Space-based stereoscopic warehouse design method and device, and electronic equipment

Abstract

The disclosure provides a space generation-based stereoscopic warehouse design method and device and electronic equipment, relates to the technical field of computers, and in particular to the technical field of space intelligence, space modeling, warehouse design and the like. The method comprises the following steps: in response to a warehouse layout operation of a user, determining a two-dimensional semantic layout, the two-dimensional semantic layout comprising a plurality of warehouse objects and adsorption information of the warehouse objects; based on the two-dimensional semantic layout, converting each warehouse object into a corresponding parameterized model by using a predefined three-dimensional conversion protocol, at least one parameterized model being included in a model set; determining an upstream and downstream topological relationship between the parameterized models according to the adsorption information and business attributes of the warehouse objects; and determining a target stereoscopic warehouse based on the plurality of parameterized models and the upstream and downstream topological relationship.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to the fields of spatial intelligence, spatial modeling, and warehouse design. Background Technology

[0002] Automated storage and retrieval systems (AS / RS) are the core carriers of modern logistics warehousing systems, relying on equipment such as racks, stacker cranes, conveyor lines, and elevators to achieve high-density storage and automated transfer of goods. Currently, AS / RS design generally uses 3D modeling tools to complete the layout construction, and then separately configures the simulation links and storage location business attributes, resulting in weak data interoperability between the design and simulation stages. Summary of the Invention

[0003] This disclosure provides a spatially generated automated warehouse design method, apparatus, and electronic equipment to solve or alleviate one or more technical problems in the prior art.

[0004] Firstly, this disclosure provides a spatially generated automated warehouse design method, including: In response to the user's warehouse layout operation, a two-dimensional semantic layout is determined, which includes multiple warehouse objects and the snapping information of the warehouse objects; Based on this two-dimensional semantic layout, using a predefined three-dimensional transformation protocol, each warehouse object is converted into a corresponding parameterized model, and at least one parameterized model is included in the model set. Based on the adsorption information and business attributes of warehouse objects, determine the upstream and downstream topological relationships between parameterized models; The target automated warehouse is determined based on multiple parametric models and upstream and downstream topological relationships.

[0005] Secondly, this disclosure provides a space-generated automated warehouse design device, comprising: The layout determination module is used to determine a two-dimensional semantic layout in response to the user's warehouse layout operation. The two-dimensional semantic layout includes multiple warehouse objects and the snapping information of the warehouse objects. The model conversion module is used to convert each repository object into a corresponding parameterized model based on the two-dimensional semantic layout and using a predefined three-dimensional conversion protocol. At least one parameterized model is included in the model collection. The topology determination module is used to determine the upstream and downstream topological relationships between parameterized models based on the adsorption information and business attributes of warehouse objects. The warehouse determination module is used to determine the target automated warehouse based on multiple parametric models and upstream and downstream topological relationships.

[0006] Thirdly, an electronic device is provided, comprising: At least one processor; and The memory is communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform any of the methods described in the present disclosure.

[0007] Fourthly, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform any of the methods according to embodiments of the present disclosure.

[0008] Fifthly, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the methods according to embodiments of the present disclosure.

[0009] This disclosure relies on two-dimensional semantic layout, predefined three-dimensional conversion protocol and adsorption information to automatically deduce the target automated warehouse. It transforms manual operations such as parametric model placement, position calibration, upstream and downstream topology relationships, and attribute configuration into automatic system execution, eliminating the need for repeated manual adjustments and verifications in complex three-dimensional space. At the same time, it reduces human errors such as position misalignment, topology misconnection, and parameter mismatch caused by manual operations, thereby improving the efficiency and accuracy of automated warehouse design.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments provided according to this disclosure and should not be construed as limiting the scope of this disclosure.

[0012] Figure 1 This is a flowchart illustrating the implementation of a space-based automated warehouse design method according to an embodiment of the present disclosure. Figure 2 This is a schematic diagram illustrating the design and simulation process of an automated warehouse according to an embodiment of the present disclosure; Figure 3 This is a flowchart illustrating the process of determining a parameterized model according to an embodiment of the present disclosure; Figure 4 This is a structural schematic diagram of an automated warehouse design device 400 according to an embodiment of the present disclosure; Figure 5 This is a structural schematic diagram of an automated warehouse design device 500 according to an embodiment of the present disclosure; Figure 6 This is a structural block diagram of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0013] The present disclosure will now be described in further detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0014] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0015] In the current field of automated storage and retrieval system (AS / RS) design, existing technologies utilize 3D modeling to design AS / RS. For example, designers typically place equipment models such as shelves, stacker cranes, conveyor lines, platforms, and Automated Guided Vehicles (AGVs) directly in a 3D scene. By adjusting the spatial position, dimensional parameters, and orientation of these equipment models, the overall layout of the AS / RS is completed. Then, the designers manually configure the connections and automated operation processes between different equipment models in a simulation tool.

