A logistics management system based on the Internet of Things

Abstract

The present invention discloses 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 control module, wherein the delivery material classification module is used to classify materials that need to be delivered, the optimal delivery calculation and planning module is used to calculate and plan the optimal delivery path and optimal delivery time of an unmanned aerial vehicle; the delivery cost calculation control module is used to calculate and control the total delivery cost of the unmanned aerial vehicle and collaborative equipment according to the occurrence cycle and the delivery path. The system classifies the materials that need to be delivered according to the occurrence cycle of the emergency event based on the delivery of materials by the unmanned aerial vehicle under emergency events, and performs optimal calculation and planning based on the delivery path and delivery time of the unmanned aerial vehicle, and synchronously controls the total delivery cost in the entire delivery process, thereby solving the problem of delayed delivery of emergency materials and a substantial increase in the cost of each link of equipment collaboration due to inconsistent collaboration.

Description

Technical Field

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

[0002] Drone delivery is a logistics method that uses drones as the core technology and uses 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, save manpower, and deliver goods to consignees faster and more efficiently. Especially in the case of public health emergencies, using drones to directly serve the emergency area can not only avoid the failure to deliver goods to the disaster area in time due to road damage, but also avoid direct contact between delivery personnel and patients when public health emergencies occur. In the existing drone delivery logistics management system, drones often work together with other equipment to ensure the safety of materials during transportation and meet the complexity of cargo transportation distances. However, in the process of collaborative operation, there will be inconsistencies in coordination, resulting in the inability to complete the pre-set itinerary, resulting in the extension of the delivery of emergency materials and a significant increase in the cost of each link of the collaborative equipment. Therefore, it is necessary to design a logistics management system based on the Internet of Things to improve the consistency of delivery coordination and reduce the cost of delay consumption. 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 technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a logistics management system based on the Internet of Things, including 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 that need 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 drones; the delivery cost calculation and control module is used to calculate and control the total delivery cost of drones and collaborative equipment according to the occurrence cycle and delivery path.

[0005] According to the above technical solution, the material delivery classification module includes an occurrence cycle definition module, a collaborative equipment establishment module, and a material demand point location definition module; the occurrence cycle definition module is used to define the cycle of emergency events based on the three-day optimal rescue time for emergency rescue; the collaborative equipment establishment module is used to use movable collaborative equipment to delineate and establish material supply points according to the area of ​​the emergency; the material demand point location definition module is used to establish a coordinate system based on the material supply point as the center of the circle, and define the location of the material demand point that needs to be delivered.

[0006] According to the above technical solution, the optimal delivery calculation and planning module includes a delivery path planning module, a material supply clustering point module, a delivery time calculation module, and an objective function optimization module. The delivery path planning module is used to plan the drone delivery path based on collaborative equipment; the material supply clustering point module selects material demand points as initial material supply clustering points during the drone delivery process; the delivery time calculation module is used to calculate the optimal delivery time under different drone delivery paths; the objective function optimization module is used to optimize the delivery path selection for each delivery with minimization of the drone's total delivery time as the objective function.

[0007] According to the above technical solution, the delivery cost calculation control module includes: a collaborative equipment cost module, a drone delivery cost calculation module, and a drone delivery cost control module. The collaborative equipment cost module is used to calculate the collaborative equipment costs generated in the mobile state and the fixed state when the material supply points are demarcated according to the emergency area; the drone delivery cost calculation module is used to calculate the material transportation cost and fixed maintenance cost generated by the drone in the fixed state; the drone delivery cost control module is used to control the delivery cost generated by the drone under the optimal delivery path according to the improved strategy.

[0008] According to the above technical solution, the operating method of the system includes the following steps:

[0009] Step 1: Classify the types of materials to be delivered, the urgency coefficient and the time requirements according to the occurrence cycle of the emergency;

[0010] Step 2: Plan the delivery path of the drone based on collaborative equipment;

[0011] Step 3: Calculate the optimal delivery time of the drone under different delivery paths;

[0012] Step 4: The drone delivers materials from the material supply point according to the planned delivery route within the delivery area;

[0013] Step 5: Calculate and control the total delivery cost of drones and collaborative equipment based on the occurrence cycle and delivery path.

