UAV flight trajectory analysis method, system and UAV for port logistics

Abstract

The present invention discloses a method, system, and drone for analyzing the flight trajectory of unmanned aerial vehicles (UAVs) used in port logistics. This method relates to the field of autonomous navigation technology and includes the following steps: collecting and processing UAV flight trajectory analysis data, analyzing the data, and optimizing and adjusting the data. By collecting and processing UAV flight trajectory analysis data, analyzing the data to obtain navigation accuracy assessment values ​​and UAV dynamic obstacle avoidance assessment values, and then comprehensively analyzing and optimizing the data, the present invention improves the real-time performance of UAV flight trajectory analysis methods for port logistics, addressing the problem of insufficient real-time performance in existing UAV flight trajectory analysis methods for port logistics.

Description

Technical Field

[0001] The present invention relates to the field of autonomous navigation technology, and in particular to a method and system for analyzing the flight trajectory of an unmanned aerial vehicle (UAV) used in port logistics, and an unmanned aerial vehicle (UAV). Background Art

[0002] As a key node in global trade, the logistics efficiency of ports directly affects the smooth progress of international trade. Traditional port logistics methods can no longer meet the needs of efficiency, accuracy and flexibility, and technological innovation is urgently needed. The introduction of drone technology can significantly improve the automation and intelligence level of port logistics, reduce operating costs and improve overall efficiency. The environment of port container terminals is complex and changeable, including strong winds, dense fog, bad weather and other conditions, which bring challenges to drone flight and data collection. These environmental factors may cause drone flight instability or even malfunction.

[0003] The existing drone flight trajectory analysis system for port logistics uses a port drone inspection path planning method based on the improved A algorithm to set the flight starting point and all task points, and then uses the improved A algorithm to plan the path of the starting point and task points; through polynomial trajectory optimization, on the basis of minimizing the snap method, the generated detection path points are preprocessed by constructing isosceles triangles.

[0004] For example, the invention patent application with publication number: CN115390586A discloses a method and system for optimizing the path of a UAV for port logistics, including: S1, open-loop scheduling optimization, with the goal of minimizing the time of each UAV task, the input is the UAV position and task position, and the output is the shortest path for each task; S2, constraint calculation, constraining the carrying capacity, running speed, and path point capacity of the UAV; S3, model predictive control, predicting the amount of power after the task is completed, and after each task is completed, performing other tasks can obtain a prediction of the amount of cargo transported per unit power, and optimizing control to maximize the amount of cargo transported per unit power; it can improve the working efficiency of the UAV by optimizing the task path and task time of the port logistics UAV, thereby extending the working time of the UAV, reducing the prediction inaccuracy caused by the difference between the expected power consumption and the actual power consumption, and realizing precise control of the UAV to meet the large-scale use needs of the port.

[0005] For example, the invention patent application with publication number CN103809597A discloses a flight path planning method for a UAV and a UAV, which includes: obtaining depth information of the UAV's flight environment, and generating a two-dimensional grid map of the flight environment based on the depth information, the flight environment including obstacles; constructing a potential function between each grid and the corresponding obstacle based on the position of each grid and the obstacle in the two-dimensional grid map; obtaining a weighted graph of the grids in the two-dimensional grid map based on the position of the UAV and the potential function; and determining the flight path based on the weighted graph.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] When the drone is flying inside a warehouse without GPS, the gyroscope has zero bias instability, and the actual flight path is offset from the planned path due to inertial navigation errors, resulting in an unreliable flight path. When the drone detects multiple dynamic obstacles such as moving cranes and transport vehicles at the same time, the path re-planning response time is long, and there is a problem of insufficient dynamic obstacle avoidance capability in dense scenes. When the drone is flying according to the initial plan, the environment has changed, such as the crane moving to a new position. Because the obstacle avoidance system does not update the obstacle position in real time, a collision occurs, and the trajectory cannot be dynamically corrected. The real-time performance of the drone flight trajectory analysis method for port logistics is insufficient. Summary of the Invention

[0008] The embodiments of the present application solve the problem of insufficient real-time performance of the drone flight trajectory analysis method for port logistics in the prior art by providing a drone flight trajectory analysis method, system and drone for port logistics, thereby improving the real-time performance of the drone flight trajectory analysis method for port logistics.

[0009] An embodiment of the present application provides a drone flight trajectory analysis method for port logistics, comprising the following steps: collecting and processing drone flight trajectory analysis data; analyzing the drone flight trajectory analysis data to obtain a navigation accuracy evaluation value and a drone dynamic obstacle avoidance evaluation value; obtaining a drone flight trajectory analysis real-time evaluation value through comprehensive analysis of the navigation accuracy evaluation value and the drone dynamic obstacle avoidance evaluation value; comparing the navigation accuracy evaluation value, the drone dynamic obstacle avoidance evaluation value, and the drone flight trajectory analysis real-time evaluation value with a first determination value of the navigation accuracy evaluation value, a second determination value of the drone dynamic obstacle avoidance evaluation value, and a third determination value of the drone flight trajectory analysis real-time evaluation value to obtain a drone flight trajectory analysis optimization and adjustment method.

[0010] Furthermore, the specific steps of collecting and processing drone flight trajectory analysis data are as follows: collecting drone flight trajectory analysis raw data through drone detection and positioning equipment; cleaning and denoising the drone flight trajectory analysis raw data to obtain drone flight trajectory analysis data; collecting continuous image frames by using a binocular camera, extracting key points and their descriptors from the image using a feature extraction algorithm, matching feature points between continuous frames using a feature point matching algorithm, estimating the position and posture of the drone through visual odometry technology, analyzing the cumulative scale error caused by feature point matching error, recording the change of scale error over time to form time series data, and calculating the rate of growth of scale error over time, i.e., scale drift rate, by fitting the time series data; the drone flight trajectory analysis data includes navigation accuracy data and drone dynamic obstacle avoidance data.

[0011] Furthermore, the specific steps of obtaining the navigation accuracy evaluation value are as follows: the navigation accuracy data includes the heading error rate, the scale drift rate, the obstacle avoidance response time, the horizontal coordinate of the actual position of the drone and the vertical coordinate of the actual position of the drone; the heading error rate threshold, the scale drift rate threshold, the obstacle avoidance response time threshold, the standard value of the horizontal coordinate of the drone position, the standard value of the vertical coordinate of the drone position, the weight factor of the heading error rate, the weight factor of the drone position error coefficient, the weight factor of the scale drift rate and the weight factor of the obstacle avoidance response time are obtained from the drone flight trajectory analysis database; the analysis results of the proportion of the heading error rate threshold and the heading error rate are averaged in different navigation accuracy detection sections and recorded as the first component of the navigation accuracy evaluation value; the horizontal coordinate of the actual position of the drone is coupled with the standard value of the horizontal coordinate of the drone position and the vertical coordinate of the actual position of the drone is coupled with the standard value of the vertical coordinate of the drone position The second component of the navigation accuracy evaluation value is obtained by processing; the analysis result of the proportion of the scale drift rate threshold and the scale drift rate is recorded as the third component of the navigation accuracy evaluation value; the analysis result of the proportion of the obstacle avoidance response time and the obstacle avoidance response time threshold is averaged in different navigation accuracy detection segments and recorded as the fourth component of the navigation accuracy evaluation value; the coupling result of the first component of the navigation accuracy evaluation value and the third component of the navigation accuracy evaluation value and the coupling result of the second component of the navigation accuracy evaluation value and the fourth component of the navigation accuracy evaluation value are analyzed to obtain the navigation accuracy evaluation value; the navigation accuracy evaluation value represents the quantitative data of the degree of closeness between the UAV position information and the true position by the first component of the navigation accuracy evaluation value, the second component of the navigation accuracy evaluation value, the third component of the navigation accuracy evaluation value and the fourth component of the navigation accuracy evaluation value.

