High-density stereoscopic warehouse delivery scheduling and transportation system and method based on RGV

Abstract

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse delivery scheduling and transportation, and in particular to a high-density stereoscopic warehouse delivery scheduling and transportation system and method based on RGV trolleys. Background Art

[0002] The manufacturing industry has an increasing demand for automated, intelligent production models and modern, intelligent logistics, transportation and warehousing systems. High-density high-bay warehouses are an advanced storage model adopted to cope with limited storage space. Through technologies such as high-bay stacking and vertical storage, warehouse space can be maximized, storage density can be increased, and floor space can be reduced. As one of the core equipment of intelligent logistics, RGV trolleys play an important role in automated high-bay warehouses.

[0003] In a high-density stereoscopic warehouse, goods are stored at different heights and locations, and the task of the RGV trolley is to perform precise storage and retrieval operations according to the outbound order. Multiple RGV trolleys have path conflicts and traffic congestion problems during the outbound process, which in turn lead to task delays. Therefore, how to use digital twin technology to analyze the optimal execution path of the outbound task and improve the efficiency of the outbound scheduling and transportation system is the problem to be solved by the present invention. To this end, a high-density stereoscopic warehouse outbound scheduling and transportation system and method based on RGV trolleys are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-density warehouse dispatching and transportation system and method based on RGV trolleys to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: First, a high-density warehouse dispatch and transportation system based on RGV vehicles includes a dispatch management center, which is communicatively connected to a virtual model building module, a task analysis and planning module, a task allocation module, and a real-time monitoring intervention module, wherein the modules are electrically connected. The virtual model building module is used to build a virtual model of the warehouse and RGV trolley, synchronously map the shelf height, aisle layout and real-time positioning of the RGV trolley, and synchronize the physical status of the warehouse and RGV trolley in real time; The task parsing and planning module is used to receive and parse outbound orders, generate an initial task queue, and simulate different path plans for task allocation based on the warehouse and RGV car virtual models; The task allocation module is used to intelligently allocate tasks to corresponding RGVs for execution based on the outbound order priority and the current location of the RGV; The real-time monitoring and intervention module is used to visualize the real-time trajectory of the RGV, task progress and abnormal events, and support manual emergency intervention.

[0006] A further improvement of the technical solution of the present invention is that the virtual model building module specifically includes: Through the sensor network deployed in the warehouse, real-time data on shelf height, aisle layout, and the physical status of RGV vehicles, such as position, speed, and load, is collected. Laser scanning and visual recognition technology are used to obtain three-dimensional spatial information of the warehouse, and the collected data is pre-processed to remove noise and redundant information. Based on the pre-processed data, a virtual model of the warehouse and RGV is constructed using modeling software. The number of shelves, heights, and positions, as well as the layout and dimensions of the aisles, are represented in the virtual model. The appearance, dimensions, and motion characteristics of the RGV are also modeled. Establish a real-time data communication channel between the virtual model and the physical entity, transmit the real-time data collected by the sensor to the virtual model through the Internet of Things technology, and update the cargo storage status of the shelf, the position and movement status information of the RGV trolley in real time in the virtual model to ensure that the virtual model can accurately reflect the real-time changes of the physical entity.

[0007] A further improvement of the technical solution of the present invention is that: the task parsing and assigning module includes a task receiving and parsing unit and a path planning simulation unit; The task receiving and parsing unit is used to receive outbound orders, parse task requirements, clarify outbound order priorities, cargo locations, and RGV trolley load status, and generate an initial task queue with a time window. The path planning simulation unit is used to simulate the time-space path conflicts of multiple RGVs in combination with the virtual model, generate collision-free trajectories of different path solutions, and optimize the outbound path.

[0008] A further improvement of the technical solution of the present invention is that the task receiving and parsing unit specifically includes: Receive outbound order information through the interface with the warehouse management system, and perform preliminary formatting and integrity checks on the received order data to ensure the accuracy and completeness of the order information. At the same time, classify and mark the orders; Detailed analysis of each outbound order extracts key mission-critical information, including cargo location (shelf number, floor height, aisle), outbound quantity, priority, and cargo type. Combining the warehouse's real-time inventory information with a 3D spatial indexing algorithm, the spatial coordinates of the cargo are generated to ensure mission feasibility. Obtain the load parameters of the currently idle RGV trolleys, analyze the remaining load and the assigned task volume of the idle RGV trolleys, establish a task-equipment matching matrix, screen the candidate equipment set that meets the load requirements, and assign time window constraints to each task; The time axis projection method is used to verify whether the time window constraint overlaps with the existing task queue. If there is a conflict, the priority arbitration mechanism is triggered to adjust the current task time window. The parsed tasks are sorted in descending order of priority and ascending order of time window urgency to generate an initial task queue. The initial task queue contains the task unique identifier, cargo space coordinates, candidate RGV vehicle list and time window constraint.

