Scheduling management method and system for unmanned finished product warehouse area

Abstract

The invention discloses a scheduling management method and system for an unmanned finished product warehouse area, and the method comprises the following steps: analyzing material attributes and order information when materials are warehoused, selecting a raw material area FIFO order scheduling strategy or a finished product area customer order clustering strategy according to the type of the warehouse area, candidate stack positions are generated in combination with a warehouse-area-row-column-layer-stack position five-level coding system, the optimal stack position is determined after stack safety verification, and a crown block operation instruction is generated; when the crown block works, acquiring starting and target coordinates, loading the digital twin model of the reservoir area to generate an initial path, and re-planning a high-risk path by adopting an improved path planning algorithm introducing a load coefficient and a congestion index through conflict risk assessment; and during multi-vehicle collaborative operation, task collaboration and path conflict avoidance are realized based on a distributed multi-agent architecture and a preset communication protocol.

Description

Technical Field

[0001] This application relates to the technical field of intelligent warehousing, and particularly to a scheduling management method and system for an unmanned finished product storage area. Background Art

[0002] In the production logistics link of the steel manufacturing industry, the overhead crane is the core lifting equipment, and its scheduling efficiency directly affects the turnover rate of the storage area and the continuity of production. The existing methods have the following core pain points: (1) Lack of intelligent decision-making in stack location management: Manual allocation of stack locations results in a stack inversion rate of 25%-30% in the slab storage area. For example, in the steel coil storage area during steel production, the deformation rate of steel coils caused by improper "pin" - shaped stacking rules exceeds 15%; (2) Coarse overhead crane path planning: When multiple overhead cranes operate in parallel, the space conflict rate of the overhead crane operation path reaches 18%, and the average ineffective operation distance accounts for 32% of the total path; (3) Low efficiency in loading and shipping: Currently, the probability of manual vehicle allocation is widespread in the steel industry. Through big data analysis, manual vehicle allocation leads to an average waiting time of vehicles exceeding 35 minutes, and the turnover rate of parking spaces in the storage area is only 40% of that of the intelligent system; (4) Insufficient information collaboration: There is a gap between production data and logistics data, and the deviation rate of order delivery cycles exceeds 12%.

[0003] Therefore, there is currently a lack of a stack location coding system and an intelligent scheduling model for steel materials characteristics, making it difficult to meet the high - efficiency operation requirements of an unmanned storage area. Summary of the Invention

[0004] This application provides a scheduling management method and system for an unmanned finished product storage area to solve the above problems.

[0005] On the one hand, this application provides a scheduling management method for an unmanned finished product storage area, including the following steps: When materials are warehoused, analyze the material attributes and order information, select the FIFO order scheduling for the raw material area or the customer order clustering strategy for the finished product area according to the type of storage area, generate candidate stack locations in combination with the five - level coding system of warehouse - area - row - column - layer - stack location, and determine the optimal stack location and generate an overhead crane operation instruction after stack safety verification; When the overhead crane operates, obtain the starting and target coordinates, load the digital twin model of the storage area to generate an initial path, through conflict risk assessment, use an improved path planning algorithm introducing load factor and congestion index to re - plan the high - risk paths, and when multiple vehicles cooperate, realize task cooperation and path conflict avoidance based on the distributed multi - agent architecture and the preset communication protocol.

[0006] In an implementation manner of this application, the implementation steps of the customer order clustering strategy for the finished product area include: Extract order features, dynamically group the order materials with the same customer and the same demand features through a clustering algorithm, allocate near - outbound areas or centralized storage areas according to the grouping results, and update the clustering results and adjust the storage areas in real - time when the order changes.

[0007] In one implementation of this application, the stacking safety verification step includes: extracting the diameter and yield strength parameters of the bottom layer material and the mass parameters of the upper layer material to be stored, substituting them into a preset mechanical safety constraint formula, verifying whether the bottom layer bearing limit is greater than or equal to the upper layer load, and retaining candidate stacking positions that meet the constraints.

