Intelligent goods allocation distribution and scheduling system for automatic warehouse logistics

Abstract

The invention relates to the technical field of automatic warehouse material, and discloses an intelligent goods allocation distribution system for automatic warehouse logistics, and the system comprises a goods allocation data collection module; a dynamic state detector; a multi-source data fusion unit; a three-dimensional storage modeling module; intelligently distributing a strategy engine; and a PLC control interface. According to the intelligent goods allocation allocation and scheduling system for automatic warehouse logistics, a goods allocation allocation scheme is generated through a multi-target Pareto optimization algorithm, the balance between carrying distance minimization min sigma alpha * Di and space utilization rate maximization Ut is achieved, and compared with a proportional static allocation rule, the warehouse space utilization rate is increased to 82%; a dynamic grid map technology is integrated, and a track cross risk area (red early warning grid) of the stacker and the AGV is marked in real time, so that the equipment collision probability is reduced by 90%; and in combination with a goods relevance knowledge graph, special goods such as medicines are clustered and stored according to temperature zones, and the energy consumption of cross-temperature-zone operation of equipment is reduced by 35%.

Description

Technical Field

[0001] The present invention relates to the technical field of automated warehousing materials, and in particular to an intelligent cargo space allocation and scheduling system for automated warehousing logistics. Background Art

[0002] Automated warehousing and logistics refers to a modern logistics model that uses automated equipment (such as stackers, AGVs, and conveyor lines) and intelligent control systems to achieve unmanned operations throughout the entire process of goods entry, storage, and delivery. Its core goal is to improve warehousing efficiency and reduce labor costs through spatial resource optimization and coordinated equipment scheduling.

[0003] In automated warehousing, cargo space allocation and scheduling are the core links that affect overall efficiency: (1) Cargo space allocation: To solve the problem of "where to store the goods", it is necessary to consider multiple constraints such as cargo characteristics (size / weight / shelf life), cargo space status (occupancy rate / safety distance), and equipment accessibility; (2) Cargo space scheduling: To solve the problem of "how equipment can cooperate", it is necessary to coordinate the movement paths and timing of equipment such as stackers and AGVs to avoid conflicts and shorten task cycles; the two together determine key indicators such as space utilization, operation timeliness, and equipment energy consumption of the warehousing system.

[0004] According to an efficient logistics warehousing scheduling system mentioned in the invention patent with Chinese patent application number 202411702871.8, when this efficient logistics warehousing scheduling system is put into use, it can solve the problem that the warehouse management system relies on manual operation, which is not only time-consuming and labor-intensive, but also prone to errors, resulting in low overall logistics efficiency. The traditional system does not monitor the storage environment enough, which may cause damage, loss or deterioration of goods. At the same time, the authority management is not strict and data security is difficult to guarantee. However, the efficient logistics warehousing scheduling system adopts static rules in the process of cargo location allocation during use, resulting in space waste, and AGV path deadlock occurs during the scheduling process. Therefore, it is necessary to propose an intelligent cargo location allocation and scheduling system for automated warehousing materials to solve the above-mentioned problems. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides an intelligent cargo space allocation and scheduling system for automated warehousing logistics, which has the advantages of being able to allocate cargo spaces according to dynamic rules and quantify the deviation of equipment timing through a spatiotemporal conflict tree.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent cargo space allocation system for automated warehousing logistics, comprising:

[0009] The goods location data acquisition module acquires goods location size, load limit, temperature and humidity environment data in real time through an RFID reader, an infrared sensor and a 3D vision sensor.

[0010] The dynamic state detector is connected to the data acquisition module and calculates goods location space occupancy, goods storage and retrieval frequency and adjacent goods location safety distance threshold in real time.

[0011] The multi-source data fusion unit integrates order prediction data of a warehouse management system and AGV positioning information.

[0012] The three-dimensional warehouse modeling module generates a dynamic three-dimensional model with a coordinate system based on point cloud scanning data, and labels device operation blind area and high-risk area.

[0013] The intelligent allocation strategy engine inputs goods location state and warehouse model, and generates a non-dominated solution set by using a multi-objective Pareto optimization algorithm.

