Logistics unmanned aerial vehicle intelligent storage cabinet management system and method

Abstract

The invention discloses a logistics unmanned aerial vehicle intelligent storage cabinet management system and method, and relates to the technical field of intelligent storage cabinets, and the method comprises the steps: determining available unmanned aerial vehicles from a logistics terminal, arranging a queue according to the carrying capacity, constructing electronic tags for to-be-delivered objects, clustering the electronic tags, and matching the electronic tags with the unmanned aerial vehicles; acquiring an object electronic tag to determine a target address, and further determining a target intelligent storage cabinet and docking with the storage robot to complete delivery; the storage robot reads the electronic tag of the object and transmits the electronic tag to the target cabinet, the cabinet retrieves the optimal matching empty cabinet for unlocking, and the robot completes cabinet storage operation; the system comprises an unmanned aerial vehicle dispatching and matching module, an address routing and delivery module and an intelligent storage module, efficient collaborative operation of the logistics unmanned aerial vehicle and the intelligent storage cabinet is achieved, and the logistics delivery efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent storage cabinets, and specifically relates to a management system and method for intelligent storage cabinets for logistics drones. Background Art

[0002] With the continuous development of the economy and the continuous advancement of science and technology, the logistics industry has ushered in unprecedented opportunities for change; the booming development of e-commerce has made people put forward higher requirements for the efficiency and accuracy of logistics distribution.

[0003] Existing technologies in the field of logistics and distribution mainly rely on traditional ground transportation and manual delivery models, which have many disadvantages. First, low efficiency, traffic congestion, slow manual sorting and delivery are prominent problems, making it difficult to meet the demand for fast logistics under rapid urban development. Second, insufficient flexibility, fixed delivery time, and inability to flexibly adjust according to user needs. Recipients may face long waits or be unable to receive their packages in time. Third, labor costs are constantly rising, and the traditional model relies heavily on manpower, resulting in high logistics costs and weakening corporate competitiveness. Fourth, traditional warehouse management is inefficient, space utilization is limited, inventory management is complex, and storage resources are easily wasted. For some special items such as fresh and fragile items, the traditional model is difficult to guarantee their quality and safety during transportation and storage.

[0004] In order to solve the above problems, the present invention proposes a logistics drone intelligent storage cabinet management system and method. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a logistics drone intelligent storage cabinet management system and method to solve the problem of low overall operational efficiency of the existing technology in the current logistics industry.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A logistics drone intelligent storage cabinet management method, the method includes the following: Step 1: Determine the available logistics drones from the logistics terminal and arrange them according to the carrying capacity of each logistics drone to obtain a logistics drone fleet set; Obtain the objects to be delivered, construct electronic tags corresponding to the objects to be delivered, cluster the objects to be delivered according to the electronic tags, and match the objects to be delivered with logistics drones based on the clustering results; Step 2: Obtain the electronic tag of the object to be delivered, determine the target address of the object to be delivered, determine the target smart storage cabinet for the object to be delivered based on the area where the target address is located, and connect with the storage robot equipped with the target smart storage cabinet to complete the delivery of the object to be delivered; Step three, the storage robot reads the electronic tag of the to-be-delivered object and transmits to the target intelligent storage cabinet, the target intelligent storage cabinet retrieves the empty cabinet most matched with the to-be-delivered object and unlocks, and the storage robot completes the to-be-delivered object storage cabinet operation.

[0007] As a further scheme of the present application, in the step one, the specific way of obtaining the logistics drone queue set is: Obtain the total number of available logistics drones equipped at the current time of the logistics terminal, denoted as j; Arrange the j logistics drones in ascending order according to the carrying capacity of the logistics drones, and obtain the logistics drone sequence A1, A2,..., Aj; Sequence the carrying capacity of each of the j logistics drones in A1, A2,..., Aj, and obtain the carrying capacity sequence BC_1, BC_2,..., BC_j corresponding to the logistics drone sequence; Take the carrying capacity BC_1 of the logistics drone A_1 as the reference value of the first category Q1; Determine the carrying capacity BC_2 of the next logistics drone A2 of the logistics drone A1 in A1, A2,..., Aj, and calculate the difference rate between BC_2 and BC_1; If the difference rate between BC_2 and BC_1 is less than the difference rate threshold preset by the operator, then BC_2 is included in the first category Q1; Otherwise, take BC_2 as the reference value of the second category Q2, and continue to traverse A1, A2,..., Aj and BC_1, BC_2,..., BC_j until the logistics drone Aj and its carrying capacity BC_j; Obtain the total number of all categories, denoted as n; Calculate the mean value of the carrying capacity of each of the n categories, arrange the n categories in ascending order according to the mean value of the carrying capacity, and obtain the carrying capacity category queue Q1`, Q2`,..., Qn`; Based on the carrying capacity of each logistics drone in A1, A2,..., Aj, and combined with the carrying capacity category queue Q1`, Q2`,..., Qn`, the logistics drone queue set V1, V2,..., Vn is obtained.

[0008] As a further scheme of the present application, in the step one, the specific way of obtaining the logistics drone queue set is: Obtain the maximum carrying weight W, the maximum carrying outer contour size C of the to-be-delivered object, and the maximum carrying fragile level F of the to-be-delivered object of any logistics drone from the cloud database; The maximum fragility level F of the object to be delivered is determined by the operator based on the performance of the logistics drone. The maximum outer dimensions C of the object to be delivered include the length H, width K, and height G of the maximum outer dimensions of the object to be delivered that the logistics drone can carry. The carrying capacity R associated with the logistics drone is calculated using R=α×W+β×(H+K+G)+γ×F, where α, β, and γ are weight coefficients preset by the operator, and α, β, and γ are all greater than 0, and α+β+γ=1.

