Dynamic Coordination Processing Method Based on Drone and Logistics Locker Delivery Tasks

Abstract

This invention discloses a dynamic coordination method for delivery tasks based on drones and logistics lockers. This invention relates to the field of collaborative delivery technology between drones and logistics lockers. First, it acquires basic information about the delivery drones and, combined with cargo weight, route, and airspace characteristics, constructs four intervals: delivery power consumption, remaining power, delivery time, and arrival time, forming a delivery data list. Then, it divides the time intervals according to a unified time granularity, aggregates drones, and determines their return capability, generating a drone charging demand list. Subsequently, it collects real-time dynamic information on charging locations, splits resources according to the time dimension, and matches time-period demand to allocate dedicated charging locations for drones. Finally, the drones standardize their speed adjustment within the delivery time intervals, ensuring accurate arrival at the corresponding logistics lockers and automatic unloading and storage. This method achieves precise dynamic matching of delivery and charging resources, improving scheduling efficiency and resource utilization.

Description

Technical Field

[0001] This invention relates to the field of collaborative delivery technology between drones and logistics lockers, specifically a dynamic coordination method for delivery tasks based on drones and logistics lockers. Background Technology

[0002] The collaborative delivery of goods using drones and logistics lockers has become an important development direction in the low-altitude logistics field. In this process, achieving dynamic matching between drone delivery tasks and logistics locker charging resources is crucial to ensuring operational compliance and scheduling efficiency. However, existing coordination solutions have not yet formed a complete technical support system, revealing the following shortcomings in practical applications:

[0003] First, the lack of precise range calculation based on cargo weight and route differences, relying solely on single-dimensional data, leads to distorted assessment of drone return capability and an inability to accurately identify charging needs.

[0004] Secondly, the lack of effective integration of route speed limit standards and the impact of traffic control waiting times has resulted in unclear definition of arrival time intervals, making it difficult to achieve orderly collection of drones according to time periods;

[0005] Third, the failure to allocate resources according to time to accommodate the arrival times of drones has created a contradiction between competition for charging spots and idle resources.

[0006] Fourth, it is impossible to stably lock the total delivery time within the preset range, causing the actual arrival time to deviate from the matching time period, thus disrupting the coordination logic between delivery and charging resources;

[0007] Therefore, there is an urgent need for a dynamic coordination method for drone and logistics locker delivery tasks. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a dynamic coordination and processing method for delivery tasks based on drones and logistics lockers, which solves the problems of resource imbalance between drones and logistics lockers, inefficient scheduling, and uncontrolled speed regulation during time periods.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a dynamic coordination processing method for delivery tasks based on drones and logistics lockers, comprising:

[0010] S1. Obtain basic information for each delivery drone, construct delivery power consumption range and remaining power range by combining cargo weight and delivery route mileage, construct delivery time range and arrival time range based on airspace traffic characteristics of delivery route, bind a unique device ID to the delivery drone, and integrate the four ranges to form a drone delivery data list. The basic information includes the initial power of the drone when it takes off, the actual weight of the cargo to be transported, the shortest flight path and the longest flight path of the drone to the corresponding logistics cabinet, and the fixed path of the drone returning empty to the nest.

[0011] S2. Divide the arrival time intervals of each delivery drone into a uniform time granularity to form a drone list for each delivery time period. Combine the minimum power consumption of the delivery drone returning empty to the nest to determine the return capability and obtain a charging demand list for each delivery time period.

[0012] S3. Collect dynamic information of charging positions at the logistics cabinet in real time, split the charging position resources by time dimension, and match the charging position resources of each time period with the charging demand list of the corresponding time period by delivery time period, and allocate a dedicated charging position for each delivery drone that needs to be charged. The dynamic information includes the total number of charging positions in the logistics cabinet, the number of charging positions that are currently idle, and the expected time point when the charging position that is in use will be released after charging.

[0013] S4. When the delivery drone performs a cargo delivery task, it adjusts its flight speed within its own delivery time range and completes the automatic unloading and storage of the cargo into the corresponding logistics cabinet after arriving at the cabinet.

[0014] As a further aspect of the present invention, the specific operation for constructing the delivery power consumption range and the remaining power range is as follows:

[0015] The actual weight data of the goods to be delivered is retrieved through the drone dispatch system. At the same time, the shortest and longest flight path mileage of the delivery drone to the corresponding logistics cabinet is extracted using the geographic information system (GIS), and two sets of mileage data are recorded.

[0016] Based on the power consumption characteristics per unit mileage corresponding to the weight of the goods, the minimum power consumption for the delivery drone to complete the shortest path delivery and the maximum power consumption for the longest path delivery are calculated respectively. The minimum power consumption and the maximum power consumption are used as the interval boundary to form the delivery power consumption interval.

