Goods distribution data processing method and system of freight unmanned aerial vehicle

Abstract

The invention discloses a cargo distribution data processing method and system for a freight unmanned aerial vehicle. The method comprises the steps of collecting multiple items of data; meteorological influence evaluation values and airspace control comprehensive indexes of different routes are calculated respectively, flight path evaluation indexes of different routes are further obtained, and the route with the minimum value serves as a final flight path; calculating a cargo priority index to obtain a task allocation comprehensive evaluation index, and presetting a threshold to judge whether the current task allocation scheme is reasonable and whether the freight unmanned aerial vehicle can execute the current task allocation scheme; calculating a flight deviation degree and an anomaly judgment comprehensive value, and presetting a threshold value to judge whether the flight path of the unmanned aerial vehicle is normal; and a distribution deviation evaluation value is calculated, a cargo distribution data comprehensive utilization index is further obtained, a distribution data utilization effect grade is judged, and improvement measures are taken. According to the scheme, the safety, efficiency and intelligent level of freight unmanned aerial vehicle distribution can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air cargo delivery, and in particular to a cargo delivery data processing method and system for a cargo drone. Background Art

[0002] In today's rapidly developing logistics industry, the efficiency and accuracy of cargo delivery are crucial. With the gradual maturity of drone technology, cargo drones have shown great potential in the field of cargo delivery. However, in actual applications, cargo drone cargo delivery faces many challenges. For example, how to reasonably plan the flight path of the drone according to the characteristics and destination of the cargo to ensure efficient and safe delivery; how to scientifically allocate delivery tasks and improve the overall delivery efficiency under the premise of considering the drone's battery power, load and other conditions, etc., all need to be solved urgently.

[0003] To solve these problems, some existing cargo delivery data processing technologies mainly use simple rules and algorithms to plan flight paths and assign tasks. For example, in terms of flight path planning, a traditional path planning algorithm is used to plan a relatively short path based on the destination coordinates and preset map information. In terms of task allocation, preliminary task allocation is carried out based on the priority of the cargo and simple status information of the drone, such as power and load.

[0004] However, existing technologies still have obvious flaws. In terms of flight path planning, traditional algorithms often fail to fully consider the dynamic changes of real-time meteorological data and airspace control information, resulting in the planned path being suboptimal or even impossible to execute in actual flight. In terms of task allocation, simply allocating tasks based on cargo priority and the basic status of the drone without comprehensively considering more influencing factors, such as the remaining range, can easily lead to unreasonable task allocation and affect delivery efficiency. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a cargo delivery data processing method and system for a cargo drone, which collects multiple data; calculates the meteorological impact assessment values ​​and airspace control comprehensive indicators of different routes respectively, further obtains the flight path assessment indicators of different routes, and takes the route with the minimum value as the final flight path; calculates the cargo priority index, and then obtains the task allocation comprehensive assessment index, and presets a threshold to judge whether the current task allocation plan is reasonable and whether the cargo drone can execute the current task allocation plan; calculates the flight deviation and the abnormality judgment comprehensive value, and presets a threshold to judge whether the drone flight route is normal; calculates the distribution deviation assessment value, and then obtains the cargo distribution data comprehensive utilization index, and presets a threshold to judge the cargo distribution data utilization effect level, and takes corresponding improvement measures, thereby solving the problems of unreasonable flight path planning and low distribution efficiency.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A cargo delivery data processing method of a cargo drone, comprising: Step 1: Collect meteorological data, airspace information data, cargo information data and drone flight data; Step 2: Generate multiple different delivery routes based on cargo information data; calculate the meteorological impact assessment value FW of different routes based on meteorological data, and calculate the airspace control comprehensive index FC of different routes based on airspace information data; calculate the flight path assessment index FT of different routes based on the meteorological impact assessment value FW and the airspace control comprehensive index FC; compare the flight path assessment index FT of all routes, and select the route with the smallest FT value as the final flight path; Step 3: Calculate the cargo priority index P based on the cargo information data, and calculate the task allocation comprehensive evaluation index MSK according to the meteorological impact assessment value FW, the flight path assessment index FT, the cargo priority index P and the flight data; preset the task allocation comprehensive evaluation index threshold, compare the task allocation comprehensive evaluation index MSK with the task allocation comprehensive evaluation index threshold, and judge whether the current task allocation plan is reasonable and whether the cargo drone can execute the current task allocation plan according to the comparison result; if the current task allocation plan cannot be executed, replace the cargo drone or re-determine the delivery route; if the current task allocation plan can be executed, execute the delivery task according to the route of the current task allocation plan; Step 4: If the cargo drone starts to perform the delivery task, the flight deviation SIL is calculated based on the flight data and the task allocation comprehensive evaluation index MSK; the abnormality judgment comprehensive value JL is calculated based on the airspace information data, flight data and the flight deviation SIL; the abnormality judgment comprehensive value threshold is preset, and the abnormality judgment comprehensive value JL is compared with the abnormality judgment comprehensive value threshold, and whether the drone flight route is normal is determined based on the comparison result; if the drone flight route is abnormal, a warning signal is issued; Step 5: If the UAV flight route is normal, calculate the distribution deviation evaluation value TE according to the abnormal judgment comprehensive value JL, cargo information data and flight data; calculate the cargo distribution data comprehensive utilization index PEI according to the distribution deviation evaluation value TE and flight data; preset the cargo distribution data comprehensive utilization index threshold, compare the cargo distribution data comprehensive utilization index PEI with the cargo distribution data comprehensive utilization index threshold, judge the UAV data utilization effect level according to the comparison result, and take corresponding improvement measures.

[0007] In the preferred embodiment of the cargo delivery data processing method of the above-mentioned cargo drone, the method for calculating the meteorological impact assessment value FW and the airspace control comprehensive index FC is: Meteorological data include wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD for different routes; Airspace information data include airspace traffic flow NA within the flight area on different routes and the minimum distance DI to the nearest no-fly zone; According to the wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD on different routes, the meteorological impact assessment value FW of different routes is calculated. The calculation formula is: ;

[0008] Among them, H0 is the standard reference humidity; K1 is The weight coefficient of K is 0.2~0.4; K2 is the weight coefficient of temperature TS, which is 0.1~0.3; K3 is the weight coefficient of air pressure PA, which is 0.3~0.5; K4 is The weight coefficient is 0.2~0.4; and K1+K2+K3+K4=1; According to the airspace traffic flow NA within the flight area on different routes and the minimum distance DI to the nearest no-fly zone, the comprehensive airspace control index FC of different routes is calculated. The calculation formula is: ; Among them, LE is the restriction level of airspace control.

[0009] In the preferred embodiment of the cargo delivery data processing method of the above cargo drone, the method of selecting the flight path is: According to the meteorological impact assessment value FW and the comprehensive index of airspace control FC, the flight path assessment index FT of different routes is calculated. The calculation formula is: ; Among them, a is the weight coefficient of the meteorological impact assessment value FW, which is 0.3~0.5; b is the weight coefficient of the airspace control comprehensive index FC, which is 0.5~0.7; and a+b=1; The flight path evaluation index FT of all routes is compared, and the route with the smallest FT value is selected as the final flight path.