[0009] As a further solution of the present invention, in step 1, the specific method of constructing electronic tags corresponding to the objects to be delivered and clustering the objects to be delivered according to the electronic tags is as follows: S41. Obtain all objects currently waiting for delivery at the logistics terminal, and determine the weight w, maximum outer dimension c, and fragility level f of all objects waiting for delivery from the cloud database; The maximum outer dimensions c include the length h, width k, and height g of the object to be delivered; Synchronize the destination address dz and waybill number yd of the item to be delivered; S42. Combine the weight w, maximum outer dimension c, fragility level f, destination address dz, and waybill number yd of the object to be delivered to construct an electronic tag ET for the object to be delivered, where ET = {w, c, f, dz, yd}. S43. Calculate the load requirement r of the object to be delivered using r = α × w + β × (h + k + g) + γ × f; S44. Repeat step S43 to determine the load requirements of all objects to be delivered, and form a load requirement data set Ω = {r1, r2, ..., rm}, where m represents the total number of objects to be delivered and the load requirements; S45. Eliminate the objects to be delivered in Ω whose carrying requirements are higher than the largest carrying capacity in the logistics drone sequence A1, A2, ..., Aj, and give a reminder to the operator based on the eliminated objects to be delivered; S46. Obtain the bearer capacity category queues Q1', Q2', ..., Qn' to determine the bearer capacity interval of each bearer capacity category; The carrying capacity range is composed of the lowest carrying capacity and the highest carrying capacity in the carrying capacity category; Cluster the carrying demand dataset Ω based on the carrying capacity interval of each carrying capacity category; Carrying demands that fall within the same carrying capacity range are grouped into the same carrying demand category; The total number of load demand categories obtained after clustering is recorded as o, where o≤n; S47. Arrange the o bearer demand categories in ascending order according to the bearer demand values ​​to obtain bearer demand category queues DC1, DC2, ..., DCo; S48. Cluster all objects to be delivered according to the classification of the load demand category queues DC1, DC2, ..., DCo to obtain the category queues of objects to be delivered PS1, PS2, ..., PSo.

[0010] As a further solution of the present invention, in step 1, the specific method of matching the objects to be delivered with the logistics drones based on the clustering results is as follows: S51. Obtain the to-be-delivered object category queues PS1, PS2, ..., PSo, the carrying capacity category queues Q1`, Q2`, ..., Qn`, and the logistics drone queue sets V1, V2, ..., Vn; S52, obtaining the carrying capacity category in Q1`, Q2`, ..., Qn` corresponding to the first to-be-delivered object category PS1 in PS1, PS2, ..., PSo; S53, matching the determined carrying capacity category with V1, V2, ..., Vn to determine the corresponding logistics drone queue; S54. Obtain the carrying capacity of the first object to be delivered in the first object category PS1, and sequentially search for the first logistics drone in the logistics drone queue whose carrying capacity is greater than the carrying capacity of the first object to be delivered, and select it as the logistics drone matched with the first object to be delivered; S55, repeat step S54 to determine the logistics drones that match all the objects to be delivered in the first category of objects to be delivered PS1; S56. Repeat steps S52 to S55 to determine the logistics drones that match all the objects to be delivered in PS1, PS2, ..., PSo.

[0011] As a further solution of the present invention, in step 2, the electronic tag of the object to be delivered is obtained, the target address of the object to be delivered is determined, and the target smart storage cabinet of the object to be delivered is determined based on the area where the target address is located. The specific method is: Get the electronic tag ET associated with any object to be delivered; Extract the target address dz from the electronic tag ET; Map the target address dz to the corresponding delivery area code AR according to the area division rules predefined by the operator; Get all smart storage cabinets associated with the delivery area code AR to form a smart storage cabinet set SC_Set; Filter the smart storage cabinets whose current status is available from SC_Set to form an available smart storage cabinet subset AT_SC_Set, where the available status means that the smart storage cabinet is online, connected to the network, and has at least one empty cabinet; Sort all smart storage cabinets in AT_SC_Set in ascending order of their straight-line distance to the target address dz; Extract the first smart storage cabinet in the sorted AT_SC_Set and determine whether there is any empty cabinet in the first smart storage cabinet whose length, height and width meet the length h, width k and height g of the maximum outer contour dimensions of the object to be delivered; If so, the first smart storage cabinet is used as the target smart storage cabinet for the item to be delivered; If it does not exist, continue traversing AT_SC_Set until the target smart storage cabinet for the object to be delivered is determined; If the target smart storage cabinet for the object to be delivered is still not determined after traversing AT_SC_Set, the operator is reminded that the target smart storage cabinet for the object to be delivered cannot be determined.

[0012] As a further solution of the present invention, in step 2, the specific method of docking with the storage robot equipped with the target smart storage cabinet to complete the delivery of the object to be delivered is: S71. The logistics drone obtains the target smart storage cabinet associated with the object to be delivered, the smart storage cabinet address of the target smart storage cabinet, and the waybill number yd of the object to be delivered from the cloud database; S72: The logistics drone sends a docking request to the target smart storage cabinet. The docking request includes at least the waybill number yd of the object to be delivered. S73. After receiving the docking request, the target smart storage cabinet dispatches its equipped storage robot to receive the object to be delivered and sends a response message including the location of the handover area to the logistics drone. The handover area and its location are predetermined by the operator. S74. The logistics drone navigates to the designated delivery area based on the received delivery area location information; S75. The storage robot moves to the handover area; S76. The logistics drone places the items to be delivered in the handover area; S77. The storage robot confirms and grabs the object to be delivered through its equipped perception system; S78. The storage robot sends a handover completion confirmation signal to the logistics drone and the target smart storage cabinet; S79. After receiving the handover completion confirmation signal, the logistics drone returns to the logistics terminal.

[0013] As a further solution of the present invention, in step 3, the storage robot reads the electronic tag of the object to be delivered and transmits it to the target smart storage cabinet. The target smart storage cabinet retrieves the empty cabinet that best matches the object to be delivered and unlocks it in the following specific manner: The storage robot reads the electronic tag ET of the object to be delivered and transmits it to the target smart storage cabinet; The target smart storage cabinet extracts the maximum outer dimensions c of the object to be delivered from the electronic tag ET, including length h, width k, and height g; The target smart storage cabinet retrieves the length, width, height, empty cabinet number, and empty cabinet location of all its currently empty cabinets; Evaluate whether the length, width, and height of all empty cabinets meet the maximum outer contour dimension c of the object to be delivered, and calculate the volume based on the length, width, and height to calculate the space utilization efficiency of each empty cabinet for storing the object to be delivered; The empty cabinet with the highest space utilization efficiency is used as the optimal matching empty cabinet for the items to be delivered; The empty cabinet number and empty cabinet location associated with the best matching empty cabinet are extracted and transmitted to the storage robot, and the target intelligent storage cabinet performs the unlocking operation on the best matching empty cabinet.

