Multi-unmanned aerial vehicle logistics battery replacement cooperative scheduling optimization method for endurance constraint

Abstract

The invention relates to the technical field of unmanned aerial vehicle logistics management, in particular to a multi-unmanned aerial vehicle logistics battery replacement cooperative scheduling optimization method for endurance constraint, and the method comprises the steps: obtaining a power state optimization factor through the residual electric quantity of an unmanned aerial vehicle; the logistics working condition urgency degree is determined; acquiring an endurance constraint coefficient based on the electric state optimization factor and the logistics working condition urgency degree; determining the endurance operation alarm degree by using the distance between the unmanned aerial vehicle and the nearest battery swap station and the endurance constraint coefficient; obtaining a corrected endurance operation alarm degree based on a total vacancy position ratio in the nearest battery swap station and a battery swap duration mean value; according to the corrected endurance operation alarm degree, determining a machine power demand performance degree; the logistics power conversion urgent degree is obtained by using the machine power demand performance degree; based on the logistics power conversion urgency degree, determining a power conversion scheduling early warning index; and carrying out battery replacement early warning according to the battery replacement scheduling early warning index. According to the method, the multi-unmanned aerial vehicle logistics battery replacement cooperative scheduling optimization efficiency facing the endurance constraint can be improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) logistics management technology, specifically to a multi-UAV logistics battery swapping collaborative scheduling optimization method oriented towards endurance constraints. Background Technology

[0002] Multi-drone logistics is a new logistics model based on low-altitude economy and drone technology. It utilizes the collaborative operation of multiple drones to transport and deliver goods in scenarios such as intercity travel, islands, and mountainous areas. Its core value lies in improving efficiency, reducing costs, and expanding coverage. With further policy improvements, continuous technological breakthroughs, and ongoing infrastructure development, multi-drone logistics is expected to become an important model in the future logistics industry, driving high-quality economic and social development.

[0003] In battery swapping scheduling constrained by range limitations, traditional methods typically employ a static evaluation strategy based on a battery threshold: when a drone's real-time battery level falls below a preset threshold, a battery swapping warning is triggered, guiding the drone to the nearest battery swapping station. However, in real-world scenarios, the drone's real-time spatial location and the dynamic charging status of the battery swapping station significantly impact the accuracy of the warning. For example, when a drone is far from a battery swapping station and available charging slots are scarce, or the progress of swapping at occupied slots is lagging, a higher weight should be assigned to the warning. Furthermore, implicit competition and preemption among multiple drones for limited battery swapping resources further exacerbate the analytical biases of traditional battery swapping scheduling strategies, making the drone range constraint problem more prominent and hindering the improvement of overall battery swapping collaborative scheduling efficiency. Summary of the Invention

[0004] This invention provides a collaborative scheduling optimization method for multi-UAV logistics battery swapping oriented to address existing problems, which is based on the constraint of endurance.

[0005] The multi-UAV logistics battery swapping collaborative scheduling optimization method of the present invention, oriented towards endurance constraints, adopts the following technical solution: One embodiment of the present invention provides a collaborative scheduling optimization method for multi-UAV logistics battery swapping oriented to meet range constraints. The method includes the following steps: By utilizing the remaining battery power of the target drone at the current moment and at historical moments, the real-time electrical state optimization factor of the target drone is obtained; The real-time logistics urgency of the target drone is determined based on the remaining delivery time of the target drone at the current moment, the distance between the target drone and the destination at the current moment, and the expected total delivery distance of the target drone. Based on the real-time electrical state optimization factor and the real-time logistics urgency of the target UAV, the real-time endurance constraint coefficient of the target UAV is obtained. The real-time endurance operation alarm level of the target drone is determined by using the distance between the target drone and its nearest battery swapping station at the current moment, as well as the real-time endurance constraint coefficient of the target drone. Based on the total vacancy rate of the target drone in the nearest battery swapping station at the current moment and the average battery swapping time of all occupied battery swapping slots in the nearest battery swapping station of the target drone at the current moment, the real-time endurance operation alarm level of the target drone is corrected, and the corrected real-time endurance operation alarm level of the target drone is obtained. Based on the real-time endurance alarm level of the target drone after correction, determine the real-time power consumption performance of the target drone; By utilizing the real-time power consumption performance of the target drone, the real-time urgency of the target drone for logistics battery swapping can be obtained; Based on the real-time urgency of battery swapping for the target drone, determine the real-time battery swapping scheduling and early warning indicators for the target drone. Battery swapping early warning is generated based on the real-time battery swapping scheduling and early warning indicators of the target drone.

[0006] Furthermore, the specific steps for obtaining the real-time electrical state priority factor of the target drone by utilizing its remaining battery power at the current and historical times are as follows: Obtain the remaining battery power of the target drone at the current time and at historical times; where historical times refer to every time within a preset time period from the current time. With time sequence as the horizontal axis and the remaining battery power of the target drone at the current moment and at historical moments as the vertical axis, a curve of the remaining battery power of the target drone is constructed. The least squares method was used to fit the remaining battery power change curve of the target UAV, and the slope of the fitted line for the remaining battery power was obtained. The slope of the fitted straight line of the remaining power is added to the zero-prevention coefficient to obtain the sum. The reciprocal of the sum is multiplied by the remaining power of the target UAV at the current moment to obtain the product. The normalized value of the product is determined as the real-time electrical state optimization factor of the target UAV.

