An out-of-warehouse task scheduling method and device for a high-bay warehouse and related media

Abstract

The application discloses a high rack warehouse out-of-warehouse task scheduling method and device and related media, the method comprises the following steps: using high rack layout data to model time cost, and then marking the repeated route section and repeated time of logistics to obtain a logistics carrying coincidence degree model; using the logistics carrying coincidence degree model to match and process to obtain in-warehouse inventory information; calculating the out-of-warehouse demand according to the in-warehouse inventory information, selecting a lane and allocating a storage location to obtain lane storage allocation information; traversing the time cost of the candidate platform of the lane storage allocation information to obtain a time cost set; screening the minimum value in the time cost set to process to obtain an out-of-warehouse task scheduling instruction. The minimum value in the time cost set is screened, and the platform with the minimum time cost is combined with the lane, and the out-of-warehouse task scheduling instruction is obtained after integration, so that the out-of-warehouse task scheduling allocation result of the high rack warehouse is matched with the actual operation time consumption, and the overall out-of-warehouse efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of logistics and distribution technology, and in particular to a method, apparatus and related media for scheduling outbound tasks in a high-bay warehouse. Background Technology

[0002] With the development of smart logistics, enterprises are generally building automated warehouse management systems. High-bay warehouses improve space utilization and enhance inbound and outbound operational capabilities through three-dimensional storage. In existing technologies, the scheduling of outbound tasks in high-bay warehouses usually focuses on the rule-based allocation of order and inventory information. When generating outbound operation plans, the actual handling time differences caused by the spatial layout of aisles, platforms, and external handling equipment are not taken into account in the scheduling decision. This results in a mismatch between the allocation of outbound tasks among aisles and platforms and the actual operation time, which can easily lead to local operation delays and reduce overall outbound efficiency. Summary of the Invention

[0003] This invention provides a method, apparatus, and related medium for scheduling outbound tasks in elevated warehouses, aiming to solve the technical problem in the prior art where the scheduling and allocation results of outbound tasks in elevated warehouses do not match the actual operation time, thus reducing the overall outbound efficiency.

[0004] In a first aspect, embodiments of the present invention provide a method for scheduling outbound tasks in a high-bay warehouse, including:

[0005] Obtain the layout data of the elevated warehouse and use the layout data to model the time cost, thereby obtaining the time cost model;

[0006] Based on the time cost model, the repetitive route segments and repetitive time of the logistics corresponding to the marked logistics are integrated to obtain the logistics handling overlap model;

[0007] The logistics handling overlap model is used to match the corresponding outbound orders and in-stock batch attributes to obtain in-stock inventory information;

[0008] Based on the in-stock inventory information, the outbound demand is calculated to obtain inventory candidate information;

[0009] Based on the inventory candidate information, select lanes and allocate storage locations to obtain lane storage location allocation information;

[0010] The time cost of iterating through the candidate platforms of the roadway storage allocation information is integrated to obtain a time cost set.

[0011] The minimum value in the set of time costs is selected to combine the platform and the roadway with the minimum time cost. The combination result is then integrated with the roadway storage location allocation information to obtain the outbound task scheduling instruction.

[0012] Secondly, embodiments of the present invention provide an outbound task scheduling device for a high-bay warehouse, comprising:

[0013] The data acquisition unit is used to acquire the layout data of the elevated warehouse and use the layout data of the elevated warehouse to perform time cost modeling to obtain a time cost model.

[0014] The data calculation unit is used to integrate the repeatability route segments and repeatability time of the logistics corresponding to the time cost model to obtain the logistics handling overlap model.

[0015] The data matching unit is used to match the corresponding outbound orders and in-stock batch attributes using the logistics handling overlap model to obtain in-stock inventory information;

[0016] An inventory calculation unit is used to calculate outbound demand based on the in-stock inventory information to obtain inventory candidate information;

[0017] A storage location allocation unit is used to select lanes and allocate storage locations based on the inventory candidate information to obtain lane storage location allocation information.

[0018] The cost calculation unit is used to traverse the candidate platforms of the roadway storage allocation information and integrate them to obtain a time cost set.

[0019] The instruction output unit is used to filter the minimum value in the time cost set, so as to combine the platform and the roadway with the minimum time cost, and integrate the combination result with the roadway storage location allocation information to obtain the outbound task scheduling instruction.

[0020] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the outbound task scheduling method of the high-bay warehouse of the first aspect.

[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the outbound task scheduling method of the high-bay warehouse of the first aspect.

[0022] This invention provides a method for scheduling outbound tasks in a high-bay warehouse, including: acquiring high-bay warehouse layout data; using the high-bay warehouse layout data to perform time cost modeling to obtain a time cost model; integrating the repetition route segments and repetition times of the logistics corresponding to the time cost model to obtain a logistics handling overlap model; using the logistics handling overlap model to match the corresponding outbound orders and in-stock batch attributes to obtain in-stock inventory information; calculating outbound demand based on the in-stock inventory information to obtain inventory candidate information; selecting aisles and allocating storage locations based on the inventory candidate information to obtain aisle storage location allocation information; iterating through the time costs of candidate platforms in the aisle storage location allocation information to obtain a time cost set; filtering the minimum value in the time cost set to combine the platform with the lowest time cost with the aisle, and integrating the combination result with the aisle storage location allocation information to obtain an outbound task scheduling instruction. This invention filters out the minimum value in the time cost set and combines the platform and aisle with the minimum time cost to obtain the outbound task scheduling instruction. In this way, the outbound task scheduling and allocation result of the elevated warehouse matches the actual operation time, thereby improving the overall outbound efficiency.

[0023] This invention also provides an outbound task scheduling device, computer equipment, and storage medium for elevated warehouses, which have the same beneficial effects as described above. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating an outbound task scheduling method for a high-bay warehouse, provided as an embodiment of the present invention;

[0026] Figure 2 This is a schematic block diagram of an outbound task scheduling device for an elevated warehouse, provided as an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0029] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0031] Please see below. Figure 1 , Figure 1 The flowchart of an outbound task scheduling method for a high-bay warehouse provided in an embodiment of the present invention is shown, specifically including steps S101 to S107.

[0032] S101. Obtain the layout data of the elevated warehouse and use the layout data of the elevated warehouse to perform time cost modeling to obtain the time cost model.

