Evaluation method and system for low-altitude UAV logistics transportation route network capacity

Abstract

The present invention provides a method and system for evaluating the network capacity of a low-altitude UAV logistics transportation route, which relates to the field of UAV logistics transportation technology, and specifically includes: generating an estimated delay time coefficient based on the collected route length, free flow speed and traffic flow data; generating a meteorological evaluation index by combining temperature, rainfall and wind speed, and generating a flight energy consumption coefficient based on the meteorological evaluation index, flight speed and basic energy consumption; generating an adaptability coefficient based on the battery power, the power required to perform the current task and the load; combining the above coefficients to generate a transportation evaluation model, evaluate the current route, generate a comprehensive evaluation index, and compare it with a preset transportation evaluation threshold to obtain an evaluation level of the current logistics transportation route network capacity. The present invention constructs a comprehensive transportation evaluation model, which significantly improves the efficiency of urban distribution and also provides reliable data support for the intelligent scheduling and resource allocation of UAVs.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle logistics transportation technology, and specifically to a method and system for evaluating the capacity of a low-altitude unmanned aerial vehicle logistics transportation route network. Background Art

[0002] In the field of modern urban logistics, with the growing demand for e-commerce and fast delivery, drones are gradually being widely used as an emerging mode of transportation. However, despite the advantages of fast and efficient drones, they still face many challenges in actual operation. First, due to the complex urban traffic environment, congestion and frequent intersections in the transportation network, drones are prone to delays on specific routes. In addition, meteorological changes have a direct impact on the flight safety and energy consumption of drones. Bad weather may cause flight cancellations or delays, thereby increasing transportation costs and customer dissatisfaction. In addition, existing drone logistics systems often lack real-time data monitoring and comprehensive evaluation capabilities. In the face of complex urban environments, it is difficult to effectively optimize routes and transportation strategies, which limits their application potential in intelligent distribution.

[0003] The shortcomings of existing technologies are that many existing logistics systems rely on static and preset route planning and lack the ability to respond to real-time traffic and environmental changes. This results in a significant impact on transportation timeliness in the event of traffic congestion or emergencies, and it is often difficult to meet customer needs; and existing technologies usually fail to effectively integrate data from different sources, such as traffic flow, weather conditions, and the status information of the drone itself. This data island phenomenon makes the decision-making process lack comprehensiveness and accuracy, resulting in low transportation efficiency.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for evaluating the capacity of a low-altitude UAV logistics transport route network to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for evaluating the capacity of a low-altitude UAV logistics transport route network, the specific steps include:

[0008] Step 1: Collect traffic flow data at the intersections of the city’s distribution centers and transportation routes; obtain real-time meteorological data in the transportation area, including temperature, rainfall, and wind speed, and collect real-time drone battery power, flight speed, and load data; collect the length of each route for drone logistics transportation, and collect the free flow speed and basic energy consumption of drones during flight;

[0009] Step 2: Generate an estimated delay time coefficient based on route length, free flow speed and traffic flow data; generate a meteorological assessment index by combining temperature, rainfall and wind speed, and generate a flight energy consumption coefficient based on the meteorological assessment index, flight speed and basic energy consumption; generate an adaptability coefficient based on the battery power of the drone, the power required to perform the current mission and the load data;

[0010] Step 3: Combine the estimated delay time coefficient, flight energy consumption coefficient and adaptability coefficient to generate a transportation evaluation model, generate the weights in the transportation evaluation model through the hierarchical analysis method, use the transportation evaluation model to evaluate the current route, and generate the corresponding comprehensive evaluation index;

[0011] Step 4: Compare the comprehensive evaluation index with the preset transport evaluation threshold. Based on the comparison results, generate an evaluation grade of the current UAV logistics transport route network capacity. Classify the current UAV logistics transport route network capacity into good, average, and poor grades, and formulate corresponding measures.

