An unmanned aerial vehicle hoisting task optimization method and device

Abstract

The application discloses a kind of unmanned aerial vehicle hoisting task optimization method and device, and the application relates to unmanned aerial vehicle scheduling technical field, comprising the following steps: based on hoisting starting point and target construction site determines hoisting path boundary, and the point cloud data of this area is collected, set along the way node, obtain the signal strength of each node and analyze its fluctuation, filter out the effective path point of signal stability, according to the distance of effective path point and target construction site Priority is set, with hoisting starting point as center, determine search range, select several candidate nodes from it, and collect wind speed variation, terrain complexity and cargo swing angle, calculate the risk cost of each node, select the candidate node with the lowest risk cost as the next waypoint, update the current effective path point of unmanned aerial vehicle, until unmanned aerial vehicle reaches target construction site, complete hoisting transport task, realize more safe and reliable transport movement.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) scheduling technology, specifically to a method and apparatus for optimizing UAV hoisting tasks. Background Technology

[0002] Photovoltaic construction and hoisting tasks in complex mountainous environments are an extremely challenging application scenario. It requires drones to hoist and transport goods in construction areas with high altitudes, significant terrain undulations, and harsh environments. Under the current technological background, photovoltaic construction and hoisting tasks mainly rely on traditional ground transportation methods or manual operation of hoisting equipment. Although these methods can effectively complete the task on flat terrain, their application efficiency and safety are greatly reduced in mountainous environments. Mountainous terrain not only has physical obstacles such as steep slopes and protruding rocks, but may also be significantly affected by weather conditions, such as strong winds and other severe weather. In such environments, traditional hoisting methods face the problems of low transportation efficiency, high energy consumption, and even the inability to guarantee construction safety.

[0003] Current drone hoisting technology typically uses a pre-defined path for task planning, which means that a fixed flight path is generated based on the geographical information between the hoisting starting point and the target construction site, and then the drone flies along this path. In the prior art, CN116088583A discloses a method, apparatus, device, and storage medium for planning unmanned aerial vehicle (UAV) mission paths. Specifically, the method first creates UAV objects, a lifting platform object, a hangar object, and a takeoff position object based on the current number of UAVs. Then, predicates are added to all objects. Next, the initial state is initialized based on target detection results. Then, the target state, i.e., the target position of the UAV, is determined according to the mission instructions. Finally, actions and operations are added to control the UAV. The lifting platform can rise and fall. Finally, an intelligent planning language is used to plan the UAV's mission path, and the UAV's position information is returned through target detection to ensure the UAV's position is secure. While no collision will occur, this approach, which selects the shortest fixed path, ignores the effects of wind speed changes and turbulence. These factors are very common in mountainous environments and may lead to decreased flight stability of the drone, increasing the risk of swaying or even falling of the hoisted cargo. In addition, due to the significant terrain undulations in mountainous environments, signal transmission is easily blocked and interfered with, resulting in large fluctuations in signal strength. This situation not only affects communication between the drone and the control terminal but also prevents the drone navigation system from accurately positioning itself, thereby increasing flight risks. Therefore, relying solely on a simple navigation system and geographic data for flight path calculation is unlikely to meet the requirements of drone hoisting missions in complex mountainous environments.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for optimizing unmanned aerial vehicle (UAV) hoisting tasks, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An optimization method for drone hoisting tasks includes the following steps: S1: Based on the starting point and target construction location of the UAV hoisting mission, determine the hoisting path boundary, collect surface point cloud data of the mountain within the hoisting path boundary, and set up several nodes along the hoisting path boundary based on the point cloud data. S2: Obtain the signal strength of each node along the route and analyze its fluctuation amplitude, so as to retain the nodes along the route whose signal strength meets the stability requirements as valid path points; S3: Determine the priority of each valid path point based on the distance between the valid path point and the target construction site, and set the hoisting start point and the target construction site as the valid path points with the lowest and highest priorities, respectively; S4: Take the hoisting starting point as the current effective path point and use it as the center to determine the search range. Within the search range, select several effective path points as candidate nodes for the next stage according to priority. Collect the wind speed change of each candidate node, the terrain complexity of the shortest path between the current effective path point and each candidate node, and the swing angle of the hoisted goods at the current effective path point. Calculate the risk cost of going to each candidate node. S5: Select the candidate node with the lowest risk and cost as the next waypoint. After the UAV reaches the waypoint, update the current valid path point of the UAV. Repeat the iteration until the UAV reaches the target construction site and completes the hoisting and transportation task.