[0016] The advantage of this design is that it can intuitively present the effect of a three-dimensional warehouse. However, the equipment model itself is more of a visual and spatial object, and the business semantics of the equipment model need to be understood and configured manually later.

[0017] Specifically, the core deficiency of existing 3D model design solutions lies in the fact that the editing logic of equipment models always revolves around geometric appearance. Although equipment objects such as shelves, tracks, and conveyor lines can be freely placed and combined, core business data such as location numbers, layer depths, equipment ports, connectable directions, and accessibility relationships are not automatically generated into stable structured datasets as the equipment models are placed. After designers adjust the position and size of the equipment models, the simulation side often needs to manually re-check all the relationships and connections between the equipment models.

[0018] The core deficiency of existing automated warehouse simulation configuration methods lies in the reliance on manual connection or confirmation of equipment connections. When adjacent equipment models in the 3D scene move, rotate, or change size, manually maintained connections are prone to omissions, failures, or mismatches with actual spatial positions, potentially leading to anomalies such as "visually adjacent equipment models, but the simulation path is not actually connected."

[0019] For stacker crane automated warehouse scenarios, existing technologies generally treat the racks as a single 3D model. Designers need to simultaneously understand parameters such as rack layout, aisle parameters, column distribution, and storage location status in a complex 3D space. This makes parameter adjustment operations unintuitive and makes it difficult to generate addressable storage location data in batches, hindering subsequent automated warehouse simulation.

[0020] Figure 1 This is a flowchart illustrating the implementation of a space-generation-based automated warehouse design method according to an embodiment of the present disclosure, including: S110. In response to the user's warehouse layout operation, determine a two-dimensional semantic layout, which includes multiple warehouse objects and the snapping information of the warehouse objects; S120. Based on two-dimensional semantic layout, using a predefined three-dimensional transformation protocol, each warehouse object is converted into a corresponding parameterized model, and at least one parameterized model is included in the model set. S130. Determine the upstream and downstream topological relationships between parameterized models based on the adsorption information and business attributes of warehouse objects; S140. Based on multiple parametric models and upstream and downstream topological relationships, determine the target automated warehouse.

[0021] In this embodiment of the disclosure, warehouse layout operations include a series of interactive actions performed by the user in the visual interactive interface of the automated warehouse design system, such as dragging and placing warehouse objects, editing size parameters, moving and rotating positions, and switching and adjusting views. In one example, the warehouse layout operation is based on two-dimensional parametric interaction, eliminating the need for editing in a complex three-dimensional space.

[0022] In this embodiment of the disclosure, in response to a user's warehouse layout operation, a two-dimensional semantic layout can be determined. This two-dimensional semantic layout may contain multiple warehouse objects and the adsorption information of each warehouse object. In one example, the two-dimensional semantic layout can be understood as the layout of multiple warehouse objects in a two-dimensional space, where each warehouse object has semantic information such as adsorption information and business attributes.

[0023] In one example, the warehouse object can be defined in a two-dimensional space. For instance, the user can create, drag, and configure parameters of the warehouse object in a two-dimensional view. These operations are performed in two-dimensional space without needing to directly edit the warehouse object in three-dimensional space. Simultaneously, the warehouse object carries complete three-dimensional business attributes. Specifically, this disclosure allows binding three-dimensional business attributes such as the warehouse layer number and the three-dimensional bounding box (i.e., bounding box information) to the warehouse object during its generation, so that the corresponding parametric model can be directly generated subsequently through a three-dimensional transformation protocol.

[0024] In one example, a warehouse object can refer to an automated warehouse element with an independent business identity. For example, it can include shelf objects and non-shelf objects, where non-shelf objects can include logistics equipment objects such as conveyor lines, platforms, tracks, and elevators.

[0025] In this embodiment of the disclosure, the snapping information can be considered as structured association data automatically generated and bound to the corresponding warehouse object during the warehouse layout operation. In one example, the snapping information may include two parts: the first part is the geometric layout data after snapping the warehouse object, such as the spatial position, orientation, port coordinates, etc. of each warehouse object after snapping multiple warehouse objects (i.e., geometric layout data); the second part is the explicit connection information of the warehouse object, such as port information indicating the connection relationship between a warehouse object and other warehouse objects, allowed passage directions, etc.

[0026] Furthermore, this disclosure can utilize a predefined 3D transformation protocol to convert each repository object in a 2D semantic layout into a corresponding parametric model.