[0017] Based on the initial power consumption of the delivery drone at takeoff, the minimum and maximum power consumption of the delivery power consumption range are deducted respectively to obtain the remaining power range after the delivery drone arrives at the corresponding logistics cabinet and unloads the goods.

[0018] As a further aspect of the present invention, the specific operation for constructing the delivery time interval and the arrival time interval is as follows:

[0019] The airspace management system retrieves the airspace access characteristics data corresponding to the shortest and longest flight paths, including the speed limit standards and control waiting time of the corresponding paths. The three sets of data, namely path mileage, speed limit standards, and control waiting time, are bound together. If there is no airspace control waiting time for the corresponding path, the airspace control waiting time of the path is uniformly assigned to 0.

[0020] Calculate the base flight time of the shortest and longest flight paths, and add the control waiting time of the corresponding paths to obtain the time 1 and time 2 corresponding to the delivery drone carrying goods. Select min(time 1, time 2) as the lower limit of the delivery time interval, and select max(time 1, time 2) as the upper limit of the delivery time interval.

[0021] The actual takeoff time of each delivery drone is obtained through the drone scheduling system. The lower limit of the interval between the actual takeoff time and the delivery time is used as the lower limit of the arrival time interval, and the upper limit of the interval between the actual takeoff time and the delivery time is used as the upper limit of the arrival time interval, thus determining the arrival time interval.

[0022] As a further aspect of the present invention, for the shortest flight path, the basic flight time is calculated as mileage / speed limit standard; for the longest flight path, the basic flight time is calculated as mileage / low speed level; the speed of the UAV is divided into three levels: low speed level, medium speed level, and high speed level, and all three levels are less than the speed limit standard.

[0023] As a further aspect of the present invention, the specific details of forming the drone list for each delivery period include:

[0024] Set a fixed time granularity, the duration of which is not less than the fixed total time taken for a single delivery drone to complete landing and unloading at the corresponding logistics cabinet and start charging preparation. Use this time granularity as a unified standard for dividing the arrival time intervals of all delivery drones.

[0025] Starting from 00:00 on the same day, the entire day is divided into continuous and non-overlapping delivery periods according to the time granularity determined above, and each delivery period is marked with a unique time period number and a specific time range.

[0026] Extract the arrival time interval of each delivery drone: If the arrival time interval overlaps with the time range of a certain delivery time period, then the delivery drone is classified into the corresponding delivery time period. The overlap includes partial overlap and complete inclusion.

[0027] After all delivery drones have been identified, a list of drones corresponding to each delivery time period is compiled, which includes the drone's device ID and remaining battery level.

[0028] As a further aspect of the present invention, obtaining the minimum power consumption for the drone to return to its nest empty specifically includes:

[0029] Based on the drone list corresponding to the delivery time period, for each delivery drone in the drone list, the unique empty return route from the corresponding logistics cabinet to the drone nest is extracted by the geographic information system (GIS), and the mileage of the route is recorded.

[0030] The power consumption per unit flight distance of the delivery drone when it is unloaded is retrieved through the drone equipment management system.

[0031] The power consumption required for a delivery drone to fly empty from the corresponding logistics cabinet to the drone nest is calculated as follows: delivery drone empty return path mileage × power consumption per unit flight mileage when empty. The result is the minimum power consumption of a single delivery drone for empty return.

[0032] As a further aspect of the present invention, the return capability is determined by combining the minimum power consumption: if the minimum value of the remaining power range is not lower than the minimum power consumption, it is determined that no charging is required and the vehicle can return directly empty; if the maximum value of the remaining power range is lower than the minimum power consumption, it is determined that charging is required and the vehicle cannot return directly empty.

[0033] As a further aspect of the present invention, the method for calculating the expected completion time of charging and releasing of the currently used charging position is as follows:

[0034] For each charging station in use at the logistics cabinet, the real-time remaining power of the drone being charged is collected through the charging station sensor module. The drone equipment management system retrieves the full battery charge and rated charging efficiency of the drone and binds the three sets of data with the corresponding charging station number to form a unique data group corresponding to the charging station number and the drone being charged.

[0035] Based on the dedicated data set, first determine the amount of electricity that the drone needs to replenish from its current remaining power to a full charge. Then divide it by its rated charging efficiency to calculate the time required to complete the full charge. This time is the remaining charging time for a single drone.

[0036] The current precise time is obtained by the high-precision clock module built into the logistics cabinet. The current precise time is added to the remaining charging time to obtain the estimated time when the charging position in use is expected to complete charging and be released. Each delivery period after the estimated time when charging is completed and released is taken as the estimated release period.

[0037] As a further aspect of the present invention, the specific operation of splitting charging position resources according to the time dimension is as follows:

[0038] All charging spots are divided into two categories and their attributes are recorded separately: the first category is currently available charging spots, and its time attribute is marked as {available for use at all times from the current time}; the second category is charging spots that are currently in use, and the expected time point of completion of charging and release is directly extracted, and its time attribute is marked as {expected time point of completion of charging and release, expected release period}.