[0014] As a further solution of the present invention, in step 3, the specific manner in which the storage robot completes the operation of storing the object to be delivered is as follows: The storage robot receives the empty cabinet number and empty cabinet location associated with the best matching empty cabinet; The storage robot automatically navigates to the location of the optimal empty cabinet based on the empty cabinet number and location, places the item to be delivered in the optimal empty cabinet, and sends a storage completion signal to the target smart storage cabinet; The target intelligent storage cabinet receives the cabinet storage completion signal and performs a locking operation on the best matching empty cabinet, and updates the available state of the best matching empty cabinet to an unavailable state.

[0015] A logistics drone intelligent storage cabinet management system, the system includes the following: The drone scheduling and matching module determines the available logistics drones from the logistics terminal and arranges them according to the carrying capacity of each logistics drone to obtain the logistics drone queue set; Obtain the objects to be delivered, construct electronic tags corresponding to the objects to be delivered, cluster the objects to be delivered according to the electronic tags, and match the objects to be delivered with logistics drones based on the clustering results; The address routing and delivery module obtains the electronic tag of the object to be delivered, determines the target address of the object to be delivered, determines the target smart storage cabinet for the object to be delivered based on the area where the target address is located, and connects with the storage robot equipped with the target smart storage cabinet to complete the delivery of the object to be delivered; The intelligent storage module and storage robot read the electronic tags of the objects to be delivered and transmit them to the target intelligent storage cabinet. The target intelligent storage cabinet retrieves the empty cabinet that best matches the objects to be delivered and unlocks it. The storage robot completes the cabinet storage operation of the objects to be delivered.

[0016] Beneficial effects of the present invention: (1) This invention combines weight, size, and fragility into multiple factors and assigns them adjustable weight coefficients to scientifically quantify the carrying capacity of drones and the carrying requirements of objects, making the evaluation more in line with the actual scenario and laying the foundation for accurate matching. Secondly, the difference rate threshold is used to dynamically classify drones into an orderly queue, and the carrying requirements of objects are clustered based on the same logic, thus constructing a structured resource pool and task pool, achieving clear management of complex resources and tasks. (2) The present invention systematically matches the priority order of the load-carrying capacity category queue and the load-carrying demand category queue, ensuring that high-load-carrying capacity drones give priority to serving high-demand items, effectively avoiding the waste of resources or delivery failures caused by insufficient capacity due to "a big horse pulling a small cart". At the same time, the present invention automatically removes items that exceed the maximum capacity of the drone and issues an alarm, further ensuring the safety and reliability of delivery. (3) The present invention uses electronic tags to accurately locate the target area and dynamically screens available storage cabinets based on real-time status. Combined with distance priority sorting and size intelligent matching algorithms, it ensures that the most suitable cabinet resources are quickly locked for each object to be delivered, optimizes cabinet utilization and drone delivery paths, and reduces invalid attempts and waiting time. At the same time, the standardized automatic docking process with the storage robot (including request response, collaborative navigation, perception and grasping, and signal confirmation) realizes seamless collaboration among "drone-storage cabinet-robot", greatly reducing the need for manual intervention and the risk of handover errors. (4) The present invention dynamically calculates the space utilization efficiency of each empty cabinet based on the outer contour dimensions of the object, accurately selects the optimal matching cabinet grid, maximizes the utilization rate of storage space, avoids the waste of resources of storing small objects in large grids, and significantly reduces hardware costs; secondly, the storage robot and the smart cabinet form a closed-loop collaboration - the robot is responsible for navigation and placement, and the smart cabinet automatically performs opening and closing locks and status updates. The unmanned operation of the entire process greatly improves the efficiency of cabinet storage, while eliminating misplacement or delays caused by manual intervention; finally, the real-time status synchronization mechanism ensures the accuracy of cabinet grid data, providing a reliable basis for subsequent logistics scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 It is a structural diagram of the system of the present invention; Figure 2 Schematic diagram of the process of the method described in Example 2 of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention. Example 1

[0020] A logistics drone intelligent storage cabinet management system, such as Figure 1 As shown, this system includes the following: This system realizes logistics transportation and management by combining logistics drones and smart storage cabinets. This system mainly includes the following modules: drone scheduling and matching module, address routing and delivery module, and smart storage module. The ultimate goal of multi-module collaboration is to maximize the utilization rate and delivery efficiency of drones, minimize human intervention, and provide users with fast, accurate, and convenient terminal delivery and self-pickup services.

[0021] Among them, the drone scheduling and matching module is mainly used to monitor and manage the logistics drones and objects to be delivered in the logistics terminal; The logistics drones include various models and uses, and are distinguished by their carrying capacity. The carrying capacity also covers the maximum carrying weight W of the corresponding logistics drone, the maximum outer contour dimension C of the object to be delivered, and the maximum fragility level F of the object to be delivered. That is, the carrying capacity of the logistics drone is jointly determined by the three parameters (W, C, F) mentioned above. It should also be noted that the maximum outer contour dimension C of the object to be delivered includes the length H, width K, and height G of the maximum outer contour dimension of the object. The object is the object to be delivered.

[0022] If the shape of the object to be delivered is irregular, the point cloud data detector equipped in the logistics terminal will scan the irregular-shaped object to be delivered and determine the minimum circumscribed rectangle of the shape of the object to be delivered, and the length, width and height of the minimum circumscribed rectangle will be used as the length, width and height of the object to be delivered.

[0023] The step of determining the available logistics drones from the logistics terminal is determined by monitoring the status of all registered logistics drones (the status of the logistics drones includes available and unavailable).

[0024] Next, the logistics drones are arranged according to the carrying capacity of all available logistics drones. After the arrangement, a logistics drone queue set is obtained. A logistics drone queue set includes at least one logistics drone (the arrangement of logistics drones is updated in real time).

[0025] As described above, after completing the logistics drone scheduling, the next step is to process the objects to be delivered. The logistics terminal determines the electronic tags of the objects to be delivered. The electronic tags of any object to be delivered include the following information: weight, maximum outer dimensions, object fragility level, destination address, and waybill number; The information in the electronic tags is obtained from a cloud database equipped by the logistics terminal, and the data stored in the cloud database includes the information in the electronic tags of each object to be delivered.

