Transportation route evaluation method and system for urban low-altitude unmanned aerial vehicle logistics

Abstract

The invention discloses a transportation route evaluation method and system for urban low-altitude unmanned aerial vehicle logistics, and relates to the technical field of electric digital data processing. The transportation route evaluation method for urban low-altitude unmanned aerial vehicle logistics comprises the following steps: initial dual-objective curve analysis; dynamic environment correction evaluation; and hovering energy consumption evaluation. The interference degree of the initial double-target curve is evaluated through the obtained first environment data, whether the first double-target curve is obtained or not is judged, if yes, dynamic environment correction evaluation is carried out based on the obtained second environment data, whether the second double-target curve is obtained or not is judged, and if yes, dynamic environment correction evaluation is carried out. If yes, hovering energy consumption evaluation is carried out based on the obtained third environment data, and whether a third double-target curve is obtained or not is judged, so that the effect of improving the accuracy of transportation route evaluation of urban low-altitude unmanned aerial vehicle logistics is achieved. The problem that in the prior art, balance between transportation stability and dynamic risks of urban low-altitude unmanned aerial vehicle logistics on a transportation route is not fully considered is solved.

Description

Technical Field

[0001] The present invention relates to the field of electronic digital data processing technology, and in particular to a transportation route evaluation method and system for urban low-altitude unmanned aerial vehicle logistics. Background Art

[0002] With the explosive growth of smart city and e-commerce logistics demand, the application of drone technology in terminal delivery is gradually breaking through technical bottlenecks and becoming a key solution to the "last mile" problem. However, the high complexity of urban low-altitude environments (such as dense buildings, dynamic obstacles, and electromagnetic interference) poses severe challenges to the safety, timeliness, and cost-effectiveness of drone logistics routes. Traditional route planning methods rely on static maps or single sensor data, making it difficult to respond in real time to dynamic constraints such as unexpected obstacles, weather changes, and airspace control, leading to a significant challenge in balancing transportation efficiency and risk.

[0003] In the existing technology, an estimated delay time coefficient is generated by collecting route length, free flow speed and traffic flow data, and then the temperature, rainfall and wind speed are combined to generate a meteorological assessment index. The flight energy consumption coefficient is generated based on the meteorological assessment index, flight speed and basic energy consumption. At the same time, an adaptability coefficient is generated based on the battery power, the power required to perform the current task and the load. Finally, the above coefficients are combined to generate a transportation assessment model, evaluate the current route, generate a comprehensive assessment index, and compare it with the preset transportation assessment threshold to obtain the assessment level of the current logistics transportation route network capacity.

[0004] For example, the patent announcement with the announcement number CN114117322B discloses a method for evaluating the capacity of an urban low-altitude UAV logistics transportation route network, including: collecting basic information of the route network structure of the target area, and constructing a topological structure diagram of the route network structure after structuring and informatizing the basic information; based on the topological structure diagram, obtaining the maximum UAV flow accommodated by the route network per unit time, and constructing a first objective function with the maximum flow of the overall route network as the upper-level planning; based on the topological structure diagram, according to the Wardrop system optimal principle, constructing a second objective function with the minimum total impedance of the overall route network as the lower-level planning; based on the first objective function and the second objective function, according to the Kuhn-Tucker theorem and through a genetic algorithm, obtaining the logistics transportation route network capacity of the area within the target area.

[0005] For example, the invention patent announcement with announcement number: CN116934194B discloses a real-time transportation management system based on big data, including: a monitoring module, a data acquisition module, a data processing module, a judgment and decision module and a data storage module; the monitoring module is used to provide real-time monitoring and display of vehicle location and road condition analysis, and to track and supervise transportation activities in real time; the data acquisition module is used to collect transportation activity data in real time; the data processing module is used to perform real-time analysis and calculation on the transportation activity data fed back by the data acquisition module, and obtain the vehicle operation status compliance index, traffic congestion compliance index, cargo distribution efficiency, and meteorological condition compliance index, and calculate the transportation risk assessment coefficient through the vehicle operation status compliance index, traffic congestion compliance index, cargo distribution efficiency, and meteorological condition compliance index; the judgment and decision module is used to compare and analyze the transportation risk assessment value calculated by the data processing module, and provide decision support functions for abnormal situations; the data storage module is used to store real-time collected data, historical data, processed data, and perform classified management.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: Existing models usually rely on static data for transport route assessment, which cannot fully respond to and update dynamic risks, resulting in a lag in transport route assessment. Secondly, static prediction models and energy consumption models usually lack consideration of dynamic factors in the drone logistics process, which in turn affects the drone's endurance and efficiency during the logistics transportation process. There is a problem of insufficient consideration of the balance between transportation stability and dynamic risks in urban low-altitude drone logistics on transport routes. Summary of the Invention

[0007] The embodiments of the present application provide a method for evaluating the transportation routes of urban low-altitude UAV logistics, thereby solving the problem in the prior art of insufficient consideration of the balance between transportation stability and dynamic risks of urban low-altitude UAV logistics on the transportation routes, and achieving improved accuracy in the evaluation of transportation routes of urban low-altitude UAV logistics.

[0008] An embodiment of the present application provides a method for evaluating the transportation route of urban low-altitude UAV logistics, comprising the following steps: Step 1, evaluating the degree of interference of the initial dual-target curve based on the first environmental data corresponding to the target transportation area of the low-altitude UAV at the beginning of the logistics transportation, and judging whether to obtain the first dual-target curve, wherein the initial dual-target curve is used to visualize the fitting relationship between the task completion efficiency and the collision risk probability of the urban low-altitude UAV logistics on the transportation route; Step 2, if the first dual-target curve is obtained, performing a dynamic environmental correction evaluation based on the obtained second environmental data, and judging whether to obtain the second dual-target curve; Step 3, if the second dual-target curve is obtained, performing a hovering energy consumption evaluation based on the obtained third environmental data, and judging whether to obtain the third dual-target curve.

