude and latitude data are generated, which ensures the accuracy and reliability of the data and provides a solid foundation for the subsequent three-dimensional coordinate construction and path planning of the tower. Through the screened measurement points, the actual three-dimensional coordinates of the tower head and other parameter values ​​are calculated to generate accurate longitude and latitude data.

[0034] Step S4: Obtain tower equipment parameters; construct a tower three-dimensional coordinate system based on precise longitude and latitude data; perform correction tower structure coordinate data analysis based on tower equipment parameters and tower three-dimensional coordinate system to generate correction tower structure coordinate data; perform inspection point three-dimensional coordinate calculation based on correction tower structure coordinate data to generate inspection point three-dimensional coordinate data; In an embodiment of the present invention, detailed parameters of the pole tower, including height, shape, size, components, etc., are collected, and these parameters are obtained by analyzing the pole tower design drawings or using field measured data. Using precise longitude and latitude data, a three-dimensional coordinate system of the pole tower is established with the tower head as the reference point, and geographic information system (GIS) technology is used to assist in the precise construction of the coordinate system. Using the pole tower equipment parameters and three-dimensional coordinate system data, the pole tower model is rotated and translated to match the spatial position of the actual pole tower, and the model is adjusted using three-dimensional modeling software to ensure that the pole tower model completely corresponds to the actual structure. Based on the corrected pole tower structure coordinate data, the three-dimensional coordinates of the key structural points that need to be covered by the inspection are calculated, and professional software or written algorithms are used to automatically identify and mark the locations of the inspection points.

[0035] Step S5: Acquire the drone flight related data; collect the inspection area terrain data based on GIS technology and the inspection point three-dimensional coordinate data to generate the inspection area terrain data; perform cruise point comprehensive constraint range data based on the drone flight related data and the inspection area terrain data to generate the cruise point comprehensive constraint range data; perform cruise node three-dimensional coordinate design on the inspection point three-dimensional coordinate data based on the cruise point comprehensive constraint range data to generate the cruise point three-dimensional coordinate data; In an embodiment of the present invention, the flight performance specification data and measured flight data of the UAV are collected, including flight speed, maximum flight altitude, endurance time, etc., and these data are analyzed to determine the flight capabilities and limitations of the UAV. GIS technology and three-dimensional coordinate data of inspection points are used to collect detailed terrain data of the inspection area, such as height, slope, existing obstacles, etc., and terrain analysis tools are used to identify terrain features that may affect the flight of the UAV. Combined with the UAV flight capability and terrain obstacle information, the basic and comprehensive constraints of the cruise point are designed, such as safe height, obstacle avoidance distance, etc., and the inspection area is subjected to comprehensive constraint analysis using analysis software to determine the optimal layout of the cruise point. According to the comprehensive constraint range data of the cruise point, the space around the inspection point is specifically designed for the cruise node to ensure that the UAV can effectively cover each inspection point, and the flight route of the UAV is designed using the path planning algorithm to ensure safe and efficient passage through all cruise points.

[0036] Step S6: Perform optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data.

[0037] In an embodiment of the present invention, a path planning algorithm, such as an A* algorithm or a Dijkstra algorithm, is used to perform node sequence planning on the three-dimensional coordinate data of the cruise point, determine the optimal flight order, consider the flight capability and safety requirements of the drone, and terrain obstacles, and generate cruise path node sequence data. According to the node sequence data, an efficient path connecting all cruise points is planned, considering avoiding terrain obstacles and flight restricted areas, and using computer-aided design (CAD) software or professional path planning tools for path design. Real-time weather conditions, airspace restrictions and other environmental impact data are collected to evaluate their impact on the predetermined flight path, and combined with real-time data, the flight plan is adjusted to cope with unforeseen environmental changes. The collected real-time environmental data is weighted and analyzed to evaluate its specific impact on the cruise path, such as the obstacle avoidance distance that needs to be increased, the flight altitude that needs to be adjusted, etc., and optimization techniques such as genetic algorithms are applied to dynamically optimize the cruise path according to weighted influencing factors to generate the final optimized cruise path data.

[0038] Preferably, step S1 comprises the following steps: Step S11: using the preset laser ranging conditions to adjust the ranging parameters of the first laser rangefinder to generate an adjusted first laser rangefinder; Step S12: collecting test longitude, latitude and height data of the tower head according to the adjusted first laser rangefinder to generate test longitude, latitude and height data; Step S13: performing data cleaning processing on the test longitude and latitude height data to generate cleaned test longitude and latitude height data; Step S14: Calculate the test accuracy according to the cleaning test latitude and longitude data corresponding to the preset real latitude and longitude data to generate test accuracy evaluation data; Step S15: Perform effective distance measurement range analysis based on the test accuracy evaluation data to generate effective distance measurement range data.

[0039] The present invention can ensure that the equipment achieves optimal performance in a specific operating environment and improves the accuracy and stability of ranging by adjusting the ranging parameters of the first laser rangefinder under preset conditions. The adjusted laser rangefinder can optimize the data acquisition quality according to the needs of the actual operating environment and reduce the impact of environmental interference on the measurement results. The adjusted and optimized laser rangefinder is used to collect the test longitude and latitude data of the tower head to obtain high-precision geographical location data, providing reliable support for the accurate positioning of the tower. The optimized ranging parameters can improve the speed and efficiency of data acquisition, shorten the operation time, and improve the efficiency of the entire inspection process. The collected test longitude and latitude data are cleaned and processed to remove noise and outliers, which can ensure the data quality of subsequent analysis and application and improve the accuracy and reliability of the data. The cleaned data is closer to the actual situation, providing a solid foundation for subsequent accuracy evaluation and effective ranging range analysis. By comparing with the preset real longitude and latitude data, the test data is calculated for accuracy, the measurement accuracy of the laser rangefinder is intuitively evaluated, and the effectiveness of data acquisition is verified. The test accuracy evaluation data provides a basis for further parameter adjustment and equipment calibration of the laser rangefinder, which helps to ensure the long-term stable operation of the equipment. Based on the effective range analysis of the accuracy evaluation data, the optimal operating range of the laser rangefinder under different environmental conditions is determined, and the guiding principles for optimizing the ranging operation are optimized. The clear effective range data provides an accurate reference for the flight altitude and position of the drone, making the flight planning more in line with the actual ranging requirements, ensuring the efficiency and accuracy of the inspection activities.

