Method for multi-terminal coordinated optimization of order picking path

Abstract

The invention relates to a method for multi-terminal coordinated optimization of a picking path. The method comprises the following steps: converting the physical layout of each warehouse into a graph structure; obtaining an order and checking goods; on the basis of the graph structure, real-time position information and the like of each PDA are obtained; screening out the nearest distance to the target shelf number and the advancing direction towards the target shelf number; issuing a picking task list; scanning the bar code number by using the PDA; generating ex-warehouse list detail data; and updating the ex-warehouse list state to be audited, and notifying the background server. According to the method, the physical layout of the warehouse is converted into a graph structure, and factors such as channel distance and width are comprehensively considered, so that intelligent planning and optimization of the picking path are realized; by acquiring the state information of the PDA and the goods picking vehicle in real time, accurate distribution and efficient execution of tasks are realized; the goods picking efficiency and accuracy are improved, the goods picking cost can be remarkably reduced, the logistics operation efficiency is improved, and an intelligent and efficient solution is provided for warehouse management.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a method for multi-terminal coordinated optimization of picking paths. Background Art

[0002] In a multi-PDA, multi-warehouse picking environment, existing picking methods typically include:

[0003] Task allocation: The backend server assigns tasks to pickers, usually based on simple rules such as the order of tasks or fixed areas of the warehouse.

[0004] Path planning: Pickers generate picking paths based on the SKU (minimum stock keeping unit, the basic unit for measuring inventory in and out) list on the task order, either manually or using a simple algorithm (such as using the A* algorithm or Dijkstra algorithm to plan a path for a single PDA).

[0005] Scanning and verification: Pickers use PDAs to scan the storage location number and barcode number to verify the picked goods and storage locations.

[0006] Real-time feedback: Pickers will feed back the picking results to the backend server in real time for subsequent review and audit.

[0007] The technical problems it has are:

[0008] Unbalanced task allocation: Existing task allocation methods often fail to fully consider the current location of pickers, warehouse distribution, and task urgency, resulting in unbalanced task allocation, with some pickers having too much work to do and some having too little.

[0009] Low path planning efficiency: Existing path planning methods are usually based on simple algorithms, such as the nearest neighbor algorithm. These methods are inefficient in complex environments with multiple warehouses and multiple PDAs, and can easily cause pickers to walk back and forth in the warehouse, increasing unnecessary path length.

[0010] Imperfect exception handling: The existing system lacks an effective mechanism for handling abnormal situations (such as inventory adjustments and product out-of-stock situations), which requires pickers to handle them manually, increasing operational complexity and error rates.

[0011] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0012] In response to the defects in the prior art, the purpose of the present invention is to provide a method for multi-terminal coordinated optimization of picking paths. By converting the physical layout of the warehouse into a graph structure and comprehensively considering factors such as channel distance and width, intelligent planning and optimization of picking paths are achieved; by obtaining the status information of PDAs and picking trucks in real time, accurate allocation and efficient execution of tasks are achieved; the solution also supports functions such as order clustering, intelligent prediction, manual task transfer and review mechanism, which further improves picking efficiency and accuracy, can significantly reduce picking costs, improve logistics operation efficiency, and provide an intelligent and efficient solution for warehouse management.

[0013] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0014] A method for multi-terminal coordinated optimization of picking paths, characterized by comprising:

[0015] The physical layout of each warehouse is converted into a graph structure, with shelves as nodes, aisles as edges between nodes, the distance between aisles as edge weights, and aisle width as priority.

[0016] Get the order, extract the product SKUs in the order one by one, send a stock query request to each warehouse, and each warehouse will return the product inventory and shelf number;

[0017] Based on the graph structure, the real-time location information of each PDA and the direction of travel of the picking truck loaded with the PDA are obtained;

[0018] Based on the real-time location information of the PDA and the direction of the picking truck, the PDA and picking truck closest to the target shelf number and heading towards the target shelf number are selected;

[0019] Issue a picking task order, which includes at least the detailed location information of the product SKU: location number, aisle number, shelf number, and barcode number;

[0020] The picking cart moves along the travel direction to the corresponding shelf number and uses the PDA to scan the barcode number;

[0021] After verifying that the picked goods are consistent with the picking task list, generate the detailed data of the outbound order;

[0022] The PDA device updates the outbound order status to pending review and notifies the backend server.

