Unmanned aerial vehicle logistics transportation capability analysis method

Abstract

The invention discloses an unmanned aerial vehicle logistics transportation capability analysis method, and belongs to the technical field of unmanned aerial vehicle transportation capability quantification. The problem that in the prior art, a traditional method for carrying out logistics transportation through an unmanned aerial vehicle does not relate to unmanned aerial vehicle transportation capacity analysis is solved. The method comprises the following steps: acquiring data according to evaluation indexes to obtain an element matrix corresponding to the evaluation indexes; performing unmanned aerial vehicle influence factor abnormal data identification to obtain an updated evaluation index element matrix of the unmanned aerial vehicle; constructing an unmanned aerial vehicle flight distance prediction model, inputting the updated evaluation index element matrix of the unmanned aerial vehicle to train the model, and outputting a predicted unmanned aerial vehicle flight distance; and determining coordinates of logistics transportation points according to the flight area of the unmanned aerial vehicle, and determining all logistics transportation points in the logistics distribution range of the unmanned aerial vehicle by calculating the distance between the numbered logistics transportation points and the predicted flight distance of the unmanned aerial vehicle. The method effectively supports unmanned aerial vehicle transportation risk management, and can be applied to analysis of unmanned aerial vehicle transportation capability.

Description

Technical Field

[0001] The invention relates to a method for analyzing the logistics transportation capacity of an unmanned aerial vehicle, and belongs to the technical field of quantification of the transportation capacity of unmanned aerial vehicles. Background Art

[0002] The application of drones in the logistics industry is changing the way goods are delivered, especially in terms of improving efficiency and reducing costs. In urban areas, drones can effectively avoid ground traffic congestion and directly deliver small goods to the destination quickly, greatly improving the speed and timeliness of delivery. In addition, drones have lower operating costs than traditional means of transportation such as trucks or motorcycles, especially in short-distance and small-batch delivery scenarios. This cost advantage is more obvious. Therefore, the use of drones not only improves the efficiency of logistics distribution, but also significantly reduces the overall logistics costs, especially in the express delivery industry and urgent delivery services.

[0003] From the perspective of environmental impact and sustainability, drone transportation also shows its advantages. Due to the reduction in the use of ground transportation, drone delivery plays an important role in alleviating traffic congestion and reducing carbon emissions. Especially when drones are powered by electricity and the energy comes from renewable energy, their negative impact on the environment will be further reduced, which not only helps the logistics industry to shift to a more environmentally friendly operating model, but also promotes the environmental sustainability of the entire society. As an efficient, environmentally friendly and flexible delivery method, drones can play a significant role in alleviating urban traffic congestion, reducing carbon emissions, increasing delivery speed and reducing operating costs. By analyzing the transportation capacity of drones in detail, companies and policymakers can better evaluate their applicability and benefits in specific scenarios. Analysis of drone transportation capacity can also reveal the challenges and opportunities faced by drone transportation in terms of technology, economies of scale and regulatory compliance, providing data support and decision-making basis for future technological improvements and policy formulation.

[0004] In the prior art, a patent document with a publication (announcement) number of CN112520040A discloses a drone and a drone system for logistics transportation, which designs a drone for logistics transportation and has the ability to automatically load and unload goods, which is achieved through a cargo bin and an intelligent sliding box at the bottom of the frame. The box design includes a reverse-threaded slide plate, which fixes the goods by clamping them with springs, thereby preventing the goods from falling during transportation and achieving shock absorption protection, significantly improving the efficiency and quality of logistics transportation; however, the prior art does not consider the impact of various factors on the drone's transportation capacity during the flight of the drone, resulting in unclear actual logistics transportation capacity of the drone, making it difficult to meet actual transportation needs.

[0005] Therefore, a method for analyzing the logistics transportation capabilities of drones is needed. Summary of the invention

[0006] A brief overview of the present invention is provided below in order to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important parts of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is merely to present certain concepts in a simplified form as a prelude to a more detailed description discussed later.

[0007] In view of this, in order to solve the problem that the traditional method of using drones for logistics transportation in the prior art does not involve drone transportation capacity analysis, the present invention provides a drone logistics transportation capacity analysis method.

[0008] The technical solution is as follows: A method for analyzing the logistics transportation capacity of unmanned aerial vehicles includes the following steps: S1. Collect data based on the evaluation indicators, build a quantitative analysis evaluation index system for the flight distance of UAVs, and obtain the element matrix corresponding to the evaluation indicators; S2. According to the element matrix corresponding to the evaluation index, identify the abnormal data of the UAV influencing factors and obtain the updated UAV evaluation index element matrix; S3. Build a UAV flight distance prediction model, input the updated UAV evaluation index element matrix to train the model, and output the predicted UAV flight distance; S4. Determine the coordinates of the logistics transportation points according to the drone flight area, and determine all the logistics transportation points within the drone logistics distribution range by calculating the distance relationship between the numbered logistics transportation points and the predicted drone flight distance.

