Low-altitude logistics distribution method based on distributed connection warehouse network

Abstract

The invention discloses a low-altitude logistics distribution method based on a distributed connection warehouse network, and belongs to the technical field of logistics distribution. The method comprises the steps of constructing a distributed logistics connection warehouse network, carrying out cargo distribution and flight task planning, carrying out unmanned aerial vehicle flight management and scheduling execution, carrying out flight supervision, carrying out inventory management and state updating, and carrying out task evaluation and model optimization. Wherein the construction of the distributed logistics connection warehouse network is based on geographic information data, population density data and logistics demand prediction data, and a clustering analysis and optimization algorithm is used to determine the number, geographic positions and service models of connection warehouses. According to the method, reasonable site selection and layout are carried out on the connection bins, and efficient distribution and scheduling of the cargoes among the connection bins are realized by using big data analysis and an intelligent algorithm, so that the cargo transportation efficiency can be effectively improved, and the logistics cost is saved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of logistics distribution, and more particularly relates to a low-altitude logistics distribution method based on a distributed connection warehouse network. BACKGROUND

[0002] With the rapid development of the e-commerce industry and the increasing demand for timeliness of logistics distribution, the drawbacks of the traditional ground logistics distribution mode, such as traffic congestion and limited distribution range, are increasingly prominent.

[0003] As a new distribution mode, low-altitude logistics distribution uses unmanned aerial vehicles for cargo transportation, has the advantages of fast speed and being unaffected by ground traffic, and can effectively improve the efficiency of logistics distribution. However, the biggest defect of low-altitude logistics distribution is the lack of a reasonable transfer and scheduling mechanism, resulting in low cargo transportation efficiency and high logistics cost.

[0004] For example, in the patent CN112781592A, a low-altitude logistics unmanned aerial vehicle path planning method and system are disclosed. The method of the application comprises: first, using a grid method to divide the flight environment of the logistics unmanned aerial vehicle into grids, and determining the danger degree of each free grid; second, constructing a logistics unmanned aerial vehicle path planning model based on the target function and the maneuverability constraint condition of the logistics unmanned aerial vehicle; third, determining the flight path of the logistics unmanned aerial vehicle path planning model; and finally, using a bidirectional crossover judgment method to optimize the flight path to obtain an optimized flight path. The bidirectional crossover judgment method introduced in the application can simplify the flight path of the logistics unmanned aerial vehicle, optimize the flight path, effectively solve the problem of redundant path points in the path planned by the original algorithm, and is beneficial to improving the logistics transportation efficiency and reducing the logistics cost. However, the industry still needs more diverse designs to solve the problems of low efficiency and high cost of low-altitude logistics distribution. SUMMARY

[0005] 1. Problems to be solved

[0006] In view of at least some of the problems existing in the prior art, the present application provides a low-altitude logistics distribution method based on a distributed connection warehouse network, which aims to solve the problems of low efficiency and high cost of existing low-altitude logistics distribution.

[0007] 2. Technical solutions

[0008] In order to solve the above problems, the technical solutions adopted by the present application are as follows:

[0009] The low-altitude logistics distribution method based on the distributed connection warehouse network of the present application comprises the following steps:

[0010] S1, constructing a distributed logistics connection warehouse network

[0011] Based on geographic information data, population density data and logistics demand prediction data, the number, geographic location and service range of the transfer warehouse are determined by using clustering analysis and optimization algorithm, and a distributed logistics transfer warehouse network is constructed.

