Urban low-altitude environment unmanned aerial vehicle cruise height evaluation method and system based on one-dimensional and multi-dimensional evaluation indexes

Abstract

The invention discloses an urban low-altitude environment unmanned aerial vehicle cruise height evaluation method and system based on one-dimensional and multi-dimensional evaluation indexes in the technical field of unmanned aerial vehicle operation management. The method comprises the steps that the evaluation indexes of the urban low-altitude environment unmanned aerial vehicle cruise height are acquired; obtaining a multi-factor matrix of the evaluation indexes by using a maximum information coefficient and an expert scoring method; calculating a fusion weight of the evaluation indexes according to the multi-factor matrix, and constructing an unmanned aerial vehicle cruise height evaluation model; and evaluating the cruise height of the unmanned aerial vehicle by using the cruise height evaluation model of the unmanned aerial vehicle. The method focuses on the specific cruise height of the unmanned aerial vehicle, evaluates the cruise height layer of the unmanned aerial vehicle in an allowable operation range, comprehensively considers safety, economic and social influence factors, and provides a theoretical basis for the selection and optimization of the subsequent cruise height.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) operation management technology, and in particular to a method and system for evaluating the cruising altitude of UAVs in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators. Background Technology

[0002] With the rapid development of drone technology, drones are increasingly being used in urban low-altitude environments, such as logistics delivery, environmental monitoring, and emergency rescue. However, the complexity and diversity of urban low-altitude environments place higher demands on the selection of drone cruising altitudes. Current drone operation management often focuses on the feasibility of operating routes, neglecting the impact of cruising altitude on operational sustainability; or it considers only a single factor, such as flight safety or energy consumption, restricting drone operations to higher altitudes to avoid collisions, thus wasting the lower low-altitude airspace within the operational range and resulting in low operational efficiency of drones in urban environments.

[0003] Therefore, there is an urgent need for a comprehensive evaluation and selection method for drone cruising altitude that takes into account multiple factors in drone operation management, so as to conduct a more extensive and comprehensive evaluation of the cruising altitude within the operational range, in order to improve the operational efficiency and safety of drones in urban low-altitude environments. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators. It focuses on the specific cruising altitude of UAVs, evaluates the cruising altitude layers of UAVs within the allowable operating range, and comprehensively considers safety, economic and social impact factors, providing a more scientific and objective theoretical basis for the selection of UAV cruising altitude in the strategic planning stage.

[0005] To address the aforementioned technical problems, the present invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators, including:

[0007] Evaluation indicators for the cruising altitude of drones in urban low-altitude environments;

[0008] The multi-factor matrix of the evaluation index is obtained using the maximum information coefficient and expert scoring method;

[0009] The fusion weights of the evaluation indicators are calculated based on the multi-factor matrix, and a drone cruising altitude evaluation model is constructed.

[0010] The drone cruise altitude evaluation model is used to evaluate the drone cruise altitude.

[0011] Optionally, the evaluation indicators for obtaining the cruising altitude of UAVs in urban low-altitude environments include:

[0012] Calculate a single-dimensional evaluation index based on the drone model, drone operating speed, drone payload, altitude of the drone during the cruise phase, and environmental information at that altitude;

[0013] The Informed-RRT* algorithm is used to perform path planning between each pair of flight mission points at a specified cruise altitude to obtain specific path information of the UAV.

[0014] Based on the drone model, drone operating speed, drone payload, drone specific path information, drone altitude during the cruise phase, environmental information at that altitude, and drone specific path information, calculate multi-dimensional evaluation indicators.

[0015] By aggregating the single-dimensional and multi-dimensional evaluation indicators, an initial evaluation indicator set is obtained. The initial evaluation indicator set is then normalized to obtain an evaluation indicator for the cruising altitude of UAVs in urban low-altitude environments.