[0009] A further improvement of the technical solution of the present invention is that the process of establishing the task-device matching matrix includes: Obtain the load parameters of the currently idle RGV trolleys from the RGV trolley's control system, including the maximum load, the amount of assigned tasks, and the current load, and record the current position and status of each idle RGV trolley; Based on the acquired load parameters, the remaining load capacity of each idle RGV is calculated by subtracting the current load from the maximum load of the RGV. At the same time, the interference of the assigned tasks on the RGV's task execution capability is analyzed to determine whether the RGV can take on new tasks. The matching value is calculated based on the cargo weight of the task, the distance between the task location and the current location of the RGV, and the remaining load factor of the RGV. A task-equipment matching matrix is ​​established and sorted in descending order according to the matching value to quantify the matching degree between each task and each idle RGV. Based on the task-equipment matching matrix, a set of candidate equipment that meets the load requirements is screened out. For each task, according to its cargo weight and urgency requirements, RGV carts with high matching degree and sufficient remaining load are selected from the matching matrix as candidate equipment, and a time window constraint is assigned to each task.

[0010] A further improvement of the technical solution of the present invention is that the path planning simulation unit specifically includes: The virtual model construction module obtains the virtual model of the warehouse and RGV, including shelf layout, aisle information, and the initial position of the RGV. It also generates an initial task queue, obtains the cargo space coordinates, candidate RGV list, and time window constraints for the task, and maps the relevant data of the initial task queue to the virtual model to ensure that the simulation environment is consistent with the actual warehouse state. Based on the virtual model, the time and space paths of multiple RGVs are simulated when performing tasks. Through time axis projection and spatial position analysis, potential conflicts between different RGV paths are detected. The position and movement direction of each RGV at different time points are analyzed, and collision points and congestion areas are identified. The time and location information of the conflict are recorded to form a conflict report. Based on the conflict detection results, the path planning strategy of the RGV is adjusted, and a genetic algorithm is used to generate collision-free trajectories for different path solutions. Through multiple simulation iterations, the path solution is gradually optimized, and the optimal path solution for each iteration is recorded, thereby reducing path conflicts and task delays and improving outbound efficiency. After multiple iterations, the path plan with the highest fitness is selected as the optimal path plan. The optimal path plan is simulated and verified to ensure that it has no collisions and meets the time window constraints of the task. If the simulation verification finds new conflicts or does not meet the time window constraints, the parameters of the genetic algorithm are adjusted and the optimization iteration is repeated until the generated path plan has no conflicts in the simulation and meets all task requirements. The optimized path plan is then output and fed back to the task allocation module.

[0011] A further improvement of the technical solution of the present invention is that the process of identifying the collision point and the congested area is as follows: For each task, the start and end times are determined based on the task's time window constraints. The time axis projection method is used to project the task time window of each RGV onto the time axis to form a time interval. The task time intervals of different RGVs are checked for overlap. If two time intervals overlap, there is a time conflict. For each task, the position of the RGV at different time points is calculated based on the spatial coordinates of the cargo and the motion path of the RGV. The spatial position analysis method is used to detect whether the positions of different RGVs at the same time point overlap. The positions of different RGVs at the same time point are compared. If the positions are the same, there is a spatial conflict, and the time and location of the conflict are recorded. At each time point, check whether there are multiple RGVs at the same or adjacent locations, record the time and location information of all conflicts, identify potential collision points and congestion areas, and generate a conflict report. The conflict report includes: conflict time, conflict location, involved RGV vehicle numbers, and conflict type (time conflict, space conflict, or both).

[0012] A further improvement of the technical solution of the present invention is that the task allocation module specifically includes: The initial task queue with a time window is obtained from the task analysis and planning module, which includes the task priority, cargo space coordinates, and candidate RGV list information. At the same time, the current position, load status, and remaining load capacity of all RGVs are obtained from the RGV control system as the basic data for task allocation to ensure the accuracy and real-time performance of task allocation. Combine the current position of the RGV and the location of the cargo in the task to calculate the task allocation efficiency and then intelligently allocate the task to the corresponding RGV; For each task, the RGV with the highest matching degree is selected for assignment, and a path plan is generated based on the time window constraints of the task and the current position of the RGV.