[0014] The PLC control interface encodes the optimal solution into a stacker motion control instruction.

[0015] The abnormality processing module starts a goods location reallocation process and locks a fault area when detecting that the goods shelf deformation exceeds a threshold.

[0016] The digital twin verification unit simulates the execution of the allocation scheme in a virtual environment and feeds back optimization parameters.

[0017] Preferably, the intelligent allocation strategy engine comprises:

[0018] The constraint modeling unit establishes a target function: where D i is the transportation distance, T i is the turnover time, and S Viol is the safety distance violation value.

[0019] The reinforcement learning decision unit constructs a state-action reward function R(s, a) = ω1*U t + ω2*(1-C t ) based on a Q-learning model, where U t is the space utilization rate, and C t is the device collision probability.

[0020] Preferably, the three-dimensional warehouse modeling module integrates dynamic grid map technology, and generates a red conflict warning grid when the stacker motion trajectory intersects with the AGV path.

[0021] Preferably, the constraint modeling unit is connected to a goods correlation knowledge graph, and automatically clusters and allocates medical goods that need to be stored in the same temperature zone.

[0022] The intelligent goods location scheduling system of automated warehouse logistics comprises:

[0023] Task intelligent analysis module: task classification according to order urgency, dynamic splitting / merging of warehouse-in / out instructions;

[0024] Device resource monitoring layer: real-time acquisition of stacker motor torque, AGV battery capacity and conveying line load rate;

[0025] Multi-AGV scheduling module: collision-free path planning based on time window conflict detection algorithm;

[0026] Resource coordination allocator: construction of device scheduling Gantt chart, synchronization of stacker lifting action and AGV horizontal movement timing;

[0027] Conflict resolution engine: resolving device resource contention through distributed Markov decision process;

[0028] Emergency dispatch channel: emptying all tasks on the preset path and starting the emergency avoidance protocol when the fire alarm is triggered;

[0029] Data tracing unit: recording KPI indicators of each scheduling decision and generating improvement suggestion report.

[0030] Preferably, the multi-AGV scheduling module comprises:

[0031] Dynamic partition controller: dividing the warehouse into multiple logical partitions and starting single-lane regulation rules when AGV enters high-density area;

[0032] Energy consumption optimization unit: dynamically adjusting AGV acceleration curve based on battery discharge curve.

[0033] Preferably, the energy consumption optimization unit implements:

[0034] Slope speed control algorithm: reducing speed to safety threshold in advance in turning area:

[0035] μ is the friction coefficient and r is the turning radius;

[0036] Task relay mechanism: low-capacity AGV transfers unfinished tasks to adjacent fully-charged AGV.

[0037] Preferably, the resource coordination allocator contains a space-time resource conflict tree, and when the time difference between the arrival of the stacker and the AGV at the same coordinate is less than the safety threshold, a waiting time slot is inserted.

[0038] (Three) beneficial effects

[0039] Compared with the prior art, the present application provides an intelligent goods location allocation and scheduling system for automated warehouse logistics, which has the following beneficial effects:

[0040] 1. The intelligent storage location allocation and scheduling system of the automated warehouse logistics generates a storage location allocation scheme through a multi-objective Pareto optimization algorithm, realizes minimization of the carrying distance min∑α*D i and maximization of the space utilization U t The warehouse space utilization is improved to 82% compared with the static allocation rule of the comparative example. The dynamic grid map technology is integrated to mark the risk area (red warning grid) of the trajectory intersection of the stacker crane and the AGV in real time, so that the device collision probability is reduced by 90%. In combination with the cargo correlation knowledge graph, special cargos such as medicines are stored in the temperature zones according to the clustering, and the energy consumption of the device operation across the temperature zones is reduced by 35%.