[0016] Optionally, the single-dimensional evaluation indicators include fall safety risk, fall economic loss, take-off and landing energy consumption, building density, and ground noise impact;

[0017] The fall safety risk is calculated using the following formula:

[0018] ,

[0019] in, Indicates a fall safety risk. Indicates the correction factor. , This indicates the energy required to cause a specified fatality rate from an impact. This represents the energy required for a collision to cause death, where S represents the shielding parameter. , This indicates the momentum of the drone or cargo at the point of impact. , Indicates the quality of the drone or cargo. This indicates the speed at which the drone or cargo reaches the point of impact. , This indicates the horizontal velocity of the drone or cargo upon arrival at the point of impact. , This indicates the horizontal velocity of the drone before it began to fall. This indicates the velocity of the drone or cargo in the vertical direction upon reaching the point of impact. , Represents gravitational acceleration. Indicates the drone's fall altitude;

[0020] The economic loss from the fall is calculated using the following formula:

[0021] ,

[0022] in, Indicates the economic loss from the fall. This indicates the momentum of the drone or cargo at the point of impact. This represents the total cost of providing social assistance when a drone or cargo crash covers a unit area. Indicates the area affected by debris after a drone or cargo crashes. , Represents a constant. This represents the minimum circumscribed sphere radius of the drone or cargo. Indicates the minimum pedestrian radius for a drone or cargo. Indicates the horizontal distance traveled after a drone or cargo crashes. , Indicates pedestrian height, Indicates the gliding angle. Represents the tangent function;

[0023] The takeoff and landing energy consumption is calculated using the following formula:

[0024] ,

[0025] in, This indicates the energy consumption for takeoff and landing. Indicates takeoff and landing altitude. Indicates takeoff and landing speed. Indicates the working efficiency of the propeller blades. Indicates the motor's operating efficiency. Indicates the efficiency of the electronic control unit (ECS). Indicates the blade area, Indicates the correction factor. Indicates the total weight of the drone. Indicates time, Indicates air density, , This represents the air density at standard atmospheric sea level. This represents the standard atmospheric sea level altitude and temperature. Indicates the drone's operating altitude. This represents the temperature gradient below the troposphere. Represents the gas constant of air;

[0026] The density of the buildings is calculated using the following formula:

[0027] ,

[0028] in, Indicates the density of buildings. This represents the total area of ​​one-dimensional obstacles on the ground in the scene. Indicates the total area of ​​the scene;

[0029] The impact on ground noise is calculated using the following formula:

[0030] ,

[0031] in, Indicates the impact on ground noise. This indicates the noise level of the drone operation as it propagates to the ground. , This indicates the initial noise level of the drone during operation. Represents an exponential function. Indicates the attenuation coefficient. This indicates the distance sound waves can travel.

[0032] Optionally, the multidimensional evaluation indicators include total path length and airborne noise level;

[0033] The total path length is calculated using the following formula:

[0034] ,

[0035] in, Indicates the total path length. This represents the number of task point pairs that need to be constructed for the flight routes. Indicates the first Distance between mission points;

[0036] The airborne noise level is calculated using the following formula:

[0037] ,

[0038] in, Indicates the noise level during air operation. This indicates the path length of the drone passing through the building noise protection zone. This indicates the flight speed of the drone during the cruise phase. This indicates the power of the drone propeller rotation noise. , Indicates the harmonic order. Indicates the number of propeller blades. Indicates the number of motors. Indicates the propeller radius. Indicates the tip Mach number. This indicates the distance of the UAV from the measurement point. Indicates the area of ​​the propeller disk. Indicates the absorbed power. Indicates the thrust of the motor. Indicates the angle between the UAV and the measurement point. Represents the cosine function. Indicates the width of the building's noise protection zone. , Indicates reference sound pressure level. Indicates sound pressure level. .

[0039] Optionally, the normalization of the initial evaluation index set is achieved by the following formula:

[0040] ,

[0041] in, Indicates the first One evaluation indicator, Represents the initial set of evaluation indicators. Indicates the first value in the initial set of evaluation indicators. One initial evaluation indicator, Represents the initial set of evaluation indicators The maximum value in.

