Cloud logistics warehousing operation method and system based on AR technology

Abstract

The present invention provides a cloud logistics warehousing operation method and system based on AR technology, which relates to the fields of AR technology and cloud logistics warehousing technology. The present invention obtains customer demand order clusters based on the cloud logistics warehousing management system and generates warehousing orders including AR warehousing orders. The AR warehouse management system then generates operation tasks based on the AR warehousing orders and sends the operation tasks to the AR warehouse operation end; the operator at the AR warehouse operation end then uses the AR intelligent warehouse operating system to perform the operation tasks; finally, after the operation tasks are completed, information feedback and updates are performed between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehousing management system. The present invention can meet the personalized storage needs of users for warehouses and has comprehensive functions; it also has a higher warehouse turnover rate and can reduce the total operation time.

Description

Technical Field

[0001] The present invention relates to the field of AR technology and cloud logistics warehousing technology, and in particular to a cloud logistics warehousing operation method and system based on AR technology. Background Art

[0002] With the rapid development of science and technology and the logistics industry, cloud logistics warehousing management platforms and their operational methods are playing a vital role in the logistics market. Cloud logistics warehousing management platforms can integrate warehousing demand and resources within the logistics market and fulfill warehousing orders through mutual selection. By integrating scattered warehousing demand and resources, this technology improves the utilization of warehousing resources within the logistics market and reduces both the warehousing service costs and the service selection costs for small-scale warehousing needs. However, existing cloud logistics warehousing operational models are increasingly failing to meet users' high demands for logistics speed and warehousing convenience. Therefore, a new cloud logistics warehousing operational model is urgently needed to meet these needs.

[0003] At present, the operation mode of the cloud logistics warehousing management platform is: first, through demand integration, warehousing demand orders are obtained, and warehousing orders are concluded according to the principle of mutual selection between the supply and demand parties. The orders are then sent to the corresponding automated warehouses or warehouse operators based on RFID scanning. The warehouse operators complete the corresponding warehousing tasks and feed back the information to the cloud warehousing management platform.

[0004] However, existing cloud logistics and warehousing management platforms lack dedicated cloud logistics and warehousing services that integrate with AR warehouse management systems, making them unable to meet the personalized AR warehouse storage needs of platform users. Furthermore, if AR technology is to be integrated with cloud logistics and warehousing operations, existing technologies are unable to decompose and cluster warehousing demand orders received from cloud logistics service platforms. Furthermore, AR warehouse management systems are unable to appropriately allocate orders to received clustered order clusters. Consequently, existing cloud logistics and warehousing operations technologies are unable to meet users' personalized AR warehouse storage needs and suffer from a single-function problem. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a cloud logistics warehousing operation method and system based on AR technology, which solves the problem that the existing cloud logistics warehousing operation technology cannot meet the user's personalized AR warehouse storage needs and has a single function.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] In a first aspect, the present invention first proposes a cloud logistics warehousing operation method based on AR technology, the method comprising:

[0010] Obtaining a customer demand order cluster based on a cloud logistics warehousing management system, and generating a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order;

[0011] The AR warehouse management system generates an operation task based on the AR warehousing order and sends the operation task to the AR warehouse operation end;

[0012] The operator of the AR warehouse operation end performs the operation task using the AR intelligent warehouse operation system;

[0013] After the operation task is completed, information feedback and updates are carried out between the AR intelligent warehouse operating system, AR warehouse management system, and cloud logistics warehouse management system.

[0014] Preferably, the cloud-based logistics warehousing management system obtains a customer demand order cluster and generates a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order including:

[0015] S11. After obtaining the warehousing demand order filled in by the customer based on the cloud logistics warehousing management system, decompose the warehousing demand order to obtain a demand order;

[0016] S12, clustering the demand orders with the minimum transportation cost as the goal to generate customer demand order clusters;

[0017] S13. Using a genetic algorithm to solve the objective function corresponding to the target to generate a storage order, wherein the storage order includes an AR storage order.

[0018] Preferably, the objective function is:

[0019]

[0020] The constraints of the objective function include:

[0021] constraint

[0022] If the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr, then both will be placed in the same order cluster; Ot ij =1 means that the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr; Ot ij =0 means that the straight-line distance between the cargo addresses of order i and order j is not less than the given acceptable clustering distance dr;

[0023] constraint Indicates that for each order i, there is only one warehouse providing service;

[0024] Among them, D ij Indicates the distance from the cargo address to the target warehouse; C ij represents unit variable logistics cost; C0 represents batch fixed logistics cost; Ot ij is the decision variable; N i Indicates the quantity of goods for demand order i. ij Indicates whether demand order i is served by AR warehouse j.