[0007] Furthermore, the logic for determining the specific boundary of the hoisting path is as follows: using the hoisting starting point and the target construction location as the major axis diameter, and using the set minor axis diameter, construct an elliptical closed boundary for the hoisting path; The logic behind pre-setting a number of nodes along the route based on surface point cloud data of the mountainous area within the hoisting path boundary is as follows: The mountainous area within the hoisting path boundary is divided into several sub-regions of equal area. Point cloud data of the mountain surface in each sub-region is collected. The pre-set number of nodes along the route in each sub-region is determined based on the average undulation of the point cloud data within that sub-region. The formula for adjusting the pre-set number of nodes along the route is as follows: In the formula, Preset the number of corrected nodes along the path in the s-th sub-region. This represents the initial preset number of nodes along the route within the sub-region. Let be the average undulation of the s-th sub-region, which is characterized by the standard deviation of the height of each point in the point cloud data corresponding to that sub-region. Here, s is the reference value for the degree of fluctuation, and s is the index of the sub-region; The method for setting along-path nodes in a sub-region is as follows: based on the preset number of along-path nodes after correction in the s-th sub-region, the same number of along-path nodes are randomly set in the sub-region.

[0008] Furthermore, the logic for determining whether the signal strength meets the stability requirements is as follows: The signal strength of each node along the route during the historical monitoring period is obtained, normalized, and the mean and variance of the signal strength of each node during the historical monitoring period are calculated. The signal stability coefficient is then calculated based on the mean and variance of the signal strength. This signal stability coefficient characterizes the stability of the communication signal at the nodes along the route. The specific formula for calculating the signal stability coefficient is as follows: In the formula, Let be the signal stability coefficient of the i-th node along the path. Let be the average signal strength of the i-th node along the route during the historical monitoring period. Let Variance be the signal strength variance of the i-th node along the route during the historical monitoring period. The signal strength variance is used to characterize the amplitude of signal strength fluctuations. and These are the weighting coefficients for signal strength and signal strength fluctuation, respectively, and i is the index of the node along the path. and and All are greater than 0; The variance of signal strength fluctuation at the i-th node along the route during the historical monitoring period. The specific formula used for the calculation is as follows: In the formula, Let be the normalized signal strength value of the i-th node along the route at the j-th sampling time, where j is the index of the sampling time within the historical monitoring period. , This represents the total number of sampling times within the historical monitoring period. The logic behind retaining path nodes whose signal strength meets stability requirements as valid path points is as follows: Set a signal stability threshold, compare the signal stability coefficient with the signal stability threshold, remove path nodes whose signal stability coefficient is greater than or equal to the signal stability threshold, and retain path nodes whose signal stability coefficient is less than the signal stability threshold, and record them as valid path points.

[0009] Furthermore, the specific steps for determining the priority of each effective path point based on the distance between the effective path point and the target construction site are as follows: A three-dimensional coordinate system is established, with the direction from the hoisting start point to the target construction site as the positive x-axis, the direction perpendicular to the x-axis as the y-axis, and the Z-axis determined using the right-hand rule. The three-dimensional coordinates of the hoisting start point, the target construction site, and each effective path point are then determined. Based on these three-dimensional coordinates, the distance between each effective path point and the target construction site is calculated. Combining this with the distance between the hoisting start point and the target construction site, a priority coefficient for each effective path point is calculated. This priority coefficient characterizes the priority of each effective path point. The specific formula for calculating the priority coefficient is as follows: In the formula, Let be the priority coefficient of the p-th valid path point. Let p be the distance between the p-th valid path point and the target construction site, where p is the index of the valid path point. The distance between the hoisting starting point and the target construction site is calculated using the coordinates of each point. The priority coefficient of the p-th valid path point is also considered. The value is inversely proportional to the priority of the valid path point.

[0010] Furthermore, the logic for determining the specific search range is as follows: set a search radius to form a closed circular search range, which serves as the single search range for the UAV, and the single search range includes at least two candidate nodes; The specific logic for determining candidate nodes is as follows: among the valid path points within the search range, select valid path points with higher priority than the current valid path point as candidate nodes, that is, select valid path points with priority coefficients lower than the current valid path point priority coefficients as candidate nodes.

[0011] Furthermore, the specific logic for collecting the wind speed changes of each candidate node is as follows: obtain the real-time wind speed of each candidate node within the real-time monitoring period, perform normalization processing, and calculate the mean and variance of the real-time wind speed of each candidate node within the real-time monitoring period. Based on the real-time mean and variance of wind speed at each candidate node, the terrain complexity of the shortest path from the current effective path point to each candidate node, and the swing angle of the hoisted cargo at the current effective path point, a risk cost index for traveling to each candidate node is calculated. This risk cost index characterizes the risk cost of traveling to each candidate node. The specific formula used to calculate the risk cost index is as follows: In the formula, Let be the risk cost index of the q-th candidate node. Let be the wind field risk factor for the q-th candidate node. This is the terrain risk factor for the shortest path from the current valid path point to the q-th candidate node, used to characterize the terrain complexity of the shortest path from the current valid path point to each candidate node. and These are the weighting coefficients for wind field risk and topographic risk, respectively. and and All are greater than 0, where q is the candidate node index; Among them, wind farm risk factors The wind field risk factor is calculated by coupling the wind speed changes at each candidate node with the swing amplitude of the hoisted cargo at the current effective path point. The specific formula used is as follows: In the formula, Let be the average real-time wind speed of the q-th candidate node. To monitor in real time the maximum swing angle of the hoisted goods at the current effective path point within a given time period, Let be the real-time wind speed variance of the q-th candidate node; Terrain risk factor of the shortest path from the current valid path point to the q-th candidate node Specifically, the calculation is performed using the average undulation of the terrain and the slope of the terrain. The specific formula used for the calculation is as follows: In the formula, The average terrain undulation is the shortest path from the current valid path point to the q-th candidate node. It represents the average slope of the shortest path from the current valid path point to the q-th candidate node.