[0039] The two types of charging slot resources are split into time slots: ① Currently idle charging slots are directly assigned to all unfinished delivery time slots; ② Charging slots currently in use are assigned to the expected release time slots based on their expected completion time. After the split is completed, the number of available charging slots is counted one by one according to the delivery time slot.

[0040] Based on delivery time periods, the charging slot resources for each time period are matched with the corresponding charging demand list, and a dedicated charging slot is allocated to each delivery drone that needs to be charged, ensuring that the number of delivery drones that need to be charged during any delivery time period does not exceed the number of charging slots available during that time period.

[0041] As a further aspect of the present invention, the specific operation of adjusting the flight speed according to the delivery time interval is as follows:

[0042] The delivery drone flies at a constant speed along the loaded delivery path. Throughout the flight, the flight control timing module collects its current cumulative flight time in real time and compares it with the shortest and longest times within the locked delivery time range, then adjusts its speed accordingly.

[0043] If the current cumulative flight time is less than the shortest time in the delivery time interval, and the completed delivery route mileage reaches 90% or more of the total route mileage, the delivery drone will reduce its flight speed to a low speed and maintain a constant speed until the remaining route is completed.

[0044] If the current cumulative flight time is more than 80% of the longest time in the delivery time interval, and the completed delivery route mileage is less than 70% of the total route mileage, the delivery drone will increase its flight speed to the high speed setting and maintain a constant speed until the remaining route is completed.

[0045] Except for the two situations mentioned above, the delivery drones always maintain a constant speed at medium speed.

[0046] This invention provides a dynamic coordination method for delivery tasks based on drones and logistics lockers, which has the following advantages compared with the prior art:

[0047] (1) This invention integrates key factors such as cargo weight, route characteristics, and airspace traffic rules to construct four major intervals: delivery power consumption, remaining power, delivery time, and arrival time. This avoids the one-sidedness of single-dimensional calculation and accurately derives the return capability and arrival time range of the drone.

[0048] (2) This invention splits charging position resources with a unified time granularity and matches charging demand according to delivery time period, breaking through the limitations of static allocation mode. At the same time, by collecting charging position status in real time and accurately calculating release time, the charging position resources are precisely aligned with the arrival time of the drone, which avoids competition for charging positions during the time period and reduces resource idleness and waste.

[0049] (3) By establishing a standardized speed adjustment mechanism, this invention enables the drone to dynamically adjust its flight speed within the delivery time range, ensuring that the actual arrival time falls precisely within the matching time period, and avoiding resource mismatch caused by arrival time deviation while meeting airspace compliance requirements. Attached Figure Description

[0050] Figure 1 This is a flowchart of the steps of the present invention;

[0051] Figure 2 This is a flowchart illustrating the steps involved in adjusting the flight speed according to the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] like Figure 1 This invention provides a dynamic coordination processing method based on delivery tasks of drones and logistics cabinets;

[0054] As an embodiment of this application, the specific steps include the following:

[0055] S1. Obtain basic information for each delivery drone, combine cargo weight and delivery route mileage to construct delivery power consumption range and remaining power range, construct delivery time range and arrival time range based on airspace traffic characteristics of delivery route, bind a unique device ID to each drone, and integrate the four ranges to form a drone delivery data list.

[0056] S2. Divide the arrival time intervals of each delivery drone into a uniform time granularity to form a drone list for each delivery time period. Combine the minimum power consumption of the drone returning empty to the nest to determine the return capability and obtain a charging demand list for each delivery time period.

[0057] S3. Real-time collection of dynamic information on charging positions at the logistics cabinet, splitting charging position resources by time dimension, and matching the charging position resources of each time period with the charging demand list of the corresponding time period by delivery time period, so as to allocate a dedicated charging position for each drone that needs to be charged.

[0058] S4. When the delivery drone performs a cargo delivery task, it adjusts its flight speed within its own delivery time range and completes the automatic unloading and storage of the cargo into the corresponding logistics cabinet after arriving at the cabinet.

[0059] As a second embodiment of this application, it is implemented based on the first embodiment, except that this embodiment includes:

[0060] S1. Obtain all known basic information about each delivery drone, including the initial battery level of the drone upon takeoff, the actual weight of the goods to be transported, the shortest and longest flight paths of the drone to the corresponding logistics cabinet, and the fixed path of the drone returning empty to the nest.

[0061] By considering the impact of cargo weight and delivery route mileage on drone power consumption, the minimum and maximum power consumption for each delivery drone to complete the delivery of the cargo are directly calculated, forming a delivery power consumption range. The specific operation is as follows:

[0062] The actual weight data of the goods to be delivered is retrieved through the drone dispatch system. At the same time, the shortest and longest flight path mileage of the drone to the corresponding logistics cabinet is extracted using the geographic information system (GIS), and two sets of mileage data are recorded.

