Logistics scheduling system

Abstract

The embodiment of the invention provides a logistics scheduling system, and belongs to the technical field of logistics. The system comprises a task layer which is used for generating an order task; the feedback layer is used for collecting dynamic resource data related to execution of the order task in a logistics site; the decision-making layer is respectively in communication connection with the task layer and the feedback layer, and the decision-making layer is used for adjusting a path planning strategy according to the dynamic resource data and generating a task execution instruction according to the adjusted path planning strategy; and the execution layer is in communication connection with the decision-making layer, and the execution layer is used for scheduling corresponding transportation equipment to execute the order task according to the task execution instruction. According to the embodiment of the invention, the scheduling flexibility of the transportation equipment can be improved.

Description

Technical Field

[0001] This application relates to the field of logistics technology, and in particular to a logistics scheduling system. Background Technology

[0002] In modern logistics and warehousing scenarios, Automated Guided Vehicles (AGVs) have become core equipment for improving automation and intelligence levels. AGVs replace traditional manual operations through autonomous navigation, efficiently completing tasks such as material handling, picking, and sorting, thus greatly improving logistics efficiency.

[0003] The efficient collaborative operation of AGVs relies on an AGV scheduling system. However, existing AGV scheduling systems suffer from poor scheduling flexibility. Summary of the Invention

[0004] The main objective of this application is to propose a logistics scheduling system that can improve the scheduling flexibility of transportation equipment.

[0005] To achieve the above objectives, a first aspect of this application proposes a logistics scheduling system, the system comprising: Task layer, which is used to generate order tasks; Feedback layer, which is used to collect dynamic resource data related to the execution of the order task in the logistics site; The decision layer is communicatively connected to the task layer and the feedback layer. The decision layer is used to adjust the path planning strategy according to the dynamic resource data and generate task execution instructions according to the adjusted path planning strategy. An execution layer, which is communicatively connected to the decision layer, is used to schedule corresponding transportation equipment to execute the order task according to the task execution instruction.

[0006] The logistics scheduling system proposed in this application constructs a four-layered system—task layer, feedback layer, decision layer, and execution layer—that is both separate and collaborative. This decouples functions such as task generation, scheduling, and environmental perception, thereby improving the system's scalability. Furthermore, by sensing real-time dynamic changes in global resources at the logistics site and adjusting path planning strategies accordingly, the system's flexibility is significantly enhanced. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of a logistics scheduling system provided in an embodiment of this application; Figure 2 This is another schematic diagram of the logistics scheduling system provided in the embodiments of this application; Figure 3This is a flowchart of an embodiment of the operation scheduling module adjusting the path planning strategy provided in this application; Figure 4 This is a flowchart of an embodiment of the warehouse optimization module provided in this application for determining candidate warehouse locations for each order task; Figure 5 This is a flowchart of another embodiment of the warehouse optimization module provided in this application for determining candidate warehouse locations for each order task; Figure 6 This is a flowchart of an embodiment of the operation scheduling module for determining candidate transportation equipment provided in this application. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0009] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., used in the specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0010] First, let's analyze some of the terms used in this application: Automated Guided Vehicles (AGVs): These are unmanned intelligent handling robots equipped with automated navigation systems (such as magnetic strips, lasers, vision, or SLAM technology). AGVs can autonomously, safely, and efficiently complete the transportation, loading, unloading, and handling of materials (such as parts, finished products, pallets, bins, and cages) along predetermined or dynamically planned paths in designated environments such as warehouses and factories. They are key equipment for realizing automated logistics and flexible production.

[0011] Currently, in modern logistics and warehousing scenarios, Automated Guided Vehicles (AGVs) have become core equipment for improving automation and intelligence levels. AGVs replace traditional manual operations through autonomous navigation, efficiently completing tasks such as material handling, picking, and sorting, thus greatly improving logistics efficiency.

[0012] The efficient collaborative operation of AGVs relies on an AGV scheduling system. However, AGV scheduling systems in related technologies typically adopt a centralized control architecture, which has the following drawbacks: 1. Low flexibility: Task allocation depends on fixed rules and cannot dynamically respond to environmental changes (such as road congestion); 2. Low accuracy: Lack of global real-time data support leads to low efficiency in task allocation and execution; 3. Poor scalability: High coupling between system modules makes it difficult to adapt to complex site layouts or add new functional modules.

[0013] Based on this, embodiments of this application provide a logistics scheduling system that can improve the scheduling flexibility of transportation equipment (such as AGVs).