[0040] In the embodiment of the present invention, the environmental conditions of the target tower, such as distance, height and surrounding environment, are evaluated to determine the optimal laser ranging conditions. According to the laser ranging conditions, the parameters of the first laser rangefinder, including the transmission power, receiving sensitivity, ranging frequency, etc., are adjusted to meet the measurement requirements of the tower head. A series of pre-tests are performed to verify whether the adjusted laser rangefinder parameters meet the expected measurement effect and ensure the ranging accuracy and stability. The first laser rangefinder is a laser tester device for testing. According to the approximate position of the tower, the tower head is accurately positioned and aligned to ensure that the measurement line of the laser rangefinder is accurate. The adjusted first laser rangefinder is started to perform continuous or multi-point measurement on the tower head, and the longitude, latitude and height data of each measurement point are recorded. The collected data is preliminarily screened to remove obvious outliers or erroneous data to generate test longitude, latitude and height data. The test data is further analyzed using data processing software, and erroneous data caused by equipment errors, environmental interference, etc. are identified and eliminated. The remaining data is smoothed to reduce the impact of random errors, improve the reliability of the data, and generate clean test longitude, latitude and height data. Compare and analyze the cleaned test latitude and longitude data with the preset real latitude and longitude data, calculate the measurement error and accuracy, record the results of the accuracy evaluation, including the average error, maximum error and accuracy level, generate test accuracy evaluation data, and provide reference data for subsequent steps. According to the test accuracy evaluation data, analyze and determine the effective ranging range of the laser rangefinder under different environmental conditions, including the minimum and maximum effective measurement distances, evaluate the applicability of the effective ranging range in actual tower inspections, generate effective ranging range data, and ensure that the drone can obtain high-precision latitude and longitude data within this range.

[0041] Preferably, step S2 comprises the following steps: Step S21: using the built-in monitoring equipment of the drone device to collect pole tower recognition image data and generate pole tower recognition image data; Step S22: Analyze the distance between the drone and the tower according to the tower recognition image data to generate drone-tower distance data; Step S23: performing tower recognition image update processing on the tower recognition image data according to the drone-tower distance data, and obtaining updated tower recognition image data and conventional tower recognition image data respectively; Step S24: Integrate the updated pole tower recognition image data and the conventional pole tower recognition image data to generate effective pole tower recognition image data.

[0042] The present invention uses the built-in monitoring equipment of the drone device to collect high-definition images, which greatly enhances the recognition ability of the pole tower. Especially in complex environments, the real-time collected pole tower image data provides the drone with instant environmental information, which helps to quickly respond and adjust the flight path or strategy. The pole tower recognition image data is analyzed to calculate the precise distance between the drone and the pole tower to ensure that the drone can operate within a safe distance. The generated drone-tower spacing data is crucial to avoid collisions and ensure the safety of drones and equipment, especially in automatic flight mode. According to the actual distance between the drone and the pole tower, the collected image data is optimized to improve image quality and recognition accuracy. The image data obtained after the update processing is distinguished from the conventional data to select the most suitable image set for analysis according to actual needs. The updated pole tower recognition image data is integrated with the conventional image data to form a comprehensive and detailed pole tower image data set, which provides a rich source of information for subsequent recognition and analysis. The integrated effective pole tower recognition image data set improves the availability and reliability of the data, ensuring that the image processing and analysis of the inspection task are based on the most accurate and comprehensive information.

[0043] In the embodiment of the present invention, according to the flight altitude and the position of the target pole tower, the parameters (such as focal length, exposure, angle) of the built-in monitoring equipment of the drone equipment are adjusted to ensure a clear pole tower image. The drone shoots the pole tower at multiple angles through the built-in monitoring equipment on the predetermined flight trajectory to obtain sufficient image data to cover all aspects of the pole tower. The captured images are preliminarily screened in real time or afterwards to eliminate images of poor quality (such as blurred, overexposed or severely blocked images). The pole tower recognition image data is analyzed by using an image processing algorithm to estimate the relative position and distance between the drone and the pole tower. Based on the image analysis results, the actual distance between the drone and the pole tower is calculated and recorded to generate drone-tower spacing data; according to the drone-tower spacing data, it is determined whether the distance exceeds the preset effective ranging range. If it exceeds the effective range, the approach measurement strategy is executed to adjust the drone position to shorten the distance. After adjusting the position, the pole tower image data is re-collected to obtain updated pole tower recognition image data. For the case where the effective range is not exceeded, the original image data is retained as conventional pole tower recognition image data. Distinguish between updated pole tower identification image data and regular pole tower identification image data to ensure accurate classification of data, merge updated image data and regular image data, use data processing algorithms to optimize data sets, ensure that non-duplicate and highest quality images are selected, compile the screened and integrated image data into a valid pole tower identification image data set to provide accurate visual information support for subsequent steps.

[0044] Preferably, step S23 includes the following steps: When the UAV-tower distance data is greater than the effective distance measurement range data, the UAV approach operation is performed on the UAV equipment based on the preset approach measurement strategy, and the pole tower recognition image data is updated according to the UAV approach operation to obtain updated pole tower recognition image data; When the drone-tower distance data is not greater than the effective ranging range data, the tower recognition image data is marked as regular tower recognition image data.