[0063] When a drone is flying with a load, its power consumption is positively correlated with the weight of the cargo and the mileage of the delivery route. That is, the heavier the cargo, the higher the power consumption per unit mileage; the longer the mileage, the higher the total power consumption. These two factors are the core variables affecting delivery power consumption. If only the mileage is considered and the impact of cargo weight on power consumption is ignored, the power consumption calculation will be distorted.

[0064] Based on the power consumption characteristics per unit mileage corresponding to the weight of the goods, the minimum power consumption for the drone to complete the shortest path delivery and the maximum power consumption for the longest path delivery are calculated respectively. The minimum power consumption and the maximum power consumption are used as the interval boundary to form the delivery power consumption interval.

[0065] The interval form is used instead of precise values ​​because drones are easily affected by airflow and flight attitude during actual flight, making it impossible to calculate power consumption with absolute precision. The interval form can accommodate this uncertainty.

[0066] Based on the initial power consumption of the drone at takeoff, the minimum and maximum power consumption of the delivery power consumption range are deducted respectively to obtain the remaining power range of the drone after it arrives at the corresponding logistics cabinet and unloads the goods. This range is the key to determining whether the drone has the ability to return empty and provides accurate data support for subsequent drone charging demand identification and charging position resource matching.

[0067] Based on the airspace characteristics of the delivery route, the shortest and longest delivery times for drones carrying goods are calculated to form a delivery time range. The specific operation is as follows:

[0068] The airspace management system retrieves the airspace access characteristics data corresponding to the shortest and longest flight paths, including the speed limit standards and control waiting time of the corresponding paths. The three sets of data, namely path mileage, speed limit standards, and control waiting time, are bound together. If there is no airspace control waiting time for the corresponding path, the airspace control waiting time of the path is uniformly assigned to 0.

[0069] Airspace control waiting period is one of the core constraints on drone flight time. If only the flight path speed limit is considered and the control waiting period is ignored, the delivery time calculation will deviate too much from the actual flight time. By setting the uncontrolled waiting period to 0, the data format can be standardized.

[0070] For the shortest flight path, the basic flight time is calculated as mileage / speed limit, and the control waiting time for that path is added to obtain the first time for the drone to complete the shortest path delivery of goods. For the longest flight path, the basic flight time is calculated as mileage / low speed level, and the control waiting time for that path is added to obtain the second time for the drone to complete the shortest path delivery of goods. min(time one, time two) is selected as the lower limit of the delivery time range, and max(time one, time two) is selected as the upper limit of the delivery time range. The speed of the drone is divided into low speed level, medium speed level, and high speed level, and all three levels are less than the speed limit.

[0071] The path mileage is fixed geographical data, while the speed limit standard is the compliant flight speed value under the path. The two are combined to obtain the time for the drone to fly under the speed limit standard on the path. The basic flight time calculated in this way corresponds to the maximum time consumed on the shortest flight path and the longest flight path, respectively. The control waiting period time is then added to completely define the actual flight time range of the drone in the delivery time range.

[0072] The actual takeoff time of each delivery drone is obtained through the drone scheduling system. Combined with the delivery time interval, the lower limit of the actual takeoff time plus the lower limit of the delivery time interval is used as the lower limit of the arrival time interval, and the upper limit of the actual takeoff time plus the upper limit of the delivery time interval is used as the upper limit of the arrival time interval, thus determining the arrival time interval of the drone to the corresponding logistics cabinet.

[0073] S2. Divide the arrival time intervals of all delivery drones into a unified time granularity to form a drone list corresponding to each delivery time interval. The specific operation is as follows:

[0074] Set a fixed time granularity, the duration of which is not less than the fixed total time taken for a single drone to complete landing and unloading at the corresponding logistics cabinet and start charging preparation. Use this time granularity as a unified standard for dividing the arrival time intervals of all drones.

[0075] The entire process of a drone from landing to unloading to charging preparation is an inseparable and complete operation. Only when the time granularity is greater than or equal to the total time of the process can it be ensured that a single drone completes all the preparatory work and officially enters the charging and matching state within the same delivery period. If the time granularity is less than the total time, the operation process of a single drone will be split into two different delivery periods, making it impossible to accurately attribute its charging needs to a certain period, which will directly cause information confusion in subsequent charging location matching.

[0076] Starting from 00:00 on the same day, the entire day is divided into continuous and non-overlapping delivery periods according to the time granularity determined above, and each delivery period is marked with a unique time period number and a specific time range.