[0033] S102. Based on the repeatability route segments and repeatability time of the logistics corresponding to the time cost model, integrate them to obtain the logistics handling overlap model.

[0034] S103. Use the logistics handling overlap model to match the corresponding outbound orders and in-stock batch attributes to obtain in-stock inventory information;

[0035] S104. Calculate the outbound demand based on the in-stock inventory information to obtain inventory candidate information;

[0036] S105. Select a lane and allocate storage locations based on the inventory candidate information to obtain lane storage location allocation information;

[0037] S106. The time cost of iterating through the candidate platforms of the roadway storage allocation information is integrated to obtain a time cost set.

[0038] S107. Filter the minimum value in the set of time costs to combine the platform and the roadway with the minimum time cost, and integrate the combination result with the roadway storage location allocation information to obtain the outbound task scheduling instruction.

[0039] In step S101, the layout data of the elevated warehouse is obtained, and the time cost model is performed using the layout data to obtain the time cost model. The time cost model is used for subsequent calculations on combinations of different lanes and different platforms.

[0040] In one embodiment, step S101 includes:

[0041] Obtain the layout data of the elevated warehouse; wherein, the layout data of the elevated warehouse includes information on aisles, platforms, conveyors, guide cars, and shuttle cars;

[0042] The coordinates of each logistics handling station in the elevated warehouse layout data are extracted to generate map nodes, and then integrated to obtain a directed graph object;

[0043] The distance between each node is calculated based on the directed graph object, and the transportation time cost from the starting address to the platform is converted according to the node distance to obtain the transportation cost parameters outside the alley.

[0044] Based on the parameters for configuring single-depth and double-depth storage locations corresponding to the external transportation cost parameters, the total time cost parameters are obtained.

[0045] The total time cost parameters are enumerated and combined to obtain the time cost model.

[0046] In this embodiment, the layout data of the elevated warehouse is acquired. This layout data includes at least the arrangement information of external logistics equipment such as stacker crane aisles, outbound platforms, conveyors, AGV trolleys, and circular shuttles. Simultaneously, information on outbound stations related to outbound operations, such as outbound aisle exits, robotic arm workstations, and full pallet outbound exits, can also be acquired. The elevated warehouse layout data may also include the station's number, type, accessibility, direction of travel, and planar coordinates, used to subsequently map spatial locations to handling times.

[0047] After obtaining the layout data of the elevated warehouse, the planar coordinates (X, Y) of the external logistics handling stations are extracted. Each station is written into a map node set, and the connection relationships between nodes are configured through a visual map editor, specifying the inbound and outbound connections of nodes to construct a directed graph object. For each connection in the directed graph object, the spatial distance between the two ends of the connection is calculated, and the distance data is recorded as an edge attribute. In the case of multiple connections, the distances from the starting address to the target address are accumulated to obtain the path distance from the warehouse exit to the target platform address. In the conversion of spatial distance to time cost, the handling speed parameters of the external logistics handling equipment can be combined to convert the path distance into handling time cost, resulting in the handling cost CostOut of the external logistics equipment. The CostOut can cover the time cost of different handling segments such as conveyor segments, AGV segments, and circular shuttle segments, and different speed parameters can be set according to the station type to match the conversion result with the actual handling capacity. Since the path distances from different lanes to different platforms vary, the above distance calculation and time conversion are performed on each combination of lane and platform to obtain lane-to-platform transport time data that can be used for comparison.

[0048] Meanwhile, the time cost of warehouse aisles is parameterized. For each stacker crane aisle, during the project planning or actual testing phase, the average operation time is calculated based on the stacker crane's acceleration and speed parameters, and this average operation time is written into the configuration as the stacker crane time cost (CostIn) within the aisle; for example, the time cost for a stacker crane to perform one outbound task can be set to 80 (seconds). In double-depth storage scenarios, if outbound operations involve transferring goods, the time cost within the aisle is included at 2 CostIn to reflect the additional aisle time consumed by double-depth operations.

[0049] After obtaining CostOut and CostIn, the time cost of the elevated warehouse is defined as CostT, where CostT = CostIn + CostOut. Enumeration and combination are performed on the set of lanes and platforms, calculating and storing the corresponding CostT for each, thus obtaining the time cost model. For example, in an elevated warehouse with 12 lanes and 14 exit platforms, one time cost record is configured for each lane and each platform, totaling 12 * 14 = 168 records. In specific projects, combining spatial distance and equipment speed acceleration calculations, the time cost from a lane to each exit equipment or platform can be distributed between 60 seconds and 260 seconds. These data collectively constitute a time cost model that can directly participate in subsequent scheduling calculations.

[0050] In step S102, logistics handling overlap processing is introduced based on time cost data. Operating route nodes and route segments are set for AGV routes, circular shuttle routes, and main transport lines. Nodes or areas prone to handling congestion are identified, and their corresponding route segments are marked as overlap route segments. The overlap time of these overlap route segments is calculated based on the number of tasks en route simultaneously. When the task volume exceeds the number of devices that can operate in parallel, the task proportion of each target address is calculated based on the total number of devices to obtain the effective task volume for each area. Simultaneously, a tiered congestion time calculation method is introduced for congested path segments. For example, different congestion times are assigned when the task volume falls into different ranges, and these congestion times are written into the predicted cost item for use in subsequent platform selection.

[0051] In one embodiment, step S102 includes:

[0052] Based on the time cost model, route nodes and route segment diagrams are set up respectively to obtain node diagram objects;

[0053] Start and end nodes are set for the node graph object, and blocked nodes are filtered to mark the repetition route segments and repetition time, and the repetition data is obtained by integration.

[0054] The repeatability data is set according to the number of repeating stations on the main transport line, and the repeatability time is calculated according to the number of tasks outside the warehouse. The task cost data is then integrated.

[0055] The number of tasks per path segment is calculated based on the number of tasks in transit using the task cost data, thus obtaining path task allocation data;

[0056] The congestion time of the path task allocation data is calculated and written into the prediction cost item to obtain the logistics handling overlap model.