[0026] Among them, the weight, maximum outer dimensions and object fragility level are all determined by the logistics terminal. The target address is the delivery address of the object to be delivered. The waybill number serves as the unique identifier of the object to be delivered, which is used to distinguish it from other objects to be delivered. The maximum outer dimensions include the length, width and height of the maximum outer dimensions of the object to be delivered.

[0027] Based on the electronic tags of the objects to be delivered, the objects to be delivered are clustered, and the objects to be delivered with similar electronic tag features are classified into one category. Each category of objects to be delivered after clustering is matched with the logistics drone queue set (one category of objects to be delivered is delivered by a logistics drone in one queue). It should be noted here that when clustering the objects to be delivered, it is also necessary to verify whether each object to be delivered is eligible for delivery by a logistics drone. For example, extra-large objects to be delivered cannot be delivered by logistics drones, and they need to be marked separately, and corresponding reminders should be given to the operator.

[0028] Next, once the object to be delivered is matched with the logistics drone, it will enter the address routing and delivery module for the next step of processing. First, the logistics drone will obtain the electronic tag of the object to be delivered and extract the target address from it. The logistics drone will determine the target smart storage cabinet corresponding to the object to be delivered based on the target address and the logistics terminal, and automatically navigate (automatic navigation technology is part of the existing technology and will not be described in detail in this solution) to the target smart storage cabinet corresponding to the target address; The target smart storage cabinet is equipped with a storage robot, which will complete the handover and delivery of the objects to be delivered with the logistics drone.

[0029] Then it enters the intelligent storage module. The storage robot reads the electronic tag of the object to be delivered and transmits the read electronic tag of the object to be delivered to the target intelligent storage cabinet. The target intelligent storage cabinet retrieves the best matching empty cabinet associated with the object to be delivered, and the target intelligent storage cabinet unlocks the best matching empty cabinet. The storage robot completes the cabinet storage operation of the object to be delivered. After the cabinet storage operation is completed, the target intelligent storage cabinet completes the locking operation of the best matching empty cabinet. At this point, the whole process ends. The subsequent operation is completed by the user of the object to be delivered through the target intelligent storage cabinet to complete the pickup operation. Example 2

[0030] A logistics drone intelligent storage cabinet management method, such as Figure 2 As shown, this method includes the following steps: The following are the specific implementation steps of this method: First, determine the logistics terminal, which is mainly used for temporarily storing objects to be delivered and performing distribution and delivery operations on the objects to be delivered. First, determine all registered logistics drones from the determined logistics terminal, and then further determine the total number of available logistics drones (for example, logistics drones that are in use or charging status are unavailable).

[0031] The total number of logistics drones determined is recorded as j, and then the j logistics drones determined are sorted in ascending order according to the carrying capacity of the logistics drones, and the sorted result is recorded as a logistics drone sequence, expressed as: A1, A2, ..., Aj, where A1 to Aj represent the first obtained logistics drone to the last obtained logistics drone, that is, the jth logistics drone.

[0032] Then obtain the carrying capacity of each logistics drone in the logistics drone sequence A1, A2, ..., Aj. The specific steps to determine the carrying capacity of the logistics drone are as follows (this step should be determined when the logistics drone is registered at the logistics terminal, and the carrying capacity determined for each logistics drone should be stored in the cloud database for subsequent retrieval): First, identify any logistics drone and use it as an example to obtain the maximum load weight W, the maximum outer dimension C of the object to be delivered, and the maximum fragility level F of the object to be delivered. Among them, the maximum load weight W, the maximum load outer dimension C of the object to be delivered, and the maximum load fragility level F of the object to be delivered are all determined by the operator based on the actual performance of the logistics drone. For example, the maximum load weight W, the maximum load outer dimension C of the object to be delivered, and the maximum load fragility level F of the object to be delivered of a large logistics drone are all stronger than those of a small logistics drone.

[0033] The maximum outer dimension C of the object to be delivered includes the length H, width K and height G of the maximum outer dimension of the object to be delivered.

[0034] Next, the carrying capacity R associated with the corresponding logistics drone is calculated using the following formula: R=α×W+β×(H+K+G)+γ×F, where α, β, and γ are weight coefficients preset by the operator, and α, β, and γ are all greater than 0, and α+β+γ=1.

[0035] After the above steps, the carrying capacity of each logistics drone in the logistics drone sequence A1, A2, ..., Aj can be determined. Then the carrying capacity of each logistics drone is extracted, and the j extracted carrying capacities are serialized. The serialization order corresponds to the logistics drone sequence A1, A2, ..., Aj. The serialized result is recorded as the carrying capacity sequence, expressed as: BC_1, BC_2, ..., BC_j, that is, A1 corresponds to BC_1, until Aj corresponds to BC_j.

[0036] Next, obtain the carrying capacity BC_1 of the first logistics drone A_1 in the logistics drone sequence A1, A2, ..., Aj, and use the carrying capacity BC_1 as the benchmark value of the first category Q1.

[0037] Then obtain the determined logistics drone sequence A1, A2, ..., Aj, and extract the carrying capacity of the logistics drone next to the logistics drone A1 from the logistics drone sequence A1, A2, ..., Aj in sequence, which is logistics drone A2 in this case. Then obtain the carrying capacity BC_2 of logistics drone A2, and then calculate the difference rate of the carrying capacity between logistics drones A_1 and logistics drones A2. The calculation method is: |BC_2-BC_1|*100%. The subsequent methods for calculating the difference rate of carrying capacity are all as described above.

[0038] Then, the operator-preset difference rate threshold is obtained, and the calculation result of |BC_2-BC_1|*100% (i.e., the difference rate between the carrying capacity BC_2 and the carrying capacity BC_1) is compared with the operator-preset difference rate threshold; If the difference between the carrying capacity BC_2 and the carrying capacity BC_1 is less than the difference threshold preset by the operator, then the carrying capacity BC_2 is included in the first category Q1 where the carrying capacity BC_1 is located; If the difference rate between BC_2 and BC_1 is greater than or equal to the difference rate threshold preset by the operator, then the carrying capacity BC_2 will not be included in the first category Q1 where the carrying capacity BC_1 is located, and the carrying capacity BC_2 will be used as the benchmark value for the second category Q2. The logistics drone sequence A1, A2, ..., Aj and the carrying capacity sequence BC_1, BC_2, ..., BC_j will be traversed backward in order until the last logistics drone Aj and its carrying capacity BC_j are traversed.

