A method and system for evaluating a transport route of urban low-altitude unmanned aerial vehicle logistics

Abstract

The application discloses a kind of urban low-altitude unmanned aerial vehicle logistics transport route evaluation method and system, it is related to electric digital data processing technical field.The urban low-altitude unmanned aerial vehicle logistics transport route evaluation method includes the following steps: initial double-target curve analysis;Dynamic environment correction evaluation;Hovering energy consumption evaluation.The interference degree of initial double-target curve is evaluated by the first environment data obtained, whether the first double-target curve is obtained is judged, if yes, then based on the second environment data obtained, dynamic environment correction evaluation is carried out, whether the second double-target curve is obtained is judged, if yes, then based on the third environment data obtained, hovering energy consumption evaluation is carried out, whether the third double-target curve is obtained is judged, reach the effect of improving the accuracy of urban low-altitude unmanned aerial vehicle logistics transport route evaluation, solve the problem that the balance between transport stability and dynamic risk of urban low-altitude unmanned aerial vehicle logistics on transport route is not fully considered in prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital data processing, and in particular to a method and system for evaluating the transport route of urban low-altitude unmanned aerial vehicle logistics. BACKGROUND

[0002] With the explosive growth of smart city and e-commerce logistics demand, the application of unmanned aerial vehicle technology in the end of distribution gradually breaks through the technical bottleneck and becomes the key solution to the "last mile" problem. However, the high complexity of urban low-altitude environment (such as dense buildings, dynamic obstacles, electromagnetic interference, etc.) poses a serious challenge to the safety, timeliness and economy of the flight route of unmanned aerial vehicle logistics. Traditional flight route planning methods rely on static maps or single sensor data, which are difficult to respond to sudden obstacles, weather changes and airspace restrictions in real time, resulting in prominent problems in the balance between transport efficiency and risk.

[0003] In the prior art, the estimated delay time coefficient is generated by collecting the flight route length, free flow speed and traffic flow data, and then the temperature, rainfall and wind speed are combined to generate a weather assessment index. The flight energy consumption coefficient is generated according to the weather assessment index, flight speed and basic energy consumption, and the adaptability coefficient is generated according to the battery capacity, the amount of electricity required to perform the current task and the load. Finally, the above coefficients are combined to generate a transport evaluation model, which evaluates the current flight route, generates a comprehensive evaluation index, and compares it with the preset transport evaluation threshold to obtain the evaluation level of the current logistics transport flight route network capacity.

[0004] For example, the patent with the announcement number: CN114117322B, a method for evaluating the transport route network capacity of urban low-altitude unmanned aerial vehicle logistics, includes: collecting the basic information of the flight route network structure of the target area, and after structuring and informatizing the basic information, constructing a topological structure diagram of the flight route network structure; based on the topological structure diagram, obtaining the maximum unmanned aerial vehicle flow that the flight route network can accommodate in unit time, and constructing a first objective function with the maximum overall flight route network flow as the upper planning; based on the topological structure diagram, according to the Wardrop system optimization principle, constructing a second objective function with the minimum overall flight route network impedance as the lower planning; based on the first objective function and the second objective function, according to the Kuhn-Tucker theorem and through the genetic algorithm, obtaining the logistics transport flight route network capacity of the target area.

[0005] For example, the invention patent announcement No. CN116934194B, a kind of real-time transportation management system based on big data, includes: monitoring module, data acquisition module, data processing module, judgment and decision module and data storage module;Monitoring module is used to provide real-time monitoring display vehicle position and road condition analysis, real-time tracking and supervision to transport activities;Data acquisition module is used to collect real-time transport activity data;Data processing module is used to analyze and calculate the transport activity data fed back by data acquisition module in real time, obtain vehicle running state compliance index, traffic congestion condition compliance index, goods distribution efficiency, weather condition compliance index, calculate transport risk assessment coefficient through vehicle running state compliance index, traffic congestion condition compliance index, goods distribution efficiency, weather condition compliance index;Judgment and decision module is used to compare and analyze the transport risk assessment value calculated by data processing module, and provide decision support function for abnormal situation;Data storage module is used to store real-time acquisition data, historical data, processing data, and carry out classified management.

[0006] But in the process of implementing the technical scheme of the embodiments of the present application, it is found that the above-mentioned technology at least has the following technical problems:

[0007] The existing model usually depends on static data for transport route evaluation, which cannot fully respond and update dynamic risk, so that the transport route evaluation is lagging behind, secondly, the static prediction model and the energy consumption model usually lack consideration of dynamic factors in the process of unmanned aerial vehicle logistics, thereby affecting the endurance and efficiency of unmanned aerial vehicle in the process of logistics transportation, and there is a problem that the balance between transport stability and dynamic risk of urban low-altitude unmanned aerial vehicle logistics on the transport route is not fully considered. SUMMARY

[0008] The embodiments of the present application provide a kind of transport route evaluation method of urban low-altitude unmanned aerial vehicle logistics, solve the problem that the balance between transport stability and dynamic risk of urban low-altitude unmanned aerial vehicle logistics on the transport route is not fully considered in the prior art, and realize the improvement of the transport route evaluation accuracy of urban low-altitude unmanned aerial vehicle logistics.