[0008] In one implementation of this application, the steps of constructing the digital twin model include: generating three-dimensional point cloud data by scanning the warehouse area with a preset frequency using a lidar, mapping the spatial positions of the overhead crane, shelves, materials and obstacles, establishing a real-time mirror of the physical space and the digital space, and monitoring the crane's movement status and spatial coordinates in real time.

[0009] In one implementation of this application, the multi-objective weight function of the improved path planning algorithm is: W = α·L + β·T + (1-α-β)·C, where L is the path length, T is the estimated travel time, C is the conflict risk coefficient, α is the load coefficient, and β is the congestion index. The Pareto optimal path is generated based on this function.

[0010] In one implementation of this application, the task allocation steps for multi-vehicle collaborative operation include: the scheduling system publishes the task, each crane intelligent agent participates in bidding based on its own distance cost, load capacity and idle time, and the crane with the lowest overall cost undertakes the task.

[0011] In one implementation of this application, the method further includes: receiving vehicle reservation information and parsing vehicle parameters, integrating material attributes, order information and vehicle parameters to generate a pre-loading scheme, generating a final loading scheme through an optimization algorithm after verifying load balance and compliance, allocating parking spaces and coordinating the planning of the overhead crane path to execute loading instructions.

[0012] In one implementation of this application, the optimization algorithm constructs a multi-objective optimization function based on a mixed integer programming model. The function considers load balance, concentration of orders from the same customer, and cross-regional operation costs, and adjusts the priority of each objective through weight coefficients.

[0013] This application also provides a scheduling and management system for an unmanned finished goods warehouse, including: a multi-dimensional intelligent stacking location selection module, which adopts a five-level coding system and is configured with a FIFO order scheduling unit for raw materials, a customer order clustering unit for finished goods, and a stacking safety verification unit to generate the optimal stacking location; a crane operation path dynamic optimization module, which constructs a three-dimensional dynamic spatial model of the warehouse based on digital twin technology and is configured with an improved path planning algorithm unit and a distributed multi-agent collaborative scheduling unit to realize crane path optimization and multi-crane collaboration; and an intelligent automatic loading module, which integrates a multi-source data fusion unit and a loading optimization unit based on a mixed integer programming model to generate loading schemes.

[0014] In one implementation of this application, the stacking safety verification unit of the multi-dimensional stacking location intelligent selection module has a built-in mechanical safety constraint formula, the overhead crane operation path dynamic optimization module realizes spatial conflict early warning through lidar scanning and digital twin modeling, and the intelligent automatic loading module supports loading standard compliance verification.

[0015] This application provides a scheduling and management method and system for unmanned finished goods warehouses, which has the following beneficial effects:

[0016] (1) Realize intelligent recommendation of stacking positions and optimization of crane path to improve crane operation efficiency, shorten vehicle dispatch cycle and increase warehouse turnover rate.

[0017] (2) Reduce stacking costs and labor costs, reduce the risk of material damage, and reduce quality loss.

[0018] (3) Construct a full-process digital twin model to achieve data connectivity, support seamless integration with other systems, and form an information closed loop. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 A flowchart illustrating the overall scheduling and management process for an unmanned finished goods warehouse area, as provided in this application embodiment;

[0021] Figure 2 A flowchart for recommending stack locations provided in the embodiments of this application;

[0022] Figure 3 A flowchart of overhead crane path optimization provided in the embodiments of this application;

[0023] Figure 4 This is an automated order fulfillment and shipping flowchart provided for an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] This application provides a scheduling and management method and system for unmanned finished goods warehouses. The technical solution proposed in this application will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1 This application provides a flowchart for the scheduling and management of an unmanned finished goods warehouse. The overall design includes a three-tiered module system: multi-dimensional intelligent pallet selection, dynamic optimization of overhead crane operation paths, and intelligent automatic order fulfillment. This closed-loop design, from perception to decision-making to execution, forms a closed-loop management system of "pallet recommendation - path optimization - order fulfillment and dispatch," meeting the high-efficiency operation requirements of the unmanned warehouse.