[0023] On the basis of the above technical solution, when obtaining the real-time location information of the PDA, the working status of the PDA is further inquired. If it is busy, the PDA and the corresponding picking cart are skipped.

[0024] Based on the above technical solution, orders are clustered according to the warehouse and SKU of the goods in the order, and goods with the same location number and adjacent shelf number are grouped together to reduce the ineffective movement of PDAs between and within warehouses.

[0025] On the basis of the above technical solution, the number of PDAs and order processing capacity of each warehouse are predicted according to historical data, and orders are proportionally allocated to warehouses with non-zero and non-negative inventory according to order processing capacity.

[0026] On the basis of the above technical solution, based on the greedy algorithm, the task is preferentially assigned to the PDA that is closest to the target shelf number and has a lighter load.

[0027] On the basis of the above technical solution, when there are more than two PDAs in the same channel number, the travel path of the picking truck that is closer to the target shelf number is adjusted first.

[0028] Based on the above technical solution, a task transfer request is manually initiated on the PDA side and broadcast to nearby PDAs. After receiving manual confirmation from a certain PDA, a picking task order transfer operation is initiated on the PDA side and the background server is notified to update the information.

[0029] Based on the above technical solution, the reviewer scans the product barcode through the PDA and reviews it with the order. If the product quantity or product barcode is inconsistent with the order, the abnormal data will be recorded and the backend server will be notified.

[0030] Based on the above technical solution, during the picking process, the power level of the PDA is monitored in real time. When the power level is lower than the preset threshold, the current task of the PDA is suspended, and the unfinished tasks are reallocated to other idle PDAs. At the same time, the background server is notified to update the task allocation information.

[0031] Based on the above technical solution, in the warehouse layout graph structure, different node attributes are set for different types of goods, and the weight and priority of the channel are adjusted according to the characteristics of the goods;

[0032] The product characteristics include: weight, volume, and fragility.

[0033] The method for multi-terminal coordinated optimization of picking paths described in the present invention has the following beneficial effects:

[0034] By converting the physical layout of the warehouse into a graph structure and comprehensively considering factors such as aisle distance and width, intelligent planning and optimization of picking paths are achieved; by obtaining real-time status information of PDAs and picking trucks, accurate task allocation and efficient execution are achieved; the solution also supports functions such as order clustering, intelligent forecasting, manual task transfer and review mechanisms, further improving picking efficiency and accuracy, significantly reducing picking costs, improving logistics operation efficiency, and providing an intelligent and efficient solution for warehouse management. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention has the following accompanying drawings:

[0036] The accompanying drawings are provided for a better understanding of the present invention and are not intended to limit the present invention.

[0037] Figure 1 Flowchart of Example 1 of a method for multi-terminal coordination and optimization of picking paths described in the present invention. DETAILED DESCRIPTION

[0038] The present invention will be described in further detail below with reference to the accompanying drawings. The detailed description, which is provided for illustrative purposes only and includes various details to aid understanding of the embodiments of the present invention, should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted from the following description.

[0039] like Figure 1 As shown, the method of multi-terminal coordinated optimization of picking paths described in the present invention includes:

[0040] The physical layout of each warehouse is converted into a graph structure, with shelves as nodes, aisles as edges between nodes, the distance between aisles as edge weights, and aisle width as priority.

[0041] Get the order, extract the product SKUs in the order one by one, send a stock query request to each warehouse, and each warehouse will return the product inventory and shelf number;

[0042] Based on the graph structure, the real-time location information of each PDA and the direction of travel of the picking truck loaded with the PDA are obtained;

[0043] Based on the real-time location information of the PDA and the direction of the picking truck, the PDA and picking truck closest to the target shelf number and heading towards the target shelf number are selected;

[0044] Issue a picking task order, which includes at least the detailed location information of the product SKU: location number, aisle number, shelf number, and barcode number;

[0045] The picking cart moves along the travel direction to the corresponding shelf number and uses the PDA to scan the barcode number;

[0046] After verifying that the picked goods are consistent with the picking task list, generate the detailed data of the outbound order;

[0047] The PDA device updates the outbound order status to pending review and notifies the backend server.