[0013] A further improvement of the technical solution of the present invention is that the real-time monitoring intervention module specifically includes: Collect data in real time from the RGV control system and task allocation module, including the RGV's real-time position, movement speed, task execution status, and abnormal events in the system. At the same time, pre-process the collected data to remove noise and redundant information to ensure data accuracy and reliability; Visualize the collected data and display the real-time trajectory, task progress, and abnormal events of the RGV through a graphical interface; Real-time analysis of collected data to detect abnormal events, including RGV deviations from planned tracks, mission delays, and equipment failures. Once an abnormal event is detected, an alarm mechanism is immediately triggered, notifying the operator through sound, pop-up windows, or text messages. At the same time, the abnormal event is detailed on the monitoring interface, providing information on the time, location, and type of the abnormality. The system supports operators to manually intervene through the monitoring interface and make emergency adjustments to the operation of the RGV. Operators can manually adjust the RGV's running path, speed or task allocation based on real-time monitoring information to resolve abnormal events or optimize system operation. At the same time, the operator's operation log of manual intervention is recorded.

[0014] In the second aspect, the RGV-based high-density warehouse outbound dispatching and transportation method is implemented based on the above-mentioned RGV-based high-density warehouse outbound dispatching and transportation system, and includes the following steps: S1. Build a virtual model of the warehouse and RGVs, synchronize shelf layout, aisle information, and RGV status in real time, and provide a digital twin foundation for outbound dispatch. S2: Receive outbound orders, analyze task requirements, clarify priorities, cargo locations, and RGV load status, and generate an initial task queue with a time window. S3. Combine virtual models to simulate the spatiotemporal paths of multiple RGVs, detect potential conflicts, generate collision-free trajectories, optimize outbound paths, and ensure efficient task execution. S4, intelligently assign tasks based on task priority and the current location of the RGV, generate path planning, and optimize the path plan through genetic algorithms to reduce conflicts and delays; S5. Real-time collection of RGV vehicle operation data, visualization of trajectory, task progress and abnormal events, support for manual emergency intervention, and ensure stable system operation.

[0015] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared to the prior art: The present invention provides a high-density three-dimensional warehouse outbound dispatching and transportation system and method based on RGV trolleys. By constructing a virtual model of the warehouse and RGV trolleys and combining it with digital twin technology, it can synchronize the status of physical entities in real time, accurately simulate the time and space paths of multiple RGV trolleys, and effectively avoid path conflicts and traffic congestion. It not only significantly improves the efficiency of outbound operations, but also greatly reduces task delays caused by path conflicts, ensuring the accuracy and timeliness of outbound operations, thereby improving overall logistics efficiency.