[0039] At this point, the carrying capacity associated with all logistics drones in the logistics drone sequence A1, A2, ..., Aj is clustered, and the total number of all categories is counted and recorded as n, where n does not exceed j; Next, the average value of the carrying capacity associated with each of the n categories is calculated, and the determined n categories are sorted in ascending order according to the value of the average carrying capacity. The sorted result is recorded as the carrying capacity category queue, expressed as: Q1`, Q2`, ..., Qn`, where Q1` is not equal to Q1, and Q1`, Q2`, ..., Qn` represent the sorted categories.

[0040] Get the first carrying capacity category Q1` in the carrying capacity category queue Q1`, Q2`, ..., Qn`, determine the logistics drones belonging to the carrying capacity category Q1` from the logistics drone sequence A1, A2, ..., Aj, and classify them to obtain a logistics drone queue, recorded as V1; Similarly, determine the logistics drone queues corresponding to the remaining carrying capacity categories of the carrying capacity category queue, and sort the determined logistics drone queues in the order of the carrying capacity category queues Q1`, Q2`, ..., Qn` to obtain the logistics drone queue set, expressed as: V1, V2, ..., Vn.

[0041] At this point, the logistics drone queue set V1, V2, ..., Vn and the carrying capacity category queues Q1`, Q2`, ..., Qn` have been determined. Next, we need to determine the items to be delivered that are currently stored at the logistics terminal and match each item to the corresponding logistics drone. The specific steps are as follows: First, all items currently awaiting delivery are obtained from the identified logistics terminal. For each item, its physical characteristics must be determined. These characteristics can be obtained from a cloud database and include the item's weight w, maximum outer dimensions c, and fragility level f. The maximum outer dimension c also includes the length h, width k and height g of the maximum outer dimension corresponding to the object to be delivered.

[0042] Then, the target address dz and the waybill number yd of the to-be-delivered object are obtained from the cloud database, and are combined with the physical characteristics to obtain an electronic tag ET of the to-be-delivered object, which is represented as ET = {w, c, f, dz, yd}.

[0043] Then, the length h, the width k, and the height g of the maximum outer contour size c in the electronic tag of any to-be-delivered object are extracted, and the carrying demand r of the corresponding to-be-delivered object is calculated by r = a x w + b x (h + k + g) + g x f.

[0044] Then, the above steps are repeated to determine the carrying demand associated with each of the to-be-delivered objects currently in the logistics terminal, and the total number of to-be-delivered objects and the total number of carrying demands are counted, and the total number of to-be-delivered objects is equal to the total number of carrying demands, and both are recorded as m. Then, the m carrying demands are aggregated to obtain a carrying demand data set Ω, which is represented as Ω = {r1, r2,..., rm}.

[0045] The to-be-delivered objects with carrying demands higher than the maximum carrying capacity of the to-be-delivered objects in the logistics drone sequence A1, A2,..., Aj in the carrying demand data set Ω are removed, indicating that the to-be-delivered objects with corresponding carrying demands cannot be delivered by the carrying capacity of the logistics drone, and the waybill numbers of all removed to-be-delivered objects are sent to the operator, and the operator is reminded that the to-be-delivered object cannot be delivered.

[0046] Then, the carrying capacity category queue Q1`, Q2`,..., Qn` determined in the above content is obtained, the first carrying capacity category Q1` is obtained, the minimum carrying capacity and the maximum carrying capacity in the carrying capacity category Q1` are extracted and combined, and the interval formed by the two is used as the carrying capacity interval associated with the carrying capacity category Q1`. Similarly, the carrying capacity intervals associated with all carrying capacity categories in the carrying capacity category queue Q1`, Q2`,..., Qn` are determined. Then, the carrying demand data set Ω = {r1, r2,..., rm} is clustered according to all carrying capacity intervals. For example, in a specific example, the carrying capacity interval of the carrying capacity category Q1` is 10-30, and the carrying demands r1, r2, and r3 are 15, 25, and 35, respectively. The carrying demands r1 and r2 are clustered into one category, and the carrying demand r3 is clustered into another category.

[0047] Finally, all the bearer demands in the bearer demand dataset Ω table = {r1, r2, ..., rm,} are clustered to obtain several bearer demand categories, and the total number of the several bearer demand categories is recorded as o, where o is less than or equal to n.

[0048] Then, the o bearer demand categories are sorted in ascending order according to the bearer demand values, and the sorted result is recorded as a bearer demand category queue, denoted as: DC1, DC2, ..., DCo.

[0049] Let us cluster all the objects to be delivered at the logistics terminal according to the clustering results of the load demand category queues DC1, DC2, ..., DCo. That is, we cluster each object to be delivered according to its respective load demand, and finally obtain the clustering result of all objects to be delivered, which is recorded as the category queue of objects to be delivered, expressed as: PS1, PS2, ..., PSo.

[0050] At this point, the object category queues PS1, PS2, ..., PSo and the load demand category queues DC1, DC2, ..., DCo have been determined. Next, it is necessary to match the object category queues PS1, PS2, ..., PSo with the logistics drones in the logistics drone queue set V1, V2, ..., Vn. The specific steps are as follows: First, obtain the first to-be-delivered object category PS1 in the to-be-delivered object category queues PS1, PS2, ..., PSo, and determine the carrying demand category associated with the to-be-delivered object category PS1 in the carrying demand category queues DC1, DC2, ..., DCo. Then, based on the determined carrying demand category, determine the matching carrying capacity category from the determined carrying capacity category queues Q1`, Q2`, ..., Qn`. Then, based on the determined carrying capacity category, determine the logistics drone queue that matches the logistics drone queue set V1, V2, ..., Vn. At this point, the logistics drone queue that matches the to-be-delivered object category PS1 is determined.