[0014] Reference Figure 1 , Figure 1 This is a schematic diagram of a logistics scheduling system provided in an embodiment of this application. In some embodiments of this application, the logistics scheduling system includes a task layer, a feedback layer, a decision layer, and an execution layer. The task layer is used to generate order tasks. The feedback layer is used to collect dynamic resource data related to the execution of order tasks in the logistics site. The decision layer is communicatively connected to both the task layer and the feedback layer, and is used to adjust the route planning strategy based on the dynamic resource data, and generate task execution instructions based on the adjusted route planning strategy. The execution layer is communicatively connected to the decision layer, and is used to schedule corresponding transportation equipment to execute order tasks according to the task execution instructions.

[0015] Specifically, the task layer can refer to the initiator and demand source of scheduling tasks (e.g., the task layer may include business modules). The task layer is used to generate order tasks, such as inbound tasks (storing goods in a designated storage location), outbound tasks (picking goods from a designated storage location and sending them out), and transfer tasks (moving goods between different storage locations within the logistics area). The task layer can define attributes such as priority and urgency for each order task to provide a basis for the decision-making layer's scheduling strategy. For example, an order task may carry the following data: task ID, task type, task start location, task end location, goods information, priority, and generation timestamp. The task layer can send task requests generated based on order tasks to the decision-making layer through asynchronous message queues or other methods.

[0016] The feedback layer refers to the set of functional modules in a logistics scheduling system responsible for comprehensively sensing and collecting various real-time changing information within the logistics site, providing a real and dynamic data foundation for the decision-making layer. For example, the feedback layer can collect dynamic resource data related to order execution. Dynamic resource data refers to the constantly changing physical state or information set within the logistics site over time and depending on task execution, such as environmental resource data (e.g., path congestion, temporary dynamic obstacles) and storage location resource data (e.g., storage location occupancy or vacancy status). The feedback layer can collect dynamic resource data through various IoT sensors deployed within the logistics site.

[0017] The decision-making layer receives task requests from the task layer and real-time data (i.e., dynamic resource data) from the feedback layer, performs calculations, optimizations, and decisions to generate specific task execution instructions. Task execution instructions can refer to a set of specific commands that can be directly understood and executed by the execution layer, such as the task start position, task end position, and suggested running path. The decision-making layer can determine the suggested running path based on a path planning strategy; that is, the path planning strategy can refer to the policy rules used by the decision-making layer to calculate the movement path of transportation equipment (such as AGVs) from the task start position to the task end position. It is understood that the path planning strategy is not fixed in advance; the decision-making layer can perform real-time or near-real-time optimization based on the dynamic resource data provided by the feedback layer, thereby always seeking the globally optimal scheduling solution.

[0018] The execution layer can refer to the control module (such as RCS, i.e., robot control system) in a logistics scheduling system that directly interacts with the transportation equipment and controls the equipment to perform specific actions. For example, the execution layer can receive task execution instructions sent by the decision layer (such as AGV-001, traveling along path-A at speed XX to warehouse location XX to perform a pickup operation), and decompose the task execution instructions into drive commands that the underlying controller of the transportation equipment can recognize, thereby achieving precise control of the transportation equipment.

[0019] This application's embodiments construct a logistics scheduling system comprising four separate yet collaborative layers: a task layer, a feedback layer, a decision-making layer, and an execution layer. This system transforms logistics scheduling from static decision-making to dynamic intelligent decision-making. By sensing real-time dynamic changes in global resources at the logistics site and using this as a basis for decision optimization and proactive dynamic adjustments, it significantly improves adaptability, response speed, and overall efficiency in complex and ever-changing logistics environments.

[0020] Reference Figure 2In some embodiments, the logistics site has main roads and secondary roads, with the permitted operating speed of the main roads being greater than that of the secondary roads. Dynamic resource data includes road congestion data. The feedback layer includes a road congestion data acquisition module, which collects road congestion data for the target main road corresponding to the order task. The decision layer includes an operation scheduling module, which is communicatively connected to the road congestion data acquisition module. The operation scheduling module adjusts the route planning strategy when the target main road is determined to be congested based on the road congestion data, and generates task execution instructions based on the adjusted route planning strategy.

[0021] Specifically, a logistics site can refer to the physical area where this logistics scheduling system is applied, typically a closed or semi-closed environment such as a warehouse, distribution center, or production workshop requiring automated material handling. The internal layout of the logistics site can include a network of pathways for transport equipment and various functional work areas (such as loading / unloading areas, storage areas, picking areas, charging areas, etc.). A main road can refer to a high-speed passageway within the logistics site's pathway network, defined by the logistics scheduling system as carrying the main traffic flow and connecting various major work areas. Main roads enable long-distance, rapid movement of transport equipment within the logistics site. A secondary road can refer to a medium-speed passageway within the logistics site's pathway network, defined by the logistics scheduling system as carrying secondary traffic flow and connecting main roads, specific work points, or internal circulation within work areas. Secondary roads can divert traffic from main roads and provide access to specific work locations. It is understood that the distinction between main and secondary roads is based on the logistics site's layout and logistics logic; therefore, this application does not specifically limit the location of main and secondary roads within the logistics site. The key distinguishing feature between arterial and secondary roads is that arterial roads allow high-speed traffic, while secondary roads only allow medium or low-speed traffic (i.e., the permitted operating speed of arterial roads is greater than that of secondary roads). Thus, under ideal conditions without congestion, prioritizing arterial roads can effectively shorten order execution time, thereby improving the overall scheduling efficiency of logistics facilities.

