Distribution path planning method and system based on low-altitude economy

Abstract

The invention provides a distribution path planning method and system based on low-altitude economy, and relates to the technical field of path planning, and the method comprises the steps: obtaining distribution order information, and determining a three-dimensional space through combining low-altitude airspace characteristics; and extracting path data conforming to the similar scene of the current delivery order from the historical delivery data, and generating an initial preset path in the three-dimensional space by combining the basic elements. And analyzing the surrounding environment of the initial preset path to obtain a sensitive area, and performing identification by using a space-time correlation algorithm in combination with low-altitude flight data to obtain a plurality of interference levels. And formulating a differential adjustment strategy according to different interference levels, and adjusting the initial preset path to generate a distribution path. According to the invention, the sensitive area is identified by using the image sensitivity identification method, and the interference level is evaluated by using the space-time association algorithm to formulate the distribution path, so that the distribution efficiency and safety are improved, the adaptability of path planning is enhanced, and the user experience and privacy protection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a distribution path planning method and system based on low-altitude economy. Background Art

[0002] With the rapid rise of the low-altitude economy, drone delivery has gradually become a new favorite in the logistics industry, showing huge development potential. However, existing technologies still have many shortcomings in drone delivery path planning, which seriously restricts the further development of the drone delivery industry. Traditional path planning methods often only provide relatively coarse two-dimensional path planning, completely ignoring the complex three-dimensional characteristics of low-altitude airspace, such as altitude restrictions and the distribution of obstacles in various shapes. This makes the planned path seriously disconnected from the actual low-altitude flight environment. Drones may face various risks during actual flight, such as collisions with obstacles, which may not only lead to delivery failures but also pose a threat to surrounding public safety.

[0003] At the same time, current path planning methods lack consideration for sensitive areas and various interference factors surrounding drone flight environments. When performing delivery missions, drones may inadvertently intrude into sensitive areas such as residential areas, core commercial areas, and even classified units. This behavior is likely to seriously infringe on the privacy of others and may also trigger a series of complex legal disputes and security issues. Furthermore, existing technologies fail to fully account for the uncertainty of interference factors such as weather changes and electromagnetic interference. This results in drones lacking effective adaptability in complex and changing real-world environments. Once encountering sudden interference, they are prone to flight failures, which in turn affects the timeliness and reliability of delivery.

[0004] Furthermore, existing drone delivery routing systems offer little protection for privacy. Drones can easily cover large areas during flight, and with minimal care, they could capture sensitive information about areas like homes and businesses, creating the risk of privacy breaches. This not only harms the legitimate rights and interests of individuals and businesses, but also significantly hinders social acceptance and trust in drone delivery.

[0005] In view of this, the present invention aims to propose a new distribution path planning method and system based on low-altitude economy. Summary of the Invention

[0006] The present invention provides a distribution path planning method and system based on low-altitude economy, which are used to solve the defects of the existing technology, such as low path planning accuracy, insufficient consideration of sensitive areas and interference factors in the surrounding environment, and insufficient privacy protection.

[0007] The present invention provides a distribution path planning method based on low-altitude economy, comprising: Obtain delivery order information, determine the three-dimensional space based on the characteristics of low-altitude airspace, collect the basic elements of the delivery task, and collect low-altitude flight data in real time.

[0008] Path data that matches the same scenarios as the current delivery order is extracted from historical delivery data, and the initial preset path is generated in three-dimensional space by combining basic elements.

[0009] The surrounding environment of the initial preset path is analyzed according to the image recognition method to obtain sensitive areas, and the spatiotemporal correlation algorithm is used in combination with low-altitude flight data to identify multiple interference levels.

[0010] Differentiated adjustment strategies are formulated according to different interference levels, and the initial preset path is adjusted to generate a delivery path.

[0011] The present invention provides a distribution path planning method based on low-altitude economy, comprising: the steps of determining a three-dimensional space comprising: The geographic center of the target delivery area is used as the coordinate origin, and the direction of the coordinate axis is determined to establish a three-dimensional coordinate system.

[0012] Get the geographic coordinates of the delivery start and end points based on the delivery order information and convert them into coordinate points in the three-dimensional coordinate system.