[0009] The embodiment of the present application provides a transport route assessment system for urban low-altitude UAV logistics, comprising: an initial dual-objective curve analysis module, a dynamic environment correction assessment module, and a hovering energy consumption assessment module; Among them, the initial dual-target curve analysis module is used to evaluate the degree of interference of the initial dual-target curve based on the first environmental data of the target transportation area corresponding to the low-altitude UAV at the beginning of logistics transportation, and determine whether to obtain the first dual-target curve. The initial dual-target curve is used to visualize the fitting relationship between the task completion efficiency and collision risk probability of urban low-altitude UAV logistics on the transportation route; the dynamic environment correction evaluation module is used to perform dynamic environment correction evaluation based on the obtained second environmental data if the first dual-target curve is obtained, and determine whether to obtain the second dual-target curve; the hovering energy consumption evaluation module is used to perform hovering energy consumption evaluation based on the obtained third environmental data if the second dual-target curve is obtained, and determine whether to obtain the third dual-target curve.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Evaluate the interference degree of the initial dual-target curve through the obtained first environmental data to determine whether to obtain the first dual-target curve. If so, perform a dynamic environmental correction evaluation based on the obtained second environmental data to determine whether to obtain the second dual-target curve. If so, perform a hovering energy consumption evaluation based on the obtained third environmental data to determine whether to obtain the third dual-target curve, thereby improving the accuracy of the initial dual-target curve update, and further improving the accuracy of the transportation route assessment of urban low-altitude UAV logistics, effectively solving the problem of insufficient consideration of the balance between transportation stability and dynamic risks in the transportation route of urban low-altitude UAV logistics in the existing technology.

[0011] 2. The difference between the obtained second city electromagnetic field intensity and the second city electromagnetic field intensity set value in the database is corrected through the second city electromagnetic field intensity weight factor to obtain the second city electromagnetic field intensity impact score. At the same time, the obtained second city electromagnetic field intensity impact score is coupled with the glass curtain wall reflected light intensity impact score, and the result is superimposed and corrected with the actual environment correction weight impact score to obtain the dynamic environment correction impact score, thereby improving the accuracy of obtaining the dynamic environment correction impact score, and further improving the efficiency and reliability of urban low-altitude UAV logistics transportation.

[0012] 3. By obtaining the wind direction deviation angle of the low-altitude UAV at the current avoidance moment, the air resistance impact score and the battery internal resistance impact score are obtained at the same time. The obtained air resistance impact score and battery internal resistance impact score are coupled with the sine function processing result of the obtained wind direction deviation angle to obtain the hovering energy consumption impact score, thereby improving the accuracy of obtaining the hovering energy consumption impact score, and thus achieving a more accurate assessment of the flight safety and logistics efficiency of urban low-altitude UAVs.

[0013] 4. By judging whether the obtained hovering energy consumption impact score is equal to 0, the system can determine whether to automatically update the second dual-objective curve and generate a more optimized third dual-objective curve to adapt to different flight conditions. Secondly, by introducing the energy consumption correction coefficient adjustment amount, the system can adjust the energy consumption correction coefficient in the drone controller according to the actual hovering energy consumption. This not only helps to reduce energy consumption, but also improves the reliability and safety of drone logistics, thereby significantly improving the transportation efficiency of urban low-altitude drone logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a method for evaluating urban low-altitude drone logistics transportation routes provided in an embodiment of the present application; Figure 2 This is an example diagram of the third dual-objective curve provided in an embodiment of the present application; Figure 3 A schematic structural diagram of a transport route assessment system for urban low-altitude drone logistics provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The embodiments of the present application solve the problem of insufficient consideration of the balance between transportation stability and dynamic risk of urban low-altitude UAV logistics on the transportation route in the prior art by providing a method and system for transport route assessment of urban low-altitude UAV logistics. The method evaluates the degree of interference of the initial dual-target curve based on the first environmental data of the target transportation area corresponding to the low-altitude UAV at the beginning of logistics transportation, and judges whether to obtain the first dual-target curve based on the obtained initial dual-target curve interference score. If the first dual-target curve is obtained, a dynamic environmental correction evaluation is performed based on the obtained second environmental data to obtain the dynamic environmental correction impact score. At the same time, it is judged whether to obtain the second dual-target curve based on the obtained dynamic environmental correction impact score. If the second dual-target curve is obtained, a hovering energy consumption evaluation is performed based on the obtained third environmental data to obtain the hovering energy consumption impact score. At the same time, it is judged whether to obtain the third dual-target curve based on the obtained hovering energy consumption impact score, thereby improving the accuracy of transport route assessment of urban low-altitude UAV logistics.

[0016] The technical solution in the embodiments of this application is to solve the problem of insufficient consideration of the balance between transportation stability and dynamic risks in the transportation routes of urban low-altitude drone logistics. The overall idea is as follows: The interference degree of the initial dual-target curve is evaluated by the obtained first environmental data to determine whether the first dual-target curve is obtained. If so, a dynamic environmental correction evaluation is performed based on the obtained second environmental data to determine whether the second dual-target curve is obtained. If so, a hovering energy consumption evaluation is performed based on the obtained third environmental data to determine whether the third dual-target curve is obtained, thereby achieving the effect of improving the accuracy of transportation route evaluation for urban low-altitude UAV logistics.

[0017] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0018] like Figure 1As shown, it is a flow chart of a method for evaluating the transportation route of urban low-altitude UAV logistics provided by an embodiment of the present application. The method for evaluating the transportation route of urban low-altitude UAV logistics provided by an embodiment of the present application includes the following steps: Step 1, evaluating the degree of interference of the initial dual-target curve based on the first environmental data of the target transportation area corresponding to the low-altitude UAV at the beginning of logistics transportation, and judging whether to obtain the first dual-target curve. The initial dual-target curve is used to visualize the fitting relationship between the task completion efficiency and the collision risk probability of the urban low-altitude UAV logistics on the transportation route; Step 2, if the first dual-target curve is obtained, the dynamic environment correction evaluation is performed based on the obtained second environmental data during the dynamic environment correction process of the low-altitude UAV, and judging whether to obtain the second dual-target curve; Step 3, if the second dual-target curve is obtained, the hovering energy consumption evaluation is performed based on the obtained third environmental data during the logistics transportation process of the low-altitude UAV, and judging whether to obtain the third dual-target curve.

[0019] In this embodiment, if the first dual-target curve is obtained, the second dual-target curve obtained refers to the curve after the first dual-target curve is updated; if the first dual-target curve is not obtained, the second dual-target curve obtained refers to the curve after the initial dual-target curve is updated; similarly, if the first dual-target curve is obtained but the second dual-target curve is not obtained, the third dual-target curve obtained refers to the curve after the first dual-target curve is updated.

[0020] It should be noted that in the transportation routes of urban low-altitude drone logistics, the efficiency-risk trade-off curve (i.e., the dual-objective curve, including the initial dual-objective curve, the first dual-objective curve, the second dual-objective curve, and the third dual-objective curve) is used to intuitively display the trade-off relationship between transportation efficiency (such as path length and time consumption) and risk level (such as collision probability and meteorological threats) of different transportation route plans. The horizontal axis of the initial dual-objective curve is the task completion efficiency (the value range is between 0 and 1, and 1 represents the optimal value of task completion efficiency), and the vertical axis is the collision risk probability (the value range is between 0 and 1, and 0 represents no collision risk).