[0009] The embodiment of the application provides a kind of city low altitude unmanned vehicle logistics transport route evaluation method, comprising the following steps: step one, the interference degree of initial double-target curve is evaluated based on the first environment data of target transport area corresponding to low altitude unmanned vehicle at the beginning of logistics transport, judge whether to obtain the first double-target curve, the initial double-target curve is used to visualize the fitting relationship between the task completion efficiency and collision risk probability of city low altitude unmanned vehicle logistics on transport route;Step two, if the first double-target curve is obtained, dynamic environment correction evaluation is carried out based on the second environment data obtained, judge whether to obtain the second double-target curve;Step three, if the second double-target curve is obtained, hover energy consumption evaluation is carried out based on the third environment data obtained, judge whether to obtain the third double-target curve.

[0010] The embodiment of the application provides a kind of city low altitude unmanned vehicle logistics transport route evaluation system, comprising: initial double-target curve analysis module, dynamic environment correction evaluation module and hover energy consumption evaluation module;

[0011] Wherein, the initial double-target curve analysis module is used to evaluate the interference degree of initial double-target curve based on the first environment data of target transport area corresponding to low altitude unmanned vehicle at the beginning of logistics transport, judge whether to obtain the first double-target curve, the initial double-target curve is used to visualize the fitting relationship between the task completion efficiency and collision risk probability of city low altitude unmanned vehicle logistics on transport route;The dynamic environment correction evaluation module is used to carry out dynamic environment correction evaluation based on the second environment data obtained if the first double-target curve is obtained, judge whether to obtain the second double-target curve;The hover energy consumption evaluation module is used to carry out hover energy consumption evaluation based on the third environment data obtained if the second double-target curve is obtained, judge whether to obtain the third double-target curve.

[0012] One or more technical solutions provided in the embodiment of the application have at least the following technical effects or advantages:

[0013] 1, the interference degree of initial double-target curve is evaluated by the first environment data obtained, judge whether to obtain the first double-target curve, if yes, dynamic environment correction evaluation is carried out based on the second environment data obtained, judge whether to obtain the second double-target curve, if yes, hover energy consumption evaluation is carried out based on the third environment data obtained, judge whether to obtain the third double-target curve, so as to realize the improvement of initial double-target curve updating accuracy, further realize the improvement of city low altitude unmanned vehicle logistics transport route evaluation accuracy, effectively solve the problem that the balance between transport stability and dynamic risk of city low altitude unmanned vehicle logistics on transport route is not fully considered in prior art.

[0014] 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 by the second city electromagnetic field intensity weight factor, the second city electromagnetic field intensity influence score is obtained, and the coupling processing result of the obtained second city electromagnetic field intensity influence score and the glass curtain wall reflected light intensity influence score is superimposed and corrected with the actual environment correction weight influence score to obtain the dynamic environment correction influence score, so as to realize the improvement of the accuracy of the dynamic environment correction influence score, and then realize the improvement of the city low-altitude unmanned aerial vehicle logistics transportation efficiency and reliability.

[0015] 3, by obtaining the wind direction offset angle of the low-altitude unmanned aerial vehicle at the current avoidance moment, simultaneously obtaining the air resistance influence score and the battery internal resistance influence score, coupling processing the coupling processing result of the obtained air resistance influence score and the battery internal resistance influence score with the sine function processing result of the obtained wind direction offset angle, obtaining the hovering energy consumption influence score, so as to improve the accuracy of the hovering energy consumption influence score, and then realize more accurate evaluation of the flight safety and logistics efficiency of the city low-altitude unmanned aerial vehicle.

[0016] 4, by judging whether the obtained hovering energy consumption influence score is equal to 0, further judging whether the system automatically updates the second double-target curve, generating a more optimized third double-target curve to adapt to different flight conditions, and secondly, by introducing the energy consumption correction coefficient adjustment amount, the system can adjust the energy consumption correction coefficient in the unmanned aerial vehicle controller according to the actual situation of the hovering energy consumption, which not only helps to reduce the energy consumption, but also improves the reliability and safety of the unmanned aerial vehicle logistics, and further significantly improves the transportation efficiency of the city low-altitude unmanned aerial vehicle logistics. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flow chart of a city low-altitude unmanned aerial vehicle logistics transportation route evaluation method provided by the embodiment of the present application;

[0018] Figure 2 An example diagram of the third double-target curve provided by the embodiment of the present application;

[0019] Figure 3 A structural schematic diagram of a city low-altitude unmanned aerial vehicle logistics transportation route evaluation system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0020] The embodiment of the application provides a kind of urban low-altitude unmanned vehicle logistics transport route evaluation method and system, solve the problem that the balance between the transport stability and dynamic risk of urban low-altitude unmanned vehicle logistics on transport route is not sufficient in prior art, the interference degree of initial double-target curve is evaluated by the first environmental data of corresponding target transport area of low-altitude unmanned vehicle when logistics transport starts, whether the first double-target curve is obtained is judged based on the initial double-target curve interference score obtained, if the first double-target curve is obtained, dynamic environment correction evaluation is carried out based on the second environmental data obtained to obtain dynamic environment correction influence score, whether the second double-target curve is obtained is judged based on the dynamic environment correction influence score obtained, if the second double-target curve is obtained, hovering energy consumption evaluation is carried out based on the third environmental data obtained to obtain hovering energy consumption influence score, whether the third double-target curve is obtained is judged based on the hovering energy consumption influence score obtained, the improvement of the transport route evaluation accuracy of urban low-altitude unmanned vehicle logistics is realized.