[0016] The present invention provides a high-density warehouse delivery scheduling and transportation system and method based on RGV trolleys. According to the priority of the delivery order and the real-time position and load status of the RGV trolley, the system intelligently allocates tasks to the most suitable RGV trolley. The task allocation strategy based on multi-dimensional matching ensures that high-priority tasks are executed first, while maximizing the use of RGV trolley resources, avoiding resource idleness and overload, and improving resource utilization and overall system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 This is a schematic diagram of the functional modules of the system of the present invention; Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1, as Figure 1 As shown, the present invention provides a high-density stereoscopic warehouse outbound dispatching and transportation system based on RGV trolleys, including a dispatching management center, which is communicatively connected to a virtual model building module, a task parsing and planning module, a task allocation module, and a real-time monitoring intervention module, wherein the modules are electrically connected; The virtual model construction module is used to build a virtual model of the warehouse and RGV cart, synchronously map the shelf height, channel layout and real-time positioning of the RGV cart, synchronize the physical status of the warehouse and RGV cart in real time, and collect the physical status data of the shelf height, channel layout and position, speed and load of the RGV cart in real time through the sensor network deployed in the warehouse. Laser scanning and visual recognition technology are used to obtain the three-dimensional spatial information of the warehouse, and the collected data is pre-processed to remove noise and redundant information. Among them, by integrating UWB positioning base station (positioning accuracy ±2cm), inertial measurement unit (IMU, acceleration / angular velocity), pressure sensor (load monitoring) and encoder (speed / mileage measurement) on the RGV cart, full-state perception is achieved, the Kalman filter algorithm is used to correct the spatiotemporal consistency of the UWB positioning data, the RANSAC algorithm is combined to eliminate outliers in the point cloud, the principal component analysis (PCA) is used to reduce the dimension of the point cloud data, and the event-driven sampling strategy is combined to reduce the communication load. According to the pre-processed data, the modeling software is used to build a virtual model of the warehouse and RGV cart, and the virtual model is used in the virtual model. In the model, the number, height, and position of the shelves, as well as the layout and size of the aisles, are represented. At the same time, the appearance, size, and motion characteristics of the RGV are modeled so that it can operate according to actual physical laws in the virtual environment. By mapping the collected physical state data into the virtual model, a preliminary correspondence between the virtual model and the physical entity is achieved. The shelf surface mesh is generated using the Poisson reconstruction algorithm, and the topological parameters of the layer height, column spacing, and load-bearing beam position are extracted to form a structured model. The multi-body dynamics modeling method is used to decompose the trolley into components: the body, drive wheels, and forks. The mass, inertia matrix, and kinematic constraints of each component are defined. A collision detection algorithm (GJK algorithm) is embedded in the virtual model to ensure that the RGV follows Newton's laws of motion and interacts with the shelves and aisle boundaries in real time. A real-time data communication channel is established between the virtual model and the physical entity. The real-time data collected by the sensors is transmitted to the virtual model through the Internet of Things technology. The shelf's cargo storage status, the position and motion status of the RGV are updated in real time in the virtual model to ensure that the virtual model can accurately reflect the real-time changes of the physical entity. The task parsing and planning module is used to receive and parse outbound orders, generate the initial task queue, and simulate different path solutions for task allocation based on the warehouse and RGV vehicle virtual models. The task parsing and allocation module includes a task receiving and parsing unit and a path planning simulation unit. Among them, the task receiving and parsing unit is used to receive outbound orders, parse task requirements, clarify outbound order priority, cargo location and RGV trolley load status, generate an initial task queue with a time window, receive outbound order information through the interface with the warehouse management system, and perform preliminary formatting and integrity check on the received order data to ensure the accuracy and completeness of the order information. At the same time, the order is classified and marked, and each outbound order is analyzed in detail to extract key task requirement information, including cargo location (shelf number, layer height, channel), outbound quantity, priority and cargo type, and generate a task queue based on the warehouse's real-time inventory information and three-dimensional spatial indexing algorithm. Cargo space coordinates, ensure the feasibility of the task, obtain the load parameters of the current idle RGV trolley, analyze the remaining load and the assigned task volume of the idle RGV trolley, establish a task-equipment matching matrix, screen the candidate equipment set that meets the load requirements, assign time window constraints to each task, and verify whether the time window constraints overlap with the existing task queue through the time axis projection method. If there is a conflict, the priority arbitration mechanism is triggered to adjust the current task time window. The parsed tasks are sorted in descending order of priority and ascending order of time window urgency to generate an initial task queue. The initial task queue contains the task unique identifier, cargo space coordinates, candidate RGV trolley list and time window constraints; The calculation expression of the time axis projection method is: ; Where, For the new task T new With the existing task T exist The length of time window overlap, For the new task T new The time window start time, For the new task T new The end time of the time window, For the existing task T exist The time window start time, For the existing task T exist The time window end time, the time window overlap time length is a non-negative number, when the new task T new With the existing task T exist When the time windows do not overlap, , when time windows overlap, is the length of the overlapping part; The process of establishing the task-equipment matching matrix includes: The load parameters of the currently idle RGV trolleys, including the maximum load, the assigned task volume, and the current load volume, are obtained from the RGV trolley's control system. The current position and status of each idle RGV trolley are recorded. Based on the obtained load parameters, the remaining load capacity of each idle RGV trolley is calculated by subtracting the current load volume from the maximum load volume of the RGV trolley. At the same time, the interference of the assigned task volume on the task execution capability of the RGV trolley is analyzed to determine whether the RGV trolley can undertake the new task. The matching value is calculated based on the cargo weight of the task, the distance between the task location and the current location of the RGV trolley, and the remaining load factor of the RGV trolley. A task-equipment matching matrix is ​​established and sorted in descending order according to the size of the matching value to quantify the matching degree between each task and each idle RGV trolley. Based on the task-equipment matching matrix, a set of candidate equipment that meets the load requirements is screened. For each task, based on its cargo weight and urgency requirements, RGV trolleys with high matching degrees and sufficient remaining load volume are selected from the matching matrix as candidate equipment, and a time window constraint is assigned to each task. The calculation process of the matching value