[0042] Optionally, a multi-factor matrix of the evaluation indicators can be obtained using the maximum information coefficient and expert scoring method, including:

[0043] Calculate the correlation between any two evaluation indicators using the maximum information coefficient. , thus obtaining a symmetric matrix ;

[0044] Using the Delphi 0-1 expert rating method, the judgment matrix of the evaluation index is constructed. ;

[0045] According to the symmetric matrix And judgment matrix The multifactor matrix (MFIM) is obtained through encoding.

[0046] Optionally, the degree of correlation Calculated using the following formula:

[0047] ,

[0048] in, Indicators With evaluation indicators The degree of correlation between them Indicates sample size 0.6 Indicators The number of arrays, Indicators The number of arrays, Indicators With evaluation indicators Mutual information between them , Indicators With evaluation indicators The joint probability distribution, Indicators Marginal probability distribution, Indicators The marginal probability distribution;

[0049] The symmetric matrix The expression is:

[0050] ,

[0051] in, Indicates the total number of evaluation indicators;

[0052] The judgment matrix The expression is:

[0053] ,

[0054] in, This indicates that the first evaluation indicator is relative to the second. The importance of each evaluation indicator;

[0055] The expression for the Multifactor Matrix (MFIM) is:

[0056]

[0057] in, Indicates the evaluation indicators.

[0058] Optionally, the calculation of the fusion weights of the evaluation indicators based on the multi-factor matrix is ​​achieved through the following formula:

[0059]

[0060] in, Indicates the first The weights of the combined evaluation indicators The coefficient representing the proportion of objective weights. Indicates the first The objective weights of the evaluation indicators are determined through the MFIM matrix. row division The sum of all the values ​​is obtained. Indicates the first The subjective weights of the evaluation indicators are determined through the MFIM matrix. Columns The sum of all the values ​​is obtained.

[0061] Optionally, the expression for the UAV cruise altitude evaluation model is as follows:

[0062]

[0063] in, This indicates the evaluation result of the drone's cruise altitude. Indicates the first One evaluation indicator.

[0064] Secondly, the present invention provides an urban low-altitude environment drone cruising altitude evaluation system based on single-dimensional and multi-dimensional evaluation indicators, comprising:

[0065] The evaluation index module is used to obtain evaluation indexes for the cruising altitude of drones in urban low-altitude environments.

[0066] The multi-factor matrix module is used to: obtain the multi-factor matrix of the evaluation indicators using the maximum information coefficient and expert scoring method;

[0067] The model building module is used to: calculate the fusion weights of the evaluation indicators based on the multi-factor matrix, and build an evaluation model for the drone's cruising altitude;

[0068] The cruise altitude evaluation module is used to: evaluate the cruise altitude of the UAV using the UAV cruise altitude evaluation model.

[0069] Compared with existing technologies, the beneficial effects achieved by this invention are as follows:

[0070] 1. This invention differs from the path and network evaluation commonly used in UAV operation and management. It focuses on the specific cruising altitude of the UAV, evaluates the UAV cruising altitude layer within the permitted operating range, considers safety, economic and social impact factors, selects evaluation indicators closely related to cruising altitude, and conducts comprehensive indicator collection. At the same time, based on the coupling between indicators and path, the indicators are divided into single-dimensional indicators and multi-dimensional indicators, and calculated step by step.

[0071] 2. To scientifically determine the weights of indicators, this invention utilizes the MIC value to mine the data relationship between indicator values ​​and altitude, uses mathematical methods to determine the degree of objective correlation between indicators, and combines expert scoring methods to obtain the subjective correlation values ​​between indicators. By obtaining and integrating indicator weights from both objective and subjective perspectives, a drone cruise altitude evaluation system is constructed, providing a theoretical basis for the subsequent selection and optimization of drone cruise altitudes. Attached Figure Description

[0072] Figure 1 This is a flowchart illustrating the method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators, according to an embodiment of the present invention.

