A distributed unmanned aerial vehicle logistics distribution system

Abstract

The application relates to the technical field of unmanned aerial vehicle distribution, and particularly discloses a distributed unmanned aerial vehicle logistics distribution system. The system comprises a user interaction system, an order distribution system, a path planning system, an energy management system, a monitoring and feedback system and an unmanned aerial vehicle warehouse. A user generates an order by using the user interaction system, the order distribution system receives the order, combines real-time order quantity and the distribution position of the unmanned aerial vehicle warehouse, and distributes the order to the optimal unmanned aerial vehicle warehouse, the path planning system selects appropriate unmanned aerial vehicles, a distribution mode and a distribution path based on the contents of goods information, order information, weather information and unmanned aerial vehicle warehouse information in combination with a multi-target decision AI algorithm, the energy management system calculates and provides the consumed energy of the unmanned aerial vehicles, and the monitoring and feedback system monitors the flight state in real time and simultaneously provides real-time distribution information to the user. The system enhances the utilization efficiency and flight safety of the unmanned aerial vehicles, improves the timeliness of distribution and optimizes the user experience through the distributed warehouse layout.

Description

Technical Field

[0001] This invention relates to the field of drone delivery technology, specifically a distributed drone logistics delivery system. Background Technology

[0002] In recent years, with the rapid development of drone technology, drones have been widely used in many industries. Especially in the field of logistics and delivery, drones have gradually become an important delivery tool due to their advantages such as high efficiency, flexibility, and low cost.

[0003] However, many current drone delivery missions typically rely on a single drone to deliver goods directly from the warehouse to the destination. But due to the limited range and payload capacity of drones, long-distance, high-payload delivery missions often encounter problems such as insufficient drone energy and decreased stability, which significantly reduces delivery efficiency and safety. How to improve delivery efficiency, ensure flight safety, optimize energy use, and rationally allocate delivery resources remains a key issue that the industry urgently needs to address.

[0004] Furthermore, existing drone warehouses or delivery stations are mostly centrally managed, meaning a single centralized warehouse is responsible for deliveries in the surrounding area. This layout leads to excessively long delivery distances and significant resource waste. Especially in areas with high delivery demand, the scheduling of delivery tasks and the allocation of resources often fail to respond promptly to market needs, resulting in poor delivery timeliness and low user satisfaction.

[0005] Therefore, there is an urgent need for a distributed drone logistics delivery system to solve the problems existing in the current technology. Summary of the Invention

[0006] The purpose of this invention is to provide a distributed unmanned aerial vehicle (UAV) logistics delivery system to solve the problems mentioned in the background art.

[0007] This invention is a distributed drone logistics delivery system, including a user interaction system, an order allocation system, a route planning system, an energy management system, a monitoring and feedback system, and a drone warehouse; The order allocation system, route planning system, and monitoring and feedback system all establish data links with the drone warehouse and drone terminal through wireless communication networks to obtain real-time drone warehouse information and real-time drone operation status. The user interaction system is used to interact with users and generate order requirements; The order allocation system is used to receive order requests and parse them to obtain detailed order information. Combining the distribution location of drone warehouses and the real-time operation status of drones, the system allocates the orders to the optimal drone warehouse. The path planning system, based on order information, weather information, optimal drone warehouse, and air traffic information, combines multi-objective intelligent AI algorithms to make decisions and schedules, select drone models, delivery methods and delivery routes, and send control signals to the drone warehouse; The energy management system acquires drone model, delivery route, weather information and order information, calculates the energy required by the drone to perform the task according to the preset energy consumption calculation model, and provides the drone warehouse with the energy consumption data required by the drone. The monitoring and feedback system is used to monitor the flight status in real time and issue an alarm when there is an anomaly, while also providing users with real-time delivery information; The drone warehouses are located in major logistics hubs in the city and are used to park drones. They also provide functions such as drone transfer, maintenance, refueling, and battery replenishment.