[0057] In this embodiment, nodes and route segments for the AGV's operating route are first set around the external logistics handling map to construct a route node map, and regional path nodes are defined in this map. These regional path nodes can be divided according to the actual operating range, covering either a local area or the entire map area. For AGV route planning, start and destination addresses are set according to the handling start and end nodes, and reachable route segments between these nodes are written into the route node map. When a handling blockage node exists in the route, the route segment corresponding to that blockage node is marked as a repeatability route segment for subsequent calculation of repeatability time. For the circular shuttle, pick-up and drop-off stations are defined, and a circular route is set, with path nodes and route segments on the circular route written into the route node map. For the conveyor, repeating paths are defined according to the number of repeating stations on the main conveyor line, and the route segments corresponding to the repeating paths are written into the route node map, thus allowing the routes of different logistics equipment to be uniformly described and retrieved under the same node map object.

[0058] After obtaining the node graph object, start and end nodes are set on the node graph object, and blocked nodes in the node graph object are filtered and labeled to obtain repeatability data. The repeatability data includes at least the repeatability route segment identifier, the set of nodes covered by the repeatability route segment, the number of repeating stations or the length of repeating paths in the repeatability route segment, and the repeatability time record item associated with the repeatability route segment. For the main transport line scenario, the number of repeating stations is used as the basic quantity of station repeatability, and the station repeatability is written into the repeatability data; for AGV and circular shuttle scenarios, the route segment corresponding to the blocked node is used as the repeatability route segment, and the positional relationship of the repeatability route segment in the node graph object is written into the repeatability data for subsequent aggregation by region. In the calculation of repeatability time, the number of tasks outside the warehouse is used as the task quantity caliber. The task quantity represents the total amount of tasks of logistics handling outside the warehouse in the same period, and can be split according to the number of platform tasks at each target address under the condition of meeting the buffer. Set the number of equipment resources as the upper limit for the number of tasks. For example, in a scenario with 40 AGVs, the upper limit for the number of tasks is 40. Count the number of tasks in transit to obtain the number of tasks in transit for each platform. When the sum of the number of tasks in transit for each platform does not exceed the number of equipment resources, the number of tasks in transit for each platform is directly written into the task cost data. When the sum of the number of tasks in transit for each platform exceeds the number of equipment resources, the number of tasks is calculated based on the proportion of the number of tasks in transit for each platform, using the number of equipment resources as the total base. This yields the effective number of tasks for each platform. Furthermore, the effective number of tasks for each platform is merged according to the regional path nodes to obtain the final number of tasks for each region, which is then written into the task cost data.

[0059] Regarding the allocation of tasks across route segments, task cost data can be used to calculate the number of tasks for each repetitive route segment. Specifically, for each repetitive route segment, the set of route segments it covers is counted, and the number of regional tasks falling into that route segment is accumulated to obtain route task allocation data. The route task allocation data includes the route segment identifier, the number of tasks in the route segment, and the identifier of the region to which the route segment belongs. The congestion time of the route task allocation data is then calculated and written into the predicted cost item. The number of tasks in a route segment is mapped to congestion time according to the tiered rule: less than 10 tasks are recorded as 10 seconds, within 20 tasks as 30 seconds, and within 30 tasks as 60 seconds. When a repetitive route segment contains multiple route segments, the congestion time of each segment is summarized to obtain the regional congestion time, and the regional congestion time is written into the logistics handling overlap model as a predicted cost item. This allows the predicted cost item to be added when calculating the off-site handling time to reflect the impact of congestion time caused by overlapping routes.

[0060] In step S103, outbound orders are obtained and attribute information related to inventory batches is extracted. The in-stock inventory is filtered in combination with batch attribute matching rules, and the inventory that meets the conditions is included in the outbound candidate range according to the inventory turnover strategy. The inventory turnover batch can be set according to the first-in-first-out (FIFO) approach, and overdue batches and tolerable batches are processed separately to ensure that order demand and turnover strategy are compatible.

[0061] In one embodiment, step S103 includes:

[0062] Obtain outbound orders and parse outbound batch attributes. Associate the outbound batch attributes with the logistics handling overlap model to obtain matching task objects.

[0063] Set batch attribute matching rules for the matching task object to obtain a matching rule object; wherein, the batch attribute matching rules include exact matching, fuzzy matching, and priority matching;

[0064] Configure an outbound strategy based on the matching rule object to select at least one of the batch attributes of the database and configure the sorting relationship to obtain an outbound strategy object;

[0065] Set inventory turnover batches and configure cycle spans for the outbound strategy object to obtain in-stock inventory information.

[0066] In this embodiment, after obtaining the outbound order, the material identifier, required quantity, batch preference, and batch-related attribute fields in the order are parsed to extract the in-stock batch attributes. These in-stock batch attributes may include at least production date and other attribute fields used for turnover sorting, and may be expanded to include batch attribute fields related to project application, such as inventory type and packaging type. The in-stock batch attributes are then associated with a logistics handling overlap model, binding the attribute requirements on the order side with information such as the target address and work area corresponding to the off-site handling heat, resulting in matching task objects. This ensures that subsequent screening and sorting of inventory candidates maintains a consistent data caliber with the off-site handling status.

[0067] After obtaining the matching task object, batch attribute matching rules are set for it, generating a matching rule object. The batch attribute matching rules include at least three types: exact matching, fuzzy matching, and priority matching. Under exact matching, the system only allows the selection of inventory batches that match the parameters passed in the outbound order into the candidate range. Under fuzzy matching, when the exact matching inventory is insufficient, the system allows the selection of inventory batches from lower-ranked batches and marks the selection result as the fuzzy matching source. Under priority matching, the system first selects the specified matching inventory batch; when the specified batch is insufficient, it then replenishes the demand from other matching inventory batches, recording the priority source and the replenishment source separately.

[0068] After the matching rule object is generated, the outbound strategy is configured to obtain the outbound strategy object. Depending on the specific project, one or more attributes are selected from the inventory batch attributes as sorting fields, and ascending or descending order is configured to construct batch sorting rules. In scenarios where the production date is the primary sorting field, batches are sorted according to the first-in, first-out (FIFO) principle. Considering the flexibility of FIFO in engineering applications, one week can be set as an inventory turnover batch, and the cycle span can be set as a configurable parameter. When the production date of an inventory batch exceeds a preset overdue limit, the batch is included in the mandatory priority outbound range, with allocation starting from the earliest batch. When the inventory batch does not exceed the overdue limit, a tolerance level can be set, such as half a month, allowing batches within this tolerance range to enter the outbound queue to participate in equipment matching and subsequent cost calculation processes.