[0025] Preferably, the AR warehouse management system generates a task based on the AR storage order, including:

[0026] Construct the order allocation objective function with the goal of minimizing the maximum working time of a single operator;

[0027] Using intelligent algorithms to solve the above order allocation objective function to generate job tasks;

[0028] The order allocation objective function is:

[0029]

[0030] Among them, the constraints of the order allocation objective function include:

[0031] constraint The constraint that each order cluster can only be completed by one operator;

[0032] constraint represents the constraint that each operator completes at least one order;

[0033] Among them, X ij A flag indicating whether the jth order cluster is completed by the i-th operator, and q represents the number of order clusters; p represents the number of operators; t represents the working time per unit workload; θ i represents the work efficiency of the i-th operator; β j represents the workload of the jth order cluster.

[0034] Preferably, the information feedback and update between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system includes:

[0035] The AR smart warehouse operating system generates task logs and task completion information and transmits them to the AR warehouse management system. The AR warehouse management system saves the task logs and feeds back the task completion information to the cloud logistics warehouse management system.

[0036] The cloud logistics warehousing management system updates the task progress and inventory information based on the task completion information.

[0037] In a second aspect, the present invention further proposes a cloud logistics warehousing operation system based on AR technology, the system comprising:

[0038] A warehousing order generation module is used to obtain a customer demand order cluster based on the cloud logistics warehousing management system and generate a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order;

[0039] An operation task generation and distribution module is used for the AR warehouse management system to generate operation tasks based on the AR storage order and distribute the operation tasks to the AR warehouse operation end;

[0040] An operation task operation module is used for the operator of the AR warehouse operation end to perform the operation task using the AR intelligent warehouse operating system;

[0041] The information feedback and update module is used to provide information feedback and update between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system after the operation task is completed.

[0042] Preferably, the warehousing order generation module obtains a customer demand order cluster based on the cloud logistics warehousing management system, and generates a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order including:

[0043] S11. After obtaining the warehousing demand order filled in by the customer based on the cloud logistics warehousing management system, decompose the warehousing demand order to obtain a demand order;

[0044] S12, clustering the demand orders with the minimum transportation cost as the goal to generate customer demand order clusters;

[0045] S13. Using a genetic algorithm to solve the objective function corresponding to the target to generate a storage order, wherein the storage order includes an AR storage order.

[0046] Preferably, the objective function is:

[0047]

[0048] The constraints of the objective function include:

[0049] constraint

[0050] If the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr, then both will be placed in the same order cluster; Ot ij=1 means that the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr; Ot ij =0 means that the straight-line distance between the cargo addresses of order i and order j is not less than the given acceptable clustering distance dr;

[0051] constraint Indicates that for each order i, there is only one warehouse providing service;

[0052] Among them, D ij Indicates the distance from the cargo address to the target warehouse; C ij represents unit variable logistics cost; C0 represents batch fixed logistics cost; Ot ij is the decision variable; N i Indicates the quantity of goods for demand order i. ij Indicates whether demand order i is served by AR warehouse j.

[0053] Preferably, the AR warehouse management system generates a task based on the AR storage order, including:

[0054] Construct the order allocation objective function with the goal of minimizing the maximum working time of a single operator;

[0055] Using intelligent algorithms to solve the above order allocation objective function to generate job tasks;

[0056] The order allocation objective function is:

[0057]

[0058] Among them, the constraints of the order allocation objective function include:

[0059] constraint The constraint that each order cluster can only be completed by one operator;

[0060] constraint represents the constraint that each operator completes at least one order;

[0061] Among them, X ij A flag indicating whether the jth order cluster is completed by the i-th operator, and q represents the number of order clusters; p represents the number of operators; t represents the working time per unit workload; θ i represents the work efficiency of the i-th operator; β j represents the workload of the jth order cluster.

[0062] Preferably, the information feedback and update between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system includes:

[0063] The AR smart warehouse operating system generates task logs and task completion information and transmits them to the AR warehouse management system. The AR warehouse management system saves the task logs and feeds back the task completion information to the cloud logistics warehouse management system.

[0064] The cloud logistics warehousing management system updates the task progress and inventory information based on the task completion information.

[0065] (3) Beneficial effects

[0066] The present invention provides a cloud logistics warehousing operation method and system based on AR technology.

[0067] Compared with the existing technology, it has the following beneficial effects:

[0068] 1. This invention uses a cloud logistics warehousing management system to obtain customer demand order clusters and generate warehouse orders, including AR warehouse orders. The AR warehouse management system then generates job tasks based on the AR warehouse orders and sends them to the AR warehouse operation end. The AR warehouse operation end operator then uses the AR intelligent warehouse operating system to perform the job tasks. Finally, after the job tasks are completed, information feedback and updates are generated between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehousing management system. This invention can meet users' specific AR warehouse storage needs and is comprehensive in functionality. It also increases warehouse turnover and reduces total operation time.