[0014] According to the above technical solution, the process of classifying according to the occurrence cycle of emergencies includes: defining the cycle of emergencies into three stages according to the three-day optimal rescue time for emergency rescue: the first day corresponds to the first stage, the importance of the categories of materials that need to be delivered in the first stage is level one, and the corresponding urgency coefficient is the largest; the second day corresponds to the second stage, the importance of the categories of materials that need to be delivered in the second stage is level two, and the corresponding urgency coefficient is in the middle; the third day corresponds to the third stage, the importance of the categories of materials that need to be delivered in the third stage is level three, and the urgency coefficient of the third stage is the smallest, wherein the importance of the categories of materials that need to be delivered is proportional to the demand for materials at different stages of the occurrence cycle of emergencies.

[0015] According to the above technical solution, the material supply point is delineated according to the area of ​​the emergency event. The material supply point is established through a movable collaborative device, which has a fixed state and a mobile state. A coordinate system is established based on the material supply point as the center of the circle, and the position that needs to be delivered to the material demand point is defined as the target position K = (x i ,y i ), a movable collaborative device can be used with multiple drones for delivery. The delivery path of the drone is: starting from the fixed-state collaborative device carrying the delivery materials, the rescue materials are transported to one or more material demand points according to the maximum flight distance of the drone during the delivery process, and the return path is selected according to the collaborative device in the fixed state with the nearest distance. When the collaborative device in the fixed state with the nearest distance is reached, one material delivery is ended, and the next material delivery plan is judged according to the hardware equipment status of the drone. According to the judgment result, the drone that needs to carry out the next delivery is planned according to the above delivery path.

[0016] According to the above technical solution, in the process of drone delivery of materials: by selecting n material demand points as initial material supply clustering points, calculating the distance between each material demand point and each initial material supply clustering point, each material demand point is assigned to the initial material supply clustering point closest to the material demand point, the initial material supply clustering point and the assigned material demand point constitute a cluster, and further calculating the path distance from the material demand point to the initial material supply clustering point, judging the relationship between the distance and the maximum distance that the drone material delivery can reach, when the 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, and when the distance is outside the maximum distance that the drone can reach for material delivery, the material demand point is removed from the initial material supply clustering point, and a second judgment and division is performed from each material demand point to the divided initial material supply clustering point according to the above conditions to ensure that the delivery path of the drone based on the material supply point is optimal when delivering materials.

[0017] According to the above technical solution, in 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, there is a situation where the drone can only deliver materials from one material supply cluster point to one material demand point, and there is a situation where materials can be delivered from one material supply cluster point to multiple continuous material demand points. In these two cases, the delivery time of the drone is calculated and planned, and the distance from the material supply cluster point to each material demand point and between the material demand points is calculated by the flight speed of the drone. The flight time of the drone from the material supply cluster point to each material demand position is calculated, and the flight time collection of the drone from the material demand point to any other material demand point is arranged;

[0018] Planning of drone delivery time: After the drone completes the single delivery route planning based on the material demand point from the material supply cluster point, the end point of each route record planned based on each material demand point is used as the starting point of the next emergency delivery route, and the single delivery route allocation is repeated until all emergency material demand points are delivered. The delivery route selection for each delivery is optimized with the minimization of the total delivery time of the drone as the objective function, and further determines whether the length of the optimal delivery path meets the limit of the maximum flight distance of the drone. If it meets the limit, the total time required for delivery is calculated based on the delivery path. The total time required for delivery is obtained by 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, the current material demand point is marked as an optimization point and sorted from small to large according to the delivery distance that needs to be optimized. The drone that meets the maximum flight distance limit of the drone based on the delivery distance can be matched from the current delivery path that exceeds the plan. Similarly, the optimal delivery time is ensured when the drone delivers materials based on the material demand point.