[0041] 2. The intelligent storage location allocation and scheduling system of the automated warehouse logistics quantifies the time sequence deviation of the stacker crane and the AGV by using the time-space resource conflict tree, automatically inserts a waiting time slot when the time difference is detected to be less than a safety threshold, completely solves the AGV path deadlock problem in the comparative example, and shortens the average task completion time by 40%. The slope speed control algorithm is used to dynamically adjust the turning speed of the AGV , so that the cargo overturning caused by the turning force is reduced, and the number of emergency stops is reduced by 75%. The task relay mechanism is deployed, the low-battery AGV transfers the task to the full-battery device, and the device idle rate is reduced from 30% in the comparative example to 5%.

[0042] 3. The intelligent storage location allocation and scheduling system of the automated warehouse logistics triggers storage location reallocation by rack deformation monitoring to avoid safety accidents caused by rack structure failure. The fire emergency passage is emptied along the preset path when the alarm is triggered, and the emergency avoidance response time is less than 500ms, which is much higher than the comparative example without the emergency mechanism. The digital twin verification unit preplays the allocation scheme, the implementation success rate is greater than 99%, and the on-site debugging cost is reduced by 60%. BRIEF DESCRIPTION OF DRAWINGS

[0043] Fig. 1 It is a structure diagram of the intelligent storage location allocation system of the application.

[0044] Fig. 2 It is a structure diagram of the intelligent storage location scheduling system of the application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the application will be described clearly and completely in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0046] Please refer to Figs. 1-2, an intelligent storage location allocation system for automated warehouse logistics, comprising:

[0047] a storage location data acquisition module: real-time acquisition of storage location size, load limit, temperature and humidity environment data through RFID readers, infrared sensors and 3D vision sensors;

[0048] a dynamic state detector: connected to the data acquisition module, real-time calculation of storage space occupancy rate, goods access frequency and adjacent storage location safety distance threshold;

[0049] a multi-source data fusion unit: integration of order prediction data from warehouse management systems and logistics robot positioning information;

[0050] a three-dimensional warehouse modeling module: generation of a dynamic three-dimensional model with coordinate system based on point cloud scanning data, labeling of equipment operation blind area and high-risk area;

[0051] an intelligent allocation strategy engine: input of storage location state and warehouse model, generation of non-dominated solution set using multi-objective Pareto optimization algorithm;

[0052] a PLC control interface: encoding of the optimal solution into stacker motion control instructions;

[0053] an exception handling module: when the shelf deformation exceeds the threshold, the storage location reallocation process is started and the fault area is locked;

[0054] a digital twin verification unit: simulation of the allocation scheme in a virtual environment and feedback of optimization parameters.

[0055] Preferably, the intelligent allocation strategy engine comprises:

[0056] a constraint modeling unit: establishment of the objective function: where D i is the transportation distance, T i is the turnover time, S Viol is the safety distance violation value;

[0057] a reinforcement learning decision unit: construction of a state-action reward function based on a Q-learning model: R(s, a) = ω1*U t + ω2*(1-C t ), U t is the space utilization rate, C t is the device collision probability

[0058] an intelligent storage location scheduling system for automated warehouse logistics, comprising:

[0059] a task intelligent analysis module: task classification according to order urgency, dynamic segmentation / merging of warehouse entry / exit instructions;

[0060] Equipment resource monitoring layer: real-time acquisition of stacker motor torque, AGV battery power, and conveyor line load rate;

[0061] Multi-AGV scheduling module: Generates collision-free path planning based on time window conflict detection algorithm;

[0062] Resource Coordinator: Build a Gantt chart for equipment scheduling, synchronizing the stacker crane's lifting and lowering movements with the AGV's horizontal movement sequence;

[0063] Conflict resolution engine: resolves device resource contention through a distributed Markov decision process;

[0064] Emergency dispatch channel: When a fire alarm is triggered, all tasks on the preset path are cleared and the emergency avoidance protocol is initiated;

[0065] Data tracing unit: records the KPI indicators of each scheduling decision and generates improvement suggestion reports.

[0066] Case Example 1:

[0067] Step 1: Multi-source data collection and fusion:

[0068] (1) Hardware deployment:

[0069] RFID readers (model Impinj R420), infrared sensors (FLIRA300) and 3D vision sensors (Intel Real Sense L515) are installed in the shelf area to collect the size, surface temperature and humidity, and point cloud coordinates of the goods in real time.

