Logistics management system based on Internet of Things

Abstract

The invention discloses a logistics management system based on the Internet of Things, and the system comprises a delivery material classification module, an optimal delivery calculation planning module, and a delivery cost calculation control module. The optimal delivery calculation planning module is used for calculating and planning the optimal delivery path and the optimal delivery time of the unmanned aerial vehicle; the delivery cost calculation control module is used for calculating and controlling the total delivery cost of the unmanned aerial vehicle and the cooperative equipment according to the occurrence period and the delivery path, and the system classifies the materials needing to be delivered according to the occurrence period of the emergency through material delivery based on the unmanned aerial vehicle under the emergency; and optimal calculation planning is carried out based on the delivery path and the delivery time of the unmanned aerial vehicle, and the total delivery cost in the whole delivery process is synchronously controlled, so that the problems that the delivery of emergency materials is prolonged and the cost of each link of equipment collaboration is greatly increased under the condition of inconsistent collaboration are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and specifically to a logistics management system based on the Internet of Things. Background Art

[0002] Drone delivery is a logistics method with drones as the core technology, using drones as carriers to transport items to designated locations. As a new type of logistics delivery method, it helps to improve logistics efficiency, reduce logistics costs, and save manpower, and can deliver goods to the consignee faster and better. Especially in the application of public health emergencies, using drones to directly serve the emergency area can not only avoid the inability to deliver goods to the disaster area in time due to road damage, but also avoid direct contact between delivery personnel and patients during public health emergencies. In the existing drone delivery logistics management system, drones often operate in coordination with other devices to ensure the safety of materials during transportation and meet the complexity under the cargo transportation distance. However, during the coordinated operation, there may be inconsistent coordination, resulting in the inability to complete the pre-set itinerary, causing the extension of the delivery of emergency supplies and a significant increase in the costs of each link of the coordinated devices. Therefore, it is necessary to design a logistics management system based on the Internet of Things that improves delivery coordination consistency and reduces delay consumption costs. Summary of the Invention

[0003] The purpose of the present invention is to provide a logistics management system based on the Internet of Things to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solutions: A logistics management system based on the Internet of Things includes a delivery material classification module, an optimal delivery calculation and planning module, and a delivery cost calculation and control module. The delivery material classification module is used to classify the materials to be delivered according to the occurrence cycle of emergencies; the optimal delivery calculation and planning module is used to calculate and plan the optimal delivery path and optimal delivery time of the drone; the delivery cost calculation and control module is used to calculate and control the total delivery cost of the drone and the coordinated devices according to the occurrence cycle and the delivery path.

[0005] According to the above technical solutions, the delivery material classification module includes an occurrence cycle definition module, a coordinated device establishment module, and a material demand point location definition module; the occurrence cycle definition module is used to define the cycle of generating emergencies according to the three-day best rescue time for emergency rescue; the coordinated device establishment module is used to delimit and establish material supply points according to the area of emergencies by using movable coordinated devices; the material demand point location definition module is used to establish a coordinate system with the material supply point as the center and define the location of the material demand point to be delivered.

[0006] According to the above technical solution, the optimal delivery calculation and planning module includes a delivery route planning module, a material supply clustering point module, a delivery time calculation module, and an objective function optimization module. The delivery route planning module is used to plan the UAV delivery route based on collaborative devices; the material supply clustering point module selects material demand points as initial material supply clustering points during the process of UAV delivering materials; the delivery time calculation module is used to calculate the optimal delivery time of the UAV under different delivery routes; the objective function optimization module is used to optimize the delivery route selection for each delivery with the minimum total delivery time of the UAV as the objective function.

[0007] According to the above technical solution, the delivery cost calculation and control module includes: a collaborative device cost module, a UAV delivery cost calculation module, and a UAV delivery cost control module. The collaborative device cost module is used to calculate the collaborative device costs generated in the moving state and the fixed state when determining the material supply points according to the emergency area; the UAV delivery cost calculation module is used to calculate the material transportation cost and the fixed maintenance cost generated by the UAV in the fixed state; the UAV delivery cost control module is used to control the delivery cost generated by the UAV under the optimal delivery route according to the improvement strategy.