[0069] 2. The present invention adopts the cloud logistics concept to realize the resource integration of the AR warehouse system and proposes an order clustering method from the cloud logistics warehousing system to the AR warehouse system to share the fixed logistics costs and achieve economies of scale.

[0070] 3. The present invention proposes an order allocation method on the AR warehouse system side to minimize the total warehouse operation time, so as to improve the warehouse turnover rate and reduce the total operation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0072] Figure 1 This is a flow chart of a cloud logistics warehousing operation method based on AR technology of the present invention;

[0073] Figure 2This is a diagram illustrating an embodiment of a cloud logistics warehousing operation method based on AR technology according to the present invention;

[0074] Figure 3 This is a flowchart of order generation in an embodiment of the present invention;

[0075] Figure 4 This is a flowchart of order allocation in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0077] The embodiments of the present application provide a cloud logistics warehousing operation method and system based on AR technology, which solves the problem that existing cloud logistics warehousing operation technology cannot meet users' personalized AR warehouse storage needs and has a single function, thereby achieving the purpose of improving warehouse turnover rate and reducing the total operation time of logistics warehousing operations.

[0078] The technical solution in the embodiments of the present application is to solve the above technical problems, and the overall idea is as follows:

[0079] In order to solve the problem that existing cloud logistics warehousing operation technology cannot meet the personalized storage needs of users' AR warehouses and has a single function, this application proposes a cloud logistics warehousing operation technology based on AR technology. This technology uses the cloud logistics model to integrate the resources of AR warehouse orders, expands the implementation method of cloud logistics warehousing, realizes data transmission from the cloud logistics warehousing management system to the AR warehouse management system, and incorporates the warehousing resources of independent AR warehouses into the resource integration scope of cloud logistics warehousing capabilities, making the cloud logistics warehousing service model more diversified, meeting the personalized warehousing needs of valuables and fragile items, and providing a real-time and visual operation method for cloud logistics warehousing functions. In addition, the design of an order allocation method with the goal of minimizing working time is implemented on the AR warehouse management system side, reducing the total warehouse operation time and improving the warehouse turnover rate.

[0080] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0081] Cloud logistics warehousing refers to establishing a resource integration platform under the goal of achieving economies of scale through resource integration, and using this platform as a basis to realize the concentration and integration of warehousing resources and warehousing demands in the logistics market, thereby achieving the effect of allocating fixed costs and improving the utilization rate of warehousing resources. The operating model of cloud logistics warehousing is as follows: the cloud logistics warehousing management platform integrates idle warehousing resources and personalized warehousing demands added to the platform, and the supply and demand parties reach warehousing transactions and conduct warehousing activities through mutual selection or platform matchmaking. The cloud logistics warehousing management system is the system that operates cloud logistics warehousing. Based on the cloud logistics warehousing management system, the technical solution of this application is proposed.

[0082] Example 1:

[0083] In the first aspect, the present invention first proposes a cloud logistics warehousing operation method based on AR technology, see Figure 1-2 , the method comprising:

[0084] S1. Obtaining a customer demand order cluster based on a cloud logistics warehousing management system, and generating a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order;

[0085] S2. The AR warehouse management system generates an operation task based on the AR warehousing order and sends the operation task to the AR warehouse operation end;

[0086] S3. The operator of the AR warehouse operation terminal performs the operation task using the AR intelligent warehouse operating system;

[0087] S4. After the operation task is completed, information feedback and updates are performed between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system.

[0088] Optionally, this embodiment obtains customer demand order clusters based on the cloud logistics warehousing management system, and generates warehousing orders including AR warehousing orders. Then, the AR warehouse management system generates job tasks based on the AR warehousing orders, and sends the job tasks to the AR warehouse operation end; then the operator at the AR warehouse operation end uses the AR intelligent warehouse operating system to perform the job tasks; finally, after the job tasks are completed, information feedback and updates are performed between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehousing management system. The present invention can meet the user's special AR warehouse storage personalized needs and has comprehensive functions; and the warehouse turnover rate is higher, which can reduce the total operation time.

[0089] The following is combined with Figure 1-4 , and explanations of specific steps S1-S4 are provided to describe in detail the implementation process of an embodiment of the present invention.

[0090] S1. Obtain a customer demand order cluster based on a cloud logistics warehousing management system, and generate a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order.

[0091] Based on the customer needs of the cloud logistics service platform, a demand order cluster in the new state is generated in the cloud logistics warehousing management system, that is, a warehousing task. Then, based on the nature of the order and personalized needs, the cloud logistics warehousing management system determines whether the warehousing method is an AR warehousing task. Specifically, the specific method of generating a customer demand order cluster is as follows:

[0092] S11. After obtaining the warehousing demand order filled in by the customer based on the cloud logistics warehousing management system, the warehousing demand order is decomposed to obtain a demand order.