[0012] The present invention also provides a drone hoisting task optimization device, which is used to execute the above-described drone hoisting task optimization method, comprising: The preset node generation module is used to determine the lifting path boundary based on the lifting start point and target construction location of the UAV lifting mission, collect surface point cloud data of the mountain within the lifting path boundary, and set several nodes along the way based on the point cloud data within the lifting path boundary. The effective node filtering module is used to obtain the signal strength of each node along the route and analyze its fluctuation amplitude, so as to retain the nodes along the route whose signal strength meets the stability requirements as effective path points. The priority definition module is used to determine the priority of each valid path point based on the distance between the valid path point and the target construction site, setting the hoisting start point and the target construction site as the valid path points with the lowest and highest priorities, respectively. The risk cost analysis module is used to take the hoisting starting point as the current effective path point and determine the search range centered on it. Within the search range, several effective path points are selected as candidate nodes for the next stage according to priority. The module also collects the wind speed changes of each candidate node, the terrain complexity of the shortest path between the current effective path point and each candidate node, and the swing angle of the hoisted cargo at the current effective path point to calculate the risk cost of going to each candidate node. The task path generation module is used to select the candidate node with the lowest risk and cost as the next waypoint. After the UAV arrives at the waypoint, the current valid path point of the UAV is updated. This process is repeated until the UAV arrives at the target construction site and completes the hoisting and transportation task.

[0013] Compared with the prior art, the beneficial effects of the present invention are: First, by using the pre-set nodes along the route based on the point cloud data of the mountain surface, the drone can design a more flexible path in complex terrain. Directly selecting the shortest path often fails to take into account the undulations of the actual terrain, obstacles and other environmental factors. By planning the flight route through the nodes along the route, the safest, most stable and adjustable optimal path can be achieved, reducing the risk of encountering unexpected events during flight and lowering the risk of the drone going out of control or crashing.

[0014] Meanwhile, by filtering the set nodes through signal stability, the communication of the drone is guaranteed. Even in the event of loss of control or crash, the accident location can be quickly located and corresponding countermeasures can be taken immediately. By setting effective path nodes at key locations, the drone can avoid these dangerous areas, thereby achieving safer and more reliable transportation. Secondly, by dynamically evaluating the priority of effective waypoints, the flight path is flexibly adjusted during the drone's flight. This risk-based selection mechanism enables the drone to choose the optimal waypoint when faced with various factors such as changes in wind speed, terrain complexity, and cargo swaying, significantly reducing flight risks. By continuously updating waypoints and collecting environmental parameters in real time, the drone can achieve efficient path optimization in complex construction environments, ultimately ensuring the successful completion of the lifting task. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a curve showing the real-time mean wind speed versus the sway angle. Figure 3 The curve is a fitting curve of average terrain undulation versus average slope. Figure 4 A bar chart showing the risk cost index and risk factors; Figure 5 This is a schematic diagram of the overall device structure of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example: Please see Figures 1-4 The present invention provides a technical solution: An optimization method for drone hoisting tasks includes the following steps: S1: Based on the starting point and target construction location of the UAV hoisting mission, determine the hoisting path boundary, collect surface point cloud data of the mountain within the hoisting path boundary, and set several nodes along the hoisting path boundary based on the point cloud data.

[0019] The logic for determining the specific boundaries of the hoisting path is as follows: using the hoisting start point and the target construction location as the major axis diameter, and using the set minor axis diameter, construct an elliptical closed boundary for the hoisting path; The major axis of the hoisting path is the straight-line distance between the starting point and the target location. The line connecting the starting point and the target location is taken as the direction of the major axis, and the length of this line is used as the core control parameter of the entire hoisting path. The diameter of the minor axis is set according to the actual hoisting needs and is used to determine the width of the path. The width can be set according to safety requirements, space limitations for hoisting operations, and the swing range of the hoisting equipment. The length of the minor axis is generally set to 0.3 to 0.6 times the length of the major axis. The specific reasons are as follows: if the length of the minor axis is too small, there will be too few selectable nodes along the way, making it impossible to determine the optimal node along the way, and the beneficial effects of the solution cannot be fully realized. If the length of the minor axis is too large, the computational load in finding the optimal node along the way will increase significantly, failing to meet the real-time requirements and increasing the consumption of computing resources.