[0045] When the present invention executes the approach measurement strategy, the UAV automatically adjusts the flight path according to the preset strategy to ensure that it approaches the target pole tower in the safest and most effective way. This dynamic path adjustment improves the flexibility and efficiency of flight operations. The images collected by the UAV after the approach operation can obtain higher-definition pole tower images due to the closer distance, thereby greatly improving the accuracy of pole tower identification and analysis. The automated execution of this process reduces the need for manual operation, improves the automation level of inspection tasks, and also reduces the complexity of operations. The pole tower identification image data is directly marked as conventional pole tower identification image data, which simplifies the data processing process, allowing subsequent analysis to be directly based on the confirmed valid data, avoiding unnecessary approach operations, saving flight time and energy consumption, and allowing the UAV to complete more inspection tasks in the shortest time. By preliminarily screening and classifying the data at the data collection stage, the subsequent data processing steps are simplified, and the overall inspection efficiency is improved.

[0046] Preferably, step S3 comprises the following steps: Step S31: Perform dynamic visual calibration processing on the UAV equipment based on the effective tower recognition image data to generate a calibrated UAV equipment; Step S32: using the calibrated second laser rangefinder built into the UAV device to collect the target longitude and latitude data of the tower head, generating the target longitude and latitude data, and performing time series matching processing on the target longitude and latitude data to generate time series longitude and latitude data; Step S33: Analyze the available frame longitude and latitude data on the time series longitude and latitude data to generate available frame longitude and latitude data; Step S34: Analyze the precise longitude and latitude data of the tower head based on the available frame longitude and latitude data to generate precise longitude and latitude data.

[0047] The present invention performs dynamic visual calibration on the drone based on effective pole tower recognition image data to ensure that the drone's photography equipment maintains optimal performance in the pole tower inspection mission, especially in terms of focus and image stability. The dynamic visual calibration process enhances the image acquisition accuracy of the drone under various flight conditions, as well as the accuracy of the subsequent longitude and latitude data acquisition using the second seasonal light detector, especially the operational performance in complex environments or severe weather conditions. The calibrated drone uses the built-in second laser rangefinder to collect the target longitude and latitude data of the pole tower head, quickly obtains high-precision position data, improves the efficiency and accuracy of data acquisition, and after the time series matching process, the generated time series longitude and latitude data ensures the consistency of the data in the time series, provides accurate time tags for subsequent data analysis, and enhances the application value of the data. By performing available frame analysis on the time series longitude and latitude data, high-quality and reliable data frames are screened out, and abnormal data caused by flight jitter, environmental interference and other factors are excluded, ensuring the basic data quality of data analysis. The selected available frame longitude and latitude data simplifies the data processing process, reduces the calculation burden of subsequent analysis, and improves the overall processing efficiency and speed. Accurate longitude and latitude data analysis based on available frame longitude and latitude data ensures the most accurate location information of the tower head, greatly improves positioning accuracy, and provides strong support for the subsequent inspection task execution. Accurate longitude and latitude data is not only crucial for the execution of inspection tasks, but also provides key support for the analysis and decision-making of inspection results, especially in inspection activities that require delicate operations, such as close-up shooting, observation at specific angles, etc.

[0048] In the embodiment of the present invention, the collected effective pole tower recognition image data is analyzed to identify key feature points in the image, such as the outline of the pole tower, the tower head and other features. According to the image data analysis results, the parameters of the built-in monitoring equipment of the drone equipment, such as focal length, angle, exposure, etc., are dynamically adjusted to improve the recognition accuracy and clarity of the image. After the parameter adjustment is completed, a series of calibration tests are performed to ensure that the monitoring equipment can accurately capture the image of the pole tower and generate a calibrated drone equipment. The second laser rangefinder built into the calibrated drone equipment is used to accurately locate and measure the tower head of the pole tower, collect the target longitude and latitude data, and perform time-series matching processing on the collected target longitude and latitude data to ensure the consistency and integrity of the data for subsequent analysis and application. The longitude and latitude data after time-series matching are analyzed to identify and select data frames with high quality and good stability, that is, available frame longitude and latitude data, so as to perform more accurate data processing. According to the analysis results, a high-quality available frame longitude and latitude data set is screened and generated. Comprehensively analyze the selected available frame latitude, longitude and height data, identify latitude and longitude data with higher precision through comparison, averaging or other statistical methods, improve the accuracy and reliability of the data, and generate precise latitude and longitude data of the tower head.

[0049] Preferably, step S4 comprises the following steps: Step S41: Obtain tower equipment parameters; Step S42: Using three-dimensional simulation technology to establish a three-dimensional digital model of the tower based on the tower equipment parameters to generate a three-dimensional model of the tower; Step S43: constructing a three-dimensional coordinate system of the tower according to the precise longitude, latitude and height data; Step S44: performing a pole tower rotation parameter analysis based on the pole tower three-dimensional model to generate pole tower rotation parameters, and performing a pole tower translation parameter analysis based on the pole tower three-dimensional model to generate pole tower translation parameters; Step S45: performing tower transfer parameter fitting analysis processing according to the tower rotation parameters and the tower translation parameters to generate fitting tower transfer parameters, and constructing a tower transfer matrix according to the fitting tower transfer parameters; Step S46: according to the tower transfer matrix, the three-dimensional tower model is transferred to the three-dimensional tower coordinate system for tower structure coordinate mapping processing to generate tower structure coordinate data, and defect node correction processing is performed on the tower structure coordinate data to generate corrected tower structure coordinate data; Step S47: Calculate the three-dimensional coordinates of the inspection point based on the corrected tower structure coordinate data to generate the three-dimensional coordinate data of the inspection point.