[0019] According to the above technical solution, the delivery cost of the collaborative equipment is the transportation cost of transporting materials in the mobile state formed when the material supply point is demarcated according to the emergency area, and the maintenance cost in the 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 equipment 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 = the transportation cost of the collaborative equipment in the mobile state + the maintenance cost in the fixed state + the material transportation cost generated by the drone from the material supply point established by the collaborative equipment in the fixed state to the material demand point + the fixed maintenance cost generated from the initial departure to the end of the delivery;

[0020] The total delivery cost Q of the drone is calculated 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 supply point established by the collaborative equipment in a fixed state to the material demand point + the fixed maintenance cost P0 generated from the initial departure to the end of the delivery, where the material transportation cost P varies with the corresponding stage of the emergency cycle T and the different levels of importance K of the material category, that is, the urgency coefficient reflected according to the corresponding stage of the occurrence cycle T and the importance K of the material category The urgency factor decreases gradually in the three stages of the optimal rescue time. The larger the value is, the larger the flight speed V and the corresponding flight time t of the drone delivering materials during the emergency cycle T are. Therefore, the transportation cost P of the drone delivering materials is also larger, and the fixed maintenance cost P0 generated at this time is also larger. Similarly, based on the process of drones from material supply points to material demand points, due to the different importance of material categories and the stages of occurrence, there are situations where drones can only deliver materials from one material supply cluster point to one material demand point, and there are situations where materials can be delivered from one material supply cluster point to multiple continuous material demand points. In these two cases, the delivery cost of drones is formed Different influencing factors are calculated separately by using the above calculation method to calculate the transportation cost P and fixed maintenance cost P0 under all delivery paths of the drone, and the calculated results are arranged in the cost set in descending order. Then, the path planning of the n material demand points in the current delivery path of the drone is randomly exchanged with the goal of reducing the path distance. When the planned path cost is reduced, the improved delivery path is retained, otherwise the original path is maintained according to the delivery path. By relocating the n consecutive material demand points in the delivery path in the path, the delivery cost generated by the drone under the optimal delivery path is controlled.

[0021] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by providing a delivery material classification module, an optimal delivery calculation planning module, and a delivery cost calculation control module, classifies the types of materials to be delivered, the urgency coefficient, and the time requirements according to the occurrence cycle of the emergency; further plans the drone delivery path based on the collaborative equipment; and calculates the optimal delivery time under different drone delivery paths; enables the drone to perform material delivery in the delivery area from the material supply point according to the planned delivery path; finally, the total delivery cost of the drone and the collaborative equipment is calculated and controlled in combination with the occurrence cycle and the delivery path, which solves the problem of extended delivery of emergency materials and a substantial increase in the cost of each link of equipment coordination due to inconsistent coordination, improves the coordination consistency of drones and collaborative equipment in material delivery in emergencies, and reduces the delay consumption cost caused by inconsistent coordination. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0023] Figure 1 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

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Embodiment 1: This embodiment can be applied to the scenario of drone material delivery management. The method can be executed by a logistics management system based on the Internet of Things provided by this embodiment. The method specifically includes the following steps:

[0026] Step 1: Classify the types of materials to be delivered, the urgency coefficient and the time requirements according to the occurrence cycle of the emergency;

[0027] Step 2: Plan the delivery path of the drone based on collaborative equipment;

[0028] Step 3: Calculate the optimal delivery time of the drone under different delivery paths;

[0029] Step 4: The drone delivers materials from the material supply point according to the planned delivery route within the delivery area;

[0030] Step 5: Calculate and control the total delivery cost of drones and collaborative equipment based on the occurrence cycle and delivery path.