[0048] In this embodiment, a weight is assigned to each edge, which represents the distance of the channel between nodes. At the same time, the width of the channel is stored as priority information, which can improve the efficiency of subsequent path search operations;

[0049] Obtain order information from the order management system, extract the product SKUs in the order one by one through the order parsing module, and use the network communication interface to send a stock query request containing the extracted product SKUs to the inventory management system of each warehouse. After receiving the request, the inventory management system of each warehouse checks the inventory of the corresponding product and returns the inventory information and the shelf number of the product. When sending the stock query request, a timeout mechanism must be set. If no response is received within the specified time, an error log will be recorded and the corresponding retry operation will be performed.

[0050] Based on the PDA positioning system and the picking cart positioning and direction monitoring system, a graph structure is used to obtain the real-time location information of each PDA and the direction of travel of the picking cart carrying the PDA. For example, to ensure the accuracy and reliability of the positioning system, indoor positioning technology (such as Wi-Fi positioning, Bluetooth positioning, or ultra-wideband positioning) can be used to obtain the real-time location of the PDA, and sensors installed on the picking cart (such as gyroscopes and accelerometers) can be used to obtain the direction of travel information.

[0051] The screening of the PDA and picking cart that are closest to the target shelf number and moving in the direction of the target shelf number can be implemented by calculating the shortest path from their location to the target shelf, while considering whether the direction of travel is consistent with the shortest path direction. If multiple PDAs meet the conditions, further screening can be performed according to certain rules (such as PDA load, PDA remaining power, etc.) to obtain the screening result of the optimal PDA;

[0052] The picking cart moves to the corresponding shelf number according to the received task order information. After arriving at the shelf, the PDA is used to scan the barcode number. If the PDA fails to scan the barcode number, an error message should be provided, and rescanning should be allowed or exception handling operations should be performed (such as reporting the error to the backend server).

[0053] During the product verification and delivery order generation process, the picked products are verified to see if they match the picking task order, including information such as the product's SKU and quantity. If they match, delivery order detail data is generated, which includes detailed product information, picking time, picking personnel, and other information. If they do not match, the abnormal information (such as inconsistent product information, possible reasons, etc.) is recorded and the backend server is notified for corresponding processing.

[0054] Example 1 of using graph structure: Path generation considering weight (channel distance)

[0055] Suppose there is a simple warehouse layout graph with three shelves A, B, and C, and aisles connecting them. Represent the shelves as nodes, the aisles as edges, and the distance of the aisles as the weight of the edge.

[0056] The channel distance from node A to node B is 5 meters (weight is 5).

[0057] The channel distance from node A to node C is 10 meters (weight is 10).

[0058] The channel distance from node B to node C is 3 meters (weight is 3).

[0059] When you need to pick up a product from shelf A and deliver it to shelf C, there are two possible paths from A to C:

[0060] Path 1: A->B->C, total distance is 5+3=8 meters.

[0061] Path 2: A->C, total distance is 10 meters.

[0062] Therefore, the path A->B->C is preferred as the picking path.

[0063] Example 2 of using graph structure: Path generation considering priority (channel width)

[0064] Assume there are aisles of different widths in the warehouse. Wider aisles can allow more picking trucks to pass through at the same time and avoid congestion during peak hours.

[0065] There are two channels from node X to node Y. Channel 1 has a width of 2 meters (low priority) and channel 2 has a width of 4 meters (high priority).

[0066] The distance from node X to node Y through channel 1 is 8 meters (weight 8).

[0067] The distance from node X to node Y through channel 2 is 10 meters (weight is 10).

[0068] Under normal circumstances, considering only the weight, channel 1 may be selected as the path because it is shorter. However, if it is currently a busy period in the warehouse, and you want to prioritize the use of wider channels to avoid traffic jams and improve the overall picking flow, then even if channel 2 is longer, you want the system to prioritize channel 2 as the picking path. In this case, an adjustment factor parameter can be introduced. When calculating the path cost, the weight of the low-priority channel remains unchanged; while the weight of the high-priority channel can be multiplied by a coefficient less than 1;

[0069] Adjusted cost of channel 1 = 8 (weight) × 1 (normal priority) = 8.