[0050] The present invention obtains the detailed parameters of the pole tower equipment to provide accurate basic information for the subsequent three-dimensional model construction, ensuring the accuracy and practicality of the model. The detailed equipment parameters help to deeply understand the design characteristics and structural complexity of the pole tower, and provide support for accurate inspection and model analysis. The three-dimensional model of the pole tower established by three-dimensional simulation technology provides an intuitive basis for the visual analysis and inspection of the pole tower. The three-dimensional model can be used for various simulation tests, including structural stress analysis and environmental impact simulation, which improves the efficiency of pole tower maintenance and improvement. The three-dimensional coordinate system of the pole tower constructed according to the precise longitude and latitude data provides an accurate reference for the spatial positioning of the pole tower, which is an important basis for subsequent analysis and correction processing, ensuring the correct position of the three-dimensional model in the actual geographic space, and improving the accuracy and practicality of the model application. The generated pole tower rotation parameters and translation parameters provide a basis for the fine adjustment of the pole tower model in three-dimensional space, ensuring the accurate alignment of the model with the actual pole tower. These parameters help to accurately calibrate the pole tower model to reflect its exact position and direction in the actual environment. Through the fitting analysis and processing of the tower transfer parameters, the position and direction of the tower model in three-dimensional space are further optimized, and the accuracy and practicality of the model are improved. The generated fitting tower transfer parameters simplify the model adjustment process and improve the efficiency and accuracy of parameter application. Using the tower transfer matrix, the tower three-dimensional model is transferred to the tower three-dimensional coordinate system for structural coordinate mapping, ensuring that every detail of the model is accurately reflected in the three-dimensional coordinate system. The defect node correction processing of the tower structure coordinate data is carried out to optimize the tower defect identification process and improve the accuracy and efficiency of inspection. The three-dimensional coordinates of the inspection point are calculated based on the corrected tower structure coordinate data, ensuring that the inspection covers all key parts of the tower without omission. The generated three-dimensional coordinate data of the inspection point provides accurate navigation for the flight planning and inspection operations of the drone, improving the inspection efficiency and the quality of data collection.

[0051] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes: Step S41: Obtain tower equipment parameters; In the embodiment of the present invention, detailed parameters of the tower, including height, shape, size, components, etc., are collected, and these parameters are obtained by analyzing the tower design drawings or using field measured data. Step S42: Using three-dimensional simulation technology to establish a three-dimensional digital model of the tower based on the tower equipment parameters to generate a three-dimensional model of the tower; In the embodiment of the present invention, professional 3D modeling software, such as Autodesk 3ds Max, Maya or SolidWorks, is selected to create a preliminary model of the tower in the 3D modeling software according to the tower equipment parameters, and the details of the model are adjusted to ensure that the size and shape of each part are consistent with the actual tower, especially the connection parts and complex structures of the tower are accurately simulated to generate a 3D model of the tower.

[0052] Step S43: constructing a three-dimensional coordinate system of the tower according to the precise longitude, latitude and height data; In the embodiment of the present invention, the pole tower vertex (the node corresponding to the precise longitude and latitude height data) or other position suitable as a reference point is selected as the origin of the three-dimensional coordinate system, the directions of the X, Y, and Z axes of the three-dimensional coordinate system are determined, the ground on which the pole tower stands is taken as the XY plane, and the vertical direction is taken as the Z axis. The precise longitude and latitude height data are input into the modeling software to construct the three-dimensional coordinate system of the pole tower so that its position in the three-dimensional space is consistent with the actual one.

[0053] Step S44: performing a pole tower rotation parameter analysis based on the pole tower three-dimensional model to generate pole tower rotation parameters, and performing a pole tower translation parameter analysis based on the pole tower three-dimensional model to generate pole tower translation parameters; In the embodiment of the present invention, by comparing the pole tower model with the field GPS data, the angle that the model needs to rotate around the Z axis is analyzed to ensure the correctness of the model direction, and the rotation angle is calculated using the built-in tool of the software, and the rotation parameters are recorded. According to the three-dimensional coordinate system of the pole tower, the distance that the three-dimensional model of the pole tower needs to move along the X, Y, and Z axes is analyzed to make the position of the model completely consistent with the field measurement data, the translation distance is calculated, and the translation parameters are recorded.

[0054] Step S45: performing tower transfer parameter fitting analysis processing according to the tower rotation parameters and the tower translation parameters to generate fitting tower transfer parameters, and constructing a tower transfer matrix according to the fitting tower transfer parameters; In the embodiment of the present invention, the rotation parameters and translation parameters of the tower are integrated to prepare for fitting analysis. Use the functions of mathematical software or 3D modeling software to fit and analyze the rotation and translation parameters, and calculate the optimal parameter combination to make the position of the tower model in 3D space as consistent as possible with the position of the actual tower. Based on the transfer parameters obtained by fitting, construct a tower transfer matrix containing interrelated rotation and translation information.

[0055] Step S46: according to the tower transfer matrix, the three-dimensional tower model is transferred to the three-dimensional tower coordinate system for tower structure coordinate mapping processing to generate tower structure coordinate data, and defect node correction processing is performed on the tower structure coordinate data to generate corrected tower structure coordinate data; In an embodiment of the present invention, in a three-dimensional modeling software, a transfer matrix is ​​applied to transfer the three-dimensional model of the pole tower to the correct three-dimensional coordinate system position, and a structural coordinate mapping process is performed on the transferred pole tower model to ensure that each part of the model is in the correct three-dimensional space position, and the mapped pole tower structure is checked to identify possible defective nodes (such as broken or twisted parts in the model), and corresponding correction processing is performed. After the correction of the defective nodes is completed, the structural coordinate data of the corrected pole tower model is recorded.

[0056] Step S47: Calculate the three-dimensional coordinates of the inspection point based on the corrected tower structure coordinate data to generate the three-dimensional coordinate data of the inspection point.

[0057] In the embodiment of the present invention, according to the function and structural characteristics of the tower, the key nodes that need to be inspected, such as insulators, connectors, etc., are identified, and these nodes are counted and marked. The corrected tower structure coordinate data is used to calculate the precise position of each inspection point in the tower three-dimensional coordinate system, and generate the inspection point three-dimensional coordinate data, including the three-dimensional coordinates of the inspection point, including the values ​​of the X, Y, and Z axes.

