Cross-wind condition truck intermodal unmanned aerial vehicle hovering risk assessment method and system

Abstract

The present application relates to a kind of truck intermodal unmanned aircraft hovering risk assessment method and system under crosswind condition, belong to unmanned aircraft transportation safety technical field.The method includes: obtaining truck intermodal unmanned aircraft and environmental wind relevant information and designing test scene;Using fluid dynamics simulation technology obtains the aerodynamic characteristics of unmanned aircraft under the coupling effect of crosswind and truck induced flow field, constructs unmanned aircraft aerodynamic characteristics dataset;Based on unmanned aircraft aerodynamic characteristics dataset, establish unmanned aircraft hovering risk level assessment model, the risk level of crosswind to the hovering state of unmanned aircraft is quantified.The present application realizes the objective, quantification evaluation of unmanned aircraft hovering risk under complex coupling flow field, provides decision basis for the safe operation of truck-unmanned aircraft intermodal mode.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) transportation safety technology, specifically relating to a method and system for assessing the hovering risk of UAVs used in crosswind transport. Background Technology

[0002] With the intelligent upgrading of the logistics and transportation industry, the truck-drone intermodal transport model is gradually becoming an important solution for last-mile delivery and long-distance freight transfer. This model uses drones to hover around moving trucks to complete cargo loading and unloading or material handover, significantly improving transportation efficiency and reducing labor costs. However, when drones hover around trucks, they face extremely complex flow environments: on the one hand, moving trucks induce non-uniform flow fields such as wakes and crossflows, altering the surrounding aerodynamic characteristics; on the other hand, the crosswinds frequently encountered in outdoor transportation scenarios further superimpose with the flow field induced by the trucks, causing drones to encounter unstable aerodynamic loads and attitude disturbances, which in severe cases can lead to hovering instability, collisions with trucks, and other safety accidents. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for assessing the hovering risk of drones in crosswind intermodal transport. By integrating multi-dimensional parameters of drones, trucks, and the environment, and using computational fluid dynamics simulation and quantification formulas, a correlation model between aerodynamic characteristics, transient response, and stability indicators is established. Correction coefficients and risk thresholds are defined, enabling accurate assessment of drone hovering risk under crosswind and truck-induced flow field coupling conditions, and providing an operable technical means for safe operation in intermodal transport scenarios.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for assessing the hovering risk of unmanned aerial vehicles (UAVs) used in crosswind transport under crosswind conditions, comprising:

[0005] Acquire information related to truck intermodal transport drones and environmental wind, and design test scenarios;

[0006] Fluid dynamics simulation technology was used to obtain the aerodynamic characteristics of UAVs under the coupling effect of crosswind and truck-induced flow field, and a dataset of UAV aerodynamic characteristics was constructed.

[0007] Based on the aerodynamic characteristics dataset of UAVs, a hovering risk level assessment model is established to quantify the risk level of crosswinds on the hovering state of UAVs.

[0008] Furthermore, the environmental wind-related information of the truck transport drone includes: the number of drone rotors n, the drone rotor size D. r Total length of the truck L t Total width of the truck W t Total height H of the truck t Truck speed V tDrone hovering altitude difference Δh, drone rotor speed ω, crosswind speed V s Wind direction angle α.

[0009] Furthermore, the experimental scenario was designed to include setting the length of the computational domain to 10L. t 9W in width t The height is 5H t Import the simplified geometric models of the drone and truck into the computational domain; set the forward speed of the truck as V. t The crosswind vector is defined using the velocity inlet, and the crosswind speed is V. s The wind direction angle is α; the outlet is set as a pressure outlet.

[0010] Furthermore, fluid dynamics simulation technology was used to obtain the aerodynamic characteristics of the UAV under the coupling effect of crosswind and truck-induced flow field, and a UAV aerodynamic characteristic dataset was constructed, specifically including:

[0011] Fluid dynamics simulation technology was used to monitor and collect core aerodynamic parameters and flow field characteristics of the UAV during the simulation process. These core aerodynamic parameters included: UAV lift L, UAV drag D, UAV lateral force Y, UAV surface static pressure distribution P, and rolling moment coefficient C. lr The flow field characteristic data includes: vorticity distribution Ω, vortex intensity I. e ;

[0012] Based on the monitored hydrostatic pressure distribution P on the UAV surface, the hydrostatic pressure asymmetry P on the UAV surface is calculated. asym The calculation formula is: P asym =(P max -P min ) / (P max +P min ), where P max P is the maximum static pressure on the surface of the drone. min Minimum static pressure on the surface of the drone;

[0013] Calculate the aerodynamic parameters of the UAV, including:

[0014] Lift coefficient Cl:

[0015]

[0016] Drag coefficient Cd:

[0017]

[0018] Lateral force coefficient Cy:

[0019]

[0020] Rolling torque Mr :

[0021] M r

[0022] Based on the calculated aerodynamic parameters of the UAV, a dataset of UAV aerodynamic characteristics is constructed.