[0027] This application constructs a five-level coding system architecture: adopting a hierarchical coding rule of "warehouse-area-row-layer-stacking position" to build a precise spatial positioning system for stacking positions, providing "digital address" support for the automated identification, scheduling, and management of the warehousing system. An example of the five-level coding system architecture is shown in the table below:

[0028]

[0029] Furthermore, the finished product area scheduling strategy is as follows: First, order characteristics (such as steel coil specifications, quantity, delivery date, and customer ID) are extracted, and order materials with "same customer and same demand characteristics" are dynamically grouped using the K-means clustering algorithm. For example, high-priority orders are clustered into "urgent groups" and assigned pallet positions near the outbound area; multiple batches of orders from the same customer are clustered into "collaborative groups" and stored centrally to simplify picking. When orders change (such as order insertion or modification), the algorithm updates the clustering results in real time and adjusts the material storage area to ensure dynamic matching of "order demand - storage location" and improve outbound efficiency. Shortening the picking path (centralized storage of materials in the same group) and reducing the order splitting rate (batch processing of collaborative groups) supports the efficient fulfillment of "multi-variety, small-batch" orders. In this embodiment, the pallet position recommendation process is as follows: Figure 2 As shown.

[0030] Furthermore, during steel coil stacking, the weight of the upper layer exerts compressive stress on the lower layer, requiring the condition that "the lower layer's load-bearing capacity is greater than or equal to the upper layer's load" to prevent plastic deformation. Based on mechanics of materials, the formula for the load-bearing capacity is derived as follows:

[0031]

[0032] Where D is the diameter of the bottom coil, which determines the contact area (S∝D2) and affects the pressure distribution; σs is the yield strength, the critical strength of the bottom steel coil material against deformation; n is the number of stacking layers, and for two stacking layers, n=2; mupper is the mass of the top coil, which directly determines the load size; and g is the acceleration due to gravity.

[0033] Constraint logic: The left side, Fsafe, represents the theoretical load-bearing limit of the bottom steel coil (determined by material strength, contact area, and stacking method), while the right side represents the actual load on the upper steel coil. This is achieved by constraining F... safe ≥m upper •g, to ensure the safety of the stacking structure.

[0034] After "generating candidate stacking positions", the system automatically extracts the mechanical parameters (D, σ) of the bottom steel coil. s ) and the mass m of the steel coils to be stored in the upper layer upper Substitute the data into the model for verification: if the constraints are met, retain the candidate stack location; if not, exclude the stack location to avoid the risk of material damage (such as steel coil deformation or coating damage).

[0035] In the section on the dynamic optimization model for overhead crane operation paths, as described in this embodiment, the dynamic optimization process for overhead crane operation paths is as follows: Figure 3 As shown. The three-dimensional spatial conflict early warning system adopts a real-time perception system driven by digital twins. The core logic is: based on LiDAR scanning and digital twin modeling, a three-dimensional dynamic spatial model of the warehouse area is constructed to monitor the crane's movement status in real time and provide early warning of collision risks. To construct the digital twin, LiDAR scans the warehouse area at a frequency of 10Hz or higher, generating three-dimensional point cloud data and mapping the spatial positions of the crane, shelves, steel coils, and obstacles. Through geometric fitting and coordinate mapping, a real-time mirror of the "physical space - digital space" is established; in the crane coordinates (x, y, z), x corresponds to the shelf row direction (horizontal one-dimensional), y corresponds to the column direction (horizontal two-dimensional), and z corresponds to the shelf height (vertical dimension); the motion vector includes velocity (vx, vy, vz), acceleration (ax, ay, az), and orientation angle (θ), which are used to predict future trajectories.