[0009] Further, in said S1, the evaluation indexes include battery capacity, motor efficiency, propeller diameter, fuselage mass, load, flight speed, flight mode, wind speed, wind direction, temperature, humidity, flight altitude and flight distance; The quantitative analysis and evaluation index system of UAV flight distance is specifically expressed as follows: The battery capacity element matrix corresponding to the battery capacity is , , is the number of drones, For the Battery capacity corresponding to each drone; The motor efficiency element matrix corresponding to the motor efficiency is: , , For the Motor efficiency corresponding to each drone; The propeller diameter element matrix corresponding to the propeller diameter is ZJ, , For the Propeller diameter corresponding to each drone; The fuselage mass element matrix corresponding to the fuselage mass is: , , For the The body mass corresponding to each drone; The load element matrix corresponding to the load is: , , For the The corresponding load of each drone; The flight speed element matrix corresponding to the flight speed is: , , For the The flight speed of each drone; The flight mode element matrix corresponding to the flight mode is , , For the The flight modes corresponding to each drone; The wind speed element matrix corresponding to the wind speed is , , For the Wind speed corresponding to each drone; The wind direction element matrix corresponding to the wind direction is , , For the The wind direction corresponding to each drone; The temperature element matrix corresponding to the temperature is , , For the The temperature corresponding to each drone; The humidity element matrix corresponding to humidity is , , For the Humidity corresponding to each drone; The flight height element matrix corresponding to the flight height is: , , For the The flight altitude corresponding to each drone; The flight distance element matrix corresponding to the flight distance is , , For the The flight distance of each drone.

[0010] Furthermore, in S2, the process of identifying abnormal battery capacity data is as follows: The average battery capacity of each drone , The standard deviation of the battery capacity of each drone and the relative distance between any data and the mean , comparing the relative distance between individual data points and the mean The relationship with 3 is , to determine the battery capacity of the drone Normal data is retained. , to determine the battery capacity of the drone The above operation is performed on all data, and all abnormal data are deleted to obtain the updated battery capacity element matrix. , , For the updated Battery capacity data, is the amount of data after deleting abnormal data; The average battery capacity of each drone It is expressed as: ; The standard deviation of the battery capacity of each drone It is expressed as: ; The relative distance between any data and the mean It is expressed as: ; According to the abnormal battery capacity data identification process, the updated motor efficiency element matrix is ​​established , Updated propeller diameter element matrix , Updated fuselage mass element matrix , updated load element matrix , Updated flight speed element matrix , Updated flight mode element matrix , Updated wind speed element matrix , Updated wind direction element matrix , Updated temperature element matrix , updated humidity element matrix , updated flight height element matrix and the updated flight distance element matrix .

[0011] Furthermore, in S3, the UAV flight distance prediction model includes an input layer, 7 hidden layers, a self-attention layer, 2 residual layers and an output layer; The neural network structure of the established UAV flight distance prediction model is connected in the following order: Input layer: Updated battery capacity element matrix , Updated motor efficiency element matrix , Updated propeller diameter element matrix , Updated fuselage mass element matrix , updated load element matrix , Updated flight speed element matrix , Updated flight mode element matrix , Updated wind speed element matrix , Updated wind direction element matrix , Updated temperature element matrix , updated humidity element matrix , updated flight height element matrix and the updated flight distance element matrix As input; Hidden layer 1: Number of neurons: 512, activation function: ReLU, batch normalization; Hidden layer 2: number of neurons: 256, activation function: ReLU, batch normalization; Self-attention layer: input the output matrix X of hidden layer 2; initialize the weight matrix and establish the initial weight query matrix in turn , initial weight key matrix and the initial weight matrix , transform the output matrix X into a query matrix , key matrix K and value matrix V, , , ; Calculate the query matrix The similarity with the key matrix K gives the attention score matrix A, ,in, is the scaling factor; the softmax function is used to normalize the attention score matrix A so that the sum of the elements in each row is 1, and the normalized matrix B is obtained and output. ; Hidden layer 3: Number of neurons: 128, activation function: ReLU, batch normalization; Residual layer 1: The output of hidden layer 1 is connected to the output of hidden layer 3 through residual connection. Hidden layer 4: number of neurons: 64, activation function: ReLU, batch normalization; Hidden layer 5: number of neurons: 32, activation function: ReLU, batch normalization; Hidden layer 6: number of neurons: 16, activation function: ReLU, batch normalization; Residual layer 2: The output of hidden layer 4 is connected to the output of hidden layer 6 through residual connection; Hidden layer 7: Number of neurons: 8, activation function: ReLU, batch normalization; Output layer: Number of neurons: 1, outputs the predicted flight distance of the drone.