[0012] Furthermore, the specific steps for obtaining the UAV dynamic obstacle avoidance evaluation value are as follows: the UAV dynamic obstacle avoidance data includes obstacle avoidance response time, path replanning response time, scale drift rate, actual horizontal coordinate of the UAV obstacle avoidance action and actual vertical coordinate of the UAV obstacle avoidance action; obtaining the obstacle avoidance response time threshold, path replanning response time threshold, scale drift rate threshold, standard value of the horizontal coordinate of the UAV obstacle avoidance action, standard value of the vertical coordinate of the UAV obstacle avoidance action, weight factor of the obstacle avoidance response time, weight factor of the path replanning response time, weight factor of the scale drift rate and weight factor of the dynamic trajectory deviation coefficient from the UAV flight trajectory analysis database; averaging the analysis results of the ratio of the obstacle avoidance response time to the obstacle avoidance response time threshold in different UAV dynamic obstacle avoidance detection segments, and recording them as the first component of the UAV dynamic obstacle avoidance evaluation value; averaging the analysis results of the ratio of the path replanning response time to the path replanning response time threshold in different UAV dynamic obstacle avoidance detection segments, and recording them as the first component of the UAV dynamic obstacle avoidance evaluation value. The segments are averaged and recorded as the second component of the UAV dynamic obstacle avoidance evaluation value; the analysis result of the ratio of the scale drift rate to the scale drift rate threshold is recorded as the third component of the UAV dynamic obstacle avoidance evaluation value; the actual horizontal coordinate of the UAV obstacle avoidance action and the standard value of the horizontal coordinate of the UAV obstacle avoidance action, as well as the actual vertical coordinate of the UAV obstacle avoidance action and the standard value of the vertical coordinate of the UAV obstacle avoidance action, are processed to obtain the fourth component of the UAV dynamic obstacle avoidance evaluation value; the first component of the UAV dynamic obstacle avoidance evaluation value, the second component of the UAV dynamic obstacle avoidance evaluation value, the third component of the UAV dynamic obstacle avoidance evaluation value and the fourth component of the UAV dynamic obstacle avoidance evaluation value are coupled to obtain the UAV dynamic obstacle avoidance evaluation value; the UAV dynamic obstacle avoidance evaluation value represents the quantitative data of the UAV dynamic obstacle avoidance evaluation value first component, the UAV dynamic obstacle avoidance evaluation value second component, the UAV dynamic obstacle avoidance evaluation value third component and the UAV dynamic obstacle avoidance evaluation value fourth component on the ability of the UAV to avoid obstacles in a complex and dynamic environment.

[0013] Furthermore, the specific steps of the comprehensive analysis to obtain the real-time evaluation value of the UAV flight trajectory analysis are as follows: the UAV dynamic obstacle avoidance data includes the obstacle data update frequency threshold, the navigation precision accuracy evaluation value weight factor, the obstacle data update frequency weight factor and the UAV dynamic obstacle avoidance evaluation value weight factor; the navigation precision accuracy evaluation value is averaged at different navigation precision accuracy detection points and recorded as the first component of the UAV flight trajectory analysis real-time evaluation value; the analysis results of the proportion of the obstacle data update frequency and the obstacle data update frequency threshold are averaged at different UAV flight trajectory analysis real-time detection points and recorded as the second component of the UAV flight trajectory analysis real-time evaluation value; The human-machine dynamic obstacle avoidance evaluation value is averaged at different UAV dynamic obstacle avoidance detection points and recorded as the third component of the UAV flight trajectory analysis real-time evaluation value; the coupling results of the first component of the UAV flight trajectory analysis real-time evaluation value and the second component of the UAV flight trajectory analysis real-time evaluation value are analyzed with the third component of the UAV flight trajectory analysis real-time evaluation value to obtain the UAV flight trajectory analysis real-time evaluation value; the UAV flight trajectory analysis real-time evaluation value represents the quantitative degree of real-time data processing of the UAV during flight by the first component of the UAV flight trajectory analysis real-time evaluation value, the second component of the UAV flight trajectory analysis real-time evaluation value and the third component of the UAV flight trajectory analysis real-time evaluation value.

[0014] Furthermore, the specific steps of obtaining the UAV flight trajectory analysis optimization and adjustment method are: obtaining a first judgment value of the navigation accuracy evaluation value, a second judgment value of the UAV dynamic obstacle avoidance evaluation value and a third judgment value of the UAV flight trajectory analysis real-time evaluation value from the UAV flight trajectory analysis database; the UAV flight trajectory analysis optimization and adjustment method includes a navigation accuracy optimization and adjustment method, a UAV dynamic obstacle avoidance optimization and adjustment method and a UAV flight trajectory analysis real-time optimization and adjustment method; if the navigation accuracy evaluation value is greater than or equal to the first judgment value of the navigation accuracy evaluation value, there is no need to optimize and adjust the navigation accuracy; if the navigation accuracy evaluation value is lower than the first judgment value of the navigation accuracy evaluation value, the visual SLAM data is fused through Kalman filtering to suppress the angle error; directly connected to the port BIM system, the coordinates of dynamic obstacle cranes and containers are obtained in real time to generate a 4D flight corridor, the digital twin platform receives crane positioning data, dynamically updates the flight restricted area, and the UAV adjusts its path according to the real-time corridor to avoid the signal blocking area.

[0015] Furthermore, the specific steps of the UAV dynamic obstacle avoidance optimization and adjustment method are: if the UAV dynamic obstacle avoidance evaluation value is lower than or equal to the second judgment value of the UAV dynamic obstacle avoidance evaluation value, there is no need to optimize and adjust the UAV dynamic obstacle avoidance; if the UAV dynamic obstacle avoidance evaluation value is greater than the second judgment value of the UAV dynamic obstacle avoidance evaluation value, then based on the rule engine, the ultrasonic data is processed to trigger emergency avoidance; radar and visual data are integrated to generate the optimal path using deep reinforcement learning.

[0016] Furthermore, the specific steps of the real-time optimization and adjustment method of the drone flight trajectory analysis are as follows: if the real-time evaluation value of the drone flight trajectory analysis is greater than or equal to the third judgment value of the real-time evaluation value of the drone flight trajectory analysis, there is no need to optimize and adjust the real-time performance of the drone flight trajectory analysis; if the real-time evaluation value of the drone flight trajectory analysis is lower than the third judgment value of the real-time evaluation value of the drone flight trajectory analysis, the surrounding obstacles are detected through millimeter wave radar and vision, and the data is uploaded to the blockchain. Based on the pheromone concentration and real-time obstacle data, the weight of each flight segment is calculated, and the optimal path is output, such as the path with the smallest total weight, bypassing the mobile crane, and selecting the container gap. If the priority of the new task is higher than the current task, path replanning is triggered.

[0017] An embodiment of the present application provides a drone flight trajectory analysis system for port logistics, including a drone flight trajectory analysis data acquisition and processing module, a drone flight trajectory analysis data analysis module, a comprehensive analysis module and an optimization and adjustment module: a drone flight trajectory analysis data acquisition and processing module: used to acquire and process drone flight trajectory analysis data; a drone flight trajectory analysis data analysis module: used to analyze the drone flight trajectory analysis data to obtain a navigation accuracy evaluation value and a drone dynamic obstacle avoidance evaluation value; a comprehensive analysis module: used to obtain a drone flight trajectory analysis real-time evaluation value through comprehensive analysis of the navigation accuracy evaluation value and the drone dynamic obstacle avoidance evaluation value; an optimization and adjustment module: used to compare and analyze the navigation accuracy evaluation value, the drone dynamic obstacle avoidance evaluation value and the drone flight trajectory analysis real-time evaluation value with a first determination value of the navigation accuracy evaluation value, a second determination value of the drone dynamic obstacle avoidance evaluation value and a third determination value of the drone flight trajectory analysis real-time evaluation value to obtain a drone flight trajectory analysis optimization and adjustment method.

[0018] An embodiment of the present application provides a drone, including a drone cargo hold adopting a carbon fiber honeycomb sandwich structure, equipped with an adaptive electromagnetic suspension system, and real-time adjustment of the cargo hold damping through electrorheological fluid; the drone fuselage is sprayed with a polyurethane coating containing microcapsules; the drone motor adopts a closed magnetic levitation bearing; and the drone belly is coated with a superhydrophobic material.

[0019] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0020] 1. By collecting and processing drone flight trajectory analysis data, analyzing the drone flight trajectory analysis data, obtaining the navigation accuracy evaluation value and the drone dynamic obstacle avoidance evaluation value, comprehensive analysis and optimization adjustment, the real-time performance of the drone flight trajectory analysis method for port logistics is improved, and the problem of insufficient real-time performance of the drone flight trajectory analysis method for port logistics in the existing technology is solved.

[0021] 2. By analyzing the UAV flight trajectory analysis data, measuring the angular velocity and acceleration based on the cold atom interferometer, eliminating the zero bias instability of the traditional MEMS gyroscope, and fusing the visual SLAM data through Kalman filtering, the cumulative error is suppressed, thereby optimizing the navigation accuracy.

[0022] 3. Through comprehensive analysis, the real-time evaluation value of the UAV flight trajectory analysis is obtained, which can provide instant flight trajectory analysis results, quickly respond to various changes in flight, and then prevent accidents and optimize flight paths, which can reduce the operation and maintenance costs of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a method for analyzing drone flight trajectories for port logistics provided in an embodiment of the present application;

[0024] Figure 2 Schematic diagram of the real-time evaluation value function for drone flight trajectory analysis provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of a drone flight trajectory system for port logistics provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application solve the problem of insufficient real-time performance of the drone flight trajectory analysis method for port logistics in the prior art by providing a drone flight trajectory analysis method, system and drone for port logistics. By collecting and processing drone flight trajectory analysis data, analyzing the drone flight trajectory analysis data, obtaining navigation accuracy evaluation values ​​and drone dynamic obstacle avoidance evaluation values, and comprehensively analyzing and optimizing and adjusting, the real-time performance of the drone flight trajectory analysis method for port logistics is improved.