[0021] The technical scheme in the embodiment of the application is to solve the problem that the balance between the transport stability and dynamic risk of urban low-altitude unmanned vehicle logistics on transport route is not sufficient, and the general idea is as follows:

[0022] The interference degree of initial double-target curve is evaluated by the first environmental data obtained, whether the first double-target curve is obtained is judged, if yes, dynamic environment correction evaluation is carried out based on the second environmental data obtained, whether the second double-target curve is obtained is judged, if yes, hovering energy consumption evaluation is carried out based on the third environmental data obtained, whether the third double-target curve is obtained is judged, the effect of improving the transport route evaluation accuracy of urban low-altitude unmanned vehicle logistics is achieved.

[0023] In order to better understand the above technical scheme, the above technical scheme will be described in detail in combination with the drawings of the specification and specific embodiments.

[0024] As Figure 1As shown, a flow chart of a transport route evaluation method of urban low-altitude unmanned aerial vehicle logistics provided by an embodiment of the present application, the transport route evaluation method of urban low-altitude unmanned aerial vehicle logistics provided by an embodiment of the present application includes the following steps: Step one, based on the first environmental data of the target transport area corresponding to the low-altitude unmanned aerial vehicle at the beginning of the logistics transport, the disturbance degree of the initial double-target curve is evaluated, and it is judged whether the first double-target curve is obtained. The initial double-target curve is used to visualize the fitting relationship between the task completion efficiency and the collision risk probability of urban low-altitude unmanned aerial vehicle logistics on the transport route; Step two, if the first double-target curve is obtained, dynamic environment correction evaluation is performed based on the obtained second environmental data during the dynamic environment correction of the low-altitude unmanned aerial vehicle, and it is judged whether the second double-target curve is obtained; Step three, if the second double-target curve is obtained, hovering energy consumption evaluation is performed based on the obtained third environmental data during the logistics transport of the low-altitude unmanned aerial vehicle, and it is judged whether the third double-target curve is obtained.

[0025] In the embodiment, if the first double-target curve is obtained, the second double-target curve obtained refers to the curve after updating the first double-target curve, and if the first double-target curve is not obtained, the second double-target curve obtained refers to the curve after updating the initial double-target curve. Similarly, if the first double-target curve is obtained but the second double-target curve is not obtained, the third double-target curve obtained refers to the curve after updating the first double-target curve.

[0026] It should be noted that in the transport route of urban low-altitude unmanned aerial vehicle logistics, the efficiency-risk trade-off curve (i.e. double-target curve, including initial double-target curve, first double-target curve, second double-target curve and third double-target curve) is used to intuitively show the trade-off relationship between transport efficiency (such as path length, time consumption) and risk level (such as collision probability, weather threat) of different transport route schemes. The abscissa of the initial double-target curve is the task completion efficiency (the value range is between 0 and 1, and 1 represents the optimal value of the task completion efficiency), and the ordinate is the collision risk probability (the value range is between 0 and 1, and 0 represents no collision risk).

[0027] The third bi-objective curve in the example is used for the selection of the transport route decision scene of the urban low-altitude unmanned aerial vehicle logistics, therefore, by evaluating the logistics transport efficiency and stability of the low-altitude unmanned aerial vehicle in stages and updating the bi-objective curve after the end of the evaluation of each stage, it is helpful to realize the real-time matching between the task completion efficiency and the collision risk probability, through dynamic environment evaluation, optimization, unmanned aerial vehicle control optimization, the balance between the transport stability and the dynamic risk of the urban low-altitude unmanned aerial vehicle logistics on the transport route is improved, and then the real-time and effective updating of the bi-objective curve of the urban low-altitude unmanned aerial vehicle in the logistics transport process is realized, which is especially suitable for complex urban low-altitude environments such as turbulent urban central business districts (CBD, Central Business District) and other unmanned aerial vehicle dense areas.

[0028] As shown in Figure 2 , the example diagram of the third bi-objective curve provided by the embodiment of the application, Figure 2 the collision risk probability of point a is 0.8, the task completion efficiency is 0.2, at this time the corresponding decision scene is: high risk and low efficiency; the collision risk probability of point b is 0.65, the task completion efficiency is 0.4, at this time the corresponding decision scene is: suboptimal solution; the collision risk probability of point c is 0.5, the task completion efficiency is 0.6, at this time the corresponding decision scene is: balance point; the collision risk probability of point d is 0.4, the task completion efficiency is 0.75, at this time the corresponding decision scene is: high efficiency and medium risk; the collision risk probability of point e is 0.35, the task completion efficiency is 0.9, at this time the corresponding decision scene is: emergency task optional.

[0029] Further, the step of obtaining the initial bi-objective curve is: based on the obtained task priority, the corresponding collision risk threshold and transport efficiency threshold are mapped from the database, and the obtained collision risk threshold and transport efficiency threshold are input into the reference bi-objective curve in the database to obtain the initial bi-objective curve.

[0030] Suppose the obtained task priority is 0.2, then the corresponding collision risk threshold is 20%, the transport efficiency threshold is also 20%, the collision risk probability (vertical coordinate) on the reference bi-objective curve is 0.4, and the task completion efficiency (horizontal coordinate) is 0.6, at this time the vertical coordinate of the obtained initial bi-objective curve is 0.4+0.4*20%=0.48, and the horizontal coordinate is 0.6+0.6*20%=0.72, that is, it is right to the reference bi-objective curve in the database.