[0020] The center of the hoisting path is located between the starting point and the target location. Based on the set major axis length and minor axis width, a regular elliptical region is constructed with the center point as the reference, resulting in a closed elliptical region with the starting point and target location as the main constraints and the width as the set value. This region is the boundary of the hoisting path, ensuring that the object remains within this safe range throughout the hoisting process.

[0021] The logic behind pre-setting a number of nodes along the route based on surface point cloud data of the mountainous area within the hoisting path boundary is as follows: The mountainous area within the hoisting path boundary is divided into several sub-regions of equal area. Point cloud data of the mountain surface in each sub-region is collected. The pre-set number of nodes along the route in each sub-region is determined based on the average undulation of the point cloud data within that sub-region. The formula for adjusting the pre-set number of nodes along the route is as follows: In the formula, Preset the number of corrected nodes along the path in the s-th sub-region. This represents the initial preset number of nodes along the route within the sub-region. Let be the average undulation of the s-th sub-region, which is characterized by the standard deviation of the height of each point in the point cloud data corresponding to that sub-region. 's' is a reference value for the degree of fluctuation, and 's' is the index of the sub-region.

[0022] The method for setting along-path nodes in a sub-region is as follows: based on the preset number of along-path nodes after correction in the s-th sub-region, the same number of along-path nodes are randomly set in the sub-region.

[0023] It should be noted that the formula compares the average undulation of each sub-region. Compared with reference value To adjust the number of nodes, areas with higher undulation and greater terrain complexity are more difficult to determine the optimal along-path nodes. Therefore, more nodes are needed to ensure the selection of the best along-path nodes. If the undulation of a certain sub-region is significantly higher than the reference value, i.e. Greater than If the fluctuation is low, the number of nodes will increase; conversely, if the fluctuation is low, the number of nodes will decrease. This helps to allocate resources reasonably and ensure that there are enough sampling points in more complex areas.

[0024] The standard deviation of the elevation of each point in the point cloud data of this sub-region is used to characterize the terrain. The larger the standard deviation, the more pronounced the topographic relief in the region. and Proportional. The initial preset number of nodes along the route. The specific settings can be determined based on expert experience. Refer to the experience values ​​of similar projects in the past. Experts will provide historical data for specific types of terrain or images to help determine a reasonable initial number of nodes. For relatively flat or simple terrain, the number of nodes can be relatively small, usually 10-15 along the route. If the terrain has large undulations or changes, 20-30 along the route can be set to ensure sufficient coverage and information collection.

[0025] S2: Obtain the signal strength of each node along the route and analyze its fluctuation amplitude, so as to retain the nodes along the route whose signal strength meets the stability requirements as valid path points.

[0026] The logic for determining whether signal strength meets stability requirements is as follows: The signal strength of each node along the route is obtained during a historical monitoring period, typically 0.5 to 1 hour before the lifting operation. This historical monitoring period is then normalized, and the mean and variance of the signal strength at each node are calculated. A signal stability coefficient is then calculated based on the mean and variance of the signal strength. This coefficient characterizes the stability of the communication signal at each node. The specific formula for calculating the signal stability coefficient is as follows: In the formula, Let be the signal stability coefficient of the i-th node along the path. Let be the average signal strength of the i-th node along the route during the historical monitoring period. Let Variance be the signal strength variance of the i-th node along the route during the historical monitoring period. The signal strength variance is used to characterize the amplitude of signal strength fluctuations. and These are the weighting coefficients for signal strength and signal strength fluctuation, respectively, and i is the index of the node along the path. and and All are greater than 0; It should be noted that the signal stability coefficient of the nodes along the route Used to characterize the stability of communication signals, The larger the value, the more unstable the communication signal of the i-th node along the way; The mean signal strength It is one of the core factors in evaluating signal stability. A higher mean value usually indicates better signal quality and is beneficial to communication stability. The formula uses an inverse proportional relationship, namely... This means that as the mean signal strength increases, the signal stability coefficient... The signal strength decreases, and vice versa; this setting reflects the positive impact of signal strength on the stability of communication signals. With signal stability coefficient Inversely proportional; Signal strength variance Variance is used to characterize the degree of fluctuation in signal strength. If the variance is large, it means that the signal strength changes drastically, which usually leads to communication instability. Using variance as an additional term can intuitively reflect the impact of signal fluctuation on stability. The larger the variance, the lower the signal stability. The variance of signal strength fluctuation at the i-th node along the route during the historical monitoring period. The specific formula used for the calculation is as follows: In the formula, Let be the normalized signal strength value of the i-th node along the route at the j-th sampling time, where j is the index of the sampling time within the historical monitoring period. , This represents the total number of sampling times within the historical monitoring period. In practical communication, signal stability is usually more important. Even if the mean signal strength is high, large signal fluctuations (i.e., large variance) can still lead to communication instability. Therefore, when evaluating signal stability, signal fluctuations need to be given higher weight. and and All are greater than 0.