[0008] According to the above technical solution, the operation method of the system includes the following steps: Step 1: Classify the material categories, emergency degree coefficients, and time requirements of the materials to be delivered according to the occurrence cycle of the emergency. Step 2: Plan the UAV delivery route based on collaborative devices. Step 3: Calculate the optimal delivery time of the UAV under different delivery routes. Step 4: The UAV performs material delivery in the delivery area from the material supply point according to the planned delivery route. Step 5: Calculate and control the total delivery costs of the UAV and collaborative devices in combination with the occurrence cycle and the delivery route.

[0009] According to the above technical solution, the process of classification according to the occurrence cycle of the emergency includes: defining the cycle of the emergency as three stages according to the three-day best rescue time for emergency rescue: the first day corresponds to the first stage, the importance level of the material categories to be delivered in the first stage is level one, and the corresponding emergency degree coefficient is the largest; the second day corresponds to the second stage, the importance level of the material categories to be delivered in the second stage is level two, and the corresponding emergency degree coefficient is medium; the third day corresponds to the third stage, the importance level of the material categories to be delivered in the third stage is level three, and the emergency degree coefficient in the third stage is the smallest. Among them, the importance level of the material categories to be delivered is proportional to the demand degree of the materials in different stages of the emergency occurrence cycle.

[0010] According to the above technical solution, the material supply points are delimited based on the area of the emergency. The material supply points are established through movable collaborative devices, which have a fixed state and a mobile state. A coordinate system is established with the material supply point as the center. The position to be delivered to the material demand point is defined as the target position. A movable collaborative device can be paired with multiple drones for delivery. The delivery path of the drones is as follows: starting from the fixed-state collaborative device after carrying the delivery materials, the relief materials are transported to one or more material demand points according to the maximum flight distance during the drone delivery process, and the return path is selected according to the fixed-state collaborative device at the nearest distance. When arriving at the fixed-state collaborative device at the nearest distance, one material delivery is completed. It is judged whether to plan the next material delivery according to the hardware device status of the drone, and according to the judgment result, the drones that need to carry out the next delivery are planned for material delivery according to the above delivery path.

[0011] According to the above technical solution, during the process of the drone delivering materials: by selecting n material demand points as the initial material supply clustering points, calculating the distances between each material demand point and the initial material supply clustering points, and allocating each material demand point to the initial material supply clustering point closest to the material demand point. The initial material supply clustering point and the allocated material demand points form a cluster. Further calculate the path distance from the material demand point to the initial material supply clustering point, and judge the relationship between this distance and the maximum distance that the drone can reach for material delivery. When this distance is within the maximum distance that the drone can reach for material delivery, the material demand point is retained in the initial material supply clustering point. When this distance is outside the maximum distance that the drone can reach for material delivery, the material demand point is excluded from the initial material supply clustering point. The second judgment and division are carried out for each material demand point to the divided initial material supply clustering point according to the above conditions to ensure the optimal delivery path when the drone delivers materials based on the material supply point.

[0012] According to the above technical solution, during the process of the drone from the material supply point to the material demand point, due to the different importance levels of the material categories and the occurrence stages, there are two situations: the drone can only deliver materials from one material supply clustering point to one material demand point, and the drone can deliver materials from one material supply clustering point to a continuous number of material demand points. In these two situations, calculate and plan the delivery time of the drone. Calculate the distances between the material supply clustering point and each material demand point and between the material demand points. Through the flight speed of the drone, calculate the flight time of the drone from the material supply clustering point to each material demand position, and arrange the collection of flight times of the drone from one material demand point to any other material demand point. Drone delivery time planning: After the single - delivery path planning of the drone based on the material demand points from the material - providing clustering points is completed, the end point of each path record planned for each material demand point is used as the starting point of the next emergency delivery path. The single - delivery path allocation is repeated until all emergency material demand points are delivered. The delivery path selection for each delivery is optimized with the minimum total delivery time of the drone as the objective function, and further determine whether the length of the optimal delivery path meets the limit of the maximum flight distance of the drone. If it meets the requirement, calculate the total time required for delivery based on this delivery path. The total time required for delivery is obtained through the given average speed of the drone's flight. If the length of the optimal delivery path exceeds the limit of the maximum flight distance of the drone, mark the current material demand point as an optimization point, sort it in ascending order according to the delivery distance to be optimized, and match the drones whose flyable distance based on the delivery distance can meet the limit of the maximum flight distance of the drone from the current delivery path that exceeds the plan. Also ensure that the delivery time is optimal when the drone delivers materials based on the material demand points.