[0031] In an embodiment of the present invention, the process of classifying according to the occurrence cycle of an emergency event includes: defining the cycle of the emergency event into three stages according to the three-day optimal rescue time of emergency rescue: the first day corresponds to the first stage, the importance of the material category to be delivered in the first stage is level one, and the corresponding urgency coefficient is the largest; the second day corresponds to the second stage, the importance of the material category to be delivered in the second stage is level two, and the corresponding urgency coefficient is in the middle; the third day corresponds to the third stage, the importance of the material category to be delivered in the third stage is level three, and the urgency coefficient of the third stage is the smallest, wherein the importance of the material category to be delivered is proportional to the demand for materials at different stages of the occurrence cycle of the emergency event;

[0032] For example, the material supply point is defined according to the area of ​​the emergency. The material supply point is established by a movable collaborative device and has a fixed state and a mobile state. A coordinate system is established based on the material supply point as the center of the circle. The position to be delivered to the material demand point is defined as the target position K=(x i ,y i ), a movable collaborative device can be used with multiple drones for delivery. The delivery path of the drone is: starting from the fixed-state collaborative device carrying the delivery materials, the rescue materials are transported to one or more material demand points according to the maximum flight distance of the drone during the delivery process, and the return path is selected according to the collaborative device in the fixed state with the nearest distance. When the collaborative device in the fixed state with the nearest distance is reached, one material delivery is ended, and the next material delivery plan is judged according to the hardware equipment status of the drone. According to the judgment result, the drone that needs to carry out the next delivery is planned according to the above delivery path.

[0033] In an embodiment of the present invention, during the process of drone delivery of materials: by selecting n material demand points as initial material supply clustering points, calculating the distance between each material demand point and each initial material supply clustering point, and assigning 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 assigned material demand point constitute a cluster, and further calculating the path distance from the material demand point to the initial material supply clustering point, judging the relationship between the distance and the maximum distance reachable by the drone for material delivery, when the distance is within the maximum distance reachable by the drone for material delivery, the material demand point is retained in the initial material supply clustering point, and when the distance is outside the maximum distance reachable by the drone for material delivery, the material demand point is removed from the initial material supply clustering point, and a second judgment and division is performed from each material demand point to the divided initial material supply clustering point according to the above conditions to ensure that the delivery path of the drone when delivering materials based on the material supply point is optimal.

[0034] For example, in the process of the drone moving from the material supply point to the material demand point, due to the different importance of material categories and the different stages of occurrence, there is a situation where the drone can only deliver materials from one material supply cluster point to one material demand point, and there is a situation where the drone can deliver materials from one material supply cluster point to multiple consecutive material demand points. In these two situations, the delivery time of the drone is calculated and planned;

[0035] Exemplarily, the distance from the material supply cluster point to each material demand point and between the material demand points is calculated, and the flight time of the drone from the material supply cluster point to each material demand location is calculated through the flight speed of the drone. The flight time collection of the drone starting from the material demand point to any other material demand point is arranged, and the drone delivery time is planned: after the drone completes the single delivery path planning from the material supply cluster point based on the material demand point, the end point of each path record planned based on each material demand point is used as the starting point of the next emergency delivery path, and the single delivery path allocation is repeated until all emergency material demand points are fully delivered, and the total delivery time of the drone is minimized. The delivery path selection for each delivery is optimized for the objective function, and further judgment is made as to whether the length of the optimal delivery path meets the UAV's maximum flight distance limit. If so, the total time required for delivery is calculated based on the delivery path. The total time required for delivery is obtained by the given average flight speed of the UAV. If the optimal delivery path length exceeds the UAV's maximum flight distance limit, the current material demand point is marked as an optimization point and sorted from small to large according to the delivery distance that needs to be optimized. The UAV that meets the UAV's maximum flight distance limit based on the delivery distance that exceeds the current planned delivery path is matched. This also ensures that the UAV's delivery time is optimal when delivering materials based on the material demand point.

[0036] In the embodiment of the present invention, the dispatch cost of the collaborative device is the transportation cost of transporting materials in the mobile state formed when the material supply point is demarcated according to the emergency area, and the maintenance cost in the fixed state. The dispatch cost of the drone is the material transportation cost generated by the drone 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 dispatch. The total dispatch cost = the transportation cost of the collaborative device in the mobile state + the maintenance cost in the fixed state + the material transportation cost 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 dispatch.