[0093] Customers fill out a warehousing request order, including customer information, cargo information, and warehousing method. For customer information, the customer simply selects the customer entry, and the Cloud Logistics Warehousing Management System executes a SQL create query to obtain customer information, including customer name, contact information, address, and level. For cargo information, the customer fills in attributes such as cargo name, quantity, weight, volume, unit price, and address. Multiple cargo types and batches can be added. The customer selects the warehousing method, which can be standard warehouse inbound, standard warehouse outbound, AR warehouse inbound, or AR warehouse outbound.

[0094] The cloud logistics warehouse management system processes customer orders based on operation type T, cargo batch B, cargo address D i (x i ,y i ) to decompose and obtain the unique batch of demand orders O with the same address and the same category. i (T i ,D i ,N i ,P i ,V i ,Q i ); where N i Indicates the quantity of goods in the order; P i Indicates unit price; V i Indicates the volume of cargo; Q i Indicates the quality of goods.

[0095] S12. Cluster the demand orders with the minimum transportation cost as the goal to generate customer demand order clusters.

[0096] Taking the minimum transportation cost as the objective function, the demand order O i Perform order clustering to achieve intensive economy and generate customer demand order clusters. i To the target warehouse W j The distance is Dij ; Unit variable logistics cost is C ij ;Batch fixed logistics cost is C0; determine the transport order O i and order O j The decision variable for whether they are in the same order cluster is Ot ij , the minimum transportation cost is expressed as:

[0097]

[0098] Among them, the first double sum represents the transportation cost, which is the transportation distance multiplied by the number of goods in the transportation order multiplied by the unit transportation cost. ij Denotes the unit transportation cost from the location of the goods in demand order i to AR warehouse j, D ij N represents the transportation distance from the cargo location of demand order i to AR warehouse j, i It represents the quantity of goods in demand order i; J ij Indicates whether demand order i is serviced by AR warehouse j. If so, J equals 1, otherwise it equals 0. Since an order can only be serviced by one warehouse, while a warehouse can serve multiple orders, if J is not set, the combined transportation costs will result in a single order being serviced by multiple warehouses, which is inconsistent with reality.

[0099] Among them, the constraints of the objective function include:

[0100]

[0101] If the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr, then both orders will be placed in the same order cluster. ij =1 means that the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr; Ot ij =0 means that the straight-line distance between the cargo addresses of order i and order j is not less than the given acceptable clustering distance dr;

[0102] constraint It means that for each order i, there is only one warehouse providing service for it.

[0103] S13. Using a genetic algorithm to solve the objective function corresponding to the target to generate a storage order, wherein the storage order includes an AR storage order.

[0104] The objective function corresponding to the minimum transportation cost is solved by genetic algorithm, and the customer order cluster Ot is finally determined. k The warehouse address W corresponding to the order cluster k. When solving the problem, you can directly use the MATLAB genetic algorithm toolbox to solve the objective function. Open the Optimization toolbox in MATLAB, select ga in Solver and click Open. Next, edit the objective function, save the above model objective function as an m function file, and fill the objective function file reference into the Fitness function. Fill in the number of variables, equality constraints, inequality constraints, value boundaries and other parameters, and then click Start to start running and get the results.

[0105] The cloud logistics warehouse management system loads the calculated customer order cluster and warehouse address into the order attributes and generates warehouse order information in the newly created state. Among them, warehouse orders include ordinary warehouse orders and AR warehouse orders.

[0106] In this example, we take the AR warehousing order under the cloud warehousing platform as an example. Figure 3 ,Now the cloud logistics service platform customers fill in a batch of demand orders, including inbound demand, outbound demand, and distribution demand. According to the order decomposition, 45 unique batches of demand orders with the same address and category are obtained. 45}, demand order example: O1{"inbound",(117.3,31.8),20,17.5,2.3,20}. Take the minimum logistics cost as the objective function to cluster the orders of the same operation type. It is known that among the 45 orders, O1, O2, O4, O8, O 12 , O 19 , O 24 , O 31 , O 42 For AR incoming orders, the goods address of each order D i (x i ,y i ) and AR warehouse address W j (x j ,y j ), unit variable logistics cost C ij , the preset acceptable cluster distance dr = 0.025, the batch fixed logistics cost C0 = 300 are substituted into the model for solution. The result is the order cluster Ot{(O1,O 31 ),(O2,O 12 ,O 19 ,O 42 ),(O4,O8,O 24 )} and the storage address W{W1(Ot1,Ot2),W2(Ot3)} corresponding to the order cluster, and finally generate the incoming cluster order clusters of different AR warehouses.

[0107] In addition, in order to ensure the accuracy of the information of newly created orders, in actual operations, the cloud logistics warehouse administrator of the cloud logistics warehouse management system will review the orders in the newly created state in the above steps, check the customer information and order attributes, and place the order if they are accurate. Otherwise, the order request will be returned and the reason for the return will be filled in again, and a work log will be generated.