[0012] Furthermore, the collection of traffic flow data is specifically as follows:

[0013] Collect traffic flow at intersections where distribution centers and transportation routes pass , and based on the data provided by the traffic control department, collect the maximum traffic flow at the intersection within a week ;

[0014] Collect meteorological data of the transportation area, specifically: obtain the temperature of the transportation area in real time , rainfall and wind speed , where temperature The unit is degrees Celsius; rainfall The unit is millimeter; wind speed The unit is meters per second;

[0015] Collect the power required for the current task , UAV free flow speed and basic energy consumption The specific logic is: set the parameters and mission requirements of the drone in the flight simulator, simulate the mission execution process, observe and record the battery consumption, and extract the power required to complete the mission from the simulation results. ; Simulate the flight of the drone under different conditions to observe its speed performance without interference, and record the free flow speed based on the simulation results ;Simulate the hovering and flight process of the drone, record the energy consumption data, and extract the basic energy consumption from the simulation results ;

[0016] The specific route length of drone logistics transportation is:

[0017] Use the GPS module on the drone to record each coordinate point of the drone's flight; when the drone takes off and lands, record its starting and ending GPS coordinate points, and use the Haversing formula to calculate the distance between the two points:

[0018] ;

[0019] in, Indicates The length of the route, represents the radius of the Earth, and Respectively The latitude and longitude of the flight start and end of each route, and is the difference in latitude and longitude between two points, The number indicating the route of drone logistics transportation.

[0020] Furthermore, the route is generated based on the route length, free flow speed and traffic flow data. The estimated delay time coefficient is based on the formula:

[0021] ;

[0022] in, For route The estimated delay factor of It is a transport route Length, is the free flow speed of the drone during flight, It is a route traffic flow, It is a route maximum traffic flow.

[0023] Furthermore, the temperature, rainfall and wind speed are combined to generate a meteorological assessment index. The specific logic is:

[0024] The real-time temperature collected Compare with the preset temperature threshold, if it exceeds the threshold, calculate the real-time temperature The absolute value of the difference between the temperature threshold ;

[0025] ;

[0026] in, is the meteorological assessment index, Real-time temperature The absolute value of the difference between the temperature threshold, is the rainfall, is the wind speed, is the constant correction exponent.

[0027] Furthermore, the flight energy consumption coefficient is generated according to the meteorological assessment index, flight speed and basic energy consumption. , the formula is as follows:

[0028] ;

[0029] in, is the flight energy consumption coefficient, is the basic energy consumption, is the meteorological assessment index for the transport area, is its preset proportionality factor, is the flight speed of the drone, is its preset scaling factor, and , greater than zero, is the constant correction exponent;

[0030] Further, the fitness coefficient is generated The logic is as follows:

[0031] Combine the drone's battery power, the power required to perform the current mission, and the load data to calculate the adaptability coefficient according to the following formula: :

[0032] ;

[0033] in, is the adaptability coefficient, is the current battery power of the drone, The amount of power required to perform the current task. is the load data, The preset scale factor is greater than zero, is the constant correction exponent.

[0034] Furthermore, the current route is evaluated using the transport evaluation model. , generate the corresponding comprehensive evaluation index The process includes: The estimated delay time coefficient , Flight energy consumption coefficient and the adaptability coefficient Combined to generate a transportation assessment model, and the transportation assessment model is used to assess the current route , generate the corresponding comprehensive evaluation coefficient , the formula is as follows:

[0035] ;

[0036] in, For route The comprehensive evaluation index of For route The estimated delay time coefficient, is the flight energy consumption coefficient, is the adaptability coefficient, , and are the weights of adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient, respectively. is the constant correction exponent.

[0037] Furthermore, the specific logic of generating the weights in the transportation assessment model through the hierarchical analysis method is as follows:

[0038] The three indicators of adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient are marked, and the relative importance between them is determined by the nine-scale method to construct a judgment matrix, in which the adaptability coefficient is marked as 1, the estimated delay time coefficient is marked as 2, and the flight energy consumption coefficient is marked as 3. The constructed judgment matrix is:

[0039] ;

[0040] in, , The mark indicating the indicator, Indicators For indicators The importance of , ;

[0041] Each element in the judgment matrix is ​​normalized, and the specific formula of the normalization process is:

[0042] ;

[0043] in, for Normalized value;

[0044] The weight value of each indicator is calculated based on the value of each element in the judgment matrix after normalization. The specific formula is as follows:

[0045] ;

[0046] in, For indicators The weight value of .

[0047] The weights of the adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient are calculated based on the following formula:

[0048] ;

[0049] ;

[0050] ;

[0051] in, Represents the normalized adaptability coefficient relative to the index The importance of Indicates the normalized estimated delay time coefficient relative to the index The importance of Indicates the normalized flight energy consumption coefficient relative to the index The importance of.