[0010] In the preferred embodiment of the cargo delivery data processing method of the above-mentioned cargo drone, the method for determining whether the cargo drone can execute the current task allocation plan is: The cargo information data includes the total time TU and the current time TN expected to complete the delivery task; The flight data includes the remaining battery power EL, estimated payload DL, estimated flight speed VS, remaining range RE and accumulated flight time TD of the drone; According to the total time TU expected to complete the delivery task and the current time TN, the cargo priority index P is calculated using the following formula: ; Among them, PO is the initial priority; S is the expedited delivery quantitative score; ω1 is the weight coefficient of the initial priority PO, which ranges from 0.1 to 0.3; ω2 is The weight coefficient is 0.3~0.4; ω3 is the weight coefficient of the expedited delivery quantitative score S, which is 0.3~0.5; and ω1+ω2+ω3=1; According to the meteorological impact assessment value FW, the flight path assessment index FT, the cargo priority index P, the remaining power EL, the expected load DL, the expected flight speed VS, the remaining range RE and the accumulated flight time TD, the task allocation comprehensive assessment index MSK is calculated based on the following formula: ; Among them, VC is the value of the goods; DP is the maximum load of the drone; MM is the mechanical failure probability of the drone; Preset the comprehensive evaluation index threshold Mth of task allocation; When MSK≤Mth, the current task allocation plan is judged to be reasonable, and the cargo drone can execute the current task allocation plan, and then the delivery task is executed according to the route of the current task allocation plan; When MSK>Mth, the current task allocation plan is judged to be unreasonable, the cargo drone cannot execute the current task allocation plan, and the cargo drone is replaced or the delivery route is re-determined.

[0011] In the preferred embodiment of the cargo delivery data processing method of the above cargo drone, the method for calculating the flight deviation SIL is: The flight data also includes the current flight speed VM, current flight altitude HG and current acceleration A of the drone; The flight deviation SIL is calculated based on the task allocation comprehensive evaluation index MSK, the current flight speed VM, the current flight altitude HG and the current acceleration A. The formula is as follows: ; Among them, V0 is the standard reference flight speed; H0 is the standard reference flight altitude; A0 is the standard reference acceleration; EQ is the energy consumption rate of the UAV; MSKmax is the theoretical maximum value of the comprehensive index of task allocation; φ1 is The weight coefficient is 0.2~0.4; φ2 is The weight coefficient is 0.3~0.5; φ3 is The weight coefficient is between 0.1 and 0.5, and φ1+φ2+φ3=1.

[0012] In the preferred embodiment of the cargo delivery data processing method of the above-mentioned cargo drone, the method for judging whether the flight route of the drone is normal is: The flight data also includes the actual flight route, the distance flown OA, the current load WM and the actual flight speed VAC; The abnormality judgment comprehensive value JL is calculated based on the airspace traffic flow NA, the minimum distance to the nearest no-fly zone DI, the actual flight route flight distance OA, the current load WM, the actual flight speed VAC, the expected flight speed VS and the flight deviation SIL. The formula is as follows: ; Among them, NAmax is the maximum airspace traffic flow; LPT is the total distance of the preset flight route; Preset the abnormality judgment comprehensive value threshold Jth; When JL≤Jth, the flight route of the drone is judged to be normal; When JL>Jth, the UAV flight route is judged to be abnormal and a warning signal is issued.

[0013] In the preferred embodiment of the cargo delivery data processing method of the above-mentioned cargo drone, the method for calculating the delivery deviation evaluation value TE is: The cargo information data also includes the actual delivery time TC of the cargo; The flight data also includes the actual amount of electricity EA consumed by the drone; According to the anomaly determination comprehensive value JL, the actual delivery time TC of the goods, the total time TU for the expected completion of the delivery task, and the actual power consumption EA of the drone, calculate the delivery deviation evaluation value TE. The calculation formula is: ; Among them, ERE is the expected power consumption in the initial plan of the goods delivery task; e is the natural constant.

[0014] In the preferred solution of the above method for processing goods delivery data of a freight drone: The method for calculating the comprehensive utilization index PEI of goods delivery data is: The flight data also includes the flight frequency FN of the drone; According to the delivery deviation evaluation value TE and the flight frequency FN of the drone, calculate the comprehensive utilization index PEI of goods delivery data. The formula is: ; Among them, SR is the flight stability index; OT is the overtime of each goods delivery task; OTavg is the average overtime of all goods delivery tasks; D is the battery aging degree of the drone; FDR is the failed delivery ratio.

[0015] In the preferred solution of the above method for processing goods delivery data of a freight drone: The method for judging the utilization effect level of goods delivery data is: The comprehensive utilization index threshold of goods delivery data includes the comprehensive utilization index threshold one PEI1 and the comprehensive utilization index threshold two PEI2 of goods delivery data, and PEI1 < PEI2; When PEI < PEI1, it is judged that the utilization effect of goods delivery data is excellent; When PEI1 ≤ PEI < PEI2, it is judged that the utilization effect of goods delivery data is good, and improvement measure one is taken; When PEI ≥ PEI2, it is judged that the utilization effect of goods delivery data is poor, and improvement measure two is taken.

[0016] The present invention also discloses a system for processing goods delivery data of a freight drone, which is used to implement the above method for processing goods delivery data of a freight drone, and includes: A data acquisition module, which is used to acquire meteorological data, airspace information data, goods information data, and flight data of the drone; The flight path planning module is used to generate multiple different delivery routes based on the cargo information data; calculate the meteorological impact assessment value FW of different routes based on the meteorological data, and calculate the airspace control comprehensive index FC of different routes based on the airspace information data; calculate the flight path assessment index FT of different routes based on the meteorological impact assessment value FW and the airspace control comprehensive index FC; compare the flight path assessment index FT of all routes, and select the route with the smallest FT value as the final flight path; The task allocation module is used to calculate the cargo priority index P based on the cargo information data, and calculate the task allocation comprehensive evaluation index MSK according to the meteorological impact evaluation value FW, the flight path evaluation index FT, the cargo priority index P and the flight data; preset the task allocation comprehensive evaluation index threshold, compare the task allocation comprehensive evaluation index MSK with the task allocation comprehensive evaluation index threshold, and judge whether the current task allocation plan is reasonable and whether the cargo drone can execute the current task allocation plan according to the comparison result; if the current task allocation plan cannot be executed, replace the cargo drone or re-determine the delivery route; if the current task allocation plan can be executed, execute the delivery task according to the route of the current task allocation plan; The monitoring module calculates the flight deviation SIL based on the flight data and the task allocation comprehensive evaluation index MSK if the cargo drone starts to perform the delivery task; calculates the abnormality judgment comprehensive value JL based on the airspace information data, flight data and the flight deviation SIL; presets the abnormality judgment comprehensive value threshold, compares the abnormality judgment comprehensive value JL with the abnormality judgment comprehensive value threshold, and determines whether the drone flight route is normal based on the comparison result; if the drone flight route is abnormal, issues a warning signal; In the delivery completion processing module, if the UAV flight route is normal, the delivery deviation evaluation value TE is calculated according to the abnormal judgment comprehensive value JL, cargo information data and flight data; the cargo delivery data comprehensive utilization index PEI is calculated according to the delivery deviation evaluation value TE and flight data; the cargo delivery data comprehensive utilization index threshold is preset, and the cargo delivery data comprehensive utilization index PEI is compared with the cargo delivery data comprehensive utilization index threshold, and the data utilization effect level of the UAV is judged according to the comparison result, and corresponding improvement measures are taken.

[0017] (III) Beneficial effects The present invention provides a cargo delivery data processing method and system for a cargo drone, which has the following beneficial effects: (1) By comprehensively collecting meteorological data, airspace information data, cargo information data, and drone flight data, it can provide rich and accurate basic information for subsequent calculations and decisions. These data cover all key aspects that affect drone delivery tasks, allowing the entire system to perform analysis based on reliable data, thereby improving the scientificity and rationality of decision-making and laying a solid foundation for efficient and safe cargo delivery.

[0018] (2) Calculate the meteorological impact assessment value and the comprehensive index of airspace control, and further derive the flight path assessment index. By comparison, select the route with the smallest index value as the final route. This method can fully consider the impact of meteorological conditions and airspace control on flight, select the most suitable flight route, effectively reduce flight risks, improve flight efficiency, reduce unnecessary energy consumption and time costs, and ensure that drones can complete delivery tasks safely and quickly.