[0108] S2. The AR warehouse management system generates an operation task based on the AR warehousing order and sends the operation task to the AR warehouse operation end.

[0109] After step S1, ordinary warehousing orders are processed in the usual manner. AR warehousing orders are transferred via the data interface between the cloud logistics warehousing management system and the AR warehouse management system. After the transfer, an operation task is generated in the AR warehouse management system (an operation task refers to a warehousing task with a corresponding relationship of "xx order cluster - xx operator" generated from the warehousing tasks of multiple order clusters). The AR warehouse administrator then sends the operation task to the AR warehouse operation end.

[0110] Specifically, the AR warehouse manager sends the task cluster to the AR warehouse operation end and allocates the order cluster based on the shortest maximum working time of a single operator. The allocation works as follows:

[0111] (3.1) Define variables. The AR warehouse has p operators, and the operator's work efficiency is θ(θ1,θ2,....,θ p ) T , q order clusters are delivered from the AR warehouse manager, and the workload of the order cluster is β(β1,β2,...,β q ) T , the working time per unit workload is t, X ij A flag indicating whether the jth order cluster is completed by the i-th operator, that is:

[0112]

[0113] (3.2) The maximum working time of a single operator is:

[0114]

[0115] The order allocation objective function is:

[0116] Among them, the constraints of the order allocation objective function include:

[0117] constraint The constraint that each order cluster can only be completed by one operator;

[0118] constraint Represents the constraint that each operator completes at least one order.

[0119] (3.3) Using intelligent algorithms (such as particle swarm optimization and annealing algorithm) to solve the above objective function, we can obtain the optimal method for allocating order clusters with the minimum working time. According to the obtained allocation method, we can assign order clusters to each operator. We call the corresponding relationship of "xx order cluster - xx operator" a warehouse operation task.

[0120] See also Figure 4 In this example, it is assumed that AR warehouse 001 has 4 operators, and the operator's work efficiency is θ(0.9, 0.8, 0.7, 0.88) T , according to the order O{O1,O 31 O2,O 12 ,O 19 ,O 42}, the workload of the order cluster is β(20,30,26,50,13,9) T , the working time per unit workload is 3 minutes, and the data is substituted into the solution algorithm to solve the model. The final allocation result is: AR warehouse operator 001 is responsible for orders O1, O 31 , AR warehouse operator 002 is responsible for order O2, AR warehouse operator 003 is responsible for order O 19 , O 42 , AR warehouse operator 004 is responsible for order O 12 .

[0121] S3. The operator at the AR warehouse operation end uses the AR intelligent warehouse operating system to perform the operation task.

[0122] Based on the type of work, the AR warehouse operator wears a Hololens2 mixed reality head-mounted display device equipped with an AR intelligent warehouse operating system to perform the assigned work tasks. Specifically, the AR warehouse operator performs warehouse work in the following steps:

[0123] (4.1) The AR warehouse operator wears the Hololens 2 mixed reality headset and stands in the starting position in front of the physical warehouse. He opens the AR warehouse training operating system and gently pinches the front with his right thumb and index finger to open the AR warehouse interface.

[0124] (4.2) The AR warehouse operator aligns the warehouse operation menu directly in front of them and gently pinches their index finger and thumb to select the task type: Inbound, Outbound, Picking, Inventory, or Distribution. After selecting the task type, they align their index finger and thumb with the Get Order button to retrieve the work order cluster from the AR warehouse administrator. If the task type is Inbound, proceed to step (4.3); if the task type is Outbound, proceed to step (4.4); if the task type is Distribution, proceed to step (4.5).

[0125] (4.3) If the order cluster obtained in step (4.2) is an incoming order, the AR warehouse operator extracts the incoming goods from the temporary storage area. The system automatically generates the optimal path from the AR warehouse operator's position to the target storage location based on the path algorithm. The path is marked in the form of a dynamic arrow from the AR warehouse operator's feet to the target storage location. The AR warehouse operator delivers the goods to the target storage location according to the path prompts, and aligns the Hololens2 mixed reality head-mounted display device with the storage location AR virtual QR code label to verify the storage location information according to the system prompts. After the information is verified, the AR virtual QR code label of the incoming material is scanned. After verifying that the material and quantity are correct, the AR warehouse operator puts the incoming goods into the storage location. The system updates the storage location information and task status information, and the incoming task is completed. The AR warehouse operator can choose to obtain the next order or end the task as needed.