[0022] Determining whether a logistics site is congested can be achieved through a road congestion data collection module in the feedback layer. Specifically, this module can include various sensors deployed at key nodes within the logistics site, enabling the collection of global data, such as road congestion data, AGV positions, and obstacle information. Road congestion data refers to real-time data used to quantify the smoothness of traffic flow on target arterial roads related to order tasks within the path network. For example, road congestion data can be determined based on the number of transport vehicles passing through the target arterial road per unit time, the ratio of the average travel speed of transport vehicles on the target arterial road to their permitted operating speed, etc. Based on road congestion data, it can be determined whether the target arterial road is congested. It can be understood that the target arterial road can refer to the preferred passageway when executing order tasks under ideal, congestion-free conditions.

[0023] The decision-making layer includes an operation and scheduling module, which establishes a continuous communication connection with the road congestion data acquisition module, enabling it to receive real-time road congestion data from the front end. The operation and scheduling module can be pre-set with thresholds or models for judging road congestion. Thus, when the target arterial road is determined to be congested based on road congestion data, the module can adjust its route planning strategy. Adjusting the route planning strategy can refer to strategic changes taken to avoid congestion, such as initiating alternative route evaluation, changing route priority, or temporarily changing road grade attributes (e.g., changing a secondary arterial road to a temporary arterial road). Finally, the operation and scheduling module can generate the final task execution instruction based on the optimized and adjusted strategy, thereby avoiding congested paths at the source of the instruction and guiding transportation equipment to choose smoother routes.

[0024] This application embodiment dynamically adjusts the global path planning strategy to transform passive congestion response into proactive congestion avoidance. This reduces the waiting and queuing time of transportation equipment on the path, greatly improving the overall traffic efficiency and throughput capacity of the path network.

[0025] Reference Figure 3 In some embodiments, the planning priority of the main road is higher than that of the secondary road, and the operation scheduling module may adjust the route planning strategy, including but not limited to the following steps S310 to S320: Step S310: Determine the target secondary road that is closest to the transportation equipment; Step S320: When the road congestion data collection module determines that the target secondary arterial road is not congested, update the planning priority of the target secondary arterial road.

[0026] The execution scheduling module generates task execution instructions based on the adjusted path planning strategy, which may include, but is not limited to, the following steps S330 to S340: Step S330: Perform route planning based on the updated planning priorities of the target secondary arterial roads and non-target main arterial roads to obtain the target planned route; Step S340: Generate task execution instructions based on the target planned path.

[0027] In step S310 of some embodiments, after the operation scheduling module determines that the target arterial road (i.e., the preferred passageway when executing order tasks under ideal, congestion-free conditions) on which the original planned path (i.e., the original path planning strategy) depends is congested, it initiates an alternative path optimization operation (i.e., adjusts the path planning strategy). Specifically, based on the current location of the transport equipment, it can first search and calculate the distance (the travel distance in the actual path network, not the straight-line geometric distance) between one or more alternative secondary arterial roads and the current location of the transport equipment in the logistics site. Through distance comparison, the closest secondary arterial road is finally selected as the target secondary arterial road. The target secondary arterial road can be used as a preferred alternative passageway. It is understood that the transport equipment mentioned in "adjusting the path planning strategy" is not the equipment that ultimately executes the order task, but rather a logical object to be considered in the path planning strategy. The transport equipment that ultimately executes the order task can be determined comprehensively based on factors such as the global task allocation algorithm and real-time equipment status.

[0028] In step S320 of some embodiments, the operation scheduling module communicates with the road congestion data acquisition module to obtain real-time road congestion data of the determined target secondary arterial road. When it is determined from the road congestion data that the target secondary arterial road is not congested, it indicates that the target secondary arterial road has good traffic conditions and can be used as an efficient alternative route. At this time, the operation scheduling module can dynamically update the planning priority of the target secondary arterial road (planning priority can indicate the degree to which a route is preferred; by default, the planning priority of arterial roads is higher than that of secondary arterial roads). For example, the priority value of the target secondary arterial road can be temporarily increased to a level equivalent to or higher than that of other uncongested arterial roads, thereby making the target secondary arterial road a preferred option in subsequent route planning.