[0019] (3) Calculate the cargo priority index and the comprehensive evaluation index of task allocation, and compare them with the preset threshold to determine the rationality of the task allocation plan and whether the drone can perform the task. This method can comprehensively consider the impact of various factors on task allocation, ensure the scientificity and rationality of task allocation, avoid resource waste or inability to complete tasks due to improper task allocation, and improve the operating efficiency and reliability of the overall distribution system.

[0020] (4) Calculate the flight deviation and the comprehensive value of abnormality judgment, and compare them with the threshold to determine whether the flight route is normal. This method can monitor the flight status of the drone in real time and detect potential flight abnormalities in time so that corresponding measures can be taken to ensure the safety of the drone and the goods, reduce the possibility of accidents, and improve the stability of the delivery service.

[0021] (5) Calculate the distribution deviation assessment value, and then obtain the comprehensive utilization index of cargo distribution data, and compare it with the threshold to determine the level of data utilization effect and take improvement measures. This helps to comprehensively evaluate the data utilization of drones in the distribution process, optimize the system in a targeted manner based on the evaluation results, improve the utilization efficiency and quality of data, continuously improve the cargo distribution data processing system, and improve the overall distribution service level. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the steps of a method for processing cargo delivery data of a cargo drone according to the present invention; Figure 2 This is a schematic diagram of the working steps of step 3 in a method for processing cargo delivery data of a cargo drone of the present invention; Figure 3 The present invention is a structural schematic diagram of a cargo delivery data processing system of a cargo drone. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 2 The present invention provides a method for processing cargo delivery data of a cargo drone, comprising: Step 1: Collect meteorological data, airspace information data, cargo information data and drone flight data.

[0025] Comprehensive step one: Comprehensive collection of meteorological data, airspace information data, cargo information data and drone flight data provides a rich and reliable foundation for accurate analysis and decision-making in subsequent links. The integration of these multi-dimensional data can ensure the scientificity and accuracy of subsequent calculations and judgments.

[0026] Step 2: Generate multiple different delivery routes based on cargo information data; calculate the meteorological impact assessment value FW of different routes based on meteorological data, and calculate the airspace control comprehensive index FC of different routes based on airspace information data; calculate the flight path assessment index FT of different routes according to the meteorological impact assessment value FW and the airspace control comprehensive index FC; compare the flight path assessment index FT of all routes, and select the route with the smallest FT value as the final flight path.

[0027] Step 201: The cargo information data includes the cargo's starting point, destination, weight, volume, whether it is special cargo and other cargo distribution information. Based on the cargo's distribution starting point, destination, weight, volume, whether it is special cargo and other cargo distribution information, GIS geographic information system software or drone path planning software is used to generate multiple different delivery routes.

[0028] Step 202: Calculate the meteorological impact assessment value FW and the airspace control comprehensive index FC. The specific method is as follows: Meteorological data include wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD for different routes.

[0029] It should be noted that by installing anemometers, wind vanes, thermometers, barometers and hygrometers at different monitoring points in the distribution areas of different routes, meteorological data at different locations are collected in real time. Combined with the distribution routes, the data points on the routes are screened to obtain the wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD on different routes.

[0030] The airspace information data includes the airspace traffic flow NA within the flight area on different routes and the minimum distance DI to the nearest no-fly zone.

[0031] It should be noted that by using ground radar or radar system carried by UAV, the flight area on different routes is scanned within time T to obtain the number of scanned aircraft n i , calculate the airspace traffic flow NA, the calculation formula is: ; Among them, n i is the number of aircraft scanned in the i-th time, i is the scanning sequence number; m is the number of scans, which is a positive integer.

[0032] It should be noted that This part is calculated by the average number of aircraft scanned in each of the m scans. It is a time conversion coefficient, which converts the average number of aircraft calculated based on time T into the number of aircraft per hour. Through the calculation of this coefficient, the airspace traffic flow NA finally obtained represents the number of aircraft appearing in the flight area per hour, which is used to measure the traffic congestion of the airspace.

[0033] The current location information of the UAV is obtained through the GIS geographic information system, and the spatial analysis function provided by the system is used to calculate the distance between the UAV location and the boundaries of all no-fly zones, and the minimum value is found, which is the minimum distance DI to the nearest no-fly zone.

[0034] According to the wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD on different routes, the meteorological impact assessment value FW of different routes is calculated. The calculation formula is: ; Among them, H0 is the standard reference humidity; K1 is The weight coefficient of K is 0.2~0.4; K2 is the weight coefficient of temperature TS, which is 0.1~0.3; K3 is the weight coefficient of air pressure PA, which is 0.3~0.5; K4 is The weight coefficient is between 0.2 and 0.4; and K1+K2+K3+K4=1.

[0035] It should be noted that to obtain the standard reference humidity H0, daily humidity data in the distribution area for the past 5-10 years are collected, and the average humidity in different seasons and time periods is calculated, which is used as the standard reference humidity H0 for the area.

[0036] It should be noted that The effects of wind speed and direction on flight are considered; It indicates that the change of humidity has a potential impact on the electronic equipment and structural materials of the drone, and this impact changes nonlinearly with the difference between humidity and the standard reference humidity H0. This formula comprehensively considers five important parameters: wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD. By multiplying each parameter with its corresponding weight coefficient and then summing them, the meteorological impact assessment value FW of different routes is obtained.

[0037] According to the airspace traffic flow NA within the flight area on different routes and the minimum distance DI to the nearest no-fly zone, the comprehensive airspace control index FC of different routes is calculated. The calculation formula is: ; Among them, LE is the restriction level of airspace control.

[0038] It should be noted that to obtain the restriction level LE of airspace control, the normal airspace traffic flow NAM is obtained based on historical flight data, and the restriction level LE of airspace control is calculated based on the airspace traffic flow NA and the normal traffic flow NAM. The calculation formula is: ; Among them, E is the emergency coefficient.

[0039] It should be noted that It reflects the impact of airspace traffic flow on airspace control. It represents the cube of airspace traffic flow, highlighting the importance of traffic flow to airspace control. It is used to balance the impact of traffic flow and aircraft distance factors in the numerator, so as to more accurately calculate the comprehensive airspace control index FC.

[0040] Step 203: Select a flight path. The specific method is as follows: According to the meteorological impact assessment value FW and the comprehensive index of airspace control FC, the flight path assessment index FT of different routes is calculated. The calculation formula is: ; Among them, a is the weight coefficient of the meteorological impact assessment value FW, which ranges from 0.3 to 0.5; b is the weight coefficient of the airspace control comprehensive index FC, which ranges from 0.5 to 0.7; and a+b=1.

[0041] It should be noted that this formula comprehensively considers two important parameters: the meteorological impact assessment value FW and the airspace control comprehensive index FC. Each parameter has a corresponding weight coefficient, which indicates the importance of different parameters when calculating the flight path assessment index FT. The flight path assessment index FT is obtained by multiplying each parameter with its corresponding weight coefficient and then summing them.

[0042] The flight path evaluation index FT of all routes is compared, and the route with the smallest FT value is selected as the final flight path.

[0043] Comprehensive steps 201~203: This method can effectively avoid the risks brought by adverse weather conditions and strict airspace control. This scientific path selection method not only improves the safety of drone flights, but also shortens flight time and reduces energy consumption, ensuring that goods can be delivered to their destinations in an efficient and stable manner.