[0012] Furthermore, in said S4, a three-dimensional coordinate system is established for the logistics transportation area, the coordinates of the logistics transportation points are determined, and the first The coordinates of the logistics transport points are marked as , ,in, =1, 2, 3, ..., F, where F is the total number of logistics transportation points, For the The horizontal coordinate of the logistics transportation point, For the The vertical coordinate of the logistics transportation point, For the The vertical coordinates of the logistics transportation points are marked as the coordinates of the first drone landing pad. , ,in, is the horizontal coordinate of the drone landing pad, is the y value of the coordinate of the drone landing pad, is the z value of the coordinates of the drone landing pad; For the first logistics transport point , , calculate the distance between the first logistics transportation point and the drone landing pad ; The first distance between the first logistics transport point and the drone landing pad It is expressed as: ; The predicted flight distance of the first drone based on the output , compare the first distance The predicted flight distance of the first drone The relationship between When , it indicates that the distance between the first logistics transportation point and the drone landing pad exceeds the flight range of the first drone, and it is determined that the first logistics transportation point is not within the logistics distribution range of the first drone. When , it indicates that the first logistics transportation point is within the logistics distribution range of the first drone. Based on this, the distance between other logistics transportation points and the first logistics transportation point is calculated. ; The distance between other logistics transportation points and the first logistics transportation point It is expressed as: ; The distance between all other logistics transportation points and the first logistics transportation point Sort from small to large and number the logistics transport points in the above order. The coordinates of the logistics transport points are , ,in, After the number The horizontal coordinate of the logistics transportation point, After the number The vertical coordinate of the logistics transportation point, After the number The vertical coordinates of the logistics transportation points, the coordinates of the first logistics transportation point after numbering are , ,in, is the horizontal coordinate of the first logistics transportation point after numbering, is the vertical coordinate of the first logistics transportation point after numbering, is the vertical coordinate of the first logistics transportation point after numbering, and the first logistics transportation point is calculated The first logistics transport point after the number The first center point coordinates between , ,in, is the horizontal coordinate of the first center point, , is the ordinate of the first center point, , is the vertical coordinate of the first center point, , calculate the distance between the first center point and the first drone landing pad ; The second distance between the first center point and the first drone landing pad It is expressed as: ; Compare the second distance The predicted flight distance of the first drone The distance between When it is, it indicates the first logistics transportation point after the number Not within the range of the first drone logistics delivery, when When it is, it indicates the first logistics transportation point after the number In the first drone logistics delivery range, Based on this, the coordinates of the second logistics transportation point are , ,in, is the horizontal coordinate of the second logistics transportation point after numbering, is the ordinate of the second logistics transport point after numbering. The vertical coordinate of the second logistics transportation point after numbering is calculated to obtain the first logistics transportation point The first logistics transport point after the number And the second logistics transport point after the number The coordinates of the second center point between , ,in, is the horizontal coordinate of the second midpoint, , is the ordinate of the second midpoint, , is the vertical coordinate of the second midpoint, , calculate the distance between the second center point and the first drone landing pad ; The third distance between the second center point and the first drone landing pad It is expressed as: ; Compare the third distance The predicted flight distance of the first drone The distance between When it is, it indicates the second logistics transportation point after the number Not within the range of the first drone logistics delivery, when When it is, it indicates the second logistics transportation point after the number Within the first drone logistics delivery range; Repeat the above steps to determine all logistics transportation points within the entire drone logistics distribution range.

[0013] The beneficial effects of the present invention are as follows: the present invention systematically evaluates the performance of drones by establishing a quantitative analysis and evaluation index system for drone flight distance, and clarifies the flight capabilities of drones under different conditions; the present invention identifies abnormal data related to data on factors affecting the flight distance of drones, ensures the accuracy and reliability of the data, and improves the accuracy of drone flight distance estimation; the present invention establishes a drone flight distance prediction model that can predict the flight capabilities of drones under specific conditions, optimizes the use of drones, and ensures that drones can plan missions within the range allowed by their endurance, avoiding mid-trip power depletion or other risks; the present invention clarifies the transportation capacity of drones by determining all logistics transportation points within the logistics distribution range of all drones, and effectively supports distribution efficiency optimization, transportation risk management and cost control. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 The figure is a flow chart of a method for analyzing the logistics transportation capacity of unmanned aerial vehicles. DETAILED DESCRIPTION