[0031] Specifically, the first environment data acquisition step is: acquiring the running temperature of the low-altitude unmanned aerial vehicle corresponding to the target transportation area at the beginning of the logistics transportation; when the acquired running temperature is not greater than the low temperature setting value in the database (set by the unmanned aerial vehicle manufacturer of the low-altitude logistics transportation, usually -10°C), a heating mode switching instruction is sent; otherwise, the actual rainfall of the low-altitude unmanned aerial vehicle corresponding to the target transportation area at the beginning of the logistics transportation is acquired, and the heating mode switching instruction is used to improve the payload of the low-altitude unmanned aerial vehicle corresponding to the target transportation area at the beginning of the logistics transportation; when the acquired actual rainfall is not greater than the maximum historical rainfall in the database, the first urban electromagnetic field intensity of the low-altitude unmanned aerial vehicle corresponding to the target transportation area at the beginning of the logistics transportation is acquired; otherwise, it is determined that the rainfall has a delay effect on the urban 5G signal; the first environment data of the low-altitude unmanned aerial vehicle corresponding to the target transportation area at the beginning of the logistics transportation is acquired, and the first environment data includes the running temperature, the actual rainfall and the first urban electromagnetic field intensity.

[0032] The specific process of evaluating the interference degree of the initial double-target curve is: the difference degree between the acquired running temperature and the running temperature setting value in the database is corrected by a running temperature weight factor to obtain a running temperature influence score; the difference degree between the acquired first urban electromagnetic field intensity and the first urban electromagnetic field intensity setting value in the database is corrected by a first urban electromagnetic field intensity weight factor to obtain a first urban electromagnetic field intensity influence score; when the acquired actual rainfall is not greater than the maximum historical rainfall in the database, the acquired running temperature influence score and the acquired first urban electromagnetic field intensity influence score are coupled to obtain an initial double-target curve interference score; when the acquired actual rainfall is greater than the maximum historical rainfall in the database, the first rainfall influence score (indicating the ratio of the acquired actual rainfall to the maximum historical rainfall in the database) and the first urban electromagnetic field intensity influence score are superimposed to obtain a second urban electromagnetic field intensity influence score, and the running temperature influence score and the second urban electromagnetic field intensity influence score are coupled to obtain the initial double-target curve interference score.

[0033] Wherein, the specific limit expression of the initial double-target curve interference score is:

[0034]

[0035] In the formula, represents the initial double-target curve interference score of the low-altitude unmanned aerial vehicle corresponding to the target transportation area at the beginning of the logistics transportation, represents the first urban electromagnetic field intensity influence score of the low-altitude unmanned aerial vehicle corresponding to the target transportation area at the beginning of the 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.

[0036] 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.

[0037] 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).

[0038] 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.

[0039] 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.

[0040] The historical rainfall maximum value represents the maximum value of historical rainfall before the start of the logistics transportation of the low-altitude unmanned aerial vehicle; the operating temperature has the same unit as the operating temperature set value, both in degrees Celsius (℃); the actual rainfall, the initial rainfall, and the historical rainfall maximum value have the same unit, all in millimeters per hour (mm / h); the urban electromagnetic field intensity has the same unit as the urban electromagnetic field intensity set value (including the first urban electromagnetic field intensity set value and the second urban electromagnetic field intensity set value), both in volts per meter (V / m); wherein the operating temperature is monitored by a digital temperature sensor, the actual rainfall and the initial rainfall are monitored by an X-band weather radar, and the urban electromagnetic field intensity (including the first urban electromagnetic field intensity and the second urban electromagnetic field intensity) is monitored by a three-axis electromagnetic field sensor.

[0041] The database stores preset weight factors closely related to the initial double-target curve interference score, and a predefined mapping relationship is established between the weight factors and the corresponding first urban electromagnetic field intensity and operating temperature. Notably, this mapping is not arbitrarily set and can be one-to-one or many-to-one. For example, in actual applications, the first urban electromagnetic field intensity and the operating temperature obtained in real time can be directly input into the preset mapping relationship to quickly and accurately obtain the first urban electromagnetic field intensity weight factor and the operating temperature weight factor that match the first urban electromagnetic field intensity and the operating temperature.

[0042] Importantly, to ensure consistency and comparability of the evaluation, the value range of the first urban electromagnetic field intensity weight factor and the operating temperature weight factor in the present example is limited to 0 to 1, and the sum of the two is 1.

[0043] The aforementioned database is a database established before the design of the urban low-altitude unmanned aerial vehicle logistics transportation route evaluation method for storing various set data. The database includes but is not limited to the current avoidance time, and various numerical values therein are directly set by technicians. The preset initial double-target curve interference score can be determined according to the actual logistics transportation scenario of the urban low-altitude unmanned aerial vehicle, for example, the preset initial double-target curve interference score is represented by the result of summing and averaging the historical initial double-target curve interference scores of the low-altitude unmanned aerial vehicle at the start of historical logistics transportation in the database. In addition, various numerical values in the database can be set and fine-tuned by technicians according to actual debugging.

[0044] It should be understood that when At this time, the initial double-target curve interference score increases with the increase of the first urban electromagnetic field intensity, the actual rainfall and the operating temperature. Among them, the increase of the first electromagnetic field intensity may interfere with the communication system of the low-altitude unmanned aerial vehicle, leading to unstable 5G signal. The increase of the actual rainfall will affect the flight stability and visibility of the unmanned aerial vehicle, increasing the flight risk. The increase of the operating temperature may affect the battery performance of the unmanned aerial vehicle and the reliability of mechanical parts.