[0044] Step 3: Calculate the cargo priority index P based on the cargo information data, and calculate the task allocation comprehensive evaluation index MSK according to the meteorological impact assessment value FW, the flight path assessment index FT, the cargo priority index P and the flight data; preset the task allocation comprehensive evaluation index threshold, compare the task allocation comprehensive evaluation index MSK with the task allocation comprehensive evaluation index threshold, and judge whether the current task allocation plan is reasonable and whether the cargo drone can execute the current task allocation plan according to the comparison result; if the current task allocation plan cannot be executed, replace the cargo drone or re-determine the delivery route; if the current task allocation plan can be executed, execute the delivery task according to the route of the current task allocation plan.

[0045] Step 301: Determine whether the cargo drone can execute the current task allocation plan. The specific method is as follows: The cargo information data includes the total time TU expected to complete the delivery task and the current time TN.

[0046] It should be noted that the prediction model is established by analyzing the completion time of delivery tasks with similar goods, similar routes, and similar weather and airspace conditions in the past. When a new delivery task is generated, the relevant features of the current task are input, such as the weight, volume, destination, current weather and airspace conditions of the goods, etc., and the model predicts the total time TU expected to complete the delivery task.

[0047] Cargo drones and ground control equipment are usually equipped with clock modules, which can directly obtain the current system time as TN.

[0048] The flight data includes the remaining battery power EL, estimated payload DL, estimated flight speed VS, remaining range RE and accumulated flight time TD of the drone.

[0049] It should be noted that the remaining power EL can be obtained in real time through the power sensor configured on the drone.

[0050] Before loading the cargo, use an electronic scale to measure the cargo weight, and then add the weight of the drone's own fixed equipment to get the estimated load DL.

[0051] Refer to the historical flight data of cargo drones and analyze the actual flight speed under similar conditions as the expected flight speed VS.

[0052] By establishing a cruising range model, the remaining power and estimated load acquired in real time are substituted into the model, and combined with the estimated flight speed, the remaining cruising range RE is obtained.

[0053] According to the timing device installed on the drone, the timing starts from the moment the drone takes off and the flight time is recorded in real time, thus obtaining the cumulative flight time TD.

[0054] According to the total time TU expected to complete the delivery task and the current time TN, the cargo priority index P is calculated using the following formula: ; Among them, PO is the initial priority; S is the expedited delivery quantitative score; ω1 is the weight coefficient of the initial priority PO, which ranges from 0.1 to 0.3; ω2 is ω3 is the weight coefficient of the expedited delivery quantitative score S, which is 0.3~0.5; and ω1+ω2+ω3=1.

[0055] It should be noted that the initial priority PO is obtained, the value of the goods is obtained, and the value of the goods is scored: 1 point for a value below 100 yuan, 2 points for 100-500 yuan, 3 points for 500-2000 yuan, 4 points for 2000-10000 yuan, and 5 points for more than 10000 yuan; in this way, the value score VP of the goods is obtained. The urgency score EY of the goods is determined according to the urgency of the delivery time required by the customer: 5 points for a delivery time of less than 1 hour, 4 points for 1-3 hours, 3 points for 3-6 hours, 2 points for 6-12 hours, and 1 point for more than 12 hours. Then, based on the value score VP of the goods and the urgency score EY of the goods, the initial priority PO is calculated, and the calculation formula is: ; in, The weight coefficient of the cargo value score VP is 0.3~0.4; is the weight coefficient of the cargo urgency score EY, ranging from 0.6 to 0.7; and .

[0056] Get the expedited delivery quantitative score S, get the expedited fee EC paid by the customer and the required advance time t, and calculate the expedited delivery quantitative score S. The calculation formula is: ; Among them, LI is the loss coefficient caused by failure to expedite delivery; α1 is the weight coefficient of the expedited fee EC paid by the customer, which ranges from 0.2 to 0.3; α2 is the weight coefficient of the required advance time t, which ranges from 0.3 to 0.4; α3 is the weight coefficient of the loss coefficient LI caused by failure to expedite delivery, which ranges from 0.4 to 0.5; and α1+α2+α3=1.

[0057] It should be noted that It represents the ratio of the remaining delivery time to the total delivery time. When the remaining time is less, the smaller the ratio is, which means the higher the urgency of the goods and the higher the priority should be. This formula takes into account three important parameters: the total time TU expected to complete the delivery task, the current time TN, and the expedited delivery quantitative score S. Each parameter has a corresponding weight coefficient, which indicates the importance of different parameters when calculating the cargo priority index P. The cargo priority index P is obtained by multiplying each parameter by its corresponding weight coefficient and then summing them.

[0058] According to the meteorological impact assessment value FW, the flight path assessment index FT, the cargo priority index P, the remaining power EL, the expected load DL, the expected flight speed VS, the remaining range RE and the accumulated flight time TD, the task allocation comprehensive assessment index MSK is calculated based on the following formula: ; Among them, VC is the value of the goods; DP is the maximum load of the drone; MM is the probability of mechanical failure of the drone.

[0059] It should be noted that the value of goods VC can be obtained directly from the order for goods delivery.

[0060] The maximum payload DP of the drone can be directly obtained from the drone's product manual.

[0061] Obtain the mechanical failure probability MM of the drone by analyzing the drone's operating data over the past period of time, including the number of failures, flight time, flight mileage, and other information. Calculate the ratio of the frequency of failures to the total operating time or mileage to obtain the mechanical failure probability. For example, if the drone has 10 mechanical failures within 1000 hours of flight time, then the mechanical failure probability MM = 10 / 1000 = 0.01.

[0062] It should be noted that This means that tasks with high priority, great value and appropriate paths will receive higher scores in the comprehensive evaluation and are more likely to be assigned first. This reflects the impact of the remaining power and estimated load of the drone on task allocation. The more remaining power, the better the drone's ability to perform tasks; It represents the relative payload margin, which is multiplied by the remaining power, indicating that drones with sufficient power and larger payload margin are more suitable for assigned tasks. The role of UAV performance, reliability and other relevant factors in task allocation are comprehensively considered. This means that under adverse weather conditions, the comprehensive evaluation of task allocation will be reduced, and the task allocation strategy needs to be reconsidered. The influence of airspace control restriction level, remaining range and accumulated flight time are considered respectively. Strict airspace control, short remaining range or long accumulated flight time will increase the denominator, resulting in a decrease in MSK value, thus affecting the priority of task allocation. By dividing the numerator by the denominator, many factors related to tasks and drones are comprehensively considered to obtain the comprehensive evaluation index MSK for task allocation.

[0063] Preset the threshold value Mth of the comprehensive evaluation index of task allocation; collect relevant data of multiple cargo drone delivery tasks in the past, including the weather impact evaluation value FW, the flight path evaluation index FT, the cargo priority index P, the remaining power EL, the expected load DL, the expected flight speed VS, the remaining range RE and the accumulated flight time TD, etc. Analyze these data, calculate the comprehensive evaluation index MSK of task allocation for each cargo drone delivery task, further calculate the average value MSKavg of the comprehensive evaluation index of task allocation, and use the average value MSKavg of the comprehensive evaluation index of task allocation as the threshold value Mth of the comprehensive evaluation index of task allocation.

[0064] When MSK≤Mth, the current task allocation plan is judged to be reasonable, and the cargo drone can execute the current task allocation plan, and then the delivery task is executed according to the route of the current task allocation plan; When MSK>Mth, the current task allocation plan is judged to be unreasonable, the cargo drone cannot execute the current task allocation plan, and the cargo drone is replaced or the delivery route is re-determined.

[0065] Comprehensive step three: By calculating the comprehensive evaluation index of task allocation and comparing it with the preset threshold, the rationality of the task allocation plan and the execution capability of the drone can be accurately judged. This process avoids unreasonable task allocation, optimizes resource allocation, and improves the success rate of delivery task execution.