[0070] Adjusted cost of channel 2 = 10 (weight) × 0.8 (high priority adjustment factor) = 8.

[0071] Even though the original distance of Aisle 2 is longer, its adjusted cost is the same as or lower than Aisle 1 after taking priority into account. In this case, the system will prioritize Aisle 2 as the picking path to ensure smoother picking operations during busy hours and avoid traffic congestion and delays caused by narrow aisles.

[0072] Example 3 of using graph structure: Comprehensive consideration of weight and priority

[0073] Based on the same principle as the above example, consider a more complex warehouse layout with four shelves D, E, F, and G and aisles connecting them.

[0074] The channel distance from node D to node E is 6 meters and the channel width is 3 meters (medium priority).

[0075] The channel distance from node D to node F is 8 meters and the channel width is 4 meters (high priority).

[0076] The channel distance from node F to node G is 5 meters and the channel width is 4 meters (low priority).

[0077] The channel distance from node E to node G is 7 meters and the channel width is 3 meters (medium priority).

[0078] Assume that you need to reach node G from node D.

[0079] Path 1: D->E->G, with a total distance of 6 + 7 = 13 meters. Considering priority, assuming the adjustment factor for the medium-priority channel is 0.9, the adjusted cost is (6 × 0.9) + (7 × 0.9) = 11.7.

[0080] Path 2: D->F->G, with a total distance of 8 + 5 = 13 meters. Taking priority into account, the adjustment factor for high-priority channels is 0.8, and the adjustment factor for low-priority channels is 1, resulting in an adjusted cost of (8 × 0.8) + (5 × 1) = 11.4.

[0081] Although the total distance of the two paths is the same, the system will prioritize path D->F->G as the picking path because path 2 contains a high-priority channel and has a lower cost after adjustment. At the same time, the system comprehensively considers the priority factors brought by distance and channel width to achieve more optimized picking path planning.

[0082] This embodiment introduces weight (channel distance) to measure the length or cost of the path, and introduces priority (channel width) as an additional factor. The weights of different channels are adjusted according to different warehouse operating conditions and business needs, so that the appropriate picking path can be selected more flexibly in path generation, thereby improving picking efficiency and the overall operational smoothness of the warehouse.

[0083] On the basis of the above technical solution, when obtaining the real-time location information of the PDA, the working status of the PDA is further inquired. If it is busy, the PDA and the corresponding picking cart are skipped.

[0084] By querying the PDA's working status in real time, the system can quickly identify which PDAs are busy, thus avoiding assigning new picking tasks to these PDAs. This ensures a more rational allocation of picking resources, reduces wait times, and improves overall picking efficiency. Based on this understanding of the PDA's working status, the system can more accurately select the PDA closest to the target shelf and the corresponding picking cart. This helps reduce inefficient movement during the picking process, further optimizes picking paths, and reduces picking costs.

[0085] Based on the above technical solution, orders are clustered according to the warehouse and SKU of the goods in the order, and goods with the same location number and adjacent shelf number are grouped together to reduce the ineffective movement of PDAs between and within warehouses.

[0086] By clustering orders and grouping items with the same location number or adjacent shelf numbers, we can significantly reduce inefficient movement of PDAs within and between warehouses. Pickers can focus on items in a specific area, reducing travel time and waiting time, thereby increasing picking speed.

[0087] On the basis of the above technical solution, the number of PDAs and order processing capacity of each warehouse are predicted according to historical data, and orders are proportionally allocated to warehouses with non-zero and non-negative inventory according to order processing capacity.

[0088] Allocating orders proportionally to warehouses with non-zero or non-negative inventory levels can fully utilize the resources of each warehouse, maximize its potential for processing orders, and achieve optimal allocation of overall resources.

[0089] By predicting order processing capacity and allocating orders proportionally, the workload of each warehouse can be balanced, preventing picking delays and inefficiency caused by overload of individual warehouses, and ensuring the smooth operation of the entire warehousing system.