[0013] According to the above - mentioned technical solution, the delivery cost of the collaborative device is the transportation cost of transporting materials in the moving state generated when the material - providing points are delimited according to the emergency event area, and the maintenance cost in the fixed state. The delivery cost of the drone is the material transportation cost generated from the material - providing point established by the collaborative device in the fixed state to the material demand point, and the fixed maintenance cost generated from the initial departure to the end of the delivery. The total delivery cost generated = the transportation cost of transporting materials by the collaborative device in the moving state + the maintenance cost in the fixed state + the material transportation cost generated by the drone from the material - providing point established by the collaborative device in the fixed state to the material demand point + the fixed maintenance cost generated from the initial departure to the end of the delivery; Calculate the total delivery cost Q of the drone for each delivery path, that is, the total delivery cost Q of the drone = the material transportation cost P generated by the drone from the material - providing point established by the collaborative device in the fixed state to the material demand point + the fixed maintenance cost generated from the initial departure to the end of the delivery , where the material transportation cost P varies with the different stages of the emergency event occurrence cycle T and the different levels of the importance degree K of the material category, that is, according to the emergency degree coefficient reflected by the corresponding stage of the occurrence cycle T and the importance degree K of the material category decreases gradually in the three stages of the optimal rescue time. The emergency degree coefficient is larger, the flight speed V and the corresponding flight time t of the drone delivering materials in the emergency event occurrence cycle T are also larger. Therefore, the material transportation cost P of the drone delivering materials is also larger, and the fixed maintenance cost generated at this time The greater it is. Similarly, based on the process of the drone from the material supply point to the material demand point, due to the different importance levels of material categories and the stages at which they occur, there are two situations: the drone can only deliver materials from one material supply clustering point to one material demand point, and the drone can deliver materials from one material supply clustering point to multiple consecutive material demand points. In these two situations, different influencing factors are formed for the delivery cost of the drone. By calculating the transportation cost P and the fixed maintenance cost under all delivery paths of the drone according to the above calculation method Calculate them separately, arrange the calculated results in descending order in the cost set, and then, with the goal of reducing the path distance, randomly swap the path planning of n material demand points in the current delivery path of the drone. If the planned path cost decreases, the improved delivery path is retained; otherwise, the original path is maintained for delivery according to the delivery path. By repositioning n consecutive material demand points in the delivery path, control the delivery cost generated by the drone under the optimal delivery path.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention is provided with a delivered material classification module, an optimal delivery calculation and planning module, and a delivery cost calculation and control module. By classifying the material categories, emergency degree coefficients, and time requirements of the materials to be delivered according to the occurrence cycle of emergencies; further planning the delivery path of the drone based on collaborative devices; and calculating the optimal delivery time under different delivery paths of the drone; enabling the drone to perform material delivery within the delivery area from the material supply point according to the planned delivery path; and finally, calculating and controlling the total delivery cost of the drone and the collaborative devices in combination with the occurrence cycle and the delivery path, it solves the problems of the extension of the delivery of emergency materials and the substantial increase in the costs of all links of device collaboration caused by inconsistent collaboration, improves the collaborative consistency of the drone and the collaborative devices in material delivery during emergencies, and reduces the delay consumption cost caused by inconsistent collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation to the present invention. In the drawings: Figure 1 It is a schematic diagram of the module composition of a logistics management system based on the Internet of Things provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment 1: This embodiment can be applied to the scenario of unmanned aerial vehicle (UAV) material delivery management. This method can be executed by a logistics management system based on the Internet of Things provided in this embodiment. The method specifically includes the following steps: Step 1: Classify the material categories, emergency level coefficients, and time requirements to be delivered according to the occurrence cycle of emergencies. Step 2: Plan the UAV delivery routes based on collaborative devices. Step 3: Calculate the optimal delivery time for different UAV delivery routes. Step 4: The UAV performs material delivery within the delivery area from the material supply point according to the planned delivery route. Step 5: Calculate and control the total delivery cost of the UAV and collaborative devices in combination with the occurrence cycle and delivery route.