[0069] After configuring the outbound strategy object, set the inventory turnover batches and configure the cycle span for the outbound strategy object to generate in-stock inventory information. Multiple batch attribute configuration rules can be preset, such as configuring strict matching or priority matching conditions for attribute fields like inventory type and packaging type. These attribute conditions, along with the aforementioned batch sorting rules, are then applied to the inventory data filtering process. Ultimately, the output in-stock inventory information includes at least the set of inventory batches that meet the outbound order requirements, the available quantity of each batch, and its source marker under the matching rules. It can also retain the target address or work area marker associated with the logistics handling overlap model, providing input data for subsequent steps such as calculating candidate inventory information, allocating storage space in lanes, and traversing time costs.

[0070] In step S104, in order to reduce the number of handling operations, the allocatability of the picking area outside the warehouse and the inventory in transit can be determined. When there is inventory that can be directly allocated, that part of the inventory is used first, and the remaining demand is then entered into the subsequent time cost calculation process to obtain inventory candidate information.

[0071] In step S105, the inventory candidate information is used as input, and the aisle and storage location allocation is performed in combination with the high-bay warehouse storage location form. The dual-depth storage locations are distinguished according to the operation characteristics of shallow and deep storage locations. Under the premise of meeting the outbound requirements, the selection of executable storage locations is completed, and the aisle storage location allocation information is output.

[0072] In one embodiment, step S105 includes:

[0073] The inventory candidate information is parsed to obtain a storage location type object;

[0074] The cargo location type objects are initially selected within the aisle according to the allocation priority and the loose pallet priority rule to obtain the initial allocation objects;

[0075] In a scenario where loose pallet outbound is permitted, the initial allocation object is subjected to loose pallet optimization allocation and storage location resource clearing and update to obtain loose pallet allocation objects.

[0076] The loose pallet allocation objects are checked according to the cases where shallow storage locations and deep storage locations meet the outbound requirements, and shallow storage location priority allocation parameters and deep storage location priority allocation parameters are obtained respectively.

[0077] By combining the shallow storage location priority allocation parameters and the deep storage location priority allocation parameters, the roadway storage location allocation information is obtained.

[0078] In this embodiment, the inventory candidate information is parsed to extract fields such as aisle identifier, location depth type, location occupancy status, pallet type, and material and batch attributes corresponding to each candidate inventory, generating location type objects. For scenarios using double-depth locations in automated high-bay warehouses, the location closer to the stacker crane is marked as a shallow location, and the corresponding location inside it is marked as a deep location, so as to distinguish the differences in accessibility and scheduling order caused by different depth locations in subsequent verification. After the location type objects are generated, the locations in the aisles are initially selected according to the allocation priority and combined with the loose pallet priority rule to obtain the initial allocation objects. Specifically, after being allocated to a certain aisle, it is first determined whether there is loose pallet inventory in the aisle and whether the current outbound demand is insufficient to meet the conditions for full pallet outbound; in business scenarios that allow loose pallet outbound, loose pallet inventory is included in the priority allocation scope, and inventory that can meet the outbound demand is selected first from loose pallet locations to improve the availability of locations in the aisles and reduce the space occupation problem caused by long-term loose pallet retention. In scenarios where loose pallet outbound shipments are permitted, the initial allocation targets undergo loose pallet optimization and storage location resource updates to obtain the loose pallet allocation targets. During this process, the selected loose pallet storage locations are subject to demand deduction, inventory locking, and outbound occupancy marking. After the loose pallets are allocated, the corresponding storage locations are cleared to ensure that the storage location resources can be reused for subsequent inbound or relocation. If the loose pallet allocation is insufficient to cover the outbound demand, the remaining demand is handled by the subsequent shallow and deep storage location verification process to ensure the continuity and executability of the allocation results within the same aisle.

[0079] After the loose pallet allocation objects are generated, further verification processing can be performed based on various scenarios where shallow and deep storage locations meet the outbound requirements, resulting in shallow storage location priority allocation parameters and deep storage location priority allocation parameters. During verification, the scenario where shallow storage locations meet the outbound requirements is processed first, and the shallow storage location priority allocation parameters are determined in the following order: When the shallow storage location meets the outbound requirements and the deep storage location stores different materials, the shallow storage location is allocated first; for example, if the requirement is for material A, the shallow storage location is for material A, and the deep storage location is for material B, this combination is placed with a higher priority to reduce the handling of unrelated materials. If the shallow storage location meets the outbound requirements and the deep storage location stores different materials but from different batches, this combination is processed in a later order. If the shallow storage location meets the outbound requirements and the deep storage location is empty, the shallow storage location is selected first to reduce subsequent transfer opportunities. If the shallow storage location meets the outbound requirements and the deep storage location contains the same product and batch, this combination is placed in a later order to avoid unnecessary disturbance to the deep storage inventory of the same product and batch. When the shallow storage location priority allocation parameters cannot fully cover the demand or when inventory needs to be retrieved from deep storage locations, the deep storage location priority allocation parameters are then calculated. Specifically, when a deep storage location meets the outbound demand and depth 1 is already allocated and awaiting outbound, this deep storage location is selected first, making it possible to schedule the outbound operations of shallow storage locations and outbound operations of deep storage locations consecutively in time, thereby reducing the additional operational cost required for retrieval from deep storage locations. When a deep storage location meets the outbound demand and shallow storage locations are already stored, this situation is placed in a lower priority, so that the corresponding transfer or sequence adjustment arrangement is triggered only when it is necessary to select a deep storage location. Finally, the shallow storage location priority allocation parameters and the deep storage location priority allocation parameters are merged to output unified lane storage location allocation information. The lane storage location allocation information includes at least lane identifier, storage location identifier, correspondence between shallow and deep storage locations, material and batch identifiers of the selected inventory, and awaiting outbound status markers, which are used for subsequent steps to traverse the time cost of candidate platforms and generate outbound task instructions.

[0080] In one embodiment, after step S105, the method further includes:

[0081] Obtain the set of platforms to be allocated corresponding to the roadway storage allocation information;

[0082] Determine whether the same loading order in the set of platforms to be assigned has been assigned to a platform, and based on the determination result, exclude or lock the assigned platforms to obtain the loading platform relationship information.