[0058] Preferably, step S5 comprises the following steps: Step S51: Acquire UAV flight related data, wherein the UAV flight related data includes flight performance specification data and measured flight data; Step S52: performing cruise point basic constraint range analysis according to the flight performance specification data and the measured flight data to generate cruise point basic constraint range data; Step S53: collecting terrain data of the inspection area based on GIS technology and the three-dimensional coordinate data of the inspection points to generate terrain data of the inspection area; Step S54: performing terrain obstacle node identification processing based on the inspection area terrain data to generate terrain obstacle node data; Step S55: performing cruise point comprehensive constraint range data according to cruise point basic constraint range data and terrain obstacle node data to generate cruise point comprehensive constraint range data; Step S56: Design the three-dimensional coordinates of the cruise node for the three-dimensional coordinate data of the inspection point according to the comprehensive constraint range data of the cruise point to generate the three-dimensional coordinate data of the cruise point.

[0059] The present invention optimizes the flight plan of the UAV by obtaining flight performance specification data and measured flight data, ensuring that the execution of the flight mission not only meets the performance characteristics of the UAV, but also takes into account the actual flight experience. Accurate flight correlation data helps to evaluate the flight safety boundary of the UAV, prevent potential flight risks, and ensure the safe execution of the inspection mission. The basic constraint range analysis of the cruise point combined with the flight performance specification data and the measured flight data can ensure that the planned flight path and cruise point setting meet the actual flight capability of the UAV, increase the rationality and feasibility of the flight path, and through accurate basic constraint range analysis, effectively avoid the potential safety hazards encountered in the flight of the UAV, optimize the flight path, and thus improve the efficiency of task execution. The terrain data of the inspection area collected by GIS technology can accurately identify obstacles in the environment, such as mountains, buildings, etc., and provide important environmental information for flight planning. Based on accurate terrain data, the flight path planning of the UAV can be more accurate, avoid unnecessary detours and flight risks, and ensure flight efficiency and safety. Obstacle node identification processing of terrain data can provide early warning for UAV flight and avoid collision with obstacles during flight. By identifying and marking terrain obstacle nodes, the location of the cruise point can be determined more accurately, ensuring that the cruise point avoids these obstacle areas and improving inspection efficiency and safety. The cruise point comprehensive constraint range data generated by combining the basic constraint range data of the cruise point with the terrain obstacle node data can fully consider various constraints in the UAV flight and provide a comprehensive reference for the setting of the cruise point. The determination of the comprehensive constraint range helps to avoid potential flight risks and ensure that the UAV flies within a safe range while achieving the purpose of inspection. The three-dimensional coordinate design of the cruise node based on the comprehensive constraint range data ensures that each inspection point can be accurately covered by the UAV, greatly improving the accuracy and quality of the inspection, reducing energy consumption, improving flight efficiency, and ensuring the efficient completion of the inspection task.

[0060] In an embodiment of the present invention, the flight performance specification data of the drone, including the maximum flight speed, maximum take-off weight, maximum endurance time, flight altitude limit, etc., are collected from the technical manual or product specification provided by the drone manufacturer. The measured flight data of the drone, including the flight performance data under different weather conditions and geographical environments, are collected through actual flight tests or historical flight records, so as to obtain the flight-related data of the drone. The flight performance specification data and the measured flight data of the drone are analyzed to determine the operation limit and performance boundary of the drone. According to the flight performance analysis results, the basic constraints to be considered when designing the cruise point are determined, such as safe flight altitude, obstacle avoidance distance, flight speed, etc. The terrain data of the inspection area, including terrain undulations, building heights, tree coverage areas, etc., are collected using GIS technology and satellite map services, and the three-dimensional coordinate data of the inspection point is combined with the terrain data to ensure that the location of the inspection point is accurately represented on the topographic map. The collected terrain data is deeply analyzed to identify obstacles that may affect the flight of the drone, such as tall buildings, mountains, large trees, etc., and all identified obstacles are marked on the topographic map to generate detailed terrain obstacle node data. Combine the basic constraint range data of the cruise point with the terrain obstacle node data, analyze and determine the comprehensive constraint conditions of the cruise point design, and formulate the comprehensive constraint range data of the cruise point based on the comprehensive analysis results to ensure that the setting of the cruise point is consistent with the flight capabilities of the drone and can avoid terrain obstacles. According to the comprehensive constraint range data of the cruise point and the three-dimensional coordinates of the inspection point, design the specific location of each cruise point to ensure that all inspection points can be effectively covered. For each designed cruise point position, calculate its precise coordinates in the three-dimensional coordinate system of the inspection area, record the three-dimensional coordinate data of all cruise points, and perform verification to generate the three-dimensional coordinate data of the cruise point.

[0061] Preferably, step S52 includes the following steps: Perform measured flight capability data analysis based on flight performance specification data and measured flight data to generate measured flight capability data; The basic constraint range of the cruise point is designed according to the measured flight capability data, and the basic constraint range data of the cruise point is generated.

[0062] The present invention analyzes the flight performance specification data and the measured flight data to generate measured flight capability data that can accurately confirm the current flight performance of the UAV, including key performance indicators such as maximum flight distance, altitude, and speed. This ensures that flight planning is based on the most accurate and up-to-date performance data. The analysis process helps to identify the difference between the manufacturer's specifications and the actual flight performance, and provides a basis for adjusting the flight plan and improving the configuration of the UAV. The cruise point basic constraint range designed according to the measured flight capability data takes into account the actual flight capability of the UAV and ensures that the cruise point is set within the safe flight range of the UAV. This reduces safety risks during flight. Through the accurately designed cruise point basic constraint range, the flight path of the UAV can be optimized to ensure that the flight mission is both efficient and energy-saving. For example, it prevents the UAV from flying to areas beyond its flight capability range, reducing unnecessary energy consumption and flight time.