[0007] Furthermore, the specific steps for determining the real-time logistics urgency of the target drone based on its remaining delivery time at the current moment, its distance from the destination at the current moment, and its estimated total delivery distance are as follows: Obtain the remaining delivery time of the target drone at the current moment, the distance between the target drone and the destination at the current moment, and the estimated total delivery distance of the target drone; The difference between the target drone's estimated total delivery distance and its current distance from the destination is calculated. The product of the reciprocal of the absolute value of the difference and the reciprocal of the target drone's remaining delivery time at the current moment is used to determine the real-time logistics urgency of the target drone.

[0008] Furthermore, the specific steps for obtaining the real-time endurance constraint coefficient of the target UAV based on the real-time electrical state priority factor and the real-time logistics urgency are as follows: The sum is obtained by adding the zero-prevention coefficient to the real-time electrical state optimization factor of the target drone, and then multiplying the reciprocal of the sum by the real-time logistics urgency of the target drone. The normalized value of the product is determined as the real-time endurance constraint coefficient of the target drone.

[0009] Furthermore, the specific steps for determining the real-time endurance operation alarm level of the target drone using the distance between the target drone and its nearest battery swapping station at the current moment, and the real-time endurance constraint coefficient of the target drone, are as follows: Obtain the nearest battery swapping station for the target drone at the current moment, and calculate the distance between the target drone and its nearest battery swapping station at the current moment; Calculate the maximum distance between all drones and their nearest battery swapping station at the current moment, and determine the maximum distance as the first distance; The ratio is obtained by dividing the distance between the target drone and its nearest battery swapping station at the current moment by the first distance, and then multiplying the ratio by the real-time endurance constraint coefficient of the target drone to obtain the real-time endurance operation alarm level of the target drone.

[0010] Furthermore, the process of correcting the real-time endurance alarm level of the target drone based on the total vacancy rate of the nearest battery swapping station and the average battery swapping duration of all occupied battery swapping slots in the nearest battery swapping station at the current moment, and obtaining the corrected real-time endurance alarm level of the target drone, includes the following specific steps: Calculate the percentage of total vacancy slots in the nearest battery swapping station for the target drone at the current moment, and take the reciprocal of the percentage of total vacancy slots as the first reciprocal; Calculate the average battery swapping time of all occupied bits in the nearest battery swapping station for the target drone at the current moment, and take the reciprocal of the average battery swapping time as the second reciprocal; Multiply the first reciprocal by the second reciprocal to obtain the product, add 1 to the hyperbolic tangent function value of the product to obtain the sum, and multiply the sum by the real-time endurance operation alarm level of the target UAV to obtain the corrected real-time endurance operation alarm level of the target UAV.

[0011] Furthermore, the specific steps for determining the real-time power consumption of the target drone based on the corrected real-time endurance alarm level are as follows: Obtain several nearby battery swapping stations for the target drone at the current time, then calculate the corrected real-time endurance operation alarm level of the target drone relative to each nearby battery swapping station and calculate the average value, and determine the average value as the comprehensive endurance operation alarm level of the target drone. A scatter plot of battery swapping analysis is constructed based on the current position of each UAV, and several neighboring UAVs of the target UAV are obtained in the scatter plot of battery swapping analysis. Calculate the Euclidean distance between the target UAV and each neighboring UAV and take the average value, then use the average value as the first average value; The average of the real-time endurance alarm values ​​of all nearby drones after correction is determined as the second average. The sum is obtained by adding the zero-prevention coefficient to the first mean, and the reciprocal of the sum, the comprehensive endurance operation alarm level of the target drone, and the second mean are multiplied to obtain the product. The normalized value of the product is determined as the real-time power consumption performance of the target drone.

[0012] Furthermore, the specific steps for obtaining the real-time urgency of battery swapping for the target drone by utilizing its real-time power consumption demand are as follows: With time sequence as the horizontal axis and the mean Euclidean distance between the target UAV and all neighboring UAVs as the vertical axis, a curve showing the interval variation of neighboring UAVs is constructed. With time sequence as the horizontal axis and the average real-time endurance alarm level of all neighboring drones after correction as the vertical axis, a curve of endurance alarm change of neighboring drones is constructed. The least squares method was used to fit the interval change curve and the endurance alarm change curve of the neighboring drones respectively, and the slope of the fitted line for the interval change of the neighboring drones and the slope of the fitted line for the endurance alarm change were obtained. The slope of the fitted line for the interval change of neighboring drones is added to the zero-prevention coefficient to obtain the sum. The reciprocal of the sum, the real-time power consumption performance of the target drone, and the slope of the fitted line for the endurance alarm change of neighboring drones are multiplied to obtain the product. The normalized value of the product is determined as the real-time logistics power swap urgency of the target drone.

[0013] Furthermore, the specific steps for determining the real-time battery swapping scheduling and early warning indicators for the target drone based on the real-time urgency of the target drone's battery swapping are as follows: Calculate the average real-time logistics battery swapping urgency of all drones, and subtract the average real-time logistics battery swapping urgency of all drones from the real-time logistics battery swapping urgency of the target drone to obtain the difference. The normalized value of the difference is determined as the real-time battery swapping scheduling early warning indicator for the target drone.