[0027] The technical solution in the embodiment of the present application is to solve the above-mentioned problem of insufficient real-time performance of the drone flight trajectory analysis method in port logistics. The overall idea is as follows:

[0028] By collecting and processing UAV flight trajectory analysis data, analyzing the UAV flight trajectory analysis data, obtaining the navigation accuracy evaluation value and the UAV dynamic obstacle avoidance evaluation value, and comprehensively analyzing and optimizing the adjustment, the real-time performance of the UAV flight trajectory analysis method for port logistics is improved.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] like Figure 1 As shown, it is a flow chart of the UAV flight trajectory analysis method for port logistics provided in an embodiment of the present application, and the method includes the following steps: collecting and processing UAV flight trajectory analysis data; analyzing the UAV flight trajectory analysis data to obtain a navigation accuracy evaluation value and a UAV dynamic obstacle avoidance evaluation value; obtaining a UAV flight trajectory analysis real-time evaluation value through comprehensive analysis of the navigation accuracy evaluation value and the UAV dynamic obstacle avoidance evaluation value; comparing the navigation accuracy evaluation value, the UAV dynamic obstacle avoidance evaluation value and the UAV flight trajectory analysis real-time evaluation value with the first determination value of the navigation accuracy evaluation value, the second determination value of the UAV dynamic obstacle avoidance evaluation value and the third determination value of the UAV flight trajectory analysis real-time evaluation value to obtain a UAV flight trajectory analysis optimization and adjustment method.

[0031] In this embodiment, a drone automatic battery swap station is deployed on top of the terminal quay crane, using a robotic arm and visual positioning to achieve 90-second battery replacement. The battery pack integrates a supercapacitor module to support instantaneous high-current discharge (peak power 20kW) to cope with emergency climbs in gusts of wind. The proton exchange membrane fuel cell (power density 3kW / kg) has a range of 350km. The port hydrogen production station provides green hydrogen energy, and its carbon footprint is reduced by 92% compared to diesel trailers.

[0032] Furthermore, the specific steps of collecting and processing drone flight trajectory analysis data are as follows: collecting drone flight trajectory analysis raw data through drone detection and positioning equipment; cleaning and denoising the drone flight trajectory analysis raw data to obtain drone flight trajectory analysis data; collecting continuous image frames by using a binocular camera, extracting key points and their descriptors from the image using a feature extraction algorithm, matching feature points between continuous frames through a feature point matching algorithm, estimating the position and posture of the drone through visual odometry technology, analyzing the cumulative scale error caused by feature point matching error, recording the change of scale error over time to form time series data, and calculating the rate of growth of scale error over time, i.e., scale drift rate, by fitting the time series data; the drone flight trajectory analysis data includes navigation accuracy data and drone dynamic obstacle avoidance data.

[0033] In this embodiment, millimeter-wave radar is used to identify large obstacles such as cranes and trucks with a detection distance of 200m; binocular camera AI is used to identify container gaps, with a minimum passable width of 1.2m; an ultrasonic array covers 360° near-field detection and identifies small obstacles such as barbed wire and cables with a response time of less than 50ms. The millimeter-wave radar pre-screens obstacles and defines safety boundaries; the binocular camera identifies passable areas such as container gaps; and the ultrasonic array monitors sudden near-field obstacles such as a forklift in real time.

[0034] Furthermore, the specific steps for obtaining the navigation accuracy evaluation value are as follows: the navigation accuracy data include the heading error rate, the scale drift rate, the obstacle avoidance response time, the horizontal coordinate of the actual position of the UAV and the vertical coordinate of the actual position of the UAV; the heading error rate threshold, the scale drift rate threshold, the obstacle avoidance response time threshold, the standard value of the horizontal coordinate of the UAV position, the standard value of the vertical coordinate of the UAV position, the weight factor of the heading error rate, the weight factor of the UAV position error coefficient, the weight factor of the scale drift rate and the weight factor of the obstacle avoidance response time are obtained from the UAV flight trajectory analysis database; the analysis results of the proportion of the heading error rate threshold and the heading error rate are averaged in different navigation accuracy detection sections, and recorded as the first component of the navigation accuracy evaluation value; the horizontal coordinate of the actual position of the UAV is coupled with the standard value of the horizontal coordinate of the UAV position and the vertical coordinate of the actual position of the UAV is coupled with the standard value of the vertical coordinate of the UAV position The second component of the navigation accuracy evaluation value is obtained by processing; the analysis result of the proportion of the scale drift rate threshold and the scale drift rate is recorded as the third component of the navigation accuracy evaluation value; the analysis result of the proportion of the obstacle avoidance response time and the obstacle avoidance response time threshold is averaged in different navigation accuracy detection segments and recorded as the fourth component of the navigation accuracy evaluation value; the coupling result of the first component of the navigation accuracy evaluation value and the third component of the navigation accuracy evaluation value and the coupling result of the second component of the navigation accuracy evaluation value and the fourth component of the navigation accuracy evaluation value are analyzed to obtain the navigation accuracy evaluation value; the navigation accuracy evaluation value represents the quantitative data of the degree of closeness between the UAV position information and the true position, which is jointly calculated by the first component of the navigation accuracy evaluation value, the second component of the navigation accuracy evaluation value, the third component of the navigation accuracy evaluation value and the fourth component of the navigation accuracy evaluation value.

[0035] In this embodiment, the specific method for obtaining the navigation accuracy evaluation value through analysis is as follows:

[0036]

[0037] ρ1+ρ2+ρ3+ρ4=1;

[0038] The preset navigation accuracy detection points are numbered in sequence, S0 represents the number of the navigation accuracy detection point under the T0-th navigation accuracy detection segment, S0 = 1, 2, ..., S, S represents the total number of navigation accuracy detection points.

[0039] The preset navigation accuracy time is divided into navigation accuracy detection segments of the same length, T0 represents the number of the navigation accuracy detection segment, T0 = 1, 2, ..., T, T represents the total number of navigation accuracy detection segments.

[0040] It represents the navigation accuracy evaluation value of the S0th navigation accuracy detection point.

[0041] W1 represents the heading angle error rate threshold, which is a preset heading angle error rate threshold obtained from the UAV flight trajectory analysis database, and can be the average heading angle error rate under the preset navigation accuracy detection segment from the historical database.

[0042] It represents the heading angle error rate in the T0th navigation accuracy detection segment, and the maximum deviation angle between the UAV heading angle and the preset trajectory in the navigation accuracy detection segment, reflecting the impact of gyroscope zero bias instability on direction control.

[0043] Represents the UAV position error coefficient at the S0th navigation accuracy detection point.

[0044] ω1 represents the scale drift rate threshold, which is a preset scale drift rate threshold obtained from the UAV flight trajectory analysis database, and can be the average value of the scale drift rate under the preset navigation accuracy detection segment from the historical database.

[0045] It represents the scale drift rate under the T0th navigation accuracy detection segment, indicating the rate at which the scale of the system changes with the path due to the cumulative error. It is the ratio of the absolute difference between the preset path length and the actual path length of the UAV in the navigation accuracy detection segment to the actual path length.

[0046] Indicates the obstacle avoidance response time in the T0th navigation accuracy detection segment.

[0047] B1 represents the obstacle avoidance response time threshold, which is a preset obstacle avoidance response time threshold obtained from the UAV flight trajectory analysis database, and can be the average obstacle avoidance response time under the preset navigation accuracy detection segment from the historical database.

[0048] Indicates the horizontal coordinate of the actual position of the UAV at the S0th navigation accuracy detection point.

[0049] X1 represents the standard value of the horizontal coordinate of the drone position, which is the preset standard value of the horizontal coordinate of the drone position obtained from the drone flight trajectory analysis database, and can be the average value of the horizontal coordinate of the drone position under the preset navigation accuracy detection point from the historical database.

[0050] It represents the vertical coordinate of the actual position of the UAV at the S0th navigation accuracy detection point.

[0051] Y1 represents the standard value of the vertical coordinate of the UAV position, which is the preset standard value of the vertical coordinate of the UAV position obtained from the UAV flight trajectory analysis database, and can be the average value of the vertical coordinate of the UAV position under the preset navigation accuracy detection points from the historical database.

[0052] ρ1 is the preset heading angle error rate weight factor obtained from the UAV flight trajectory analysis database.

[0053] ρ2 is the preset UAV position error coefficient weight factor obtained from the UAV flight trajectory analysis database.

[0054] ρ3 is the preset scale drift rate weight factor obtained from the UAV flight trajectory analysis database.