[0018] In the embodiment of the present invention, the process of classification according to the occurrence cycle of emergencies includes: defining the cycle of generating emergencies as three stages according to the three-day best rescue time for emergency rescue: the first day corresponds to the first stage, the importance level of the material categories to be delivered in the first stage is level one, and the corresponding emergency level coefficient is the largest; the second day corresponds to the second stage, the importance level of the material categories to be delivered in the second stage is level two, and the corresponding emergency level coefficient is medium; the third day corresponds to the third stage, the importance level of the material categories to be delivered in the third stage is level three, and the emergency level coefficient in the third stage is the smallest. Among them, the importance level of the material categories to be delivered is proportional to the demand degree of materials in different stages of the emergency occurrence cycle; Exemplarily, the material supply points are delimited according to the area of the emergency. The material supply points are established through movable collaborative devices, which have a fixed state and a mobile state. A coordinate system is established with the material supply point as the center. The position to be delivered to the material demand point is defined as the target position. A movable collaborative device can be paired with multiple drones for delivery. The delivery path of the drones is as follows: starting from the fixed-state collaborative device carrying the delivery materials, the relief materials are transported to one or more material demand points according to the maximum flight distance that the drones can fly during the delivery process, and the return path is selected according to the fixed-state collaborative device at the nearest distance. When reaching the fixed-state collaborative device at the nearest distance, a material delivery is completed. It is judged whether to plan the next material delivery according to the hardware device status of the drones. According to the judgment result, the drones that need to carry out the next delivery are planned for material delivery according to the above delivery path.

[0019] In the embodiment of the present invention, during the process of the drones delivering materials: by selecting n material demand points as the initial material supply clustering points, calculating the distances between each material demand point and the initial material supply clustering points, and allocating each material demand point to the initial material supply clustering point closest to the material demand point, the initial material supply clustering point and the allocated material demand points form a cluster. Further, the path distance from the material demand point to the initial material supply clustering point is calculated, and the relationship between this distance and the maximum distance that the drones can reach for material delivery is judged. When this distance is within the maximum distance that the drones can reach for material delivery, the material demand point is retained in the initial material supply clustering point. When this distance is outside the maximum distance that the drones can reach for material delivery, the material demand point is excluded from the initial material supply clustering point. The second judgment and division are carried out for each material demand point to the divided initial material supply clustering points according to the above conditions to ensure the optimal delivery path when the drones deliver materials based on the material supply points.

[0020] Exemplarily, during the process of the drones flying from the material supply point to the material demand point, due to the different importance levels of the material categories and the stages at which they occur, there are situations where the drones can only deliver materials from one material supply clustering point to one material demand point, and there are also situations where the drones can deliver materials from one material supply clustering point to a continuous number of material demand points. In these two situations, the delivery time of the drones is calculated and planned; Exemplarily, calculate the distances from the material supply clustering points to each material demand point and between the material demand points. Based on the flight speed of the unmanned aerial vehicle (UAV), calculate the flight time of the UAV from the material supply clustering points to each material demand location. Arrange the collection of flight times of the UAV from one material demand point to any other material demand point. Planning of the UAV delivery time: After the single delivery path planning based on the material demand points from the material supply clustering points by the UAV is completed, use the end point recorded for each path planned based on each material demand point as the starting point for the next emergency delivery path. Repeat the single delivery path allocation until all emergency material demand points are completely delivered. Optimize the selection of the delivery path for each delivery with the minimum total delivery time of the UAV as the objective function, and further determine whether the length of the optimal delivery path meets the limit of the maximum flight distance of the UAV. If it meets the requirement, calculate the total time required for the delivery based on this delivery path. The total time required for the delivery is obtained through the given average flight speed of the UAV. If the length of the optimal delivery path exceeds the limit of the maximum flight distance of the UAV, mark the current material demand point as an optimization point, sort it in ascending order according to the delivery distance to be optimized, and match the UAV whose flyable distance based on the delivery distance meets the limit of the maximum flight distance of the UAV from the current delivery path that exceeds the plan, and also ensure the optimal delivery time when the UAV delivers materials based on the material demand points.