[0083] Determine whether the loading platform relationship information belongs to the same customer, and mark the platforms belonging to the same customer as candidate platforms to obtain candidate platform information;

[0084] Based on the candidate platform information, the selected platform will be set as the outbound shipping address, and the platform allocation rule result will be updated.

[0085] In this embodiment, a set of waiting-to-be-allocated platforms corresponding to the roadway storage location allocation information is obtained. This set of platforms can be obtained by filtering the platform list in the elevated warehouse layout data and can carry platform availability status, occupancy status, and business attribute fields associated with loading operations for subsequent item-by-item judgment. After obtaining the set of waiting-to-be-allocated platforms, the availability of platforms is determined, and unavailable platforms are directly removed. For available platforms, it is further determined whether the platform is empty to exclude platforms that are currently occupied or occupied by operations, so that the set of waiting-to-be-allocated platforms remains a candidate basic set that can participate in scheduling calculations. Based on the above, using the loading order as the primary key, a consistency check is performed on the set of platforms to be assigned to determine whether the same loading order has already been assigned to a platform: if the same loading order already has an assigned platform, the system locks the assigned platform and excludes other platforms from the candidate range of the loading order to maintain the continuity of loading operations for the same loading order on the same platform; if the same loading order has not yet been assigned a platform, platforms that meet the availability and idle conditions are retained for subsequent screening processes. The results of the above locking or exclusion processing are recorded as loading platform relationship information, which includes at least the loading order identifier, the locked platform identifier, and the set of excluded platform identifiers for subsequent tracking and verification.

[0086] After obtaining the loading platform relationship information, the next step is to determine whether the candidate platforms belong to the same customer. Specifically, customer identifiers are extracted from the loading order or outbound order, and the available platforms in the loading platform relationship information are matched according to the customer identifiers. When a platform with the same customer identifier as the current loading order exists, it is marked as a candidate platform, and candidate platform information is obtained. When no platform with the same customer exists, platforms that have met the availability, idleness, and loading order consistency checks can be retained as candidate platforms to ensure that subsequent time cost calculations still have executable input. The candidate platform information includes at least a set of candidate platform identifiers and their corresponding labeling status, which is used to proceed to the subsequent steps for time cost traversal of candidate platforms.

[0087] Based on the candidate platform information, after the selected platform is selected, it will be set as the outbound delivery address, and the platform status will be updated to "in use" to reflect that the platform has been occupied and to prevent concurrent scheduling from repeatedly allocating it. At the same time, the update result will be written into the platform allocation rule result, which may include the selected platform identifier, the corresponding loading and delivery note identifier, the customer identifier, and the platform status update record. This will be used together with the lane storage location allocation information to participate in the generation and issuance of subsequent outbound task scheduling instructions.

[0088] In step S106, after filtering based on conditions such as platform availability, platform idle status, loading and unloading order association, and customer consistency, a candidate platform set is obtained. The storage location allocation information for each lane is traversed through the candidate platforms, and the time consumption of the tasks already assigned in the lane is added to the transportation time cost from the lane to the platform. The total time consumption for each candidate platform is calculated and summarized to obtain a time cost set.

[0089] In one embodiment, step S106 includes:

[0090] Based on the roadway storage allocation information, the spatial distance from the roadway to the candidate platform is traversed and converted into time cost to obtain traversal calculation data;

[0091] The actual movement cost from the starting point to the target node is calculated using the traversal calculation data, and the number of tasks that have been allocated and distributed to each roadway is counted to obtain the allocated time cost data.

[0092] The estimated cost from the target node to the destination is calculated using the allocated time cost data to obtain the estimated cost data;

[0093] Based on the estimated cost data, the off-site logistics handling cost from each lane exit to the candidate platform is calculated to obtain the platform traversal cost data.

[0094] The estimated cost data and the platform traversal cost data are allocated to the lowest cost roadway in ascending order, and the comprehensive roadway cost data is updated to obtain the roadway comprehensive cost data.

[0095] The total cost value of each platform is recorded sequentially using the comprehensive cost data of the tunnel, and then integrated to obtain a time cost set.

[0096] In this embodiment, given the information on the allocation of storage locations in the storage lanes and the information on candidate platforms, the total outbound time for each candidate platform is evaluated one by one, and the time cost set is obtained by summarizing the results. The storage lanes are regarded as parallel operation units, and the platforms are regarded as outbound delivery addresses. Under the same candidate platform, the in-warehouse operation time and the out-of-warehouse handling time for each storage lane are uniformly converted, and a comparable total cost value is output at the platform level.

[0097] Specifically, based on the aisle storage location allocation information, the spatial distance from each aisle to each candidate platform is traversed. This spatial distance is then converted into off-site handling time according to the logistics handling speed, resulting in traversal calculation data. This traversal calculation data can establish a two-dimensional mapping between aisle identifiers and platform identifiers, recording the handling time from the aisle exit to the candidate platform target address, which is used for subsequent calculation of off-site logistics handling costs. Simultaneously, the number of stacker crane tasks currently assigned and issued to each aisle is read, and the time required for one task to be executed by the aisle is used as the aisle operation baseline parameter to obtain the allocated time cost data for each aisle. For example, if an aisle has been assigned 2 tasks, and the time to execute one task is set to 80 seconds, then the allocated time cost for that aisle can be recorded as Gsc01 = 2 * 80 = 160, where G represents the actual movement cost (known) from the starting point to the current node, used to characterize the accumulated operation time of that aisle.