[0073] Figure 2 This diagram illustrates the impact of falling debris according to an embodiment of the present invention.

[0074] Figure 3 This diagram illustrates a noise protection zone provided according to an embodiment of the present invention. Detailed Implementation

[0075] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0076] It should be noted that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0077] Example 1:

[0078] This invention discloses a method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators, with reference to... Figure 1 As shown, the specific steps include the following:

[0079] S1 is an evaluation index for the cruising altitude of drones in urban low-altitude environments.

[0080] S2, use the maximum information coefficient and expert scoring method to obtain the multi-factor matrix of the evaluation index;

[0081] S3, calculate the fusion weights of the evaluation indicators based on the multi-factor matrix, and construct an evaluation model for the drone's cruising altitude;

[0082] S4. Use the UAV cruise altitude evaluation model to evaluate the UAV cruise altitude.

[0083] Specifically, in step S1, evaluation indicators related to the cruising altitude of UAVs in urban low-altitude environments are selected based on safety, economic, and social impact factors, including:

[0084] Step S101 defines a single-dimensional indicator as an indicator that, given a fixed UAV model, operating speed, and payload, is only related to the altitude of the UAV during its cruise phase and the environment determined by that altitude, and is unrelated to the specific path the UAV takes. A multi-dimensional indicator is defined as an indicator that, given a fixed UAV model, operating speed, payload, and cruise altitude, is related to the specific path the UAV takes at a specified cruise altitude.

[0085] Step S102: Select fall safety risk, fall economic loss, take-off and landing energy consumption, building density, and ground noise impact as single-dimensional indicators, obtain calculation data for each indicator, and perform single-dimensional indicator calculation.

[0086] Step S103: Perform path planning between each flight mission point pair at the specified cruise altitude to obtain specific path information of the UAV;

[0087] Step S104: Select total path length and airborne noise level as multi-dimensional indicators, obtain specific path information and calculation data of each indicator, and perform multi-dimensional indicator calculation.

[0088] Step S105: Aggregate the single-dimensional evaluation index and the multi-dimensional evaluation index to obtain an initial evaluation index set, and normalize the initial evaluation index set to obtain the evaluation index of the drone's cruising altitude in the urban low-altitude environment.

[0089] In step S102, the single-dimensional evaluation index includes fall safety risk, fall economic loss, take-off and landing energy consumption, building density, and ground noise impact.

[0090] The crash safety risk is the safety risk posed by a drone falling to the ground from a designated cruising altitude, quantified as the probability of fatality upon drone crash, and calculated using the following formula:

[0091] ,

[0092] in, Indicates a fall safety risk. Indicates the correction factor. , This indicates the energy required to cause a specified fatality rate of 50% in an impact. The energy required to cause death from an impact is represented by 34 J; S represents the shielding parameter. , This indicates the momentum of the drone or cargo at the point of impact. , Indicates the quality of the drone or cargo. This indicates the speed at which the drone or cargo reaches the point of impact. , This indicates the horizontal velocity of the drone or cargo upon arrival at the point of impact. , This indicates the horizontal velocity of the drone before it began to fall. This indicates the velocity of the drone or cargo in the vertical direction upon reaching the point of impact. , Represents gravitational acceleration. Indicates the drone's fall altitude;

[0093] refer to Figure 2 As shown, the impact range of debris from a drone falling to the ground is not limited to the impact point, but extends to a distance from that point. The economic loss caused by the damage is directly related to this distance. The economic loss from the fall is the economic loss caused to the ground by the drone falling from its cruising altitude, and is calculated using the following formula:

[0094] ,

[0095] in, Indicates the economic loss from the fall. This indicates the momentum of the drone or cargo at the point of impact. This represents the total cost of providing social assistance when a drone or cargo crash covers a unit area. Indicates the area affected by debris after a drone or cargo crashes. , Represents a constant. This represents the minimum circumscribed sphere radius of the drone or cargo. Indicates the minimum pedestrian radius for a drone or cargo. Indicates the horizontal distance traveled after a drone or cargo crashes. , Indicates pedestrian height, Indicates the gliding angle. Represents the tangent function;