[0126] (4.4) If the order cluster obtained in step (4.2) is an outbound order, the system automatically generates an optimal path from the AR warehouse operator's position to the target storage location based on the path algorithm. The path is marked as a dynamic arrow from the AR warehouse operator's feet to the target storage location. The AR warehouse operator follows the path prompts to find the target storage location. After arriving at the target storage location, the system reminds the AR warehouse operator to align the Hololens2 mixed reality head-mounted display device with the storage location's AR virtual QR code label to verify the storage location information. After verifying the storage location information, the system reminds the AR warehouse operator to align the Hololens2 mixed reality head-mounted display device with the storage location's AR virtual QR code label to verify the cargo information. After the AR warehouse operator completes picking at the target storage location, the system automatically generates an optimal path from the AR warehouse operator's position to the outbound packaging area based on the path algorithm. The path is marked as a dynamic arrow from the AR warehouse operator's feet to the outbound packaging area. The AR warehouse operator follows the path prompts to reach the outbound packaging area. After packaging is completed, the system updates the inventory information and task status information, and the outbound task is completed. The AR warehouse operator can choose to obtain the next order or end the task as needed.

[0127] (4.5) If the order cluster obtained in step (4.2) is a distribution order, the system first automatically generates the optimal path from the AR warehouse operator's position to the warehouse location of the goods to be distributed based on the path algorithm. The path is marked with a dynamic arrow from the feet of the AR warehouse operator to the target warehouse location. The AR warehouse operator follows the path prompts to find the target warehouse location. After arriving at the target warehouse location, the system reminds the AR warehouse operator to align the Hololens2 mixed reality head-mounted display device with the AR virtual QR code label of the warehouse location to verify the warehouse location information. After verifying the warehouse location information, the system prompts the AR warehouse operator to align the Hololens2 mixed reality head-mounted display device with the AR virtual QR code label of the goods to be distributed in the warehouse location to verify the goods information. After the AR warehouse operator completes picking at the target warehouse location, the system automatically generates each optimal path from the AR warehouse operator's position to the target distribution warehouse location based on the quantity and type of goods to be distributed according to the path algorithm. The path is marked with a dynamic arrow from the feet of the AR warehouse operator to the distribution warehouse location. The AR warehouse operator follows the route prompts to each target distribution location and scans the AR virtual QR code label at the location. After verifying the location, the operator places the corresponding batch of goods to be distributed into the target location. The next distribution subtask is then followed in the same manner to distribute the goods to the target location. After all subtasks are completed, the system updates the inventory information and task status, and the distribution task is completed. The AR warehouse operator can then choose to obtain the next order or end the task as needed.

[0128] In this embodiment, the AR warehouse operator 001 is used as an example to illustrate a specific operation process of the above operation. AR warehouse operator 001 points to the warehouse operation menu option in front of him, pinches his index finger and thumb to select the operation type as warehousing. After selecting the operation type, the index finger and thumb are aligned with the Get Order button to obtain the work orders O1 and O2 from the AR warehouse manager. 31 . Then the orders are executed in sequence, and the goods to be entered into the temporary storage area are extracted. The system automatically generates the optimal path from the AR warehouse operator's position to the target storage location based on the path algorithm. The path is marked in the form of a dynamic arrow from the feet of the AR warehouse operator to the target storage location. The AR warehouse operator follows the path prompts to deliver the goods to the target storage location, and according to the system prompts, aligns the Hololens2 mixed reality head-mounted display device with the AR virtual QR code label of the storage location to verify the storage location information. After the information is verified, the AR virtual QR code label of the incoming material is scanned. After verifying that the material and quantity are correct, the AR warehouse operator puts the goods to be entered into the storage location, the system updates the storage location information and task status information, and the warehousing task is completed. The AR warehouse operator can choose to obtain the next order or end the task as needed.

[0129] S4. After the operation task is completed, information feedback and updates are performed between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system.

[0130] After the task in S3 is completed, the AR intelligent warehouse operating system generates a task log and transmits task completion information to the AR warehouse management system. The AR warehouse management system saves the task log and feeds the task completion information back to the cloud logistics warehouse management system. The cloud logistics warehouse management system updates the task progress and inventory information based on the task information fed back by the AR warehouse management system. At this point, the information exchange and task progress between the cloud logistics warehouse management system and the AR warehouse management system are complete.

[0131] At this point, the entire process of the cloud logistics warehousing operation method based on AR technology in this embodiment is completed.

[0132] Example 2:

[0133] In a second aspect, the present invention further provides a cloud logistics warehousing operation system based on AR technology, the system comprising:

[0134] A warehousing order generation module is used to obtain a customer demand order cluster based on the cloud logistics warehousing management system and generate a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order;

[0135] An operation task generation and distribution module is used for the AR warehouse management system to generate operation tasks based on the AR storage order and distribute the operation tasks to the AR warehouse operation end;

[0136] An operation task operation module is used for the operator of the AR warehouse operation end to perform the operation task using the AR intelligent warehouse operating system;

[0137] The information feedback and update module is used to provide information feedback and update between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system after the operation task is completed.