[0014] Furthermore, the specific steps for issuing a battery swapping early warning based on the real-time battery swapping scheduling early warning indicators of the target UAV are as follows: When the real-time battery swapping scheduling warning index of the target drone exceeds the preset index threshold, a battery swapping warning is issued to the target drone.

[0015] The beneficial effects of the technical solution of the present invention are as follows: The embodiments of the present invention propose a multi-UAV logistics battery swapping collaborative scheduling optimization method oriented towards endurance constraints. First, the power state optimization factor is obtained by observing the real-time power status of the UAVs. Then, the logistics urgency is obtained based on the remaining distance and duration of the UAVs' real-time task execution. Subsequently, the endurance constraint coefficient is obtained by combining the power state optimization factor and the logistics urgency. Based on the endurance constraint coefficient, the endurance operation alarm degree is obtained by combining the distance between the UAV and the adjacent battery swapping station. Then, the endurance operation alarm degree is corrected by combining the charging gap performance of the adjacent battery swapping station. Then, a battery swapping analysis scatter plot is constructed based on the endurance operation alarm degree. The real-time power demand performance of each UAV is obtained by monitoring the distance between the UAV and the adjacent UAVs in the scatter plot. Then, the logistics battery swapping urgency is obtained by combining the short-term interval and alarm degree change trend of the adjacent UAVs. Finally, the real-time battery swapping scheduling early warning index of each UAV is obtained from the urgency parameter. Compared with traditional methods, this invention can combine the real-time location of the drone in the actual scenario, the charging status of the battery swapping station, and the resource contention relationship between multiple drones for the battery swapping station to obtain more accurate drone battery swapping scheduling results, thereby improving the efficiency of multi-drone logistics battery swapping collaborative scheduling optimization under range constraints. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the steps of the multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints according to the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the multi-UAV logistics battery swapping collaborative scheduling optimization method provided by the present invention, which is oriented towards endurance constraints.

[0021] It should be noted that the present invention aims to obtain real-time battery swapping scheduling and early warning indicators based on the performance of multiple UAV logistics battery swapping operations.

[0022] Multi-drone logistics is a new logistics model based on low-altitude economy and drone technology. Multi-drone logistics battery swapping has been widely applied in intercity, island, and mountainous cargo transportation and delivery scenarios. However, the range constraint in the drone logistics battery swapping field stems from limitations in drone performance, environmental conditions, mission requirements, or the battery swapping system itself, which restricts the actual sustainable operation capability of drones. Traditional methods address this range constraint by using threshold analysis based on the drone's real-time battery level. This traditional method has low efficiency in collaborative scheduling of logistics battery swapping. Therefore, this invention combines the real-time location of drones in actual scenarios, the charging status of battery swapping stations, and the resource contention relationships among multiple drones for battery swapping stations to obtain more accurate drone battery swapping scheduling results.

[0023] Please see Figure 1 The diagram illustrates a flowchart of a multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints provided by an embodiment of the present invention. The method includes the following steps: Step S001: Utilize the remaining battery power of the target drone at the current moment and at historical moments to obtain the real-time electrical state optimization factor of the target drone.

[0024] It should be noted that the target drone refers to the currently monitored drone. The purpose of this embodiment is to obtain real-time battery swapping scheduling and early warning indicators for each drone by combining the location, operating conditions, and mutual influence relationships between drones and battery swapping stations. Therefore, firstly, the endurance constraint coefficient is obtained based on the drone's battery status and the operating conditions of the drone's mission. Based on the endurance constraint coefficient, the endurance operation alarm degree is obtained by combining the interval of battery swapping stations and real-time charging performance. Then, a battery swapping analysis scatter plot is constructed based on the alarm degree parameters. Based on the location and high-precision mutual influence performance of multiple drones in the scatter plot, the urgency of logistics battery swapping is obtained. Finally, the real-time battery swapping scheduling and early warning indicators for drones are obtained based on the urgency of logistics battery swapping.

[0025] Considering that the main role of drones in the drone logistics battery swapping scenario is cargo delivery, the task requirements of each drone are different. For example, when the delivery location of the drone performing the logistics task in real time is far away and the required delivery time is shorter, the demand for sufficient power of the drone is higher. At the same time, the real-time power status of the drone can reflect its endurance under the task conditions. Therefore, this step analyzes the endurance constraint coefficient based on the real-time power status of the drone and the urgency of performing the logistics task.

[0026] First, the real-time battery level of a drone can reflect its endurance to a certain extent. In actual logistics delivery flights, the power consumption is not constant, but is affected by multiple factors such as flight status and environment, showing a complex nonlinear trend of short-term battery power variation. Therefore, in order to ensure that the battery level of the drone always meets the needs of logistics tasks, it is necessary to evaluate and analyze the real-time battery status of the drone.

[0027] Step S001 further includes steps S0011-S0014: Step S0011: Obtain the remaining battery power of the target drone at the current time and at historical times; where historical times are each time within a preset time period from the current time.

[0028] It should be noted that the remaining battery power of the target drone at the current moment and at historical moments can be obtained through the drone's onboard sensors.