[0015] In order to make the technical solutions and advantages of the embodiments of the present invention more clearly understood, the exemplary embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0016] refer to Figure 1 The present embodiment is described in detail, a method for analyzing the logistics transportation capacity of a drone, specifically comprising the following steps: S1. Collect data based on the evaluation indicators, build a quantitative analysis evaluation index system for the flight distance of UAVs, and obtain the element matrix corresponding to the evaluation indicators; S2. According to the element matrix corresponding to the evaluation index, identify the abnormal data of the UAV influencing factors and obtain the updated UAV evaluation index element matrix; S3. Build a UAV flight distance prediction model, input the updated UAV evaluation index element matrix to train the model, and output the predicted UAV flight distance; S4. Determine the coordinates of the logistics transportation points according to the drone flight area, and determine all the logistics transportation points within the drone logistics distribution range by calculating the distance relationship between the numbered logistics transportation points and the predicted drone flight distance.

[0017] Furthermore, in S1, the quantitative analysis and evaluation index system of the UAV flight distance includes battery capacity, motor efficiency, propeller diameter, fuselage mass, load, flight speed, flight mode, wind speed, wind direction, temperature, humidity, flight altitude and flight distance; The battery capacity determines the total amount of electrical energy that the drone can use, thus affecting its continuous flight time. The battery capacity element matrix corresponding to the battery capacity is: , , is the number of drones, For the Battery capacity corresponding to each drone; Motor efficiency refers to the efficiency of the motor in converting electrical energy into mechanical energy. A high-efficiency motor can increase the flight time. The motor efficiency element matrix corresponding to the motor efficiency is: , , For the Motor efficiency corresponding to each drone; The propeller size affects the thrust and flight efficiency of the drone. The propeller diameter directly affects the thrust and aerodynamic performance it provides. The propeller diameter element matrix corresponding to the propeller diameter is: , , For the Propeller diameter corresponding to each drone; The fuselage mass affects the take-off and flight stability of the UAV. The fuselage mass element matrix corresponding to the fuselage mass is: , , For the The body mass corresponding to each drone; The load refers to the extra weight that the drone can carry in addition to its own weight. The load element matrix corresponding to the load is , , For the The corresponding load of each drone; The flight speed directly affects the flight distance and time. The flight speed element matrix corresponding to the flight speed is: , , For the The flight speed of each drone; The flight mode determines different energy consumption and operating conditions. The flight modes include manual mode, stable mode, hovering mode, heading mode, return mode, follow mode, waypoint flight, and track flight. For quantitative comparison, the above modes are coded as 1-8 in sequence. The flight mode element matrix corresponding to the flight mode is , , For the The flight modes corresponding to each drone; Wind speed will affect the flight efficiency and control of the drone. The wind speed element matrix corresponding to the wind speed is: , , For the Wind speed corresponding to each drone; Wind direction affects the flight path and energy consumption of the drone. The wind direction angle is used to quantitatively analyze and compare the wind direction. The wind direction element matrix corresponding to the wind direction is: , , For the The wind direction corresponding to each drone; Temperature affects battery performance and air density, which in turn affects flight. The temperature element matrix corresponding to temperature is: , , For the The temperature corresponding to each drone; High humidity will affect the efficiency and reliability of electronic equipment. The humidity element matrix corresponding to humidity is: , , For the Humidity corresponding to each drone; The flight altitude affects the air density and flight efficiency. The flight altitude element matrix corresponding to the flight altitude is: , , For the The flight altitude corresponding to each drone; The flight distance element matrix corresponding to the flight distance is , , For the The flight distance corresponding to each drone; Specifically, the flight distance of the drone directly affects the logistics transportation capacity. Therefore, the flight distance of the drone is used as a quantitative indicator of the logistics transportation capacity of the drone. At the same time, there are many factors that affect the flight distance of the drone, including battery capacity, motor efficiency, propeller diameter, fuselage mass, load, flight speed, flight mode, wind speed, wind direction, temperature, humidity, flight altitude, etc. In order to quantify the impact of each factor on the flight distance of the drone, it is necessary to first clarify the quantitative analysis indicators of each factor, and establish the corresponding element matrix based on the data collected from public channels.