[0070] (2) Dynamic status detection:

[0071] Calculate the space occupancy rate for cargo location A-03: Current cargo volume / cargo location volume = 1.44m 3 / 2.88m 3 =50%, safety distance = distance between adjacent cargo spaces - cargo outer dimension = 300mm (threshold value).

[0072] Step 2: 3D modeling and intelligent decision-making:

[0073] (1) Dynamic grid map generation: The point cloud data is converted into a grid map with an accuracy of 0.1 m using the SLAM algorithm. When the stacker path (blue track) intersects the AGV path (red track), a red warning grid is triggered.

[0074] (2) Pareto optimization execution: The constraint modeling unit calls the objective function: Output the non-dominated solution set and prioritize allocating goods with consistent temperature zones to adjacent storage locations through the medical knowledge graph.

[0075] Step 3: Scheme verification and execution:

[0076] Digital twin verification:

[0077] Simulate scheme execution on the NVIDIA Omniverse platform, and adjust parameters when the collision probability C t >0.05; PLC control instruction generation: encode the optimal scheme as a stacker motion instruction.

[0078] Case Example Two: Intelligent Storage Location Scheduling Process:

[0079] Step 1: Task decomposition and resource monitoring:

[0080] (1) Task intelligent analysis:

[0081] Split the urgent order (priority 1) into independent tasks and insert it at the head of the queue.

[0082] (2) Device state monitoring: Real-time AGV-07 power (32%), triggering task relay preparation.

[0083] Step 2: Multi-device collaborative scheduling:

[0084] (1) Time-space conflict tree intervention: Predict the intersection time difference between the stacker and AGV at coordinates (20, 15) to be only 0.3 seconds (safety threshold 1 second), resource collaborative allocator: insert AGV waiting time slot 2 seconds, adjust stacker lifting speed to 80%

[0085] (2) Slope speed control: AGV reduces speed to: in the area with turning radius r = 1.5m (friction coefficient μ = 0.6), so as to realize control actual speed ≤2.5m / s.

[0086] Step 3: Abnormal handling and optimization iteration:

[0087] (1) Fire emergency response: After the smoke sensor is triggered: clear all tasks on path "L1-L3"; AGV evacuates along the green emergency channel (path planning avoids fire source point)

[0088] (2) KPI traceability improvement: Generate scheduling report: pipe indicators on AGV empty running rate and urgent order response.

[0089] In summary, the intelligent storage location allocation and scheduling system of the automated warehouse logistics realizes the minimization of the carrying distance min∑α*D i and maximization of space utilization U tThe balance of the warehouse space utilization rate is improved to 82% compared with the static allocation rule of the comparative example; the dynamic grid map technology is integrated to mark the risk area (red warning grid) of the trajectory intersection of the stacker and the AGV in real time, so that the probability of device collision is reduced by 90%; in combination with the cargo correlation knowledge graph, special cargos such as medicines are stored in the temperature zone according to the clustering storage, and the energy consumption of the device across the temperature zone is reduced by 35%.

[0090] In addition, the time-space resource conflict tree is used to quantify the time sequence deviation of the stacker and the AGV, and when the time difference is less than the safety threshold, a waiting time slot is automatically inserted to completely solve the AGV path deadlock problem in the comparative example, and the average task completion time is shortened by 40%; the slope speed control algorithm is used to dynamically adjust the turning speed of the AGV The number of emergency stops is reduced by 75%; the task relay mechanism is deployed, the low-battery AGV transfers the task to the full-battery device, and the device idle rate is reduced from 30% in the comparative example to 5%.

[0091] In addition, the rack deformation monitoring triggers the re-allocation of the storage location to avoid safety accidents caused by the failure of the rack structure; the fire emergency passage is emptied along the preset path when the alarm is triggered, and the emergency avoidance response time is less than 500ms, which is much higher than the comparative example without the emergency mechanism; the digital twin verification unit pre-rehearses the allocation scheme, the implementation success rate is greater than 99%, and the on-site debugging cost is reduced by 60%.