[0052] Furthermore, the comprehensive evaluation index is compared with the preset transport evaluation threshold, and the evaluation level of the current logistics transport route network capacity of the drone is generated according to the comparison result. The specific logic is as follows:

[0053] when When the current transport route network capacity is poor, the current route network is seriously congested, and the transport efficiency is low, it is necessary to immediately re-evaluate the route network and change the route;

[0054] when When the current transport route network capacity is judged to be average, and the route network is congested to a certain extent, new take-off and landing points can be added to optimize the route;

[0055] when When the current transport route network capacity is judged to be good and the conditions are suitable, logistics transportation will continue along the current route;

[0056] in, is a comprehensive evaluation index. Evaluate thresholds for transport.

[0057] The present invention further provides a system for evaluating the capacity of a low-altitude UAV logistics transport route network, which is used to execute the above-mentioned method for evaluating the capacity of a low-altitude UAV logistics transport route network, and includes:

[0058] The data collection module is used to place sensors at the intersections of the city's distribution centers and transportation routes to collect traffic flow data; use the meteorological API to obtain meteorological data of the transportation area every hour, including temperature, rainfall and wind speed; integrate the GPS module on the drone to collect the drone's battery power, flight speed and load data in real time; collect the length of each route for drone logistics transportation, and collect the drone's free flow speed and basic energy consumption during flight;

[0059] The data analysis module is used to generate an estimated delay time coefficient based on route length, free flow speed and traffic flow data; generate a meteorological assessment index by combining temperature, rainfall and wind speed, and generate a flight energy consumption coefficient based on the meteorological assessment index, flight speed and basic energy consumption; and generate an adaptability coefficient based on the battery power of the drone, the power required to perform the current mission and load data;

[0060] A comprehensive evaluation index generation module is used to generate a transportation evaluation model by combining the estimated delay time coefficient, the flight energy consumption coefficient and the adaptability coefficient, generate the weights in the transportation evaluation model through the hierarchical analysis method, evaluate the current route using the transportation evaluation model, and generate the corresponding comprehensive evaluation index;

[0061] The intelligent analysis module is used to compare the comprehensive evaluation index with the preset transport evaluation threshold, and generate an evaluation level of the current UAV logistics transport route network capacity based on the comparison results, and divide the current UAV logistics transport route network capacity into good, general, and poor levels, and formulate corresponding measures.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention constructs a comprehensive transportation evaluation model by placing sensors at key traffic points and on drones, combining meteorological data with real-time flight information. This model can predict the transportation delay, energy consumption and adaptability of the route in real time, providing a scientific decision-making basis for the operation of drones. By weighted evaluation of various indicators through the hierarchical analysis method, the network capacity level of drone logistics transportation routes can be accurately judged, which helps to identify potential problems and formulate corresponding optimization measures. Ultimately, this will significantly improve the efficiency of urban distribution, reduce operating costs, and improve customer satisfaction, while also providing reliable data support for the intelligent scheduling and resource allocation of drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1It is a schematic diagram of the overall method flow of the present invention;

[0065] Figure 2 It is a schematic diagram of the overall system module of the present invention. DETAILED DESCRIPTION

[0066] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0067] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0068] Example:

[0069] See also Figure 1 , the present invention provides a technical solution:

[0070] A method for evaluating the capacity of a low-altitude UAV logistics transport route network, the specific steps include:

[0071] Step 1: Collect traffic flow data at the intersections of the city’s distribution centers and transportation routes; obtain real-time meteorological data in the transportation area, including temperature, rainfall, and wind speed, and collect real-time drone battery power, flight speed, and load data; collect the length of each route for drone logistics transportation, and collect the free flow speed and basic energy consumption of drones during flight;

[0072] In this embodiment, the collection of traffic flow data specifically includes:

[0073] Collect traffic flow at intersections where distribution centers and transportation routes pass , and based on the data provided by the traffic control department, collect the maximum traffic flow at the intersection within a week ; The traffic flow refers to the number of vehicles passing through the intersection per hour;

[0074] The intersections of the distribution center and the transportation route specifically refer to the key nodes where the distribution center and the ground transportation network intersect during the flight of the drone when carrying out logistics transportation;

[0075] Collect meteorological data of the transportation area, specifically: obtain the temperature of the transportation area in real time , rainfall and wind speed , where temperature The unit is degrees Celsius; rainfall The unit is millimeter; wind speed The unit is meters per second;