[0027] In this embodiment of the disclosure, the three-dimensional transformation protocol may be a structured transformation rule between a two-dimensional semantic layout and a three-dimensional parametric model, used to map two-dimensional business data (such as warehouse objects) to three-dimensional computable objects (such as parametric models).

[0028] In one example, the 3D conversion protocol may include a basic mapping rule layer. For instance, the 3D conversion protocol may predefine the correspondence between the attributes of each type of warehouse object and the parameterized model, and then map the warehouse object directly to the corresponding parameterized model based on the correspondence.

[0029] It is understood that, through the basic mapping rule layer, this disclosure can determine the type of the parametric model corresponding to the warehouse object. The specific parameters of the parametric model (such as size, position, etc.) can be determined by the generation parameters of the warehouse object and the parameter relationships between warehouse objects. The generation parameters of the warehouse object and the parameter relationships between warehouse objects can be determined through the parameter linkage adaptation strategy in the 3D conversion protocol and the positional relationships between different warehouse objects.

[0030] The 3D conversion protocol can include a parameter linkage and adaptation strategy between different warehouse objects. This strategy can be used to determine the generation parameters of warehouse objects and the parameter relationships between them, thereby determining the size of the parameterized model corresponding to the warehouse object. For example, the width of the internal conveyor line of a four-way vehicle will automatically and synchronously adjust with the width parameter of the docking target track. After the shelf height parameter is modified, the internal lifting stroke dimension of the corresponding hoist can be automatically adjusted in linkage.

[0031] The 3D conversion protocol can include the positional relationship between different warehouse objects. This positional relationship can also be used to clarify the parameter relationship between warehouse objects, and then determine the positional relationship between the corresponding parametric models of the warehouse objects based on the parameter relationship, so as to realize the spatial coordinate alignment of multiple different types of parametric models such as conveyor line ports, elevator entrances and exits, and track connection positions.

[0032] In this embodiment, parameterized models are generally not stored in isolation, but rather categorized into corresponding model sets according to business logic. For example, multiple connected shelf models in three-dimensional space (i.e., the parameterized models corresponding to the warehouse objects and the shelf objects) can be automatically aggregated into the same model set (which can be called the shelf model set), sharing a unified location indexing system. Similarly, parameterized models of tracks, stacker cranes, conveyor lines, etc., within a connected aisle in three-dimensional space can be aggregated into the same model set (which can be called the actuator group model set).

[0033] Furthermore, this disclosure can determine the upstream and downstream topological relationships of the parameterized model based on the adsorption information and business attributes of the warehouse object.

[0034] In this embodiment of the disclosure, the upstream and downstream topology can be a structured connection relationship with a clear logistics flow direction, derived based on adsorption information and business attributes. For example, the upstream and downstream topology can clearly mark the upstream starting object and the downstream ending object between two parameterized models, defining the path for goods to be transported (such as initial goods) to flow from the upstream starting object to the downstream ending object.

[0035] The method for determining upstream and downstream topological relationships will be described in detail later.

[0036] In this embodiment of the disclosure, the target automated warehouse can be determined by aggregating multiple parametric models and their upstream and downstream topological relationships. For example, this disclosure can traverse all parametric models to eliminate anomalies such as spatial overlap and logistics location interference between them; at the same time, it can traverse all upstream and downstream topological relationships to verify the continuity of flow and the legality of port connections, complete missing connections in isolated parametric models, and eliminate invalid topological relationships that do not conform to logistics logic.

[0037] Next, this disclosure can aggregate the validated parametric model and upstream and downstream topological relationships to determine the target automated warehouse.

[0038] Using the above method, this disclosure relies on two-dimensional semantic layout, predefined three-dimensional conversion protocol and adsorption information to automatically deduce the target automated warehouse. It transforms manual operations such as parametric model placement, position calibration, upstream and downstream topological relationships, and attribute configuration into automatic system execution, eliminating the need for repeated manual adjustments and verifications in complex three-dimensional space. At the same time, it reduces human errors such as position misalignment, topological misconnection, and parameter mismatch caused by manual operations, thereby improving the efficiency and accuracy of automated warehouse design.

[0039] Figure 2 This is a schematic flowchart illustrating the design and simulation of an automated warehouse according to an embodiment of the present disclosure.

[0040] This disclosure does not use the traditional three-dimensional warehouse design model of manually maintaining all relationships in the three-dimensional scene, but instead directly generates structured warehouse objects with clear business meanings during the design stage.

[0041] In one example, users can quickly generate independent shelves (i.e., shelf objects) using a parametric configuration method with both plan and elevation views that is more adapted to the design logic of shelf objects. Simultaneously, various non-shelf objects such as tracks, conveyor lines, elevators, and platforms can be quickly generated based on the generated template. This disclosure can also automatically and completely record the business attributes for each warehouse object.