[0018] Furthermore, in S2, the process of identifying abnormal battery capacity data is as follows: The average battery capacity of each drone , The standard deviation of the battery capacity of each drone and the relative distance between any data and the mean , comparing the relative distance between individual data points and the mean The relationship with 3 is , to determine the battery capacity of the drone Normal data is retained. , to determine the battery capacity of the drone The above operation is performed on all data, and all abnormal data are deleted to obtain the updated battery capacity element matrix. , For the updated Battery capacity data, is the amount of data after deleting abnormal data; The average battery capacity of each drone It is expressed as: ; The standard deviation of the battery capacity of each drone It is expressed as: ; The relative distance between any data and the mean It is expressed as: ; According to the abnormal battery capacity data identification process, the updated motor efficiency element matrix is ​​established , Updated propeller diameter element matrix , Updated fuselage mass element matrix , updated load element matrix , Updated flight speed element matrix , Updated flight mode element matrix , Updated wind speed element matrix , Updated wind direction element matrix , Updated temperature element matrix , updated humidity element matrix , updated flight height element matrix and the updated flight distance element matrix , , , , , , , , , , , , ,in, For the updated The motor efficiency data corresponding to each drone, For the updated The motor efficiency data corresponding to each drone, For the updated The data of the fuselage mass corresponding to each drone, For the updated The corresponding load data of each drone, For the updated The flight speed data of each drone, For the updated The data of the flight modes corresponding to each drone, For the updated The wind speed data corresponding to each drone, For the updated The wind direction data corresponding to each drone, For the updated The temperature data corresponding to each drone, For the updated The humidity data corresponding to each drone, For the updated The data of the flight altitude corresponding to each drone, For the updated The data of flight distance corresponding to each drone; Specifically, the number of elements in all element matrices needs to remain the same, so the number of elements in the updated element matrix, that is, the amount of data after deleting abnormal data, remains .

[0019] Furthermore, in S3, the UAV flight distance prediction model includes an input layer, 7 hidden layers, a self-attention layer, 2 residual layers and an output layer; The neural network structure of the established UAV flight distance prediction model is connected in the following order: Input layer: Updated battery capacity element matrix , Updated motor efficiency element matrix , Updated propeller diameter element matrix , Updated fuselage mass element matrix , updated load element matrix , Updated flight speed element matrix , Updated flight mode element matrix , Updated wind speed element matrix , Updated wind direction element matrix , Updated temperature element matrix , updated humidity element matrix , updated flight height element matrix and the updated flight distance element matrix As input; Hidden layer 1: Number of neurons: 512, activation function: ReLU, batch normalization; Hidden layer 2: number of neurons: 256, activation function: ReLU, batch normalization; Self-attention layer: input the output matrix X of hidden layer 2; initialize the weight matrix and establish the initial weight query matrix in turn , initial weight key matrix and the initial weight matrix , transform the output matrix X into a query matrix , key matrix K and value matrix V, , , ; Calculate the query matrix The similarity with the key matrix K gives the attention score matrix A, ,in, is the scaling factor; the softmax function is used to normalize the attention score matrix A so that the sum of the elements in each row is 1, and the normalized matrix B is obtained and output. , the output after normalization is a weighted average value matrix, that is, each element in the sequence adjusts its representation according to its correlation with other elements; the normalized matrix B captures the important relationships within the output matrix X of hidden layer 2 and serves as the input of the next layer; Hidden layer 3: Number of neurons: 128, activation function: ReLU, batch normalization; Residual layer 1: The output of hidden layer 1 is connected to the output of hidden layer 3 through residual connection. Hidden layer 4: number of neurons: 64, activation function: ReLU, batch normalization; Hidden layer 5: number of neurons: 32, activation function: ReLU, batch normalization; Hidden layer 6: number of neurons: 16, activation function: ReLU, batch normalization; Residual layer 2: The output of hidden layer 4 is connected to the output of hidden layer 6 through residual connection; Hidden layer 7: Number of neurons: 8, activation function: ReLU, batch normalization; Output layer: Number of neurons: 1, outputs the predicted flight distance of the drone; Specifically, when the UAV flight distance prediction model is applied, it only needs to input the battery capacity, motor efficiency, propeller diameter, fuselage mass, load, flight speed, flight mode, wind speed, wind direction, temperature, humidity, and flight altitude data of the UAV to output the estimated flight distance of the UAV; Compared with traditional mathematical models, neural network models have advantages in UAV flight distance prediction. First, neural network models can effectively capture and model the complex nonlinear relationship between input features, enabling neural network models to handle problems that are difficult to solve with traditional linear models. For example, the flight distance of a UAV is affected by many factors, and the interactions between these factors may be very complex. Neural networks can automatically learn and extract effective features from large amounts of data, reducing the need for manual selection and design of features. In the prediction of UAV flight distance, the network can automatically learn the key factors that determine the flight distance from the raw data without the need for specialized domain knowledge to guide feature engineering.