[0023] Furthermore, based on the UAV aerodynamic characteristic dataset, a UAV hovering risk level assessment model is established to quantify the risk level of crosswinds on the UAV hovering state, including:

[0024] (1) Define the core evaluation indicators, including:

[0025] Transient response index: Roll angle deviation Δθ r Transient response time τ;

[0026] Stability index: Damping ratio Natural frequency f n ;

[0027] (2) Based on the UAV aerodynamic characteristic dataset, the transient response index is calculated using the following formula:

[0028] Roll angle deviation Δθ r :

[0029]

[0030] Transient response time τ:

[0031]

[0032] (3) Based on the transient response index and the UAV aerodynamic characteristic dataset, the stability index is calculated using the following formula:

[0033] Damping ratio :

[0034]

[0035] Natural frequency f n :

[0036]

[0037] (4) Construct a hovering risk level assessment model for UAVs based on core evaluation indicators, and calculate the hovering risk level R:

[0038] .

[0039] Furthermore, based on the UAV aerodynamic characteristic dataset, a UAV hovering risk level assessment model is established to quantify the risk level of crosswinds on the UAV hovering state, and also includes:

[0040] (5) Classify risk levels based on hovering risk level R:

[0041] When R < 0.3, the drone has stable attitude, fast transient response, and safe hovering, and is judged as low risk;

[0042] When 0.3≤R<0.6, the attitude deviation is controllable and the response time is within the allowable range. The hovering position needs to be optimized, and it is judged as medium risk.

[0043] When R≥0.6, the attitude deviation exceeds the limit, the stability is insufficient, and there is a risk of hovering instability, which is judged as high risk.

[0044] This invention also provides a hovering risk assessment system for truck transport drones under crosswind conditions, which performs the hovering risk assessment method for truck transport drones under crosswind conditions as described above, including:

[0045] The data acquisition and scenario construction module is used to acquire parameters of trucks, drones, and ambient wind, and to construct a computational fluid dynamics simulation domain containing a simplified geometric model;

[0046] The simulation and aerodynamic characteristic calculation module is used to perform fluid dynamics simulations, obtain the core aerodynamic parameters and flow field data of the UAV in the coupled flow field, calculate the aerodynamic parameters of the UAV, and construct the UAV aerodynamic characteristic dataset.

[0047] The risk assessment module is used to establish a hovering risk level assessment model for UAVs based on the UAV aerodynamic characteristic dataset, and to quantify the risk level of crosswinds to the UAV hovering state.

[0048] The present invention also provides an electronic device, comprising: a processor and a memory; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the hovering risk assessment method for truck transport drones under crosswind conditions as described above.

[0049] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the hovering risk assessment method for truck transport drones under crosswind conditions as described above.

[0050] Compared with existing technologies, the present invention has the following beneficial effects: By integrating multi-dimensional parameters of drones, trucks, and the environment, and using computational fluid dynamics simulation and quantification formulas, the present invention establishes a correlation model between aerodynamic characteristics, transient response, and stability indicators, clarifies correction coefficients and risk thresholds, and achieves accurate assessment of drone hovering risk under crosswind and truck-induced flow field coupling environment, providing an operable technical means for safe operation in intermodal transport scenarios. Attached Figure Description

[0051] Figure 1 This is a flowchart of the hovering risk assessment method for truck intermodal transport drones under crosswind conditions provided in an embodiment of the present invention;

[0052] Figure 2 This is a flowchart of the present invention for acquiring information related to truck intermodal transport drones and environmental wind and designing test scenarios;

[0053] Figure 3 This is a schematic diagram of the CFD virtual simulation platform built according to the present invention;

[0054] Figure 4 This invention uses fluid dynamics simulation technology to obtain the aerodynamic characteristics of a UAV under the coupling effect of crosswind and truck-induced flow field, and constructs a UAV aerodynamic characteristic dataset.