[0045] Through this mutual influence mechanism, it is helpful to more comprehensively understand the influence of various factors in the urban low-altitude environment on the flight of the unmanned aerial vehicle, so as to more accurately evaluate the flight performance of the unmanned aerial vehicle under certain conditions, thereby realizing the improvement of the transportation route evaluation accuracy of the urban low-altitude unmanned aerial vehicle logistics, and effectively solving the problem that the balance between transportation stability and dynamic risk on the transportation route of the urban low-altitude unmanned aerial vehicle logistics is not fully considered in the prior art.

[0046] Further, the specific process of determining whether to obtain the first double-target curve is: when the obtained initial double-target curve interference score is greater than the preset initial double-target curve interference score in the database, a dynamic environment correction evaluation instruction is sent and the obtained first interference correction factor is input into the initial double-target curve to obtain the first double-target curve, otherwise the initial double-target curve is not updated; the first interference correction factor represents the difference between the obtained initial double-target curve interference score and the preset initial double-target curve interference score in the database.

[0047] In the embodiment, the first interference correction factor is used to correct the initial double-target curve, so that it more accurately reflects the influence of the current environmental data on the relationship between the task completion efficiency and the collision risk probability. The system inputs the obtained first interference correction factor into the initial double-target curve, dynamically adjusts the double-target curve according to the current environmental data, and obtains a more accurate double-target curve suitable for the current logistics transportation environment, i.e. the first double-target curve, which helps to improve the flight safety and efficiency of the unmanned aerial vehicle, reduce the collision risk, and optimize the transportation path.

[0048] Further, the second environment data includes a second urban electromagnetic field intensity, a glass curtain wall reflected light intensity, and a dynamic correction weight; the second urban electromagnetic field intensity represents quantified data of an interference degree of an electromagnetic field intensity of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process on the urban 5G signal; the glass curtain wall reflected light intensity represents quantified data of an influence degree of reflected light of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process on the flight safety of the low-altitude unmanned aerial vehicle; the dynamic correction weight includes an actual visibility correction weight, an actual traffic flow correction weight, and an actual strong wind safety correction weight; the actual visibility correction weight represents a product of an initial visibility correction weight of the first double-target curve and a second rainfall influence score; the second rainfall influence score is used to reflect rainfall of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process; the actual traffic flow correction weight is used to reflect a traffic jam degree of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process; and the actual strong wind safety correction weight is used to reflect a transportation resistance of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process.

[0049] In the present example, the traffic jam degree refers to a traffic condition between the low-altitude logistics transportation unmanned aerial vehicles; the second rainfall influence score represents a ratio of an absolute value of a difference between actual rainfall of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process and initial rainfall and the initial rainfall; the actual traffic flow correction weight represents a ratio of an absolute value of a difference between an actual traffic jam coefficient of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process and an initial traffic jam coefficient and the initial traffic jam coefficient; and the actual strong wind safety correction weight represents a ratio of an absolute value of a difference between an actual environmental wind speed of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the logistics transportation process and an initial environmental wind speed and the initial environmental wind speed, wherein the initial rainfall, the initial traffic jam coefficient, and the initial environmental wind speed are initial data of the low-altitude unmanned aerial vehicle at the beginning of the logistics transportation.

[0050] Specifically, a specific flow of dynamically correcting and evaluating the environment based on the obtained second environment data is as follows:

[0051] The difference between the obtained second city electromagnetic field intensity and the second city electromagnetic field intensity set value in the database is corrected by a second city electromagnetic field intensity weight factor to obtain a second city electromagnetic field intensity influence score; 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 by a glass curtain wall reflected light intensity weight factor to obtain a glass curtain wall reflected light intensity influence score; the result of coupling processing of the obtained second city electromagnetic field intensity influence score and the glass curtain wall reflected light intensity influence score is superimposed and corrected with an actual environment correction weight influence score to obtain a dynamic environment correction influence score; the actual environment correction weight influence score represents the result of coupling processing of an actual visibility correction weight influence score, an actual traffic flow correction weight influence score and an actual strong wind safety correction weight influence score; the actual visibility correction weight influence score is used to reflect the difference between an actual visibility correction weight and an initial visibility correction weight; the actual traffic flow correction weight influence score is used to reflect the difference between an actual traffic flow correction weight and an initial traffic flow correction weight; the actual strong wind safety correction weight influence score is used to reflect the difference between an actual strong wind safety correction weight and an initial strong wind safety correction weight; and the dynamic environment correction influence score is used to quantify the interference degree of the second environment data on the dynamic environment correction stability of the low-altitude unmanned aerial vehicle.

[0052] The specific limit expression of the dynamic environment correction influence score is:

[0053] ;

[0054] In the formula, represents the dynamic environment correction influence score of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the dynamic environment correction process, represents the second city electromagnetic field intensity influence score of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the dynamic environment correction process, represents the glass curtain wall reflected light intensity influence score of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the dynamic environment correction process, represents the actual environment correction weight influence score of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the dynamic environment correction process.

[0055] The specific limit expression of the second city electromagnetic field intensity influence score is: In the formula, represents the second city electromagnetic field intensity weight factor, represents the second city electromagnetic field intensity of the low-altitude unmanned aerial vehicle corresponding to the target transportation area in the dynamic environment correction process, represents the second city electromagnetic field intensity set value.