[0036] Conflict early warning mechanism: Based on the digital twin model, calculate the spatial distance d and relative motion trend between the overhead crane and surrounding entities (such as other overhead cranes, rack columns, and suspended objects): When d < safety threshold (such as 0.5m), or if it is predicted that the distance will exceed the threshold within the next 3 seconds, trigger the conflict risk coefficient C (C∈[0,1], the higher the value, the greater the risk), providing constraints for path planning.

[0037] This application employs an improved Dijkstra algorithm for multi-constraint dynamic path planning. The core logic involves introducing a load coefficient α (0.1-1.0) and a congestion index β (0-1) as weighting factors to construct a multi-objective weighting function. This overcomes the limitations of the traditional "shortest path" approach and adapts to the physical constraints of overhead crane operations. Weighting formula:

[0038] W = α·L + β·T + (1-α-β)·C

[0039] Parameter description: L is the path length, T is the estimated travel time, C is the conflict risk coefficient, α is the load coefficient, and β is the congestion index.

[0040] Algorithm execution logic: Based on the priority queue pathfinding of the traditional Dijkstra algorithm, W is used as the edge weight (replacing simple distance), and the nodes in the warehouse area (shelf rows and columns, crane standby positions, etc.) are traversed to generate the Pareto optimal path that is "short in distance, short in time, low in conflict, and adaptable to the load".

[0041] Furthermore, the multi-vehicle collaborative scheduling adopts autonomous negotiation of distributed intelligent agents. In this embodiment, the automatic delivery process is as follows: Figure 4 As shown. Core architecture: It adopts a distributed multi-agent (MAS) architecture, and realizes information interaction and task collaboration between the two-vehicle units through the MQTT protocol, breaking through the global bottleneck of "single-vehicle optimization".

[0042] The MAS intelligent agent is designed as follows: Each crane acts as an independent agent, possessing a three-layer architecture: "perception layer (LiDAR + digital twin), decision-making layer (improved Dijkstra algorithm + ant colony algorithm), and execution layer (crane control system)".

[0043] Perception layer: Real-time acquisition of its own status (location, load, task) and global status (location of other cranes, path occupancy);

[0044] Decision-making level: Based on task priority (such as urgent orders, high load), independently decide on the path or participate in task bidding;

[0045] Execution layer: Receives path instructions, controls the crane's movement, and provides feedback on the execution status.

[0046] The MQTT communication mechanism adopts a "publish-subscribe" model. The crane periodically publishes "heartbeat packets" (including location, load, and task progress) to the message broker, and subscribes to "conflict warning" and "task broadcast" topics. For example, when crane A is planning a route, it discovers that the route segment is occupied by crane B by subscribing to messages, and automatically triggers a route avoidance strategy (such as waiting or detouring).

[0047] The collaborative strategy is as follows: Task allocation: Using the contract network protocol, the scheduling system publishes tasks, and the overhead cranes bid based on their own "distance cost", "load capacity" and "idle time", and the crane with the "lowest overall cost" will ultimately take the task; Path conflict: Through virtual area locking (such as marking the path segment as "occupied" when the crane is traveling) and dynamic speed adjustment (such as slowing down and yielding when meeting), multi-vehicle interlocking is avoided.

[0048] Furthermore, the intelligent automated freight matching model is as follows: First, it integrates multi-source data, including a vehicle parameter library (load / volume limits, axle load distribution), a material attribute library (steel type, specifications, location), and an order information library (customer, destination, priority); the mixed integer programming model is as follows:

[0049] minZ=ω1·E+ω2·(1-C)+ω3·K

[0050] Where E represents load balance, C represents the concentration of orders from the same customer, K represents the cost of cross-regional operations, and ω i These are the weighting coefficients.

[0051] Dynamic load allocation algorithm: Supports compliance verification of GB1589-2016 standard and achieves ±2.5% load deviation control.