[0012] S2, cargo allocation and flight task planning

[0013] Using logistics order system and sensor technology, real-time acquisition of cargo information, combined with the storage capacity of transfer warehouse, current inventory, unmanned aerial vehicle carrying capacity and range, the optimal transfer path for cargo is allocated and flight task is generated through dynamic programming algorithm; wherein the dynamic programming algorithm takes minimizing transportation cost and delivery time as objective function for solving;

[0014] S3, unmanned aerial vehicle flight management and dispatch execution

[0015] Real-time weather data, airspace control information and traffic flow conditions are obtained, and safe and efficient flight route is planned for unmanned aerial vehicle through path planning algorithm; and real-time communication system is used to monitor and dynamically adjust the flight state;

[0016] S4, flight supervision

[0017] During the whole flight, the flight state of unmanned aerial vehicle is monitored in real time through real-time communication system, when the unmanned aerial vehicle deviates from the planned flight route or abnormal situation occurs, the unmanned aerial vehicle is adjusted in time through the communication system to ensure it flies according to the planned route;

[0018] S5, inventory management and state update

[0019] The goods in the transfer warehouse are classified and stored, and an inventory management model is established; and according to the frequency of warehouse in and out, shelf life, inventory optimization algorithm is used to determine the best inventory level and update in real time;

[0020] S6, task evaluation and model optimization

[0021] After the unmanned aerial vehicle completes the delivery task, the platform collects relevant flight data, performs task feedback and effect evaluation; according to the evaluation result, the scheduling strategy and optimization algorithm in flight task planning are iteratively adjusted to improve the intelligent level of subsequent logistics scheduling and task execution; wherein the relevant flight data at least includes task completion time, path deviation, material distribution state.

[0022] In step S1, the construction of the distributed hub network is the basic step to achieve efficient logistics scheduling of low-altitude UAVs. The design not only needs to meet the spatial distribution of current logistics demand, but also needs to have adaptability to future demand changes. Therefore, this step uses a combination of multi-source heterogeneous data fusion, clustering analysis and multi-objective optimization to systematically complete the network architecture design and node layout. The specific steps are as follows,

[0023] First, the following core data is obtained:

[0024] Geographic information data: including terrain and landform (such as mountains, rivers, green land, etc.), traffic road network, city function zoning (such as residential area, industrial area, commercial area) and building height restriction, etc.

[0025] Population density data: combined with national / territorial census data, operator real-time location service data and mobile internet device heat map, dynamic population activity intensity atlas is obtained;

[0026] Historical logistics order data: statistics of delivery frequency, order type, delivery time period, cargo weight and size, etc. to form a multi-dimensional logistics demand portrait;

[0027] Future logistics demand prediction data: using time series prediction algorithm and machine learning model (such as XGBoost, LSTM, etc.) to predict the order growth rate and delivery density of each region in the future based on historical data and consumer behavior trends;

[0028] Then, the above data is input into the GIS platform for visual fusion analysis, and clustering algorithms such as K-Means, DBSCAN or spectral clustering are used to divide the city and surrounding areas into several logistics high-correlation zones. Each zone is considered as a candidate hub service area, and the internal logistics demand tends to be homogeneous, with stable supply and demand patterns.

[0029] On this basis, a multi-objective optimization model is introduced to optimize the hub network structure with the following objective functions:

[0030] Minimize total construction and operation cost (including land rental, warehouse construction, energy, maintenance, labor cost);

[0031] Maximize service coverage efficiency (including order response rate and average delivery time within the delivery radius);

[0032] Minimize the total length of warehouse transfer paths to reduce the number of relays, shorten the flight distance and time;

[0033] Enhance network redundancy to avoid delivery interruption caused by single point failure.

[0034] The constraint conditions of the optimization model include:

[0035] There must be at least one docking station node in each service area;

[0036] The area covered by each docking station shall not exceed its effective service radius (e.g. 5km);

[0037] Prioritize setting up warehouses in existing commercial facilities, rooftop platforms or industrial parks to reduce new infrastructure investment;

[0038] For remote areas or areas with low logistics density, the use of mobile docking warehouses (such as container warehouses and temporary warehouses) is encouraged for flexible coverage.