[0021] In the embodiment of the present invention, the delivery cost of the collaborative device is the transportation cost of transporting materials in the moving state generated when the material supply points are delimited according to the emergency event area, and the maintenance cost in the fixed state. The delivery cost of the UAV is the material transportation cost generated from the material supply point established by the collaborative device in the fixed state to the material demand point, and the fixed maintenance cost generated from the initial departure to the end of the delivery. The total delivery cost generated = the transportation cost of transporting materials by the collaborative device in the moving state + the maintenance cost in the fixed state + the material transportation cost generated from the material supply point established by the collaborative device in the fixed state to the material demand point by the UAV + the fixed maintenance cost generated from the initial departure to the end of the delivery; Exemplarily, calculate the total delivery cost Q of the UAV for each delivery path, that is, the total delivery cost Q of the UAV = the material transportation cost P generated from the material supply point established by the collaborative device in the fixed state to the material demand point + the fixed maintenance cost generated from the initial departure to the end of the delivery , where the material transportation cost P varies with the different stages of the emergency event occurrence cycle T and the different levels of the importance K of the material category, that is, according to the emergency coefficient reflected by the corresponding stage of the occurrence cycle T and the importance K of the material category decreases gradually in the three stages of the optimal rescue time, and the emergency coefficient The larger it is, the greater the flight speed V and the corresponding flight time t of the drone delivering supplies during the emergency occurrence period T. Therefore, the transportation cost P of the drone delivering supplies is also greater, and the fixed maintenance cost generated at this time is also greater. Similarly, based on the fact that during the process of the drone flying from the supply point to the demand point, due to the importance level of the supply category and the different stages of occurrence, there are two situations: the drone can only deliver supplies from one supply clustering point to one demand point, and the drone can deliver supplies from one supply clustering point to multiple consecutive demand points. In these two situations, different influencing factors are formed for the delivery cost of the drone. By calculating the transportation cost P and the fixed maintenance cost separately according to the above calculation method, arranging the calculated results in descending order in the cost set, and then randomly swapping the path planning of n demand points of the drone in the current delivery path with the goal of reducing the path distance. If the planned path cost decreases, the improved delivery path is retained; otherwise, the original path is maintained for delivery. By repositioning n consecutive demand points in the delivery path, the delivery cost generated by the drone under the optimal delivery path is controlled.

[0022] Embodiment 2: Embodiment 2 of the present invention provides an Internet of Things-based logistics management system, Figure 1 which is a schematic diagram of the module composition of an Internet of Things-based logistics management system provided by Embodiment 2 of the present invention, as Figure 1 shown. The system includes: A delivery material classification module, used for classifying the materials to be delivered according to the occurrence period of emergencies; An optimal delivery calculation and planning module, used for calculating and planning the optimal delivery path and optimal delivery time of the drone; A delivery cost calculation and control module, used for calculating and controlling the total delivery cost of the drone and collaborative devices according to the occurrence period and delivery path.

[0023] In some embodiments of the present invention, the delivery material classification module includes: An occurrence period definition module, used for defining the period of generating emergencies according to the three-day best rescue time for emergency rescue; A collaborative device establishment module, used for delimiting and establishing supply points using movable collaborative devices according to the area of emergencies; A demand point location definition module, used for establishing a coordinate system with the supply point as the center and defining the location of the demand points to which supplies need to be delivered.