[0051] Then, the first object to be delivered is determined from the extracted object category PS1, and the carrying capacity requirement of the object to be delivered is obtained. Then, the logistics drone queues that match the object category PS1 determined in the above content are sequentially searched, and the first logistics drone with a carrying capacity greater than the carrying capacity requirement of the first object to be delivered in the object category PS1 is determined from the logistics drone queues as the logistics drone that matches the first object to be delivered; Repeat the above steps to determine the logistics drones that match all the objects in the delivery category PS1. If there is any object in the delivery category PS1 that does not match the logistics drone, the corresponding object will be placed in the waiting queue until a new logistics drone is added to the logistics drone queue that matches the delivery category PS1. Continue to repeat the above steps to determine all logistics drone queues that match the categories of all objects to be delivered in the queues PS1, PS2, ..., PSo and all logistics drones that match the objects to be delivered.

[0052] At this point, the logistics drone that matches any object to be delivered has been determined. The logistics drone then obtains the corresponding object to be delivered and simultaneously reads the electronic tag ET of the object to be delivered. The target address dz of the object to be delivered is determined from the electronic tag ET. Obtaining a delivery area code AR that matches the target address dz based on the determined target address dz and the area division rules predefined by the operator. The delivery area codes AR represent different delivery areas determined by the operator within the logistics terminal. One delivery area code AR corresponds to one delivery area, and one delivery area is bound to several matching smart storage cabinets. Next, all smart storage cabinets associated with the delivery area code AR matching the target address dz are obtained and formed into a smart storage cabinet set, which is recorded as SC_Set.

[0053] Then, the smart storage cabinets currently in the available state are selected from the determined smart storage cabinet set SC_Set, and all the smart storage cabinets in the available state are combined to obtain the available smart storage cabinet subset, which is recorded as AT_SC_Set. The available state means that the smart storage cabinet is online, connected to the network, and has at least one empty cabinet. Then obtain the actual addresses of all smart storage cabinets in the smart storage cabinet subset AT_SC_Set, and determine the straight-line distances between the actual addresses of all smart storage cabinets and the target address dz, and sort all smart storage cabinets in the smart storage cabinet subset AT_SC_Set in order from short to long according to the determined straight-line distances to obtain the sorted smart storage cabinet subset AT_SC_Set.

[0054] Sequentially extract the first smart storage cabinet in the smart storage cabinet subset AT_SC_Set, and determine whether there is any empty cabinet in the first smart storage cabinet whose length, height and width meet the maximum outer contour dimensions of length h, width k and height g recorded in the electronic tag ET of the object to be delivered.

[0055] If it is determined that there is any empty cabinet in the first smart storage cabinet whose length, height and width meet the maximum outer contour dimensions of length h, width k and height g recorded in the electronic tag ET of the object to be delivered, the first smart storage cabinet will be used as the target smart storage cabinet for the object to be delivered.

[0056] On the other hand, if there is no empty cabinet in the first smart storage cabinet whose length, height and width match the maximum outer dimensions (length h, width k and height g) recorded in the electronic tag ET of the object to be delivered, the smart storage cabinet subset AT_SC_Set is traversed until the target smart storage cabinet for the object to be delivered is determined; If the target smart storage cabinet for the object to be delivered is still not determined after traversing the smart storage cabinet subset AT_SC_Set, the operator needs to be reminded that "the target smart storage cabinet for the object to be delivered cannot be determined" and the waybill number of the object to be delivered is sent to the operator.

[0057] After the target smart storage cabinet for the object to be delivered is determined, the next step is to carry out the delivery operation. The matching logistics drone obtains the smart storage cabinet address of the target smart storage cabinet from the cloud database and obtains the waybill number yd of the object to be delivered from the electronic tag ET of the object to be delivered; The logistics drone sends a docking request to the target smart storage cabinet, and the docking request includes the waybill number yd of the object to be delivered.

[0058] Then, when the target smart storage cabinet receives the docking request sent by the logistics drone, it dispatches the storage robot equipped with the target smart storage cabinet to prepare to receive the objects to be delivered, and sends a response message containing the location of the handover area to the logistics drone, where the handover area and the location of the handover area are determined by the operator when installing the smart storage cabinet.

[0059] Next, the logistics drone adaptively navigates to the designated delivery zone based on the received response information about the delivery zone's location. Simultaneously, the storage robot also moves to the delivery zone. The logistics drone places the item to be delivered in the delivery zone, and the storage robot then uses its perception system (including cameras and ultrasonic sensors) to identify and grab the item to be delivered. The storage robot then sends a handover completion confirmation signal to the logistics drone and the target smart storage cabinet respectively. After receiving the handover completion confirmation signal, the logistics drone returns to the logistics terminal.

[0060] The storage robot then reads the electronic tag ET of the object to be delivered and transmits it to the target smart storage cabinet. The analysis and computing terminal in the target smart storage cabinet extracts the maximum outer dimensions c of the object to be delivered from the electronic tag ET, including length h, width k, and height g. The target smart storage cabinet searches all currently empty cabinets, and the retrieved information includes the length, width, height, number, and location of the empty cabinet; Next, the length, width, and height of all empty cabinets are evaluated to see if they meet the maximum outer dimensions c of the object to be delivered. Empty cabinets that meet the maximum outer dimensions c of the object to be delivered are extracted, and the space utilization efficiency of all empty cabinets that meet the maximum outer dimensions c of the object to be delivered is calculated. The space utilization efficiency is calculated by determining the volume of the empty cabinet by the length, width and height of the empty cabinet, and determining the volume of the object to be delivered by the maximum outer contour dimension c of the object to be delivered, that is, length h, width k and height g. Then, the volumes of the two are compared to determine the space utilization efficiency.

[0061] The target smart storage cabinet determines the empty cabinet with the highest space utilization efficiency and uses it as the optimal matching empty cabinet for the objects to be delivered. It then obtains the empty cabinet number and empty cabinet location associated with the optimal matching empty cabinet and transmits them to the storage robot. The storage robot completes the cabinet storage operation for the objects to be delivered. At the same time, the target smart storage cabinet unlocks the optimal matching empty cabinet.

[0062] When the storage robot receives the information that the target smart storage cabinet has performed an unlocking operation on the best matching empty cabinet, as well as the empty cabinet number and empty cabinet location associated with the best matching empty cabinet, it automatically navigates to the location of the best matching empty cabinet according to the empty cabinet number and empty cabinet location, places the object to be delivered in the best matching empty cabinet, and sends a storage completion signal to the target smart storage cabinet.