[0039] The resulting distributed docking network features a "center-periphery-branch" structure. A central hub features a high-density, high-throughput master node, while a number of high-frequency relay and branch nodes are deployed in the periphery, forming an efficient, interconnected distribution grid. Inter-dock scheduling, coupled with an algorithmic approach, creates a "drone relay + intra-dock load-swap" model, enabling long-distance, multi-hop, low-latency, and highly efficient intelligent delivery.

[0040] In some embodiments, in step S2, the cargo information includes the weight, volume, and destination information of the cargo; the transportation cost includes the flight energy consumption cost of the drone and the loading and unloading cost at the docking station; the delivery time includes the transit time of the cargo between each docking station and the flight time of the drone; the flight mission includes the flight path, take-off and landing sequence, and drone matching plan.

[0041] In some embodiments, in step S3, weather data is provided by a weather monitoring station, including relevant meteorological information on wind speed, wind direction, rainfall, and lightning; airspace information is provided by an aviation management agency; and the path planning algorithm at least considers obstacle avoidance, congestion, and no-fly zones;

[0042] In some embodiments, in step S4, for flight missions with higher mission levels, routes crossing complex airspace or abnormal weather, the platform must undergo manual secondary approval and confirmation before the flight can be launched.

[0043] In some embodiments, in step S5, the inventory management model combines the first-in-first-out principle with the economic order quantity model, and the inventory optimization algorithm optimizes the inventory structure with the goal of minimizing inventory costs and out-of-stock risks.

[0044] This step is the key to achieving efficient and coordinated operation of the distributed docking warehouse network. Specific methods include:

[0045] Goods in the docking warehouse are intelligently classified and stored according to categories, volumes, timeliness requirements, etc., and item-level tracking is achieved by installing RFID electronic tags;

[0046] A distributed inventory management system is constructed to synchronize the inventory quantity of each warehouse, the in-out warehouse record and the replenishment prediction demand in real time. The system adopts an edge computing architecture based on IoT to improve the response speed.

[0047] According to factors such as the shelf life, turnover rate and popularity of goods, the replenishment priority and storage area are dynamically adjusted, and the first-in-first-out (FIFO) principle and economic order quantity (EOQ) model are combined to realize the cost-optimal inventory strategy.

[0048] A safety threshold of inventory is set, and when the inventory of a certain warehouse decreases to the threshold, the system automatically triggers a replenishment warning and automatically interfaces with the supplier or the central warehouse.

[0049] A mechanism of sharing the remaining amount between warehouses and an intelligent allocation strategy are supported, that is, when the inventory of a certain warehouse is short and the neighboring warehouse has surplus goods, the shortest flight path and the optimal delivery opportunity are automatically matched to complete cross-warehouse allocation, thereby improving the collaborative efficiency and resource utilization of the entire network.

[0050] In some embodiments, in step S6, the path deviation is obtained by comparing the actual flight trajectory recorded by the UAV navigation system with the planned path; and the delivery material state includes material integrity, delivery accuracy and temperature control data.

[0051] In some embodiments, in step S6, the flight data further includes the number of airspace interference events during the flight of the UAV and the battery energy consumption curve data.

[0052] In some embodiments, in step S6, the model optimization is based on scheduling efficiency, resource consumption and task reliability as evaluation indexes; wherein the scheduling efficiency includes two sub-indexes of material delivery time consumption and task response delay rate; the resource consumption includes two sub-indexes of UAV battery energy consumption and idle rate of the docking warehouse; and the task reliability includes two sub-indexes of flight trajectory deviation rate and material damage rate.

[0053] 3. Advantages

[0054] Compared with the prior art, the present application has the following advantages:

[0055] (1) The low-altitude logistics distribution method based on the distributed docking warehouse network can reduce the flight distance and time of the UAV, improve the efficiency of goods distribution, and deliver goods to customers faster by constructing a distributed docking warehouse network and reasonably planning the goods transfer path. At the same time, the distributed deployment through the docking warehouse network can form interactions between multiple docking warehouses, effectively solve the problem of insufficient endurance and carrying capacity of a single UAV, expand the coverage of low-altitude logistics distribution, and realize the delivery of goods in remote areas.