[0055] ρ4 is the preset obstacle avoidance response time weight factor obtained from the UAV flight trajectory analysis database.

[0056] By obtaining a mapping table of weight factors from a database, the corresponding weight factors are quickly extracted based on the current heading error rate, drone position error coefficient, scale drift rate, and obstacle avoidance response time. For example, the weight factor for the heading error rate, the weight factor for the drone position error coefficient, the weight factor for the scale drift rate, and the weight factor for the obstacle avoidance response time are determined. This mapping table defines a clear set of association rules that convert the specific values ​​of the heading error rate, drone position error coefficient, scale drift rate, and obstacle avoidance response time into their corresponding weight factors. This mechanism effectively enables dynamic acquisition of weight factors, whether achieving a one-to-one exact match or a many-to-one relationship where multiple parameters are aggregated into a single weight.

[0057] The heading error rate indicates the deviation between the actual heading angle of the drone and the predetermined heading angle. A higher heading error rate means that the drone's heading control is not accurate enough, which will prolong the time it takes for the drone to react after detecting an obstacle, and the obstacle avoidance response time will be longer. The heading error will cause the drone to have position deviation during flight, thereby affecting the scale drift rate. The scale drift rate refers to the deviation rate between the actual path length and the expected path length of the drone during flight. The higher the heading error rate, the higher the scale drift rate. The heading error will directly cause the drone's actual flight path to deviate from the predetermined path, thereby affecting the drone's actual position horizontal and vertical coordinates. The higher the heading error rate, the greater the deviation between the drone's actual position and the predetermined position. The obstacle avoidance response time affects the drone's obstacle avoidance action execution, and thus indirectly affects the drone's actual position. The longer the obstacle avoidance response time, the less likely the drone will be able to avoid obstacles in time, resulting in a deviation between the actual position and the predetermined position.

[0058] There is a negative correlation between the heading angle error rate and the navigation accuracy assessment value, indicating that the greater the deviation between the actual heading of the drone and the planned heading, the more likely the drone will deviate from the planned flight path. The larger the heading angle error rate, the smaller the navigation accuracy assessment value. There is a negative correlation between the obstacle avoidance response time and the navigation accuracy assessment value, indicating that the longer the drone takes to react after detecting an obstacle, the more likely the drone will deviate from the planned path during obstacle avoidance. The longer the obstacle avoidance response time, the smaller the navigation accuracy assessment value. There is a negative correlation between the scale drift rate and the navigation accuracy assessment value, indicating that the greater the drone's position deviation and the higher the scale drift rate, the smaller the navigation accuracy assessment value. There is a negative correlation between the sum of the squares of the difference between the drone's actual position abscissa and the standard value of the drone's position abscissa and the sum of the squares of the difference between the drone's actual position ordinate and the standard value of the drone's position ordinate, and the navigation accuracy assessment value. This means that the greater the deviation between the drone's flight path and the planned path, the greater the sum of the squares of the difference between the drone's actual position abscissa and the standard value of the drone's position abscissa and the sum of the squares of the difference between the drone's actual position ordinate and the standard value of the drone's position ordinate, and the smaller the navigation accuracy assessment value.

[0059] Furthermore, the specific steps for obtaining the UAV dynamic obstacle avoidance evaluation value are as follows: the UAV dynamic obstacle avoidance data includes obstacle avoidance response time, path replanning response time, scale drift rate, actual horizontal coordinate of the UAV obstacle avoidance action and actual vertical coordinate of the UAV obstacle avoidance action; obtaining the obstacle avoidance response time threshold, path replanning response time threshold, scale drift rate threshold, standard value of the horizontal coordinate of the UAV obstacle avoidance action, standard value of the vertical coordinate of the UAV obstacle avoidance action, weight factor of the obstacle avoidance response time, weight factor of the path replanning response time, weight factor of the scale drift rate and weight factor of the dynamic trajectory deviation coefficient from the UAV flight trajectory analysis database; averaging the analysis results of the ratio of the obstacle avoidance response time to the obstacle avoidance response time threshold in different UAV dynamic obstacle avoidance detection segments, and recording them as the first component of the UAV dynamic obstacle avoidance evaluation value; averaging the analysis results of the ratio of the path replanning response time to the path replanning response time threshold in different UAV dynamic obstacle avoidance detection segments The average processing is performed and recorded as the second component of the UAV dynamic obstacle avoidance evaluation value; the analysis result of the ratio of the scale drift rate to the scale drift rate threshold is recorded as the third component of the UAV dynamic obstacle avoidance evaluation value; the actual horizontal coordinate of the UAV obstacle avoidance action and the standard value of the horizontal coordinate of the UAV obstacle avoidance action and the actual vertical coordinate of the UAV obstacle avoidance action and the standard value of the vertical coordinate of the UAV obstacle avoidance action are processed to obtain the fourth component of the UAV dynamic obstacle avoidance evaluation value; the first component of the UAV dynamic obstacle avoidance evaluation value, the second component of the UAV dynamic obstacle avoidance evaluation value, the third component of the UAV dynamic obstacle avoidance evaluation value and the fourth component of the UAV dynamic obstacle avoidance evaluation value are coupled to obtain the UAV dynamic obstacle avoidance evaluation value; the UAV dynamic obstacle avoidance evaluation value represents the quantitative data of the UAV dynamic obstacle avoidance evaluation value first component, the UAV dynamic obstacle avoidance evaluation value second component, the UAV dynamic obstacle avoidance evaluation value third component and the UAV dynamic obstacle avoidance evaluation value fourth component on the ability of the UAV to avoid obstacles in complex and dynamic environments.

[0060] In this embodiment, the specific method for analyzing and obtaining the drone dynamic obstacle avoidance evaluation value is as follows:

[0061]

[0062] The preset UAV dynamic obstacle avoidance detection points are numbered in sequence, R0 represents the number of the UAV dynamic obstacle avoidance detection point under the K0th UAV dynamic obstacle avoidance detection segment, R0 = 1, 2, ..., R, R represents the total number of UAV dynamic obstacle avoidance detection points.

[0063] The preset UAV dynamic obstacle avoidance time is divided into UAV dynamic obstacle avoidance detection segments of the same length M. K0 represents the number of the UAV dynamic obstacle avoidance detection segment, K0 = 1, 2, ..., K, where K represents the total number of UAV dynamic obstacle avoidance detection segments.

[0064] Represents the UAV dynamic obstacle avoidance evaluation value of the R0th UAV dynamic obstacle avoidance detection point.

[0065] It represents the obstacle avoidance response time of the K0th UAV in the dynamic obstacle avoidance detection segment.

[0066] G1 represents the obstacle avoidance response time threshold, which is a preset obstacle avoidance response time threshold obtained from the UAV flight trajectory analysis database, and can be the average obstacle avoidance response time under the preset UAV dynamic obstacle avoidance detection segment from the historical database.

[0067] It represents the path replanning response time of the K0th UAV in the dynamic obstacle avoidance detection segment, which is the total time from detecting a new obstacle to generating a new path and starting execution.

[0068] U1 represents the path replanning response time threshold, which is the preset path replanning response time threshold obtained from the UAV flight trajectory analysis database. It can be the average path replanning response time under the preset UAV dynamic obstacle avoidance detection segment from the historical database.

[0069] It represents the scale drift rate of the K0th UAV in the dynamic obstacle avoidance detection segment.

[0070] V1 represents the scale drift rate threshold, which is a preset scale drift rate threshold obtained from the UAV flight trajectory analysis database, and can be the average scale drift rate under the preset UAV dynamic obstacle avoidance detection segment from the historical database.

[0071] It represents the dynamic trajectory deviation coefficient of the K0th UAV in the dynamic obstacle avoidance detection segment.

[0072] Represents the actual horizontal coordinate of the UAV obstacle avoidance action in the K0th UAV dynamic obstacle avoidance detection segment.

[0073] It represents the standard value of the horizontal coordinate of the UAV obstacle avoidance action, which is the preset standard value of the horizontal coordinate of the UAV obstacle avoidance action obtained from the UAV flight trajectory analysis database, and can be the average value of the horizontal coordinate of the UAV obstacle avoidance action under the preset UAV dynamic obstacle avoidance detection segment from the historical database.

[0074] It represents the actual vertical coordinate of the UAV obstacle avoidance action in the K0th UAV dynamic obstacle avoidance detection segment.

[0075] It represents the standard value of the vertical coordinate of the UAV obstacle avoidance action, which is the preset standard value of the vertical coordinate of the UAV obstacle avoidance action obtained from the UAV flight trajectory analysis database, and can be the average value of the vertical coordinate of the UAV obstacle avoidance action under the preset UAV dynamic obstacle avoidance detection segment from the historical database.

[0076] is the preset obstacle avoidance response time weight factor obtained from the UAV flight trajectory analysis database.

[0077] The weight factor for the response time of replanning the preset path obtained from the UAV flight trajectory analysis database.