[0076] Collect the power required for the current task , UAV free flow speed and basic energy consumption The specific logic is: set the parameters and mission requirements of the drone in the flight simulator, simulate the mission execution process, observe and record the battery consumption, and extract the power required to complete the mission from the simulation results. ; Simulate the flight of the drone under different conditions to observe its speed performance without interference, and record the free flow speed based on the simulation results ;Simulate the hovering and flight process of the drone, record the energy consumption data, and extract the basic energy consumption from the simulation results ;

[0077] The specific route length of drone logistics transportation is:

[0078] Use the GPS module on the drone to record each coordinate point of the drone's flight; when the drone takes off and lands, record its starting and ending GPS coordinate points, and use the Haversing formula to calculate the distance between the two points:

[0079] ;

[0080] in, Indicates The length of the route, represents the radius of the Earth, and Respectively The latitude and longitude of the flight start and end of each route, and is the difference in latitude and longitude between two points, The number that represents the route of the UAV logistics transportation. The Haversing formula is designed to calculate the distance between two points on a sphere, so it can accurately take into account the curvature of the earth. In the case of longer distances or large terrain changes, using the distance formula between two points may lead to significant errors, while the Haversing formula provides more accurate measurements. In aviation navigation, the shortest route is along the great circle route. The Haversing formula can accurately calculate the great circle distance, reflecting the shortest path of the actual route, and is suitable for UAV navigation planning.

[0081] This step can obtain multi-dimensional, real-time operating environment data, including traffic flow, weather conditions, and the status information of the drone itself, breaking through the limitations of the previous single data source and forming a more comprehensive and accurate monitoring network, thus providing a solid data foundation for subsequent transportation evaluation. By using sensors to collect traffic flow data, traffic congestion can be identified in real time, thus providing a key reference for the route planning of drones; meteorological data obtained by the meteorological API can promptly reflect the impact of weather changes on flight safety and energy consumption; whether it is the battery power, flight speed or load information of the drone, it can help better evaluate the adaptability and transportation capacity of the drone in a specific environment. This comprehensive data collection not only improves the adaptability to the transportation environment, but also provides an important basis for subsequent model construction and evaluation, thereby significantly improving transportation efficiency and safety.

[0082] Step 2: Generate an estimated delay time coefficient based on route length, free flow speed and traffic flow data; generate a meteorological assessment index by combining temperature, rainfall and wind speed, and generate a flight energy consumption coefficient based on the meteorological assessment index, flight speed and basic energy consumption; generate an adaptability coefficient based on the battery power of the drone, the power required to perform the current mission and the load data;

[0083] In this embodiment, the route is generated based on the route length, free flow speed and traffic flow data. The estimated delay time coefficient is based on the formula:

[0084] ;

[0085] in, For route The estimated delay factor of It is a transport route Length, is the free flow speed of the drone during flight, It is a route traffic flow, It is a route The maximum traffic flow. When it increases, the mileage of drone logistics transportation routes increases, and the estimated delay coefficient Increase; when the route Traffic flow When it increases, it will affect the efficiency of logistics transportation and estimate the delay coefficient Increase; that is, , and the estimated delay factor There is a positive correlation.

[0086] The temperature, rainfall and wind speed are combined to generate a meteorological assessment index The specific logic is:

[0087] The real-time temperature collected Compare with the preset temperature threshold, if it exceeds the threshold, calculate the real-time temperature The absolute value of the difference between the temperature threshold ;

[0088] ;

[0089] in, is the meteorological assessment index, Real-time temperature The absolute value of the difference between the temperature threshold, is the rainfall, is the wind speed, is a constant correction exponent; when When it increases, it means that the temperature in the transport area exceeds the threshold. High or low temperature conditions may cause the flight efficiency of the drone to decrease under specific temperature conditions. Therefore, the meteorological assessment index Increase; when rainfall When it increases, visibility will be reduced, affecting the visual navigation and sensor performance of the drone, increasing the risk during flight, and rainfall may cause the product to become damp and deteriorate. Therefore, the meteorological assessment index Increase; when wind speed When it increases, it will cause airflow instability, thus affecting the flight stability of the drone. Therefore, the meteorological assessment index Increase; that is, , , Meteorological Assessment Index There is a positive correlation.