is: Use the maximum load of the RGV trolley to subtract the current load to get the remaining load, determine whether the RGV trolley has enough load capacity to bear the weight of the cargo of the task, divide the cargo weight of the task by the remaining load capacity of the RGV trolley, calculate the load matching degree, and analyze the matching degree between the remaining load capacity of the RGV trolley and the weight of the cargo of the task. If the ratio is close to 1, it means that the remaining load capacity of the RGV trolley is suitable for the task. If the ratio is greater than 1, it means that the remaining load capacity of the RGV trolley is not enough to bear the task. If the ratio is significantly less than 1, it means that the remaining load capacity of the RGV trolley exceeds the requirements of the task, and the task is analyzed. The distance between the cargo location and the current location of the RGV is used to calculate the impact of the distance factor on the matching degree. The farther the distance, the smaller the value, indicating that the distance has a greater negative impact on the matching degree. The closer the distance, the closer the value is to 1, indicating that the distance has a smaller impact on the matching degree. The remaining load of the RGV is divided by the maximum load to obtain the remaining load ratio. The unused portion of the RGV's load capacity is analyzed. The larger the ratio, the more sufficient the RGV's load capacity. The load matching degree, the impact of the distance factor on the matching degree, and the remaining load ratio are multiplied together to obtain the matching degree value, that is, the degree of matching between the task and the RGV. The calculation expression of the matching value is: ; Where M i,j is the matching value between task i and RGV car j, indicating the matching degree between the task and the RGV car, W task,i is the cargo weight of task i, is the maximum load of RGV trolley j, is the current load of RGV trolley j, D i,j is the distance between the cargo location of task i and the current location of RGV car j. Approaching or D i,j When it is very large, M i,j will approach 0 only when and D i,j =0, M i,j =1; The path planning simulation unit is used to simulate the space-time path conflicts of multiple RGVs in combination with the virtual model, generate collision-free trajectories for different path solutions, optimize the outbound path, obtain the virtual model of the warehouse and RGV trolley from the virtual model construction module, including the shelf layout, channel information and the initial position of the RGV trolley, and synchronize the generated initial task queue, obtain the cargo space coordinates of the task, the candidate RGV trolley list and the time window constraint, map the relevant data of the initial task queue to the virtual model, ensure that the simulation environment is consistent with the actual warehouse status, simulate the space-time paths of multiple RGV trolleys when performing tasks based on the virtual model, detect potential conflicts between different RGV trolley paths through time axis projection method and spatial position analysis, analyze the position and movement direction of each RGV trolley at different time points, identify collision points and congestion areas, and record the time and location information of the conflict to form a conflict report. According to the conflict detection results, the path planning strategy of the RGV car is adjusted, and the genetic algorithm is used to generate collision-free trajectories for different path solutions. Through multiple simulation iterations, the path solution is gradually optimized, and the optimal path solution of each iteration is recorded to reduce path conflicts and task delays and improve outbound efficiency. After multiple iterations, the path solution with the highest fitness is selected as the optimal path solution. The optimal path solution is simulated and verified to ensure that it is collision-free and meets the time window constraints of the task. If the simulation verification finds new conflicts or does not meet the time window constraints, the parameters of the genetic algorithm are adjusted and the optimization iteration is repeated until the generated path solution is conflict-free in the simulation and meets all task requirements. The optimized path solution is then output, including the motion path, task allocation and time arrangement of each RGV car, and the optimized path solution is fed back to the task allocation module to provide guidance for the actual operation of the RGV car. In addition, the identification process of collision points and congestion areas is as follows: For each task, determine the start time and end time of the task according to the time window constraint of the task, use the time axis projection method to project the task time window of each RGV car onto the time axis to form a time interval, detect whether the task time intervals of different RGV cars overlap, if the two time intervals have overlapping parts, there is a time conflict, for each task, calculate the position of the RGV car at different time points according to the spatial coordinates of the cargo and the motion path of the RGV car, use the spatial position analysis method to detect whether the positions of different RGV cars at the same time point overlap, compare the positions of different RGV cars at the same time point, if the positions are the same, there is a spatial conflict, record the time point and position of the conflict, for each time point, check whether there are multiple RGV cars at the same or adjacent positions, record the time and position information of all conflicts, identify potential collision points and congestion areas, and form a conflict report, which includes: conflict time, conflict location, RGV car number involved, and conflict type (time conflict, space conflict, or both); The optimization process of the path plan is: According to the conflict detection results, the potential conflict points and conflict types of each task are analyzed, and optimization goals including reducing path conflicts, shortening task completion time and reducing path length are set. The priority and time window constraints of each task are determined, and the parameters of the genetic algorithm are defined, including population size, crossover rate, mutation rate and number of iterations. The population is initialized and multiple initial path plans are generated. Each path plan is represented as a chromosome. The genes in the chromosome represent the position and movement direction of the RGV car at different time points. Each path plan is encoded to ensure that the path plan meets the time window constraints of the task and the cargo space coordinates. The path conflicts and task completion time are comprehensively analyzed. and path length, design a fitness function, evaluate the fitness of each path plan, and calculate its fitness value. The higher the fitness value, the better the path plan. According to the fitness value, the path plan with higher fitness is selected to enter the next generation population. Two parent path plans are randomly selected, and a new child path plan is generated through a crossover operation. The newly generated child path plan is mutated to randomly change the values ​​of certain genes to increase the diversity of the population. The fitness of the newly generated child path plan is evaluated and its fitness value is calculated. Repeat the selection, crossover, mutation and fitness evaluation operations to gradually optimize the path plan and record the optimal path plan for each iteration. The expression of the fitness function is: ; Where Fitness(P) is the fitness value output by the fitness function, Conflict(P) is the number of conflicts in path plan P, Time(P) is the task completion time of path plan P, Length(P) is the total path length of path plan P, α, β, and γ are weight coefficients, which are adjusted according to the optimization goal; The task allocation module is used to intelligently allocate tasks to the corresponding RGVs for execution based on the outbound order priority and the current location of the RGVs; The real-time monitoring and intervention module is used to visualize the real-time trajectory, mission progress, and abnormal events of the RGV, and supports emergency manual intervention.