[0027] The logic behind retaining path nodes whose signal strength meets stability requirements as valid path points is as follows: Set a signal stability threshold, compare the signal stability coefficient with the signal stability threshold, remove path nodes whose signal stability coefficient is greater than or equal to the signal stability threshold, and retain path nodes whose signal stability coefficient is less than the signal stability threshold and record them as valid path points. The signal stability threshold is specifically set based on the signal strength during stable communication combined with expert experience.

[0028] S3: Determine the priority of each valid path point based on the distance between the valid path point and the target construction site, and set the hoisting start point and the target construction site as the valid path points with the lowest and highest priorities, respectively.

[0029] The specific steps for determining the priority of each effective path point based on the distance between the effective path point and the target construction site are as follows: A three-dimensional coordinate system is established, with the direction from the hoisting start point to the target construction site as the positive x-axis, the direction perpendicular to the x-axis as the y-axis, and the Z-axis determined using the right-hand rule. The three-dimensional coordinates of the hoisting start point, the target construction site, and each effective path point are then determined. Based on these three-dimensional coordinates, the distance between each effective path point and the target construction site is calculated. Combining this distance with the calculation of the distance between the hoisting start point and the target construction site, a priority coefficient for each effective path point is calculated. This priority coefficient characterizes the priority of each effective path point. The specific formula for calculating the priority coefficient is as follows: In the formula, Let be the priority coefficient of the p-th valid path point. Let p be the distance between the p-th valid path point and the target construction site, where p is the index of the valid path point. The distance between the hoisting starting point and the target construction site is calculated using the coordinates of each point. The priority coefficient of the p-th valid path point is also considered. The value is inversely proportional to the priority of the valid path point.

[0030] It should be noted that priority is defined based on the distance between valid waypoints and the target construction site. The closer the distance, the higher the priority, meaning that the waypoint should be considered earlier during task execution. Therefore, the priority coefficient should be inversely proportional to the distance. along with The increase in priority coefficients indicates that the farther away from the target construction site, the higher the priority coefficient, thus reflecting its relatively low priority.

[0031] Distance Divide by the distance between the hoisting starting point and the target construction site This method standardizes the distances between valid path points, allowing for horizontal comparison of priority coefficients across different path points. This eliminates the influence of different distance units or scales, ensuring the rationality and consistency of priorities. Through this standardization, the priority coefficient value can vary from 0 to 1, making the hierarchy of priorities more intuitive and facilitating subsequent decision-making.

[0032] S4: Take the hoisting starting point as the current valid path point and use it as the center to determine the search range. Within the search range, select several valid path points as candidate nodes for the next stage according to priority. Collect the wind speed changes of each candidate node, the terrain complexity of the shortest path between the current valid path point and each candidate node, and the swing angle of the hoisted goods at the current valid path point. Calculate the risk cost of going to each candidate node.

[0033] The logic for determining the search range is as follows: set a search radius to form a closed circular search range, which serves as the single search range for the UAV. The single search range includes at least two candidate nodes. The specific logic for determining candidate nodes is as follows: among the valid path points within the search range, select valid path points with higher priority than the current valid path point as candidate nodes, that is, select valid path points with priority coefficients lower than the priority coefficients of the current valid path point.

[0034] The logic for collecting wind speed changes at each candidate node is as follows: obtain the real-time wind speed of each candidate node within the real-time monitoring period, where the real-time monitoring period is generally set to 5 to 10 minutes before the drone arrives at the corresponding candidate node, perform normalization processing, and calculate the mean and variance of the real-time wind speed of each candidate node within the real-time monitoring period. Based on the real-time mean and variance of wind speed at each candidate node, the terrain complexity of the shortest path from the current effective path point to each candidate node, and the swing angle of the hoisted cargo at the current effective path point, a risk cost index for traveling to each candidate node is calculated. This risk cost index characterizes the risk cost of traveling to each candidate node. The specific formula used to calculate the risk cost index is as follows: In the formula, Let be the risk cost index of the q-th candidate node. Let be the wind field risk factor for the q-th candidate node. This is the terrain risk factor for the shortest path from the current valid path point to the q-th candidate node, used to characterize the terrain complexity of the shortest path from the current valid path point to each candidate node. and These are the weighting coefficients for wind field risk and topographic risk, respectively. and and All are greater than 0, where q is the candidate node index; It should be noted that the risk cost index of the q-th candidate node This reflects the level of risk associated with traveling to that node, aiming to quantify the overall risk of traveling to each candidate node. This allows for the effective assessment and comparison of the safety of different paths when selecting a path or node. The larger the value, the greater the risk and cost of going to that node.

[0035] in, By considering the mean and variance of real-time wind speed, the potential threat posed by wind speed changes to hoisting operations can be reflected. For example, in high wind conditions, the safety of hoisting operations is significantly reduced, and wind speed fluctuations, i.e., wind speed variance, also lead to additional risks. Therefore, wind field risk factors are used to characterize the risks.