[0096] Takeoff and landing energy consumption refers to the energy consumed by the UAV to climb to a designated cruising altitude. Assuming the UAV flies at a constant speed during vertical takeoff and landing, the power system does work to overcome its own power. Assuming the air density and speed are the same during takeoff and landing, the energy consumption can be calculated using twice the takeoff energy consumption, specifically using the following formula:

[0097] ,

[0098] in, This indicates the energy consumption for takeoff and landing. Indicates takeoff and landing altitude. Indicates takeoff and landing speed. Indicates the working efficiency of the propeller blades. Indicates the motor's operating efficiency. Indicates the efficiency of the electronic control unit (ECS). Indicates the blade area, Indicates the correction factor. Indicates the total weight of the drone. Indicates time, Indicates air density, , This represents the air density at standard atmospheric sea level. This represents the standard atmospheric sea level altitude and temperature. Indicates the drone's operating altitude. This represents the temperature gradient below the troposphere. Represents the gas constant of air;

[0099] Building density is the ratio of the area of ​​buildings at a specified cruising altitude within the area covered by the drone to the total area of ​​the entire region, calculated using the following formula:

[0100] ,

[0101] in, Indicates the density of buildings. This represents the total area of ​​one-dimensional obstacles on the ground in the scene. Indicates the total area of ​​the scene;

[0102] The ground noise impact refers to the degree of influence of noise generated by the drone during operation on people on the ground, and is calculated using the following formula:

[0103] ,

[0104] in, Indicates the impact on ground noise. This indicates the noise level of the drone operation as it propagates to the ground. , This indicates the initial noise level of the drone during operation. Represents an exponential function. Indicates the attenuation coefficient. This indicates the distance sound waves can travel.

[0105] In step S103, path planning between each pair of flight mission points at the specified cruise altitude is specifically performed as follows: The Informed-RRT* algorithm is used to plan the UAV path between the two points. After randomly sampling to obtain feasible paths, the algorithm narrows the search range by the ellipse range determined by the feasible paths and iteratively searches for the optimal solution. At the same time, a dynamic adjustment of the search step size is introduced. In the early stage of the search, feasible solutions are quickly traversed by a large search step size, and in the later stage, the final path is optimized by a small step size. This improves the search efficiency while having a high probability of obtaining the global optimal solution, in order to obtain the specific flight path of the UAV.

[0106] In step S104, the multidimensional evaluation indicators include total path length and airborne noise level;

[0107] The total path length is the sum of the path lengths of the UAV between each pair of mission points at the specified cruising altitude, calculated using the following formula:

[0108] ,

[0109] in, Indicates the total path length. This represents the number of task point pairs that need to be constructed for the flight routes. Indicates the first Distance between mission points;

[0110] refer to Figure 3 As shown, a noise protection zone of a certain width is constructed centered on the building. This noise protection zone represents the area within which the drone's operation causes noise pollution to the building. The noise source is primarily considered to be the drone's propeller rotation noise, and the aerial noise is calculated using the following formula:

[0111] ,

[0112] in, Indicates the noise level during air operation. This indicates the path length of the drone passing through the building noise protection zone. This indicates the flight speed of the drone during the cruise phase. This indicates the power of the drone propeller rotation noise. , Indicates the harmonic order. Indicates the number of propeller blades. Indicates the number of motors. Indicates the propeller radius. Indicates the tip Mach number. This indicates the distance of the UAV from the measurement point. Indicates the area of ​​the propeller disk. Indicates the absorbed power. Indicates the thrust of the motor. Indicates the angle between the UAV and the measurement point. Represents the cosine function. Indicates the width of the building's noise protection zone. , Indicates reference sound pressure level. Indicates sound pressure level. .