[0013] Obtain the height limit of the target delivery area and the height of the obstacle position, and convert them into a geometric body in a three-dimensional coordinate system to obtain the obstacle influence range.

[0014] And calculate the safe flight altitude based on the altitude limit and the height of the obstacle.

[0015] The present invention provides a distribution path planning method based on low-altitude economy, and the steps of extracting path data include: The basic geographical features, airspace environment features and order attribute features of the current delivery order are integrated into the current scene feature vector.

[0016] The incomplete paths and incorrect coordinates in the historical delivery data are removed, and the missing features are supplemented by reverse calculation to obtain the integrated delivery data.

[0017] The integrated delivery data is extracted according to the current scene feature vector to obtain the historical scene feature vector, and the cosine similarity algorithm is used to calculate the similarity between the current scene feature vector and the historical scene feature vector.

[0018] The path data is obtained by filtering the historical delivery data based on the historical scene feature vectors that meet the preset threshold similarity.

[0019] The present invention provides a distribution path planning method based on low-altitude economy, wherein the steps of generating an initial preset path include: Key feature points in the route data are extracted as the skeleton points of the initial route, and intermediate points are inserted according to the size and complexity of the target delivery area.

[0020] Align the start and end points of the historical path with the current start and end points, and adjust the position of the middle point by the scaling factor.

[0021] Determine whether the adjusted midpoint reaches the obstacle influence range, and if so, make adjustments in the vertical or horizontal direction.

[0022] The cubic B-spline curve is used to connect the corrected intermediate points and skeleton points to generate the initial preset path. The formula is:

[0023] Where, is the cubic B-spline basis function, is the control vertex, 、 、 yes The three-dimensional coordinates of the path point at that moment, Is a parameter on the path The corresponding three-dimensional coordinate points, is the number of control vertices, is a path parameter.

[0024] The present invention provides a distribution path planning method based on low-altitude economy, and the steps of analyzing and obtaining sensitive areas include: With the initial preset path as the center, the analysis boundary is set according to the impact radius of different area types, and image data of the target delivery area is collected.

[0025] The building outline and function are identified based on the image data, and the minimum straight-line distance between the building outline and the initial preset path is calculated.

[0026] Determine whether the minimum straight-line distance reaches the preset area impact threshold. If so, the current building is regarded as a suspected area. The suspected area is identified as a sensitive area by comparing the difference between mobile phone signaling during the day and at night, street view maps and no-photography signs.

[0027] The present invention provides a distribution path planning method based on low-altitude economy, wherein the steps of identifying and obtaining multiple interference levels include: Spatial features, temporal features and environmental interference features related to sensitive areas are extracted from low-altitude flight data.

[0028] The improved Apriori algorithm is used to identify the association patterns between spatial features, temporal features, environmental interference features and sensitive area types, and the spatiotemporal correlation index is calculated.

[0029] It is determined whether the spatiotemporal correlation index reaches a preset correlation threshold, and if so, multiple interference levels are obtained by dividing the levels based on the interference impact index.

[0030] The present invention provides a distribution path planning method based on low-altitude economy, and the steps of formulating a differentiated adjustment strategy include: According to different interference levels, corresponding adjustment targets are set for flight threats. For each level, the minimum straight-line distance is adjusted according to the analysis boundary to obtain the path deviation strategy.

[0031] The flight parameter adjustment strategy is obtained by adjusting the flight altitude according to the activity layer of the sensitive area and reducing the exposure time around the sensitive area.

[0032] The anti-interference mode is switched according to different levels of electromagnetic interference, and the device adjustment strategy is obtained by performing privacy protection on sensitive areas.

[0033] The path adjusted by the path deviation strategy, flight parameter adjustment strategy and equipment adjustment strategy is switched in real time according to the UAV's endurance, estimated time and low-altitude flight data to obtain differentiated adjustment strategies.

[0034] The present invention provides a distribution path planning method based on low-altitude economy, and the steps of adjusting and generating the distribution path include: Path deviation parameters, height constraint parameters, speed constraint parameters and prohibited area parameters are extracted from the differentiated adjustment strategy as key adjustment parameters.