[0063] In the embodiment of the present invention, the flight performance specification data (such as maximum take-off weight, maximum endurance time, flight speed, etc.) of the UAV and the measured flight data (such as actual flight time, speed, altitude, etc. under specific conditions) are summarized and collated, and the flight performance specification data is compared with the measured flight data to analyze the performance of the UAV in actual operation, identify possible performance differences or limiting factors, and based on the above analysis, comprehensively evaluate the actual flight capability of the UAV, including flight performance under different environmental conditions (such as wind speed, temperature), and adaptability to specific tasks (such as long-term inspections). According to the evaluation results, a detailed measured flight capability data report is generated to point out the advantages and limitations of the UAV, providing a basis for the design of the cruise point. According to the measured flight capability data, the basic flight constraints of the cruise point are defined, such as the minimum flight altitude, maximum flight distance, safe obstacle avoidance distance, etc., to ensure the safety and efficiency of the UAV flight.

[0064] Preferably, step S54 includes the following steps: Based on the preset convolutional neural network algorithm, terrain obstacle recognition and classification processing are performed on the terrain data of the inspection area to generate terrain obstacle data; The terrain data of the inspection area is processed for terrain obstacle node identification according to the terrain obstacle data to generate terrain obstacle node data.

[0065] The present invention uses a preset convolutional neural network (CNN) algorithm to automatically identify and classify terrain obstacles in the inspection area. This automated processing greatly improves the speed and efficiency of data processing. Compared with traditional manual identification methods, it can process more data in a shorter time. Convolutional neural networks are widely used due to their high accuracy in image recognition and classification. By processing the terrain data of the inspection area through this advanced algorithm, various terrain obstacles, such as mountains, buildings, trees, etc., can be more accurately identified, thereby improving the accuracy of obstacle identification. The terrain obstacle node identification processing performed according to the terrain obstacle data provides accurate terrain obstacle information for the planning of the UAV flight path, ensuring that the flight planning can effectively avoid these obstacles and reduce the risk of collision during the flight. The generated terrain obstacle node data makes the UAV flight safer, reduces potential flight accidents by avoiding obstacles in flight, and improves the safety of the UAV in performing inspection tasks. The clear identification of terrain obstacle nodes also helps to optimize the flight route of the UAV, bypass obstacles by selecting the best flight path, reduce flight time, and improve the overall efficiency and effect of the inspection.

[0066] In the embodiment of the present invention, a pre-trained convolutional neural network model suitable for image recognition and classification is selected, such as VGG-16, ResNet-50 or other deep learning models suitable for terrain feature recognition. The model selection criteria include recognition accuracy, processing speed and sensitivity to terrain features. The selected model is fine-tuned using the marked terrain obstacle image data to enhance the model's recognition ability for specific terrain obstacles. The fine-tuning process may include adjusting the last few layers of the model to adapt to specific obstacle classification tasks, and pre-processing the terrain data of the inspection area, including cropping, scaling and normalization, to meet the requirements of the convolutional neural network input. The pre-processed terrain image data is input into the convolutional neural network model to automatically recognize and classify terrain obstacles. The output result will include the type of obstacle (such as trees, buildings, mountains, etc.) and the position in the image. According to the recognition results of the CNN model, the location coordinates, type and other relevant information of each identified obstacle are sorted out, and the identified terrain obstacle data is mapped to the terrain map of the inspection area to ensure that the position of each obstacle matches the actual terrain. This step may require the support of GIS software to accurately mark the location of obstacles on the terrain map, generate detailed data records for each identified terrain obstacle node, including the precise location (latitude and longitude) of the node, obstacle type, size and possible impact assessment on drone inspections, and manually verify and optimize the generated terrain obstacle node data to correct possible identification errors or omissions.

[0067] Preferably, step S6 comprises the following steps: Step S61: using a path planning algorithm to perform cruise path node sequence planning processing on the cruise point three-dimensional coordinate data to generate cruise path node sequence data; Step S62: performing cruise path planning processing on the cruise point three-dimensional coordinate data according to the cruise path node sequence data to generate cruise path data; Step S63: Acquire real-time environmental impact data; Step S64: performing a weighted influencing factor analysis on the cruise path data according to the real-time environmental impact data to generate weighted influencing cruise path data; Step S65: Utilize a genetic algorithm to perform cruise path optimization and adjustment processing on the weighted cruise path data to generate optimized cruise path data.

[0068] The present invention uses a path planning algorithm to process the three-dimensional coordinate data of the cruise point. The generated cruise path node sequence data ensures that the route of the drone inspection is optimized, and each inspection point is visited in the most effective order. By optimizing the cruise path node sequence, the drone can complete all inspection tasks in the shortest time, thereby improving the inspection efficiency and reducing energy consumption. The cruise path is planned for the three-dimensional coordinate data of the cruise point according to the cruise path node sequence data. The generated cruise path data ensures the continuity and logic of the drone flight, avoids invalid flight and repeated coverage, and the carefully planned cruise path reduces safety hazards during flight, such as avoiding flying into a no-fly zone or a dangerous area, ensuring the safe execution of the drone inspection task. The acquired real-time environmental impact data (such as weather conditions, wind speed, etc.) enables the cruise path planning to adapt to real-time environmental changes, improves the adaptability and flexibility of the inspection task, and the application of real-time environmental data enables the flight plan to be adjusted in real time according to the actual situation to cope with sudden environmental changes. By analyzing the weighted influencing factors of the cruise path data, it is possible to identify and manage the risks that may be encountered during the flight, such as adjusting the flight altitude to avoid strong wind areas. The weighted impact analysis makes the cruise path planning more personalized and tailors the most suitable flight strategy according to the actual environmental conditions. By optimizing and adjusting the weighted influencing cruise path data using genetic algorithms, it is possible to find the most efficient cruise path under the current environmental conditions, ensuring the maximum efficiency of the UAV flight. The optimized cruise path data not only takes into account flight safety and efficiency, but also adapts to the complex and changeable actual environmental conditions, improving the success rate and reliability of the inspection mission.