[0113] In step S105, the initial evaluation index set is normalized using the following formula:

[0114] ,

[0115] in, Indicates the first One evaluation indicator, Represents the initial set of evaluation indicators. Indicates the first value in the initial set of evaluation indicators. One initial evaluation indicator, Represents the initial set of evaluation indicators The maximum value in.

[0116] In step S2, the multi-factor matrix of the evaluation index is obtained using the maximum information coefficient and expert scoring method, including:

[0117] Step S201: Since the calculation results are the values ​​of each indicator at a specified cruising altitude, which are discrete variables, the correlation between the two indicators is calculated using the maximum information coefficient method for discrete variables. , thus obtaining a symmetric matrix ;

[0118] Step S202: Construct the judgment matrix of the evaluation index using the Delphi 0-1 expert scoring method. ;

[0119] Step S203, according to the symmetric matrix And judgment matrix The multifactor matrix (MFIM) is obtained through encoding.

[0120] In step S201, the degree of correlation Calculated using the following formula:

[0121] ,

[0122] in, Indicators With evaluation indicators The degree of correlation between them Indicates sample size 0.6 Indicators The number of arrays, Indicators The number of arrays, Indicators With evaluation indicators Mutual information between them , Indicators With evaluation indicators The joint probability distribution, Indicators Marginal probability distribution, Indicators The marginal probability distribution;

[0123] The symmetric matrix The expression is:

[0124] ,

[0125] in, Indicates the total number of evaluation indicators;

[0126] In step S202, this embodiment designs a questionnaire to determine the scale values ​​and their meanings, as shown in the table below:

[0127]

[0128] Collect ratings from multiple experts and construct a judgment matrix A, in the following form:

[0129] ,

[0130] in, This indicates that the first evaluation indicator is relative to the second. The importance of each evaluation indicator.

[0131] In step S203, the expression for the multi-factor matrix MFIM is:

[0132]

[0133] in, Indicates the evaluation indicators.

[0134] In step S3, the calculation of the fusion weights of the evaluation indicators based on the multi-factor matrix is ​​achieved through the following formula:

[0135]

[0136] in, Indicates the first The weights of the combined evaluation indicators The coefficient representing the proportion of objective weights. Indicates the first The objective weights of the evaluation indicators are determined through the MFIM matrix. row division The sum of all the values ​​is obtained. Indicates the first The subjective weights of the evaluation indicators are determined through the MFIM matrix. Columns The sum of all the values ​​is obtained.

[0137] In step S4, the expression for the UAV cruise altitude evaluation model is as follows:

[0138]

[0139] in, This indicates the evaluation result of the drone's cruise altitude. Indicates the first One evaluation indicator.

[0140] In summary, this embodiment proposes an evaluation method for the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators. Focusing on the evaluation of UAV cruising altitude, it selects evaluation indicators from three aspects: safety, economy, and social impact, and classifies them into static multi-dimensional indicators based on their characteristics. For indicator values ​​at different altitude levels, objective weights are obtained through MIC value calculation and MIC matrix eigenvector extraction. Five types of judgment scale values ​​are established as expert scoring evaluation standards to obtain subjective weights for each indicator. Two types of weight coefficients are set to obtain the cruising altitude evaluation expression. Through data analysis and mathematical expression construction, a more scientific basis for selecting UAV cruising altitude is provided, contributing to the scientific and efficient development of UAV operation management.

[0141] Example 2:

[0142] Based on the same inventive concept as Embodiment 1, this embodiment of the invention discloses a system for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators, comprising:

[0143] The evaluation index module is used to obtain evaluation indexes for the cruising altitude of drones in urban low-altitude environments.

[0144] The multi-factor matrix module is used to: obtain the multi-factor matrix of the evaluation indicators using the maximum information coefficient and expert scoring method;

[0145] The model building module is used to: calculate the fusion weights of the evaluation indicators based on the multi-factor matrix, and build an evaluation model for the drone's cruising altitude;

[0146] The cruise altitude evaluation module is used to: evaluate the cruise altitude of the UAV using the UAV cruise altitude evaluation model.