[0056] (2) The low-altitude logistics distribution method based on the distributed connection warehouse network of the application, through intelligent clustering and optimization algorithm based on multi-source data, realizes the connection warehouse site selection scheme with high adaptability in the heterogeneous environment of cities, suburbs and the like, effectively reduces the infrastructure investment cost; at the same time, supports flexible expansion, node adjustment and mobile deployment of the warehouse network structure in the later period, and has good elastic adaptability.

[0057] (3) The low-altitude logistics distribution method based on the distributed connection warehouse network of the application, through the inventory management system with automatic identification, real-time monitoring, cross-warehouse allocation and prediction replenishment functions, realizes dynamic inventory coordination of multiple nodes in the distributed connection warehouse network, and greatly reduces the out-of-stock risk and inventory accumulation rate.

[0058] (4) The low-altitude logistics distribution method based on the distributed connection warehouse network of the application, through the closed-loop mechanism of task execution-data evaluation-strategy optimization, can continuously improve the operation efficiency, intelligent scheduling level and task robustness of the entire distributed low-altitude logistics connection warehouse network, and ensure the stable, reliable and efficient operation of the system in the changing urban environment. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 The flowchart of the low-altitude logistics distribution method based on the distributed connection warehouse network of the application. DETAILED DESCRIPTION

[0060] In order to further understand the content of the application, the application will be described in detail in combination with the drawings.

[0061] In the description of the application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0062] The application will be further described below in combination with specific embodiments.

[0063] As shown in the drawings, the low-altitude logistics distribution method based on the distributed connection warehouse network of the embodiment comprises the following steps, Figure 1

[0064] Step S1, constructing a distributed connection warehouse network

[0065] ​Taking a city as an example, the geographic information data of the city is collected, and it is found through GIS analysis that the central area of the city is densely built and congested, while the suburbs and surrounding towns are relatively open. At the same time, according to the population census data and real-time monitoring, it is determined that the population density in the central area of the city is large, and the logistics demand is strong, while the population density in the surrounding towns is relatively small, but the logistics demand also has a certain scale. At the same time, through the analysis of historical logistics order data and market trend prediction, the type and quantity of logistics demand in different areas are estimated;

[0066] The geographic information data, population density data and logistics demand prediction data are input into the clustering analysis model to divide the city into several logistics demand clustering areas. For example, the central business district, residential area and other areas with similar logistics demand are clustered into one category. Then, using optimization algorithms, considering the high land cost in the city center and the relatively low land cost in the suburbs, the construction cost, operation cost and distribution efficiency are taken as constraint conditions for calculation.

[0067] After several iterations, it is determined to set 3 transfer warehouses in the city center, and to use the rooftop space of high-rise buildings to meet the high-density logistics demand. In the surrounding towns, 1-2 transfer warehouses are set up, and the appropriate location is selected according to the size of the town and the distribution of logistics demand, and finally the distributed transfer warehouse network architecture of the city is constructed.

[0068] Step S2, cargo allocation and flight task planning

[0069] Suppose a logistics order system receives a batch of goods, including ordinary goods with a weight of 5kg, a volume of 0.1 cubic meters and a destination in a certain community in the suburbs of the city, and urgent documents with a weight of 2kg, a volume of 0.05 cubic meters and a destination in a certain office building in the city center. Through the logistics order system and sensor technology, the information of these goods is obtained in real time; at the same time, through the inventory management system, the storage capacity and current inventory of each transfer warehouse, as well as the carrying capacity and flight range of each unmanned aerial vehicle are known.