[0037] For example, the total delivery cost Q of the drone is calculated 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 supply point established by the coordinated equipment in a fixed state to the material demand point + the fixed maintenance cost P0 generated from the initial departure to the end of the delivery, where the material transportation cost P varies with the corresponding stage of the emergency cycle T and the different levels of importance K of the material category, that is, the urgency coefficient reflected according to the corresponding stage of the occurrence cycle T and the importance K of the material category The urgency factor decreases gradually in the three stages of the optimal rescue time. The larger the value is, the larger the flight speed V and the corresponding flight time t of the drone delivering materials during the emergency cycle T are. Therefore, the transportation cost P of the drone delivering materials is also larger, and the fixed maintenance cost P0 generated at this time is also larger. Similarly, based on the process of drones from material supply points to material demand points, due to the different importance of material categories and the stages of occurrence, there are situations where drones can only deliver materials from one material supply cluster point to one material demand point, and there are situations where materials can be delivered from one material supply cluster point to multiple continuous material demand points. In these two cases, the delivery cost of drones is formed Different influencing factors are calculated separately by using the above calculation method to calculate the transportation cost P and fixed maintenance cost P0 under all delivery paths of the drone, and the calculated results are arranged in the cost set in descending order. Then, the path planning of the n material demand points in the current delivery path of the drone is randomly exchanged with the goal of reducing the path distance. When the planned path cost is reduced, the improved delivery path is retained, otherwise the original path is maintained according to the delivery path. By relocating the n consecutive material demand points in the delivery path in the path, the delivery cost generated by the drone under the optimal delivery path is controlled.

[0038] Embodiment 2: Embodiment 2 of the present invention provides a logistics management system based on the Internet of Things. Figure 1 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, such as Figure 1 As shown, the system includes:

[0039] The material delivery classification module is used to classify the materials that need to be delivered according to the occurrence cycle of emergencies;

[0040] The optimal delivery calculation and planning module is used to calculate and plan the optimal delivery path and optimal delivery time of the drone;

[0041] The delivery cost calculation control module is used to calculate and control the total delivery cost of drones and collaborative equipment based on the occurrence cycle and delivery path.

[0042] In some embodiments of the present invention, the delivery material classification module includes:

[0043] The occurrence cycle definition module is used to define the cycle of emergency events according to the three-day optimal rescue time for emergency rescue;

[0044] A collaborative equipment establishment module is used to use movable collaborative equipment to define and establish material supply points according to the area of ​​the emergency;

[0045] The material demand point location definition module is used to establish a coordinate system based on the material supply point as the center of the circle, and define the location of the material demand point that needs to be delivered.

[0046] In some embodiments of the present invention, the optimal dispatch calculation planning module includes:

[0047] The delivery path planning module is used to plan the delivery path of drones based on collaborative devices;

[0048] The material supply clustering point module selects material demand points as initial material supply clustering points during the process of drone delivery of materials;

[0049] The delivery time calculation module is used to calculate the optimal delivery time of the drone under different delivery paths;

[0050] The objective function optimization module is used to optimize the delivery path selection for each delivery with the minimization of the total delivery time of the drone as the objective function.

[0051] In some embodiments of the present invention, the dispatch cost calculation control module includes:

[0052] The collaborative equipment cost module is used to calculate the collaborative equipment costs generated in the mobile state and the fixed state when the material supply points are demarcated according to the emergency area;

[0053] The drone delivery cost calculation module is used to calculate the material transportation cost and fixed maintenance cost generated by the drone from a fixed state;