[0066] Step 4: If the cargo UAV starts to perform the delivery task, the flight deviation SIL is calculated according to the flight data and the task allocation comprehensive evaluation index MSK; the abnormality judgment comprehensive value JL is calculated according to the airspace information data, flight data and the flight deviation SIL; the abnormality judgment comprehensive value threshold is preset, and the abnormality judgment comprehensive value JL is compared with the abnormality judgment comprehensive value threshold, and whether the UAV flight route is normal is judged according to the comparison result; if the UAV flight route is abnormal, a warning signal is issued.

[0067] Step 401: Calculate the flight deviation SIL, the specific method is as follows: The flight data also includes the current flight speed VM, current flight altitude HG and current acceleration A of the drone.

[0068] It should be noted that the flight speed is measured by using sensors such as a pitot tube or Doppler radar configured on the drone to obtain the current flight speed VM. The current flight altitude HG is measured by using a barometric altimeter or laser altimeter configured on the drone. An accelerometer is installed on the drone to obtain the current acceleration A through accelerometer measurement.

[0069] The flight deviation SIL is calculated based on the task allocation comprehensive evaluation index MSK, the current flight speed VM, the current flight altitude HG and the current acceleration A. The formula is: ; Among them, V0 is the standard reference flight speed; H0 is the standard reference flight altitude; A0 is the standard reference acceleration; EQ is the energy consumption rate of the UAV; MSKmax is the theoretical maximum value of the comprehensive index of task allocation; φ1 is The weight coefficient is 0.2~0.4; φ2 is The weight coefficient is 0.3~0.5; φ3 is The weight coefficient is between 0.1 and 0.5, and φ1+φ2+φ3=1.

[0070] It should be noted that the standard reference flight speed V0 can be obtained through the product manual or technical documentation of the drone.

[0071] Obtain the standard reference flight altitude H0, which is determined according to the specific mission type and airspace management regulations of the drone. For example, when performing urban express delivery missions, the standard reference flight altitude is set to 100 meters based on urban airspace restrictions and flight safety requirements; and when performing agricultural plant protection missions, the standard reference flight altitude is set to 5 meters based on the height of the crops and the requirements of the spraying operation, so as to obtain the standard reference flight altitude H0.

[0072] The standard reference acceleration A0 can be directly obtained from the technical data of the drone.

[0073] Obtain the energy consumption rate EQ of the drone and perform energy consumption tests on the drone in a laboratory or actual flight environment. By measuring the battery power consumption or fuel consumption of the drone under different flight conditions such as different speeds, altitudes, and loads, and combining parameters such as flight time or flight distance, calculate the energy consumption rate. For example, record the power d consumed by the drone in a flight mission and the flight mileage h, and calculate the energy consumption rate EQ, EQ=d / h.

[0074] Obtain the theoretical maximum value MSKmax of the comprehensive index of task allocation, and simulate and calculate the comprehensive evaluation index of task allocation by using computer simulation software to construct various extreme flight conditions, such as thunderstorm weather, hurricane weather, etc. By continuously adjusting the parameters and testing the value range of MSK, find its theoretical maximum value, and thus obtain the theoretical maximum value MSKmax of the comprehensive index of task allocation.

[0075] It should be noted that It reflects the square of the velocity deviation, which can better highlight the impact of larger deviations. Highlight the squared effect of height deviation. Emphasize the square of the acceleration deviation. Multiply the above items by their respective weight coefficients and then sum them up, comprehensively considering the deviation of flight speed, altitude and acceleration from the standard reference values, and fully reflecting the difference between the actual flight state of the drone and the ideal state in these parameters. It reflects the energy usage of the drone during flight. The impact of task allocation on flight deviation is mainly considered. By dividing the numerator by the denominator, the flight deviation SIL is obtained by comprehensively considering factors such as flight parameter deviation, energy consumption rate and task allocation comprehensive evaluation index.

[0076] Step 402: Determine whether the flight path of the drone is normal. The specific method is as follows: The flight data also includes the actual flight route, the distance flown OA, the current load WM and the actual flight speed VAC.

[0077] It should be noted that since the GPS module on the drone can record the drone's location information in real time, the distance between two points can be calculated by recording the coordinates of the take-off point and multiple time points during the flight. By adding up these distances, the actual flight route's flight distance OA can be obtained. For example, at different times t1 and t2, the coordinates of the drone are (x1, y1) and (x2, y2) respectively. The distance between the two points is calculated as , calculate and accumulate multiple times to get OA.

[0078] By installing a weighing sensor at the cargo loading part of the drone, the pressure of the cargo and other loads on the drone can be sensed in real time to obtain the current load WM.

[0079] By installing a pitot tube or pitot tube on the drone, the relative speed between the drone and the surrounding air, that is, the airspeed, can be directly measured. The airspeed is the actual flight speed VAC of the drone.

[0080] The abnormality judgment comprehensive value JL is calculated based on the airspace traffic flow NA, the minimum distance to the nearest no-fly zone DI, the actual flight route flight distance OA, the current load WM, the actual flight speed VAC, the expected flight speed VS and the flight deviation SIL. The formula is as follows: ; Among them, NAmax is the maximum value of airspace traffic flow; LPT is the total distance of the preset flight route.

[0081] It should be noted that to obtain the maximum value NAmax of the airspace traffic flow, the historical traffic flow data of the airspace is collected, and the data are analyzed to find the maximum value of the traffic flow, which is used as the maximum value NAmax of the airspace traffic flow.

[0082] Obtain the total distance LPT of the preset flight route. By using the GIS platform, input the coordinates of the starting point and destination of the drone. Through the path analysis function of GIS, combined with map data such as roads, rivers, mountains and flight rules, plan the flight route and obtain the total length of the route as LPT.

[0083] It should be noted that SIL reflects the deviation of the current flight status of the drone from the standard reference value. This item takes into account the airspace traffic flow factor. It reflects the role of the distance between the drone and the no-fly zone in determining flight anomalies. It reflects the deviation between the actual flight distance and the total distance of the preset flight route. If the difference between OA and LPT is large, it means that the flight route of the drone deviates greatly from the preset route. The larger the value, the greater the impact on JL. The impact of the current load on flight is taken into account. It reflects the impact of the difference between the actual flight speed and the expected flight speed on the flight status. By combining the above factors, the abnormality judgment comprehensive value JL is obtained.

[0084] The abnormality judgment comprehensive value threshold Jth is preset; a large number of abnormality judgment comprehensive value data of UAVs in normal flight status are collected, and these data cover different flight missions, meteorological conditions, airspace environment, etc. These data are analyzed, and the abnormality judgment comprehensive value JL in each flight is calculated, and the average value of the abnormality judgment comprehensive value JLavg is further calculated, and the average value of the abnormality judgment comprehensive value JLavg is used as the abnormality judgment comprehensive value threshold Jth.

[0085] When JL≤Jth, the flight route of the drone is judged to be normal; When JL>Jth, the UAV flight route is judged to be abnormal and a warning signal is issued.

[0086] Comprehensive step 401~step 402: By calculating the flight deviation and the comprehensive value of abnormality judgment and comparing it with the threshold, the flight status of the drone can be monitored in real time and accurately. Once an abnormal flight route is found, a warning signal is issued in time, which helps to quickly take countermeasures to ensure the safety of the drone and cargo and reduce the occurrence of potential accidents.

[0087] Step 5: If the UAV flight route is normal, calculate the distribution deviation evaluation value TE according to the abnormal judgment comprehensive value JL, cargo information data and flight data; calculate the cargo distribution data comprehensive utilization index PEI according to the distribution deviation evaluation value TE and flight data; preset the cargo distribution data comprehensive utilization index threshold, compare the cargo distribution data comprehensive utilization index PEI with the cargo distribution data comprehensive utilization index threshold, judge the UAV data utilization effect level according to the comparison result, and take corresponding improvement measures.