[0138] Optionally, the warehousing order generation module obtains a customer demand order cluster based on a cloud logistics warehousing management system, and generates a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order including:

[0139] S11. After obtaining the warehousing demand order filled in by the customer based on the cloud logistics warehousing management system, decompose the warehousing demand order to obtain a demand order;

[0140] S12, clustering the demand orders with the minimum transportation cost as the goal to generate customer demand order clusters;

[0141] S13. Using a genetic algorithm to solve the objective function corresponding to the target to generate a storage order, wherein the storage order includes an AR storage order.

[0142] Optionally, the objective function is:

[0143]

[0144] The constraints of the objective function include:

[0145] constraint

[0146] If the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr, then both will be placed in the same order cluster; Ot ij =1 means that the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr; Ot ij =0 means that the straight-line distance between the cargo addresses of order i and order j is not less than the given acceptable clustering distance dr;

[0147] constraint Indicates that for each order i, there is only one warehouse providing service;

[0148] Among them, D ij Indicates the distance from the cargo address to the target warehouse; C ij represents unit variable logistics cost; C0 represents batch fixed logistics cost; Ot ij is the decision variable; N i Indicates the quantity of goods for demand order i. ij Indicates whether demand order i is served by AR warehouse j.

[0149] Optionally, the AR warehouse management system generates a task based on the AR storage order, including:

[0150] Construct the order allocation objective function with the goal of minimizing the maximum working time of a single operator;

[0151] Using intelligent algorithms to solve the above order allocation objective function to generate job tasks;

[0152] The order allocation objective function is:

[0153]

[0154] Among them, the constraints of the order allocation objective function include:

[0155] constraint The constraint that each order cluster can only be completed by one operator;

[0156] constraint represents the constraint that each operator completes at least one order;

[0157] Among them, X ij A flag indicating whether the jth order cluster is completed by the i-th operator, and q represents the number of order clusters; p represents the number of operators; t represents the working time per unit workload; θ i represents the work efficiency of the i-th operator; β j represents the workload of the jth order cluster.

[0158] Optionally, the information feedback and update between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system includes:

[0159] The AR smart warehouse operating system generates task logs and task completion information and transmits them to the AR warehouse management system. The AR warehouse management system saves the task logs and feeds back the task completion information to the cloud logistics warehouse management system.

[0160] The cloud logistics warehousing management system updates the task progress and inventory information based on the task completion information.

[0161] It can be understood that the cloud logistics warehousing operation system based on AR technology provided by the embodiment of the present invention corresponds to the above-mentioned cloud logistics warehousing operation method based on AR technology. The explanations, examples, beneficial effects, etc. of the relevant contents can refer to the corresponding contents in the cloud logistics warehousing operation method based on AR technology, and will not be repeated here.

[0162] In summary, compared with the existing technology, the present invention has the following beneficial effects:

[0163] 1. This invention uses a cloud logistics warehousing management system to obtain customer demand order clusters and generate warehouse orders, including AR warehouse orders. The AR warehouse management system then generates job tasks based on the AR warehouse orders and sends them to the AR warehouse operation end. The AR warehouse operation end operator then uses the AR intelligent warehouse operating system to perform the job tasks. Finally, after the job tasks are completed, information feedback and updates are generated between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehousing management system. This invention can meet users' specific AR warehouse storage needs and is comprehensive in functionality. It also increases warehouse turnover and reduces total operation time.

[0164] 2. The present invention adopts the cloud logistics concept to realize the resource integration of the AR warehouse system and proposes an order clustering method from the cloud logistics warehousing system to the AR warehouse system to share the fixed logistics costs and achieve economies of scale.

[0165] 3. The present invention proposes an order allocation method on the AR warehouse system side to minimize the total warehouse operation time, so as to improve the warehouse turnover rate and reduce the total operation time.

[0166] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0167] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A cloud logistics warehousing operation method based on AR technology, characterized in that: The method comprises: Obtaining a customer demand order cluster based on a cloud logistics warehousing management system, and generating a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order; The AR warehouse management system generates an operation task based on the AR warehousing order and sends the operation task to the AR warehouse operation end; The operator of the AR warehouse operation end performs the operation task using the AR intelligent warehouse operation system; After the task is completed, information feedback and updates are carried out between the AR intelligent warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system; Wherein, a warehousing order is generated based on the customer demand order cluster; the warehousing order includes an AR warehousing order including: S11. After obtaining the warehousing demand order filled in by the customer based on the cloud logistics warehousing management system, decompose the warehousing demand order to obtain a demand order; S12, clustering the demand orders with the minimum transportation cost as the goal to generate customer demand order clusters; S13. Using a genetic algorithm to solve the objective function corresponding to the target to generate a storage order, wherein the storage order includes an AR storage order; The objective function is: The constraints of the objective function include: constraint i=1,2,...,n-1; j=i+1,i+2,...,n If the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr, then both will be placed in the same order cluster; Ot ij =1 means that the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr; Ot ij =0 means that the straight-line distance between the cargo addresses of order i and order j is not less than the given acceptable clustering distance dr; constraint Indicates that for each order i, there is only one warehouse providing service; Among them, D ij Indicates the distance from the cargo address to the target warehouse; C ij represents unit variable logistics cost; C0 represents batch fixed logistics cost; Ot ij is the decision variable; N i represents the quantity of goods in demand order i; J ij Indicates whether demand order i is served by AR warehouse j.