[0008] Furthermore, the multi-objective intelligent AI algorithm aims to minimize flight distance, flight time, energy consumption, and safety. It constructs a delivery spatiotemporal map based on a geographic information system map, the optimal drone warehouse location, and the delivery address. Based on order information, the drone's maximum range, remaining drone energy, airspace constraints, and weather information, it generates a set of candidate paths that meet the flight conditions. For each candidate path, it calculates the flight distance cost, time cost, energy cost, safety risk cost, and transfer cost, forming a multi-objective comprehensive evaluation result. Based on this multi-objective comprehensive evaluation result, it jointly outputs the drone model, whether a relay delivery method is used, and the delivery route.

[0009] Furthermore, the path planning system includes a path calculation module and a drone scheduling module; The path calculation module receives order information and optimal drone warehouse information, combines weather conditions, air traffic information and standby drone list, and uses multi-objective intelligent AI algorithm to make decision-making and scheduling, and outputs drone model, delivery method and delivery route. The drone scheduling module is used to send drone scheduling control signals to the corresponding drone warehouse to execute delivery tasks based on the drone model, delivery method, and delivery route.

[0010] Furthermore, the energy management system's built-in energy consumption calculation model calculates the energy consumption of the drone during the takeoff, cruise, hovering, landing, and transfer waiting phases based on the drone model, order information, delivery route, delivery method, and weather information, and adds a preset safety margin to obtain the energy required by the drone to perform the delivery task.

[0011] Furthermore, the monitoring and feedback system includes a flight status monitoring module and a mission execution feedback module; The flight status monitoring module is used to acquire the flight parameters of the UAV in real time, including flight altitude, speed, heading, remaining battery or fuel, and flight path, and compare them with the predetermined flight parameters; when the deviation between the UAV's flight parameters and the predetermined flight parameters exceeds a set threshold, the alarm mechanism of the flight status monitoring module is triggered. The task execution feedback module is used to obtain the current location of the delivery drone, compare it with the predetermined flight route, obtain the order delivery progress and the estimated arrival time of the goods, and transmit it to the user interaction system.

[0012] Furthermore, the drone warehouse includes a drone storage module, a transfer module, a maintenance module, and a power replenishment module; The drone storage module is used to store all drones on site and to schedule them to other sub-modules in the drone warehouse to perform related functions as needed; The transfer module is used to transfer the cargo transported by the previous drone to the new drone so that the new drone can continue to carry out the transportation mission. The maintenance module is used to maintain the normal operation of various parts such as the fuselage and wings of the drone. The energy replenishment module is used to provide fuel refueling or battery replacement services for the drone based on the energy consumption data of the drone provided by the energy management system.

[0013] The present invention also provides a delivery method based on the aforementioned distributed unmanned aerial vehicle (UAV) logistics delivery system, comprising: S1: The user interaction system receives orders submitted by users, generates order requirements, and sends them to the order allocation system; S2: The order allocation system parses the order information through the built-in order parsing algorithm. Then, it combines the distribution location of the drone warehouse and the real-time operation status of the drones, and comprehensively considers the distance from the drone warehouse to the pickup and delivery locations, the number of drones currently available in the drone warehouse, the estimated take-off waiting time, the drone warehouse's refueling capability, and the weather information of the drones in the inventory area. After weighted scoring of each candidate drone warehouse, the optimal drone warehouse is selected as the delivery starting point and sent to the path calculation module. S3: The route planning system, based on order information and optimal drone warehouse information, combined with weather conditions, air traffic information and standby drone list, uses multi-objective intelligent AI algorithms to determine drone models and delivery routes, and sends them to the drone warehouse and energy management system. S4: The energy management system obtains the drone model, delivery route, weather information and order information, calculates the energy required by the drone to perform the task according to the preset energy consumption calculation model, and provides the drone warehouse with the energy consumption data required by the drone. S5: The drone warehouse dispatches the corresponding model of drone according to the control signal, and compares the remaining energy of the drone with the required energy consumption data to determine whether the drone can complete the delivery task. If it cannot complete the task, the drone is recharged and then proceeded to step S6. If the task can be completed, step S6 is performed directly. S6: Based on the generated delivery route, the route planning system determines whether the current delivery task should use a relay delivery method and sends the delivery method to the drone scheduling module. The drone scheduling module generates an interaction signal based on the received information and sends it to the drone, scheduling the drone to execute the delivery task according to the preset delivery method and delivery route. S7: After the drone arrives at the delivery location, it places the goods at the designated delivery location, and the user interaction system sends a package arrival notification to the user. S8: If the drone completes all delivery tasks or runs out of energy, it returns to the warehouse to wait or recharge; if it does not complete all delivery tasks and its energy is not exhausted, it returns to step S6 to continue performing the remaining delivery tasks.