[0078] is the preset scale drift rate weight factor obtained from the UAV flight trajectory analysis database.

[0079] It is the preset dynamic trajectory deviation coefficient weight factor obtained from the UAV flight trajectory analysis database.

[0080] By obtaining a mapping table of weight factors from a database, the corresponding weight factors are quickly extracted based on the current obstacle avoidance response time, path replanning response time, scale drift rate, and dynamic trajectory deviation coefficient. For example, the weight factor for obstacle avoidance response time, the weight factor for path replanning response time, the weight factor for scale drift rate, and the weight factor for dynamic trajectory deviation coefficient are derived. This mapping table defines a clear set of association rules that converts the specific values ​​of obstacle avoidance response time, path replanning response time, scale drift rate, and dynamic trajectory deviation coefficient into their corresponding weight factors. This mechanism effectively enables dynamic acquisition of weight factors, whether achieving a one-to-one exact match or a many-to-one relationship where multiple parameters are aggregated into a single weight.

[0081] The obstacle avoidance response time refers to the time interval from when the drone detects an obstacle to when it starts to perform obstacle avoidance actions. The path replanning response time refers to the time required for the drone to replan its flight path after detecting an obstacle. The shorter the obstacle avoidance response time, the faster the drone can make obstacle avoidance decisions, thereby providing more time for path replanning, and the shorter the path replanning response time. The higher the scale drift rate, the lower the accuracy of the drone's position and obstacle detection, which increases the obstacle avoidance response time and path replanning response time. The drone needs more time to process this inaccurate information, and the longer the obstacle avoidance response time and path replanning response time. The actual horizontal coordinate and actual vertical coordinate of the drone's obstacle avoidance action are the actual position coordinates reached by the drone after performing the obstacle avoidance action. They are the direct result of the obstacle avoidance response time and path replanning response time. Short obstacle avoidance response time and path replanning response time mean that the drone can complete the obstacle avoidance action faster and thus reach the predetermined obstacle avoidance position coordinates more accurately.

[0082] There is a positive correlation between the obstacle avoidance response time and the UAV's dynamic obstacle avoidance evaluation value. The slower the UAV reacts to obstacles, the lower the obstacle avoidance performance. The longer the obstacle avoidance response time, the greater the UAV's dynamic obstacle avoidance evaluation value. There is a positive correlation between the path replanning response time and the UAV's dynamic obstacle avoidance evaluation value. The slower the UAV replans the path after encountering an obstacle, the greater the collision risk. The longer the path replanning response time, the greater the UAV's dynamic obstacle avoidance evaluation value. There is a positive correlation between the scale drift rate and the UAV's dynamic obstacle avoidance evaluation value. The lower the accuracy of the UAV's estimation of its own position and surrounding environment, the more accurate the obstacle avoidance action will be. The higher the scale drift rate, the greater the dynamic obstacle avoidance evaluation value of the drone; the sum of the square of the difference between the actual horizontal coordinate of the drone obstacle avoidance action and the standard value of the horizontal coordinate of the drone obstacle avoidance action, and the sum of the square of the difference between the actual vertical coordinate of the drone obstacle avoidance action and the standard value of the vertical coordinate of the drone obstacle avoidance action are positively correlated with the dynamic obstacle avoidance evaluation value of the drone. A large deviation from the predetermined obstacle avoidance position indicates that the obstacle avoidance action is not executed accurately. The greater the sum of the square of the difference between the actual horizontal coordinate of the drone obstacle avoidance action and the standard value of the horizontal coordinate of the drone obstacle avoidance action, and the sum of the square of the difference between the actual vertical coordinate of the drone obstacle avoidance action and the standard value of the vertical coordinate of the drone obstacle avoidance action, the greater the dynamic obstacle avoidance evaluation value of the drone.

[0083] Furthermore, the specific steps of comprehensively analyzing and obtaining the real-time evaluation value of the UAV flight trajectory analysis are as follows: the UAV dynamic obstacle avoidance data includes the obstacle data update frequency threshold, the navigation precision accuracy evaluation value weight factor, the obstacle data update frequency weight factor and the UAV dynamic obstacle avoidance evaluation value weight factor; the navigation precision accuracy evaluation value is averaged at different navigation precision accuracy detection points, and recorded as the first component of the UAV flight trajectory analysis real-time evaluation value; the analysis results of the proportion of the obstacle data update frequency and the obstacle data update frequency threshold are averaged at different UAV flight trajectory analysis real-time detection points, and recorded as the second component of the UAV flight trajectory analysis real-time evaluation value; ... The dynamic obstacle avoidance evaluation value of the drone is averaged at different dynamic obstacle avoidance detection points of the drone and recorded as the third component of the real-time evaluation value of the drone flight trajectory analysis; the coupling results of the first component of the real-time evaluation value of the drone flight trajectory analysis and the second component of the real-time evaluation value of the drone flight trajectory analysis are analyzed with the third component of the real-time evaluation value of the drone flight trajectory analysis to obtain the real-time evaluation value of the drone flight trajectory analysis; the real-time evaluation value of the drone flight trajectory analysis represents the quantitative degree of real-time data processing of the drone during flight by the first component of the real-time evaluation value of the drone flight trajectory analysis, the second component of the real-time evaluation value of the drone flight trajectory analysis and the third component of the real-time evaluation value of the drone flight trajectory analysis.

[0084] In this embodiment, the specific method for obtaining the real-time evaluation value of the drone flight trajectory analysis is as follows:

[0085]

[0086] φ1+φ2+φ3=1;

[0087] The preset UAV flight trajectory analysis real-time detection points are numbered in sequence, L0 represents the number of the UAV flight trajectory analysis real-time detection point, L0 = 1, 2, ..., L, L represents the total number of UAV flight trajectory analysis real-time detection points.

[0088] Represents the real-time evaluation value of UAV flight trajectory analysis.

[0089] It represents the navigation accuracy evaluation value of the S0th navigation accuracy detection point.

[0090] It represents the obstacle data update frequency at the L0th UAV flight trajectory analysis real-time detection point, which is the refresh rate of the dynamic obstacle position information by the sensor or digital twin platform.

[0091] ∈1 represents the obstacle data update frequency threshold, which is the preset obstacle data update frequency threshold obtained from the UAV flight trajectory analysis database, and can be the average obstacle data update frequency under the preset UAV flight trajectory analysis real-time detection point from the historical database.

[0092] Represents the UAV dynamic obstacle avoidance evaluation value of the R0th UAV dynamic obstacle avoidance detection point.

[0093] φ1 is the preset navigation accuracy evaluation value weight factor obtained from the UAV flight trajectory analysis database.

[0094] φ2 is the update frequency weighting factor of the preset obstacle data obtained from the UAV flight trajectory analysis database.

[0095] φ3 is the preset UAV dynamic obstacle avoidance evaluation value weight factor obtained from the UAV flight trajectory analysis database.

[0096] By obtaining a mapping table of weight factors from a database, the corresponding weight factors are quickly extracted based on the current navigation accuracy assessment value, obstacle data update frequency, and the drone's dynamic obstacle avoidance assessment value. For example, the weight factor for the navigation accuracy assessment value, the weight factor for the obstacle data update frequency, and the weight factor for the drone's dynamic obstacle avoidance assessment value are calculated. This mapping table defines a clear set of association rules that convert the specific values ​​of the navigation accuracy assessment value, obstacle data update frequency, and drone's dynamic obstacle avoidance assessment value into their corresponding weight factors. This mechanism effectively achieves dynamic acquisition of weight factors, whether achieving a one-to-one exact match or a many-to-one relationship where multiple parameters are aggregated into a single weight.

[0097] Table 1 is an example table of real-time evaluation values ​​for UAV flight trajectory analysis. The example parameters in Table 1 only take the parameters under one real-time detection point of UAV flight trajectory analysis for illustration. The weight factor φ1 is set to 0.4, the weight factor φ2 is set to 0.2, the weight factor φ3 is set to 0.4, and the ∈1 obstacle data update frequency threshold is 2. Table 1 is shown below.

[0098] Table 1 Example of real-time evaluation value of UAV flight trajectory analysis

[0099]

[0100]

[0101] The navigation accuracy assessment value affects the dynamic obstacle avoidance capability of the UAV. The lower the navigation accuracy assessment value, the greater the deviation between the UAV and the predetermined trajectory during actual flight, resulting in the UAV's dynamic obstacle avoidance system being unable to accurately judge the position and motion state of the obstacle when encountering an obstacle. The higher the obstacle data update frequency, the more real-time the UAV can obtain obstacle information in the surrounding environment, thereby more accurately predicting the movement trajectory of the obstacle. The lower the UAV dynamic obstacle avoidance assessment value. The greater the navigation accuracy assessment value, the more accurate the UAV can reach the predetermined position, thereby providing more stable environmental conditions for the sensor system, which is conducive to improving the update frequency of obstacle data. The greater the obstacle data update frequency, the lower the dynamic obstacle avoidance assessment value.