[0035] Different interference levels are bound to corresponding key adjustment parameters, and the distances between the middle point and the skeleton point and the sensitive area are calculated to obtain the sensitive point distance.

[0036] Determine whether the distance to the sensitive point is less than the preset safety distance. If so, calculate the reverse direction and offset distance of the vector pointing from the node to the center of the area. The formula is:

[0037]

[0038] Where, is the sensitive point distance, It is a preset safety distance. is the safety distance threshold of the sensitive area, is the offset direction angle, , are the center coordinates of the sensitive area, , is the original coordinate of the node to be corrected, is the original angle of the computation node pointing to the center of the region, is a reversal of direction, is the total offset distance.

[0039] The intermediate points and skeleton points are corrected according to the reverse direction of the vector and the offset distance to obtain the corrected node coordinates.

[0040] For high-level interference, new avoidance nodes are added, and the speed and altitude are set in segments based on the corrected node coordinates and the distance to sensitive points to obtain the delivery path.

[0041] The present invention provides a distribution path planning method based on low-altitude economy, and the formula for correcting the node coordinates is expressed as follows:

[0042] Where, is the corrected node, is the corrected horizontal coordinate, is the corrected ordinate, is the corrected height coordinate, is the cosine of the offset direction angle, is the sine of the offset direction angle.

[0043] On the other hand, the present invention also provides a distribution path planning system based on low-altitude economy, comprising: The three-dimensional data acquisition module is used to obtain delivery order information, determine the three-dimensional space based on the characteristics of low-altitude airspace, collect the basic elements of the delivery task, and collect low-altitude flight data in real time.

[0044] The initial path generation module is used to extract path data that meets the same scenarios as the current delivery order from historical delivery data, and generate an initial preset path in three-dimensional space based on basic elements.

[0045] The level setting module is used to analyze the surrounding environment of the initial preset path based on the image recognition method to obtain sensitive areas, and use the spatiotemporal correlation algorithm combined with low-altitude flight data to identify and obtain multiple interference levels.

[0046] The path generation module is used to formulate differentiated adjustment strategies according to different interference levels and adjust the initial preset path to generate a delivery path.

[0047] The present invention provides a distribution path planning method and system based on low-altitude economy. By establishing a three-dimensional coordinate system, the information such as the distribution starting point, end point and obstacles is accurately converted into geometric bodies in the three-dimensional coordinate system, thereby solving the problem of insufficient path planning accuracy. By constructing a scene feature vector and using the cosine similarity algorithm to screen out historical path data with high similarity, the value of historical data is fully tapped. The image recognition method is used to identify sensitive areas, and the spatiotemporal correlation algorithm is used to evaluate the interference level. This makes up for the previous path planning methods' lack of consideration of sensitive areas and interference factors in the surrounding environment. In addition, according to different interference levels, differentiated adjustment strategies including path offset, flight parameter adjustment and equipment adjustment are formulated, solving the problem of the lack of real-time adjustment strategies. The results have improved distribution efficiency and safety, enhanced the adaptability of path planning, improved user experience and privacy protection, and realized intelligent and automated distribution path planning. It reduces manual intervention and improves the scientificity and rationality of path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 This is one of the flow diagrams of a distribution path planning method and system based on low-altitude economy provided by an embodiment of the present invention; Figure 2 This is the second flow chart of a distribution path planning method and system based on low-altitude economy provided by an embodiment of the present invention; Figure 3 This is the third flow chart of a distribution path planning method and system based on low-altitude economy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0051] The following combination Figure 1-Figure 3 The present invention describes a distribution path planning method and system based on low-altitude economy.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a distribution path planning method based on low-altitude economy, including: Acquire delivery order information, determine the three-dimensional space based on the characteristics of low-altitude airspace, and collect basic elements of the delivery task, collecting low-altitude flight data in real time. Low-altitude flight data can include real-time weather conditions (for areas with wind speeds > 6), dynamic air traffic control no-fly orders, ground surveillance video (such as crowds in squares), and the real-time location of drone swarms. Basic elements can include drone model (to determine flight endurance, payload, and wind resistance), delivery time requirements (such as urgent orders must be delivered within 1 hour), and cargo attributes (such as whether the item is fragile and whether temperature control is required).