[0055] Figure 5 This invention is a flowchart that quantifies the risk level of crosswinds on the hovering state of a drone by establishing a drone hovering risk level assessment model based on a drone aerodynamic characteristic dataset. Detailed Implementation

[0056] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0057] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0058] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0059] This invention provides a method for assessing the hovering risk of unmanned aerial vehicles (UAVs) used in crosswind transport under crosswind conditions, including:

[0060] Acquire information related to truck intermodal transport drones and environmental wind, and design test scenarios;

[0061] Fluid dynamics simulation technology was used to obtain the aerodynamic characteristics of UAVs under the coupling effect of crosswind and truck-induced flow field, and a dataset of UAV aerodynamic characteristics was constructed.

[0062] Based on the aerodynamic characteristics dataset of UAVs, a hovering risk level assessment model is established to quantify the risk level of crosswinds on the hovering state of UAVs.

[0063] The following is a detailed implementation process of the present invention.

[0064] like Figure 1 As shown, this invention proposes a method for assessing the hovering risk of unmanned aerial vehicles (UAVs) used in crosswind transport, comprising the following steps:

[0065] (1) Obtain information related to the truck intermodal transport drone and environmental wind and design test scenarios, such as Figure 2 As shown;

[0066] 1) The information related to the truck intermodal transport drone and environmental wind includes: the number of drone rotors (n), the size of the drone rotor (D). r ), total length of the truck (L) t ), Total width of truck (W) t ), truck total height (H) t ), Truck speed (V) t ), drone hovering altitude difference (Δh), rotor speed (ω), crosswind speed (V) s ), wind direction angle (α);

[0067] 2) Experimental scenario design: The length of the computational domain is 10L. t 9W in width t The height is 5H t To avoid interference from truck wake and boundary effects on the experiment, simplified geometric models of the UAV and truck were imported into the computational domain. The truck's forward velocity was set to Vt. A crosswind vector (wind speed Vs, wind direction α) was defined using a velocity inlet, and a pressure outlet was set to complete the experimental scenario design. Figure 3 As shown;

[0068] (2) The aerodynamic characteristics of the UAV under the coupling effect of crosswind and truck-induced flow field are obtained by using fluid dynamics simulation technology, and the UAV aerodynamic characteristic dataset is constructed. The flowchart of this step is as follows: Figure 4 As shown;

[0069] 1) Use fluid dynamics simulation technology to monitor and collect the core aerodynamic parameters and flow field characteristics of the UAV during the simulation process;

[0070] The core aerodynamic parameters monitored and collected through fluid dynamics simulation technology include: UAV lift (L), UAV drag (D), UAV lateral force (Y), UAV surface static pressure distribution (P), and rolling moment coefficient (C). lr );

[0071] The flow field characteristic data monitored and collected through fluid dynamics simulation technology includes: vorticity distribution (Ω), vortex intensity (IL). e );

[0072] Based on the monitored surface hydrostatic pressure distribution (P) of the UAV, the surface hydrostatic pressure asymmetry P of the UAV is calculated. asym =(P max - P min ) / (P max + P min ), where P max P is the maximum static pressure on the surface of the drone. min Minimum static pressure on the surface of the drone;

[0073] 2) Based on the data obtained in the preceding steps, calculate the following UAV aerodynamic parameters using the following formula:

[0074] Lift coefficient Cl:

[0075]

[0076] Drag coefficient Cd:

[0077]

[0078] Lateral force coefficient Cy:

[0079]

[0080] Rolling torque M r :

[0081] M r

[0082] 3) Based on the above UAV aerodynamic parameters, construct a UAV aerodynamic characteristic dataset.

[0083] (3) Based on the UAV aerodynamic characteristic dataset, establish a UAV hovering risk level assessment model to quantify the risk level of crosswinds on the UAV hovering state. The flowchart of this step is as follows: Figure 5 As shown;

[0084] 1) Define core evaluation indicators:

[0085] Transient response index: Roll angle deviation (Δθ) r ), transient response time (τ);

[0086] Stability index: Damping ratio ( ), natural frequency (f n );

[0087] The hovering risk level (R) is obtained by coupling and quantifying the above indicators;

[0088] 2) Based on the constructed UAV aerodynamic characteristic dataset, the transient response index is calculated using the following quantification formula:

[0089] Roll angle deviation Δθ r :

[0090]

[0091] Transient response time :

[0092]

[0093] 3) Based on the transient response index and the UAV aerodynamic characteristic dataset, the stability index is calculated using the following quantification formula:

[0094] Damping ratio (This characterizes the stability of the UAV's attitude recovery; a larger ζ value indicates better stability.)