[0102] It can be seen from Table 1 that there is a positive correlation between the navigation accuracy evaluation value and the real-time evaluation value of the UAV flight trajectory analysis. The smaller the deviation between the actual flight trajectory of the UAV and the predetermined trajectory, the greater the navigation accuracy evaluation value and the real-time evaluation value of the UAV flight trajectory analysis. There is a negative correlation between the UAV dynamic obstacle avoidance evaluation value and the real-time evaluation value of the UAV flight trajectory analysis. The UAV reacts slowly or the obstacle avoidance action is inaccurate when avoiding obstacles, resulting in the flight trajectory analysis system being unable to obtain accurate flight trajectory information in time. The greater the UAV dynamic obstacle avoidance evaluation value, the smaller the UAV flight trajectory analysis real-time evaluation value. There is a positive correlation between the obstacle data update frequency and the real-time evaluation value of the UAV flight trajectory analysis, which enables the flight trajectory analysis system to understand the changes in the surrounding environment more in real time and adjust the flight trajectory analysis results in time. The greater the obstacle data update frequency, the greater the real-time evaluation value of the UAV flight trajectory analysis.

[0103] Depend on Figure 2 As shown, this is a schematic diagram of the real-time evaluation value function of the drone flight trajectory analysis provided in an embodiment of the present application; x is the positive semi-axis of the horizontal coordinate, y is the positive semi-axis of the vertical coordinate, the weight factor φ1 is set to 0.4, the weight factor φ2 is set to 0.2, the weight factor φ3 is set to 0.4, and the ∈1 obstacle data update frequency threshold is 2.

[0104] Curve a indicates that if the dynamic obstacle avoidance evaluation value of the UAV is fixed at 1, the obstacle data update frequency is fixed at 1, the navigation accuracy evaluation value is x, and the real-time evaluation value of the UAV flight trajectory analysis is y, the real-time evaluation value of the UAV flight trajectory analysis increases with the increase of the navigation accuracy evaluation value.

[0105] Furthermore, the specific steps of obtaining the UAV flight trajectory analysis optimization and adjustment method are: obtaining a first judgment value of the navigation accuracy evaluation value, a second judgment value of the UAV dynamic obstacle avoidance evaluation value and a third judgment value of the UAV flight trajectory analysis real-time evaluation value from the UAV flight trajectory analysis database; the UAV flight trajectory analysis optimization and adjustment method includes a navigation accuracy optimization and adjustment method, a UAV dynamic obstacle avoidance optimization and adjustment method and a UAV flight trajectory analysis real-time optimization and adjustment method; if the navigation accuracy evaluation value is greater than or equal to the first judgment value of the navigation accuracy evaluation value, there is no need to optimize and adjust the navigation accuracy; if the navigation accuracy evaluation value is lower than the first judgment value of the navigation accuracy evaluation value, the visual SLAM data is fused through Kalman filtering to suppress the angle error; directly connected to the port BIM system, the coordinates of dynamic obstacle cranes and containers are obtained in real time to generate a 4D flight corridor, the digital twin platform receives crane positioning data, dynamically updates the flight restricted area, and the UAV adjusts its path according to the real-time corridor to avoid the signal blocking area.

[0106] In this embodiment, when the GPS signal is available, the quantum inertial navigation reference is calibrated, and the precise position information provided by the GPS is used to calibrate the quantum inertial navigation system, and the reference data of the quantum inertial navigation system is updated to ensure that the high-precision position information output by it is accurate, thereby improving the accuracy of the quantum inertial navigation system and reducing the cumulative error. When GPS is denied, the quantum module autonomously outputs high-precision position information. When the GPS signal is denied or unavailable, the quantum inertial navigation module autonomously outputs high-precision position information to maintain the accuracy of the UAV's positioning and attitude estimation, ensuring that the UAV can still maintain high-precision positioning and attitude estimation when GPS is unavailable. Attitude control; fuse visual SLAM data through Kalman filtering, use the Kalman filtering algorithm to fuse the position information output by the quantum inertial navigation module and the environmental perception data provided by the visual SLAM system, process the fused data, suppress cumulative errors, improve the stability and accuracy of the navigation system, and reduce error accumulation during long-term flight; directly connect with the port BIM system to obtain dynamic obstacle information in real time. The drone system is directly connected to the port's BIM (Building Information Model) system to obtain coordinate information of dynamic obstacles such as cranes and containers in real time, obtain real-time and accurate obstacle information, and provide flight path planning. Provide data support; Generate 4D flight corridors, based on the dynamic obstacle information obtained, generate 4D flight corridors including time dimensions, ensure that there are no obstacles in the flight corridor, provide a safe flight path for the drone, plan a safe flight path, and avoid collisions with obstacles; The digital twin platform receives crane positioning data, the digital twin platform receives crane positioning data from the UWB base station, monitors the real-time position of the crane, obtains high-precision crane position information, and provides data support for dynamically updating flight restricted areas; Dynamically update flight restricted areas, based on the real-time position and movement trajectory prediction of the crane, dynamically update flight restricted areas, and set flight restricted areas The zone information is transmitted to the UAV navigation system in real time to prevent the UAV from flying into the crane's moving trajectory area, ensuring flight safety; the UAV adjusts its path according to the real-time corridor, and the UAV receives real-time updated 4D flight corridor information, adjusts the flight path according to the flight corridor, avoids signal blocking areas and flight restricted areas, and ensures that the UAV always flies along a safe path to avoid collisions and signal loss; avoids signal blocking areas, identifies signal blocking areas, such as buildings, large equipment, etc. that may block GPS or communication signals, adjusts the flight path to avoid these areas, keeps the communication between the UAV and the ground control station unobstructed, and ensures the effective transmission of flight control commands.

[0107] Furthermore, the specific steps of the UAV dynamic obstacle avoidance optimization and adjustment method are as follows: if the UAV dynamic obstacle avoidance evaluation value is lower than or equal to the second judgment value of the UAV dynamic obstacle avoidance evaluation value, there is no need to optimize and adjust the UAV dynamic obstacle avoidance; if the UAV dynamic obstacle avoidance evaluation value is greater than the second judgment value of the UAV dynamic obstacle avoidance evaluation value, then the ultrasonic data is processed based on the rule engine to trigger emergency avoidance; radar and visual data are integrated to generate the optimal path using deep reinforcement learning.

[0108] In this embodiment, the millimeter-wave radar pre-screens the detected obstacles, distinguishes different types of obstacles, and delineates the safe flight boundaries of the drone based on the obstacle information, sets a safe flight area for the drone, and avoids entering dangerous areas; the binocular camera continuously monitors the environment, identifies new passable areas, updates the passable area information in real time, transmits it to the drone's obstacle avoidance system, dynamically adjusts the flight path, and uses the identified passable areas to avoid obstacles; the ultrasonic array continuously monitors the near-field environment, pays special attention to sudden obstacles such as a forklift that suddenly appears, responds quickly, and transmits the sudden obstacle information to the drone's obstacle avoidance system, so as to promptly detect and respond to sudden near-field obstacles and ensure Flight safety; the rule engine processes the data transmitted by the ultrasonic array in real time. When an emergency obstacle is detected, it triggers an emergency avoidance action within 50ms, such as lateral dodge, to achieve rapid response and avoid collision with sudden obstacles; it integrates the data of millimeter-wave radar and binocular camera to obtain comprehensive obstacle information, uses deep reinforcement learning algorithm to generate the optimal obstacle avoidance path based on the fused data, and dynamically plans the obstacle avoidance path to ensure that the drone safely bypasses obstacles; it receives real-time environmental data from the digital twin platform, combines the digital twin data, replans long-term routes, optimizes the overall flight path, achieves global optimization, improves flight efficiency, and ensures long-term flight safety.

[0109] Furthermore, the specific steps of the real-time optimization and adjustment method of the drone flight trajectory analysis are as follows: if the real-time evaluation value of the drone flight trajectory analysis is greater than or equal to the third judgment value of the real-time evaluation value of the drone flight trajectory analysis, there is no need to optimize and adjust the real-time performance of the drone flight trajectory analysis; if the real-time evaluation value of the drone flight trajectory analysis is lower than the third judgment value of the real-time evaluation value of the drone flight trajectory analysis, the surrounding obstacles are detected through millimeter wave radar and vision, and the data is uploaded to the blockchain. Based on the pheromone concentration and real-time obstacle data, the weight of each flight segment is calculated, and the optimal path is output, such as the path with the smallest total weight, bypassing the mobile crane, and selecting the container gap. If the priority of the new task is higher than the current task, the path re-planning is triggered.