[0053] The steps to determine the three-dimensional space include: Use the geographic center of the target delivery area as the coordinate origin and determine the coordinate axis directions to establish a three-dimensional coordinate system. The x-axis is due north, the y-axis is due east, and the z-axis is vertically upward.

[0054] Get the geographic coordinates of the delivery start and end points based on the delivery order information and convert them into coordinate points in the three-dimensional coordinate system.

[0055] Obtain the height limit of the target delivery area and the height of the obstacle position, and convert them into geometric bodies in the three-dimensional coordinate system to obtain the obstacle influence range. The formula is expressed as:

[0056] Where, and are the coordinates of the obstacle center, is the obstacle height, is the obstacle radius, is the vertical coordinate, and is the horizontal coordinate of the delivery starting point.

[0057] And according to the altitude limit and the height of the obstacle, the safe flight altitude is calculated. The formula is expressed as:

[0058] Where, is the flight altitude, It's a safe distance.

[0059] Path data that matches the same scenarios as the current delivery order is extracted from historical delivery data, and the initial preset path is generated in three-dimensional space based on basic elements.

[0060] The steps to extract path data include: The basic geographical features, airspace environment features and order attribute features of the current delivery order are integrated into the current scene feature vector.

[0061] The incomplete paths and incorrect coordinates in the historical delivery data are removed, and the missing features are supplemented by reverse calculation to obtain the integrated delivery data.

[0062] The historical scene feature vector is obtained by extracting the integrated delivery data according to the current scene feature vector, and the cosine similarity algorithm is used to calculate the similarity between the current scene feature vector and the historical scene feature vector. The formula is expressed as:

[0063] Where, is the current scene feature vector and the The cosine similarity of the feature vectors of historical scenes, is the total number of dimensions of the scene feature vector method, is the first The original value of the feature, It is The first historical scene feature vector The original value of the feature, is the first The standardized value of the feature, It is The first historical scene feature vector The standardized value of the feature, is the modulus of the current scene feature vector, It is The modulus of the feature vector of the historical scene.

[0064] The path data is obtained by filtering the historical delivery data based on the historical scene feature vectors that meet the preset threshold similarity.

[0065] like Figure 2 As shown, the steps of generating the initial preset path include: Key feature points in the route data are extracted as the skeleton points of the initial route, and intermediate points are inserted according to the size and complexity of the target delivery area.

[0066] Align the start and end points of the historical path with the current start and end points, and adjust the position of the middle point by the scaling factor. The formula is expressed as:

[0067]

[0068] Where, is the scaling factor, is the current starting and ending distance, is the distance between the historical starting and ending points, After adaptation The three-dimensional coordinates of the middle point, is the three-dimensional coordinate of the starting point of the current delivery order, It is the first in the historical path The three-dimensional coordinates of the original midpoint, are the original starting coordinates of the historical path.

[0069] Determine whether the adjusted midpoint reaches the obstacle influence range. If so, adjust it vertically or horizontally. The formula is:

[0070] Where, It is the revised The three-dimensional coordinates of the middle point, 、 、 are the node coordinates of the adapted but uncorrected obstacle, is the avoidance angle, is the offset distance along the avoidance direction angle.

[0071] The cubic B-spline curve is used to connect the corrected intermediate points and skeleton points to generate the initial preset path. The formula is:

[0072] Where, is the cubic B-spline basis function, is the control vertex, 、 、 yes The three-dimensional coordinates of the path point at that moment, Is a parameter on the path The corresponding three-dimensional coordinate points, is the number of control vertices, is a path parameter.

[0073] The surrounding environment of the initial preset path is analyzed according to the image recognition method to obtain sensitive areas, and the spatiotemporal correlation algorithm is used in combination with low-altitude flight data to identify multiple interference levels.

[0074] The steps to analyze and obtain sensitive areas include: With the initial preset path as the center, the analysis boundary is set according to the impact radius of different area types, and image data of the target delivery area is collected.