[0098] After obtaining the allocated time cost data, a preset algorithm based on task categories is used to evaluate candidate platforms, calculating the total time cost F for each candidate platform, where F = G + H, and H is the estimated cost from the current node to the destination. To ensure that H is calculable and consistent with actual operations, H is broken down into three components: off-site handling cost H1 related to the candidate platform, lane operation cost H2 related to outbound operations within the lane, and repetition prediction cost H3 related to overlapping off-site paths. First, the H1 cost is used to obtain a set of available and calculable candidate platforms according to the platform allocation rules. The status of each candidate platform is iteratively obtained, and idle platforms are added to the allocable list. When a platform is selected as an outbound shipping address, its status is updated to "used". Then, according to the lane platform cost model, the off-site handling time from the lane exit to the platform in the traversed calculation data is written into the H1 cost, thereby obtaining the off-site logistics handling cost from each lane to the candidate platform. Secondly, the H2 cost is used to characterize the new operation time of the outbound tasks to be assigned under the candidate platform in the aisle. Outbound tasks are assigned to the aisles one by one according to the allocation results of the inventory candidates, and a segment of aisle operation time is added to each allocation; for example, H2 can be 80 seconds as the aisle time cost for one outbound operation. After a task is assigned to an aisle, the H2 is used as the newly incurred cost for that aisle, and the G value of that aisle is updated to reflect the real-time time changes of the aisle task queue. Thirdly, the H3 cost is used to characterize the congestion time caused by the repetition of routes outside the warehouse. The logistics handling overlap model is called, and when there are multiple overlapping routes, the corresponding time cost is obtained according to the number of overlapping tasks in each route. The times of each overlapping route are then summarized to obtain the H3 cost of the candidate platform.

[0099] Based on the calculable cost items mentioned above, the allocation calculation of lanes and tasks is performed for the same candidate platform to obtain the comprehensive cost data of the lanes under that candidate platform. First, initial evaluation values ​​for each lane are constructed based on G and H1. Then, the lane with the lowest cost is selected in ascending order of cost, and the outbound tasks to be allocated are assigned to that lane. H2 is then added to update the G value of that lane. This allocation process is then repeated cyclically until the allocated inventory meets the order demand. During this process, the comprehensive time consumption status of each lane under the current candidate platform is continuously maintained, so that subsequent comparisons between platforms reflect the actual differences in the progress of parallel operations. After task allocation is completed, H3 is incorporated into the comprehensive cost data of the lanes as the repetition prediction cost, ensuring that the evaluation results for the same candidate platform simultaneously cover three sources of time consumption: in-lane operations, out-of-lane handling, and congestion prediction.

[0100] To aggregate the combined time of parallel lanes into a single comparable total cost value for a candidate platform, the total cost F1 is calculated for that candidate platform, and a Max aggregation method is used to characterize the overall completion time under parallel execution conditions. Specifically, for each lane, the G cost, the H1 cost from the lane to the platform, the time cost of new operations within the lane H2, and the repetition prediction cost H3 are calculated separately. The maximum sum of the above costs for each lane is taken as the total cost of the candidate platform, i.e., F1 = Max(G cost of each lane + H1 cost from each lane to the platform + time cost within the lane H2 + repetition prediction cost H3). The above calculation process is repeated for each candidate platform to obtain F1, F2...Fn in sequence. The total cost value corresponding to each candidate platform, along with its platform identifier, the set of lane identifiers involved in the calculation, and the assigned lane task status, are recorded together to obtain a time cost set, providing input for subsequent steps to screen the platform and lane combination with the minimum time cost.

[0101] In step S107, the minimum value is filtered from the time cost set, and the platform and aisle combination with the minimum total time is selected. The combination is then merged with the aisle storage location allocation information and the outbound task scheduling instruction is output. At the same time as the instruction is output, the usage status of the selected platform can be updated, and the outbound task is sent to the control terminal of the stacker crane and the external handling equipment to execute the handling operation from the aisle outbound outlet to the target platform.

[0102] In summary, this application incorporates the operation time of stacker cranes within the aisles and the handling time of conveyors, AGVs, and circular shuttles outside the aisles into the time cost model. It also overlays the congestion prediction cost caused by path overlap at the platform level, transforming the allocation of aisles and platforms for outbound tasks from relying solely on static rules to a comprehensive optimal choice based on actual operation time. Simultaneously, by combining inventory turnover and batch matching strategies, it achieves load balancing of tasks across aisles while satisfying the outbound strategy, reducing the probability of uneven equipment workload and local congestion, shortening outbound completion time, and improving overall delivery efficiency and scheduling stability.

[0103] Combination Figure 2 As shown, Figure 2 This is a schematic block diagram of an outbound task scheduling device for a high-bay warehouse provided in an embodiment of the present invention. The outbound task scheduling device 200 for the high-bay warehouse includes:

[0104] The data acquisition unit 201 is used to acquire the layout data of the elevated warehouse and use the layout data of the elevated warehouse to perform time cost modeling to obtain a time cost model.

[0105] Data calculation unit 202 is used to integrate the repeatability route segments and repeatability time of the logistics corresponding to the time cost model to obtain the logistics handling overlap model;

[0106] Data matching unit 203 is used to match the corresponding outbound orders and in-stock batch attributes using the logistics handling overlap model to obtain in-stock inventory information;

[0107] The inventory calculation unit 204 is used to calculate the outbound demand based on the in-stock inventory information to obtain inventory candidate information.

[0108] Storage location allocation unit 205 is used to select lanes and allocate storage locations according to the inventory candidate information to obtain lane storage location allocation information;

[0109] Cost calculation unit 206 is used to traverse the candidate platforms of the roadway storage allocation information and integrate them to obtain a time cost set;

[0110] The instruction output unit 207 is used to filter the minimum value in the time cost set, so as to combine the platform and the roadway with the minimum time cost, and integrate the combination result with the roadway storage location allocation information to obtain the outbound task scheduling instruction.

[0111] In this embodiment, the data acquisition unit 201 acquires the high-bay warehouse layout data and uses the high-bay warehouse layout data to perform time cost modeling to obtain a time cost model; the data calculation unit 202 integrates the repeatability route segments and repeatability time of the logistics corresponding to the time cost model to obtain a logistics handling overlap model; the data matching unit 203 uses the logistics handling overlap model to match the corresponding outbound orders and in-stock batch attributes to obtain in-stock inventory information; the inventory calculation unit 204 calculates the outbound demand based on the in-stock inventory information to obtain inventory candidate information; the storage location allocation unit 205 selects aisles and allocates storage locations based on the inventory candidate information to obtain aisle storage location allocation information; the cost calculation unit 206 iterates through the time costs of candidate platforms in the aisle storage location allocation information to obtain a time cost set; the instruction output unit 207 filters the minimum value in the time cost set to combine the platform with the aisle with the minimum time cost, and integrates the combination result with the aisle storage location allocation information to obtain an outbound task scheduling instruction.