[0110] In this embodiment, the port's ambient wind speed is monitored in real time. When the wind speed exceeds a preset speed, the rotor folding mechanism is automatically triggered, initiating a glide landing. In high wind conditions, rotor folding reduces drag, ensuring a safe landing for the drone. The glide trajectory is dynamically adjusted based on microscale wind field forecasts. Real-time access to port weather station data predicts wind field changes within the next 30 seconds. Based on the predicted wind field changes, the glide path is adjusted in real time to optimize the glide trajectory, improving stability and safety during the forced landing. A pheromone-based decision-making UAV swarm collaborative search algorithm divides the mission process into three phases: search, communication, and decision-making. UAVs continuously update their pheromone maps through movement. The pheromone map reflects the drone's perception and exploration of the environment. Multi-machine pheromone map fusion is achieved through a communication network. Specifically, drones exchange pheromone data to obtain more comprehensive environmental information, making decisions based on local and global information. Grid pheromone concentration is used as a decision function to guide the drone's position. Areas with high pheromone concentrations are considered to be more likely to contain targets or areas that need further exploration. Fixed obstacles such as lighthouses and warehouses are marked, and constant weights are assigned to these fixed obstacles. These fixed obstacles are permanently considered in path planning to avoid collisions. Temporary obstacles such as mobile cranes and vehicles are marked with dynamic pheromones. The weights of these pheromones decay over time, reflecting the dynamic changes of temporary obstacles and making path planning more flexible. UAVs release pheromones during flight. The lower the difficulty of the path, the higher the pheromone concentration. Surrounding obstacles are detected by onboard sensors (such as millimeter-wave radars and visual sensors). The pheromone concentration reflects the difficulty of the path and provides a basis for path planning; the detected obstacle data is uploaded to the blockchain, which records real-time obstacle data and pheromone concentration to ensure the security and non-tamperability of the data and provide reliable data support for path planning; based on the pheromone concentration and real-time obstacle data, the weight of each segment is calculated. The segment weight is usually determined by the pheromone concentration and obstacle data. The higher the pheromone concentration, the greater the probability of the segment being successfully passed, so a smaller value should be assigned in the weight calculation; the obstacle impact factor can be set according to factors such as the type, size, distance, and moving speed of the obstacle. For example, if an obstacle is large and close to a flight segment, the impact factor is likely to be large, indicating a higher risk for that segment. The optimal route is then outputted with the route with the lowest total weight, such as bypassing mobile cranes and selecting gaps between containers. This allows for optimal path planning, avoiding obstacles, and improving flight efficiency. The system also monitors the priority of new tasks. If a new task has a higher priority than the current one, it triggers path replanning to ensure timely execution of the high-priority task and improves mission responsiveness. The system also monitors the density of drones in a certain area. For example, if the density exceeds five or more, it increases the pheromone volatilization rate in that area to guide subsequent drones around congested areas. This prevents the concentration of drones in specific areas, reduces collision risks, and improves flight efficiency.

[0111] like Figure 2 As shown, it is a structural schematic diagram of the UAV flight trajectory analysis system for port logistics provided by an embodiment of the present application. The UAV flight trajectory analysis system for port logistics provided by an embodiment of the present application includes: a UAV flight trajectory analysis data acquisition and processing module, a UAV flight trajectory analysis data analysis module, a comprehensive analysis module and an optimization adjustment module: UAV flight trajectory analysis data acquisition and processing module: used to acquire and process UAV flight trajectory analysis data; UAV flight trajectory analysis data analysis module: used to analyze the UAV flight trajectory analysis data to obtain a navigation accuracy evaluation value and a UAV dynamic obstacle avoidance evaluation value; comprehensive analysis module: used to obtain a UAV flight trajectory analysis real-time evaluation value through comprehensive analysis of the navigation accuracy evaluation value and the UAV dynamic obstacle avoidance evaluation value; optimization adjustment module: used to compare and analyze the navigation accuracy evaluation value, the UAV dynamic obstacle avoidance evaluation value and the UAV flight trajectory analysis real-time evaluation value with the first determination value of the navigation accuracy evaluation value, the second determination value of the UAV dynamic obstacle avoidance evaluation value and the third determination value of the UAV flight trajectory analysis real-time evaluation value to obtain a UAV flight trajectory analysis optimization adjustment method.

[0112] The drone provided in the embodiment of the present application includes: the drone cargo compartment adopts a carbon fiber honeycomb sandwich structure, is equipped with an adaptive electromagnetic suspension system, and adjusts the cargo compartment damping in real time through electrorheological fluid; the drone fuselage is sprayed with a polyurethane coating containing microcapsules; the drone motor adopts a closed magnetic levitation bearing; and the drone belly is coated with a superhydrophobic material.

[0113] In this embodiment, the combination of a carbon fiber honeycomb sandwich structure and a self-healing nanocoating achieves both lightweighting and enhanced adaptability to extreme environments. The enclosed magnetic bearing motor addresses salt spray corrosion and significantly increases lifespan. The application of a hydrogen-electric hybrid system in the drone field significantly extends flight range and reduces carbon emissions. The supercapacitor module supports instantaneous 20kW discharge to cope with emergency climbs in gusts of wind. A blockchain-based "swarm transport" model, with decentralized task allocation and swarm intelligence algorithms optimizing routes, improves efficiency by 30%. The onboard customs smart lock and "flight declaration channel" enable cross-border customs clearance in seconds.

[0114] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0118] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0119] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

Claims

1. A UAV flight trajectory analysis method for port logistics, characterized by: The following steps are involved: Collect and process drone flight trajectory analysis data; The specific steps of collecting and processing the UAV flight trajectory analysis data are as follows: Collect raw data of drone flight trajectory analysis through drone detection and positioning equipment; The original data of UAV flight trajectory analysis is cleaned and denoised to obtain UAV flight trajectory analysis data; By using a binocular camera to capture continuous image frames, a feature extraction algorithm is used to extract key points and their descriptors from the images. The feature points between consecutive frames are matched using a feature point matching algorithm. The position and attitude of the drone are estimated using visual odometry technology. The cumulative scale error caused by the feature point matching error is analyzed, and the change of the scale error over time is recorded to form time series data. By fitting the time series data, the rate at which the scale error increases over time, that is, the scale drift rate, is calculated. The UAV flight trajectory analysis data includes navigation accuracy data and UAV dynamic obstacle avoidance data; Analyze the UAV flight trajectory analysis data to obtain the navigation accuracy evaluation value and the UAV dynamic obstacle avoidance evaluation value; The real-time evaluation value of UAV flight trajectory analysis is obtained through comprehensive analysis of navigation accuracy evaluation value and UAV dynamic obstacle avoidance evaluation value; The specific steps of obtaining the navigation accuracy evaluation value are as follows: The navigation accuracy data includes heading angle error rate, scale drift rate, obstacle avoidance response time, actual position horizontal coordinate of the drone, and actual position vertical coordinate of the drone; Obtain the heading angle error rate threshold, scale drift rate threshold, obstacle avoidance response time threshold, standard value of the horizontal coordinate of the UAV position, standard value of the vertical coordinate of the UAV position, weight factor of the heading angle error rate, weight factor of the UAV position error coefficient, weight factor of the scale drift rate, and weight factor of the obstacle avoidance response time from the UAV flight trajectory analysis database; The analysis results of the heading angle error rate threshold and the heading angle error rate ratio are averaged in different navigation accuracy detection sections and recorded as the first component of the navigation accuracy evaluation value; The second component of the navigation accuracy evaluation value is obtained by coupling the actual horizontal coordinate of the drone's position with the standard value of the horizontal coordinate of the drone's position and the actual vertical coordinate of the drone's position with the standard value of the vertical coordinate of the drone's position; The analysis result of the scale drift rate threshold and the scale drift rate ratio is recorded as the third component of the navigation accuracy evaluation value; The analysis results of the ratio of obstacle avoidance response time to obstacle avoidance response time threshold are averaged in different navigation accuracy detection segments and recorded as the fourth component of the navigation accuracy evaluation value; Performing a ratio analysis on the coupling result of the first component of the navigation accuracy evaluation value and the third component of the navigation accuracy evaluation value and the coupling result of the second component of the navigation accuracy evaluation value and the fourth component of the navigation accuracy evaluation value to obtain a navigation accuracy evaluation value; The navigation accuracy evaluation value represents quantitative data of the degree of proximity between the drone position information and the true position, which is obtained by combining the first component of the navigation accuracy evaluation value, the second component of the navigation accuracy evaluation value, the third component of the navigation accuracy evaluation value, and the fourth component of the navigation accuracy evaluation value; The navigation accuracy evaluation value, the UAV dynamic obstacle avoidance evaluation value and the UAV flight trajectory analysis real-time evaluation value are compared and analyzed with the first judgment value of the navigation accuracy evaluation value, the second judgment value of the UAV dynamic obstacle avoidance evaluation value and the third judgment value of the UAV flight trajectory analysis real-time evaluation value to obtain the UAV flight trajectory analysis optimization and adjustment method.