[0029] In step S330 of some embodiments, under the new strategy environment where the planning priority of the target secondary arterial road has been upgraded, the operation scheduling module begins global route planning. Global route planning comprehensively considers the latest status of all available paths, including: the target secondary arterial road with upgraded priority, and non-target arterial roads not reported as congested by the road congestion data collection module (i.e., other arterial and secondary arterial roads besides the target arterial road). The operation scheduling module can use optimization objectives such as "shortest total travel time" and "lowest overall travel cost" to calculate among all possible path combinations and candidate transportation equipment, ultimately determining the target transportation equipment to execute the order task and generating an optimal target planning path from the current location of the target transportation equipment to the task's endpoint. The target planning path can be a combination route that completely avoids the original congested arterial road (i.e., the target arterial road) and prioritizes the use of upgraded target secondary arterial roads and / or non-target arterial roads.

[0030] It is understandable that in some embodiments, when the task endpoint is determined to be close to the work area and reaching the task endpoint does not require passing through the main road, the target planning path determined by the above method may be a combination of different secondary roads, so as to reach the task endpoint more efficiently.

[0031] In step S340 of some embodiments, after determining the target planning path, the operation scheduling module can transform this abstract path into specific task execution instructions that can be recognized and executed by the underlying control system of the transportation equipment (such as the execution layer).

[0032] The route planning strategy adjustment mechanism provided in this application, when detecting congestion on the target main road, intelligently identifies the nearest and unobstructed target secondary road as the most critical alternative, and dynamically increases the planning priority of the target secondary road, ensuring that it receives the same level of attention in route decision-making as the uncongested main road. In this way, the scheduling system can plan the optimal route that effectively avoids congestion while maintaining driving efficiency. This approach realizes a shift from fixed rule allocation to intelligent decision-making driven by real-time data (i.e., road congestion data), improving the task execution efficiency of transportation equipment and the adaptability and flexibility of the logistics scheduling system in responding to emergencies.

[0033] In some embodiments, the logistics site further includes a work area, which represents the area where operations are performed on goods corresponding to order tasks. The work area is connected to main roads and / or secondary roads, and the dynamic resource data also includes storage location status data. The feedback layer also includes a status data acquisition module and a storage area optimization module. The status data acquisition module is used to collect storage location status data of the original picking area in the work area. The picking area represents the area where picking operations are performed on goods. The storage area optimization module is communicatively connected to the status data acquisition module and the task layer. When the storage location status data determines that the number of free storage locations in the original picking area is less than the task volume of the corresponding multiple order tasks, the storage area optimization module dynamically converts the original temporary storage area in the work area into a temporary picking area, and determines the candidate storage location for each order task from the original picking area and the temporary picking area according to a preset storage location allocation strategy. The temporary storage area is used to buffer goods. The task layer is also used to determine the target storage location for each order task based on the candidate storage locations. The operation scheduling module is also used to adjust the path planning strategy according to the target storage location and generate task execution instructions according to the adjusted path planning strategy.

[0034] Specifically, in addition to the route network, the logistics site also includes work areas. A work area refers to the area where substantive operations (such as loading, unloading, picking, temporary storage, and packaging) are performed on goods corresponding to an order task. Different work areas can be interconnected via main roads and / or secondary roads to form a complete logistics network. Location status data refers to a real-time data set describing the current occupancy, idle status, etc., of each specific location in the original picking area of ​​the work area (i.e., the area specifically used for picking operations under system initialization settings or normal conditions). For example, a work area may include a loading area or an unloading area. For the loading area, picking operations can refer to the allocation and loading of goods according to orders. For the unloading area, picking operations can refer to the sorting of goods unloaded from trucks according to orders.

[0035] Storage location status data can be collected by the status data acquisition module in the feedback layer. This module can refer to a central data platform deployed in the cloud, responsible for global data storage and visualization. It can also collect data such as transportation equipment status, task progress, and storage location status information. Furthermore, the feedback layer can include a storage area optimization module. This module can establish communication connections with both the status data acquisition module and the task layer. Based on the storage location status data sent by the status data acquisition module, the module can visualize and dynamically adjust storage locations (e.g., generate storage location heatmaps) and optimize the storage area layout. Specifically, when the storage area optimization module determines, based on the storage location status data, that the number of available storage locations in the original picking area is insufficient to meet the total task volume (i.e., the total number of storage locations required for this batch of orders) issued by the task layer (i.e., multiple order tasks corresponding to the same work area) (i.e., during peak periods), the module will trigger a dynamic storage area conversion operation. Dynamic warehouse conversion operation refers to dynamically converting the original temporary storage area (i.e., the area used to cache goods in the system initialization setting or normal state) in the work area (such as loading area or unloading area) corresponding to this batch of orders into a temporary picking area, thereby expanding the picking location.