[0075] The building outline and function are identified based on the image data, and the minimum straight-line distance between the building outline and the initial preset path is calculated.

[0076] Determine whether the minimum straight-line distance reaches the preset area impact threshold. If so, the current building is regarded as a suspected area. The suspected area is identified as a sensitive area by comparing the difference between mobile phone signaling during the day and at night, street view maps and no-photography signs.

[0077] The steps for identifying multiple interference levels include: Extract spatial, temporal, and environmental interference features related to sensitive areas from low-altitude flight data. Spatial features include: the minimum distance between a trajectory point and a suspected sensitive area; the difference between the flight altitude and the altitude of the area; Temporal features include: the timestamp of the trajectory point; the peak hours of human activity in the area; and environmental interference features including: the average electromagnetic interference intensity and the fluctuation of acoustic wave intensity.

[0078] The improved Apriori algorithm is used to identify the association patterns between spatial features, temporal features, environmental interference features and sensitive area types, and the spatiotemporal correlation index is calculated. The formula is expressed as follows:

[0079] Where, is the spatial correlation, is the temporal correlation, is the environmental interference correlation, 、 、 is the weight, is the spatiotemporal correlation index.

[0080] It is determined whether the spatiotemporal correlation index reaches the preset correlation threshold. If yes, multiple interference levels are obtained based on the interference impact index. The formula is:

[0081] Where, is the electromagnetic interference index, is the acoustic interference index, is the GPS interference index, 、 、 is the weight, is the interference impact index.

[0082] The levels are defined according to the value range of the interference impact index and can be divided into: Level 1 (weak interference):

[0083] Level 2 (minor disturbance):

[0084] Level 3 (moderate interference):

[0085] Level 4 (strong interference):

[0086] Level 5 (Severe Disturbance):

[0087] Differentiated adjustment strategies are formulated according to different interference levels, and the initial preset path is adjusted to generate a delivery path.

[0088] Steps to developing a differentiated adjustment strategy include: Set corresponding adjustment targets for flight threats based on different interference levels, and adjust the minimum straight-line distance based on the analysis boundary for each level to obtain a path deviation strategy. Adjustment targets can include: Level 1 (weak interference): The threat is "minor electromagnetic signal fluctuations" and the adjustment target is to "maintain a basically stable path and only record interference data" to ensure that normal flight procedures are not affected.

[0089] Level 2 (Minor Interference): The threat is "short-term privacy exposure risk (such as in residential areas)", and the adjustment goal is to "reduce perception in sensitive areas and reduce visual / acoustic interference", such as turning off non-essential sensors and reducing flight noise.

[0090] Level 3 (Moderate Interference): The threat is "continuous signal interference and security conflicts (such as dense crowds in commercial areas)." The adjusted goal is to "maintain a safe distance and control flight parameters to avoid interference peaks," such as avoiding flying during peak daytime traffic hours.

[0091] Level 4 (Strong Interference): The threat is "signal interruption and detection risk (such as around confidential units)." The adjustment goal is to "physically isolate the interference source and ensure communication and navigation stability," such as switching to anti-interference mode and passing quickly.

[0092] Level 5 (Severe Interference): The threat is "collision, loss of contact, or compliance risk (such as a military restricted area)." The goal is adjusted to "completely avoid the area and ensure physical isolation of the path and area." Any form of entry or close contact is prohibited.

[0093] For each level of analysis boundary, the minimum straight-line distance adjustment rules are refined in combination with the area type (residential / commercial / confidential unit): Basic offset logic: Based on the boundary of the sensitive area, the safety distance threshold increases by 50% for each increase in the interference level (for example, the safety distance in a level 2 area is 20 meters, and in a level 3 area it is 30 meters).

[0094] Differentiated offset direction: For residential areas, vertical deviation is preferred (increasing the flight altitude to more than 100 meters to reduce visual contact with the ground); For commercial / confidential units, priority is given to horizontal displacement (away from the core area of the region, avoiding human traffic or signal monitoring range).