[0069] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S6 in the flowchart, in this example, step S6 includes: Step S61: using a path planning algorithm to perform cruise path node sequence planning processing on the cruise point three-dimensional coordinate data to generate cruise path node sequence data; In an embodiment of the present invention, a suitable path planning algorithm is selected, such as the A* algorithm, the Dijkstra algorithm or other algorithms suitable for UAV flight planning, and the algorithm's computational efficiency and ability to adapt to complex environments are considered. The three-dimensional coordinate data of the cruise point is input into the selected path planning algorithm to ensure that all patrol points are covered. The path planning algorithm is used to calculate the optimal path node sequence connecting all cruise points, that is, to determine the order in which the UAV should visit these cruise points. According to the algorithm output, the cruise path node sequence data is generated to record the order and path of the UAV flight in detail.

[0070] Step S62: performing cruise path planning processing on the cruise point three-dimensional coordinate data according to the cruise path node sequence data to generate cruise path data; In an embodiment of the present invention, a continuous cruise path is designed based on the cruise path node sequence data to connect all nodes to ensure the continuity of the path and the feasibility of flight. When designing the cruise path, the three-dimensional coordinate data of the cruise points take into account the safe distance between the UAV and obstacles, as well as the safe area for take-off and landing, so as to plan the cruise path and generate cruise path data, including the starting and ending coordinates of each path segment, as well as the shape and length of the path.

[0071] Step S63: Acquire real-time environmental impact data; In an embodiment of the present invention, real-time environmental impact data, including wind speed, wind direction, temperature, etc., are collected through weather stations, online weather services or sensors carried by drones, and the collected environmental data are sorted and classified in preparation for impact analysis on the cruise path.

[0072] Step S64: performing a weighted influencing factor analysis on the cruise path data according to the real-time environmental impact data to generate weighted influencing cruise path data; In an embodiment of the present invention, the possible impact of real-time environmental data on the cruise path is analyzed, such as how wind speed and wind direction may change the flight speed and direction of the drone, and weighted analysis and processing are performed on the deflection angle, deflection amplitude, etc. of the cruise path according to each influencing factor to determine the degree of its impact on the cruise path so that adjustments can be made during path optimization, and weighted impact cruise path data is generated to clearly indicate which segments of the path need to be adjusted, as well as the direction and amplitude of the adjustment.

[0073] Step S65: Utilize a genetic algorithm to perform cruise path optimization and adjustment processing on the weighted cruise path data to generate optimized cruise path data.

[0074] In the embodiment of the present invention, the key parameters of the genetic algorithm, including population size, crossover probability, mutation probability, number of iterations, etc., are set to adapt to the characteristics of the cruise path optimization problem. Based on the weighted cruise path data, the initial population is generated, and each individual represents a possible cruise path solution, including the visit order of each cruise point and the path selection. The fitness function is designed to evaluate the pros and cons of the cruise path solution, and the factors considered include the comprehensive weight of the path length, flight time, energy consumption and environmental influencing factors. The fitness function is applied to each individual (cruise path solution) in the population, and its fitness is calculated to guide the subsequent selection, crossover and mutation operations. Selection is performed based on the fitness of the individual, and excellent individuals have a higher probability of being selected to produce offspring. The selected individuals produce new offspring through the crossover operation. Changes in the cruise point order or adjustments to the path selection may occur during the crossover process. The offspring individuals are mutated with a certain probability, and a new cruise point order or path selection is introduced to increase the diversity of the population. The selection, crossover and mutation operations are repeatedly performed until the termination condition is met (such as reaching the maximum number of iterations or the fitness reaches a predetermined threshold), and the individual with the highest fitness is extracted from the final population as the optimal cruise path plan, thereby generating optimized cruise path data, including the visit order of each cruise point, path selection and expected flight parameters.

[0075] The beneficial effect of the present application is that the three-dimensional spatial positioning and path planning method of a drone pole tower of the present invention aims to improve the accuracy and efficiency of drones in three-dimensional spatial positioning and path planning of pole tower structures by integrating multiple sensing technologies. By integrating multiple sensor data such as visual recognition, laser ranging, GPS, etc., accurate positioning can be achieved in complex and changeable environments to avoid accidents such as collisions caused by drones. Comprehensive consideration of multiple sensing technologies enables accurate positioning of the three-dimensional positioning information of drone pole towers, and path planning is performed based on these data, thereby significantly improving the efficiency and safety of drone inspections.

[0076] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0077] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A three-dimensional spatial positioning and path planning method for an unmanned aerial vehicle tower, characterized in that: The following steps are involved: Step S1: collecting test longitude and latitude data of the tower head according to the preset laser ranging conditions and the first laser rangefinder to generate test longitude and latitude data; performing effective ranging range data analysis based on the test longitude and latitude data to generate effective ranging range data; Step S2: Collect effective pole tower recognition image data according to the effective ranging range data and the monitoring equipment built into the drone device to generate effective pole tower recognition image data; Step S3: Based on the effective tower recognition image data and the second laser rangefinder built into the UAV equipment, accurate longitude and latitude data are collected to generate accurate longitude and latitude data; Step S4: Obtain tower equipment parameters; construct a tower three-dimensional coordinate system based on precise longitude and latitude data; perform correction tower structure coordinate data analysis based on tower equipment parameters and tower three-dimensional coordinate system to generate correction tower structure coordinate data; perform inspection point three-dimensional coordinate calculation based on correction tower structure coordinate data to generate inspection point three-dimensional coordinate data; Step S5: Acquire the drone flight related data; collect the inspection area terrain data based on GIS technology and the inspection point three-dimensional coordinate data to generate the inspection area terrain data; perform cruise point comprehensive constraint range data based on the drone flight related data and the inspection area terrain data to generate the cruise point comprehensive constraint range data; perform cruise node three-dimensional coordinate design on the inspection point three-dimensional coordinate data based on the cruise point comprehensive constraint range data to generate the cruise point three-dimensional coordinate data; Step S6: Perform optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data.