[0036] After the dynamic transformation of the warehouse area is completed, the warehouse area optimization module can determine one or more candidate warehouse locations for each of the multiple order tasks from the expanded picking warehouse locations (including idle warehouse locations in the original picking area and idle warehouse locations in the temporary picking area) according to the preset warehouse location allocation strategy. Then, for each order task, the task layer can filter the target warehouse location from the candidate warehouse locations according to preset business rules (such as order priority, customer level, etc.) (finally determining the warehouse location used to perform the picking operation). The target warehouse location information can be synchronized to the operation scheduling module, so that the operation scheduling module can adjust the route planning strategy according to the target warehouse location (such as recalculating the optimal route based on road congestion data), and generate task execution instructions based on the adjusted route planning strategy.

[0037] This application embodiment achieves dynamic and on-demand reconstruction of the work area in the logistics site through dynamic transformation of the warehouse area. This allows for rapid expansion of the system's processing capacity during peak business periods, effectively reducing processing delays or congestion caused by resource fixation. In addition, it also improves the logistics scheduling system's adaptability to order fluctuations and the flexibility of resource utilization.

[0038] Reference Figure 4 In some embodiments, the warehouse area optimization module determines the candidate warehouse location for each order task from the original picking area and the temporary picking area according to a preset warehouse location allocation strategy, which may include, but is not limited to, steps S410 to S420.

[0039] Step S410: Determine the travel time and route efficiency of the transport equipment from its current location to each available storage location in the original picking area and the temporary picking area; Step S420: Determine the candidate storage location for each order from the available storage locations in the original picking area and the temporary picking area based on the travel time and route efficiency.

[0040] In step S410 of some embodiments, the warehouse area optimization module first obtains the current location of the transport equipment in the logistics site and the coordinates of all available warehouse locations in the original picking area and the temporary picking area. Then, based on the current location and warehouse location coordinates, the warehouse area optimization module calculates the travel time and path efficiency for each potential path from the current location to each available warehouse location. The travel time can be estimated based on the network distance of the path and the speed difference allowed on main and secondary roads. Travel time can be a quantified representation of distance cost. Path efficiency can be a quantified representation of congestion cost. Path efficiency can be estimated from the path congestion information provided by the road congestion data acquisition module. Path efficiency reflects the actual smoothness of the current corresponding path and can be estimated by comprehensively considering factors such as the equipment density on the current path and the average travel speed of the transport equipment.

[0041] It is understandable that the transportation equipment mentioned in the "warehouse location allocation strategy" is not the equipment that ultimately executes the order task, but rather a logical object that needs to be considered in the warehouse location allocation strategy. The transportation equipment that ultimately executes the order task can be determined comprehensively based on factors such as the global task allocation algorithm and the real-time equipment status.

[0042] In step S420 of some embodiments, after determining the travel time and route efficiency corresponding to each available storage location, the storage area optimization module can filter candidate storage locations corresponding to each order task from multiple available storage locations based on these two indicators. For example, the storage area optimization module can substitute these two indicators into a predefined cost function for comprehensive calculation and select the multiple available storage locations with the lowest comprehensive cost as candidate storage locations. It is understood that the number of candidate storage locations corresponding to each order task can be adaptively set according to the actual situation, and there is no specific limitation on this.

[0043] This application provides a multi-dimensional and real-time dynamic warehouse location allocation strategy. This strategy comprehensively calculates the estimated travel time and route efficiency of transportation equipment to each available warehouse location and integrates these two indicators. This reduces problems such as transportation equipment waiting, increased congestion in the route network, and overall task execution delays caused by blindly allocating warehouse locations under route congestion conditions, thereby improving the flexibility of the logistics scheduling system.

[0044] Reference Figure 5In some embodiments, the warehouse area optimization module determines the candidate warehouse location for each order task from the original picking area and the temporary picking area according to a preset warehouse location allocation strategy, which may include, but is not limited to, steps S510 to S520.

[0045] Step S510: Determine the task matching degree between the order task and each available storage location in the original picking area and the temporary picking area based on the task type of the order task. Step S520: Determine the candidate storage location for each corresponding order from the available storage locations in the original picking area and the temporary picking area based on the task matching degree.