[0095] Boundary verification: After the offset, ensure that the minimum straight-line distance between the path and the sensitive area is ≥ "regional impact threshold + redundant safety distance (5 meters)" to avoid policy failure due to measurement errors.

[0096] The flight parameter adjustment strategy is obtained by adjusting the flight altitude according to the activity layer of the sensitive area and reducing the exposure time around the sensitive area.

[0097] The anti-interference mode is switched according to different levels of electromagnetic interference, and the device adjustment strategy is obtained by protecting privacy in sensitive areas.

[0098] The path adjusted by the path deviation strategy, flight parameter adjustment strategy and equipment adjustment strategy is switched in real time according to the UAV's endurance, estimated time and low-altitude flight data to obtain differentiated adjustment strategies.

[0099] Based on the drone's endurance, estimated flight time, and low-altitude flight data (such as real-time electromagnetic intensity and wind speed), set the dynamic strategy switching threshold: Endurance trigger: If the total length of the adjusted path exceeds 80% of the drone's endurance, the avoidance distance in low-level areas will be automatically reduced (for example, reducing the offset in level 2 areas from 20 meters to 15 meters) to prioritize mission completion.

[0100] Time trigger: If the estimated time exceeds 120% of the order time threshold, the "shortest path first" principle will be adopted for non-Level 5 interference areas, and the detour length will be compressed.

[0101] Interference burst trigger: When the real-time monitoring detects that the electromagnetic intensity is ≥ 150% of the safety threshold or the wind speed is ≥ the UAV's wind resistance level, the highest level avoidance strategy (such as emergency altitude increase + return backup path) will be immediately initiated.

[0102] like Figure 3 As shown, the steps for adjusting and generating a delivery route include: Path deviation parameters, height constraint parameters, speed constraint parameters and prohibited area parameters are extracted from the differentiated adjustment strategy as key adjustment parameters.

[0103] Different interference levels are bound to corresponding key adjustment parameters, and the distances between the middle point and the skeleton point and the sensitive area are calculated to obtain the sensitive point distance.

[0104] Determine whether the distance to the sensitive point is less than the preset safety distance. If so, calculate the reverse direction and offset distance of the vector pointing from the node to the center of the area. The formula is:

[0105]

[0106] Where, is the sensitive point distance, It is a preset safety distance. is the safety distance threshold of the sensitive area, is the offset direction angle, , are the center coordinates of the sensitive area, , is the original coordinate of the node to be corrected, is the original angle of the computation node pointing to the center of the region, is a reversal of direction, is the total offset distance.

[0107] According to the reverse direction of the vector and the offset distance, the intermediate point and the skeleton point are corrected to obtain the corrected node coordinates. The formula is expressed as:

[0108] Where, is the corrected node, is the corrected horizontal coordinate, is the corrected ordinate, is the corrected height coordinate, is the cosine of the offset direction angle, is the sine of the offset direction angle.

[0109] For high-level interference, new avoidance nodes are added, and the speed and altitude are set in segments based on the corrected node coordinates and the distance to sensitive points to obtain the delivery path.

[0110] Altitude segmentation: Maintain altitude above interference levels 3 and above. In other areas, fly at a low, energy-saving altitude (e.g., 50 meters). Strict no-fly zone avoidance (for Level 5 interference): For the absolute no-fly zone A, which is subject to Level 5 interference, buffer zone analysis is used to ensure the distance between the path and the zone. If the minimum distance between the initial path and A is less than the preset distance, the original path is completely abandoned and an alternative path is replanned from S to E, completely bypassing A.

[0111] Based on the same general inventive concept, the present invention also protects a distribution path planning system based on low-altitude economy, the path planning system comprising: The three-dimensional data acquisition module is used to obtain delivery order information, determine the three-dimensional space based on the characteristics of low-altitude airspace, collect the basic elements of the delivery task, and collect low-altitude flight data in real time.

[0112] The initial path generation module is used to extract path data that meets the same scenarios as the current delivery order from historical delivery data, and generate an initial preset path in three-dimensional space based on basic elements.

[0113] The level setting module is used to analyze the surrounding environment of the initial preset path based on the image recognition method to obtain sensitive areas, and use the spatiotemporal correlation algorithm combined with low-altitude flight data to identify and obtain multiple interference levels.