[0054] The drone delivery cost control module is used to control the delivery cost of the drone under the optimal delivery path according to the improved strategy.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0056] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A logistics management system based on the Internet of Things, including a delivery material classification module, an optimal delivery calculation and planning module, and a delivery cost calculation and control module, characterized in that: The delivery material classification module is used to classify the materials that need to be delivered according to the occurrence cycle of the emergency; the optimal delivery calculation planning module is used to calculate and plan the optimal delivery path and optimal delivery time of the drone; The delivery cost calculation control module is used to calculate and control the total delivery cost of drones and collaborative devices based on the occurrence cycle and delivery path; The material delivery classification module includes an occurrence cycle definition module, a collaborative equipment establishment module, and a material demand point location definition module; the occurrence cycle definition module is used to define the cycle of emergency events according to the three-day optimal rescue time for emergency rescue; the collaborative equipment establishment module is used to use mobile collaborative equipment to define and establish material supply points according to the area of ​​the emergency event; the material demand point location definition module is used to establish a coordinate system based on the material supply point as the center of the circle, and define the location of the material demand point that needs to be delivered; The optimal delivery calculation and planning module includes a delivery path planning module, a material supply clustering point module, a delivery time calculation module, and an objective function optimization module. The delivery path planning module is used to plan the UAV delivery path based on the collaborative device; the material supply clustering point module selects material demand points as initial material supply clustering points during the UAV delivery process; The delivery time calculation module is used to calculate the optimal delivery time of the drone under different delivery paths; The objective function optimization module is used to optimize the delivery path selection for each delivery by minimizing the total delivery time of the drone as the objective function; The delivery cost calculation control module includes a collaborative equipment cost module, a drone delivery cost calculation module, and a drone delivery cost control module. The collaborative equipment cost module is used to calculate the collaborative equipment costs generated in a mobile state and a fixed state when the material supply points are demarcated according to the emergency area; the drone delivery cost calculation module is used to calculate the material transportation cost and fixed maintenance cost generated by the drone in a fixed state; the drone delivery cost control module is used to control the delivery cost generated by the drone under the optimal delivery path according to the improved strategy; The operation method of the system comprises the following steps: Step 1: Classify the types of materials to be delivered, the urgency coefficient and the time requirements according to the occurrence cycle of the emergency; Step 2: Plan the delivery path of the drone based on collaborative equipment; Step 3: Calculate the optimal delivery time of the drone under different delivery paths; Step 4: The drone delivers materials from the material supply point according to the planned delivery route within the delivery area; Step 5: Calculate and control the total delivery cost of the drone and the coordinated equipment based on the occurrence cycle and the delivery path; The process of classifying according to the occurrence cycle of emergencies includes: defining the cycle of emergencies into three stages according to the three-day optimal rescue time of emergency rescue: the first day corresponds to the first stage, the importance of the category of materials to be delivered in the first stage is level one, and the corresponding urgency coefficient is the largest; the second day corresponds to the second stage, the importance of the category of materials to be delivered in the second stage is level two, and the corresponding urgency coefficient is in the middle; the third day corresponds to the third stage, the importance of the category of materials to be delivered in the third stage is level three, and the urgency coefficient of the third stage is the smallest, wherein the importance of the category of materials to be delivered is proportional to the demand for materials at different stages of the occurrence cycle of the emergencies; The material supply point is defined according to the area of ​​the emergency event. The material supply point is established by a mobile collaborative device and has a fixed state and a mobile state. A coordinate system is established based on the material supply point as the center of the circle. The position to be delivered to the material demand point is defined as the target position K=(x i ,y i ), a mobile collaborative device is matched with multiple drones for delivery. The delivery path of the drone is as follows: starting from the fixed collaborative device carrying the delivery materials, the rescue materials are delivered to one or more material demand points according to the maximum flight distance of the drone during the delivery process, and the return path is selected according to the collaborative device in the closest fixed state. When the collaborative device in the closest fixed state is reached, one material delivery is ended, and the next material delivery plan is judged according to the hardware device status of the drone. According to the judgment result, the drone that needs to make the next delivery is planned according to the above delivery path; In the process of drone delivery of materials: by selecting n material demand points as initial material supply clustering points, calculating the distance between each material demand point and each initial material supply clustering point, 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 constitute a cluster, further calculating the path distance from the material demand point to the initial material supply clustering point, judging the relationship between the distance and the maximum distance that the drone material delivery can reach, when the distance is within the maximum distance that the drone material delivery can reach, then retaining the material demand point in the initial material supply clustering point, when the distance is outside the maximum distance that the drone material delivery can reach, then excluding the material demand point from the initial material supply clustering point, performing a second judgment and division from each material demand point to the divided initial material supply clustering point according to the above conditions, to ensure that the delivery path of the drone when delivering materials based on the material supply point is optimal; In the process of the drone from the material supply point to the material demand point, due to the different importance of the material categories and the stages of occurrence, the drone may only deliver materials from one material supply cluster point to one material demand point, or may deliver materials from one material supply cluster point to multiple continuous material demand points. In these two cases, the delivery time of the drone is calculated and planned, and the distance from the material supply cluster point to each material demand point and between the material demand points is calculated by the flight speed of the drone. The flight time of the drone from the material supply cluster point to each material demand location is calculated, and the flight time collection of the drone from the material demand point to any other material demand point is arranged; Planning of drone delivery time: After the drone completes the single delivery route planning based on the material demand point from the material supply cluster point, the end point of each route record planned based on each material demand point is used as the starting point of the next emergency delivery route, and the single delivery route allocation is repeated until all emergency material demand points are delivered. The delivery route selection for each delivery is optimized with the minimization of the total delivery time of the drone as the objective function, and further determines whether the length of the optimal delivery path meets the limit of the maximum flight distance of the drone. If it meets the limit, the total time required for delivery is calculated based on the delivery path. The total time required for delivery is obtained by 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, the current material demand point is marked as an optimization point and sorted from small to large according to the delivery distance that needs to be optimized. The drone that meets the maximum flight distance limit of the drone based on the delivery distance is matched from the current delivery path that exceeds the plan, and the delivery time of the drone when delivering materials based on the material demand point is also guaranteed to be optimal.