[0090] The flight energy consumption coefficient is generated according to the meteorological assessment index, the flight speed and the basic energy consumption , the formula is as follows:

[0091] ;

[0092] in, is the flight energy consumption coefficient, is the basic energy consumption, is the meteorological assessment index for the transport area, is its preset proportionality factor, is the flight speed of the drone, is its preset scaling factor, and , greater than zero, is a constant correction index; when the basic energy consumption When it increases, the corresponding flight energy consumption coefficient Increase; when the meteorological assessment index When it increases, it means that the climate environment is getting worse, resulting in the flight energy consumption coefficient Increase; when the flight speed When it increases, the battery will be consumed faster, thus increasing the flight energy consumption coefficient Increase; that is, , , Flight energy consumption coefficient There is a positive correlation.

[0093] Generate fitness coefficient The logic is as follows:

[0094] Combine the drone's battery power, the power required to perform the current mission, and the load data to calculate the adaptability coefficient according to the following formula: :

[0095] ;

[0096] in, is the adaptability coefficient, is the current battery power of the drone, The amount of power required to perform the current task. is the load data, The preset scale factor is greater than zero, is a constant correction index; when the battery power When the speed increases, it directly improves the endurance of the drone, making the drone more flexible in performing multiple tasks, thus increasing the adaptability coefficient. Increase; when the power required to perform the current task When the battery capacity increases, the drone needs to provide more power during flight to complete the task, which means that the drone must have a higher endurance to adapt to the increased power demand. If the battery capacity is insufficient, the task may not be completed, thus reducing the adaptability coefficient. Reduce; when the load Increased weight means that the drone needs to consume more energy to overcome the extra weight during transportation, which will directly affect the drone's endurance and may result in a shorter flight time. If the drone's battery capacity cannot meet the increased energy demand, the drone's adaptability will be limited and it will not be able to complete the scheduled transportation mission, so the adaptability coefficient Reduce; that is, and There is a positive correlation, , and There is a negative correlation.

[0097] Step 3: Combine the estimated delay time coefficient, flight energy consumption coefficient and adaptability coefficient to generate a transportation evaluation model, generate the weights in the transportation evaluation model through the hierarchical analysis method, use the transportation evaluation model to evaluate the current route, and generate the corresponding comprehensive evaluation index;

[0098] In this embodiment, the current route is evaluated using the transportation evaluation model. , generate the corresponding comprehensive evaluation index The process includes: The estimated delay time coefficient , Flight energy consumption coefficient and the adaptability coefficient Combined to generate a transportation assessment model, and the transportation assessment model is used to assess the current route , generate the corresponding comprehensive evaluation coefficient , the formula is as follows:

[0099] ;

[0100] in, For route The comprehensive evaluation index of For route The estimated delay time coefficient, is the flight energy consumption coefficient, is the adaptability coefficient, , and are the weights of adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient, respectively. is a constant correction index; when the adaptability coefficient When it increases, it means that the drone can better cope with complex transportation environments and mission requirements, and the comprehensive evaluation index Increase; when the route The estimated delay time coefficient When it increases, it means that the transportation time of the drone on this route will be extended, which will directly lead to a decrease in the overall transportation efficiency. Therefore, the comprehensive evaluation index Reduce; when the flight energy consumption coefficient When it increases, it means longer flight time and lower speed of cargo transportation. This will directly affect transportation efficiency and the comprehensive evaluation index. will decrease accordingly; that is, Comprehensive Assessment Index There is a positive correlation, , Comprehensive Assessment Index There is a negative correlation.

[0101] The specific logic of generating weights in the transport assessment model through the analytic hierarchy process is as follows:

[0102] The three indicators of adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient are marked, and the relative importance between them is determined by the nine-scale method to construct a judgment matrix, in which the adaptability coefficient is marked as 1, the estimated delay time coefficient is marked as 2, and the flight energy consumption coefficient is marked as 3. The constructed judgment matrix is:

[0103] ;

[0104] in, , The mark indicating the indicator, Indicators For indicators The importance of , ;

[0105] Each element in the judgment matrix is ​​normalized, and the specific formula of the normalization process is:

[0106] ;

[0107] in, for Normalized value;

[0108] The weight value of each indicator is calculated based on the value of each element in the judgment matrix after normalization. The specific formula is as follows:

[0109] ;

[0110] in, For indicators The weight value of .