[0036] Assessing terrain complexity reflects the impact of terrain on operational risks. Complex terrain can lead to difficulties and potential dangers in lifting operations, making terrain risk assessment equally important.

[0037] Among them, wind farm risk factors The wind field risk factor is calculated by coupling the wind speed changes at each candidate node with the swing amplitude of the hoisted cargo at the current effective path point. The specific formula used is as follows: In the formula, Let be the average real-time wind speed of the q-th candidate node. To monitor in real time the maximum swing angle of the hoisted goods at the current effective path point within a given time period, Let be the real-time wind speed variance of the q-th candidate node; It should be noted that wind farm risk factors It is used to reflect the impact of wind speed changes and the swaying of hoisted goods on operational risks. The larger the value, the greater the wind speed and the more complex the wind speed changes, and the greater the risk of going to that node.

[0038] Average wind speed High wind speeds are a major factor affecting the safety of lifting operations. Higher wind speeds can cause cargo instability, increasing operational risks. (Using square root...) The form can reflect the non-linear relationship between wind speed and risk factors, emphasizing the rapid increase in risk when wind speed increases.

[0039] Wind speed variance This reflects the fluctuations in wind speed, which affect the stability of hoisting operations, and is presented in logarithmic form. This can effectively mitigate the impact of variance on risk, avoid excessive influence of extreme values ​​on risk factors, and ensure a smoother risk assessment. When wind speed fluctuations are large, variance increases, and this item will increase, thereby improving... The value indicates that unstable wind speeds will lead to higher operational risks.

[0040] Real-time monitoring of the maximum swing angle of the hoisted goods at the current effective path point within the specified time period. This refers to the swing amplitude of hoisted goods caused by wind and other external factors. It is usually expressed as the angle at which the goods deviate from the vertical direction. The larger the swing angle, the stronger the dynamic instability of the goods. and Proportional to the swing angle The value of this term decreases as the oscillation angle increases. When the oscillation angle is small, the value of this term is small, the wind field risk factor will weaken, indicating a lower risk.

[0041] Terrain risk factor of the shortest path from the current valid path point to the q-th candidate node Specifically, the calculation is performed using the average undulation of the terrain and the slope of the terrain. The specific formula used for the calculation is as follows: In the formula, The average terrain undulation is the shortest path from the current valid path point to the q-th candidate node. It represents the average slope of the shortest path from the current valid path point to the q-th candidate node.

[0042] It should be noted that the terrain risk factor of the shortest path from the current valid path point to the q-th candidate node is... This reflects the impact of terrain features on the risks of hoisting operations. The higher the value, the more complex the terrain and the higher the risk of going there.

[0043] Average terrain undulation of the shortest path from the current valid path point to the q-th candidate node This directly reflects the unevenness of the terrain in the area. Areas with significant topographic relief may encounter more obstacles and difficulties during hoisting operations, thus increasing the risk of the operation. (The square root is then used to determine the risk.) This can emphasize the impact of terrain undulation on risk, especially in areas with high undulation, where the increase in risk is non-linear, making the assessment of terrain impact more sensitive.

[0044] Average slope It is a measure of terrain inclination. A larger slope means greater gravitational forces and instability during hoisting operations. The steeper the slope, the worse the stability of the hoisted object, increasing the likelihood of accidents. The tangent function is used. This is because the tangent function increases rapidly when the slope is steep, which can effectively reflect the impact of slope on operational risks.

[0045] Changes in wind speed and wind field have a significant immediate impact on hoisting operations. Strong winds or gusts can directly affect the stability of the hoisted load, potentially causing it to sway or even become out of control, leading to safety accidents. Therefore, wind field risk occupies a relatively important position in hoisting operations. Compared to wind field risk, terrain conditions are usually relatively stable in the short term. Once the complexity of the terrain is determined, its impact will not change significantly in the short term. Therefore, the immediate impact of terrain risk is relatively small, and thus, setting... and and All are greater than 0.

[0046] S5: Select the candidate node with the lowest risk and cost as the next waypoint. After the UAV reaches the waypoint, update the current valid path point of the UAV. Repeat the iteration until the UAV reaches the target construction site and completes the hoisting and transportation task.