[0090] On the basis of the above technical solution, based on the greedy algorithm, the task is preferentially assigned to the PDA that is closest to the target shelf number and has a lighter load.

[0091] On the basis of the above technical solution, when there are more than two PDAs in the same channel number, the travel path of the picking truck that is closer to the target shelf number is adjusted first.

[0092] When there are multiple PDAs in the same aisle, it means that there are multiple picking carts operating in the same aisle. By prioritizing the path of the picking carts closer to the target shelf number, it can ensure that the picking carts in the front do not affect the passage of the picking carts behind, and the impact on the picking time is minimal. After adjusting the path of the picking carts closer to the target shelf number, the picking carts in the front can complete other picking tasks more quickly, while the picking carts behind can catch up and complete their picking tasks in time, thereby improving the scheduling efficiency of the picking carts and balancing the reasonable allocation and execution of multiple tasks.

[0093] Based on the above technical solution, a task transfer request is manually initiated on the PDA side and broadcast to nearby PDAs. After receiving manual confirmation from a certain PDA, a picking task order transfer operation is initiated on the PDA side and the background server is notified to update the information.

[0094] This embodiment can serve as a fault emergency mechanism. In actual picking operations, various emergencies may occur, such as a PDA device failure, an operator's temporary absence, or being assigned to other urgent tasks. Manually initiating a task transfer request on the PDA provides operators with a flexible approach, allowing them to adjust task assignments based on actual conditions and avoid task delays caused by equipment or personnel issues.

[0095] When a task needs to be transferred, by broadcasting a request to a nearby PDA, the resources on site can be used to quickly reallocate the task without relying on the centralized scheduling of the backend system. This operation method is closer to the actual operation scenario and is simple and convenient to operate. Operators can select the most suitable nearby PDA to receive the transferred task based on the on-site situation, which improves the operability of the task at the on-site level, reduces dependence on background intervention, and improves the timeliness of task processing. Once a manual response confirmation is received from the nearby PDA and the task transfer operation is completed, the picking task can continue without interruption, ensuring the continuity of the picking task. Without this manual transfer mechanism, it may be necessary to wait for the backend system to reallocate the task, which may cause the task to be suspended and affect the overall progress of picking.

[0096] Based on the above technical solution, the reviewer scans the product barcode through the PDA and reviews it with the order. If the product quantity or product barcode is inconsistent with the order, the abnormal data will be recorded and the backend server will be notified.

[0097] Based on the above technical solution, in the warehouse layout graph structure, different node attributes are set for different types of goods, and the weight and priority of the channel are adjusted according to the characteristics of the goods;

[0098] The product characteristics include: weight, volume, and fragility.

[0099] To optimize path planning, this embodiment adopts the following approach:

[0100] Considering product weight: Heavier items may require specialized equipment or more manpower to transport. By setting different node attributes and adjusting channel weights and priorities, picking trucks can be guided to select wider and more stable channels, avoiding safety hazards or equipment damage caused by insufficient channel load-bearing capacity. For example, when planning routes, priority will be given to main channels with greater load-bearing capacity, rather than narrow or low-load-bearing auxiliary channels, ensuring safe and efficient transportation of goods.

[0101] Considering product size: Large items may have difficulty navigating certain aisles or cause congestion. By adjusting aisle weights and priorities, picking carts can be directed to avoid narrow or difficult-to-turn aisles, making the picking path smoother. This prevents large items from blocking narrow aisles, impacting other picking operations and overall efficiency.

[0102] Consider product fragility: Fragile goods require a more stable, less bumpy transport environment. Adjusting channel priorities based on this characteristic allows the system to prioritize routes with better road conditions and less interference, reducing the risk of damage to goods due to vibration and collision, thereby ensuring product quality.

[0103] By adjusting the weights and priorities of different product aisles, we can guide their flow more efficiently within the warehouse, avoiding transportation difficulties caused by product characteristics and the resulting waste of resources, such as time wasted rerouting or addressing issues arising from these characteristics. For example, fragile goods are prioritized in flatter, smoother aisles to prevent damage during transport and reduce the resulting waste of resources. Larger goods are prioritized in spacious aisles, improving overall space utilization and transportation efficiency, and reducing unnecessary waiting and handling time. This effectively improves warehouse resource utilization.