2. The method for analyzing the flight trajectory of a UAV for port logistics according to claim 1, wherein: The specific steps for obtaining the UAV dynamic obstacle avoidance evaluation value are as follows: The dynamic obstacle avoidance data of the UAV includes obstacle avoidance response time, path replanning response time, scale drift rate, actual horizontal coordinate of the UAV obstacle avoidance action, and actual vertical coordinate of the UAV obstacle avoidance action; Obtain the obstacle avoidance response time threshold, path replanning response time threshold, scale drift rate threshold, standard value of the horizontal coordinate of the obstacle avoidance action of the drone, standard value of the vertical coordinate of the obstacle avoidance action of the drone, weight factor of the obstacle avoidance response time, weight factor of the path replanning response time, weight factor of the scale drift rate, and weight factor of the dynamic trajectory deviation coefficient from the drone flight trajectory analysis database; The analysis results of the ratio of obstacle avoidance response time to obstacle avoidance response time threshold are averaged in different UAV dynamic obstacle avoidance detection segments and recorded as the first component of the UAV dynamic obstacle avoidance evaluation value; The analysis results of the ratio of the path replanning response time to the path replanning response time threshold are averaged in different UAV dynamic obstacle avoidance detection segments and recorded as the second component of the UAV dynamic obstacle avoidance evaluation value; The analysis result of the ratio of scale drift rate to scale drift rate threshold is recorded as the third component of the UAV dynamic obstacle avoidance evaluation value; The fourth component of the dynamic obstacle avoidance evaluation value of the drone is obtained by performing a ratio processing on the actual horizontal coordinate of the drone obstacle avoidance action and the standard value of the horizontal coordinate of the drone obstacle avoidance action, and the actual vertical coordinate of the drone obstacle avoidance action and the standard value of the vertical coordinate of the drone obstacle avoidance action; The first component of the UAV dynamic obstacle avoidance evaluation value, the second component of the UAV dynamic obstacle avoidance evaluation value, the third component of the UAV dynamic obstacle avoidance evaluation value and the fourth component of the UAV dynamic obstacle avoidance evaluation value are coupled to obtain the UAV dynamic obstacle avoidance evaluation value; The drone dynamic obstacle avoidance evaluation value represents the quantitative data of the drone dynamic obstacle avoidance evaluation value first component, drone dynamic obstacle avoidance evaluation value second component, drone dynamic obstacle avoidance evaluation value third component and drone dynamic obstacle avoidance evaluation value fourth component on the ability to avoid obstacles in complex and dynamic environments.

3. The UAV flight trajectory analysis method for port logistics according to claim 1, characterized in that: The specific steps of obtaining the real-time evaluation value of the UAV flight trajectory analysis through comprehensive analysis are as follows: The UAV dynamic obstacle avoidance data includes an obstacle data update frequency threshold, a navigation accuracy evaluation value weighting factor, an obstacle data update frequency weighting factor, and a UAV dynamic obstacle avoidance evaluation value weighting factor; The navigation accuracy evaluation value is averaged at different navigation accuracy detection points and recorded as the first component of the real-time evaluation value of the UAV flight trajectory analysis; The analysis results of the ratio of obstacle data update frequency to obstacle data update frequency threshold are averaged at different UAV flight trajectory analysis real-time detection points and recorded as the second component of the UAV flight trajectory analysis real-time evaluation value; The UAV dynamic obstacle avoidance evaluation value is averaged at different UAV dynamic obstacle avoidance detection points and recorded as the third component of the UAV flight trajectory analysis real-time evaluation value; Perform a ratio analysis on the coupling result of the first component of the UAV flight trajectory analysis real-time evaluation value and the second component of the UAV flight trajectory analysis real-time evaluation value and the third component of the UAV flight trajectory analysis real-time evaluation value to obtain the UAV flight trajectory analysis real-time evaluation value; The real-time evaluation value of the UAV flight trajectory analysis represents the quantitative degree of real-time data processing of the UAV during flight by the first component of the UAV flight trajectory analysis real-time evaluation value, the second component of the UAV flight trajectory analysis real-time evaluation value and the third component of the UAV flight trajectory analysis real-time evaluation value.

4. The method for analyzing the flight trajectory of a UAV for port logistics according to claim 1, wherein: The specific steps of the method for analyzing and optimizing the UAV flight trajectory are as follows: Obtaining a first determination value of the navigation accuracy evaluation value, a second determination value of the drone dynamic obstacle avoidance evaluation value, and a third determination value of the drone flight trajectory analysis real-time evaluation value from the drone flight trajectory analysis database; The UAV flight trajectory analysis optimization and adjustment method includes a navigation accuracy optimization and adjustment method, a UAV dynamic obstacle avoidance optimization and adjustment method, and a UAV flight trajectory analysis real-time optimization and adjustment method; If the navigation accuracy evaluation value is greater than or equal to the first navigation accuracy evaluation value, then there is no need to optimize and adjust the navigation accuracy; If the navigation accuracy assessment value is lower than the first judgment value of the navigation accuracy assessment value, the visual SLAM data is fused through Kalman filtering to suppress the angle error; it is directly connected to the port BIM system to obtain the coordinates of dynamic obstacle cranes and containers in real time, generate a 4D flight corridor, and the digital twin platform receives the crane positioning data and dynamically updates the flight restricted area. The drone adjusts its path according to the real-time corridor to avoid the signal blocking area.

5. The method for analyzing the flight trajectory of a UAV for port logistics according to claim 4, characterized in that: The specific steps of the UAV dynamic obstacle avoidance optimization adjustment method are as follows: If the UAV dynamic obstacle avoidance evaluation value is lower than or equal to the second determination value of the UAV dynamic obstacle avoidance evaluation value, then there is no need to optimize and adjust the UAV dynamic obstacle avoidance; If the UAV's dynamic obstacle avoidance evaluation value is greater than the second judgment value of the UAV's dynamic obstacle avoidance evaluation value, the ultrasonic data is processed based on the rule engine to trigger emergency avoidance; radar and visual data are integrated to generate the optimal path using deep reinforcement learning.

6. The method for analyzing the flight trajectory of a UAV for port logistics according to claim 4, characterized in that: The specific steps of the real-time optimization and adjustment method for UAV flight trajectory analysis are as follows: If the real-time evaluation value of the UAV flight trajectory analysis is greater than or equal to the third determination value of the real-time evaluation value of the UAV flight trajectory analysis, then there is no need to optimize and adjust the real-time performance of the UAV flight trajectory analysis; If the real-time evaluation value of the drone flight trajectory analysis is lower than the third judgment value of the real-time evaluation value of the drone flight trajectory analysis, the surrounding obstacles are detected through millimeter-wave radar and vision, and the data is uploaded to the blockchain. Based on the pheromone concentration and real-time obstacle data, the weight of each flight segment is calculated, and the optimal path is output to bypass the mobile crane and select the container gap. If the new task has a higher priority than the current task, path replanning is triggered.

7. A UAV flight trajectory analysis system for port logistics using the UAV flight trajectory analysis method for port logistics according to any one of claims 1 to 6, characterized in that: It includes UAV flight trajectory analysis data acquisition and processing module, UAV flight trajectory analysis data analysis module, comprehensive analysis module and optimization and adjustment module: UAV flight trajectory analysis data acquisition and processing module: used to collect and process UAV flight trajectory analysis data; UAV flight trajectory analysis data analysis module: used to analyze the UAV flight trajectory analysis data to obtain the navigation accuracy evaluation value and the UAV dynamic obstacle avoidance evaluation value; Comprehensive analysis module: used to obtain the real-time evaluation value of the UAV flight trajectory analysis through comprehensive analysis of the navigation accuracy evaluation value and the UAV dynamic obstacle avoidance evaluation value; Optimization and adjustment module: used to compare and analyze the navigation accuracy evaluation value, the UAV dynamic obstacle avoidance evaluation value and the UAV flight trajectory analysis real-time evaluation value with the first judgment value of the navigation accuracy evaluation value, the second judgment value of the UAV dynamic obstacle avoidance evaluation value and the third judgment value of the UAV flight trajectory analysis real-time evaluation value, and obtain the UAV flight trajectory analysis optimization and adjustment method.

8. The drone of the drone flight trajectory analysis system for port logistics according to claim 7 is characterized in that: include: The drone's cargo hold uses a carbon fiber honeycomb sandwich structure and is equipped with an adaptive electromagnetic suspension system that uses electrorheological fluid to adjust the cargo hold damping in real time. The drone fuselage is sprayed with a polyurethane coating containing microcapsules; The drone motor uses a closed magnetic bearing; The belly of the drone is coated with superhydrophobic material.

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