[0052] The above is a scheduling and management method for an unmanned finished goods warehouse provided by an embodiment of this application. Based on the same inventive concept, an embodiment of this application also provides a scheduling and management system for an unmanned finished goods warehouse, including: a multi-dimensional intelligent stacking location selection module, which adopts a five-level coding system and is configured with a raw material area FIFO order scheduling unit, a finished goods area customer order clustering unit, and a stacking safety verification unit to generate the optimal stacking location; a crane operation path dynamic optimization module, which constructs a three-dimensional dynamic spatial model of the warehouse based on digital twin technology and is configured with an improved path planning algorithm unit and a distributed multi-agent collaborative scheduling unit to realize crane path optimization and multi-crane collaboration; and an intelligent automatic loading module, which integrates a multi-source data fusion unit and a loading optimization unit based on a mixed integer programming model to generate loading schemes.

[0053] In this application, the stacking safety verification unit of the multi-dimensional stacking location intelligent selection module has a built-in mechanical safety constraint formula, the overhead crane operation path dynamic optimization module realizes spatial conflict early warning through lidar scanning and digital twin modeling, and the intelligent automatic loading module supports loading standard compliance verification.

[0054] An example of the stacking location selection model is as follows: A hot-rolled slab warehouse receives a batch of Q235B slabs (specifications 200mm×1200mm×6000mm):

[0055] 1. The system parses the order timestamp, determines it to be batch ORD20250701, and calls the FIFO queue;

[0056] 2. Scanning the warehouse area revealed that slabs from the same order were stored in stack S3B-0604-3-01, with surrounding stacks available at S3B-0604-3-02 (empty) and S3B-0605-3-01 (empty);

[0057] 3. Based on the principle of "priority for the same order + proximity to the loading roller conveyor", stacking position S3B-0604-3-02 is recommended;

[0058] 4. After the overhead crane performs the stacking operation, the system updates the blockchain record with a timestamp of 2025-07-01T14:32:15.

[0059] The following is an example of overhead crane path optimization:

[0060] The overhead crane needs to lift the steel coil from S2A-0705-2-03 to the outlet, while another overhead crane moves from S2A-0805-2-01 to S2A-0505-2-01:

[0061] 1. The path optimization model detected a collision risk (collision probability 0.78) at the intersection of line 7 and line 8;

[0062] 2. Using the improved Dijkstra algorithm, considering the weight of the steel coil (5.8t, α = 0.6) and the congestion index (β = 0.3), a new path is generated:

[0063] S2A-0705-2-03→S2A-0704-2-03→S2A-0604-2-03→Outbound Port;

[0064] 3. The overhead crane will run along the new route, which is 12 meters longer than the original route, but the risk of conflict will be reduced to 0.12, and the total time will be reduced by 8%.

[0065] An example of an automated cargo allocation model: A logistics company orders two 30-ton trucks to transport three types of steel coils (total weight 58.6 tons, 10 coils in total). The system reads the vehicle parameters (front axle weight limit 7 tons, rear axle weight limit 23 tons); constructs a multi-objective optimization model with weights w1 = 0.4 (load balance), w2 = 0.3 (order concentration), and w3 = 0.3 (cross-regional cost); and generates a loading plan.

[0066] 4. The loading plan is synchronized to the overhead crane dispatching system. Combined with the stacking position recommendation information, the hoisting sequence is generated: first load Lu Q23456 (which needs to cross 2 warehouse areas), then load Lu Q12345. The total operation time is controlled within 16 minutes.

[0067] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0068] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A scheduling and management method for an unmanned finished goods warehouse area, characterized in that, Includes the following steps: When materials are received, the material attributes and order information are analyzed. Based on the warehouse area type, either the FIFO order scheduling strategy for raw materials or the customer order clustering strategy for finished products are selected. A five-level coding system of warehouse-area-row-column-layer-stacking position is used to generate candidate stacking positions. After stacking safety verification, the optimal stacking position is determined and an overhead crane operation instruction is generated. During overhead crane operation, the starting and target coordinates are obtained, the warehouse area digital twin model is loaded to generate an initial path, and through conflict risk assessment, high-risk paths are replanned using an improved path planning algorithm that introduces load coefficients and congestion indices. When multiple cranes are working together, task coordination and path conflict avoidance are achieved based on a distributed multi-agent architecture and a preset communication protocol.