[0111] The weights of the adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient are calculated based on the following formula:

[0112] ;

[0113] ;

[0114] ;

[0115] in, Represents the normalized adaptability coefficient relative to the index The importance of Indicates the normalized estimated delay time coefficient relative to the index The importance of Indicates the normalized flight energy consumption coefficient relative to the index The importance of.

[0116] Step 4: Compare the comprehensive evaluation index with the preset transport evaluation threshold, and generate an evaluation grade of the current UAV logistics transport route network capacity based on the comparison results. The current UAV logistics transport route network capacity is divided into good, average, and poor grades, and corresponding measures are formulated;

[0117] In this embodiment, the comprehensive evaluation index is compared with the preset transport evaluation threshold, and the evaluation level of the current logistics transport route network capacity of the drone is generated according to the comparison result. The specific logic is as follows:

[0118] when When the current transport route network capacity is poor, the current route network is seriously congested, and the transport efficiency is low, it is necessary to immediately re-evaluate the route network and change the route;

[0119] when When the current transport route network capacity is judged to be average, and the route network is congested to a certain extent, new take-off and landing points can be added to optimize the route;

[0120] when When the current transport route network capacity is judged to be good and the conditions are suitable, logistics transportation will continue along the current route;

[0121] in, is a comprehensive evaluation index. Evaluate thresholds for transport.

[0122] By comparing the comprehensive evaluation index with the preset transport evaluation threshold, a clear evaluation standard can be provided for the performance of drone logistics transportation. This standardized evaluation method makes the evaluation process more objective and transparent, which is convenient for enterprises to manage and make decisions. Through the division of evaluation levels, enterprises can quickly identify potential problems in the current logistics transportation route network. This timely feedback mechanism helps enterprises take measures before the problem expands, thereby reducing operational risks. According to different evaluation levels, enterprises can formulate corresponding improvement measures. For example, for routes with a "poor" grade, optimization and adjustment can be given priority, while for routes with a "good" grade, the status quo can be maintained or fine-tuned. This differentiated management strategy makes resource allocation more reasonable. With quantitative evaluation results, management can make decisions more quickly and formulate optimization strategies. This efficient decision-making process helps to improve the response speed and market adaptability of enterprises.

[0123] See also Figure 2 The present invention also provides a system for evaluating the capacity of a low-altitude UAV logistics transport route network. The system for evaluating the capacity of a low-altitude UAV logistics transport route network is used to execute the above-mentioned method for evaluating the capacity of a low-altitude UAV logistics transport route network, comprising:

[0124] The data collection module is used to place sensors at the intersections of the city's distribution centers and transportation routes to collect traffic flow data; use the meteorological API to obtain meteorological data of the transportation area every hour, including temperature, rainfall and wind speed; integrate the GPS module on the drone to collect the drone's battery power, flight speed and load data in real time; collect the length of each route for drone logistics transportation, and collect the drone's free flow speed and basic energy consumption during flight;

[0125] The data analysis module is used to generate an estimated delay time coefficient based on route length, free flow speed and traffic flow data; generate a meteorological assessment index by combining temperature, rainfall and wind speed, and generate a flight energy consumption coefficient based on the meteorological assessment index, flight speed and basic energy consumption; and generate an adaptability coefficient based on the battery power of the drone, the power required to perform the current mission and load data;

[0126] A comprehensive evaluation index generation module is used to generate a transportation evaluation model by combining the estimated delay time coefficient, the flight energy consumption coefficient and the adaptability coefficient, generate the weights in the transportation evaluation model through the hierarchical analysis method, evaluate the current route using the transportation evaluation model, and generate the corresponding comprehensive evaluation index;

[0127] The intelligent analysis module is used to compare the comprehensive evaluation index with the preset transport evaluation threshold, and generate an evaluation level of the current UAV logistics transport route network capacity based on the comparison results, and divide the current UAV logistics transport route network capacity into good, general, and poor levels, and formulate corresponding measures.

[0128] In the formula , , , and The specific value of is generally determined by those skilled in the art according to actual conditions. The technicians in this field collect multiple groups of sample data, set corresponding preset proportional coefficients for each group of sample data, substitute the preset proportional coefficients and the collected sample data into the formula, observe the accuracy of the model output and the rationality of the results through repeated experiments and parameter adjustments, gradually adjust these factor coefficients, and compare the performance and effect of the model under different parameter settings to find the optimal coefficient combination, screen the calculated factor coefficients and take the average, and obtain , , , and The value of .