2. According to claim 1, a logistics management system based on the Internet of Things is characterized by: The delivery cost of the collaborative equipment is the transportation cost of transporting materials in a mobile state formed when demarcating material supply points in 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 equipment 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 = the transportation cost of the collaborative equipment 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 equipment in a fixed state to the material demand point + the fixed maintenance cost generated from the initial departure to the end of the delivery; The total delivery cost Q of the drone is calculated 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 supply point established by the collaborative equipment in a fixed state to the material demand point + the fixed maintenance cost P0 generated from the initial departure to the end of the delivery, where the material transportation cost P varies with the corresponding stage of the emergency cycle T and the different levels of importance K of the material category, that is, the urgency coefficient reflected according to the corresponding stage of the occurrence cycle T and the importance K of the material category The urgency factor decreases gradually in the three stages of the optimal rescue time. The larger the value is, the larger the flight speed V and the corresponding flight time t of the drone delivering materials under the emergency cycle T are. Therefore, the transportation cost P of the drone delivering materials is also larger, and the fixed maintenance cost P0 generated at this time is also larger. Similarly, based on the different importance of material categories and the stages of occurrence in the process of drones from material supply points to material demand points, there are situations where drones can only deliver materials from one material supply cluster point to one material demand point, and there are situations where materials are delivered from one material supply cluster point to multiple continuous material demand points. In these two cases, different factors affect the delivery cost of drones. By calculating the transportation cost P and fixed maintenance cost P0 of all delivery paths of drones according to the above calculation method, the calculated results are arranged in the cost set from large to small, and then the path planning of n material demand points in the current delivery path of drones is randomly exchanged with the goal of reducing the path distance. When the planned path cost is reduced, the improved delivery path is retained, otherwise the original path is maintained according to the delivery path. By relocating n continuous material demand points in the delivery path in the path, the delivery cost generated by the drone under the optimal delivery path is controlled.

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