[0014] The distributed drone logistics delivery system provided by this invention has the following beneficial effects: (1) Traditional drone delivery systems rely on a single centralized warehouse to handle deliveries to the surrounding area. This layout results in excessively long delivery distances and significant resource waste. In contrast, distributed drone warehouses are scattered across multiple locations within a city, with each warehouse serving its surrounding area. This effectively shortens delivery routes, thereby reducing energy waste and improving delivery efficiency.

[0015] (2) In long-distance, high-load delivery missions, a single UAV may face problems such as insufficient battery power or rapid fuel consumption, which may prevent it from completing the entire mission. The relay delivery method, by setting up a transfer platform in the refueling station, hands over the delivery mission to a standby UAV, reducing the flight distance of a single UAV to perform the delivery mission, thereby reducing the amount of fuel or batteries carried by a single UAV. This allows each UAV to operate more efficiently during mission execution, improving the stability and safety of UAV operation and extending the service life of the UAV.

[0016] (3) Users can obtain the delivery progress and estimated arrival time of goods in real time through the user interaction system, which increases the transparency and predictability of the delivery process and enhances users' trust and satisfaction. Attached Figure Description

[0017] Figure 1 This invention presents an architecture diagram of a distributed unmanned aerial vehicle (UAV) logistics delivery system. Figure 2 A flowchart illustrating an embodiment of a distributed unmanned aerial vehicle (UAV) logistics delivery system according to the present invention; Figure 3This is a schematic diagram of an embodiment of a distributed unmanned aerial vehicle (UAV) logistics delivery system according to the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification 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.

[0019] In the description of the embodiments of the present invention, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0020] This invention provides a distributed unmanned aerial vehicle (UAV) logistics delivery system, such as... Figure 1 and Figure 3 As shown, it includes a user interaction system, an order allocation system, a path planning system, an energy management system, a monitoring and feedback system, and a drone warehouse; the order allocation system, the path planning system, and the monitoring and feedback system all establish data links with the drone warehouse and the drone terminal through a wireless communication network to obtain real-time drone warehouse information and real-time drone operation status.

[0021] The user interaction system is used to interact with users and generate order requirements.

[0022] The order allocation system includes an order receiving module and a warehouse selection module.

[0023] The order receiving module interfaces with the user interaction system to obtain the user's order requirements. It then parses the order requirements using a built-in order parsing algorithm. This algorithm extracts entities, standardizes fields, and maps constraints to the text and structured information in the order requirements. It obtains the cargo size, quantity, weight, pickup location, expected delivery time, and priority for each order, and maps these to the corresponding load level and delivery constraint parameters. These parameters are then used as input to the subsequent warehouse selection module and route planning system.