[0077] For example, if the time granularity is 10 minutes, it can be labeled "Time Period 1: 00:00-00:10, Time Period 2: 00:10-00:20..." to form a delivery time period framework covering the entire day;

[0078] Extract the arrival time interval of each drone and determine the time relationship between this interval and each time period in the established delivery time frame: if the arrival time interval overlaps with the time range of a certain delivery time period, then the drone is assigned to the corresponding delivery time period. The overlap includes partial overlap and complete inclusion.

[0079] After all drones have been identified, a drone list corresponding to each delivery time period is compiled, which includes the drone device ID and remaining battery range.

[0080] If time period overlap is not performed, drones will arrive at the logistics locker in an disorderly manner, which will cause competition for charging positions. However, by using interval overlap as the sole basis for classification, the group of drones that may arrive at the logistics locker within the same time window can be accurately identified, and drones can be classified in an orderly manner according to time period.

[0081] For example, the arrival time of drone A is 06:03-06:12, which overlaps with delivery time slots 37 (06:00-06:10) and 38 (06:10-06:20). Therefore, it is included in the drone list of both time slot 37 and time slot 38. The final time slot 37 list includes drones A, B, and C, and the time slot 38 list includes drones A, D, and E.

[0082] Determining the minimum power consumption for a drone to return empty to its nest specifically includes:

[0083] Based on the drone list corresponding to each delivery period, for each drone in the drone list, the unique empty return route from the corresponding logistics cabinet to the drone nest is extracted by the geographic information system (GIS), and the mileage of the route is recorded.

[0084] The power consumption characteristics per unit mileage of the drone under no-load conditions are retrieved through the drone equipment management system, that is, the power consumption per unit flight mile when no-load.

[0085] The model and wear and tear of each drone may vary, resulting in different power consumption characteristics per unit distance when airborne. At the same time, the airborne return path is a fixed geographical path, and its mileage is the core basis for calculating power consumption. Therefore, both need to be considered together.

[0086] The power consumption required for a drone to fly empty from the corresponding logistics cabinet to the drone nest is calculated as follows: drone empty return path mileage × power consumption per unit distance when flying empty. The result is the minimum power consumption for a single drone to return empty.

[0087] The return capability is determined by the minimum power consumption of the drone when returning to the nest empty: if the minimum value of the remaining power range is not lower than the minimum power consumption, it is determined that no charging is required and the drone can return empty directly; if the maximum value of the remaining power range is lower than the minimum power consumption, it is determined that charging is required and the drone cannot return empty directly.

[0088] Finally, for each delivery period, the number of drones that need charging during that period is counted, and the corresponding device IDs are recorded to form a charging demand list for each delivery period, which serves as a direct basis for subsequent matching of charging space resources.

[0089] S3. Real-time collection of dynamic information on charging positions at the logistics cabinet, including the total number of charging positions in the logistics cabinet, the number of charging positions currently in an idle state, and the estimated time when the charging positions in use will be released after charging is completed.

[0090] The method for calculating the expected completion time of charging and releasing of the currently used charging position is as follows:

[0091] For each charging station in use at the logistics cabinet, the real-time remaining power of the drone being charged is collected through the charging station sensor module. The drone equipment management system retrieves the full battery charge and rated charging efficiency of the drone and binds the three sets of data with the corresponding charging station number to form a unique data group corresponding to the charging station number and the drone being charged.

[0092] Different drones have different battery specifications and real-time charging progress, so the charging time will inevitably vary. If a uniform charging time is used to estimate the time, it will easily cause the release time to be distorted.

[0093] Based on the dedicated data set, the amount of electricity required to bring the drone from its current remaining power to full power is first determined. Then, combined with its rated charging efficiency, the time required to complete the full charge is calculated. This time is the remaining charging time for a single drone.

[0094] For example, the amount of electricity that drone A needs to replenish is calculated by subtracting the current remaining electricity of 0.4kWh from the full charge of 1.2kWh, which gives a replenishment of 0.8kWh. Based on the rated charging efficiency of 0.2kWh / minute, the remaining charging time is calculated to be 4 minutes.

[0095] The current precise time is obtained by the high-precision clock module built into the logistics cabinet. The current precise time is added to the remaining charging time to obtain the estimated time when the charging position in use is expected to complete charging and be released. Each delivery period after the estimated time when charging is completed and released is taken as the estimated release period.

[0096] For example, if the current precise time is 06:05:30 and drone A has 4 minutes of remaining charging time, the precise release time for charging station 1 is calculated to be 06:09:30. Combining this with a uniform 10-minute delivery time granularity, the expected release periods for charging station 1 are 06:09:30-06:10:00 (period 37), 06:10:00-06:20:00 (period 38), and all subsequent periods. This means that the charging station can be allocated and used from 06:09:30 onwards.