[0047] Please see Figure 5 The present invention also provides a drone hoisting task optimization device, which is used to execute the above-mentioned drone hoisting task optimization method, including: The preset node generation module is used to determine the lifting path boundary based on the lifting start point and target construction location of the UAV lifting mission, collect surface point cloud data of the mountain within the lifting path boundary, and set several nodes along the way based on the point cloud data within the lifting path boundary. The effective node filtering module is used to obtain the signal strength of each node along the route and analyze its fluctuation amplitude, so as to retain the nodes along the route whose signal strength meets the stability requirements as effective path points. The priority definition module is used to determine the priority of each valid path point based on the distance between the valid path point and the target construction site, setting the hoisting start point and the target construction site as the valid path points with the lowest and highest priorities, respectively. The risk cost analysis module is used to take the hoisting starting point as the current effective path point and determine the search range centered on it. Within the search range, several effective path points are selected as candidate nodes for the next stage according to priority. The module also collects the wind speed changes of each candidate node, the terrain complexity of the shortest path between the current effective path point and each candidate node, and the swing angle of the hoisted cargo at the current effective path point to calculate the risk cost of going to each candidate node. The task path generation module is used to select the candidate node with the lowest risk and cost as the next waypoint. After the UAV arrives at the waypoint, the current valid path point of the UAV is updated. This process is repeated until the UAV arrives at the target construction site and completes the hoisting and transportation task.

[0048] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0049] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0050] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing a UAV hoisting task, characterized in that, The specific steps include: S1: Based on the starting point and target construction location of the UAV hoisting mission, determine the hoisting path boundary, collect surface point cloud data of the mountain within the hoisting path boundary, and set up several nodes along the hoisting path boundary based on the point cloud data. S2: Obtain the signal strength of each node along the route and analyze its fluctuation amplitude, so as to retain the nodes along the route whose signal strength meets the stability requirements as valid path points; S3: Determine the priority of each valid path point based on the distance between the valid path point and the target construction site, and set the hoisting start point and the target construction site as the valid path points with the lowest and highest priorities, respectively; S4: Take the hoisting starting point as the current effective path point and use it as the center to determine the search range. Within the search range, select several effective path points as candidate nodes for the next stage according to priority. Collect the wind speed change of each candidate node, the terrain complexity of the shortest path between the current effective path point and each candidate node, and the swing angle of the hoisted goods at the current effective path point. Calculate the risk cost of going to each candidate node. S5: Select the candidate node with the lowest risk and cost as the next waypoint. After the UAV reaches the waypoint, update the current valid path point of the UAV. Repeat the iteration until the UAV reaches the target construction site and completes the hoisting and transportation task. The specific logic for collecting wind speed changes of each candidate node is as follows: obtain the real-time wind speed of each candidate node within the real-time monitoring period, perform normalization processing, and calculate the mean and variance of the real-time wind speed of each candidate node within the real-time monitoring period. Based on the real-time mean and variance of wind speed at each candidate node, the terrain complexity of the shortest path from the current effective path point to each candidate node, and the swing angle of the hoisted cargo at the current effective path point, a risk cost index for traveling to each candidate node is calculated. This risk cost index characterizes the risk cost of traveling to each candidate node. The specific formula used to calculate the risk cost index is as follows: In the formula, Let be the risk cost index of the q-th candidate node. Let be the wind field risk factor for the q-th candidate node. This is the terrain risk factor for the shortest path from the current valid path point to the q-th candidate node, used to characterize the terrain complexity of the shortest path from the current valid path point to each candidate node. and These are the weighting coefficients for wind field risk and topographic risk, respectively. and and All are greater than 0, where q is the candidate node index; Among them, wind farm risk factors The wind speed changes at each candidate node and the swing amplitude of the hoisted cargo at the current effective path point are coupled and characterized. Terrain risk factor of the shortest path from the current valid path point to the q-th candidate node Specifically, the calculation is performed using the average undulation of the terrain and the slope of the terrain.

2. The method for optimizing UAV hoisting tasks according to claim 1, characterized in that: The logic for determining the specific boundaries of the hoisting path is as follows: using the hoisting start point and the target construction location as the major axis diameter, and using the set minor axis diameter, construct an elliptical closed boundary for the hoisting path; The logic behind pre-setting a number of nodes along the route based on surface point cloud data of the mountainous area within the hoisting path boundary is as follows: The mountainous area within the hoisting path boundary is divided into several sub-regions of equal area. Point cloud data of the mountain surface in each sub-region is collected. The pre-set number of nodes along the route in each sub-region is determined based on the average undulation of the point cloud data within that sub-region. The formula for adjusting the pre-set number of nodes along the route is as follows: In the formula, Preset the number of corrected nodes along the path in the s-th sub-region. This represents the initial preset number of nodes along the route within the sub-region. Let be the average undulation of the s-th sub-region, which is characterized by the standard deviation of the height of each point in the point cloud data corresponding to that sub-region. Here, s is the reference value for the degree of fluctuation, and s is the index of the sub-region; The method for setting along-path nodes in a sub-region is as follows: based on the preset number of along-path nodes after correction in the s-th sub-region, the same number of along-path nodes are randomly set in the sub-region.