[0046] In step S510 of some embodiments, the warehouse area optimization module can parse or determine the task type of the currently assigned order task from the task layer. The task type may include outbound picking, inbound storage, etc. Then, for each vacant storage location in the original picking area and temporary picking area, the warehouse area optimization module can evaluate the relative positional relationship between the vacant storage location and key logistics nodes (such as outbound gates, inbound gates, specific work areas, etc., which can be determined according to the task type), and determine the task matching degree between the vacant storage location and the task type based on the relative positional relationship. The task matching degree can be an indicator used to quantitatively evaluate the degree of matching between the geographical location of each vacant storage location and the business logic requirements of the order task.

[0047] For example, if the task type of an order is outbound picking (i.e., retrieving goods from a storage location for shipment), then the critical logistics node is the outbound gate. In this case, the closer the available storage location is to the outbound gate, the higher the task matching degree. If the task type of an order is inbound storage (i.e., moving goods unloaded from the truck to a storage location), then the critical logistics node is the inbound gate or temporary storage area. In this case, the closer the available storage location is to the inbound gate or temporary storage area, the higher the task matching degree.

[0048] In step S520 of some embodiments, after determining the task matching degree corresponding to all available storage locations, the storage area optimization module can filter multiple available storage locations corresponding to each order task according to the task matching degree, and select one or more available storage locations with the highest matching task matching degree as candidate storage locations.

[0049] The storage location allocation strategy based on task matching degree in this application improves the storage location allocation strategy from simple physical distance calculation to intelligent decision-making related to task type. This can reduce unnecessary secondary handling and long-distance movement of goods in the logistics site.

[0050] It is understood that in some embodiments, candidate locations can also be selected based on a comprehensive evaluation of three indicators: travel time, route efficiency, and task matching degree, without any specific limitations.

[0051] In other embodiments, the warehouse area optimization module is further configured to dynamically convert the idle warehouse locations in the work area into temporary charging buffers when the number of idle warehouse locations in the work area exceeds a first preset threshold, update the warehouse location information of the work area according to the temporary charging buffers, and send the warehouse location information to the operation scheduling module. The first preset threshold is determined based on the workload of the corresponding multiple order tasks. The operation scheduling module is further configured to determine candidate transportation equipment for executing each order task based on the warehouse location information, determine the target transportation equipment for finally executing the order task based on the candidate transportation equipment and the adjusted route planning strategy, and generate task execution instructions for the target transportation equipment.

[0052] In this embodiment, the warehouse area optimization module can continuously acquire warehouse location information of the work area from the status data acquisition module. When the warehouse location optimization module determines, based on this information, that the number of vacant warehouse locations in the work area is greater than a first preset threshold, it determines that the existing warehouse location resources far exceed the potential demand of the current order task (i.e., the current period is a low point). At this time, the warehouse area optimization module can dynamically convert some vacant warehouse locations in the work area from their original function to temporary charging function, that is, convert some vacant warehouse locations in the work area into temporary charging buffers. The temporary charging buffer can refer to a temporarily designated area used to provide charging services for transportation equipment. The first preset threshold can be determined based on the task volume of multiple order tasks corresponding to the work area, or it can be determined based on historical data and real-time task volume to estimate the basic guarantee data of warehouse locations required in the current and future periods, and the first preset threshold can be determined based on this basic guarantee data. For example, when the task volume of an order task is less than 30% of the number of warehouse locations in the work area, it is considered that the current period is a low point.

[0053] After completing the warehouse location conversion, the warehouse area optimization module can update the warehouse location information based on the current warehouse location status of the work area and send the updated information to the operation scheduling module. Upon receiving the new warehouse location information, when the operation scheduling module receives a task request from the task layer (i.e., there are pending order tasks), it can filter the transportation equipment based on the new warehouse location information to obtain candidate transportation equipment for each order task. The filtering logic could include transportation equipment with low remaining battery power and whose current location or task path is close to the temporary charging buffer zone, thereby increasing the scope of candidate transportation equipment selection. Finally, the operation scheduling module determines the target transportation equipment for executing the order task from the set based on the adjusted route planning strategy (such as a route planning strategy that avoids congestion). It is understandable that the updated warehouse location information can be synchronized to the transportation equipment's navigation map through the operation scheduling module, thereby ensuring that the route planning matches the latest warehouse area.

[0054] This application embodiment realizes the intelligent conversion of idle storage space into temporary charging buffer zone. Compared with the empty driving mileage and time consumption caused by the transportation equipment going to a fixed charging station in related technologies, this application embodiment combines the charging process with task execution, thereby improving the utilization rate of transportation equipment.

[0055] Reference Figure 6 In some embodiments, the operation scheduling module determines the candidate transportation equipment to execute each order task based on the warehouse location information, which may include, but is not limited to, steps S610 to S620.