[0063] When the target smart storage cabinet receives the cabinet storage completion signal sent by the storage robot, it performs a locking operation on the best matching empty cabinet and simultaneously updates the available status of the best matching empty cabinet, setting it to an unavailable state.

[0064] Some of the data in the formulas described above are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0065] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0066] It is important to note that all user data collected in this application is collected with the user's consent and authorization. Furthermore, the use of user data is legal and compliant, and the use and processing of user data complies with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A logistics drone intelligent storage cabinet management method, characterized in that: This method includes the following: Step 1: Determine the available logistics drones from the logistics terminal and arrange them according to the carrying capacity of each logistics drone to obtain a logistics drone fleet set; Obtain the objects to be delivered, construct electronic tags corresponding to the objects to be delivered, cluster the objects to be delivered according to the electronic tags, and match the objects to be delivered with logistics drones based on the clustering results; Step 2: Obtain the electronic tag of the object to be delivered, determine the target address of the object to be delivered, determine the target smart storage cabinet for the object to be delivered based on the area where the target address is located, and connect with the storage robot equipped with the target smart storage cabinet to complete the delivery of the object to be delivered; Step 3: The storage robot reads the electronic tag of the object to be delivered and transmits it to the target smart storage cabinet. The target smart storage cabinet retrieves the empty cabinet that best matches the object to be delivered and unlocks it. The storage robot completes the storage operation of the object to be delivered.

2. A logistics drone intelligent storage cabinet management method according to claim 1, characterized in that: In step 1, the specific method of obtaining the logistics drone queue set is: Get the total number of available logistics drones equipped at the logistics terminal at the current time, recorded as j; Arrange the j logistics drones in ascending order according to their carrying capacity, and obtain the logistics drone sequence A1, A2, ..., Aj; Serialize the carrying capacity of each of the j logistics drones in A1, A2, ..., Aj, and obtain the carrying capacity sequence BC_1, BC_2, ..., BC_j corresponding to the logistics drone sequence; Take the carrying capacity BC_1 of the logistics drone A_1 as the benchmark value of the first category Q1; Determine the carrying capacity BC_2 of the next logistics drone A2 of logistics drone A1 among A1, A2, ..., Aj, and calculate the difference rate between BC_2 and BC_1; If the difference rate between BC_2 and BC_1 is less than the difference rate threshold preset by the operator, BC_2 is included in the first category Q1; Otherwise, BC_2 is used as the benchmark value of the second category Q2, and the process continues through A1, A2, ..., Aj and BC_1, BC_2, ..., BC_j until the logistics drone Aj and its carrying capacity BC_j; Get the total number of all categories, recorded as n; Calculate the mean carrying capacity of each of the n categories, and sort the n categories in ascending order according to the mean carrying capacity to obtain the carrying capacity category queues Q1`, Q2`, ..., Qn`; Based on the carrying capacity of each logistics drone in A1, A2, ..., Aj, and combined with the carrying capacity category queues Q1`, Q2`, ..., Qn`, we classify them and obtain the logistics drone queue set V1, V2, ..., Vn.

3. A logistics drone intelligent storage cabinet management method according to claim 2, characterized in that: In step 1, the carrying capacity of each logistics drone is determined as follows: Obtain the maximum load weight W, the maximum outer dimensions C of the object to be delivered, and the maximum fragility level F of the object to be delivered for any logistics drone from the cloud database; The maximum fragility level F of the object to be delivered is determined by the operator based on the performance of the logistics drone. The maximum outer dimensions C of the object to be delivered include the length H, width K, and height G of the maximum outer dimensions of the object to be delivered that the logistics drone can carry. The carrying capacity R associated with the logistics drone is calculated using R=α×W+β×(H+K+G)+γ×F, where α, β, and γ are weight coefficients preset by the operator, and α, β, and γ are all greater than 0, and α+β+γ=1.

4. A logistics drone intelligent storage cabinet management method according to claim 3, characterized in that: In step 1, the electronic tags corresponding to the objects to be delivered are constructed, and the specific method of clustering the objects to be delivered according to the electronic tags is as follows: S41. Obtain all objects currently waiting for delivery at the logistics terminal, and determine the weight w, maximum outer dimension c, and fragility level f of all objects waiting for delivery from the cloud database; The maximum outer dimensions c include the length h, width k, and height g of the object to be delivered; Synchronize the destination address dz and waybill number yd of the item to be delivered; S42. Combine the weight w, maximum outer dimension c, fragility level f, destination address dz, and waybill number yd of the object to be delivered to construct an electronic tag ET for the object to be delivered, where ET = {w, c, f, dz, yd}. S43. Calculate the load requirement r of the object to be delivered using r = α × w + β × (h + k + g) + γ × f; S44. Repeat step S43 to determine the load requirements of all objects to be delivered, and form a load requirement data set Ω = {r1, r2, ..., rm}, where m represents the total number of objects to be delivered and the load requirements; S45. Eliminate the objects to be delivered in Ω whose carrying requirements are higher than the largest carrying capacity in the logistics drone sequence A1, A2, ..., Aj, and give a reminder to the operator based on the eliminated objects to be delivered; S46. Obtain the bearer capacity category queues Q1', Q2', ..., Qn' to determine the bearer capacity interval of each bearer capacity category; The carrying capacity range is composed of the lowest carrying capacity and the highest carrying capacity in the carrying capacity category; Cluster the carrying demand dataset Ω based on the carrying capacity interval of each carrying capacity category; Carrying demands that fall within the same carrying capacity range are grouped into the same carrying demand category; The total number of load demand categories obtained after clustering is recorded as o, where o≤n; S47. Arrange the o bearer demand categories in ascending order according to the bearer demand values ​​to obtain bearer demand category queues DC1, DC2, ..., DCo; S48. Cluster all objects to be delivered according to the classification of the load demand category queues DC1, DC2, ..., DCo to obtain the category queues of objects to be delivered PS1, PS2, ..., PSo.