[0024] In some embodiments of the present invention, the optimal delivery calculation and planning module includes: A delivery path planning module for planning the UAV delivery path based on collaborative devices; A material supply clustering point module that selects material demand points as initial material supply clustering points during the process of UAV delivering materials; A delivery time calculation module for calculating the optimal delivery time of the UAV under different delivery paths; An objective function optimization module for optimizing the selection of the delivery path for each delivery with the minimum total delivery time of the UAV as the objective function.

[0025] In some embodiments of the present invention, the delivery cost calculation and control module includes: A collaborative device cost module for calculating the collaborative device costs generated in the moving state and the fixed state when determining the material supply points according to the emergency area; A UAV delivery cost calculation module for calculating the material transportation cost and the fixed maintenance cost generated by the UAV in the fixed state; A UAV delivery cost control module for controlling the delivery cost generated by the UAV under the optimal delivery path according to the improvement strategy.

[0026] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0027] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A logistics management system based on the Internet of Things, comprising a delivery material classification module, an optimal delivery calculation and planning module, and a delivery cost calculation and control module, characterized in that: The dispatched material classification module is used to classify the materials to be dispatched according to the occurrence cycle of emergencies; the optimal delivery calculation and planning module is used to calculate and plan the optimal delivery path and optimal delivery time of the UAV; The delivery cost calculation and control module is used to calculate and control the total delivery cost of the UAV and collaborative equipment according to the occurrence cycle and delivery path; The operation method of the system includes the following steps: Step 1: Classify the types of materials to be dispatched, the emergency degree coefficient, and the time requirements according to the occurrence cycle of emergencies; Step 2: Plan the UAV delivery path based on collaborative equipment; Step 3: Calculate the optimal delivery time of the UAV under different delivery paths; Step 4: The UAV performs material delivery execution in the delivery area from the material supply point according to the planned delivery path; Step 5: Calculate and control the total delivery cost of the UAV and collaborative equipment in combination with the occurrence cycle and delivery path; The process of classification according to the occurrence cycle of emergencies includes: defining the cycle of emergencies as three stages according to the three-day best rescue time for emergency rescue: the first day corresponds to the first stage, the importance level of the types of materials to be dispatched in the first stage is level one, and the corresponding emergency degree coefficient is the largest; the second day corresponds to the second stage, the importance level of the types of materials to be dispatched in the second stage is level two, and the corresponding emergency degree coefficient is medium; the third day corresponds to the third stage, the importance level of the types of materials to be dispatched in the third stage is level three, and the emergency degree coefficient in the third stage is the smallest. Among them, the importance level of the types of materials to be dispatched is proportional to the demand degree of materials in different stages of the occurrence cycle of emergencies; The material supply point is delimited according to the area of the emergency. The material supply point is established by mobile collaborative equipment, which has a fixed state and a mobile state. A coordinate system is established with the material supply point as the center. The position to which the materials need to be delivered to the material demand point is defined as the target position. One mobile collaborative equipment is paired with multiple UAV deliveries. The delivery path of the UAV is: starting from the fixed-state collaborative equipment after loading the dispatched materials, transporting the rescue materials to one or more material demand points according to the maximum flight distance during the UAV delivery process, and selecting the return path according to the fixed-state collaborative equipment at the nearest distance. When reaching the fixed-state collaborative equipment at the nearest distance, one material delivery ends. Judge whether to plan the next material delivery according to the hardware device status of the UAV, and plan the material delivery of the UAV to be delivered next according to the above delivery path according to the judgment result; During the process of the drone delivering supplies: By selecting n material demand points as the initial material supply clustering points, calculating the distances between each material demand point and each initial material supply clustering point, and allocating each material demand point to the initial material supply clustering point closest to the material demand point. The initial material supply clustering point and the allocated material demand points form a cluster. Further calculate the path distance from the material demand point to the initial material supply clustering point, and judge the relationship between this distance and the maximum distance that the drone can reach for material delivery. When this distance is within the maximum distance that the drone can reach for material delivery, keep the material demand point in this initial material supply clustering point. When this distance is outside the maximum distance that the drone can reach for material delivery, remove the material demand point from this initial material supply clustering point. Make a second judgment and division for each material demand point to the divided initial material supply clustering point according to the above conditions to ensure the optimal delivery path when the drone delivers materials based on the material supply point. During the process of the drone flying from the material supply point to the material demand point, due to the importance level of the material category and the different stages that occur, there are two situations: the drone can only deliver materials from one material supply clustering point to one material demand point, and the drone can deliver materials from one material supply clustering point to a continuous number of material demand points. In these two situations, calculate and plan the delivery time of the drone, calculate the distances between the material supply clustering point and each material demand point and between the material demand points. Through the flight speed of the drone, calculate the flight time of the drone from the material supply clustering point to each material demand location, and arrange the collection of flight times of the drone from each material demand point to any other material demand point. Planning of the drone delivery time: After the single - delivery path planning based on the material demand point from the material supply clustering point is completed, use the end point of each path record planned based on each material demand point as the starting point of the next emergency delivery path, repeat the single - delivery path allocation until all emergency material demand points are completely delivered. Optimize the selection of the delivery path for each delivery with the minimum total delivery time of the drone as the objective function, and further judge whether the length of the optimal delivery path meets the limit of the maximum flight distance of the drone. If it meets the requirement, calculate the total time required for delivery based on this delivery path. The total time required for delivery is obtained through the given average flight speed of the drone. If the length of the optimal delivery path exceeds the limit of the maximum flight distance of the drone, mark the current material demand point as an optimization point, sort it in ascending order according to the delivery distance to be optimized, match the drone whose flight distance based on the delivery distance meets the limit of the maximum flight distance of the drone from the current delivery path that exceeds the plan, and also ensure the optimal delivery time when the drone delivers materials based on the material demand point.