[0095]

[0096] Natural frequency f n (Characterizing the attitude oscillation characteristics of the UAV):

[0097]

[0098] 4) Construct a quantitative model for hovering risk level based on the above indicators:

[0099]

[0100] 5) Classify risk levels based on hovering risk level R:

[0101] Low risk: R < 0.3 (Drone attitude stability, fast transient response, and safe hovering);

[0102] Medium risk: 0.3 ≤ R < 0.6 (attitude deviation is controllable, response time is within the allowable range, hovering position needs to be optimized);

[0103] High risk: R ≥ 0.6 (attitude deviation exceeds the limit, instability is insufficient, and there is a risk of hovering instability).

[0104] In summary, this invention presents a method for assessing the hovering risk of unmanned aerial vehicles (UAVs) in crosswind conditions during intermodal freight transport. This method obtains multi-dimensional parameters of the UAV, truck, and crosswind environment through field measurements, constructs a coupled flow field simulation scenario using computational fluid dynamics simulation technology, quantitatively calculates the UAV's aerodynamic characteristics, transient response, and stability indicators, and finally quantitatively assesses the hovering risk level through a weighted model. This provides an effective technical means for the safe operation of UAVs in intermodal freight transport scenarios. The method designed in this invention overcomes the limitations of existing technologies, which are only applicable to single environments, and improves upon the shortcomings of existing technologies, such as the lack of quantitative models and the strong subjectivity of assessment results. It also compensates for the inability of existing technologies to provide actionable operational guidance.

[0105] This invention also provides a hovering risk assessment system for truck transport drones under crosswind conditions, which performs the hovering risk assessment method for truck transport drones under crosswind conditions as described above, including:

[0106] The data acquisition and scenario construction module is used to acquire parameters of trucks, drones, and ambient wind, and to construct a computational fluid dynamics simulation domain containing a simplified geometric model;

[0107] The simulation and aerodynamic characteristic calculation module is used to perform fluid dynamics simulations, obtain the core aerodynamic parameters and flow field data of the UAV in the coupled flow field, calculate the aerodynamic parameters of the UAV, and construct the UAV aerodynamic characteristic dataset.

[0108] The risk assessment module is used to establish a hovering risk level assessment model for UAVs based on the UAV aerodynamic characteristic dataset, and to quantify the risk level of crosswinds to the UAV hovering state.

[0109] The present invention also provides an electronic device, comprising: a processor and a memory; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the hovering risk assessment method for truck transport drones under crosswind conditions as described above.

[0110] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the hovering risk assessment method for truck transport drones under crosswind conditions as described above.

[0111] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0116] This patent is not limited to the above-described preferred embodiments. Anyone can derive other various forms of energy-saving and emission-reduction evaluation methods for pure electric vehicles under complex road alignment conditions based on the inspiration of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. A method for assessing the hovering risk of unmanned aerial vehicles (UAVs) used in crosswind transport under crosswind conditions, characterized in that, include: Acquire information related to truck intermodal transport drones and environmental wind, and design test scenarios; Fluid dynamics simulation technology was used to obtain the aerodynamic characteristics of UAVs under the coupling effect of crosswind and truck-induced flow field, and a dataset of UAV aerodynamic characteristics was constructed. Based on the aerodynamic characteristics dataset of UAVs, a hovering risk level assessment model is established to quantify the risk level of crosswinds on the hovering state of UAVs.

2. The method for assessing the hovering risk of unmanned aerial vehicles (UAVs) in crosswind conditions according to claim 1, characterized in that, The information related to the truck transport drone and environmental wind includes: the number of drone rotors n, the drone rotor size D. r Total length of the truck L t Total width of the truck W t Total height H of the truck t Truck speed V t Drone hovering altitude difference Δh, drone rotor speed ω, crosswind speed V s Wind direction angle α.