[0021] The third dual-objective curve in this example is used to select transport route decision scenarios for urban low-altitude UAV logistics. Therefore, by evaluating the logistics transportation efficiency and stability of low-altitude UAVs in stages and updating the dual-objective curve after the evaluation of each stage, it is helpful to achieve real-time matching between task completion efficiency and collision risk probability. Through dynamic environmental assessment, optimization, and UAV control optimization, the balance between transportation stability and dynamic risk on the transportation route of urban low-altitude UAV logistics is improved, thereby achieving real-time and effective updating of the dual-objective curve during the logistics transportation process of urban low-altitude UAVs. This is particularly suitable for complex low-altitude urban environments, such as turbulent urban central business districts (CBDs) and other areas with a high density of UAVs.

[0022] like Figure 2 As shown, it is an example diagram of the third dual-target curve provided in an embodiment of the present application. Figure 2 The collision risk probability at point a is 0.8, and the task completion efficiency is 0.2. The corresponding decision scenario is: high risk and low efficiency; the collision risk probability at point b is 0.65, and the task completion efficiency is 0.4. The corresponding decision scenario is: suboptimal solution; the collision risk probability at point c is 0.5, and the task completion efficiency is 0.6. The corresponding decision scenario is: balance point; the collision risk probability at point d is 0.4, and the task completion efficiency is 0.75. The corresponding decision scenario is: high efficiency and medium risk; the collision risk probability at point e is 0.35, and the task completion efficiency is 0.9. The corresponding decision scenario is: urgent task is optional.

[0023] Furthermore, the steps for obtaining the initial dual-objective curve are: mapping the corresponding collision risk threshold and transport efficiency threshold from the database based on the obtained task priority, and inputting the obtained collision risk threshold and transport efficiency threshold into the reference dual-objective curve in the database to obtain the initial dual-objective curve.

[0024] Assuming that the obtained task priority is 0.2, then the corresponding collision risk threshold is 20%, the transportation efficiency threshold is also 20%, the collision risk probability (ordinate) on the reference dual-objective curve is 0.4, and the task completion efficiency (abscissa) is 0.6. At this time, the ordinate of the initial dual-objective curve obtained is 0.4+0.4*20%=0.48, and the abscissa is 0.6+0.6*20%=0.72, which is right-shifted relative to the reference dual-objective curve in the database.

[0025] Specifically, the steps for obtaining the first environmental data are: obtaining the operating temperature of the low-altitude UAV corresponding to the target transportation area at the beginning of logistics transportation; when the obtained operating temperature is not greater than the low temperature setting value in the database (set by the manufacturer of the low-altitude logistics transportation UAV, usually -10°C), a heating mode switching instruction is sent; otherwise, the actual rainfall in the target transportation area corresponding to the low-altitude UAV at the beginning of logistics transportation is obtained. The heating mode switching instruction is used to increase the payload of the low-altitude UAV in the target transportation area corresponding to the beginning of logistics transportation; when the actual rainfall obtained is not greater than the maximum historical rainfall in the database, the first city electromagnetic field strength of the low-altitude UAV in the target transportation area corresponding to the beginning of logistics transportation is obtained, otherwise it is determined that the rainfall has a delay effect on the city's 5G signal; the first environmental data of the low-altitude UAV corresponding to the target transportation area at the beginning of logistics transportation is obtained, and the first environmental data includes the operating temperature, actual rainfall and the first city electromagnetic field strength.

[0026] The specific process of evaluating the interference degree of the initial dual-objective curve is as follows: the difference between the obtained operating temperature and the operating temperature setting value in the database is corrected by the operating temperature weight factor to obtain the operating temperature impact score; the difference between the obtained first city electromagnetic field intensity and the first city electromagnetic field intensity setting value in the database is corrected by the first city electromagnetic field intensity weight factor to obtain the first city electromagnetic field intensity impact score; when the actual rainfall obtained is not greater than the historical maximum rainfall in the database, the obtained operating temperature impact score and the obtained first city electromagnetic field intensity impact score are coupled to obtain the initial dual-objective curve interference score; when the actual rainfall obtained is greater than the historical maximum rainfall in the database, the obtained first rainfall impact score (indicating the ratio of the actual rainfall obtained to the historical maximum rainfall in the database) and the obtained first city electromagnetic field intensity impact score are superimposed and corrected to obtain the second city electromagnetic field intensity impact score, and the obtained operating temperature impact score and the obtained second city electromagnetic field intensity impact score are coupled to obtain the initial dual-objective curve interference score.

[0027] Among them, the specific limiting expression of the initial dual-objective curve interference score is: Where, It represents the initial dual-target curve interference score of the low-altitude UAV corresponding to the target transportation area at the beginning of logistics transportation, It represents the electromagnetic field intensity impact score of the first city in the target transportation area at the beginning of low-altitude UAV logistics transportation. It represents the electromagnetic field intensity impact score of the second city corresponding to the target transportation area at the beginning of the low-altitude UAV logistics transportation. It represents the operating temperature impact score of the low-altitude UAV in the target transportation area at the beginning of logistics transportation. It indicates the actual rainfall in the target transportation area when the low-altitude UAV starts the logistics transportation. Indicates the maximum rainfall in history.

[0028] First city electromagnetic field intensity impact score The specific restriction expression is: , where represents the electromagnetic field intensity weight factor of the first city, It indicates the electromagnetic field strength of the first city in the target transportation area when the low-altitude drone starts logistics transportation. Indicates the electromagnetic field strength setting value of the first city.

[0029] Operating temperature impact score The specific restriction expression is: , where represents the operating temperature weight factor, Indicates the operating temperature of the low-altitude UAV corresponding to the target transportation area at the beginning of logistics transportation. Indicates the operating temperature setpoint (set by the drone manufacturer for low-altitude logistics transport, usually 15°C).

[0030] Second city electromagnetic field intensity impact score The specific restriction expression is: , , where It represents the first rainfall impact score of the low-altitude UAV in the target transportation area at the beginning of logistics transportation.

[0031] In this embodiment, when the actual rainfall obtained is not greater than the maximum historical rainfall in the database, the initial dual-objective curve interference score represents the quantitative data of the degree of interference of the operating temperature and the urban electromagnetic field strength on the initial dual-objective curve; when the actual rainfall obtained is greater than the maximum historical rainfall in the database, the initial dual-objective curve interference score represents the quantitative data of the degree of interference of the first environmental data on the initial dual-objective curve.