[0056] Step S610: When the distance between the target storage location of the order task and the temporary charging buffer is less than or equal to the second preset threshold, the transportation equipment to be charged is determined. Step S620: Based on the remaining power of each transport device to be charged, determine candidate transport devices from a plurality of transport devices to be charged.

[0057] In step S610 of some embodiments, after the operation scheduling module obtains the latest storage location information through the storage area optimization module, it can determine the distance between the target storage location (i.e., the final determined goods operation location of the order task) corresponding to the currently assigned order task and the temporary charging buffer, and compare this distance with a second preset threshold. The second preset threshold can be a pre-set distance threshold used to determine whether portable charging conditions are available, and the value of the second preset threshold is not specifically limited. When the distance is less than or equal to the second preset threshold, it indicates that when executing the order task, the transportation equipment will have the opportunity to replenish its power nearby. Therefore, the operation scheduling module can determine the transportation equipment to be charged based on the remaining power of all unassigned tasks sent by the execution layer. The transportation equipment to be charged can refer to transportation equipment with remaining power lower than the preset threshold (the value is not specifically limited).

[0058] In step S620 of some embodiments, the operation scheduling module can further refine the selection of the already screened transport devices to be charged to determine candidate transport devices. For example, the operation scheduling module sorts the transport devices to be charged based on their remaining power, and selects multiple transport devices that most urgently need charging and have sufficient remaining power to execute order tasks as candidate transport devices from the sorting results. In this way, order tasks closer to the temporary charging buffer are prioritized for transport devices that need charging. For example, for candidate transport devices determined based on situational conditions, candidate transport devices determined based on remaining power can have a higher task allocation weight.

[0059] This application embodiment includes qualified transport equipment to be charged into a candidate transport equipment set, thus realizing the "charging along the way during task execution" mode, which can improve the utilization rate of transport equipment.

[0060] Understandably, in some embodiments, for heavy-duty transport equipment, order tasks that require the shortest path can be prioritized to reduce energy consumption.

[0061] In some embodiments, the logistics site is further provided with an original charging buffer, and the execution layer is further used to schedule the transport equipment to a temporary charging buffer or the original charging buffer when the remaining power of the transport equipment is equal to a third preset threshold.

[0062] Specifically, the original charging buffer zone can refer to the area used for charging during system initialization or normal operation. The execution layer can monitor the real-time remaining power of each transport device under its control and compare the remaining power with a third preset threshold. This third preset threshold can be a minimum power safety threshold set to prevent the device from running out of power and to ensure the device can safely reach the charging area (original charging buffer zone or temporary charging buffer zone), and its value is not specifically limited. When the remaining power equals the third preset threshold, it indicates that the device no longer meets the energy requirements for executing the order task and urgently needs charging. Therefore, the execution layer can schedule the transport device to run to the temporary charging buffer zone or the original charging buffer zone for charging. For example, the execution layer can report the transport device that needs charging to the operation scheduling module. The operation scheduling module can select the optimal charging buffer zone as the target based on the transport device's current location, distance from the original charging buffer zone and the temporary charging buffer zone, road congestion, etc. After determining the target, the execution layer can automatically dispatch the transport device to the selected target charging area (i.e., a charging location within the original charging buffer zone or temporary charging buffer zone) for charging according to the execution scheduling module's instructions.

[0063] The embodiments of this application implement proactive and preventative equipment scheduling based on real-time power monitoring, reducing the risk of task terminal and path blockage caused by sudden power failure of transportation equipment.

[0064] In some embodiments, the status data acquisition module is also used to collect the transportation status data of all transportation equipment and determine the efficiency analysis data of the transportation equipment based on the transportation status data.

[0065] Specifically, the status data acquisition module can also integrate the transportation status data of all transportation equipment in the logistics site. Transportation status data can be a collection of real-time information covering all dimensions of transportation equipment operation, such as real-time location, speed, remaining battery power, load, currently executing task number, and fault codes (specific codes indicating abnormal hardware or software states of transportation equipment) reported by the execution layer. The status data acquisition module can perform clear, correlated, calculated, and statistical analysis on the transportation status data to generate efficiency analysis data. Efficiency analysis data can refer to a set of indicators used to quantitatively evaluate the energy efficiency and health status of individual transportation equipment or the overall operation of a group of equipment. Specifically, it can include data such as the amount of tasks completed per unit time and the average waiting time.

[0066] This application embodiment performs efficiency analysis on all transportation equipment based on the status data acquisition module, realizing the visualization and quantification of the operational efficiency of transportation equipment, and providing data support for resource planning, equipment maintenance, etc.