[0024] The warehouse selection module establishes a communication connection with the Geographic Information System (GIS). Combining order information, drone warehouse information, and real-time drone operation status, it employs a comprehensive candidate warehouse scoring algorithm to select the optimal drone warehouse. This algorithm comprehensively considers the distance from the drone warehouse to the pickup and / or delivery location, the number of currently available drones in the warehouse, the estimated takeoff waiting time, the warehouse's refueling capabilities, and the weather risk of the area where the warehouse is located. It then assigns a weighted score to each candidate drone warehouse and selects the optimal one. This optimal drone warehouse is output as warehouse information to the path planning system. In some embodiments, when the number of available drones in the optimal drone warehouse is insufficient, a drone warehouse closer to the delivery location can be selected as the starting warehouse.

[0025] The path planning system includes a path calculation module and a drone scheduling module.

[0026] The path calculation module receives order and warehouse information, combines weather conditions, air traffic information, and a list of standby drones, and makes decisions using a multi-objective intelligent AI algorithm. The multi-objective intelligent AI algorithm aims to minimize flight distance, flight time, energy consumption, and safety. It constructs a delivery spatiotemporal map based on a geographic information system (including drone warehouse nodes, transit nodes, and receiving nodes, as well as edges of flyable paths, overlaid with no-fly zones, height-restricted zones, weather risk zones, and air traffic information). Based on the drone's maximum range, remaining energy, payload capacity, airspace constraints, and weather information, it generates a set of candidate paths that meet the flight conditions. Then, it calculates the flight distance cost, flight time cost, energy consumption cost, safety risk cost, and transit cost for each path in the candidate path set, constructs a multi-objective comprehensive evaluation function, and uniformly evaluates and ranks the candidate paths. Based on the multi-objective evaluation results, it jointly determines the drone model, delivery method (including relay delivery and regular delivery), and delivery path, and determines the corresponding transit warehouse sequence when using relay delivery. Relay delivery is a delivery method involving multiple drones. It is not used for short-distance, low-payload orders; however, for long-distance, high-payload orders, the path calculation module will arrange relay delivery. Drone model, delivery method, delivery route, and transit warehouse sequence will be sent to the drone scheduling module and energy management system.

[0027] The drone dispatch module generates control signals based on the received information and sends them to the drone warehouse. The drone warehouse then dispatches the corresponding model of drone to perform the delivery task based on the control signals.

[0028] Meanwhile, the energy management system calculates energy consumption based on information such as path information, drone model, weather conditions, order information, and whether a relay delivery method is used, transmitted from the path planning system, according to a pre-set energy consumption calculation model. The energy consumption calculation model includes energy consumption calculations for the drone's takeoff, cruise, hovering, landing, and transfer waiting phases, and comprehensively considers factors such as drone model, cargo weight, delivery route distance, drone flight altitude changes, wind speed and direction, ambient temperature, and the number of relays. Finally, it adds a safety margin to obtain the total energy required to perform the task and transmits the required energy consumption data of the drone to the drone warehouse's energy replenishment module.

[0029] Distributed drone warehouses are used to park and dispatch drones for missions. They are widely distributed throughout cities, with the distance between adjacent warehouses less than the maximum delivery radius of the drones. Each drone warehouse includes a drone storage module, a transfer module, a maintenance module, and a refueling module. The drone storage module stores all drones currently in service and dispatches them to other sub-modules within the warehouse as needed. The drones, used for delivery missions, come in various models and utilize energy forms including hybrid electric vehicles, fuel cells, and lithium batteries. The transfer module, in relay delivery mode, transfers goods transported by the original drone to a standby drone to continue the delivery mission. The maintenance module maintains the drone's fuselage, wings, and other components for proper operation. The refueling module provides refueling or battery replacement services to the drones based on energy consumption data provided by the energy management system. It should be noted that the refueling amount or battery energy provided by the refueling module should be slightly more than the energy consumption calculated by the energy management system. During operation, the drone warehouse receives control signals from the drone scheduling module, retrieves the corresponding drone model from the drone storage module, and diagnoses the drone's remaining energy and component status. Once its health is confirmed, the delivery method and route information are sent to the drone, which then takes off according to the predetermined plan to perform the delivery task. For orders requiring relay delivery, the drone performing the delivery task hands over the goods to the standby drone at the transfer module of the drone warehouse midway along the predetermined delivery route. The standby drone then takes over the delivery task. After receiving the goods, the standby drone continues along the predetermined route to its destination; the original drone can replenish its energy at the recharging module and remain on standby, or perform another delivery task at the current drone warehouse.