[0029] The preset duration can be set according to specific circumstances; 20 minutes is preferred here.

[0030] Obtain the remaining battery power of the target drone at the current time and at each time point within the previous 20 minutes.

[0031] Step S0012: Construct a curve showing the change in the remaining battery power of the target drone, with time sequence as the horizontal axis and the remaining battery power of the target drone at the current time and at historical times as the vertical axis.

[0032] Specifically, by using the time of remaining battery power as the horizontal axis and the percentage of remaining battery power of the target drone at the current time and at historical times as the vertical axis, the remaining battery power change curve of the target drone is obtained.

[0033] Step S0013: Use the least squares method to fit the remaining power change curve of the target UAV and obtain the slope of the fitted line of remaining power.

[0034] It should be noted that the least squares method is a well-known technique and will not be described in detail here.

[0035] Step S0014: Add the slope of the fitted straight line of the remaining power to the zero-prevention coefficient to obtain the sum, and multiply the reciprocal of the sum by the remaining power of the target UAV at the current moment to obtain the product. Determine the normalized value of the product as the real-time electrical state optimization factor of the target UAV.

[0036] Specifically, calculate the current real-time electrical state preference factor for the monitoring drone: ; in, This represents the real-time electrical state optimization factor of the target drone. This indicates normalization processing. This indicates the percentage of battery power remaining for the target drone at the current moment. This represents the slope of the fitted straight line representing the remaining battery power. To prevent zero coefficient.

[0037] The more real-time remaining battery power a drone has, and the slower its battery consumption is in the short term, the better its battery status.

[0038] Step S002: Determine the real-time logistics urgency of the target drone based on the remaining delivery time of the target drone at the current moment, the distance between the target drone and the destination at the current moment, and the expected total delivery distance of the target drone.

[0039] It should be noted that the main role of drones in logistics tasks is goods delivery. Considering the differences in delivered goods, delivery areas, and task timeliness, the delivery requirements for drones in performing tasks vary. For example, the farther the distance between the drone and the destination in a delivery task, and the shorter the remaining delivery time, the higher the demand for sufficient remaining battery power in the current drone logistics task. Therefore, the analysis is supplemented by monitoring the performance of the drone in performing tasks.

[0040] Step S002 further includes steps S0021 and S0022: Step S0021: Obtain the remaining delivery time of the target drone at the current moment, the distance between the target drone and the destination at the current moment, and the estimated total delivery distance of the target drone.

[0041] It should be noted that the estimated total delivery time is... and the estimated total delivery distance The cloud-based scheduling system pre-calculates the path using a path planning algorithm when assigning tasks, and then sends the path to the drone along with the task instructions.

[0042] The drone obtains its current location coordinates in real time via onboard GPS / BeiDou, and calculates the distance between the real-time location and the destination by combining these coordinates with the coordinates of the mission destination. .

[0043] During the mission, the drone's flight control system records the flight time in real time, and combines this with the estimated total delivery time. Calculate remaining delivery time .

[0044] Step S0022: Subtract the distance between the target drone and its destination at the current moment from the expected total delivery distance to obtain the difference. Then, multiply the reciprocal of the absolute value of the difference with the reciprocal of the remaining delivery time of the target drone at the current moment to determine the real-time logistics urgency of the target drone.

[0045] Specifically, calculate the urgency of the real-time logistics operations of the currently monitored drones: ; in, This indicates the urgency of the target drone's real-time logistics operations. This indicates the expected total delivery distance for the target drone. This indicates the distance between the target drone and the destination at the current moment. This indicates the remaining delivery time for the target drone at the current moment.

[0046] The greater the distance between the current real-time location of the monitored drone and the target delivery location, and the less time remains for delivery, the more urgent the logistics situation of the monitored drone in performing the delivery task.

[0047] Step S003: Based on the real-time electrical state priority factor and the real-time logistics urgency of the target UAV, obtain the real-time endurance constraint coefficient of the target UAV.

[0048] Specifically, this includes: adding a zero-prevention coefficient to the real-time electrical state optimization factor of the target drone to obtain a sum, multiplying the reciprocal of the sum by the real-time logistics urgency of the target drone to obtain a product, and determining the normalized value of the product as the real-time endurance constraint coefficient of the target drone.

[0049] Specifically, the smaller the real-time electrical state preference factor of the monitoring drone, and the higher the urgency of the logistics situation, the more the real-time endurance performance of the monitoring drone is constrained by the battery swapping demand. Therefore, the real-time endurance constraint coefficient of the current monitoring drone is calculated as follows: ; in, This represents the real-time endurance constraint coefficient of the target drone. To prevent zero coefficient.

[0050] Therefore, the real-time endurance constraint coefficients of all monitoring drones within the monitoring range are calculated and recorded.

[0051] Step S004: Using the distance between the target drone and its nearest battery swapping station at the current moment, and the real-time endurance constraint coefficient of the target drone, determine the real-time endurance operation alarm level of the target drone.