[0020] The present invention constructs a UAV flight distance prediction model based on a neural network, which mainly includes a hidden layer, a self-attention layer, a residual layer, etc., wherein the hidden layer can help the network capture the nonlinear relationship in the input data through a nonlinear activation function, and can be used to characterize the nonlinear influence of multiple factors on the flight distance of the UAV. Each hidden layer can be regarded as performing a feature conversion, gradually converting the original input into an internal representation with more predictive value for the output variable. In the above process, the deep network structure helps to extract more abstract high-level features; the self-attention layer enables the network to evaluate the importance of other features when processing each input feature, and dynamically adjusts the weight distribution by learning the contribution of different features to the prediction task, so as to more flexibly capture the interaction between the features and improve the accuracy of the prediction and the adaptability of the model; the residual layer ensures that the initial information can directly affect the final output by adding the features of the early layer to the features of the subsequent layer, which is very helpful for maintaining the information integrity of the network in the deep structure.

[0021] Furthermore, a three-dimensional coordinate system is established for the logistics transportation area to determine the coordinates of the logistics transportation points. Since the flight range of the drone is related to the coordinates of the logistics transportation points, for the convenience of description, the first The coordinates of the logistics transport points are marked as , ,in, =1, 2, 3, ..., F, where F is the total number of logistics transportation points, For the The horizontal coordinate of the logistics transportation point, For the The vertical coordinate of the logistics transportation point, For the The vertical coordinates of the logistics transportation points are marked as the coordinates of the first drone landing pad. , ,in, is the horizontal coordinate of the drone landing pad, is the y value of the coordinate of the drone landing pad, is the z value of the coordinates of the drone landing pad; For the first logistics transport point , , calculate the distance between the first logistics transportation point and the drone landing pad ; The first distance between the first logistics transport point and the drone landing pad It is expressed as: ; The predicted flight distance of the first drone based on the output , compare the first distance The predicted flight distance of the first drone The relationship between When , it indicates that the distance between the first logistics transportation point and the drone landing pad exceeds the flight range of the first drone. It is determined that the first logistics transportation point is not within the logistics distribution range of the first drone. When analyzing the flight range of the drone, the first logistics transportation point is not considered. When , it indicates that the first logistics transportation point is within the logistics distribution range of the first drone. Based on this, the distance between other logistics transportation points and the first logistics transportation point is calculated. ; The distance between other logistics transportation points and the first logistics transportation point It is expressed as: ; The distance between all other logistics transportation points and the first logistics transportation point Sort from small to large and number the logistics transport points in the above order. The coordinates of the logistics transport points are , ,in, After the number The horizontal coordinate of the logistics transportation point, After the number The vertical coordinate of the logistics transportation point, After the number The vertical coordinates of the logistics transportation points, the coordinates of the first logistics transportation point after numbering are , ,in, is the horizontal coordinate of the first logistics transportation point after numbering, is the vertical coordinate of the first logistics transportation point after numbering, is the vertical coordinate of the first logistics transportation point after numbering, and the first logistics transportation point is calculated The first logistics transport point after the number The first center point coordinates between , ,in, is the horizontal coordinate of the first center point, , is the ordinate of the first center point, , is the vertical coordinate of the first center point, , calculate the distance between the first center point and the first drone landing pad ; The second distance between the first center point and the first drone landing pad It is expressed as: ; Compare the second distance The predicted flight distance of the first drone The distance between When it is, it indicates the first logistics transportation point after the number It is not within the logistics distribution range of the first drone. In this case, the first logistics transportation point after the numbering is not considered when analyzing the flight range of the drone. ,when When it is, it indicates the first logistics transportation point after the number In the first drone logistics delivery range, Based on this, the coordinates of the second logistics transportation point are , ,in, is the horizontal coordinate of the second logistics transportation point after numbering, is the ordinate of the second logistics transport point after numbering. The vertical coordinate of the second logistics transportation point after numbering is calculated to obtain the first logistics transportation point The first logistics transport point after the number And the second logistics transport point after the number The coordinates of the second center point between , ,in, is the horizontal coordinate of the second midpoint, , is the ordinate of the second midpoint, , is the vertical coordinate of the second midpoint, , calculate the distance between the second center point and the first drone landing pad ; The third distance between the second center point and the first drone landing pad It is expressed as: ; Compare the third distance The predicted flight distance of the first drone The distance between When it is, it indicates the second logistics transportation point after the number It is not within the logistics delivery range of the first drone. In this case, the second logistics transportation point after the number is not considered when analyzing the flight range of the drone. ,when When it is, it indicates the second logistics transportation point after the number In the logistics delivery range of the first drone, all logistics transportation points within the logistics delivery range of the first drone have been determined; Repeat the above steps to determine all logistics transportation points within the range of all drone logistics distribution, that is, to identify the logistics transportation points after numbering , , …, The predicted flight distance of the first drone The distance relationship between them and the numbered logistics transport points , , …, Is it within the logistics delivery range of the first drone?