[0070] The above information is input into the dynamic programming algorithm model, with the minimum total transportation cost and delivery time as the objective function. For ordinary goods, considering its weight and volume, as well as the logistics demand situation in the suburbs, the optimal transfer path calculated by the algorithm is to first transport the goods to the transfer warehouse in the city center that is closest to the place of origin, then arrange the appropriate unmanned aerial vehicle to transport the goods to the transfer warehouse in the suburbs from the transfer warehouse in the city center, and finally deliver the goods to the destination. For urgent documents, due to the high timeliness requirement, the algorithm preferentially selects the transfer warehouse in the city center that is closest to the place of origin and has the ability to directly reach the destination, and directly transports the goods from the place of origin to the transfer warehouse in the city center, and then quickly delivers the goods to the destination, so as to realize the efficient allocation and scheduling of goods.

[0071] Step S3, unmanned aerial vehicle flight management and scheduling execution

[0072] If real-time weather data is obtained through a meteorological monitoring station during a certain delivery period, showing that there is strong wind weather in part of the city; through the aviation management department, it is known that a certain airspace is being temporarily controlled, and at the same time the traffic flow monitoring system finds that there are frequent activities of other aircraft on part of the low-altitude flight path. Input these information into the path planning algorithm, the algorithm comprehensively considers avoiding strong wind area, controlled airspace and traffic busy area, and plans a safe and efficient flight route for the UAV.

[0073] For example, a UAV originally planned to fly from a city center docking warehouse to a suburban docking warehouse, according to the algorithm planning, bypasses the strong wind area and selects a wide and uncontrolled airspace for flight. During the flight process, the flight state of the UAV is monitored in real time through the 4G / 5G communication system. When it is found that the UAV deviates slightly from the planned route due to air flow, timely adjustment instructions are sent through the communication system to ensure that the UAV flies accurately according to the planned route.

[0074] Step S4, flight supervision and manual intervention:

[0075] During the entire flight process, the monitoring system displays the position, flight speed, power and other information of the UAV in real time. When the UAV deviates (deviates from the planned route by more than a preset angle), the power is abnormal (the power is lower than the set safety threshold), or the weather suddenly changes (such as sudden strong wind, heavy rain and other severe weather), the operator can immediately manually input instructions through the operation console to realize manual intervention of the flight path and task execution state.

[0076] For example, when it is found that the UAV deviates due to the influence of strong wind, the operator quickly adjusts the flight attitude and direction of the UAV to make it return to the planned route. If the power of the UAV is too low, the operator will direct the UAV to the nearest docking warehouse or designated safe landing point for landing and charging according to the surrounding geographical environment and the position information of the UAV.

[0077] Step S5, inventory management and status update

[0078] The docking warehouse stores various goods such as electronic products and daily necessities, which are classified and stored according to factors such as the type, size and weight of the goods. Using Internet of Things technology, an electronic tag is attached to each good, and the position and inventory of the goods are located and tracked in real time through the reader.

[0079] At the same time, according to the inventory management model, combined with the first-in first-out principle and the economic order quantity model, the optimal inventory level of various goods is determined. For example, for electronic products, due to their rapid update, a lower inventory level is set, and when the inventory quantity is lower than the set threshold, the automatic replenishment warning system is triggered. The system is connected with the supplier system, and according to the preset replenishment strategy, the replenishment request is automatically sent to the supplier to replenish the goods in time, ensuring that the goods in the docking warehouse can meet the logistics distribution demand, while avoiding overstocking.

[0080] Step S6, task evaluation and model optimization

[0081] When the UAV completes the delivery task, the platform automatically collects flight data such as task completion time, path deviation (deviation distance and angle of actual flight path from predetermined path), and material state (whether intact, whether there are signs of damage, etc.). Through analysis of these data, task feedback and effect evaluation are carried out.

[0082] For example, if it is found that the task completion time in a certain area is generally longer, it may be that the traffic congestion in that area is serious or the flight path planning of the UAV is unreasonable; if the material state appears damaged, it may be that the goods have been subjected to improper vibration or collision during loading and transportation.