[0056] Glass curtain wall reflected light intensity influence score The specific limit expression is: , wherein, represents a glass curtain wall reflected light intensity weight factor, represents the glass curtain wall reflected light intensity of the corresponding target transportation area in the dynamic environment correction process of the low-altitude unmanned aerial vehicle, represents a glass curtain wall reflected light intensity set value.

[0057] Actual environment correction weight influence score The specific limit expression is: , , , , wherein, represents the actual visibility correction weight influence score of the corresponding target transportation area in the dynamic environment correction process of the low-altitude unmanned aerial vehicle, represents the actual traffic flow correction weight influence score of the corresponding target transportation area in the dynamic environment correction process of the low-altitude unmanned aerial vehicle, represents the actual strong wind safety correction weight influence score of the corresponding target transportation area in the dynamic environment correction process of the low-altitude unmanned aerial vehicle, represents the actual visibility correction weight of the corresponding target transportation area in the dynamic environment correction process of the low-altitude unmanned aerial vehicle, represents an initial visibility correction weight, represents the actual traffic flow correction weight of the corresponding target transportation area in the dynamic environment correction process of the low-altitude unmanned aerial vehicle, represents an initial traffic flow correction weight, represents the actual strong wind safety correction weight of the corresponding target transportation area in the dynamic environment correction process of the low-altitude unmanned aerial vehicle, represents an initial strong wind safety correction weight.

[0058] In this embodiment, the glass curtain wall reflected light intensity and the glass curtain wall reflected light intensity set value have the same unit, both of which are nits (nt), and the second city electromagnetic field intensity set value and the glass curtain wall reflected light intensity set value are represented by the results of summing and averaging the historical second city electromagnetic field intensity and the historical glass curtain wall reflected light intensity of the low-altitude unmanned aerial vehicle in the historical dynamic environment correction process in the database, wherein the glass curtain wall reflected light is detected by the optical fiber probe of the low-altitude unmanned aerial vehicle and the intensity of the glass curtain wall reflected light, i.e., the glass curtain wall reflected light intensity, is monitored by a brightness meter.

[0059] The second urban electromagnetic field intensity weight factor and the glass curtain wall reflected light intensity weight factor are respectively the influence degrees of the second urban electromagnetic field intensity and the glass curtain wall reflected light intensity in the database on the multi-view model updating process. Specifically, the database stores preset weight factors corresponding to the second urban electromagnetic field intensity and the glass curtain wall reflected light intensity. There is a pre-set mapping relationship between these weight factors and the second urban electromagnetic field intensity and the glass curtain wall reflected light intensity. This mapping relationship can be one-to-one or many-to-one. For example, in actual application, the real-time second urban electromagnetic field intensity and the glass curtain wall reflected light intensity can be input into this mapping relationship to quickly obtain the corresponding weight factors.

[0060] The value range of the second urban electromagnetic field intensity weight factor and the glass curtain wall reflected light intensity weight factor in the example is usually 0 to 1, and the sum of the two is 1.

[0061] It should be understood that the dynamic environment correction influence score increases with the increase of the second urban electromagnetic field intensity, the glass curtain wall reflected light intensity, the actual visibility correction weight deviation (i.e. ), the actual traffic flow correction weight (i.e. ), and the actual strong wind safety correction weight (i.e. ). Among them, the increase of the second urban electromagnetic field intensity may interfere with the sensors of the unmanned aerial vehicle, affecting its accurate judgment of the actual visibility, thereby increasing the visibility correction weight. Too large glass curtain wall reflected light intensity may cause misjudgment of the unmanned aerial vehicle vision system, especially under strong wind conditions, which may exacerbate, so the strong wind safety correction weight needs to be increased accordingly.

[0062] By considering this influence mechanism, it is helpful to more accurately calibrate the flight parameters of the unmanned aerial vehicle in the complex urban environment, improve the ability of the unmanned aerial vehicle to respond to dynamic environmental changes, and thereby improve the accuracy of the transport route evaluation of the urban low-altitude unmanned aerial vehicle logistics, effectively solving the problem that the balance between transport stability and dynamic risk of the urban low-altitude unmanned aerial vehicle logistics on the transport route is not fully considered in the prior art.

[0063] Further, the specific process of judging whether to obtain the second double-target curve is: when the obtained dynamic environment correction influence score is greater than the preset dynamic environment correction influence score in the database, a hovering energy consumption evaluation instruction is sent, and the obtained second interference correction factor and the loop frequency adjustment amount are input into the first double-target curve to obtain the second double-target curve, otherwise the first double-target curve is not updated; the second interference correction factor represents the difference between the obtained dynamic environment correction influence score and the preset dynamic environment correction influence score in the database; the loop frequency adjustment amount is used to increase the PID (Proportional-Integral-Derivative) control loop frequency in the low-altitude unmanned aerial vehicle controller.

[0064] In the embodiment, the preset dynamic environment correction influence score is represented by the sum average of the historical dynamic environment correction influence scores of the low-altitude unmanned aerial vehicle in the historical dynamic environment correction process in the database; the loop frequency adjustment amount represents the product of the difference between the obtained dynamic environment correction influence score and the preset dynamic environment correction influence score in the database and the second interference correction factor, and the increase of the PID control loop frequency is realized by adding the initial PID control loop frequency of the PID control loop in the system and the loop frequency adjustment amount, so as to obtain a larger PID control loop frequency. This increasing mode ensures that the system can adjust the frequency of the PID control loop in real time to better adapt to the changes of the dynamic environment, thereby improving the flight stability and safety of the urban low-altitude unmanned aerial vehicle.