[0147] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0148] Those skilled in the art will understand that embodiments of the present invention can provide representations of methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0149] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0150] 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.

[0151] 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.

[0152] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators, characterized in that... include: Evaluation indicators for the cruising altitude of drones in urban low-altitude environments; The multi-factor matrix of the evaluation index is obtained using the maximum information coefficient and expert scoring method; The fusion weights of the evaluation indicators are calculated based on the multi-factor matrix, and a drone cruising altitude evaluation model is constructed. The drone cruise altitude evaluation model is used to evaluate the drone cruise altitude.

2. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 1, characterized in that, The evaluation metrics for obtaining the cruising altitude of UAVs in urban low-altitude environments include: Calculate a single-dimensional evaluation index based on the drone model, drone operating speed, drone payload, altitude of the drone during the cruise phase, and environmental information at that altitude; The Informed-RRT* algorithm is used to perform path planning between each pair of flight mission points at a specified cruise altitude to obtain specific path information of the UAV. Based on the drone model, drone operating speed, drone payload, drone specific path information, drone altitude during the cruise phase, environmental information at that altitude, and drone specific path information, calculate multi-dimensional evaluation indicators. By aggregating the single-dimensional and multi-dimensional evaluation indicators, an initial evaluation indicator set is obtained. The initial evaluation indicator set is then normalized to obtain an evaluation indicator for the cruising altitude of UAVs in urban low-altitude environments.

3. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 2, is characterized in that... The single-dimensional evaluation indicators include fall safety risk, fall economic loss, take-off and landing energy consumption, building density, and ground noise impact. The fall safety risk is calculated using the following formula: ， in, Indicates a fall safety risk. Indicates the correction factor. , This indicates the energy required to cause a specified fatality rate from an impact. This represents the energy required for a collision to cause death, where S represents the shielding parameter. , This indicates the momentum of the drone or cargo at the point of impact. , Indicates the quality of the drone or cargo. This indicates the speed at which the drone or cargo reaches the point of impact. , This indicates the horizontal velocity of the drone or cargo upon arrival at the point of impact. , This indicates the horizontal velocity of the drone before it began to fall. This indicates the velocity of the drone or cargo in the vertical direction upon reaching the point of impact. , Represents gravitational acceleration. Indicates the drone's fall altitude; The economic loss from the fall is calculated using the following formula: ， in, Indicates the economic loss from the fall. This indicates the momentum of the drone or cargo at the point of impact. This represents the total cost of providing social assistance when a drone or cargo crash covers a unit area. Indicates the area affected by debris after a drone or cargo crashes. , Represents a constant. This represents the minimum circumscribed sphere radius of the drone or cargo. Indicates the minimum pedestrian radius for a drone or cargo. Indicates the horizontal distance traveled after a drone or cargo crashes. , Indicates pedestrian height, Indicates the gliding angle. Represents the tangent function; The takeoff and landing energy consumption is calculated using the following formula: ， in, This indicates the energy consumption for takeoff and landing. Indicates takeoff and landing altitude. Indicates takeoff and landing speed. Indicates the working efficiency of the propeller blades. Indicates the motor's operating efficiency. Indicates the efficiency of the electronic control unit (ECS). Indicates the blade area, Indicates the correction factor. Indicates the total weight of the drone. Indicates time, Indicates air density, , This represents the air density at standard atmospheric sea level. This represents the standard atmospheric sea level altitude and temperature. Indicates the drone's operating altitude. This represents the temperature gradient below the troposphere. Represents the gas constant of air; The density of the buildings is calculated using the following formula: ， in, Indicates the density of buildings. This represents the total area of ​​one-dimensional obstacles on the ground in the scene. Indicates the total area of ​​the scene; The impact on ground noise is calculated using the following formula: ， in, Indicates the impact on ground noise. This indicates the noise level of the drone operation as it propagates to the ground. , This indicates the initial noise level of the drone during operation. Represents an exponential function. Indicates the attenuation coefficient. This indicates the distance sound waves can travel.

4. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 2, characterized in that, The multidimensional evaluation indicators include total path length and airborne noise level; The total path length is calculated using the following formula: ， in, Indicates the total path length. This represents the number of task point pairs that need to be constructed for the flight routes. Indicates the first Distance between mission points; The airborne noise level is calculated using the following formula: ， in, Indicates the noise level during air operation. This indicates the path length of the drone passing through the building noise protection zone. This indicates the flight speed of the drone during the cruise phase. This indicates the power of the drone propeller rotation noise. , Indicates the harmonic order, Indicates the number of propeller blades. Indicates the number of motors. Indicates the propeller radius. Represents the tip Mach number. This indicates the distance of the UAV from the measurement point. Indicates the area of ​​the propeller disk. Indicates the absorbed power. Indicates the thrust of the motor. Indicates the angle between the UAV and the measurement point. Represents the cosine function. Indicates the width of the building's noise protection zone. , Indicates reference sound pressure level. Indicates sound pressure level. .

5. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 2, characterized in that, The normalization of the initial evaluation index set is achieved by the following formula: ， in, Indicates the first One evaluation indicator, Represents the initial set of evaluation indicators. Indicates the first value in the initial set of evaluation indicators. One initial evaluation indicator, Represents the initial set of evaluation indicators The maximum value in.

6. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 1, characterized in that, The multi-factor matrix of the evaluation index is obtained using the maximum information coefficient and expert scoring method, including: Calculate the correlation between any two evaluation indicators using the maximum information coefficient. , thus obtaining a symmetric matrix ; Using the Delphi 0-1 expert rating method, the judgment matrix of the evaluation index is constructed. ; According to the symmetric matrix And judgment matrix The multifactor matrix (MFIM) is obtained through encoding.

7. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 6, characterized in that, The degree of correlation Calculated using the following formula: ， in, Indicators With evaluation indicators The degree of correlation between them Indicates sample size 0.6 Indicators The number of arrays, Indicators The number of arrays, Indicators With evaluation indicators Mutual information between them , Indicators With evaluation indicators The joint probability distribution, Indicators Marginal probability distribution, Indicators The marginal probability distribution; The symmetric matrix The expression is: ， in, Indicates the total number of evaluation indicators; The judgment matrix The expression is: ， in, This indicates that the first evaluation indicator is relative to the second. The importance of each evaluation indicator; The expression for the Multifactor Matrix (MFIM) is: in, Indicates the evaluation indicators.

8. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 7, characterized in that, The calculation of the fusion weights of the evaluation indicators based on the multi-factor matrix is ​​achieved through the following formula: in, Indicates the first The weights of the combined evaluation indicators The coefficient representing the proportion of objective weights. Indicates the first The objective weights of the evaluation indicators are determined through the MFIM matrix. row division The sum of all the values ​​is obtained. Indicates the first The subjective weights of the evaluation indicators are determined through the MFIM matrix. Columns The sum of all the values ​​is obtained.

9. The method for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators according to claim 8, characterized in that, The expression for the UAV cruising altitude evaluation model is as follows: in, This indicates the evaluation result of the drone's cruise altitude. Indicates the first One evaluation indicator.

10. A system for evaluating the cruising altitude of unmanned aerial vehicles (UAVs) in urban low-altitude environments based on single-dimensional and multi-dimensional evaluation indicators, characterized in that: include: The evaluation index module is used to obtain evaluation indexes for the cruising altitude of drones in urban low-altitude environments. The multi-factor matrix module is used to: obtain the multi-factor matrix of the evaluation indicators using the maximum information coefficient and expert scoring method; The model building module is used to: calculate the fusion weights of the evaluation indicators based on the multi-factor matrix, and build an evaluation model for the drone's cruising altitude; The cruise altitude evaluation module is used to: evaluate the cruise altitude of the UAV using the UAV cruise altitude evaluation model.

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