5. A logistics drone intelligent storage cabinet management method according to claim 4, characterized in that: In step 1, the specific method of matching the objects to be delivered with the logistics drones based on the clustering results is as follows: S51. Obtain the to-be-delivered object category queues PS1, PS2, ..., PSo, the carrying capacity category queues Q1`, Q2`, ..., Qn`, and the logistics drone queue sets V1, V2, ..., Vn; S52, obtaining the carrying capacity category in Q1`, Q2`, ..., Qn` corresponding to the first to-be-delivered object category PS1 in PS1, PS2, ..., PSo; S53, matching the determined carrying capacity category with V1, V2, ..., Vn to determine the corresponding logistics drone queue; S54. Obtain the carrying capacity of the first object to be delivered in the first object category PS1, and sequentially search for the first logistics drone in the logistics drone queue whose carrying capacity is greater than the carrying capacity of the first object to be delivered, and select it as the logistics drone matched with the first object to be delivered; S55, repeat step S54 to determine the logistics drones that match all the objects to be delivered in the first category of objects to be delivered PS1; S56. Repeat steps S52 to S55 to determine the logistics drones that match all the objects to be delivered in PS1, PS2, ..., PSo.

6. A logistics drone intelligent storage cabinet management method according to claim 1, characterized in that: In step 2, the electronic tag of the object to be delivered is obtained, the target address of the object to be delivered is determined, and the target smart storage cabinet of the object to be delivered is determined based on the area where the target address is located. The specific method is: Get the electronic tag ET associated with any object to be delivered; Extract the target address dz from the electronic tag ET; According to the regional division rules predefined by the operator, the target address dz is mapped to the corresponding delivery area code AR; Get all smart storage cabinets associated with the delivery area code AR to form a smart storage cabinet set SC_Set; Filter the smart storage cabinets whose current status is available from SC_Set to form an available smart storage cabinet subset AT_SC_Set, where the available status means that the smart storage cabinet is online, connected to the network, and has at least one empty cabinet; Sort all smart storage cabinets in AT_SC_Set in ascending order of their straight-line distance to the target address dz; Extract the first smart storage cabinet in the sorted AT_SC_Set and determine whether there is any empty cabinet in the first smart storage cabinet whose length, height and width meet the length h, width k and height g of the maximum outer contour dimensions of the object to be delivered; If so, the first smart storage cabinet is used as the target smart storage cabinet for the item to be delivered; If it does not exist, continue traversing AT_SC_Set until the target smart storage cabinet for the object to be delivered is determined; If the target smart storage cabinet for the object to be delivered is still not determined after traversing AT_SC_Set, the operator is reminded that the target smart storage cabinet for the object to be delivered cannot be determined.

7. A logistics drone intelligent storage cabinet management method according to claim 6, characterized in that: In step 2, the specific method of docking with the storage robot equipped with the target smart storage cabinet to complete the delivery of the object to be delivered is: S71. The logistics drone obtains the target smart storage cabinet associated with the object to be delivered, the smart storage cabinet address of the target smart storage cabinet, and the waybill number yd of the object to be delivered from the cloud database; S72: The logistics drone sends a docking request to the target smart storage cabinet. The docking request includes at least the waybill number yd of the object to be delivered. S73. After receiving the docking request, the target smart storage cabinet dispatches its equipped storage robot to receive the object to be delivered and sends a response message including the location of the handover area to the logistics drone. The handover area and its location are predetermined by the operator. S74. The logistics drone navigates to the designated delivery area based on the received delivery area location information; S75. The storage robot moves to the handover area; S76. The logistics drone places the items to be delivered in the handover area; S77. The storage robot confirms and grabs the object to be delivered through its equipped perception system; S78. The storage robot sends a handover completion confirmation signal to the logistics drone and the target smart storage cabinet; S79. After receiving the handover completion confirmation signal, the logistics drone returns to the logistics terminal.

8. A logistics drone intelligent storage cabinet management method according to claim 7, characterized in that: In step 3, the storage robot reads the electronic tag of the object to be delivered and transmits it to the target smart storage cabinet. The target smart storage cabinet retrieves the empty cabinet that best matches the object to be delivered and unlocks it in the following manner: The storage robot reads the electronic tag ET of the object to be delivered and transmits it to the target smart storage cabinet; The target smart storage cabinet extracts the maximum outer dimensions c of the object to be delivered from the electronic tag ET, including length h, width k, and height g; The target smart storage cabinet retrieves the length, width, height, empty cabinet number, and empty cabinet location of all its currently empty cabinets; Evaluate whether the length, width, and height of all empty cabinets meet the maximum outer contour dimension c of the object to be delivered, and calculate the volume based on the length, width, and height to calculate the space utilization efficiency of each empty cabinet for storing the object to be delivered; The empty cabinet with the highest space utilization efficiency is used as the optimal matching empty cabinet for the items to be delivered; The empty cabinet number and empty cabinet location associated with the best matching empty cabinet are extracted and transmitted to the storage robot, and the target intelligent storage cabinet performs the unlocking operation on the best matching empty cabinet.

9. A logistics drone intelligent storage cabinet management method according to claim 8, characterized in that: In step 3, the specific method for the storage robot to complete the storage operation of the object to be delivered is as follows: The storage robot receives the empty cabinet number and empty cabinet location associated with the best matching empty cabinet; The storage robot automatically navigates to the location of the optimal empty cabinet based on the empty cabinet number and location, places the item to be delivered in the optimal empty cabinet, and sends a storage completion signal to the target smart storage cabinet; The target intelligent storage cabinet receives the cabinet storage completion signal and performs a locking operation on the best matching empty cabinet, and updates the available state of the best matching empty cabinet to an unavailable state.

10. A logistics drone intelligent storage cabinet management system, characterized in that: This system includes the following: The drone scheduling and matching module determines the available logistics drones from the logistics terminal and arranges them according to the carrying capacity of each logistics drone to obtain the logistics drone queue set; Obtain the objects to be delivered, construct electronic tags corresponding to the objects to be delivered, cluster the objects to be delivered according to the electronic tags, and match the objects to be delivered with logistics drones based on the clustering results; The address routing and delivery module obtains the electronic tag of the object to be delivered, determines the target address of the object to be delivered, determines the target smart storage cabinet for the object to be delivered based on the area where the target address is located, and connects with the storage robot equipped with the target smart storage cabinet to complete the delivery of the object to be delivered; The intelligent storage module and storage robot read the electronic tags of the objects to be delivered and transmit them to the target intelligent storage cabinet. The target intelligent storage cabinet retrieves the empty cabinet that best matches the objects to be delivered and unlocks it. The storage robot completes the cabinet storage operation of the objects to be delivered.

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