[0030] The monitoring and feedback system includes a flight status monitoring module and a mission execution feedback module. The flight status monitoring module tracks various flight parameters of the UAV in real time, such as flight altitude, speed, heading, remaining battery or fuel, and flight path, and compares them with predetermined flight parameters. When the deviation between the UAV's flight parameters, real-time weather or air traffic information and predetermined parameters exceeds a set threshold, an alarm mechanism is triggered, and a replanning request is sent to the path planning system.

[0031] The task execution feedback module continuously collects the drone's current location information, compares it with the predetermined flight route, calculates the actual delivery progress and estimated arrival time of the goods, and promptly feeds this information back to the user interaction system, allowing users to keep track of the current delivery status at any time.

[0032] Once the drone arrives at the delivery location, it places the goods at the designated location (e.g., a drone receiver installed at the destination). When the delivery is complete, the user interaction system sends a notification to the user, informing them that the package has been delivered to the designated location and requesting them to pick it up as soon as possible. Finally, if there are no other pending tasks, the drone is scheduled to return to the corresponding drone warehouse to prepare for the next task. Conversely, if there are pending tasks, the above process is repeated to continue operation.

[0033] This invention also provides a distributed delivery method based on the aforementioned distributed unmanned aerial vehicle (UAV) logistics delivery system, such as... Figure 2 As shown, it includes: S1: The user interaction system receives orders submitted by users, generates order requirements, and sends them to the order receiving module; S2: The order receiving module extracts entities, standardizes fields, and maps constraints for order requirements through the built-in order parsing algorithm, and generates order information to be sent to the warehouse selection module and the route calculation module. The order information includes the size, quantity, weight, pickup location, delivery location, expected delivery time, and priority of the goods. S3: The warehouse selection module combines order information, real-time drone operation status, and geographic information (GIS) system data. Based on the comprehensive scoring algorithm of candidate warehouses, it selects the optimal drone warehouse as the delivery starting point and sends it to the route planning system. S4: The path calculation module constructs a delivery spatiotemporal map and generates a candidate path set based on order information and optimal drone warehouse information, combined with weather conditions, air traffic information and standby drone list. It evaluates the path based on a multi-objective comprehensive evaluation function and jointly determines the drone model and delivery path, and sends it to the drone scheduling module and energy management system. S5: The drone dispatch module generates a control signal based on the received information and sends it to the drone warehouse. The drone warehouse dispatches the corresponding model drone based on the control signal and compares its remaining energy with the required energy provided by the energy management system in the maintenance module to determine whether the current drone can complete the delivery task. If it cannot complete the task, the drone is sent to the energy replenishment module for energy replenishment and then proceeds to step S6. If the task can be completed, proceed directly to step S6. S6: The path calculation module, based on the generated delivery path and decision results, determines whether the current delivery task should use a relay delivery method and sends the delivery method to the drone scheduling module. If the order is a short-distance, low-load order, normal delivery is used; if the order is a long-distance, high-load order, relay delivery is used, and the delivery method is sent to the drone scheduling module. The drone scheduling module generates an interaction signal based on the received information and sends it to the drone, scheduling the drone to execute the delivery task according to the preset delivery method and path. For orders requiring relay delivery, the drone executing the delivery task hands over the goods to the standby drone at the transfer module of the drone warehouse midway along the predetermined delivery path. The standby drone then takes over from the original drone to continue executing the delivery task. After receiving the goods, the standby drone continues to the destination along the predetermined path. The original drone can replenish its energy at the refueling module and remain on standby, or execute another delivery task at the current drone warehouse. S7: After the drone arrives at the delivery location, it places the goods at the designated delivery location. The user interaction system sends a notification to the user, informing them that the package has been delivered to the designated location. S8: Determine whether the drone has completed all delivery tasks or whether its energy is about to run out. If yes, the drone returns to the warehouse to wait or recharge. If not, return to step S6 to continue performing the remaining delivery tasks.