[0129] In addition, the size of the preset factor coefficient is to quantify each parameter to obtain a specific value. In order to facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preset proportional coefficient initially set by technical personnel in this field for each set of sample data. It is not unique as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0130] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0131] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for evaluating the capacity of a low-altitude UAV logistics transport route network, characterized in that: The specific steps include: Step 1: Collect traffic flow data at the intersections of the city’s distribution centers and transportation routes; obtain real-time meteorological data in the transportation area, including temperature, rainfall, and wind speed, and collect real-time drone battery power, flight speed, and load data; collect the length of each route for drone logistics transportation, and collect the free flow speed and basic energy consumption of drones during flight; Step 2: Generate an estimated delay time coefficient based on route length, free flow speed and traffic flow data; generate a meteorological assessment index by combining temperature, rainfall and wind speed, and generate a flight energy consumption coefficient based on the meteorological assessment index, flight speed and basic energy consumption; generate an adaptability coefficient based on the battery power of the drone, the power required to perform the current mission and the load data; Step 3: Combine the estimated delay time coefficient, flight energy consumption coefficient and adaptability coefficient to generate a transportation evaluation model, generate the weights in the transportation evaluation model through the hierarchical analysis method, use the transportation evaluation model to evaluate the current route, and generate the corresponding comprehensive evaluation index; Step 4: Compare the comprehensive evaluation index with the preset transport evaluation threshold, and generate the evaluation level of the current UAV logistics transport route network capacity according to the comparison results. The current UAV logistics transport route network capacity level is divided into: good, general, poor, and formulate corresponding measures; Generate fitness coefficient The specific logic is: Combine the drone's battery power, the power required to perform the current mission, and the load data to calculate the adaptability coefficient according to the following formula: : ； in, is the adaptability coefficient, is the current battery power of the drone, The amount of power required to perform the current task. is the load data, The preset scale factor is greater than zero, is the constant correction exponent; Evaluate current routes using the transport assessment model , generate the corresponding comprehensive evaluation index The process includes: The estimated delay time coefficient , Flight energy consumption coefficient and the adaptability coefficient Combined to generate a transportation assessment model, and the transportation assessment model is used to assess the current route , generate the corresponding comprehensive evaluation coefficient , the formula is as follows: ； in, For route The comprehensive evaluation index of For route The estimated delay time coefficient, is the flight energy consumption coefficient, is the adaptability coefficient, , and are the weights of adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient, respectively. is the constant correction exponent.

2. The method for evaluating the capacity of a low-altitude UAV logistics transport route network according to claim 1 is characterized by: The specific traffic flow data collected are: Collect traffic flow at intersections where distribution centers and transportation routes pass , and based on the data provided by the traffic control department, collect the maximum traffic flow at the intersection within a week ; Collect meteorological data of the transportation area, specifically: obtain the temperature of the transportation area in real time , rainfall and wind speed , where temperature The unit is degrees Celsius; rainfall The unit is millimeter; wind speed The unit is meters per second; Collect the power required for the current task , UAV free flow speed and basic energy consumption The specific logic is: set the parameters and mission requirements of the drone in the flight simulator, simulate the mission execution process, observe and record the battery consumption, and extract the power required to complete the mission from the simulation results. ; Simulate the flight of the drone under different conditions to observe its speed performance without interference, and record the free flow speed based on the simulation results ;Simulate the hovering and flight process of the drone, record the energy consumption data, and extract the basic energy consumption from the simulation results ; The specific route length of drone logistics transportation is: Use the GPS module on the drone to record each coordinate point of the drone's flight; when the drone takes off and lands, record its starting and ending GPS coordinate points, and use the Haversing formula to calculate the distance between the two points: ； in, Indicates The length of the route, represents the radius of the Earth, and Respectively The latitude and longitude of the flight start and end of each route, and is the difference in latitude and longitude between two points, The number indicating the route of drone logistics transportation.

3. The method for evaluating the capacity of a low-altitude UAV logistics transport route network according to claim 2 is characterized by: Generate routes based on route length, free flow speed and traffic volume data The estimated delay time coefficient is based on the formula: ； in, For route The estimated delay factor of It is a transport route Length, is the free flow speed of the drone during flight, It is a route traffic flow, It is a route maximum traffic flow.