[0088] Step 501: Calculate the delivery deviation evaluation value TE, the specific method is: The cargo information data also includes the actual delivery time TC of the cargo.

[0089] It should be noted that, according to the timing module equipped by the drone, when the drone arrives at the target location of the goods and completes the delivery of the goods, the system on the drone will record the time at this time as the actual delivery time TC.

[0090] The flight data also includes the actual amount of electricity EA consumed by the drone.

[0091] It should be noted that the BMS battery management system equipped with the drone monitors various battery parameters in real time, including voltage, current, temperature, etc. By integrating the battery charge and discharge current, the BMS can accurately calculate the amount of power consumed by the battery from the drone takeoff to the current moment, and obtain the actual amount of power consumed by the drone, EA.

[0092] According to the abnormal judgment comprehensive value JL, the actual delivery time TC of the goods, the total time TU expected to complete the delivery task and the actual power consumption EA of the drone, the delivery deviation evaluation value TE is calculated. The calculation formula is: ; Among them, ERE is the estimated power consumption of the initial planning of the cargo delivery task; e is a natural constant with a value of 2.71828.

[0093] It should be noted that the estimated power consumption ERE of the initial planning of the cargo delivery mission is obtained, and the power consumption model is established based on the performance parameters of the drone, such as battery capacity and energy conversion efficiency, combined with the flight distance, load, flight altitude, speed and other characteristics of the flight mission. Through this model, the estimated power consumption ERE under the initial planned flight conditions can be output.

[0094] It should be noted that reflects the square of the time deviation, which can highlight the impact of larger deviations, divided by The purpose is to normalize the deviation. The larger the value, the more serious the deviation between the actual delivery time and the total time expected to complete the delivery task, and the more obvious the effect of increasing the delivery deviation evaluation value TE. It indicates the ratio of actual power consumption to expected power consumption, reflecting the relative size of power consumption. It is the ratio of the absolute value of the power consumption deviation to the expected power consumption. The exponential function is used to further amplify the impact of this deviation. When the actual power consumption differs greatly from the expected power consumption, The value of will increase significantly, making the impact of this part on TE more prominent. It comprehensively reflects the role of power consumption deviation in distribution deviation assessment. It shows that abnormal conditions during flight will further increase the evaluation value of delivery deviation, emphasizing the impact of flight abnormalities on delivery tasks.

[0095] Step 502: Calculate the comprehensive utilization index PEI of cargo distribution data, the specific method is: The flight data also includes the flight frequency FN of the drone.

[0096] It should be noted that a counter is installed on the motor or power supply of the drone. Each time the drone is started or completes a flight mission, the counter will automatically increase the corresponding value. By reading the data of the counter, the flight frequency FN is obtained.

[0097] According to the delivery deviation evaluation value TE and the flight frequency FN of the drone, the comprehensive utilization index PEI of cargo delivery data is calculated based on the following formula: ; Among them, SR is the flight stability index; OT is the timeout time of each cargo delivery task; OTavg is the average timeout time of all cargo delivery tasks; D is the battery aging degree of the drone; FDR is the failed delivery ratio.

[0098] It should be noted that to obtain the flight stability index SR, the flight simulation software such as X-Plane and FlightGear is used to input the parameters of the drone and the flight scenes of different airflow environments to simulate the flight process of the drone. The flight stability of the drone is evaluated by observing the attitude changes, response performance and other indicators of the drone during the simulated flight. The software can directly give the flight stability index SR according to the preset stability evaluation standard.

[0099] Get the timeout time OT of each cargo delivery task. At the beginning of the cargo delivery task, record the start time of the task, and set an expected completion time based on the total time TU expected to complete the delivery task. When the task is completed, record the actual delivery time TC. By calculating the difference between the actual delivery time and the total time TU expected to complete the delivery task, the timeout time OT of each cargo delivery task is obtained = TC-TU.

[0100] Get the average timeout value OTavg of all cargo delivery tasks, and collect the timeout time OT of all cargo delivery tasks within a period of time. Add these timeout times and divide them by the total number of delivery tasks to get the average timeout value OTavg of all cargo delivery tasks.

[0101] Obtain the battery aging degree D of the drone, perform performance tests on the drone's battery regularly, and measure parameters such as battery capacity and internal resistance. As the battery is used more and more times and over time, the battery capacity will gradually decrease and the internal resistance will gradually increase. By comparing the initial performance parameters of the battery with the current performance parameters, calculate the battery aging degree. For example, if the initial capacity of the battery is C0 and the currently measured capacity is C1, then the battery aging degree D=1-(C1 / C0).

[0102] Obtain the failed delivery ratio FDR. In the logistics distribution management system, record the execution results of each cargo delivery task, including successful delivery and failed delivery. By counting the number of failed delivery tasks and the total number of delivery tasks within a period of time, divide the number of failed delivery tasks by the total number of delivery tasks to obtain the failed delivery ratio FDR.

[0103] Obtain the failed delivery ratio FDR. In the logistics distribution management system, record the execution results of each cargo delivery task, including successful delivery and failed delivery. By counting the number of failed delivery tasks and the total number of delivery tasks within a period of time, divide the number of failed delivery tasks by the total number of delivery tasks to obtain the failed delivery ratio FDR.

[0104] It should be noted that TE comprehensively considers the deviation between the actual delivery time of the goods and the initial planned time, the deviation between the actual power consumption of the drone and the initial planned power consumption, and abnormal conditions during the flight. This reflects the impact of flight stability on the utilization effect. The better the flight stability, the closer the SR value is to 1, and the larger the value of this item is, which increases the PEI. This shows that high flight stability helps to improve the utilization effect of UAVs. The relationship between the timeout of each cargo delivery task and the average timeout of all cargo delivery tasks is considered. The importance of flight frequency on utilization effect is further highlighted in the form of cubic expression, indicating that a higher flight frequency reflects the utilization efficiency of UAVs to a certain extent. The influence of the battery aging of the UAV on the utilization effect is considered. It reflects the impact of the failed delivery ratio on the utilization effect. The higher the failed delivery ratio, the greater the FDR value. By combining the above factors, the comprehensive utilization index PEI of cargo distribution data is obtained.

[0105] Step 503: Determine the level of utilization effect of the goods delivery data. The specific method is as follows: The comprehensive utilization index threshold of cargo distribution data includes the first comprehensive utilization index threshold of cargo distribution data PEI1 and the second comprehensive utilization index threshold of cargo distribution data PEI2, and PEI1 < PEI2; Use simulation software or an actual experimental environment to simulate and test the operation of the UAV in various different scenarios. Set different parameter combinations, such as different meteorological conditions like wind speed, wind direction, temperature, flight route, load condition, etc., and calculate the PEI value in each case. Through multiple simulations and experiments, observe the variation law of the PEI value and the actual performance of the UAV, so as to determine the thresholds PEI1 and PEI2 for distinguishing the levels of data utilization effect. For example, when the simulation results show that the PEI is lower than a certain value, the UAV can complete the cargo distribution task within the specified time, and the deviation between the actual delivery time and the initially planned delivery time is within 10%, and the successful task completion rate is above 95%, then this value is taken as the first comprehensive utilization index threshold of cargo distribution data PEI1. When the PEI is higher than a certain value, the flight speed of the UAV drops by more than 20% compared to the preset speed, and the failed delivery ratio exceeds 10%, then this value is taken as the second comprehensive utilization index threshold of cargo distribution data PEI2.

[0106] When PEI < PEI1, it is judged that the utilization effect of cargo distribution data is excellent.