[0021] Example 2, as Figure 1 As shown, based on Example 1, the present invention provides a technical solution: preferably, the task allocation module specifically includes: The initial task queue with a time window is obtained from the task parsing and planning module, which contains the task priority, cargo space coordinates, and candidate RGV trolley list information. At the same time, the current position, load status, and remaining load capacity of all RGV trolleys are obtained from the RGV trolley's control system as the basic data for task allocation to ensure the accuracy and real-time performance of task allocation. The task allocation efficiency is calculated based on the current position of the RGV trolley and the location of the cargo in the task, and then the task is intelligently allocated to the corresponding RGV trolley. For each task, the RGV trolley with the highest matching degree is selected for allocation, and the path planning is generated based on the time window constraint of the task and the current position of the RGV trolley. The calculation process of task allocation efficiency is: Analyze the distance between the cargo location of the task and the current location of the RGV trolley, and calculate the impact of the distance factor on the allocation efficiency. The farther the distance, the smaller the value, which means that the negative impact of the distance on the allocation efficiency is greater. The closer the distance, the closer the value is to 1, which means that the impact of the distance on the allocation efficiency is smaller. Divide the cargo weight of the task by the remaining load capacity of the RGV trolley to calculate the load matching degree. This ratio reflects the degree of matching between the remaining load capacity of the RGV trolley and the weight of the task cargo. If the ratio is close to 1, it means that the remaining load capacity of the RGV trolley is just suitable for the task. If the ratio is greater than 1, it means that the remaining load capacity of the RGV trolley is not enough to undertake the task. If the ratio is less than 1, it means that the RGV The remaining load capacity of the trolley exceeds the requirements of the task. The ratio between the time window length of the task and the total available time of the RGV trolley is analyzed, and the time window matching degree is calculated. This ratio reflects the matching degree between the time window of the task and the available time of the RGV trolley. If the ratio is close to 1, it means that the time window of the task just matches the available time of the RGV trolley. If the ratio is greater than 1, it means that the time window of the task exceeds the available time of the RGV trolley. If the ratio is less than 1, it means that the time window of the task is less than the available time of the RGV trolley. The influence of the distance factor on the allocation efficiency, the load matching degree and the time window matching degree are multiplied together to calculate the task allocation efficiency and analyze the efficiency of the task allocation to the RGV trolley. The calculation expression of task allocation efficiency is: ; Where, E i,j is the efficiency of task i assigned to RGV car j, and represents the comprehensive efficiency of task i assigned to RGV car j, D i,j is the distance between the cargo location of task i and the current location of RGV j, W task,i is the cargo weight of task i, is the maximum load of RGV trolley j, is the current load of RGV trolley j, is the end time of the time window of task i, is the start time of the time window for task i, is the total available time of RGV car j, when D i,j Very large or Approaching or Very small or When it is very large, E i,j Will approach 0 only when D i,j =0 and and When E i,j =1; The real-time monitoring and intervention module specifically includes: The control system and task allocation module of the RGV trolley collect data in real time, including the real-time position, movement speed, task execution status and abnormal events in the system of the RGV trolley. At the same time, the collected data is pre-processed to remove noise and redundant information to ensure the accuracy and reliability of the data. The collected data is visualized and the real-time trajectory, task progress and abnormal events of the RGV trolley are displayed through a graphical interface. The real-time trajectory shows the movement path of the RGV trolley in the warehouse in the form of dynamic graphics. The task progress shows the completion status of each task in the form of a progress bar or timeline. Abnormal events are displayed in the corresponding position with eye-catching icons or color marks. The graphical interface is automatically updated regularly to ensure that the operator can grasp the operation status of the system in real time and issue timely warnings. It can identify potential problems, analyze the collected data in real time, and detect whether there are any abnormal events, including RGV vehicles deviating from the planned trajectory, task delays, equipment failures, etc. Once an abnormal event is detected, the alarm mechanism will be triggered immediately, and the operator will be notified by sound, pop-up window or SMS. At the same time, the abnormal event will be marked in detail on the monitoring interface, and the time, location and type of the abnormality will be provided to help operators quickly locate and handle the problem, ensure the stable operation of the system, support operators to manually intervene through the monitoring interface, and make emergency adjustments to the operation of the RGV vehicle. Operators can manually adjust the operation path, speed or task allocation of the RGV vehicle according to real-time monitoring information to solve abnormal events or optimize system operation. At the same time, the operation log of manual intervention will be recorded.