[0052] It should be noted that the battery swapping process for drones relies on battery swapping stations. The distance between the drone and the station, as well as the charging availability of the station, will affect the battery swapping early warning assessment. For example, if the drone is far from the nearest battery swapping station and the station's charging availability is poor, a higher battery swapping early warning level should be applied. Therefore, this step obtains the endurance operation alarm level for each drone based on the endurance constraint coefficient and the station scheduling. Furthermore, there is a resource contention relationship between multiple drones for battery swapping stations. Therefore, a scatter plot of battery swapping analysis is constructed based on the endurance operation alarm level, and the real-time urgency of battery swapping for the monitored drones is obtained by combining the influence of multiple drones in the scatter plot.

[0053] First, the above assessment of battery swapping needs is based solely on the drone's real-time battery level and performance during logistics tasks. In reality, however, battery swapping for drones must be performed at battery swapping stations. If the distance between the drone and a nearby battery swapping station is significant, or if the battery swapping station has poor availability, it indicates that the drone faces greater difficulty in performing battery swapping at a nearby station. Therefore, a higher assessment of battery swapping needs should be applied to the drone. Thus, supplementary analysis is required based on the endurance constraint coefficient and the performance of the battery swapping station.

[0054] Step S004 further includes steps S0041-S0043: Step S0041: Obtain the nearest battery swapping station for the target drone at the current moment, and calculate the distance between the target drone and its nearest battery swapping station at the current moment.

[0055] It should be noted that: the drone obtains its current location coordinates in real time through onboard GPS / BeiDou, reads the fixed location coordinates of all battery swapping stations from the cloud database, calculates the Euclidean distance between the drone and each battery swapping station, and takes the battery swapping station with the minimum distance as the nearest battery swapping station at the current moment. This minimum distance is the distance between the target drone and its nearest battery swapping station.

[0056] Step S0042: Calculate the maximum distance between all drones and their nearest battery swapping station at the current moment, and determine the maximum distance as the first distance.

[0057] It should be noted that: at the current moment, the nearest battery swapping station for each drone is obtained, as well as the distance between each drone and its corresponding nearest battery swapping station, and finally the maximum distance is obtained. This is denoted as the first spacing.

[0058] Step S0043: Divide the distance between the target drone and its nearest battery swapping station at the current moment by the first distance to obtain the ratio, and multiply the ratio by the real-time endurance constraint coefficient of the target drone to obtain the real-time endurance operation alarm level of the target drone.

[0059] Specifically, the current operational alarm level of the monitored drone is calculated by combining the endurance constraint coefficient: ; in, This indicates the real-time operational alarm level of the target drone. This indicates the distance between the target drone and its nearest battery swapping station at the current moment.

[0060] Step S005: Based on the total vacancy rate of the target drone in the nearest battery swapping station at the current moment and the average battery swapping time of all occupied slots in the nearest battery swapping station of the target drone at the current moment, correct the real-time endurance operation alarm level of the target drone and obtain the corrected real-time endurance operation alarm level of the target drone.

[0061] It should be noted that the battery swapping status of battery swapping stations varies. For example, the shorter the battery swapping time of a drone in a battery swapping slot at a battery swapping station, the more likely the drone is to start swapping. The fewer empty slots at the battery swapping station, the lower the current monitoring drone's battery swapping tendency. Therefore, the battery swapping alarm level for that drone needs to be increased. Thus, the endurance operation alarm level can be adjusted based on the battery swapping station's operating status.

[0062] Step S005 further includes steps S0051-S0053: Step S0051: Calculate the percentage of total vacant slots in the nearest battery swapping station for the target drone at the current moment, and take the reciprocal of the percentage of total vacant slots as the first reciprocal.

[0063] Specifically, based on the identifier of the most recent battery swapping station, the number of currently available charging spots at that station is read from the cloud database. Total number of charging positions The total number of charging spots is a fixed attribute of the battery swapping station and is pre-stored; the number of idle charging spots is monitored in real time by the built-in sensors of the battery swapping station and periodically reported to the cloud. The total percentage of vacant charging spots is then calculated. .

[0064] It is recorded as the first reciprocal.

[0065] Step S0052: Calculate the average battery swapping time of all occupied bits in the nearest battery swapping station of the target UAV at the current time, and take the reciprocal of the average battery swapping time as the second reciprocal.

[0066] Specifically, based on the identifier of the most recent battery swapping station, a list of all currently occupied battery swapping slots at that station, along with the start time of battery swapping for each slot, is retrieved from the cloud database. The difference between the start time and the current time is used to calculate the swapping duration for each slot, and finally, the average swapping duration is calculated. .

[0067] It is written as the second reciprocal.

[0068] Step S0053: Multiply the first reciprocal by the second reciprocal to obtain the product, and add 1 to the hyperbolic tangent function value of the product to obtain the sum. Multiply the sum by the real-time endurance operation alarm degree of the target UAV to obtain the corrected real-time endurance operation alarm degree of the target UAV.

[0069] Specifically, the corrected real-time operational alarm level of the currently monitored drone is calculated: ; in, This represents the real-time endurance operation alarm level of the target drone after correction, and th represents the hyperbolic tangent function.

[0070] If the monitored drone has a high endurance constraint coefficient and the corresponding number of empty spaces in the airspace above the nearby battery swapping station is small, and the drones occupying the spaces have just started swapping batteries, then it reflects that the drone's endurance operation alarm level is higher.

[0071] Step S006: Determine the real-time power consumption of the target drone based on the corrected real-time endurance operation alarm level.