4. The method for evaluating the capacity of a low-altitude UAV logistics transport route network according to claim 1 is characterized by: Combine temperature, rainfall and wind speed to create a meteorological assessment index The specific logic is: The real-time temperature collected Compare with the preset temperature threshold, if it exceeds the threshold, calculate the real-time temperature The absolute value of the difference between the temperature threshold ; ； in, is the meteorological assessment index, Real-time temperature The absolute value of the difference between the temperature threshold, is the rainfall, is the wind speed, is the constant correction exponent.

5. The method for evaluating the capacity of a low-altitude UAV logistics transport route network according to claim 4 is characterized by: Generate flight energy consumption coefficient based on meteorological assessment index, flight speed and basic energy consumption , the formula is as follows: ； in, is the flight energy consumption coefficient, is the basic energy consumption, is the meteorological assessment index for the transport area, is its preset proportionality factor, is the flight speed of the drone, is its preset scaling factor, and , greater than zero, is the constant correction exponent.

6. The method for evaluating the capacity of a low-altitude UAV logistics transport route network according to claim 1 is characterized by: The specific logic of generating weights in the transport assessment model through the analytic hierarchy process is as follows: The three indicators of adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient are marked, and the relative importance between them is determined by the nine-scale method to construct a judgment matrix, in which the adaptability coefficient is marked as 1, the estimated delay time coefficient is marked as 2, and the flight energy consumption coefficient is marked as 3. The constructed judgment matrix is: ； in, , The mark indicating the indicator, Indicator For indicators The importance of , ; Each element in the judgment matrix is ​​normalized, and the specific formula of the normalization process is: ； in, for The normalized value; The weight value of each indicator is calculated based on the value of each element in the judgment matrix after normalization. The specific formula is as follows: ； in, For indicators The weight value of The weights of the adaptability coefficient, estimated delay time coefficient and flight energy consumption coefficient are calculated based on the following formula: ； ； ； in, Represents the normalized adaptability coefficient relative to the index The importance of Indicates the normalized estimated delay time coefficient relative to the index The importance of Indicates the normalized flight energy consumption coefficient relative to the index The importance of.

7. The method for evaluating the capacity of a low-altitude UAV logistics transport route network according to claim 1 is characterized by: The comprehensive evaluation index is compared with the preset transport evaluation threshold, and based on the comparison result, the evaluation level of the current UAV logistics transport route network capacity is generated. The specific logic is as follows: when When the current transport route network capacity is poor, the current route network is seriously congested, and the transport efficiency is low, it is necessary to immediately re-evaluate the route network and change the route; when When the current transport route network capacity is judged to be average, the route network is congested to a certain extent, and new take-off and landing points are added to optimize the route; when When the current transport route network capacity is judged to be good and the conditions are suitable, logistics transportation will continue along the current route; in, is a comprehensive evaluation index. Evaluate thresholds for transport.

8. A low-altitude UAV logistics transport route network capacity assessment system, characterized by: The low-altitude UAV logistics transport route network capacity evaluation system is used to execute a low-altitude UAV logistics transport route network capacity evaluation method according to any one of claims 1 to 7, comprising: The data collection module is used to place sensors at the intersections of the city's distribution centers and transportation routes to collect traffic flow data; use the meteorological API to obtain meteorological data of the transportation area every hour, including temperature, rainfall and wind speed; integrate the GPS module on the drone to collect the drone's battery power, flight speed and load data in real time; collect the length of each route for drone logistics transportation, and collect the drone's free flow speed and basic energy consumption during flight; The data analysis module is used to generate an estimated delay time coefficient based on route length, free flow speed and traffic flow data; generate a meteorological assessment index by combining temperature, rainfall and wind speed, and generate a flight energy consumption coefficient based on the meteorological assessment index, flight speed and basic energy consumption; and generate an adaptability coefficient based on the battery power of the drone, the power required to perform the current mission and load data; A comprehensive evaluation index generation module is used to generate a transportation evaluation model by combining the estimated delay time coefficient, the flight energy consumption coefficient and the adaptability coefficient, generate the weights in the transportation evaluation model through the hierarchical analysis method, evaluate the current route using the transportation evaluation model, and generate the corresponding comprehensive evaluation index; The intelligent analysis module is used to compare the comprehensive evaluation index with the preset transport evaluation threshold, and generate an evaluation level of the current UAV logistics transport route network capacity based on the comparison results, and divide the current UAV logistics transport route network capacity into good, general, and poor levels, and formulate corresponding measures.

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