3. The method for optimizing UAV hoisting tasks according to claim 2, characterized in that: The logic for determining whether signal strength meets stability requirements is as follows: The signal strength of each node along the route during the historical monitoring period is obtained, normalized, and the mean and variance of the signal strength of each node during the historical monitoring period are calculated. The signal stability coefficient is then calculated based on the mean and variance of the signal strength. This signal stability coefficient characterizes the stability of the communication signal at the nodes along the route. The specific formula for calculating the signal stability coefficient is as follows: In the formula, Let be the signal stability coefficient of the i-th node along the path. Let be the average signal strength of the i-th node along the route during the historical monitoring period. Let Variance be the signal strength variance of the i-th node along the route during the historical monitoring period. The signal strength variance is used to characterize the amplitude of signal strength fluctuations. and These are the weighting coefficients for signal strength and signal strength fluctuation, respectively, and i is the index of the node along the path. and and All are greater than 0; The variance of signal strength fluctuation at the i-th node along the route during the historical monitoring period. The specific formula used for the calculation is as follows: In the formula, Let be the normalized signal strength value of the i-th node along the route at the j-th sampling time, where j is the index of the sampling time within the historical monitoring period. , This represents the total number of sampling times within the historical monitoring period. The logic behind retaining path nodes whose signal strength meets stability requirements as valid path points is as follows: Set a signal stability threshold, compare the signal stability coefficient with the signal stability threshold, remove path nodes whose signal stability coefficient is greater than or equal to the signal stability threshold, and retain path nodes whose signal stability coefficient is less than the signal stability threshold, and record them as valid path points.

4. The method for optimizing UAV hoisting tasks according to claim 1, characterized in that: The specific steps for determining the priority of each effective path point based on the distance between the effective path point and the target construction site are as follows: A three-dimensional coordinate system is established, with the direction from the hoisting start point to the target construction site as the positive x-axis, the direction perpendicular to the x-axis as the y-axis, and the Z-axis determined using the right-hand rule. The three-dimensional coordinates of the hoisting start point, the target construction site, and each effective path point are then determined. Based on these three-dimensional coordinates, the distance between each effective path point and the target construction site is calculated. Combining this distance with the calculation of the distance between the hoisting start point and the target construction site, a priority coefficient for each effective path point is calculated. This priority coefficient characterizes the priority of each effective path point. The specific formula for calculating the priority coefficient is as follows: In the formula, Let p be the priority coefficient of the p-th valid path point. Let p be the distance between the p-th valid path point and the target construction site, where p is the index of the valid path point. The distance between the hoisting starting point and the target construction site is calculated using the coordinates of each point. The priority coefficient of the p-th valid path point is also considered. The value is inversely proportional to the priority of the valid path point.

5. The method for optimizing UAV hoisting tasks according to claim 4, characterized in that: The logic for determining the search range is as follows: set a search radius to form a closed circular search range, which serves as the single search range for the UAV. The single search range includes at least two candidate nodes. The specific logic for determining candidate nodes is as follows: among the valid path points within the search range, select valid path points with higher priority than the current valid path point as candidate nodes, that is, select valid path points with priority coefficients lower than the current valid path point priority coefficients as candidate nodes.

6. The method for optimizing UAV hoisting tasks according to claim 2, characterized in that: Calculate wind field risk factors The specific formula used is as follows: In the formula, Let be the average real-time wind speed of the q-th candidate node. To monitor in real time the maximum swing angle of the hoisted goods at the current effective path point within a given time period, Let be the real-time wind speed variance of the q-th candidate node; Calculate terrain risk factors The specific formula used is as follows: In the formula, The average terrain undulation is the shortest path from the current valid path point to the q-th candidate node. It represents the average slope of the shortest path from the current valid path point to the q-th candidate node.

7. A device for optimizing unmanned aerial vehicle (UAV) hoisting tasks, characterized in that: The UAV hoisting task optimization device is used to execute the UAV hoisting task optimization method according to any one of claims 1-6, including: The preset node generation module is used to determine the lifting path boundary based on the lifting start point and target construction location of the UAV lifting mission, collect surface point cloud data of the mountain within the lifting path boundary, and set several nodes along the way based on the point cloud data within the lifting path boundary. The effective node filtering module is used to obtain the signal strength of each node along the route and analyze its fluctuation amplitude, so as to retain the nodes along the route whose signal strength meets the stability requirements as effective path points. The priority definition module is used to determine the priority of each valid path point based on the distance between the valid path point and the target construction site, setting the hoisting start point and the target construction site as the valid path points with the lowest and highest priorities, respectively. The risk cost analysis module is used to take the hoisting starting point as the current effective path point and determine the search range centered on it. Within the search range, several effective path points are selected as candidate nodes for the next stage according to priority. The module also collects the wind speed changes of each candidate node, the terrain complexity of the shortest path between the current effective path point and each candidate node, and the swing angle of the hoisted cargo at the current effective path point to calculate the risk cost of going to each candidate node. The task path generation module is used to select the candidate node with the lowest risk and cost as the next waypoint. After the UAV arrives at the waypoint, the current valid path point of the UAV is updated. This process is repeated until the UAV arrives at the target construction site and completes the hoisting and transportation task.

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