[0072] Step S006 further includes steps S0061-S0065: Step S0061: Obtain several nearby battery swapping stations for the target drone at the current time, then calculate the corrected real-time endurance operation alarm level of the target drone relative to each nearby battery swapping station and calculate the average value, and determine the average value as the comprehensive endurance operation alarm level of the target drone.

[0073] It should be noted that the number of nearby battery swapping stations should be set according to specific circumstances; here, 5 is preferred. Calculate the Euclidean distance between the target drone and each battery swapping station, and sort the Euclidean distances in ascending order. The battery swapping stations corresponding to the first 5 Euclidean distances are considered the nearby battery swapping stations for the target drone.

[0074] Each nearby battery swapping station is taken as its nearest battery swapping station, and the corrected real-time endurance alarm degree of the target drone relative to each nearby battery swapping station is obtained according to the calculation method of the corrected real-time endurance alarm degree of the currently monitored drone.

[0075] Step S0062: Construct a scatter plot of battery swapping analysis based on the current position of each UAV, and obtain several neighboring UAVs of the target UAV in the scatter plot of battery swapping analysis.

[0076] It should be noted that: taking all drones currently performing logistics tasks as the object, the real-time two-dimensional position coordinates of each drone are obtained and used as the horizontal and vertical coordinates of the sample point; at the same time, the comprehensive endurance operation alarm level of each drone is used as the label value of that sample point. All sample points together constitute the scatter plot of the battery swapping analysis at the current moment.

[0077] The number of nearby drones should be set according to the specific circumstances; here, 5 is preferred.

[0078] In the scatter plot of the battery swapping analysis, the coordinates of the target drone are used as the query point. All other drones in the plot are traversed, and the Euclidean distance between the target drone and each other drone is calculated. The Euclidean distances are sorted in ascending order, and the top 5 drones with the smallest distances are selected as the target drone's neighboring drones.

[0079] Step S0063: Calculate the Euclidean distance between the target UAV and each neighboring UAV and calculate the average value, then use the average value as the first average value.

[0080] Specifically, This is denoted as the first mean.

[0081] Step S0064: Determine the average real-time endurance alarm value of all nearby drones after correction as the second average value.

[0082] Specifically, This is denoted as the second mean.

[0083] Step S0065: Add the zero-prevention coefficient to the first mean to obtain the sum, and multiply the reciprocal of the sum, the comprehensive endurance operation alarm degree of the target drone, and the second mean to obtain the product. Determine the normalized value of the product as the real-time power consumption performance of the target drone.

[0084] Specifically, calculate the current power consumption of the monitoring drone: ; in, This indicates the real-time power consumption of the target drone. This indicates the overall operational alarm level of the target drone. To prevent zero coefficient.

[0085] The closer the neighboring drone is to the currently monitored drone, and the higher the endurance alarm levels of both the currently monitored drone and the neighboring drone, the stronger the drone's power consumption performance.

[0086] Step S007: Utilize the real-time power consumption performance of the target drone to obtain the real-time urgency of the target drone's logistics battery swapping.

[0087] It should be noted that if the interval between the monitoring drone and the neighboring drones tends to converge more strongly in the short term, and the short-term increase in the battery life alarm of the neighboring drones is more significant, it reflects that the neighboring drones are more likely to compete for the battery swapping resources of the current monitoring drone. Therefore, further auxiliary analysis should be conducted based on the battery demand performance of the drone and the trend changes of the parameters of the neighboring drones in the short term.

[0088] Step S007 further includes steps S0071-S0074: Step S0071: Construct an interval variation curve of neighboring UAVs with time sequence as the horizontal axis and the mean Euclidean distance between the target UAV and all neighboring UAVs as the vertical axis.

[0089] Step S0072: With time sequence as the horizontal axis and the average real-time endurance alarm level of all neighboring drones after correction as the vertical axis, construct the endurance alarm change curve of neighboring drones.

[0090] Step S0073: Use the least squares method to fit the interval change curve and the endurance alarm change curve of the neighboring UAVs respectively, and obtain the slope of the fitted line for the interval change of the neighboring UAVs and the slope of the fitted line for the endurance alarm change.

[0091] Specifically, the slope of the fitted straight line for the interval change of neighboring drones is denoted as... The slope of the fitted straight line for the change in battery life warning is denoted as... .

[0092] Step S0074: Add the zero-prevention coefficient to the slope of the fitted straight line of the interval change of the neighboring drones to obtain the sum, and multiply the reciprocal of the sum, the real-time power consumption performance of the target drone, and the slope of the fitted straight line of the endurance alarm change of the neighboring drones to obtain the product. The normalized value of the product is determined as the real-time logistics power replacement urgency of the target drone.

[0093] Specifically, the urgency of battery swapping for the monitored drones in logistics is calculated by combining the current demand for electricity with the performance of the drones: ; in, This indicates the real-time urgency of battery swapping for the target drone. To prevent a zero coefficient, This represents the slope of the fitted straight line indicating the change in battery life warning.

[0094] If the current monitoring drone has a higher power consumption performance, and the nearby drones have a higher rate of increase in their short-term battery life alarm levels, and the nearby drones are closer to the current monitoring drone, then it reflects that the current monitoring drone has a more urgent need for battery swapping.