[0104] Based on the above technical solution, during the picking process, the power level of the PDA is monitored in real time. When the power level is lower than the preset threshold, the current task of the PDA is suspended, and the unfinished tasks are reallocated to other idle PDAs. At the same time, the background server is notified to update the task allocation information.

[0105] During the picking process, the PDA's battery level is crucial for ensuring smooth progress. Real-time monitoring of the PDA's battery level prevents tasks from being abruptly interrupted due to a PDA's depleted battery. Reassigning unfinished tasks to other idle PDAs ensures that tasks are not stalled due to device power issues, thus ensuring the continuity of the picking process. This minimizes the impact of low device power on the entire picking process, ensuring tasks are completed on schedule and on time, and improving overall picking efficiency.

[0106] To ensure stable system operation, when a PDA experiences a common battery problem, such as low battery, the system automatically adjusts task allocation rather than relying on manual intervention, reducing the risk of system failures caused by device malfunctions. This makes the entire picking system more reliable, ensuring normal operation even when some devices experience power issues. This enhances system stability in various situations and ensures the smooth progress of the entire picking process.

[0107] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0108] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by those skilled in the art based on the contents disclosed in the present invention should be included in the protection scope recorded in the claims.

Claims

1. A method for multi-terminal coordinated optimization of picking paths, characterized in that: include: The physical layout of each warehouse is converted into a graph structure, with shelves as nodes, aisles as edges between nodes, the distance between aisles as edge weights, and aisle width as priority. Get the order, extract the product SKUs in the order one by one, send a stock query request to each warehouse, and each warehouse will return the product inventory and shelf number; Based on the graph structure, the real-time location information of each PDA and the direction of travel of the picking truck loaded with the PDA are obtained; Based on the real-time location information of the PDA and the direction of the picking truck, the PDA and picking truck closest to the target shelf number and heading towards the target shelf number are selected; Issue a picking task order, which includes at least the detailed location information of the product SKU: location number, aisle number, shelf number, and barcode number; The picking cart moves along the travel direction to the corresponding shelf number and uses the PDA to scan the barcode number; After verifying that the picked goods are consistent with the picking task list, generate the detailed data of the outbound order; The PDA device updates the outbound order status to pending review and notifies the backend server.

2. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: When obtaining the real-time location information of the PDA, the working status of the PDA is further inquired. If it is busy, the PDA and the corresponding picking cart are skipped.

3. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: Orders are clustered based on the warehouse and SKU to which the goods in the order belong, and goods with the same location number and adjacent shelf numbers are grouped together to reduce ineffective movement of PDAs between and within warehouses.

4. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: The number of PDAs and order processing capacity of each warehouse are predicted based on historical data, and orders are proportionally allocated to warehouses with non-zero or non-negative inventory according to order processing capacity.

5. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: Based on the greedy algorithm, tasks are preferentially assigned to the PDA that is closest to the target shelf number and has a lighter load.

6. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: When there are more than two PDAs in the same channel number, the picking cart path that is closer to the target shelf number will be adjusted first.

7. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: Manually initiate a task transfer request on the PDA side, broadcast it to nearby PDAs, and after receiving manual confirmation from a certain PDA, initiate the picking task order transfer operation on the PDA side and notify the background server to update the information.

8. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: The reviewer scans the product barcode through the PDA and verifies it with the order. If the product quantity or product barcode is inconsistent with the order, the abnormal data will be recorded and notified to the backend server.

9. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: During the picking process, the power level of the PDA is monitored in real time. When the power level is lower than the preset threshold, the current task of the PDA is suspended, and the unfinished tasks are reallocated to other idle PDAs. At the same time, the background server is notified to update the task allocation information.

10. The method for multi-terminal coordinated optimization of picking paths according to claim 1, characterized in that: In the warehouse layout graph structure, different node attributes are set for different types of goods, and the weight and priority of channels are adjusted according to the characteristics of the goods; The product characteristics include: weight, volume, and fragility.

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