[0097] The specific steps for splitting charging space resources according to the time dimension are as follows:

[0098] All charging spots are divided into two categories and their attributes are recorded separately: The first category is currently available charging spots, and its time attribute is directly marked as {available for use at all times from the current time}; the second category is charging spots currently in use, and its expected completion time for charging and release is directly extracted and its time attribute is marked as {expected completion time for charging and release, expected release period}.

[0099] There is a fundamental difference in the available time between currently available charging slots and those currently in use. Categorizing and sorting these slots can clarify the availability boundaries of each type of resource and prevent unreleased charging slots from being mistakenly included in the current delivery time.

[0100] Based on the unified time-granularity delivery time frame established in step S2, time period matching and splitting are performed on the two types of charging slot resources: ① Currently idle charging slots are directly assigned to all unfinished delivery time periods; ② Charging slots currently in use are assigned to the expected release time period according to their expected completion time. After the splitting is completed, the number of available charging slots is counted one by one according to the delivery time period. This number is the sum of all available charging slots in the corresponding time period.

[0101] If only the number of available charging spots is statically counted without being broken down by time, it will result in charging spots being occupied by early-arriving drones and no spots available for late-arriving drones. Breaking them down by a unified delivery time frame can align the time attributes of charging spot resources with the arrival time of drones, achieving precise matching of resources and demand within the time frame.

[0102] For example, with a unified delivery time granularity of 10 minutes, time slots 37 (06:00-06:10) and 38 (06:10-06:20) are analyzed separately for the 5 charging positions: Charging position 1 is released at 06:09:30 (belonging to time slot 37), and is included in the expected release time slot, i.e., time slot 37 and subsequent time slots; Charging position 2 is released at 06:18:00 (belonging to time slot 38), and is also included in the expected release time slot, i.e., time slot 38 and subsequent time slots; Charging positions 3 to 5 are idle and are included in time slot 37 and subsequent time slots; The final statistics show that there are 4 available charging positions in time slot 37 and 5 available charging positions in time slot 38.

[0103] By matching charging slots with the charging demand list for each delivery period, a dedicated charging slot is assigned to each drone that needs charging. This ensures that the number of drones needing charging during any delivery period does not exceed the number of available charging slots for that period, thus preventing conflicts such as competition for charging slots and lack of available slots after drones arrive.

[0104] S4. When the delivery drone performs a cargo delivery task, it adjusts its flight speed within its own delivery time range and completes the automatic unloading and storage of the cargo into the corresponding logistics cabinet after arriving at the cabinet.

[0105] As a third embodiment of this application, this embodiment further discloses a method for adjusting flight speed within a delivery time range, based on embodiments one and two. Figure 2 As shown, the specific content includes:

[0106] The drone retrieves its own bound dynamic delivery path reference range to the corresponding logistics locker from the generated drone delivery data list, and loads this path reference range into the flight control system. This ensures that the entire flight can be within this reference range, and the actual flight path can be dynamically adjusted according to the real-time airspace status. Simultaneously, the drone retrieves its own delivery time interval, locks the shortest and longest time boundaries of this time interval in the flight control system, and presets the full path reference mileage. This value changes with the real-time changes of the dynamic delivery path and needs to be verified in advance based on the pre-defined dynamic delivery path reference range and fixed throughout the entire process. No matter how the actual path is dynamically adjusted due to airspace congestion or temporary control during the drone's flight, this reference mileage remains unchanged.

[0107] The reference range for the dynamic delivery route is the compliant airspace boundary for the drone to fly to the corresponding logistics cabinet. This range is based on the shortest and longest flight paths. The drone can dynamically adjust its actual flight path within this range, but must not exceed the geographical and airspace constraints of this range.

[0108] The drone flies at a constant speed along the loaded delivery route. Throughout the flight, the flight control timing module collects its current cumulative flight time in real time and compares it directly with the shortest and longest times within the locked delivery time range, executing corresponding speed adjustment actions accordingly.

[0109] If the current cumulative flight time is less than the shortest time in the delivery time interval, and the completed delivery route mileage reaches 90% or more of the total route mileage, the drone will reduce its flight speed to a low speed and maintain a constant speed until the remaining route is completed.

[0110] If the current cumulative flight time is more than 80% of the longest delivery time interval, and the completed delivery route mileage is less than 70% of the total route mileage, the drone will increase its flight speed to the high speed setting and maintain a constant speed until the remaining route is completed.

[0111] Except for the two situations mentioned above, the drone always maintains a constant speed at the medium speed setting;

[0112] The entire process involves only the three types of speed gear switching operations mentioned above, without changing the flight heading or altitude;

[0113] By accurately locking the total actual delivery time of the drone through standardized speed control, the actual arrival time of the drone is ensured to fall precisely into the delivery time slot it matches, thus achieving precise time-dimensional binding with the charging space resources of the corresponding logistics cabinet during the appropriate time slot.