[0095] Calculate and record the real-time urgency of battery swapping for each drone.

[0096] Step S008: Based on the real-time urgency of battery swapping for the target UAV, determine the real-time battery swapping scheduling and early warning indicators for the target UAV.

[0097] Specifically, this includes: calculating the average real-time logistics battery swapping urgency of all drones, subtracting the average real-time logistics battery swapping urgency of all drones from the real-time logistics battery swapping urgency of the target drone to obtain the difference, and determining the normalized value of the difference as the real-time battery swapping scheduling and early warning indicator for the target drone.

[0098] Specifically, the real-time urgency of battery swapping for each drone is obtained from the above steps. The average urgency of battery swapping for all drones currently performing logistics tasks within the overall monitoring range is calculated. The greater the real-time urgency of battery swapping for a monitored drone is compared to the overall level, the higher the necessity for early warning of battery swapping scheduling for that drone. Therefore, the real-time early warning index for battery swapping scheduling of the currently monitored drone is calculated: ; in, This indicates the real-time battery swapping scheduling and early warning indicators for the target drone. This represents the average real-time urgency of battery swapping for all drones.

[0099] Calculate and record the real-time battery swapping scheduling and early warning indicators for each UAV within the monitoring range.

[0100] Step S009: Issue a battery swapping warning based on the real-time battery swapping scheduling warning indicators of the target UAV.

[0101] Specifically, this includes issuing a battery swapping warning to the target drone when the real-time battery swapping scheduling warning index of the target drone exceeds a preset index threshold.

[0102] It should be noted that the preset threshold value is set according to the specific circumstances, and 0.85 is preferred here.

[0103] When the monitored drone meets the real-time battery swapping scheduling early warning index of 0.85 or its real-time battery level is below 15%, a battery swapping warning is issued to the drone. Upon receiving the warning, the drone prioritizes proceeding to the nearest available battery swapping station for battery swapping. The battery swapping station's sensors detect the installation status of the new battery, such as whether it is secure and whether the voltage is normal, and send a verification signal to the drone. The drone initiates a self-test program, confirming that the battery connection is normal and the system is fault-free, before sending a battery swapping completion signal to the battery swapping station. The battery swapping station's main control unit reports the battery swapping results to the cloud scheduling system, such as the battery swapping time and battery status. The cloud updates the drone's mission status and plans subsequent flight paths for the logistics execution mission.

[0104] In summary, in this embodiment of the invention, by combining the real-time location of the drone in the actual scenario, the charging status of the battery swapping station, and the resource contention relationship between multiple drones for the battery swapping station, a more accurate drone battery swapping scheduling result is obtained, which improves the efficiency of multi-drone logistics battery swapping collaborative scheduling optimization under range constraints.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative scheduling optimization method for multi-UAV logistics battery swapping oriented to meet range constraints, characterized in that, The method includes the following steps: By utilizing the remaining battery power of the target drone at the current moment and at historical moments, the real-time electrical state optimization factor of the target drone is obtained; The real-time logistics urgency of the target drone is determined based on the remaining delivery time of the target drone at the current moment, the distance between the target drone and the destination at the current moment, and the expected total delivery distance of the target drone. Based on the real-time electrical state optimization factor and the real-time logistics urgency of the target UAV, the real-time endurance constraint coefficient of the target UAV is obtained. The real-time endurance operation alarm level of the target drone is determined by using the distance between the target drone and its nearest battery swapping station at the current moment, as well as the real-time endurance constraint coefficient of the target drone. Based on the total vacancy rate of the target drone in the nearest battery swapping station at the current moment and the average battery swapping time of all occupied battery swapping slots in the nearest battery swapping station of the target drone at the current moment, the real-time endurance operation alarm level of the target drone is corrected, and the corrected real-time endurance operation alarm level of the target drone is obtained. Based on the real-time endurance alarm level of the target drone after correction, determine the real-time power consumption performance of the target drone; By utilizing the real-time power consumption performance of the target drone, the real-time urgency of the target drone for logistics battery swapping can be obtained; Based on the real-time urgency of battery swapping for the target drone, determine the real-time battery swapping scheduling and early warning indicators for the target drone. Battery swapping early warning is generated based on the real-time battery swapping scheduling and early warning indicators of the target drone.

2. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The specific steps for obtaining the real-time electrical state priority factor of the target drone by utilizing its remaining battery power at the current and historical times are as follows: Obtain the remaining battery power of the target drone at the current time and at historical times; where historical times refer to every time within a preset time period from the current time. With time sequence as the horizontal axis and the remaining battery power of the target drone at the current moment and at historical moments as the vertical axis, a curve of the remaining battery power of the target drone is constructed. The least squares method was used to fit the remaining battery power change curve of the target UAV, and the slope of the fitted line for the remaining battery power was obtained. The slope of the fitted straight line of the remaining power is added to the zero-prevention coefficient to obtain the sum. The reciprocal of the sum is multiplied by the remaining power of the target UAV at the current moment to obtain the product. The normalized value of the product is determined as the real-time electrical state optimization factor of the target UAV.

3. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The process of determining the real-time logistics urgency of the target drone based on its remaining delivery time, distance from the destination, and estimated total delivery distance includes the following specific steps: Obtain the remaining delivery time of the target drone at the current moment, the distance between the target drone and the destination at the current moment, and the estimated total delivery distance of the target drone; The difference between the target drone's estimated total delivery distance and its current distance from the destination is calculated. The product of the reciprocal of the absolute value of the difference and the reciprocal of the target drone's remaining delivery time at the current moment is used to determine the real-time logistics urgency of the target drone.

4. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The specific steps for obtaining the real-time endurance constraint coefficient of the target UAV based on the real-time electrical state preference factor and the real-time logistics urgency are as follows: The sum is obtained by adding the zero-prevention coefficient to the real-time electrical state optimization factor of the target drone, and then multiplying the reciprocal of the sum by the real-time logistics urgency of the target drone. The normalized value of the product is determined as the real-time endurance constraint coefficient of the target drone.

5. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The specific steps for determining the real-time endurance operation alarm level of the target drone by utilizing the distance between the target drone and its nearest battery swapping station at the current moment, as well as the real-time endurance constraint coefficient of the target drone, are as follows: Obtain the nearest battery swapping station for the target drone at the current moment, and calculate the distance between the target drone and its nearest battery swapping station at the current moment; Calculate the maximum distance between all drones and their nearest battery swapping station at the current moment, and determine the maximum distance as the first distance; The ratio is obtained by dividing the distance between the target drone and its nearest battery swapping station at the current moment by the first distance, and then multiplying the ratio by the real-time endurance constraint coefficient of the target drone to obtain the real-time endurance operation alarm level of the target drone.

6. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The process of correcting the real-time endurance alarm level of the target drone based on the total vacancy rate of the nearest battery swapping station at the current moment and the average battery swapping time of all occupied battery swapping slots at the nearest battery swapping station at the current moment, and obtaining the corrected real-time endurance alarm level of the target drone, includes the following specific steps: Calculate the percentage of total vacancy slots in the nearest battery swapping station for the target drone at the current moment, and take the reciprocal of the percentage of total vacancy slots as the first reciprocal; Calculate the average battery swapping time of all occupied bits in the nearest battery swapping station for the target drone at the current moment, and take the reciprocal of the average battery swapping time as the second reciprocal; Multiply the first reciprocal by the second reciprocal to obtain the product, add 1 to the hyperbolic tangent function value of the product to obtain the sum, and multiply the sum by the real-time endurance operation alarm level of the target UAV to obtain the corrected real-time endurance operation alarm level of the target UAV.

7. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The specific steps for determining the real-time power consumption of the target drone based on the corrected real-time endurance alarm level are as follows: Obtain several nearby battery swapping stations for the target drone at the current time, then calculate the corrected real-time endurance operation alarm level of the target drone relative to each nearby battery swapping station and calculate the average value, and determine the average value as the comprehensive endurance operation alarm level of the target drone. A scatter plot of battery swapping analysis is constructed based on the current position of each UAV, and several neighboring UAVs of the target UAV are obtained in the scatter plot of battery swapping analysis. Calculate the Euclidean distance between the target UAV and each neighboring UAV and take the average value, then use the average value as the first average value; The average of the real-time endurance alarm values ​​of all nearby drones after correction is determined as the second average. The sum is obtained by adding the zero-prevention coefficient to the first mean, and the reciprocal of the sum, the comprehensive endurance operation alarm level of the target drone, and the second mean are multiplied to obtain the product. The normalized value of the product is determined as the real-time power consumption performance of the target drone.

8. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The specific steps for obtaining the real-time urgency of battery swapping for the target drone by utilizing its real-time power demand are as follows: With time sequence as the horizontal axis and the mean Euclidean distance between the target UAV and all neighboring UAVs as the vertical axis, a curve showing the interval variation of neighboring UAVs is constructed. With time sequence as the horizontal axis and the average real-time endurance alarm level of all neighboring drones after correction as the vertical axis, a curve of endurance alarm change of neighboring drones is constructed. The least squares method was used to fit the interval change curve and the endurance alarm change curve of the neighboring drones respectively, and the slope of the fitted line for the interval change of the neighboring drones and the slope of the fitted line for the endurance alarm change were obtained. The slope of the fitted line for the interval change of neighboring drones is added to the zero-prevention coefficient to obtain the sum. The reciprocal of the sum, the real-time power consumption performance of the target drone, and the slope of the fitted line for the endurance alarm change of neighboring drones are multiplied to obtain the product. The normalized value of the product is determined as the real-time logistics power swap urgency of the target drone.

9. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The specific steps for determining the real-time battery swapping scheduling and early warning indicators for the target drone based on the real-time urgency of the target drone's battery swapping are as follows: Calculate the average real-time logistics battery swapping urgency of all drones, and subtract the average real-time logistics battery swapping urgency of all drones from the real-time logistics battery swapping urgency of the target drone to obtain the difference. The normalized value of the difference is determined as the real-time battery swapping scheduling early warning indicator for the target drone.

10. The multi-UAV logistics battery swapping collaborative scheduling optimization method for endurance constraints as described in claim 1, characterized in that, The specific steps for generating a battery swapping early warning based on the real-time battery swapping scheduling and early warning indicators of the target UAV are as follows: When the real-time battery swapping scheduling warning index of the target drone exceeds the preset index threshold, a battery swapping warning is issued to the target drone.

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