3. The method for assessing the hovering risk of unmanned aerial vehicles (UAVs) in crosswind conditions according to claim 2, characterized in that, The experimental scenario was designed with the following parameters: the length of the computational domain was set to 10L. t 9W in width t The height is 5H t Import the simplified geometric models of the drone and truck into the computational domain; set the forward speed of the truck as V. t The crosswind vector is defined using the velocity inlet, and the crosswind speed is V. s The wind direction angle is α; the outlet is set as a pressure outlet.

4. The method for assessing the hovering risk of a truck-freighting drone under crosswind conditions according to claim 3, characterized in that, The aerodynamic characteristics of the UAV under the coupled effects of crosswind and truck-induced flow field are obtained using fluid dynamics simulation technology, and a UAV aerodynamic characteristic dataset is constructed, which includes: Fluid dynamics simulation technology was used to monitor and collect core aerodynamic parameters and flow field characteristics of the UAV during the simulation process. These core aerodynamic parameters included: UAV lift L, UAV drag D, UAV lateral force Y, UAV surface static pressure distribution P, and rolling moment coefficient C. lr The flow field characteristic data includes: vorticity distribution Ω, vortex intensity I. e ; Based on the monitored hydrostatic pressure distribution P on the UAV surface, the hydrostatic pressure asymmetry P on the UAV surface is calculated. asym The calculation formula is: P asym =(P max -P min ) / (P max +P min ), where P max P is the maximum static pressure on the surface of the drone. min Minimum static pressure on the surface of the drone; Calculate the aerodynamic parameters of the UAV, including: Lift coefficient Cl: Drag coefficient Cd: Lateral force coefficient Cy: Rolling torque M r : M r Based on the calculated aerodynamic parameters of the UAV, a dataset of UAV aerodynamic characteristics is constructed.

5. The method for assessing the hovering risk of a truck-freighting drone under crosswind conditions according to claim 4, characterized in that, Based on a dataset of UAV aerodynamic characteristics, a UAV hovering risk level assessment model is established to quantify the risk level of crosswinds on the UAV hovering state, including: (1) Define the core evaluation indicators, including: Transient response index: Roll angle deviation Δθ r Transient response time τ; Stability index: Damping ratio Natural frequency f n ; (2) Based on the UAV aerodynamic characteristic dataset, the transient response index is calculated using the following formula: Roll angle deviation Δθ r : Transient response time τ: (3) Based on the transient response index and the UAV aerodynamic characteristic dataset, the stability index is calculated using the following formula: Damping ratio : Natural frequency f n : (4) Construct a hovering risk level assessment model for UAVs based on core evaluation indicators, and calculate the hovering risk level R: 。 6. The method for assessing the hovering risk of unmanned aerial vehicles (UAVs) in crosswind conditions according to claim 5, characterized in that, Based on a dataset of UAV aerodynamic characteristics, a UAV hovering risk level assessment model is established to quantify the risk level of crosswinds on the UAV hovering state. This also includes: (5) Classify risk levels based on hovering risk level R: When R < 0.3, the drone has stable attitude, fast transient response, and safe hovering, and is judged as low risk; When 0.3≤R<0.6, the attitude deviation is controllable and the response time is within the allowable range. The hovering position needs to be optimized, and it is judged as medium risk. When R≥0.6, the attitude deviation exceeds the limit, the stability is insufficient, and there is a risk of hovering instability, which is judged as high risk.

7. A hovering risk assessment system for unmanned aerial vehicles (UAVs) used in crosswind transport under crosswind conditions, characterized in that, The method for assessing the hovering risk of a truck transport drone under crosswind conditions as described in any one of claims 1 to 6 includes: The data acquisition and scenario construction module is used to acquire parameters of trucks, drones, and ambient wind, and to construct a computational fluid dynamics simulation domain containing a simplified geometric model; The simulation and aerodynamic characteristic calculation module is used to perform fluid dynamics simulations, obtain the core aerodynamic parameters and flow field data of the UAV in the coupled flow field, calculate the aerodynamic parameters of the UAV, and construct the UAV aerodynamic characteristic dataset. The risk assessment module is used to establish a hovering risk level assessment model for UAVs based on the UAV aerodynamic characteristic dataset, and to quantify the risk level of crosswinds to the UAV hovering state.

8. An electronic device, characterized in that, include: A processor and a memory; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the hovering risk assessment method for a truck-freighted unmanned aerial vehicle under crosswind conditions as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the hovering risk assessment method for truck transport drones under crosswind conditions as described in any one of claims 1 to 6.

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