[0114] After the drone arrives at the corresponding logistics cabinet, it completes the automatic unloading and storage of goods into the cabinet: drones that are determined not to need charging will return to the nest empty along a fixed path; drones that are determined to need charging will land at the assigned dedicated charging position to complete charging, and then return to the nest empty after charging is completed.

[0115] After a drone completes a delivery or charging task, it updates its remaining battery power and task completion status in real time. At the same time, the logistics cabinet updates the occupancy status of the corresponding charging slot and the estimated time when the drone is being charged will be released. All the updated data is transmitted back to the drone delivery data list in step S1 in real time, providing the latest data support for the interval extrapolation, time period aggregation, and charging slot matching of subsequent new delivery tasks.

[0116] Any content not described in detail in this specification is prior art known to those skilled in the art.

[0117] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A dynamic coordination method for delivery tasks based on drones and logistics lockers, characterized in that, include: S1. Obtain basic information for each delivery drone, construct delivery power consumption range and remaining power range based on cargo weight and delivery route mileage, construct delivery time range and arrival time range based on airspace characteristics of the delivery route, and bind a unique device ID to the delivery drone. Integrate the four ranges to form a drone delivery data list. The basic information includes the initial battery level of the drone at takeoff, the actual weight of the cargo to be transported, the shortest and longest flight paths of the drone to the corresponding logistics cabinet, and the fixed path of the drone returning empty to the nest. The specific operation of constructing the delivery power consumption range and remaining power range is as follows: retrieve the actual weight data of the cargo to be delivered through the drone scheduling system. At the same time, use the Geographic Information System (GIS) to extract the shortest and longest flight path mileage of the delivery drone to the corresponding logistics cabinet and record two sets of mileage data. Based on the power consumption characteristics per unit mileage corresponding to the cargo weight, calculate the minimum power consumption for the delivery drone to complete the shortest path delivery and the maximum power consumption for completing the longest path delivery, and use the minimum and maximum power consumption as the range boundaries to form the delivery power consumption range. Based on the initial power consumption of the delivery drone at takeoff, the minimum and maximum power consumption of the delivery power consumption range are deducted respectively to obtain the remaining power range of the delivery drone after it arrives at the corresponding logistics cabinet and unloads the goods. S2. Divide the arrival time intervals of each delivery drone into a unified time granularity to form a drone list for each delivery time interval. Combine the minimum power consumption of the delivery drone returning empty to the nest to determine the return capability and obtain a charging demand list for each delivery time interval. The formation of the drone list for each delivery time interval specifically includes: setting a fixed time granularity, the duration of which is not less than the fixed total time taken for a single delivery drone to complete landing and unloading at the corresponding logistics cabinet and start charging preparation, and using this time granularity as a unified standard for dividing the arrival time intervals of all delivery drones; taking 00:00 of the day as the starting point, according to the time granularity determined above, the entire day is divided into continuous and non-overlapping delivery time intervals, and each delivery time interval is marked with a unique time interval number and a specific time range; extract the arrival time interval of each delivery drone: if the arrival time interval overlaps with the time range of a certain delivery time interval, the delivery drone is assigned to the corresponding delivery time interval, the overlap includes partial overlap and complete inclusion; after all delivery drones have been determined, organize them into a drone list corresponding to each delivery time interval, the list includes the delivery drone device ID and remaining power range; S3. Collect dynamic information of charging positions at the logistics cabinet in real time, split the charging position resources by time dimension, and match the charging position resources of each time period with the charging demand list of the corresponding time period by delivery time period, and allocate a dedicated charging position for each delivery drone that needs to be charged. The dynamic information includes the total number of charging positions in the logistics cabinet, the number of charging positions that are currently idle, and the expected time point when the charging position that is in use will be released after charging. S4. When the delivery drone performs a cargo delivery task, it adjusts its flight speed within its own delivery time range and completes the automatic unloading and storage of the cargo into the corresponding logistics cabinet after arriving at the cabinet.

2. The dynamic coordination processing method based on UAV and logistics locker delivery tasks according to claim 1, characterized in that, The specific steps for constructing delivery time ranges and arrival time ranges are as follows: The airspace management system retrieves the airspace access characteristics data corresponding to the shortest and longest flight paths, including the speed limit standards and control waiting time of the corresponding paths. The three sets of data, namely path mileage, speed limit standards, and control waiting time, are bound together. If there is no airspace control waiting time for the corresponding path, the airspace control waiting time of the path is uniformly assigned to 0. Calculate the base flight time of the shortest and longest flight paths, and add the control waiting time of the corresponding paths to obtain the time 1 and time 2 corresponding to the delivery drone carrying goods. Select min(time 1, time 2) as the lower limit of the delivery time interval, and select max(time 1, time 2) as the upper limit of the delivery time interval. The actual takeoff time of each delivery drone is obtained through the drone scheduling system. The lower limit of the interval between the actual takeoff time and the delivery time is used as the lower limit of the arrival time interval, and the upper limit of the interval between the actual takeoff time and the delivery time is used as the upper limit of the arrival time interval, thus determining the arrival time interval.