2. The three-dimensional spatial positioning and path planning method of the drone tower according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using the preset laser ranging conditions to adjust the ranging parameters of the first laser rangefinder to generate an adjusted first laser rangefinder; Step S12: collecting test longitude, latitude and height data of the tower head according to the adjusted first laser rangefinder to generate test longitude, latitude and height data; Step S13: performing data cleaning processing on the test longitude and latitude height data to generate cleaned test longitude and latitude height data; Step S14: Calculate the test accuracy according to the cleaning test latitude and longitude data corresponding to the preset real latitude and longitude data to generate test accuracy evaluation data; Step S15: Perform effective distance measurement range analysis based on the test accuracy evaluation data to generate effective distance measurement range data.

3. The three-dimensional spatial positioning and path planning method of the drone tower according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: using the built-in monitoring equipment of the drone device to collect pole tower recognition image data and generate pole tower recognition image data; Step S22: Analyze the distance between the drone and the tower according to the tower recognition image data to generate drone-tower distance data; Step S23: performing tower recognition image update processing on the tower recognition image data according to the drone-tower distance data, and obtaining updated tower recognition image data and conventional tower recognition image data respectively; Step S24: Integrate the updated pole tower recognition image data and the conventional pole tower recognition image data to generate effective pole tower recognition image data.

4. The three-dimensional spatial positioning and path planning method of the drone tower according to claim 3 is characterized in that: Step S23 includes the following steps: When the UAV-tower distance data is greater than the effective distance measurement range data, the UAV approach operation is performed on the UAV equipment based on the preset approach measurement strategy, and the pole tower recognition image data is updated according to the UAV approach operation to obtain updated pole tower recognition image data; When the drone-tower distance data is not greater than the effective ranging range data, the tower recognition image data is marked as regular tower recognition image data.

5. The three-dimensional spatial positioning and path planning method of the drone tower according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Perform dynamic visual calibration processing on the UAV equipment based on the effective tower recognition image data to generate a calibrated UAV equipment; Step S32: using the calibrated second laser rangefinder built into the UAV device to collect the target longitude and latitude data of the tower head, generating the target longitude and latitude data, and performing time series matching processing on the target longitude and latitude data to generate time series longitude and latitude data; Step S33: Analyze the available frame longitude and latitude data on the time series longitude and latitude data to generate available frame longitude and latitude data; Step S34: Analyze the precise longitude and latitude data of the tower head based on the available frame longitude and latitude data to generate precise longitude and latitude data.

6. The three-dimensional spatial positioning and path planning method of a drone tower according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Obtain tower equipment parameters; Step S42: Using three-dimensional simulation technology to establish a three-dimensional digital model of the tower based on the tower equipment parameters to generate a three-dimensional model of the tower; Step S43: constructing a three-dimensional coordinate system of the tower according to the precise longitude, latitude and height data; Step S44: performing a pole tower rotation parameter analysis based on the pole tower three-dimensional model to generate pole tower rotation parameters, and performing a pole tower translation parameter analysis based on the pole tower three-dimensional model to generate pole tower translation parameters; Step S45: performing tower transfer parameter fitting analysis processing according to the tower rotation parameters and the tower translation parameters to generate fitting tower transfer parameters, and constructing a tower transfer matrix according to the fitting tower transfer parameters; Step S46: according to the tower transfer matrix, the three-dimensional tower model is transferred to the three-dimensional tower coordinate system for tower structure coordinate mapping processing to generate tower structure coordinate data, and defect node correction processing is performed on the tower structure coordinate data to generate corrected tower structure coordinate data; Step S47: Calculate the three-dimensional coordinates of the inspection point based on the corrected tower structure coordinate data to generate the three-dimensional coordinate data of the inspection point.

7. The three-dimensional spatial positioning and path planning method of a drone tower according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Acquire UAV flight related data, wherein the UAV flight related data includes flight performance specification data and measured flight data; Step S52: performing cruise point basic constraint range analysis according to the flight performance specification data and the measured flight data to generate cruise point basic constraint range data; Step S53: collecting terrain data of the inspection area based on GIS technology and the three-dimensional coordinate data of the inspection points to generate terrain data of the inspection area; Step S54: performing terrain obstacle node identification processing based on the inspection area terrain data to generate terrain obstacle node data; Step S55: performing cruise point comprehensive constraint range data according to cruise point basic constraint range data and terrain obstacle node data to generate cruise point comprehensive constraint range data; Step S56: Design the three-dimensional coordinates of the cruise node for the three-dimensional coordinate data of the inspection point according to the comprehensive constraint range data of the cruise point to generate the three-dimensional coordinate data of the cruise point.

8. The three-dimensional spatial positioning and path planning method of the drone tower according to claim 7 is characterized in that: Step S52 includes the following steps: Perform measured flight capability data analysis based on flight performance specification data and measured flight data to generate measured flight capability data; The basic constraint range of the cruise point is designed according to the measured flight capability data, and the basic constraint range data of the cruise point is generated.

9. The three-dimensional spatial positioning and path planning method of a drone tower according to claim 7, characterized in that: Step S54 includes the following steps: Based on the preset convolutional neural network algorithm, terrain obstacle recognition and classification processing are performed on the terrain data of the inspection area to generate terrain obstacle data; The terrain data of the inspection area is processed for terrain obstacle node identification according to the terrain obstacle data to generate terrain obstacle node data.

10. The three-dimensional spatial positioning and path planning method of a drone tower according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: using a path planning algorithm to perform cruise path node sequence planning processing on the cruise point three-dimensional coordinate data to generate cruise path node sequence data; Step S62: performing cruise path planning processing on the cruise point three-dimensional coordinate data according to the cruise path node sequence data to generate cruise path data; Step S63: Acquire real-time environmental impact data; Step S64: performing a weighted influencing factor analysis on the cruise path data according to the real-time environmental impact data to generate weighted influencing cruise path data; Step S65: Utilize a genetic algorithm to perform cruise path optimization and adjustment processing on the weighted cruise path data to generate optimized cruise path data.

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