[0114] The path generation module is used to formulate differentiated adjustment strategies according to different interference levels and adjust the initial preset path to generate a delivery path.

[0115] This embodiment provides a distribution path planning method and system based on low-altitude economy. Through precise three-dimensional path planning and effective use of historical data, the generated initial preset path is more in line with actual distribution needs. At the same time, the detailed analysis of the surrounding environment and the real-time adjustment strategy enable the drone to avoid sensitive areas and interference sources, thereby improving distribution efficiency and flight safety. The ability to flexibly adjust the distribution path according to different interference levels enhances the adaptability of path planning to complex and changing environments. Whether facing weather changes, obstacles or other interference factors, timely adjustments can be made. At the same time, the accurate identification and effective avoidance of sensitive areas fully respect personal privacy and commercial secrets, and enhance the user experience. At the same time, the exposure time in sensitive areas is reduced, reducing the potential risk of privacy leakage.

[0116] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distribution path planning method based on low-altitude economy, characterized in that: include: Obtain delivery order information, determine the three-dimensional space based on the characteristics of low-altitude airspace, collect basic elements of the delivery task, and collect low-altitude flight data in real time; Extracting path data that matches similar scenarios of the current delivery order from historical delivery data, and generating an initial preset path in the three-dimensional space in combination with the basic elements; Analyzing the surrounding environment of the initial preset path according to an image recognition method to obtain sensitive areas, and using a spatiotemporal correlation algorithm combined with the low-altitude flight data to identify and obtain multiple interference levels; Differentiated adjustment strategies are formulated according to different interference levels, and the initial preset path is adjusted to generate a delivery path.

2. A distribution path planning method based on low-altitude economy according to claim 1, characterized in that: The step of determining the three-dimensional space includes: Take the geographic center of the target delivery area as the coordinate origin and determine the direction of the coordinate axis to establish a three-dimensional coordinate system; Obtaining the geographic coordinates of the delivery start and end points according to the delivery order information, and converting them into coordinate points in the three-dimensional coordinate system; Obtaining the height limit of the target delivery area and the height of the obstacle position, and converting them into geometric bodies in the three-dimensional coordinate system to obtain the obstacle influence range; And calculate the safe flight altitude based on the altitude limit and the obstacle position altitude.

3. A distribution path planning method based on low-altitude economy according to claim 1, characterized in that: The step of extracting the path data comprises: Integrate the basic geographical features, airspace environment features and order attribute features of the current delivery order into the current scene feature vector; Removing incomplete path and coordinate error data from the historical delivery data, and supplementing the missing features by reverse engineering to obtain integrated delivery data; Extracting the integrated delivery data according to the current scene feature vector to obtain a historical scene feature vector, and calculating the similarity between the current scene feature vector and the historical scene feature vector using a cosine similarity algorithm; The path data is obtained by screening the historical delivery data according to historical scene feature vectors that meet a preset threshold similarity.

4. A distribution path planning method based on low-altitude economy according to claim 2, characterized in that: The step of generating the initial preset path includes: Extracting key feature points from the path data as skeleton points of the initial path, and inserting intermediate points based on the size and complexity of the target delivery area; Align the start and end points of the historical path with the current start and end points, and adjust the position of the intermediate point by the scaling factor; Determine whether the adjusted midpoint reaches the obstacle influence range, and if so, adjust along the vertical direction or the horizontal direction; The corrected intermediate point and the skeleton point are connected using a cubic B-spline curve to generate the initial preset path, which is expressed as follows: Where, is the cubic B-spline basis function, is the control vertex, 、 、 yes The three-dimensional coordinates of the path point at that moment, Is a parameter on the path The corresponding three-dimensional coordinate points, is the number of control vertices, is a path parameter.