2. The logistics management system based on the Internet of Things according to claim 1, wherein: The delivery cost of the collaborative device is the transportation cost of transporting materials in a mobile state formed by delimiting material supply points according to the emergency area, as well as the maintenance cost in a fixed state. The delivery cost of the drone is the material transportation cost generated by the drone from the material supply point established by the collaborative device in a fixed state to the material demand point, as well as the fixed maintenance cost generated from the initial departure to the end of the delivery. The total delivery cost generated = the transportation cost of transporting materials by the collaborative device in a mobile state + the maintenance cost in a fixed state + the material transportation cost generated by the drone from the material supply point established by the collaborative device in a fixed state to the material demand point + the fixed maintenance cost generated from the initial departure to the end of the delivery; Calculate the total delivery cost Q of the drone for each delivery route, that is, the total delivery cost Q of the drone = the material transportation cost P generated by the drone from the material supply point established by the collaborative device in the fixed state to the material demand point + the fixed maintenance cost generated from the initial departure to the end of the delivery , where the material transportation cost P varies with the different stages of the emergency occurrence cycle T and the different levels of the importance K of the material category, that is, according to the emergency degree coefficient reflected by the corresponding stage of the occurrence cycle T and the importance K of the material category decreases gradually in the three stages of the optimal rescue time, and the emergency degree coefficient is larger, the flight speed V and the corresponding flight time t of the drone delivering materials under the emergency occurrence cycle T are also larger. Therefore, the transportation cost P of the drone delivering materials is also larger, and the fixed maintenance cost generated at this time is also larger. Similarly, based on the process of the drone from the material supply point to the material demand point, due to the different importance of the material category and the stage of occurrence, the drone can only deliver materials from one material supply clustering point to one material demand point, and there is also a situation where materials are delivered from one material supply clustering point to multiple consecutive material demand points. In these two cases, different influencing factors are formed for the delivery cost of the drone. By calculating the transportation cost P and the fixed maintenance cost under all delivery routes of the drone according to the above calculation method, arranging the calculated results in descending order in the cost set, and then randomly swapping the path planning of n material demand points in the current delivery route of the drone with the goal of reducing the path distance. If the planned path cost decreases, the improved delivery route is retained; otherwise, the original route is maintained for delivery. By repositioning n consecutive material demand points in the delivery route, the delivery cost generated by the drone under the optimal delivery route is controlled.

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