[0112] In one embodiment, the data acquisition unit 201 is specifically used for:

[0113] Obtain the layout data of the elevated warehouse; wherein, the layout data of the elevated warehouse includes information on aisles, platforms, conveyors, guide cars, and shuttle cars;

[0114] The coordinates of each logistics handling station in the elevated warehouse layout data are extracted to generate map nodes, and then integrated to obtain a directed graph object;

[0115] The distance between each node is calculated based on the directed graph object, and the transportation time cost from the starting address to the platform is converted according to the node distance to obtain the transportation cost parameters outside the alley.

[0116] Based on the parameters for configuring single-depth and double-depth storage locations corresponding to the external transportation cost parameters, the total time cost parameters are obtained.

[0117] The total time cost parameters are enumerated and combined to obtain the time cost model.

[0118] In one embodiment, the data calculation unit 202 is specifically used for:

[0119] Based on the time cost model, route nodes and route segment diagrams are set up respectively to obtain node diagram objects;

[0120] Start and end nodes are set for the node graph object, and blocked nodes are filtered to mark the repetition route segments and repetition time, and the repetition data is obtained by integration.

[0121] The repeatability data is set according to the number of repeating stations on the main transport line, and the repeatability time is calculated according to the number of tasks outside the warehouse. The task cost data is then integrated.

[0122] The number of tasks per path segment is calculated based on the number of tasks in transit using the task cost data, thus obtaining path task allocation data;

[0123] The congestion time of the path task allocation data is calculated and written into the prediction cost item to obtain the logistics handling overlap model.

[0124] In one embodiment, the data matching unit 203 is specifically used for:

[0125] Obtain outbound orders and parse outbound batch attributes. Associate the outbound batch attributes with the logistics handling overlap model to obtain matching task objects.

[0126] Set batch attribute matching rules for the matching task object to obtain a matching rule object; wherein, the batch attribute matching rules include exact matching, fuzzy matching, and priority matching;

[0127] Configure an outbound strategy based on the matching rule object to select at least one of the batch attributes of the database and configure the sorting relationship to obtain an outbound strategy object;

[0128] Set inventory turnover batches and configure cycle spans for the outbound strategy object to obtain in-stock inventory information.

[0129] In one embodiment, the storage space allocation unit 205 is specifically used for:

[0130] The inventory candidate information is parsed to obtain a storage location type object;

[0131] The cargo location type objects are initially selected within the aisle according to the allocation priority and the loose pallet priority rule to obtain the initial allocation objects;

[0132] In a scenario where loose pallet outbound is permitted, the initial allocation object is subjected to loose pallet optimization allocation and storage location resource clearing and update to obtain loose pallet allocation objects.

[0133] The loose pallet allocation objects are checked according to the cases where shallow storage locations and deep storage locations meet the outbound requirements, and shallow storage location priority allocation parameters and deep storage location priority allocation parameters are obtained respectively.

[0134] By combining the shallow storage location priority allocation parameters and the deep storage location priority allocation parameters, the roadway storage location allocation information is obtained.

[0135] In one embodiment, the cost calculation unit 206 is specifically used for:

[0136] Based on the roadway storage allocation information, the spatial distance from the roadway to the candidate platform is traversed and converted into time cost to obtain traversal calculation data;

[0137] The actual movement cost from the starting point to the target node is calculated using the traversal calculation data, and the number of tasks that have been allocated and distributed to each roadway is counted to obtain the allocated time cost data.

[0138] The estimated cost from the target node to the destination is calculated using the allocated time cost data to obtain the estimated cost data;

[0139] Based on the estimated cost data, the off-site logistics handling cost from each lane exit to the candidate platform is calculated to obtain the platform traversal cost data.

[0140] The estimated cost data and the platform traversal cost data are allocated to the lowest cost roadway in ascending order, and the comprehensive roadway cost data is updated to obtain the roadway comprehensive cost data.

[0141] The total cost value of each platform is recorded sequentially using the comprehensive cost data of the tunnel, and then integrated to obtain a time cost set.

[0142] In one embodiment, the outbound task scheduling device 200 of the elevated warehouse is further used for:

[0143] Obtain the set of platforms to be allocated corresponding to the roadway storage allocation information;

[0144] Determine whether the same loading order in the set of platforms to be assigned has been assigned to a platform, and based on the determination result, exclude or lock the assigned platforms to obtain the loading platform relationship information.

[0145] Determine whether the loading platform relationship information belongs to the same customer, and mark the platforms belonging to the same customer as candidate platforms to obtain candidate platform information;

[0146] Based on the candidate platform information, the selected platform will be set as the outbound shipping address, and the platform allocation rule result will be updated.

[0147] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0148] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed, can perform the steps provided in the above embodiments. The storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0149] This invention also provides a computer device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the computer device may also include various network interfaces, a power supply, a graphics card, etc., to utilize the graphics card's performance to operate the model, such as for inference and training.

[0150] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0151] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for scheduling outbound tasks in an elevated warehouse, characterized in that, include: Obtain the layout data of the elevated warehouse and use the layout data to model the time cost, thereby obtaining the time cost model; Based on the time cost model, the repetitive route segments and repetitive time of the logistics corresponding to the marked logistics are integrated to obtain the logistics handling overlap model; The logistics handling overlap model is used to match the corresponding outbound orders and in-stock batch attributes to obtain in-stock inventory information; Based on the in-stock inventory information, the outbound demand is calculated to obtain inventory candidate information; Based on the inventory candidate information, select lanes and allocate storage locations to obtain lane storage location allocation information; The time cost of iterating through the candidate platforms of the roadway storage allocation information is integrated to obtain a time cost set. The minimum value in the set of time costs is selected to combine the platform and the lane with the minimum time cost, and the combination result is integrated with the lane storage location allocation information to obtain the outbound task scheduling instruction. The process of acquiring elevated warehouse layout data and using this data to model time costs to obtain a time cost model includes: acquiring elevated warehouse layout data, which includes information on aisles, platforms, conveyors, guided vehicles, and shuttle vehicles; extracting the coordinates of each logistics handling station in the elevated warehouse layout data to generate map nodes, and integrating them to obtain a directed graph object; calculating the distance between each node based on the directed graph object, and converting the handling time cost from the starting address to the platform according to the node distance to obtain the handling cost parameter outside the aisle; configuring parameters for single-depth and double-depth storage locations according to the handling cost parameter outside the aisle to obtain the total time cost parameter; and enumerating and combining the total time cost parameter to obtain the time cost model.