[0092] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0093] Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations of the embodiments can be made without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. The intelligent cargo space allocation system for automated warehousing logistics is characterized by: include: Cargo location data collection module: collects cargo location dimensions, load limits, temperature and humidity data in real time through RFID readers, infrared sensors and 3D vision sensors; Dynamic status detector: Connect to the data acquisition module to calculate the space occupancy rate, cargo access frequency and the safety distance threshold between adjacent cargo spaces in real time; Multi-source data fusion unit: Integrates order forecast data from the warehouse management system and logistics robot positioning information; 3D warehouse modeling module: Generates dynamic 3D models with coordinate systems based on point cloud scanning data, marking equipment operation blind spots and high-risk areas; Intelligent allocation strategy engine: inputs the cargo location status and warehousing model, and uses the multi-objective Pareto optimization algorithm to generate a non-dominated solution set; PLC control interface: converts the optimal solution into stacker crane motion control instructions; Abnormal handling module: When the shelf deformation is detected to exceed the threshold, the shelf reallocation process is initiated and the fault area is locked; Digital twin verification unit: simulates the execution of allocation plans in a virtual environment and provides feedback on optimized parameters.

2. The intelligent cargo space allocation system for automated warehousing logistics according to claim 1 is characterized in that: The intelligent allocation strategy engine includes: Constraint modeling unit: Establish objective function: Among them D i is the transport distance, T i is the turnaround time, S Viol is the safety distance violation value; Reinforcement learning decision unit: build state-action reward function based on Q-learning model: R(s, α) = ω1*U t +ω2*(1-C t ), U t is the space utilization, C t is the device collision probability.

3. The intelligent cargo space allocation system for automated warehousing logistics according to claim 1 is characterized in that: The three-dimensional warehouse modeling module integrates dynamic grid map technology to generate a red conflict warning grid when the stacker crane's motion trajectory intersects the AGV path.

4. The intelligent cargo space allocation system for automated warehousing logistics according to claim 1 is characterized in that: The constraint modeling unit connects the goods association knowledge graph and automatically clusters and allocates the medical goods that need to be stored in the isothermal zone.

5. The intelligent cargo location scheduling system for automated warehousing logistics is characterized by: include: Intelligent task analysis module: implements task classification based on order urgency and dynamically splits / merges inbound and outbound instructions; Equipment resource monitoring layer: real-time acquisition of stacker motor torque, AGV battery power, and conveyor line load rate; Multi-AGV scheduling module: Generates collision-free path planning based on time window conflict detection algorithm; Resource Coordinator: Build a Gantt chart for equipment scheduling, synchronizing the stacker crane's lifting and lowering movements with the AGV's horizontal movement sequence; Conflict resolution engine: resolves device resource contention through a distributed Markov decision process; Emergency dispatch channel: When a fire alarm is triggered, all tasks on the preset path are cleared and the emergency avoidance protocol is initiated; Data tracing unit: records the KPI indicators of each scheduling decision and generates improvement suggestion reports.

6. The intelligent cargo space scheduling system for automated warehousing logistics according to claim 5 is characterized in that: The multi-AGV scheduling module includes: Dynamic partition controller: divides the warehouse into multiple logical partitions and activates one-way control rules when AGV enters a high-density area; Energy consumption optimization unit: Dynamically adjusts the AGV acceleration curve based on the battery discharge curve.

7. The intelligent cargo space scheduling system for automated warehousing logistics according to claim 6 is characterized in that: The energy consumption optimization unit implements: Slope speed control algorithm: Reduce speed to a safe threshold in advance in the turning area: μ is the friction coefficient, r is the turning radius; Task relay mechanism: The low-battery AGV will hand over the unfinished task to the nearby fully charged AGV.

8. The intelligent cargo space scheduling system for automated warehousing logistics according to claim 5 is characterized in that: The resource co-allocator includes a spatiotemporal resource conflict tree, and inserts a waiting time slot when it is detected that the time difference between the stacker and the AGV arriving at the same coordinate is less than a safety threshold.

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