[0022] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative, not restrictive, with respect to the scope of the present invention, which is defined by the appended claims.

Claims

1. A method for analyzing the logistics transportation capacity of drones, It is characterized in that The following steps are involved: S1. Collect data based on the evaluation indicators, build a quantitative analysis evaluation index system for the flight distance of UAVs, and obtain the element matrix corresponding to the evaluation indicators; S2. According to the element matrix corresponding to the evaluation index, identify the abnormal data of the UAV influencing factors and obtain the updated UAV evaluation index element matrix; S3. Build a UAV flight distance prediction model, input the updated UAV evaluation index element matrix to train the model, and output the predicted UAV flight distance; S4. Determine the coordinates of the logistics transportation points according to the drone flight area, and determine all the logistics transportation points within the drone logistics distribution range by calculating the distance relationship between the numbered logistics transportation points and the predicted drone flight distance.

2. A method for analyzing the logistics transportation capacity of unmanned aerial vehicles according to claim 1, It is characterized in that In S1, the evaluation indicators include battery capacity, motor efficiency, propeller diameter, fuselage mass, load, flight speed, flight mode, wind speed, wind direction, temperature, humidity, flight altitude and flight distance; The quantitative analysis and evaluation index system of UAV flight distance is specifically expressed as follows: The battery capacity element matrix corresponding to the battery capacity is , , is the number of drones, For the Battery capacity corresponding to each drone; The motor efficiency element matrix corresponding to the motor efficiency is: , , For the Motor efficiency corresponding to each drone; The propeller diameter element matrix corresponding to the propeller diameter is , , For the Propeller diameter corresponding to each drone; The fuselage mass element matrix corresponding to the fuselage mass is: , , For the The body mass corresponding to each drone; The load element matrix corresponding to the load is: , , For the The corresponding load of each drone; The flight speed element matrix corresponding to the flight speed is: , , For the The flight speed of each drone; The flight mode element matrix corresponding to the flight mode is , , For the The flight modes corresponding to each drone; The wind speed element matrix corresponding to the wind speed is , , For the Wind speed corresponding to each drone; The wind direction element matrix corresponding to the wind direction is , , For the The wind direction corresponding to each drone; The temperature element matrix corresponding to the temperature is , , For the The temperature corresponding to each drone; The humidity element matrix corresponding to humidity is , , For the Humidity corresponding to each drone; The flight height element matrix corresponding to the flight height is: , , For the The flight altitude corresponding to each drone; The flight distance element matrix corresponding to the flight distance is , , For the The flight distance of each drone.

3. A method for analyzing the logistics transportation capacity of unmanned aerial vehicles according to claim 2, It is characterized in that In S2, the process of identifying abnormal battery capacity data is as follows: The average battery capacity of each drone , The standard deviation of the battery capacity of each drone and the relative distance between any data and the mean , comparing the relative distance between individual data points and the mean The relationship with 3 is , to determine the battery capacity of the drone Normal data is retained. , to determine the battery capacity of the drone The above operation is performed on all data, and all abnormal data are deleted to obtain the updated battery capacity element matrix. , , For the updated Battery capacity data, is the amount of data after deleting abnormal data; The average battery capacity of each drone It is expressed as: ； The standard deviation of the battery capacity of each drone It is expressed as: ； The relative distance between any data and the mean It is expressed as: ； According to the abnormal battery capacity data identification process, the updated motor efficiency element matrix is ​​established , Updated propeller diameter element matrix , Updated fuselage mass element matrix , updated load element matrix , Updated flight speed element matrix , Updated flight mode element matrix , Updated wind speed element matrix , Updated wind direction element matrix , the updated temperature element matrix , updated humidity element matrix , updated flight height element matrix and the updated flight distance element matrix .