[0107] When PEI1 ≤ PEI < PEI2, it is judged that the utilization effect of cargo distribution data is good, and the first improvement measure is taken; The first improvement measure is to regularly maintain the equipment at the monitoring points to ensure the accuracy of the data, maintain and repair the UAV equipment, replace aging sensors and batteries, and upgrade the hardware and software systems to ensure its stable operation; Regularly update the path planning scheme, adjust the planned distribution tasks, and adjust the path and sequence in combination with real-time information to reduce the overtime and failed delivery ratios.

[0108] When PEI ≥ PEI2, it is judged that the utilization effect of cargo distribution data is poor, and the second improvement measure is taken; The second improvement measure is to shorten the maintenance cycle of the equipment at the monitoring points and perform periodic calibration to ensure the accuracy of the data; Increase the frequency of maintaining and repairing the UAV equipment, replace aging sensors and batteries, and periodically upgrade the hardware and software systems to ensure its stable operation; Increase the frequency of updating the path planning scheme, regularly adjust the planned distribution tasks, and conduct periodic statistics on the obtained transportation situation to find the weak points of data utilization and make timely adjustments.

[0109] Integrate Step 501 to Step 503: By evaluating delivery deviations and data utilization effects, and taking improvement measures based on the evaluation results, it helps to continuously optimize the delivery performance of drones. This optimization mechanism based on data feedback can continuously improve the utilization efficiency of data, improve the quality of delivery services, and make the entire cargo delivery data processing system more complete and efficient.

[0110] See also Figure 3 On the other hand, the present invention also discloses a cargo delivery data processing system for a cargo drone, which is used to implement the cargo delivery data processing method for the above-mentioned cargo drone, including: Data collection module, used to collect meteorological data, airspace information data, cargo information data and UAV flight data; The flight path planning module is used to generate multiple different delivery routes based on the cargo information data; calculate the meteorological impact assessment value FW of different routes based on the meteorological data, and calculate the airspace control comprehensive index FC of different routes based on the airspace information data; calculate the flight path assessment index FT of different routes based on the meteorological impact assessment value FW and the airspace control comprehensive index FC; compare the flight path assessment index FT of all routes, and select the route with the smallest FT value as the final flight path; The task allocation module is used to calculate the cargo priority index P based on the cargo information data, and calculate the task allocation comprehensive evaluation index MSK according to the meteorological impact evaluation value FW, the flight path evaluation index FT, the cargo priority index P and the flight data; preset the task allocation comprehensive evaluation index threshold, compare the task allocation comprehensive evaluation index MSK with the task allocation comprehensive evaluation index threshold, and judge whether the current task allocation plan is reasonable and whether the cargo drone can execute the current task allocation plan according to the comparison result; if the current task allocation plan cannot be executed, replace the cargo drone or re-determine the delivery route; if the current task allocation plan can be executed, execute the delivery task according to the route of the current task allocation plan; The monitoring module calculates the flight deviation SIL based on the flight data and the task allocation comprehensive evaluation index MSK if the cargo drone starts to perform the delivery task; calculates the abnormality judgment comprehensive value JL based on the airspace information data, flight data and the flight deviation SIL; presets the abnormality judgment comprehensive value threshold, compares the abnormality judgment comprehensive value JL with the abnormality judgment comprehensive value threshold, and determines whether the drone flight route is normal based on the comparison result; if the drone flight route is abnormal, issues a warning signal; In the delivery completion processing module, if the UAV flight route is normal, the delivery deviation evaluation value TE is calculated according to the abnormal judgment comprehensive value JL, cargo information data and flight data; the cargo delivery data comprehensive utilization index PEI is calculated according to the delivery deviation evaluation value TE and flight data; the cargo delivery data comprehensive utilization index threshold is preset, and the cargo delivery data comprehensive utilization index PEI is compared with the cargo delivery data comprehensive utilization index threshold, and the data utilization effect level of the UAV is judged according to the comparison result, and corresponding improvement measures are taken.

[0111] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for processing cargo delivery data of a cargo drone, characterized in that: include: Step 1: Collect meteorological data, airspace information data, cargo information data and drone flight data; Step 2: Generate multiple different delivery routes based on cargo information data; calculate the meteorological impact assessment value FW of different routes based on meteorological data, and calculate the airspace control comprehensive index FC of different routes based on airspace information data; calculate the flight path assessment index FT of different routes based on the meteorological impact assessment value FW and the airspace control comprehensive index FC; compare the flight path assessment index FT of all routes, and select the route with the smallest FT value as the final flight path; Step 3: Calculate the cargo priority index P based on the cargo information data, and calculate the task allocation comprehensive evaluation index MSK according to the meteorological impact evaluation value FW, the flight path evaluation index FT, the cargo priority index P and the flight data; preset the task allocation comprehensive evaluation index threshold, compare the task allocation comprehensive evaluation index MSK with the task allocation comprehensive evaluation index threshold, and judge whether the current task allocation plan is reasonable according to the comparison result, and whether the cargo drone can execute the current task allocation plan; If the current task allocation plan cannot be executed, the cargo drone will be replaced or the delivery route will be re-determined; if the current task allocation plan can be executed, the delivery task will be executed according to the route of the current task allocation plan; Step 4: If the cargo drone starts to perform the delivery task, the flight deviation SIL is calculated based on the flight data and the task allocation comprehensive evaluation index MSK; the abnormality judgment comprehensive value JL is calculated based on the airspace information data, flight data and the flight deviation SIL; the abnormality judgment comprehensive value threshold is preset, and the abnormality judgment comprehensive value JL is compared with the abnormality judgment comprehensive value threshold, and whether the drone flight route is normal is determined based on the comparison result; if the drone flight route is abnormal, a warning signal is issued; Step 5: If the flight route of the drone is normal, calculate the delivery deviation evaluation value TE based on the abnormality judgment comprehensive value JL, cargo information data and flight data; calculate the cargo delivery data comprehensive utilization index PEI based on the delivery deviation evaluation value TE and flight data; Preset the threshold of the comprehensive utilization index of cargo delivery data, compare the comprehensive utilization index PEI of cargo delivery data with the threshold of the comprehensive utilization index of cargo delivery data, judge the data utilization effect level of the drone according to the comparison result, and take corresponding improvement measures.

2. The cargo delivery data processing method of a cargo drone according to claim 1, characterized in that: The method for calculating the meteorological impact assessment value FW and the airspace control comprehensive index FC is: Meteorological data include wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD for different routes; Airspace information data include airspace traffic flow NA within the flight area on different routes and the minimum distance DI to the nearest no-fly zone; According to the wind speed VW, wind direction VD, temperature TS, air pressure PA and humidity HD on different routes, the meteorological impact assessment value FW of different routes is calculated. The calculation formula is: ； Among them, H0 is the standard reference humidity; K1 is The weight coefficient of K is 0.2~0.4; K2 is the weight coefficient of temperature TS, which is 0.1~0.3; K3 is the weight coefficient of air pressure PA, which is 0.3~0.5; K4 is The weight coefficient is 0.2~0.4; and K1+K2+K3+K4=1; According to the airspace traffic flow NA within the flight area on different routes and the minimum distance DI to the nearest no-fly zone, the comprehensive airspace control index FC of different routes is calculated. The calculation formula is: ； Among them, LE is the restriction level of airspace control.

3. The cargo delivery data processing method of a cargo drone according to claim 2, characterized in that: The method for selecting a flight path is: According to the meteorological impact assessment value FW and the comprehensive index of airspace control FC, the flight path assessment index FT of different routes is calculated. The calculation formula is: ； Among them, a is the weight coefficient of the meteorological impact assessment value FW, which is 0.3~0.5; b is the weight coefficient of the airspace control comprehensive index FC, which is 0.5~0.7; and a+b=1; The flight path evaluation index FT of all routes is compared, and the route with the smallest FT value is selected as the final flight path.