3. The dynamic coordination processing method based on UAV and logistics locker delivery tasks according to claim 2, characterized in that, For the shortest flight path, the basic flight time is calculated as mileage / speed limit standard; for the longest flight path, the basic flight time is calculated as mileage / low speed level; the speed of the UAV is divided into three levels: low speed, medium speed, and high speed, and all three levels are less than the speed limit standard.

4. The dynamic coordination processing method based on UAV and logistics locker delivery tasks according to claim 1, characterized in that, The minimum power consumption required for a drone to return to its nest empty includes: Based on the drone list corresponding to the delivery time period, for each delivery drone in the drone list, the unique empty return route from the corresponding logistics cabinet to the drone nest is extracted by the geographic information system (GIS), and the mileage of the route is recorded. The power consumption per unit flight distance of the delivery drone when it is unloaded is retrieved through the drone equipment management system. The power consumption required for a delivery drone to fly empty from the corresponding logistics cabinet to the drone nest is calculated as follows: delivery drone empty return path mileage × power consumption per unit flight mileage when empty. The result is the minimum power consumption of a single delivery drone for empty return.

5. The dynamic coordination processing method based on UAV and logistics locker delivery tasks according to claim 1, characterized in that, Return capability is determined by the minimum power consumption: if the minimum value of the remaining power range is not lower than the minimum power consumption, it is determined that no charging is needed and the vehicle can return directly empty; if the maximum value of the remaining power range is lower than the minimum power consumption, it is determined that charging is required and the vehicle cannot return directly empty.

6. The dynamic coordination processing method based on UAV and logistics locker delivery tasks according to claim 1, characterized in that, The method for calculating the expected completion time of charging and releasing of the currently used charging position is as follows: For each charging station in use at the logistics cabinet, the real-time remaining power of the drone being charged is collected through the charging station sensor module. The drone equipment management system retrieves the full battery charge and rated charging efficiency of the drone and binds the three sets of data with the corresponding charging station number to form a unique data group corresponding to the charging station number and the drone being charged. Based on the dedicated data set, first determine the amount of electricity that the drone needs to replenish from its current remaining power to a full charge. Then divide it by its rated charging efficiency to calculate the time required to complete the full charge. This time is the remaining charging time for a single drone. The current precise time is obtained by the high-precision clock module built into the logistics cabinet. The current precise time is added to the remaining charging time to obtain the estimated time when the charging position in use is expected to complete charging and be released. Each delivery period after the estimated time when charging is completed and released is taken as the estimated release period.

7. The dynamic coordination processing method based on UAV and logistics locker delivery tasks according to claim 1, characterized in that, The specific steps for splitting charging space resources according to the time dimension are as follows: All charging positions are divided into two categories and their attributes are recorded separately: the first category is currently available charging positions, and its time attribute is marked as "available for use at all times from the current time"; the second category is charging positions that are currently in use, and the expected time point of completion of charging and release is directly extracted, and its time attribute is marked as "expected time point of completion of charging and release, expected release period". The two types of charging slot resources are split into time slots: ① Currently idle charging slots are directly assigned to all unfinished delivery time slots; ② Charging slots currently in use are assigned to the expected release time slots based on their expected completion time. After the split is completed, the number of available charging slots is counted one by one according to the delivery time slot. Based on delivery time periods, the charging slot resources for each time period are matched with the corresponding charging demand list, and a dedicated charging slot is allocated to each delivery drone that needs to be charged, ensuring that the number of delivery drones that need to be charged during any delivery time period does not exceed the number of charging slots available during that time period.

8. The dynamic coordination processing method based on UAV and logistics locker delivery tasks according to claim 1, characterized in that, The specific steps for adjusting flight speed based on delivery time intervals are as follows: The delivery drone flies at a constant speed along the loaded delivery path. Throughout the flight, the flight control timing module collects its current cumulative flight time in real time and compares it with the shortest and longest times within the locked delivery time range, then adjusts its speed accordingly. If the current cumulative flight time is less than the shortest time in the delivery time interval, and the completed delivery route mileage reaches 90% or more of the total route mileage, the delivery drone will reduce its flight speed to a low speed and maintain a constant speed until the remaining route is completed. If the current cumulative flight time is more than 80% of the longest time in the delivery time interval, and the completed delivery route mileage is less than 70% of the total route mileage, the delivery drone will increase its flight speed to the high speed setting and maintain a constant speed until the remaining route is completed. Except for the two situations mentioned above, the delivery drones always maintain a constant speed at medium speed.

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