5. A distribution path planning method based on low-altitude economy according to claim 4, characterized in that: The steps of analyzing and obtaining the sensitive area include: Taking the initial preset path as the center, setting the analysis boundary according to the influence radius of different area types, and collecting image data of the target delivery area; Identifying a building outline and function based on the image data, and calculating a minimum straight-line distance between the building outline and the initial preset path; Determine whether the minimum straight-line distance reaches the preset area impact threshold. If yes, take the current building as a suspected area, and identify the suspected area by comparing the difference between mobile phone signaling during the day and at night, street view maps and no-photography signs to obtain the sensitive area.

6. A distribution path planning method based on low-altitude economy according to claim 1, characterized in that: The steps for identifying multiple interference levels include: extracting spatial features, temporal features, and environmental interference features related to the sensitive area from the low-altitude flight data; Identifying the correlation pattern between the spatial feature, the temporal feature, the environmental interference feature and the sensitive area type through an improved Apriori algorithm, and calculating the spatiotemporal correlation index; It is determined whether the spatiotemporal correlation index reaches a preset correlation threshold, and if so, multiple interference levels are obtained by dividing the levels based on the interference impact index.

7. A distribution path planning method based on low-altitude economy according to claim 5, characterized in that: The steps of formulating the differentiated adjustment strategy include: Setting corresponding adjustment targets for flight threats according to different interference levels, and adjusting the minimum straight-line distance according to the analysis boundary for each level to obtain a path deviation strategy; Adjusting the flight altitude according to the activity layer of the sensitive area and reducing the exposure time around the sensitive area to obtain a flight parameter adjustment strategy; Switching anti-interference modes according to different levels of electromagnetic interference, and performing privacy protection on the sensitive areas to obtain device adjustment strategies; The path adjusted by the path deviation strategy, the flight parameter adjustment strategy and the equipment adjustment strategy is switched in real time according to the drone's endurance, the estimated time and the low-altitude flight data to obtain the differentiated adjustment strategy.

8. A distribution path planning method based on low-altitude economy according to claim 5, characterized in that: The steps of adjusting and generating the delivery path include: Extracting a path deviation parameter, a height constraint parameter, a speed constraint parameter, and a prohibited area parameter from the differentiated adjustment strategy as key adjustment parameters; Binding different interference levels to corresponding key adjustment parameters, and calculating the distances between the middle point and the skeleton point and the sensitive area to obtain sensitive point distances; Determine whether the distance to the sensitive point is less than the preset safety distance. If so, calculate the reverse direction and offset distance of the vector pointing from the node to the center of the area. The formula is: Where, is the sensitive point distance, It is a preset safety distance. is the safety distance threshold of the sensitive area, is the offset direction angle, , are the center coordinates of the sensitive area, , is the original coordinate of the node to be corrected, is the original angle of the computation node pointing to the center of the region, is a reversal of direction, is the total offset distance; Correcting the intermediate point and the skeleton point according to the reverse direction of the vector and the offset distance to obtain corrected node coordinates; For high-level interference levels, new avoidance nodes are added, and the speed and altitude are set in segments based on the corrected node coordinates and the distance to the sensitive points to obtain the delivery path.

9. A distribution path planning method based on low-altitude economy according to claim 8, characterized in that: The formula for obtaining the corrected node coordinates is expressed as follows: Where, is the corrected node, is the corrected horizontal coordinate, is the corrected ordinate, is the corrected height coordinate, is the cosine of the offset direction angle, is the sine of the offset direction angle.

10. A distribution path planning system based on low-altitude economy, adopting a distribution path planning method based on low-altitude economy as claimed in any one of claims 1 to 9, characterized in that: The path planning system includes: The 3D data acquisition module is used to obtain delivery order information, determine the 3D space based on the characteristics of low-altitude airspace, collect basic elements of the delivery task, and collect low-altitude flight data in real time; An initial path generation module is used to extract path data that meets the same scenario as the current delivery order from historical delivery data, and generate an initial preset path in the three-dimensional space based on the basic elements; A level setting module is used to analyze the surrounding environment of the initial preset path according to an image recognition method to obtain sensitive areas, and use a spatiotemporal correlation algorithm combined with the low-altitude flight data to identify and obtain multiple interference levels; The path generation module is used to formulate differentiated adjustment strategies according to different interference levels and adjust the initial preset path to generate a delivery path.

Images