[0032] The historical maximum rainfall indicates the maximum historical rainfall of the low-altitude UAV before the start of logistics transportation; the operating temperature has the same unit as the operating temperature setting value, both are degrees Celsius (℃); the actual rainfall, initial rainfall and the historical maximum rainfall have the same unit, both are millimeters / hour (mm / h); the urban electromagnetic field intensity and the urban electromagnetic field intensity setting value (including the first city electromagnetic field intensity setting value and the second city electromagnetic field intensity setting value) have the same unit, both are volts / meter (V / m). Among them, the operating temperature is monitored by a digital temperature sensor, the actual rainfall and initial rainfall are monitored by X-band weather radar, and the urban electromagnetic field intensity (including the first city electromagnetic field intensity and the second city electromagnetic field intensity) is monitored by a three-axis electromagnetic field sensor.

[0033] The database stores preset weight factors that are closely related to the initial dual-objective curve interference scores. A predefined mapping relationship is established between these weight factors and the corresponding first-city electromagnetic field strength and operating temperature. It is worth noting that this mapping is not set arbitrarily. It can be a one-to-one correspondence or a many-to-one relationship. For example, in actual applications, the real-time electromagnetic field strength and operating temperature of the first city can be directly input into this preset mapping relationship, so that the first-city electromagnetic field strength weight factor and operating temperature weight factor that match the first-city electromagnetic field strength and operating temperature can be quickly and accurately obtained.

[0034] It is particularly important that, in order to ensure consistency and comparability of the assessment, the value ranges of the first city's electromagnetic field intensity weight factor and the operating temperature weight factor in this example are limited to between 0 and 1, and the sum of the two is 1.

[0035] The aforementioned database is a database for storing various types of setting data established before the design of the transportation route evaluation method for urban low-altitude UAV logistics. The database includes but is not limited to the current avoidance moment. The various values therein are directly set by technical personnel. Among them, the setting basis of the preset initial dual-target curve interference score can be determined according to the actual logistics transportation scenario of the urban low-altitude UAV. For example, the preset initial dual-target curve interference score is represented by the sum and average of the historical initial dual-target curve interference scores of the low-altitude UAVs in the database at the beginning of historical logistics transportation. In addition, the various values in the database can be set and fine-tuned by technical personnel according to actual debugging.

[0036] It is important to understand that when The initial dual-objective curve interference score increases with the increase of the electromagnetic field intensity, actual rainfall and operating temperature in the first city. Among them, the increase in the first electromagnetic field intensity may interfere with the communication system of the low-altitude UAV, resulting in unstable 5G signals. The increase in actual rainfall will affect the flight stability and visibility of the UAV and increase the flight risk. The increase in operating temperature may affect the battery performance and the reliability of the mechanical components of the UAV.

[0037] This mutual influence mechanism helps to more comprehensively understand the impact of various factors on drone flight in urban low-altitude environments, thereby enabling a more accurate assessment of drone flight performance under specific conditions, thereby improving the accuracy of transport route assessments for urban low-altitude drone logistics and effectively solving the problem of insufficient consideration of the balance between transport stability and dynamic risks in urban low-altitude drone logistics on transport routes in existing technologies.

[0038] Furthermore, the specific process for determining whether to obtain the first dual-target curve is as follows: when the obtained initial dual-target curve interference score is greater than the initial dual-target curve interference score preset in the database, a dynamic environment correction assessment instruction is sent and the obtained first interference correction factor is input into the initial dual-target curve to obtain the first dual-target curve; otherwise, the initial dual-target curve is not updated; the first interference correction factor represents the result of mapping the degree of difference between the obtained initial dual-target curve interference score and the initial dual-target curve interference score preset in the database in the database.

[0039] In this embodiment, the first interference correction factor is used to correct the initial dual-objective curve so that it more accurately reflects the impact of current environmental data on the relationship between task completion efficiency and collision risk probability. The system inputs the obtained first interference correction factor into the initial dual-objective curve and dynamically adjusts the dual-objective curve according to the current environmental data. The system can obtain a more accurate dual-objective curve that is suitable for the current logistics and transportation environment, namely the first dual-objective curve, which helps to improve the flight safety and efficiency of the drone, reduce the risk of collision, and optimize the transportation route.

[0040] Furthermore, the second environmental data includes the electromagnetic field intensity of the second city, the reflected light intensity of the glass curtain wall and the dynamic correction weight; the electromagnetic field intensity of the second city represents the quantitative data of the interference degree of the electromagnetic field intensity of the target transportation area corresponding to the low-altitude UAV during the logistics transportation process on the city's 5G signal; the reflected light intensity of the glass curtain wall represents the quantitative data of the impact degree of the reflected light of the target transportation area corresponding to the low-altitude UAV during the logistics transportation process on the flight safety of the low-altitude UAV; the dynamic correction weight includes the actual visibility correction weight, the actual traffic flow correction weight and the actual strong wind safety correction weight; the actual visibility correction weight represents the product of the initial visibility correction weight of the first dual-target curve and the second rainfall influence score; the second rainfall influence score is used to reflect the rainfall in the target transportation area corresponding to the low-altitude UAV during the logistics transportation process; the actual traffic flow correction weight is used to reflect the traffic congestion degree of the low-altitude UAV in the target transportation area corresponding to the logistics transportation process; the actual strong wind safety correction weight is used to reflect the transportation resistance of the low-altitude UAV in the target transportation area corresponding to the logistics transportation process.

[0041] Among them, the traffic congestion level mentioned in this example refers to the traffic conditions between drones engaged in low-altitude logistics transportation; the second rainfall impact score represents the ratio of the absolute value of the difference between the actual rainfall in the target transportation area corresponding to the low-altitude drone during the logistics transportation process and the initial rainfall to the initial rainfall; the actual traffic flow correction weight represents the ratio of the absolute value of the difference between the actual traffic congestion coefficient and the initial traffic congestion coefficient in the target transportation area corresponding to the low-altitude drone during the logistics transportation process to the initial traffic congestion coefficient; the actual strong wind safety correction weight represents the ratio of the absolute value of the difference between the actual environmental wind speed in the target transportation area corresponding to the low-altitude drone during the logistics transportation process and the initial environmental wind speed to the initial environmental wind speed; among them, the initial rainfall, initial traffic congestion coefficient and initial environmental wind speed are all initial data of the low-altitude drone at the beginning of logistics transportation.