[0034] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed unmanned aerial vehicle (UAV) logistics delivery system, characterized in that, It includes a user interaction system, an order allocation system, a route planning system, an energy management system, a monitoring and feedback system, and a drone warehouse; the order allocation system, route planning system, and monitoring and feedback system all establish data links with the drone warehouse and drone terminals through wireless communication networks to obtain real-time drone warehouse information and real-time drone operation status; The user interaction system is used to interact with users and generate order requirements; The order allocation system is used to receive order requests and parse them to obtain detailed order information. Combining the distribution location of drone warehouses and the real-time operation status of drones, the system allocates the orders to the optimal drone warehouse. The path planning system, based on order information, weather information, optimal drone warehouse, and air traffic information, combines multi-objective intelligent AI algorithms to make decisions and schedules, select drone models, delivery methods and delivery routes, and send control signals to the drone warehouse; The energy management system acquires drone model, delivery route, weather information and order information, calculates the energy required by the drone to perform the task according to the preset energy consumption calculation model, and provides the drone warehouse with the energy consumption data required by the drone. The monitoring and feedback system is used to monitor the flight status in real time and issue an alarm when there is an anomaly, while also providing users with real-time delivery information; The drone warehouses are located in major logistics hubs in the city and are used to park drones. They also provide functions such as drone transfer, maintenance, refueling, and battery replenishment.

2. The distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 1, characterized in that, The order allocation system includes an order receiving module and a warehouse selection module; The order receiving module interfaces with the user interaction system to obtain the user's order requirements. It uses a built-in order parsing algorithm to parse the size, quantity, weight of the goods, as well as the pick-up and delivery locations, and outputs the order information to the warehouse selection module and the route planning system. The warehouse selection module is based on a geographic information system and comprehensively considers the distance from the drone warehouse to the pickup and delivery locations, the number of drones currently available in the drone warehouse, the estimated takeoff waiting time, the drone warehouse's refueling capabilities, and the weather information of the area where the drone warehouse is located. It selects the optimal drone warehouse as the starting point for the delivery task, and outputs this optimal drone warehouse information to the path planning system.

3. A distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 2, characterized in that, The order parsing algorithm extracts entities, standardizes fields, and maps constraints to order requirements, obtaining information such as cargo size, quantity, weight, pickup location, delivery location, expected delivery time, and priority. This information is then mapped to corresponding load levels and delivery constraint parameters, which are used as input to the subsequent warehouse selection module and route planning module.

4. A distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 1, characterized in that, The multi-objective intelligent AI algorithm aims to minimize flight distance, flight time, energy consumption, and safety. It constructs a delivery spatiotemporal map based on a geographic information system map, the optimal drone warehouse location, and the delivery address. It generates a set of candidate paths that meet the flight conditions based on order information, the drone's maximum range, remaining drone energy, airspace constraints, and weather information. For each candidate path, it calculates the flight distance cost, time cost, energy consumption cost, safety risk cost, and transfer cost, forming a comprehensive multi-objective evaluation result. Based on the multi-objective comprehensive evaluation results, the drone model, whether a relay delivery method is used, and the delivery route are jointly output.

5. A distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 1, characterized in that, The path planning system includes a path calculation module and a drone scheduling module; The path calculation module receives order information and optimal drone warehouse information, combines weather conditions, air traffic information and standby drone list, and uses multi-objective intelligent AI algorithm to make decision-making and scheduling, and outputs drone model, delivery method and delivery route. The drone scheduling module is used to send drone scheduling control signals to the corresponding drone warehouse to execute delivery tasks based on the drone model, delivery method, and delivery route.