2. The method according to claim 1, wherein The AR warehouse management system generates a task based on the AR storage order, including: Construct the order allocation objective function with the goal of minimizing the maximum working time of a single operator; Using intelligent algorithms to solve the above order allocation objective function to generate job tasks; The order allocation objective function is: Among them, the constraints of the order allocation objective function include: constraint j=1,2,...,q represents the constraint that each order cluster can only be completed by one operator; constraint i=1,2,...,p represents the constraint that each operator completes at least one order; Among them, X ij A flag indicating whether the jth order cluster is completed by the i-th operator, and q represents the number of order clusters; p represents the number of operators; t represents the working time per unit workload; θ i represents the work efficiency of the i-th operator; β j represents the workload of the jth order cluster.

3. The method according to claim 1, wherein The information feedback and update between the AR smart warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system include: The AR smart warehouse operating system generates task logs and task completion information and transmits them to the AR warehouse management system. The AR warehouse management system saves the task logs and feeds back the task completion information to the cloud logistics warehouse management system. The cloud logistics warehousing management system updates the task progress and inventory information based on the task completion information.

4. A cloud logistics warehousing operation system based on AR technology, characterized by: The system comprises: A warehousing order generation module is used to obtain a customer demand order cluster based on the cloud logistics warehousing management system and generate a warehousing order based on the customer demand order cluster; the warehousing order includes an AR warehousing order; An operation task generation and distribution module is used for the AR warehouse management system to generate operation tasks based on the AR storage order and distribute the operation tasks to the AR warehouse operation end; An operation task operation module is used for the operator of the AR warehouse operation end to perform the operation task using the AR intelligent warehouse operating system; An information feedback and update module is used to provide information feedback and updates between the AR smart warehouse operating system, AR warehouse management system, and cloud logistics warehouse management system after the task is completed; Wherein, a warehousing order is generated based on the customer demand order cluster; the warehousing order includes an AR warehousing order including: S11. After obtaining the warehousing demand order filled in by the customer based on the cloud logistics warehousing management system, decompose the warehousing demand order to obtain a demand order; S12, clustering the demand orders with the minimum transportation cost as the goal to generate customer demand order clusters; S13. Using a genetic algorithm to solve the objective function corresponding to the target to generate a storage order, wherein the storage order includes an AR storage order; The objective function is: The constraints of the objective function include: constraint i=1,2,...,n-1; j=i+1,i+2,...,n If the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr, then both will be placed in the same order cluster; Ot ij =1 means that the straight-line distance between the goods addresses of order i and order j is less than the given acceptable clustering distance dr; Ot ij =0 means that the straight-line distance between the cargo addresses of order i and order j is not less than the given acceptable clustering distance dr; constraint i=1,2,...,n means that for each order i, there is only one warehouse providing service; Among them, D ij Indicates the distance from the cargo address to the target warehouse; C ij represents unit variable logistics cost; C0 represents batch fixed logistics cost; Ot ij is the decision variable; N i represents the quantity of goods in demand order i; J ij Indicates whether demand order i is served by AR warehouse j.

5. The system according to claim 4, wherein: The AR warehouse management system generates a task based on the AR storage order, including: Construct the order allocation objective function with the goal of minimizing the maximum working time of a single operator; Using intelligent algorithms to solve the above order allocation objective function to generate job tasks; The order allocation objective function is: Among them, the constraints of the order allocation objective function include: constraint j=1,2,...,q represents the constraint that each order cluster can only be completed by one operator; constraint i=1,2,...,p represents the constraint that each operator completes at least one order; Among them, X ij A flag indicating whether the jth order cluster is completed by the i-th operator, and q represents the number of order clusters; p represents the number of operators; t represents the working time per unit workload; θ i represents the work efficiency of the i-th operator; β j represents the workload of the jth order cluster.

6. The system according to claim 4, wherein: The information feedback and update between the AR smart warehouse operating system, the AR warehouse management system, and the cloud logistics warehouse management system include: The AR smart warehouse operating system generates task logs and task completion information and transmits them to the AR warehouse management system. The AR warehouse management system saves the task logs and feeds back the task completion information to the cloud logistics warehouse management system. The cloud logistics warehousing management system updates the task progress and inventory information based on the task completion information.

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