[0042] Specifically, the specific process of performing dynamic environmental correction assessment based on the acquired second environmental data is as follows: The difference between the obtained electromagnetic field intensity of the second city and the set value of the electromagnetic field intensity of the second city in the database is corrected by the electromagnetic field intensity weight factor of the second city to obtain the electromagnetic field intensity impact score of the second city; the difference between the obtained reflected light intensity of the glass curtain wall and the set value of the reflected light intensity of the glass curtain wall in the database is corrected by the glass curtain wall reflected light intensity weight factor to obtain the glass curtain wall reflected light intensity impact score; the result of coupling the obtained electromagnetic field intensity impact score of the second city and the reflected light intensity impact score of the glass curtain wall is superimposed and corrected with the actual environment correction weight influence score to obtain the dynamic environment correction influence score; the actual environment correction weight influence score It represents the result of coupling processing of the actual visibility correction weight influence score, the actual traffic flow correction weight influence score and the actual strong wind safety correction weight influence score; the actual visibility correction weight influence score is used to reflect the degree of difference between the actual visibility correction weight and the initial visibility correction weight; the actual traffic flow correction weight influence score is used to reflect the degree of difference between the actual traffic flow correction weight and the initial traffic flow correction weight; the actual strong wind safety correction weight influence score is used to reflect the degree of difference between the actual strong wind safety correction weight and the initial strong wind safety correction weight; the dynamic environment correction impact score is used to quantify the degree of interference of the second environment data on the stability of the dynamic environment correction of low-altitude UAVs.

[0043] The specific limiting expression of the dynamic environmental correction impact fraction is: ; Where, It represents the dynamic environment correction impact score of the target transportation area during the dynamic environment correction process of the low-altitude UAV. It represents the impact score of the second city electromagnetic field intensity in the target transportation area during the dynamic environment correction process of the low-altitude UAV. It represents the impact score of the glass curtain wall reflected light intensity in the target transportation area during the dynamic environment correction process of the low-altitude UAV. It represents the actual environmental correction weight impact score of the target transportation area during the dynamic environmental correction process of the low-altitude UAV.

[0044] Second city electromagnetic field intensity impact score The specific restriction expression is: , where represents the second city electromagnetic field intensity weight factor, It indicates the electromagnetic field intensity of the second city corresponding to the target transportation area during the dynamic environment correction process of the low-altitude UAV. Indicates the electromagnetic field strength setting value of the second city.

[0045] The intensity of reflected light from the glass curtain wall affects the score The specific restriction expression is: , where Represents the weight factor of the reflected light intensity of the glass curtain wall, It represents the reflected light intensity of the glass curtain wall corresponding to the target transportation area during the dynamic environment correction process of the low-altitude UAV. Indicates the setting value of the reflected light intensity of the glass curtain wall.

[0046] Actual environment correction weight impact score The specific restriction expression is: , , , , where It represents the influence score of the actual visibility correction weight of the low-altitude UAV corresponding to the target transportation area during the dynamic environment correction process. It represents the influence score of the actual traffic flow correction weight of the target transportation area in the dynamic environment correction process of the low-altitude UAV. It represents the actual strong wind safety correction weight impact score of the target transportation area during the dynamic environment correction process of the low-altitude UAV. It represents the actual visibility correction weight of the low-altitude UAV corresponding to the target transportation area during the dynamic environment correction process. represents the initial visibility correction weight, It represents the actual traffic flow correction weight of the target transportation area during the dynamic environment correction process of the low-altitude UAV. represents the initial traffic flow correction weight, It represents the actual strong wind safety correction weight of the target transport area during the dynamic environment correction process of the low-altitude UAV. Represents the initial strong wind safety correction weight.

[0047] In this embodiment, the units of the glass curtain wall reflected light intensity and the glass curtain wall reflected light intensity setting value are the same, both are nits (nt). The second city electromagnetic field intensity setting value and the glass curtain wall reflected light intensity setting value are respectively represented by the sum and average of the historical second city electromagnetic field intensity and the historical glass curtain wall reflected light intensity of the low-altitude drone in the historical dynamic environment correction process in the database. Among them, the glass curtain wall reflected light is detected by the optical fiber probe of the low-altitude drone and the intensity of the glass curtain wall reflected light is monitored by the brightness meter, that is, the glass curtain wall reflected light intensity.

[0048] The second city's electromagnetic field intensity weighting factor and the glass curtain wall's reflected light intensity weighting factor are the degrees of influence of the second city's electromagnetic field intensity and the glass curtain wall's reflected light intensity, respectively, as pre-set in the database, on the multi-view model update process. Specifically, the database stores preset weighting factors corresponding to the second city's electromagnetic field intensity and the glass curtain wall's reflected light intensity. These weighting factors are mapped to the second city's electromagnetic field intensity and the glass curtain wall's reflected light intensity in a pre-set manner. This mapping relationship can be one-to-one or many-to-one. For example, in actual applications, the real-time second city's electromagnetic field intensity and glass curtain wall's reflected light intensity can be input into this mapping relationship to quickly obtain the corresponding weighting factors.

[0049] In this example, the second city electromagnetic field intensity weight factor and the glass curtain wall reflected light intensity weight factor typically range from 0 to 1, and their sum is 1.

[0050] It should be understood that the dynamic environment correction impact score varies with the electromagnetic field intensity of the second city, the intensity of the reflected light from the glass curtain wall, and the actual visibility correction weight deviation (i.e. ), actual traffic flow correction weight (i.e. ) and the actual strong wind safety correction weight (i.e. ), among which, the increase in the electromagnetic field strength in the second city may interfere with the drone's sensors and affect its accurate judgment of the actual visibility, thereby increasing the visibility correction weight; and the excessive intensity of light reflected from the glass curtain wall may cause misjudgment of the drone's visual system, especially in strong wind conditions, this misjudgment may be exacerbated, so it is necessary to increase the strong wind safety correction weight accordingly.

[0051] By considering this influencing mechanism, it is helpful to more accurately calibrate the flight parameters of drones in complex urban environments, improve the ability of drones to cope with dynamic environmental changes, and thus improve the accuracy of transportation route assessment for urban low-altitude drone logistics. It effectively solves the problem of insufficient consideration of the balance between transportation stability and dynamic risks on transportation routes in urban low-altitude drone logistics in existing technologies.