4. A method for analyzing the logistics transportation capacity of unmanned aerial vehicles according to claim 3, It is characterized in that In S3, the UAV flight distance prediction model includes an input layer, 7 hidden layers, a self-attention layer, 2 residual layers and an output layer; The neural network structure of the established UAV flight distance prediction model is connected in the following order: Input layer: Updated battery capacity element matrix , Updated motor efficiency element matrix , Updated propeller diameter element matrix , Updated fuselage mass element matrix , updated load element matrix , Updated flight speed element matrix , Updated flight mode element matrix , Updated wind speed element matrix , Updated wind direction element matrix , Updated temperature element matrix , updated humidity element matrix , updated flight height element matrix and the updated flight distance element matrix As input; Hidden layer 1: Number of neurons: 512, activation function: ReLU, batch normalization; Hidden layer 2: number of neurons: 256, activation function: ReLU, batch normalization; Self-attention layer: inputs the output matrix X of hidden layer 2; Initialize the weight matrix and build the initial weight query matrix in turn , initial weight key matrix and the initial weight matrix , transform the output matrix X into a query matrix , key matrix K and value matrix V, , , ; Calculate the query matrix The similarity with the key matrix K gives the attention score matrix A, ,in, is the scaling factor; the softmax function is used to normalize the attention score matrix A so that the sum of the elements in each row is 1, and the normalized matrix B is obtained and output. ; Hidden layer 3: Number of neurons: 128, activation function: ReLU, batch normalization; Residual layer 1: The output of hidden layer 1 is connected to the output of hidden layer 3 through residual connection. Hidden layer 4: number of neurons: 64, activation function: ReLU, batch normalization; Hidden layer 5: number of neurons: 32, activation function: ReLU, batch normalization; Hidden layer 6: number of neurons: 16, activation function: ReLU, batch normalization; Residual layer 2: The output of hidden layer 4 is connected to the output of hidden layer 6 through residual connection; Hidden layer 7: Number of neurons: 8, activation function: ReLU, batch normalization; Output layer: Number of neurons: 1, outputs the predicted flight distance of the drone.

5. A method for analyzing the logistics transportation capacity of unmanned aerial vehicles according to claim 4, It is characterized in that In S4, a three-dimensional coordinate system is established for the logistics transportation area, and the coordinates of the logistics transportation points are determined. The coordinates of the logistics transport points are marked as , ,in, =1, 2, 3, ..., F, where F is the total number of logistics transportation points, For the The horizontal coordinate of the logistics transportation point, For the The vertical coordinate of the logistics transportation point, For the The vertical coordinates of the logistics transportation points are marked as the coordinates of the first drone landing pad. , ,in, is the horizontal coordinate of the drone landing pad, is the y value of the coordinate of the drone landing pad, is the z value of the coordinates of the drone landing pad; For the first logistics transport point , , calculate the distance between the first logistics transportation point and the drone landing pad ; The first distance between the first logistics transport point and the drone landing pad It is expressed as: ； The predicted flight distance of the first drone based on the output , compare the first distance The predicted flight distance of the first drone The relationship between When , it indicates that the distance between the first logistics transportation point and the drone landing pad exceeds the flight range of the first drone, and it is determined that the first logistics transportation point is not within the logistics distribution range of the first drone. When , it indicates that the first logistics transportation point is within the logistics distribution range of the first drone. Based on this, the distance between other logistics transportation points and the first logistics transportation point is calculated. ; The distance between other logistics transportation points and the first logistics transportation point It is expressed as: ； The distance between all other logistics transportation points and the first logistics transportation point Sort from small to large and number the logistics transport points in the above order. The coordinates of the logistics transport points are , ,in, After the number The horizontal coordinate of the logistics transportation point, After the number The vertical coordinate of the logistics transportation point, After the number The vertical coordinates of the logistics transportation points, the coordinates of the first logistics transportation point after numbering are , ,in, is the horizontal coordinate of the first logistics transportation point after numbering, is the vertical coordinate of the first logistics transportation point after numbering, is the vertical coordinate of the first logistics transportation point after numbering, and the first logistics transportation point is calculated The first logistics transport point after the number The first center point coordinates between , ,in, is the horizontal coordinate of the first center point, , is the ordinate of the first center point, , is the vertical coordinate of the first center point, , calculate the distance between the first center point and the first drone landing pad ; The second distance between the first center point and the first drone landing pad It is expressed as: ； Compare the second distance The predicted flight distance of the first drone The distance between When it is, it indicates the first logistics transportation point after the number Not within the range of the first drone logistics delivery, when When it is, it indicates the first logistics transportation point after the number In the first drone logistics delivery range, Based on this, the coordinates of the second logistics transportation point are , ,in, is the horizontal coordinate of the second logistics transportation point after numbering, is the ordinate of the second logistics transport point after numbering. The vertical coordinate of the second logistics transportation point after numbering is calculated to obtain the first logistics transportation point The first logistics transport point after the number And the second logistics transport point after the number The coordinates of the second center point between , ,in, is the horizontal coordinate of the second midpoint, , is the ordinate of the second midpoint, , is the vertical coordinate of the second midpoint, , calculate the distance between the second center point and the first drone landing pad ; The third distance between the second center point and the first drone landing pad It is expressed as: ； Compare the third distance The predicted flight distance of the first drone The distance between When it is, it indicates the second logistics transportation point after the number Not within the range of the first drone logistics delivery, when When it is, it indicates the second logistics transportation point after the number Within the first drone logistics delivery range; Repeat the above steps to determine all logistics transportation points within the entire drone logistics distribution range.

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