[0083] According to the evaluation results, the scheduling strategy in flight task planning (such as adjusting the goods distribution rules, optimizing the dispatch strategy of UAVs, etc.), and the optimization algorithm parameters (such as adjusting the weight coefficient in dynamic programming algorithm, the heuristic function in path planning algorithm, etc.) are iteratively adjusted. Through continuous iteration and optimization, the intelligent level of subsequent logistics scheduling and task execution is improved, thereby improving the efficiency and service quality of the entire low-altitude logistics distribution.

[0084] For example, after multiple task evaluations and analyses, it is found that after appropriately reducing the weight coefficient of transportation cost and appropriately increasing the weight coefficient of delivery time in the dynamic programming algorithm, the task completion time is significantly shortened in some areas with high time efficiency requirements, and the customer satisfaction is significantly improved.

[0085] The low-altitude logistics distribution method based on the distributed docking warehouse network of the embodiment improves the efficiency of UAV logistics distribution, expands the distribution coverage, and reduces the logistics operation cost. The use of distributed docking warehouses and network management systems realizes the intelligentization and automation of low-altitude logistics distribution management. Not only does it overcome the constraints of traditional ground logistics such as traffic congestion, but also overcomes the limitations of single UAV distribution in terms of endurance and load capacity, and improves the overall distribution efficiency.

[0086] The above describes the present application and its embodiments in a schematic manner, and the description is not restrictive, and the embodiments shown in the drawings are only one of the embodiments of the present application, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by the above, without departing from the spirit of the present application, similar structural modes and embodiments can be designed without creativity, and all of them shall belong to the protection scope of the present application.

Claims

1. A low-altitude logistics distribution method based on a distributed docking warehouse network, characterized by: The following steps are included: S1. Build a distributed logistics docking warehouse network Based on geographic information data, population density data, and logistics demand forecast data, cluster analysis and optimization algorithms are used to determine the number, location, and service scope of docking stations, and to build a distributed logistics docking station network. Cluster analysis groups areas with similar logistics demand, and the optimization algorithm uses construction cost, operating cost, and distribution efficiency as constraints. S2. Cargo distribution and flight mission planning Leveraging logistics order systems and sensor technology, cargo information is acquired in real time. Combined with the docking warehouse's storage capacity, current inventory, and the drone's carrying capacity and range, a dynamic programming algorithm is used to assign optimal docking routes and generate flight missions. The dynamic programming algorithm aims to minimize transportation costs and delivery time. S3. UAV flight management and dispatch execution Obtain real-time weather data, airspace control information, and traffic flow conditions, and use path planning algorithms to plan safe and efficient flight routes for drones; and use real-time communication systems to monitor and dynamically adjust flight status; S4. Flight supervision During the entire flight, the drone's flight status is monitored in real time through the real-time communication system. If the drone deviates from the planned flight route or encounters an abnormal situation, the drone will be adjusted in time through the communication system to ensure that it flies according to the planned route. S5. Inventory Management and Status Update Categorize and store goods in the docking warehouse and establish an inventory management model. Apply inventory optimization algorithms based on inbound and outbound frequency and shelf life to determine the optimal inventory level and update it in real time. S6. Task Evaluation and Model Optimization After the drone completes the delivery mission, the platform collects relevant flight data, conducts mission feedback and effect evaluation; based on the evaluation results, it iteratively adjusts the scheduling strategy and optimization algorithm in the flight mission planning to improve the intelligence level of subsequent logistics scheduling and mission execution; among them, the relevant flight data includes at least the mission completion time, path deviation, and material distribution status.

2. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 1, characterized in that: The step S1 specifically includes: S11. Get the following core data Geographic information data: including topography, transportation network, urban functional zoning, and building height restrictions; Population density data: Combine national / regional census data, operator real-time location service data, and mobile internet device heat maps to obtain a dynamic population activity intensity map; Historical logistics order data: collects statistics on delivery frequency, order type, delivery time period, cargo weight and size information to form a multi-dimensional logistics demand profile; Logistics demand forecast data: Using time series forecasting algorithms and machine learning models to predict order growth rates and delivery density in various regions over the next period of time based on historical logistics order data and consumer behavior trends; S12. Based on the above data, a geographic information system platform is used to conduct spatial visualization and data fusion analysis. A clustering algorithm is used to cluster the logistics demand of the city and surrounding areas. Areas with homogeneous logistics demand are divided into a service unit to provide a boundary basis for the site selection of docking warehouses. S13. Through a multi-index scoring model, a comprehensive evaluation is conducted from multiple perspectives, including spatial accessibility, infrastructure completeness, and legal support, to form a set of highly credible candidate sites. S14. Build an optimization model with the objective functions of minimizing construction and operation costs, maximizing coverage, minimizing inter-warehouse paths, and maximizing redundancy. Genetic algorithms or particle swarm algorithms are introduced to solve the problem, ultimately outputting the location and number of docking warehouses and the topological structure of inter-warehouse collaborative paths. S15. Establish a dynamic network evolution mechanism, including reserving redundant candidate warehouses, setting regular data callback strategies and AI simulation evaluation systems, so that the docking warehouse network can be flexibly adjusted with changes in time, order behavior, and urban development, ensuring long-term robustness and flexibility.

3. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 2, characterized in that: In step S2, the cargo information includes the weight, volume, and destination information of the cargo; the transportation cost includes the flight energy consumption cost of the drone and the loading and unloading cost at the docking station; the delivery time includes the transit time of the cargo between each docking station and the flight time of the drone; the flight mission includes the flight path, take-off and landing sequence, and drone matching plan.

4. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 3 is characterized by: In step S3, weather data is provided by a meteorological monitoring station, including relevant meteorological information on wind speed, wind direction, rainfall, and lightning; airspace information comes from an aviation management agency; and the path planning algorithm at least considers obstacle avoidance, congestion, and no-fly zones.

5. A low-altitude logistics distribution method based on a distributed docking warehouse network according to any one of claims 1 to 4, characterized in that: In step S4, for flight missions with higher mission levels, routes crossing complex airspace or abnormal weather, the platform must undergo manual secondary approval and confirmation before it can be launched.

6. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 5, characterized in that: In step S5, the inventory management model combines the first-in-first-out principle with the economic order quantity model, and the inventory optimization algorithm optimizes the inventory structure with the goal of minimizing inventory costs and out-of-stock risks.

7. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 6, characterized in that: In step S5, the Internet of Things technology is used to install an electronic tag on each cargo, and the cargo in the docking warehouse is located and tracked in real time through a reader / writer to accurately grasp the location and inventory quantity of the cargo; When the inventory level falls below the set threshold, the replenishment warning system is automatically triggered, which is connected to the supplier system. According to the preset replenishment strategy, a replenishment request is automatically sent to the supplier to achieve rapid replenishment, ensure sufficient supply of goods in the docking warehouse, and meet logistics distribution needs.

8. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 5, characterized in that: In step S6, the path deviation is obtained by comparing and analyzing the actual flight trajectory recorded by the drone navigation system with the planned path; the distribution material status includes material integrity, delivery accuracy and temperature control data.

9. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 8, characterized in that: In step S6, the flight data also includes the number of airspace interference events during the flight of the UAV and battery energy consumption curve data.

10. The low-altitude logistics distribution method based on a distributed docking warehouse network according to claim 9, characterized in that: In step S6, the model optimization is evaluated based on scheduling efficiency, resource consumption, and mission reliability. Scheduling efficiency includes two sub-indicators: material delivery time and mission response delay rate; resource consumption includes two sub-indicators: drone battery energy consumption and docking bay idle rate; and mission reliability includes two sub-indicators: flight trajectory deviation rate and material damage rate.

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