[0052] Furthermore, the specific process of determining whether to obtain the second dual-target curve is as follows: when the obtained dynamic environment correction impact score is greater than the dynamic environment correction impact score preset in the database, a hovering energy consumption assessment instruction is sent and the obtained second interference correction factor and loop frequency adjustment amount are input into the first dual-target curve to obtain the second dual-target curve, otherwise the first dual-target curve is not updated; the second interference correction factor represents the result of mapping the difference between the obtained dynamic environment correction impact score and the dynamic environment correction impact score preset in the database in the database; the loop frequency adjustment amount is used to increase the PID (Proportional-Integral-Derivative) control loop frequency in the low-altitude UAV controller.

[0053] In this embodiment, the preset dynamic environment correction impact score is represented by the sum and average of the historical dynamic environment correction impact scores of the low-altitude UAVs in the database during the historical dynamic environment correction process; the loop frequency adjustment amount represents the product of the difference between the obtained dynamic environment correction impact score and the dynamic environment correction impact score preset in the database and the second interference correction factor. The increase in the PID control loop frequency is achieved by adding the initial PID control loop frequency of the PID control loop in the system to the loop frequency adjustment amount, thereby obtaining a larger PID control loop frequency. This increase method ensures that the system can adjust the frequency of the PID control loop in real time to better adapt to changes in the dynamic environment, thereby improving the flight stability and safety of urban low-altitude UAVs.

[0054] Furthermore, the third environmental data includes wind direction deviation angle, air resistance and battery internal resistance; the specific process of hovering energy consumption evaluation based on the obtained third environmental data is: obtain the wind direction deviation angle of the low-altitude UAV at the current avoidance moment, and at the same time obtain the air resistance influence score and the battery internal resistance influence score; couple the result of coupling processing of the obtained air resistance influence score and the battery internal resistance influence score with the obtained sine function processing result of the wind direction deviation angle to obtain the hovering energy consumption influence score; the air resistance influence score represents the result of the air resistance weighting factor correcting the degree of difference between the air resistance of the low-altitude UAV at the current avoidance moment and the reference air resistance in the database; the battery internal resistance influence score represents the result of the battery internal resistance weighting factor correcting the degree of difference between the battery internal resistance of the low-altitude UAV at the current avoidance moment and the reference battery internal resistance in the database.

[0055] The specific limiting expression of the hovering energy consumption impact score is: ; Where, Indicates the impact score of the hovering energy consumption of the low-altitude UAV at the current avoidance moment, Indicates the wind direction deviation angle of the low-altitude drone at the current avoidance moment. Indicates the air resistance impact score of the low-altitude drone at the current avoidance moment. Indicates the battery internal resistance impact score of the low-altitude drone at the current avoidance moment.

[0056] Air resistance affects the score The specific restriction expression is: , where represents the air resistance weight factor, Indicates the air resistance of the low-altitude drone at the current avoidance moment, Indicates the reference air resistance.

[0057] Battery internal resistance impact score The specific restriction expression is: , where represents the battery internal resistance weight factor, Indicates the internal resistance of the battery of the low-altitude drone at the current avoidance moment. Indicates the reference battery internal resistance.

[0058] When the obtained wind direction deviation angle is not equal to 0 (that is, the low-altitude UAV is not on the preset transport route), the hovering energy consumption impact score represents the quantitative data of the degree of influence of the wind direction deviation angle, air resistance and battery internal resistance on the avoidance stability of the low-altitude UAV; when the obtained wind direction deviation angle is equal to 0 (that is, the low-altitude UAV is on the preset transport route), the hovering energy consumption impact score represents the quantitative data of the degree of influence of the air resistance and battery internal resistance on the avoidance stability of the low-altitude UAV.

[0059] In this embodiment, the wind direction deviation angle (between 0° and 180°) represents the angle between the ambient wind direction and the low-altitude UAV transport direction. The air resistance and the reference air resistance have the same unit, both in Newton (N). The battery internal resistance and the reference battery internal resistance have the same unit, both in ohm (Ω). The wind direction deviation angle is monitored by a wind direction sensor, the air resistance is monitored by an air resistance sensor, and the battery internal resistance is monitored by a battery internal resistance tester.

[0060] Digital temperature sensors, three-axis electromagnetic field sensors and battery internal resistance testers are usually installed inside the fuselage of low-altitude drones, while X-band weather radars, wind direction sensors and air resistance sensors are usually installed outside the fuselage of low-altitude drones. The specific installation locations of these sensors can be set according to the actual logistics and transportation scenarios of the low-altitude drones.

[0061] The database stores preset weight factors that are closely related to the hovering energy consumption impact score. A predefined mapping relationship is established between these weight factors and the corresponding air resistance and battery internal resistance. It is worth noting that this mapping is not set arbitrarily. It can be a one-to-one correspondence or a many-to-one relationship. For example, in actual applications, the real-time air resistance and battery internal resistance can be directly input into this preset mapping relationship, which can quickly and accurately obtain the air resistance weight factor and battery internal resistance weight factor that match the air resistance and battery internal resistance.

[0062] It is particularly important that, in order to ensure consistency and comparability of the evaluation, the values of the air resistance weighting factor and the battery internal resistance weighting factor in this example are limited to the range between 0 and 1, and the sum of the two is 1.

[0063] It should be understood that the hovering energy consumption impact score increases with the increase of air resistance, battery internal resistance and wind direction deviation angle. Among them, the wind direction deviation angle refers to the angle between the wind direction and the flight direction or expected direction of the drone. When the wind direction deviation angle increases, the drone will be affected by greater crosswinds during flight, resulting in increased air resistance.

[0064] When the wind direction deviation angle increases, the drone may need to increase power output to resist the influence of crosswind in order to maintain flight stability and heading, which will cause the battery discharge current to increase. The battery internal resistance will consume part of the electrical energy during the battery discharge process and convert it into heat energy. Therefore, the increase in discharge current may cause the energy consumed by the battery internal resistance to increase. Although the value of the battery internal resistance itself is not directly affected by the wind direction deviation angle, its impact on the drone's energy consumption will indirectly increase with the increase in the wind direction deviation angle.

[0065] By considering the impact of wind direction deviation angle on air resistance and battery internal resistance, the accuracy of urban low-altitude UAV logistics transportation route assessment can be improved, thereby optimizing route planning and ensuring that the UAV can maintain stability while reducing dynamic risks during transportation. This improves the accuracy of urban low-altitude UAV logistics transportation route assessment and effectively solves the problem of insufficient consideration of the balance between transportation stability and dynamic risks in urban low-altitude UAV logistics on transportation routes in existing technologies.