[0022] Example 3, as Figure 2 As shown, based on Examples 1-2, the present invention also provides a high-density warehouse outbound scheduling and transportation method based on RGV trolleys, which is implemented by a high-density warehouse outbound scheduling and transportation system based on RGV trolleys, including the following steps: S1. Build a virtual model of the warehouse and RGVs, synchronize shelf layout, aisle information, and RGV status in real time, and provide a digital twin foundation for outbound dispatch. S2: Receive outbound orders, analyze task requirements, clarify priorities, cargo locations, and RGV load status, and generate an initial task queue with a time window. S3. Combine virtual models to simulate the spatiotemporal paths of multiple RGVs, detect potential conflicts, generate collision-free trajectories, optimize outbound paths, and ensure efficient task execution. S4, intelligently assign tasks based on task priority and the current location of the RGV, generate path planning, and optimize the path plan through genetic algorithms to reduce conflicts and delays; S5. Real-time collection of RGV vehicle operation data, visualization of trajectory, task progress and abnormal events, support for manual emergency intervention, and ensure stable system operation.

[0023] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The RGV-based high-density warehouse dispatch and transportation system includes a dispatch management center, which is characterized by: The dispatch management center is communicatively connected to a virtual model building module, a task analysis and planning module, a task allocation module, and a real-time monitoring intervention module, wherein electrical signal connections are made between the modules; The virtual model building module is used to build a virtual model of the warehouse and RGV trolley, synchronously map the shelf height, aisle layout and real-time positioning of the RGV trolley, and synchronize the physical status of the warehouse and RGV trolley in real time; The task parsing and planning module is used to receive and parse outbound orders, generate an initial task queue, and simulate different path plans for task allocation based on the warehouse and RGV car virtual models; The task allocation module is used to intelligently allocate tasks to corresponding RGVs for execution based on the outbound order priority and the current location of the RGV; The real-time monitoring and intervention module is used to visualize the real-time trajectory of the RGV, task progress and abnormal events, and supports manual emergency intervention.

2. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 1 is characterized by: The virtual model building module specifically includes: Through the sensor network deployed in the warehouse, real-time data on shelf height, aisle layout, and the physical status of RGV vehicles, such as position, speed, and load, is collected. Laser scanning and visual recognition technology are used to obtain three-dimensional spatial information of the warehouse, and the collected data is pre-processed to remove noise and redundant information. Based on the pre-processed data, a virtual model of the warehouse and RGV is constructed using modeling software. The number of shelves, heights, and positions, as well as the layout and dimensions of the aisles, are represented in the virtual model. The appearance, dimensions, and motion characteristics of the RGV are also modeled. Establish a real-time data communication channel between the virtual model and the physical entity, transmit the real-time data collected by the sensor to the virtual model through the Internet of Things technology, and update the cargo storage status of the shelf, the position and movement status information of the RGV trolley in real time in the virtual model.

3. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 1 is characterized by: The task parsing and assigning module includes a task receiving and parsing unit and a path planning simulation unit; The task receiving and parsing unit is used to receive outbound orders, parse task requirements, clarify outbound order priorities, cargo locations, and RGV trolley load status, and generate an initial task queue with a time window. The path planning simulation unit is used to simulate the time-space path conflicts of multiple RGVs in combination with the virtual model, generate collision-free trajectories of different path solutions, and optimize the outbound path.

4. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 3 is characterized by: The task receiving and parsing unit specifically includes: Receive outbound order information through the interface with the warehouse management system, perform preliminary formatting and integrity check on the received order data, and classify and mark the orders; Detailed analysis of each outbound order extracts key mission-critical information, including cargo location, outbound quantity, priority, and cargo type. Combining the warehouse's real-time inventory information with a three-dimensional spatial indexing algorithm, the spatial coordinates of the cargo are generated. Obtain the load parameters of the currently idle RGV trolleys, analyze the remaining load and the assigned task volume of the idle RGV trolleys, establish a task-equipment matching matrix, screen the candidate equipment set that meets the load requirements, and assign time window constraints to each task; The time axis projection method is used to verify whether the time window constraint overlaps with the existing task queue. If there is a conflict, the priority arbitration mechanism is triggered to adjust the current task time window. The parsed tasks are sorted in descending order of priority and ascending order of time window urgency to generate an initial task queue. The initial task queue contains the task unique identifier, cargo space coordinates, candidate RGV vehicle list and time window constraint.

5. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 4 is characterized by: The process of establishing the task-device matching matrix includes: Obtain the load parameters of the currently idle RGV trolleys from the RGV trolley's control system, including the maximum load, the amount of assigned tasks, and the current load, and record the current position and status of each idle RGV trolley; Based on the acquired load parameters, the remaining load capacity of each idle RGV is calculated by subtracting the current load from the maximum load of the RGV. At the same time, the interference of the assigned tasks on the RGV's task execution capability is analyzed to determine whether the RGV can take on new tasks. The matching value is calculated based on the cargo weight of the task, the distance between the task location and the current location of the RGV, and the remaining load factor of the RGV. The task-equipment matching matrix is ​​established and sorted in descending order according to the matching value. Based on the task-equipment matching matrix, a set of candidate equipment that meets the load requirements is screened out. For each task, according to its cargo weight and urgency requirements, RGV carts with high matching degree and sufficient remaining load are selected from the matching matrix as candidate equipment, and a time window constraint is assigned to each task.

6. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 3 is characterized by: The path planning simulation unit specifically includes: The virtual model building module obtains the virtual model of the warehouse and RGV, including shelf layout, aisle information, and the initial position of the RGV. It also generates the initial task queue, obtains the cargo space coordinates, candidate RGV list, and time window constraints of the task, and maps the relevant data of the initial task queue to the virtual model. Based on the virtual model, the time and space paths of multiple RGVs are simulated when performing tasks. Through time axis projection and spatial position analysis, potential conflicts between different RGV paths are detected. The position and movement direction of each RGV at different time points are analyzed, and collision points and congestion areas are identified. The time and location information of the conflict are recorded to form a conflict report. According to the conflict detection results, the path planning strategy of the RGV car is adjusted, and a genetic algorithm is used to generate collision-free trajectories for different path plans. Through multiple simulation iterations, the path plan is gradually optimized, and the optimal path plan for each iteration is recorded; After multiple iterations, the path plan with the highest fitness is selected as the optimal path plan. The optimal path plan is simulated and verified to ensure that it has no collisions and meets the time window constraints of the task. If the simulation verification finds new conflicts or does not meet the time window constraints, the parameters of the genetic algorithm are adjusted and the optimization iteration is repeated until the generated path plan has no conflicts in the simulation and meets all task requirements. The optimized path plan is then output and fed back to the task allocation module.

7. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 6 is characterized by: The identification process of the collision point and congestion area is as follows: For each task, the start and end times are determined based on the task's time window constraints. The time axis projection method is used to project the task time window of each RGV onto the time axis to form a time interval. The task time intervals of different RGVs are checked for overlap. If two time intervals overlap, there is a time conflict. For each task, the position of the RGV at different time points is calculated based on the spatial coordinates of the cargo and the motion path of the RGV. The spatial position analysis method is used to detect whether the positions of different RGVs at the same time point overlap. The positions of different RGVs at the same time point are compared. If the positions are the same, there is a spatial conflict, and the time and location of the conflict are recorded. At each time point, check whether there are multiple RGVs at the same or adjacent locations, record the time and location information of all conflicts, identify potential collision points and congestion areas, and form a conflict report. The conflict report includes: conflict time, conflict location, RGV vehicle numbers involved, and conflict type.

8. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 3 is characterized by: The task allocation module specifically includes: Obtain the initial task queue with a time window from the task parsing and planning module, which includes the task priority, cargo space coordinates, and candidate RGV list information. At the same time, obtain the current position, load status, and remaining load capacity of all RGVs from the RGV control system; Combine the current position of the RGV and the location of the cargo in the task to calculate the task allocation efficiency and then intelligently allocate the task to the corresponding RGV; For each task, the RGV with the highest matching degree is selected for assignment, and a path plan is generated based on the time window constraints of the task and the current position of the RGV.

9. The high-density warehouse dispatching and transportation system based on RGV vehicles according to claim 8 is characterized by: The real-time monitoring intervention module specifically includes: Collect data from the RGV control system and task allocation module in real time, including the RGV's real-time position, movement speed, task execution status, and abnormal events in the system. At the same time, pre-process the collected data to remove noise and redundant information. Visualize the collected data and display the real-time trajectory, task progress, and abnormal events of the RGV through a graphical interface; Analyze the collected data in real time to detect any abnormal events. Once an abnormal event is detected, the alarm mechanism is immediately triggered and the operator is notified through sound, pop-up window or SMS. At the same time, the abnormal event is marked in detail on the monitoring interface, providing information on the time, location and type of the abnormality. It supports operators to manually intervene through the monitoring interface to make emergency adjustments to the operation of the RGV vehicle, and at the same time, record the operation log of manual intervention.

10. A method for dispatching and transporting high-density warehouses based on RGV trolleys, implemented based on the high-density warehouse dispatching and transporting system based on RGV trolleys according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Build a virtual model of the warehouse and RGVs, and synchronize shelf layout, aisle information, and RGV status in real time. S2: Receive outbound orders, analyze task requirements, clarify priorities, cargo locations, and RGV load status, and generate an initial task queue with a time window. S3, combined with the virtual model, simulates the spatiotemporal paths of multiple RGVs, detects potential conflicts, generates collision-free trajectories, and optimizes the outbound path; S4: Intelligently assign tasks and generate path planning based on task priority and the current position of the RGV vehicle; S5. Real-time collection of RGV vehicle operation data, visualization of trajectory, task progress and abnormal events, and support for manual emergency intervention.

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