[0065] Further, the third environment data includes a wind direction offset angle, air resistance and battery internal resistance; the specific process of hovering energy consumption evaluation based on the obtained third environment data is: obtaining the wind direction offset angle of the low-altitude unmanned aerial vehicle at the current avoidance moment, and obtaining the air resistance influence score and the battery internal resistance influence score; coupling the obtained air resistance influence score and the battery internal resistance influence score to obtain the hovering energy consumption influence score; the air resistance influence score represents the result of correcting the difference between the air resistance of the low-altitude unmanned aerial vehicle at the current avoidance moment and the reference air resistance in the database by the air resistance weight factor; the battery internal resistance influence score represents the result of correcting the difference between the battery internal resistance of the low-altitude unmanned aerial vehicle at the current avoidance moment and the reference battery internal resistance in the database by the battery internal resistance weight factor.

[0066] Wherein, the specific limit expression of the hovering energy consumption influence score is:

[0067] ;

[0068] In the formula, a hovering energy consumption influence score of the low-altitude unmanned aerial vehicle at the current avoidance moment, a wind direction offset angle of the low-altitude unmanned aerial vehicle at the current avoidance moment, an air resistance influence score of the low-altitude unmanned aerial vehicle at the current avoidance moment, a battery internal resistance influence score of the low-altitude unmanned aerial vehicle at the current avoidance moment.

[0069] the air resistance influence score The specific limiting expression of the air resistance influence score is: , wherein, the air resistance weight factor, the air resistance of the low-altitude unmanned aerial vehicle at the current avoidance moment, the reference air resistance.

[0070] the battery internal resistance influence score The specific limiting expression of the battery internal resistance influence score is: , wherein, the battery internal resistance weight factor, the battery internal resistance of the low-altitude unmanned aerial vehicle at the current avoidance moment, the reference battery internal resistance.

[0071] When the obtained wind direction offset angle is not equal to 0 (i.e., the low-altitude unmanned aerial vehicle is not on the preset transportation route), the hovering energy consumption influence score represents the influence degree quantization data of the wind direction offset angle, the air resistance and the battery internal resistance on the avoidance stability of the low-altitude unmanned aerial vehicle; when the obtained wind direction offset angle is equal to 0 (i.e., the low-altitude unmanned aerial vehicle is on the preset transportation route), the hovering energy consumption influence score represents the influence degree quantization data of the air resistance and the battery internal resistance on the avoidance stability of the low-altitude unmanned aerial vehicle.

[0072] In the embodiment, the wind direction offset angle (between 0° and 180°) represents the included angle between the environmental wind direction and the transportation direction of the low-altitude unmanned aerial vehicle, the air resistance and the reference air resistance have the same unit, both are Newton (N), the battery internal resistance and the reference battery internal resistance have the same unit, both are Ohm (Ω), the wind direction offset angle is obtained by monitoring through the wind direction sensor, the air resistance is obtained by monitoring through the air resistance sensor, and the battery internal resistance is obtained by monitoring through the battery internal resistance tester.

[0073] The digital temperature sensor, the three-axis electromagnetic field sensor and the battery internal resistance tester are usually installed inside the fuselage of the low-altitude unmanned aerial vehicle, and the X-band weather radar, the wind direction sensor and the air resistance sensor are usually installed outside the fuselage of the low-altitude unmanned aerial vehicle. The specific installation positions of these sensors can be set according to the actual logistics transportation scene of the low-altitude unmanned aerial vehicle.

[0074] The database stores preset weight factors closely related to the hovering energy consumption impact score, which establishes a predefined mapping relationship between the corresponding air resistance and battery internal resistance. Notably, this mapping is not arbitrary and can be one-to-one or many-to-one. For example, in practical applications, the real-time acquired air resistance and battery internal resistance can be directly input into this preset mapping relationship to quickly and accurately obtain the air resistance weight factor and battery internal resistance weight factor matching the air resistance and battery internal resistance.

[0075] Importantly, to ensure consistency and comparability of the evaluation, the value range of the air resistance weight factor and the battery internal resistance weight factor in the present example is limited to 0 to 1, and their sum is 1.

[0076] It should be understood that the hovering energy consumption impact score increases with the increase of air resistance, battery internal resistance and wind direction offset angle. The wind direction offset angle refers to the angle between the wind direction and the flight direction or intended direction of the unmanned aerial vehicle. When the wind direction offset angle increases, the unmanned aerial vehicle will be affected by more crosswind during flight, resulting in increased air resistance.

[0077] When the wind direction offset angle increases, the unmanned aerial vehicle may need to increase power output to resist the influence of crosswind in order to maintain flight stability and heading, which will result in an increase in battery discharge current. The battery internal resistance consumes part of the electrical energy during battery discharge and converts it into heat energy. Therefore, the increase in discharge current may result in an increase in energy consumption of the battery internal resistance. Although the value of the battery internal resistance itself is not directly affected by the wind direction offset angle, its impact on the energy consumption of the unmanned aerial vehicle will indirectly increase with the increase of the wind direction offset angle.