[0066] Furthermore, the specific process of determining whether to obtain the third dual-target curve is as follows: when the obtained hovering energy consumption impact score is greater than the hovering energy consumption impact score preset in the database, the obtained third interference correction factor and the energy consumption correction coefficient adjustment amount are input into the second dual-target curve to obtain the third dual-target curve, otherwise the second dual-target curve is not updated; the third interference correction factor represents the result of mapping the degree of difference between the obtained hovering energy consumption impact score and the hovering energy consumption impact score preset in the database in the database; the energy consumption correction coefficient adjustment amount is used to increase the energy consumption correction coefficient in the low-altitude UAV controller.

[0067] In this embodiment, the preset hovering energy consumption impact score is represented by the sum and average of the historical hovering energy consumption impact scores of low-altitude UAVs at historical avoidance moments in the database; the energy consumption correction coefficient adjustment amount represents the product of the absolute value of the difference between the obtained hovering energy consumption impact score and the hovering energy consumption impact score preset in the database and the third interference correction factor. The increase in the energy consumption correction coefficient is achieved by adding the original energy consumption correction coefficient in the system to the energy consumption correction coefficient adjustment amount, thereby obtaining a larger energy consumption correction coefficient. The system can dynamically adjust the energy consumption correction coefficient to more accurately reflect the energy consumption of low-altitude UAVs in a hovering state, thereby optimizing the planning and evaluation of transport routes.

[0068] like Figure 3 As shown, it is a structural schematic diagram of a transportation route evaluation system for urban low-altitude UAV logistics provided by an embodiment of the present application. The transportation route evaluation system for urban low-altitude UAV logistics provided by an embodiment of the present application includes: an initial dual-target curve analysis module, a dynamic environment correction evaluation module and a hovering energy consumption evaluation module; wherein, the initial dual-target curve analysis module is used to evaluate the degree of interference of the initial dual-target curve based on the first environmental data corresponding to the target transportation area of the low-altitude UAV at the beginning of logistics transportation, and judge whether to obtain the first dual-target curve. The initial dual-target curve is used to visualize the fitting relationship between the task completion efficiency and collision risk probability of urban low-altitude UAV logistics on the transportation route; the dynamic environment correction evaluation module is used to perform a dynamic environment correction evaluation based on the obtained second environment data if the first dual-target curve is obtained, and judge whether to obtain the second dual-target curve; the hovering energy consumption evaluation module is used to perform a hovering energy consumption evaluation based on the obtained third environment data if the second dual-target curve is obtained, and judge whether to obtain the third dual-target curve.

[0069] In this embodiment, a systematic route evaluation process is formed through the organic combination of the three modules of initial dual-objective curve analysis, dynamic environment correction evaluation and hovering energy consumption evaluation. This process can ensure the comprehensiveness, accuracy and timeliness of the route evaluation, and provide strong support for the transportation route planning of drone logistics. Secondly, by comprehensively considering factors such as task completion efficiency and collision risk probability, as well as dynamic environment and hovering energy consumption, the system can help plan safe and efficient transportation routes, which can not only reduce the collision risk of drones and improve transportation safety, but also optimize route selection and improve transportation efficiency.

[0070] To sum up, the embodiment of the present application evaluates the interference degree of the initial dual-target curve through the acquired first environmental data, determines whether to obtain the first dual-target curve, and if so, performs a dynamic environmental correction evaluation based on the acquired second environmental data to determine whether to obtain the second dual-target curve, and if so, performs a hovering energy consumption evaluation based on the acquired third environmental data to determine whether to obtain the third dual-target curve, thereby achieving an improvement in the accuracy of the initial dual-target curve update, and further achieving an improvement in the accuracy of the transportation route evaluation of urban low-altitude UAV logistics, effectively solving the problem of insufficient consideration of the balance between transportation stability and dynamic risks in the transportation route of urban low-altitude UAV logistics in the prior art.

[0071] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for evaluating the transport routes of urban low-altitude UAV logistics, characterized in that: The following steps are involved: Step 1: Evaluate the degree of interference of the initial dual-objective curve based on the first environmental data of the target transportation area corresponding to the low-altitude UAV at the beginning of logistics transportation, and determine whether to obtain the first dual-objective curve. The initial dual-objective curve is used to visualize the fitting relationship between the task completion efficiency and collision risk probability of urban low-altitude UAV logistics on the transportation route; Step 2: If the first dual-objective curve is obtained, a dynamic environmental correction evaluation is performed based on the obtained second environmental data to determine whether a second dual-objective curve is obtained; Step three: if the second dual-objective curve is obtained, a hovering energy consumption evaluation is performed based on the obtained third environmental data to determine whether a third dual-objective curve is obtained.

2. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 1, characterized in that: The steps for obtaining the initial dual-objective curve are: Based on the obtained task priority, the corresponding collision risk threshold and transportation efficiency threshold are mapped from the database, and the obtained collision risk threshold and transportation efficiency threshold are input into the reference dual-objective curve in the database to obtain an initial dual-objective curve; The steps for obtaining the first environmental data are: Obtain the operating temperature of the low-altitude UAV corresponding to the target transportation area at the beginning of logistics transportation. When the obtained operating temperature is not greater than the low temperature set value in the database, send a heating mode switching instruction. Otherwise, obtain the actual rainfall in the target transportation area corresponding to the low-altitude UAV at the beginning of logistics transportation. The heating mode switching instruction is used to increase the payload of the low-altitude UAV corresponding to the target transportation area at the beginning of logistics transportation; If the actual rainfall is no greater than the maximum historical rainfall in the database, the electromagnetic field strength of the first city in the target transport area corresponding to the low-altitude drone at the start of the logistics transport is obtained. Otherwise, it is determined that the rainfall has a delay effect on the city's 5G signal. Acquire first environmental data corresponding to the target transportation area of the low-altitude UAV at the beginning of logistics transportation, wherein the first environmental data includes operating temperature, actual rainfall and electromagnetic field strength of the first city.

3. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 2, characterized in that: The specific process of evaluating the interference degree of the initial dual-objective curve is as follows: Correct the difference between the acquired operating temperature and the operating temperature set value in the database using the operating temperature weight factor to obtain an operating temperature impact score; Correcting the difference between the obtained electromagnetic field intensity of the first city and the set value of the electromagnetic field intensity of the first city in the database using the electromagnetic field intensity weight factor of the first city to obtain an electromagnetic field intensity impact score of the first city; When the actual rainfall obtained is not greater than the maximum historical rainfall in the database, the obtained operating temperature impact score is coupled with the obtained first city electromagnetic field intensity impact score to obtain the initial dual-objective curve interference score; When the actual rainfall obtained is greater than the maximum historical rainfall in the database, the first rainfall impact score obtained is superimposed and corrected with the first city electromagnetic field strength impact score obtained to obtain the second city electromagnetic field strength impact score. At the same time, the operating temperature impact score obtained is coupled with the second city electromagnetic field strength impact score obtained to obtain the initial dual-objective curve interference score.

4. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 3, characterized in that: The specific process of determining whether to obtain the first dual-target curve is as follows: When the obtained initial dual-objective curve interference score is greater than the initial dual-objective curve interference score preset in the database, a dynamic environment correction evaluation instruction is sent and the obtained first interference correction factor is input into the initial dual-objective curve to obtain a first dual-objective curve; otherwise, the initial dual-objective curve is not updated; The first interference correction factor represents a result of mapping, in a database, the degree of difference between the acquired initial dual-objective curve interference score and the initial dual-objective curve interference score preset in the database.

5. The method for evaluating urban low-altitude UAV logistics transportation routes according to claim 1, characterized in that: The second environmental data includes the electromagnetic field intensity of the second city, the intensity of light reflected from the glass curtain wall, and a dynamic correction weight; The second urban electromagnetic field strength represents quantitative data on the degree of interference of the electromagnetic field strength of the target transportation area of the low-altitude drone during logistics transportation with the urban 5G signal; The intensity of light reflected from the glass curtain wall represents quantitative data on the degree of impact of reflected light from the target transport area on the flight safety of the low-altitude drone during logistics transportation. The dynamic correction weights include actual visibility correction weights, actual traffic flow correction weights, and actual strong wind safety correction weights; The actual traffic flow correction weight is used to reflect the traffic congestion level of the target transportation area during the logistics transportation of the low-altitude UAV; The actual strong wind safety correction weight is used to reflect the transportation resistance of the low-altitude UAV in the corresponding target transportation area during the logistics transportation process.

6. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 5, characterized in that: The specific process of performing dynamic environmental correction assessment based on the acquired second environmental data is as follows: Correcting the difference between the obtained electromagnetic field intensity of the second city and the set value of the electromagnetic field intensity of the second city in the database using the electromagnetic field intensity weight factor of the second city to obtain the electromagnetic field intensity impact score of the second city; The difference between the obtained glass curtain wall reflected light intensity and the glass curtain wall reflected light intensity set value in the database is corrected using the glass curtain wall reflected light intensity weight factor to obtain the glass curtain wall reflected light intensity influence score; The obtained second city electromagnetic field intensity impact score and glass curtain wall reflected light intensity impact score are coupled and processed, and then the result is superimposed and corrected with the actual environment correction weight impact score to obtain the dynamic environment correction impact score; The actual environment correction weighted influence score represents the result of coupling processing of the actual visibility correction weighted influence score, the actual traffic flow correction weighted influence score and the actual strong wind safety correction weighted influence score; The actual visibility correction weight impact score is used to reflect the degree of difference between the actual visibility correction weight and the initial visibility correction weight; The actual traffic flow correction weight impact score is used to reflect the degree of difference between the actual traffic flow correction weight and the initial traffic flow correction weight; The actual strong wind safety correction weight impact score is used to reflect the degree of difference between the actual strong wind safety correction weight and the initial strong wind safety correction weight; The dynamic environment correction impact score is used to quantify the degree of interference of the second environment data on the stability of the dynamic environment correction of the low-altitude UAV.

7. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 6, characterized in that: The specific process of determining whether to obtain the second dual-target curve is as follows: When the obtained dynamic environment correction impact score is greater than the dynamic environment correction impact score preset in the database, a hovering energy consumption evaluation instruction is sent and the obtained second interference correction factor and loop frequency adjustment amount are input into the first dual-target curve to obtain a second dual-target curve; otherwise, the first dual-target curve is not updated; The second interference correction factor represents a result of mapping the absolute value of the difference between the acquired dynamic environment correction impact score and the dynamic environment correction impact score preset in the database into the database; The loop frequency adjustment is used to increase the PID control loop frequency in the low-altitude UAV controller.

8. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 1, characterized in that: The third environmental data includes wind direction deviation angle, air resistance and battery internal resistance; The specific process of evaluating hovering energy consumption based on the acquired third environment data is as follows: Obtain the wind direction deviation angle of the low-altitude drone at the current avoidance moment, as well as the air resistance impact score and battery internal resistance impact score; The obtained air resistance impact score and battery internal resistance impact score are coupled together with the obtained sine function processing result of the wind direction deviation angle to obtain the hovering energy consumption impact score; The air resistance impact score represents the result of correcting the difference between the air resistance of the low-altitude UAV at the current avoidance moment and the reference air resistance in the database by the air resistance weighting factor; The battery internal resistance impact score represents the result of the battery internal resistance weight factor correcting the degree of difference between the battery internal resistance of the low-altitude UAV at the current avoidance moment and the reference battery internal resistance in the database.

9. A method for evaluating urban low-altitude UAV logistics transport routes as claimed in claim 8, characterized in that: The specific process of determining whether to obtain the third dual-target curve is as follows: When the obtained hovering energy consumption impact score is greater than the hovering energy consumption impact score preset in the database, the obtained third interference correction factor and the energy consumption correction coefficient adjustment amount are input into the second dual-objective curve to obtain a third dual-objective curve; otherwise, the second dual-objective curve is not updated; The third interference correction factor represents a result of mapping the difference between the acquired hovering energy consumption impact score and the hovering energy consumption impact score preset in the database into the database; The energy consumption correction coefficient adjustment amount is used to increase the energy consumption correction coefficient in the low-altitude UAV controller.

10. A transport route assessment system for urban low-altitude drone logistics, characterized in that: include: Initial dual-objective curve analysis module, dynamic environment correction evaluation module and hovering energy consumption evaluation module; Among them, the initial dual-objective curve analysis module is used to evaluate the degree of interference of the initial dual-objective curve based on the first environmental data of the target transportation area corresponding to the low-altitude UAV at the beginning of logistics transportation, and determine whether to obtain the first dual-objective curve. The initial dual-objective curve is used to visualize the fitting relationship between the task completion efficiency and collision risk probability of urban low-altitude UAV logistics on the transportation route; The dynamic environment correction evaluation module is used to perform a dynamic environment correction evaluation based on the acquired second environment data if the first dual-objective curve is acquired, and determine whether to acquire the second dual-objective curve; The hovering energy consumption evaluation module is configured to, if the second dual-objective curve is obtained, perform a hovering energy consumption evaluation based on the obtained third environmental data and determine whether to obtain the third dual-objective curve.

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