4. The cargo delivery data processing method of a cargo drone according to claim 3 is characterized by: The method to determine whether the cargo drone can execute the current task allocation plan is: The cargo information data includes the total time TU and the current time TN expected to complete the delivery task; The flight data includes the remaining battery power EL, estimated payload DL, estimated flight speed VS, remaining range RE and accumulated flight time TD of the drone; According to the total time TU expected to complete the delivery task and the current time TN, the cargo priority index P is calculated using the following formula: ； Among them, PO is the initial priority; S is the expedited delivery quantitative score; ω1 is the weight coefficient of the initial priority PO, which ranges from 0.1 to 0.3; ω2 is The weight coefficient is 0.3~0.4; ω3 is the weight coefficient of the expedited delivery quantitative score S, which is 0.3~0.5; and ω1+ω2+ω3=1; According to the meteorological impact assessment value FW, the flight path assessment index FT, the cargo priority index P, the remaining power EL, the expected load DL, the expected flight speed VS, the remaining range RE and the accumulated flight time TD, the task allocation comprehensive assessment index MSK is calculated based on the following formula: ； Among them, VC is the value of the goods; DP is the maximum load of the drone; MM is the mechanical failure probability of the drone; Preset the comprehensive evaluation index threshold Mth of task allocation; When MSK≤Mth, the current task allocation plan is judged to be reasonable, and the cargo drone can execute the current task allocation plan, and then the delivery task is executed according to the route of the current task allocation plan; When MSK>Mth, the current task allocation plan is judged to be unreasonable, the cargo drone cannot execute the current task allocation plan, and the cargo drone is replaced or the delivery route is re-determined.

5. The cargo delivery data processing method of a cargo drone according to claim 4, characterized in that: The method for calculating the flight deviation SIL is: The flight data also includes the current flight speed VM, current flight altitude HG and current acceleration A of the drone; The flight deviation SIL is calculated based on the task allocation comprehensive evaluation index MSK, the current flight speed VM, the current flight altitude HG and the current acceleration A. The formula is: ； Among them, V0 is the standard reference flight speed; H0 is the standard reference flight altitude; A0 is the standard reference acceleration; EQ is the energy consumption rate of the UAV; MSKmax is the theoretical maximum value of the comprehensive index of task allocation; φ1 is The weight coefficient is 0.2~0.4; φ2 is The weight coefficient is 0.3~0.5; φ3 is The weight coefficient is between 0.1 and 0.5, and φ1+φ2+φ3=1.

6. The cargo delivery data processing method of a cargo drone according to claim 5, characterized in that: The method to determine whether the flight route of the drone is normal is: The flight data also includes the actual flight route, the distance flown OA, the current load WM and the actual flight speed VAC; The abnormality judgment comprehensive value JL is calculated based on the airspace traffic flow NA, the minimum distance to the nearest no-fly zone DI, the actual flight route flight distance OA, the current load WM, the actual flight speed VAC, the expected flight speed VS and the flight deviation SIL. The formula is as follows: ； Among them, NAmax is the maximum airspace traffic flow; LPT is the total distance of the preset flight route; Preset the abnormality judgment comprehensive value threshold Jth; When JL≤Jth, the flight route of the drone is judged to be normal; When JL>Jth, the UAV flight route is judged to be abnormal and a warning signal is issued.

7. The cargo delivery data processing method of a cargo drone according to claim 6, characterized in that: The method for calculating the delivery deviation evaluation value TE is: The cargo information data also includes the actual delivery time TC of the cargo; The flight data also includes the actual amount of electricity EA consumed by the drone; According to the anomaly determination comprehensive value JL, the actual delivery time TC of the goods, the total time TU for the expected completion of the distribution task, and the actual power consumption EA of the drone, calculate the distribution deviation evaluation value TE. The calculation formula is: ； Among them, ERE is the expected power consumption in the initial plan of the goods distribution task; e is the natural constant.

8. The cargo delivery data processing method of a cargo drone according to claim 7, characterized in that: The method for calculating the comprehensive utilization index PEI of the goods distribution data is: The flight data also includes the flight frequency FN of the drone; According to the distribution deviation evaluation value TE and the flight frequency FN of the drone, calculate the comprehensive utilization index PEI of the goods distribution data. The formula used is: ； Among them, SR is the flight stability index; OT is the overtime of each goods distribution task; OTavg is the average overtime of all goods distribution tasks; D is the degree of battery aging of the drone; FDR is the failed delivery ratio.

9. The cargo delivery data processing method of a cargo drone according to claim 8, characterized in that: The method for judging the level of the utilization effect of the goods distribution data is: The comprehensive utilization index threshold of the goods distribution data includes the comprehensive utilization index threshold one PEI1 and the comprehensive utilization index threshold two PEI2 of the goods distribution data, and PEI1 < PEI2; When PEI < PEI1, it is judged that the utilization effect of the goods distribution data is excellent; When PEI1 ≤ PEI < PEI2, it is judged that the utilization effect of the goods distribution data is good, and improvement measure one is taken; When PEI ≥ PEI2, it is judged that the utilization effect of the goods distribution data is poor, and improvement measure two is taken.

10. A cargo delivery data processing system for a cargo drone, characterized in that: The method for processing the goods distribution data of the freight drone for implementing any one of the above claims 1-9 includes: A data acquisition module for acquiring meteorological data, airspace information data, goods information data, and the flight data of the drone; A flight path planning module for generating multiple different distribution routes based on the goods information data; calculating the meteorological impact evaluation value FW of different routes based on the meteorological data, and calculating the airspace control comprehensive index FC of different routes based on the airspace information data; calculating the flight path evaluation index FT of different routes according to the meteorological impact evaluation value FW and the airspace control comprehensive index FC; comparing the flight path evaluation indexes FT of all routes, and selecting the route with the minimum FT value as the final flight path; A task allocation module for calculating the goods priority index P based on the goods information data, and calculating the task allocation comprehensive evaluation index MSK according to the meteorological impact evaluation value FW, the flight path evaluation index FT, the goods priority index P, and the flight data; presetting the task allocation comprehensive evaluation index threshold, comparing the task allocation comprehensive evaluation index MSK with the task allocation comprehensive evaluation index threshold, and judging whether the current task allocation plan is reasonable and whether the freight drone can execute the current task allocation plan according to the comparison result; if the current task allocation plan cannot be executed, replace the freight drone or the distribution route and re-determine; if the current task allocation plan can be executed, execute the distribution task according to the route of the current task allocation plan; The monitoring module calculates the flight deviation SIL based on the flight data and the task allocation comprehensive evaluation index MSK if the cargo drone starts to perform the delivery task; calculates the abnormality judgment comprehensive value JL based on the airspace information data, flight data and the flight deviation SIL; presets the abnormality judgment comprehensive value threshold, compares the abnormality judgment comprehensive value JL with the abnormality judgment comprehensive value threshold, and determines whether the drone flight route is normal based on the comparison result; if the drone flight route is abnormal, issues a warning signal; In the delivery completion processing module, if the UAV flight route is normal, the delivery deviation evaluation value TE is calculated according to the abnormal judgment comprehensive value JL, cargo information data and flight data; the cargo delivery data comprehensive utilization index PEI is calculated according to the delivery deviation evaluation value TE and flight data; the cargo delivery data comprehensive utilization index threshold is preset, and the cargo delivery data comprehensive utilization index PEI is compared with the cargo delivery data comprehensive utilization index threshold, and the data utilization effect level of the UAV is judged according to the comparison result, and corresponding improvement measures are taken.

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