[0078] By considering the influence of the wind direction offset angle on the values of air resistance and battery internal resistance, the accuracy of the urban low-altitude unmanned aerial vehicle logistics transportation route evaluation can be improved, thereby optimizing the route planning and ensuring that the unmanned aerial vehicle can maintain stability and reduce dynamic risk during transportation. Thus, the accuracy of the urban low-altitude unmanned aerial vehicle logistics transportation route evaluation is improved, effectively solving the problem of insufficient balance between transportation stability and dynamic risk in the existing technology for urban low-altitude unmanned aerial vehicle logistics on the transportation route.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] In the embodiment, through the organic combination of the initial double-target curve analysis, the dynamic environment correction evaluation and the hovering energy consumption evaluation three modules, a systematic flight route evaluation process is formed, which can ensure the comprehensiveness, accuracy and timeliness of the flight route evaluation, and provide strong support for the transportation flight route planning of the unmanned aerial vehicle logistics. In addition, by comprehensively considering the task completion efficiency and the collision risk probability, as well as the dynamic environment and the hovering energy consumption and other factors, the system can help to plan a safe and efficient transportation flight route, which can not only reduce the collision risk of the unmanned aerial vehicle and improve the transportation safety, but also optimize the flight route selection and improve the transportation efficiency.

[0083] To sum up, the interference degree of the initial double-target curve is evaluated based on the acquired first environment data, it is judged whether the first double-target curve is acquired, if yes, the dynamic environment correction evaluation is performed based on the acquired second environment data, it is judged whether the second double-target curve is acquired, if yes, the hovering energy consumption evaluation is performed based on the acquired third environment data, it is judged whether the third double-target curve is acquired, so as to realize the improvement of the updating accuracy of the initial double-target curve, and then realize the improvement of the transportation flight route evaluation accuracy of the urban low-altitude unmanned aerial vehicle logistics, and effectively solve the problem that the balance between the transportation stability and the dynamic risk on the transportation flight route of the urban low-altitude unmanned aerial vehicle logistics is not fully considered in the prior art.

[0084] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0085] The present application is described with reference to flowcharts and / or block diagrams according to the method, device (system) and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows 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 the 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 produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0086] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0087] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0088] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to cover all such additional variations and modifications as fall within the scope of the present application. What is claimed is:

[0089] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

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 to obtain the second dual-objective curve, where the second environmental data includes the electromagnetic field intensity of the second city, the intensity of the light reflected from the glass curtain wall, and the dynamic correction weight; Step 3: If the second dual-target curve is obtained, a hovering energy consumption assessment is performed based on the obtained third environmental data to determine whether to obtain a third dual-target curve, where the third environmental data includes wind direction deviation angle, air resistance, and battery internal resistance; The initial dual-objective curve is obtained by mapping the corresponding collision risk threshold and transportation efficiency threshold from a database based on the obtained task priority, and inputting the obtained collision risk threshold and transportation efficiency threshold into a reference dual-objective curve in the database to obtain the initial dual-objective curve; The first environmental data acquisition step comprises: acquiring the operating temperature of the low-altitude UAV corresponding to the target transport area at the start of logistics transportation; when the acquired operating temperature is not greater than the low temperature set value in the database, sending a heating mode switching instruction; otherwise, acquiring the actual rainfall in the target transport area corresponding to the low-altitude UAV at the start of logistics transportation; the heating mode switching instruction is used to increase the payload of the low-altitude UAV corresponding to the target transport area at the start 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. Acquiring first environmental data corresponding to a target transportation area of ​​the low-altitude UAV at the start of logistics transportation, the first environmental data including operating temperature, actual rainfall, and electromagnetic field strength of the first city; 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 intensity impact score obtained to obtain the second city electromagnetic field intensity impact score. At the same time, the operating temperature impact score obtained is coupled with the second city electromagnetic field intensity impact score obtained to obtain the initial dual-objective curve interference score. The second city electromagnetic field strength represents quantitative data on the degree of interference of the electromagnetic field strength of the target transportation area corresponding to the low-altitude drone during the logistics transportation process on the city's 5G signal. The glass curtain wall reflected light intensity represents quantitative data on the degree of impact of the reflected light of the target transportation area corresponding to the low-altitude drone during the logistics transportation process on the flight safety of the low-altitude drone. The dynamic correction weight includes an actual visibility correction weight, an actual traffic flow correction weight, and an actual strong wind safety correction weight. The actual traffic flow correction weight is used to reflect the degree of traffic congestion in the target transportation area corresponding to the low-altitude drone during the logistics transportation process. The actual strong wind safety correction weight is used to reflect the transportation resistance of the low-altitude drone in the target transportation area corresponding to the logistics transportation process; 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 weight impact score represents the result of coupling processing of the actual visibility correction weight impact score, the actual traffic flow correction weight impact score, and the actual strong wind safety correction weight impact 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. The specific process of performing hovering energy consumption evaluation based on the acquired third environment data is as follows: Obtain the wind direction deviation angle of the low-altitude UAV at the current avoidance moment, and simultaneously obtain the air resistance influence score and the battery internal resistance influence score, wherein the air resistance influence 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, and the battery internal resistance influence score represents the result of correcting the 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 by the battery internal resistance weighting factor; The obtained air resistance impact score and battery internal resistance impact score are coupled together, and the obtained sine function processing result of the wind direction deviation angle is coupled together to obtain the hovering energy consumption impact score.

2. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 1, 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.

3. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 1, 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.

4. A method for evaluating urban low-altitude UAV logistics transportation routes as claimed in claim 1, 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.

5. A transport route assessment system for urban low-altitude UAV logistics, applying the urban low-altitude UAV logistics transport route assessment method according to any one of claims 1 to 4, comprising: 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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