[0067] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0068] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0069] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0071] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0072] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0073] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0074] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0076] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A logistics dispatch system characterized by, The system comprises: a task layer configured to generate order tasks; a feedback layer configured to collect dynamic resource data related to execution of the order tasks in a logistics site; a decision layer in communication connection with the task layer and the feedback layer, and configured to adjust a path planning strategy according to the dynamic resource data, and generate task execution instructions according to the adjusted path planning strategy; an execution layer in communication connection with the decision layer, and configured to dispatch corresponding transportation devices to execute the order tasks according to the task execution instructions.

2. The system of claim 1, wherein, The logistics site is provided with a main road and a secondary road, the allowable running speed of the main road is greater than the running speed of the secondary road, and the dynamic resource data comprises road congestion data; The feedback layer comprises a road congestion data collection module configured to collect road congestion data of a target main road corresponding to an order task; The decision layer comprises a running scheduling module in communication connection with the road congestion data collection module, and configured to adjust a path planning strategy when it is determined according to the road congestion data that the target main road is congested, and generate task execution instructions according to the adjusted path planning strategy.

3. The system of claim 2, wherein, The planning priority of the main road is greater than the planning priority of the secondary road, and the running scheduling module adjusts the path planning strategy, comprising: determining a target secondary road closest to the transportation device; updating the planning priority of the target secondary road when it is determined according to the road congestion data collection module that the target secondary road is not congested; The running scheduling module generates task execution instructions according to the adjusted path planning strategy, comprising: planning a path according to the planning priority of the target secondary road and the planning priority of non-target main roads after the planning priority is updated, to obtain a target planning path; generating task execution instructions according to the target planning path.

4. The system of claim 2, wherein, The logistics site is further provided with a work area, the work area represents an area for performing work on goods corresponding to an order task, the work area is in communication with the main road and / or the secondary road, and the dynamic resource data further comprises storage location state data; The feedback layer further comprises: a state data collection module configured to collect the storage location state data of an original picking area in the work area, wherein the picking area represents an area for performing picking operations on goods; a warehouse area optimization module in communication connection with the state data collection module and the task layer, and configured to dynamically convert an original temporary storage area in the work area into a temporary picking area when it is determined according to the storage location state data that the number of idle storage locations of the original picking area is less than the task amount of a plurality of order tasks corresponding to the original picking area, and determine a candidate storage location for each order task from the original picking area and the temporary picking area according to a preset storage location allocation strategy, wherein the temporary storage area is used for buffering goods. The task layer is further configured to determine a target storage location of each order task according to the candidate storage location, and the operation scheduling module is further configured to adjust a path planning strategy according to the target storage location, and generate a task execution instruction according to the adjusted path planning strategy.

5. The system of claim 4, wherein, The warehouse area optimization module determines the candidate storage location of each order task from the original picking area and the temporary picking area according to a preset storage location allocation strategy, including: determining the travel time and path traffic efficiency of the transport device from the current location to each idle storage location in the original picking area and the temporary picking area; determining the candidate storage location of each order from the idle storage locations in the original picking area and the temporary picking area according to the travel time and the path traffic efficiency.

6. The system of claim 4, wherein, The warehouse area optimization module determines the candidate storage location of each order task from the original picking area and the temporary picking area according to a preset storage location allocation strategy, including: determining the task matching degree of the order task and each idle storage location in the original picking area and the temporary picking area according to the task type of the order task; determining the candidate storage location of each order from the idle storage locations in the original picking area and the temporary picking area according to the task matching degree.

7. The system of claim 4, wherein, The warehouse area optimization module is further configured to dynamically convert the idle storage locations in the work area to a temporary charging buffer area when the number of idle storage locations in the work area is greater than a first preset threshold, update the storage location information of the work area according to the temporary charging buffer area, and send the storage location information to the operation scheduling module, wherein the first preset threshold is determined according to the task amount of the corresponding multiple order tasks. The operation scheduling module is further configured to determine the candidate transport device for executing each order task according to the storage location information, and determine the target transport device for executing the order task according to the candidate transport device and the adjusted path planning strategy, and generate a task execution instruction for the target transport device.

8. The system of claim 7, wherein, The operation scheduling module determines the candidate transport device for executing each order task according to the storage location information, including: when the distance between the target storage location of the order task and the temporary charging buffer area is less than or equal to a second preset threshold according to the storage location information, determining the transport device to be charged; determining the candidate transport device from the multiple transport devices to be charged according to the remaining power of each transport device to be charged.

9. The system of claim 7, wherein, The logistics site is further provided with an original charging buffer area, and the execution layer is further configured to schedule the transport device to run to the temporary charging buffer area or the original charging buffer area when the remaining power of the transport device is equal to a third preset threshold.

10. The system of claim 4, wherein, The state data acquisition module is further configured to acquire the transport state data of all transport devices, and determine the efficiency analysis data of the transport device according to the transport state data.

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