6. A distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 1, characterized in that, The energy management system's built-in energy consumption calculation model calculates the energy consumption of the drone during the takeoff, cruise, hovering, landing, and transit waiting phases based on the drone model, order information, delivery route, delivery method, and weather information. It also adds a preset safety margin to obtain the energy required by the drone to perform the delivery task.

7. A distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 1, characterized in that, The monitoring and feedback system includes a flight status monitoring module and a mission execution feedback module; The flight status monitoring module is used to acquire the flight parameters of the UAV in real time, including flight altitude, speed, heading, remaining battery or fuel, and flight path, and compare them with the predetermined flight parameters; when the deviation between the UAV's flight parameters and the predetermined flight parameters exceeds a set threshold, the alarm mechanism of the flight status monitoring module is triggered. The task execution feedback module is used to obtain the current location of the delivery drone, compare it with the predetermined flight route, obtain the order delivery progress and the estimated arrival time of the goods, and transmit it to the user interaction system.

8. A distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 1, characterized in that, The drone warehouse includes a drone storage module, a transfer module, a maintenance module, and a power replenishment module; The drone storage module is used to store all drones on site and to schedule them to other sub-modules in the drone warehouse to perform related functions as needed; The transfer module is used to transfer the cargo transported by the previous drone to the new drone so that the new drone can continue to carry out the transportation mission. The maintenance module is used to maintain the normal operation of the UAV fuselage and wing parts; The energy replenishment module is used to provide fuel refueling or battery replacement services for the drone based on the energy consumption data of the drone provided by the energy management system.

9. A distributed unmanned aerial vehicle (UAV) logistics delivery system according to claim 1, characterized in that, Drones come in various models and energy forms, including hybrid electric vehicles, fuel cells, and lithium batteries.

10. A delivery method based on the distributed unmanned aerial vehicle (UAV) logistics delivery system according to any one of claims 1-9, characterized in that, include: S1: The user interaction system receives orders submitted by users, generates order requirements, and sends them to the order allocation system; S2: The order allocation system parses the order information through the built-in order parsing algorithm. Then, it combines the distribution location of the drone warehouse and the real-time operation status of the drones, and comprehensively considers the distance from the drone warehouse to the pickup and delivery locations, the number of drones currently available in the drone warehouse, the estimated take-off waiting time, the drone warehouse's refueling capability, and the weather information of the drones in the inventory area. After weighted scoring of each candidate drone warehouse, the optimal drone warehouse is selected as the delivery starting point and sent to the path calculation module. S3: The route planning system, based on order information and optimal drone warehouse information, combined with weather conditions, air traffic information and standby drone list, uses multi-objective intelligent AI algorithms to determine drone models and delivery routes, and sends them to the drone warehouse and energy management system. S4: The energy management system obtains the drone model, delivery route, weather information and order information, calculates the energy required by the drone to perform the task according to the preset energy consumption calculation model, and provides the drone warehouse with the energy consumption data required by the drone. S5: The drone warehouse dispatches the corresponding model of drone according to the control signal, and compares the remaining energy of the drone with the required energy consumption data to determine whether the drone can complete the delivery task. If it cannot complete the task, the drone is recharged and then proceeded to step S6. If the task can be completed, step S6 is performed directly. S6: Based on the generated delivery route, the route planning system determines whether the current delivery task should use a relay delivery method and sends the delivery method to the drone scheduling module. The drone scheduling module generates an interaction signal based on the received information and sends it to the drone, scheduling the drone to execute the delivery task according to the preset delivery method and delivery route. S7: After the drone arrives at the delivery location, it places the goods at the designated delivery location, and the user interaction system sends a package arrival notification to the user. S8: If the drone completes all delivery tasks or runs out of energy, it returns to the warehouse to wait or recharge; if it does not complete all delivery tasks and its energy is not exhausted, it returns to step S6 to continue performing the remaining delivery tasks.

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