2. The method for scheduling outbound tasks in an elevated warehouse according to claim 1, characterized in that, The process of integrating the repetitive route segments and repetitive times of the logistics according to the time cost model to obtain the logistics handling overlap model includes: Based on the time cost model, route nodes and route segment diagrams are set up respectively to obtain node diagram objects; Start and end nodes are set for the node graph object, and blocked nodes are filtered to mark the repetition route segments and repetition time, and the repetition data is obtained by integration. The repeatability data is set according to the number of repeating stations on the main transport line, and the repeatability time is calculated according to the number of tasks outside the warehouse. The task cost data is then integrated. The number of tasks per path segment is calculated based on the number of tasks in transit using the task cost data, thus obtaining path task allocation data; The congestion time of the path task allocation data is calculated and written into the prediction cost item to obtain the logistics handling overlap model.

3. The method for scheduling outbound tasks in an elevated warehouse according to claim 1, characterized in that, The process of matching corresponding outbound orders and in-stock batch attributes using the logistics handling overlap model to obtain in-stock inventory information includes: Obtain outbound orders and parse outbound batch attributes. Associate the outbound batch attributes with the logistics handling overlap model to obtain matching task objects. Set batch attribute matching rules for the matching task object to obtain a matching rule object; wherein, the batch attribute matching rules include exact matching, fuzzy matching, and priority matching; Configure an outbound strategy based on the matching rule object to select at least one of the in-stock batch attributes and configure the sorting relationship to obtain an outbound strategy object; Set inventory turnover batches and configure cycle spans for the outbound strategy object to obtain in-stock inventory information.

4. The method for scheduling outbound tasks in an elevated warehouse according to claim 1, characterized in that, The step of selecting lanes and allocating storage locations based on the inventory candidate information to obtain lane storage location allocation information includes: The inventory candidate information is parsed to obtain a storage location type object; The cargo location type objects are initially selected within the aisle according to the allocation priority and the loose pallet priority rule to obtain the initial allocation objects; In a scenario where loose pallet outbound is permitted, the initial allocation object is subjected to loose pallet optimization allocation and storage location resource clearing and update to obtain loose pallet allocation objects. The allocation objects of the loose pallets are checked according to the situation that the shallow storage location and the deep storage location meet the outbound requirements, and the shallow storage location priority allocation parameters and the deep storage location priority allocation parameters are obtained respectively. By combining the shallow storage location priority allocation parameters and the deep storage location priority allocation parameters, the roadway storage location allocation information is obtained.

5. The method for scheduling outbound tasks in an elevated warehouse according to claim 1, characterized in that, After selecting lanes and allocating storage locations based on the inventory candidate information to obtain lane storage location allocation information, the process further includes: Obtain the set of platforms to be allocated corresponding to the roadway storage allocation information; Determine whether the same loading order in the set of platforms to be assigned has been assigned to a platform, and based on the determination result, exclude or lock the assigned platforms to obtain the loading platform relationship information. Determine whether the loading platform relationship information belongs to the same customer, and mark the platforms belonging to the same customer as candidate platforms to obtain candidate platform information; Based on the candidate platform information, the selected platform will be set as the outbound shipping address, and the platform allocation rule result will be updated.

6. The method for scheduling outbound tasks in an elevated warehouse according to claim 1, characterized in that, The time cost of traversing the candidate platforms of the roadway storage allocation information is integrated to obtain a time cost set, including: Based on the roadway storage allocation information, the spatial distance from the roadway to the candidate platform is traversed and converted into time cost to obtain traversal calculation data; The actual movement cost from the starting point to the target node is calculated using the traversal calculation data, and the number of tasks that have been allocated and distributed to each roadway is counted to obtain the allocated time cost data. The estimated cost from the target node to the destination is calculated using the allocated time cost data to obtain the estimated cost data; Based on the estimated cost data, the off-site logistics handling cost from each lane exit to the candidate platform is calculated to obtain the platform traversal cost data. The estimated cost data and the platform traversal cost data are allocated to the lowest cost roadway in ascending order, and the comprehensive roadway cost data is updated to obtain the roadway comprehensive cost data. The total cost value of each platform is recorded sequentially using the comprehensive cost data of the tunnel, and then integrated to obtain a time cost set.

7. A dispatching device for outbound tasks in an elevated warehouse, characterized in that, include: The data acquisition unit is used to acquire the layout data of the elevated warehouse and use the layout data of the elevated warehouse to perform time cost modeling to obtain a time cost model. The data calculation unit is used to integrate the repeatability route segments and repeatability time of the logistics corresponding to the time cost model to obtain the logistics handling overlap model. The data matching unit is used to match the corresponding outbound orders and in-stock batch attributes using the logistics handling overlap model to obtain in-stock inventory information; An inventory calculation unit is used to calculate outbound demand based on the in-stock inventory information to obtain inventory candidate information; A storage location allocation unit is used to select lanes and allocate storage locations based on the inventory candidate information to obtain lane storage location allocation information. The cost calculation unit is used to traverse the candidate platforms of the roadway storage allocation information and integrate them to obtain a time cost set. The instruction output unit is used to filter the minimum value in the time cost set, so as to combine the platform and the roadway with the minimum time cost, and integrate the combination result with the roadway storage location allocation information to obtain the outbound task scheduling instruction. The data acquisition unit is specifically used to acquire the layout data of the elevated warehouse; wherein, the layout data of the elevated warehouse includes information on aisles, platforms, conveyors, guided vehicles, and shuttle vehicles; extracting the coordinates of each logistics handling station in the layout data of the elevated warehouse to generate map nodes, and integrating them to obtain a directed graph object; calculating the distance between each node based on the directed graph object, and converting the handling time cost from the starting address to the platform according to the node distance, to obtain the handling cost parameter outside the aisle; configuring the parameters of single-depth storage location and double-depth storage location according to the handling cost parameter outside the aisle, to obtain the total time cost parameter; and enumerating and combining the total time cost parameter to obtain the time cost model.

8. A computer device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the outbound task scheduling method for a high-bay warehouse as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the outbound task scheduling method for the high-bay warehouse as described in any one of claims 1 to 6.

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