2. The scheduling and management method for an unmanned finished goods warehouse area according to claim 1, characterized in that, The implementation steps of the finished product area customer order clustering strategy include: extracting order features, dynamically grouping order materials with the same customer and the same demand features using a clustering algorithm, allocating near-outbound areas or centralized storage areas according to the grouping results, and updating the clustering results and adjusting the storage areas in real time when orders change.

3. The scheduling and management method for an unmanned finished goods warehouse area according to claim 1, characterized in that, Stacking safety verification specifically includes: extracting the diameter and yield strength parameters of the bottom layer material and the mass parameters of the upper layer material to be stored, substituting them into the preset mechanical safety constraint formula, verifying whether the bottom layer bearing limit is greater than or equal to the upper layer load, and retaining candidate stacking positions that meet the constraints.

4. The scheduling and management method for an unmanned finished goods warehouse area according to claim 1, characterized in that, The steps for constructing the digital twin model include: generating three-dimensional point cloud data by scanning the warehouse area with a preset frequency using lidar, mapping the spatial positions of overhead cranes, shelves, materials and obstacles, establishing a real-time mirror of the physical space and the digital space, and monitoring the movement status and spatial coordinates of the overhead cranes in real time.

5. The scheduling and management method for an unmanned finished goods warehouse area according to claim 1, characterized in that, The multi-objective weight function of the improved path planning algorithm is: W = α·L + β·T + (1-α-β)·C, where L is the path length, T is the estimated travel time, C is the conflict risk coefficient, α is the load coefficient, and β is the congestion index. The Pareto optimal path is generated based on this function.

6. The scheduling and management method for an unmanned finished goods warehouse area according to claim 1, characterized in that, The task allocation steps for multi-vehicle collaborative operations include: the scheduling system releases the task, each crane intelligent agent participates in bidding based on its own distance cost, load capacity and idle time, and the crane with the lowest overall cost undertakes the task.

7. The scheduling and management method for an unmanned finished goods warehouse area according to claim 1, characterized in that, The method further includes: receiving vehicle reservation information and parsing vehicle parameters, integrating material attributes, order information and vehicle parameters to generate a pre-loading plan, and after verifying load balance and compliance, generating a final loading plan through an optimization algorithm, allocating parking spaces and coordinating the planning of the overhead crane path to execute loading instructions.

8. A scheduling and management method for an unmanned finished goods warehouse area according to claim 7, characterized in that, The optimization algorithm is based on a mixed integer programming model to construct a multi-objective optimization function. The function considers load balance, concentration of orders from the same customer, and cross-regional operation costs, and adjusts the priority of each objective through weight coefficients.

9. A scheduling and management system for an unmanned finished goods warehouse area, employing the scheduling and management method for an unmanned finished goods warehouse area as described in any one of claims 1-8, characterized in that, include: The multi-dimensional intelligent stacking location selection module adopts a five-level coding system and is configured with a FIFO order scheduling unit in the raw material area, a customer order clustering unit in the finished product area, and a stacking safety verification unit to generate the optimal stacking location; the crane operation path dynamic optimization module constructs a three-dimensional dynamic spatial model of the warehouse area based on digital twin technology and is configured with an improved path planning algorithm unit and a distributed multi-agent collaborative scheduling unit to realize crane path optimization and multi-crane collaboration. The intelligent automatic cargo allocation module integrates a multi-source data fusion unit and a loading optimization unit based on a mixed integer programming model to generate loading schemes.

10. A scheduling and management system for an unmanned finished goods warehouse area according to claim 9, characterized in that, The stacking safety verification unit of the multi-dimensional stacking location intelligent selection module has a built-in mechanical safety constraint formula; the overhead crane operation path dynamic optimization module realizes spatial conflict early warning through lidar scanning and digital twin modeling; and the intelligent automatic loading module supports loading standard compliance verification.

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