[0042] In some implementations, the business attributes include at least one of bounding box information, warehouse layer number, boundary information, logistics direction information, type information, and input / output port information.

[0043] In this embodiment, the bounding box information can be axis-aligned three-dimensional spatial boundary data generated for each warehouse object. This disclosure defines the area occupied by the warehouse object in three-dimensional space using the maximum and minimum coordinate values ​​of the X / Y / Z dimensions. The warehouse layer number can be the physical floor or operational layer identifier to which the warehouse object belongs in three-dimensional space, and can be used for cross-layer equipment docking verification.

[0044] Boundary information can be the geometric contour boundary data of a warehouse object, recording the distribution of vertices and edges of the warehouse object's outer contour. Logistics direction information refers to the allowed cargo flow directions of the warehouse object, supporting both unidirectional and multidirectional flow. In one example, it can be used to derive the upstream and downstream topological relationships of a parametric model.

[0045] Type information can refer to the equipment classification label of a warehouse object, which can mark the warehouse object as belonging to types such as shelves, conveyor lines, elevators, AGV tracks, and docking stations. Input / output port information can refer to the structured data of the preset docking points on the warehouse object, such as recording the spatial coordinates, docking direction, and compatible equipment type of each input / output port.

[0046] It is understandable that, in the process of generating the repository object, this disclosure may bind business attributes to the repository object to provide a data foundation for subsequent processes such as determining the parameterized model and upstream and downstream topological relationships.

[0047] This disclosure eliminates the need for manual input of device attributes and manual marking of connection rules in subsequent 3D scene simulations by setting corresponding business attributes for warehouse objects. Furthermore, relying on these business attributes, this disclosure can automatically complete core operations such as determining adsorption information, deriving upstream and downstream topology relationships, and automating warehouse simulation. This reduces common errors caused by manual configuration, such as attribute omissions, parameter mismatches, and missing path connections, thereby improving the accuracy and efficiency of automated warehouse design.

[0048] In this embodiment of the disclosure, during the two-dimensional semantic layout design process, different warehouse objects can be automatically attracted based on business attributes. The results of the attraction (such as attraction information) can be recorded as high-priority candidate links, serving as the core benchmark for determining the subsequent upstream and downstream topology relationships.

[0049] Furthermore, this disclosure can automatically convert and generate corresponding parameterized models based on warehouse objects with built-in business attributes; after entering the simulation stage, this disclosure can rely on the business attributes of warehouse objects to automatically deduce the upstream and downstream topological relationships between corresponding parameterized models, thereby generating simulation results including equipment docking relationships, the starting and ending points of cargo transportation tasks, and cargo handling paths.

[0050] The following content details how to determine a two-dimensional semantic layout.

[0051] In some implementations, a two-dimensional semantic layout is determined in response to a user's warehouse layout operation, including: In response to the user's warehouse layout operation, determine multiple warehouse objects and design the layout based on multiple warehouse objects; Based on the layout design results, and at least one of the boundary information, logistics direction information, input / output port information, and type information of each warehouse object, determine the first connectivity between warehouse objects; and determine the object adsorption combination based on the first connectivity. Based on the main axis direction, boundary center, and distance between multiple warehouse objects in the object snapping group, determine the alignment position of the object snapping group, and snap multiple warehouse objects in the object snapping group based on the alignment position; Based on the adsorption results, the adsorption information of the warehouse objects is determined, and based on multiple warehouse objects and the adsorption information, a two-dimensional semantic layout is determined.

[0052] In this embodiment of the disclosure, the core data for designing an automated warehouse is a computable warehouse object, rather than a simple three-dimensional model.

[0053] Generally, common business attributes for warehouse objects (such as storage location objects and non-storage location objects) can include bounding box information, number of warehouse layers, boundary information, logistics direction information, type information, and input / output port information. For shelf objects, business attributes may also include row information, column information, layer information, depth information, storage location size information, storage location information, storage location direction information, and storage location status.

[0054] In one example, this disclosure supports warehouse object snapping during the design phase of a two-dimensional semantic layout. For example, the first connectability of a warehouse object is determined based on its business attributes. When the first connectability indicates that multiple warehouse objects can be snapped together (or the connection conditions are met), this disclosure can identify multiple warehouse objects as a snapping combination, and then automatically snap the warehouse objects in the snapping combination together and record the snapping information of each warehouse object.

[0055] Specifically, the complete execution flow from the user's warehouse layout operation to the two-dimensional semantic layout is as follows.

[0056] like Figure 2 As shown, the two-dimensional layout module 210 can respond to the user's warehouse layout operation in real time. The warehouse layout operation can include visual interactive actions such as dragging and placing warehouse objects, moving their positions, rotating their angles, and editing their size parameters. This allows multiple independent warehouse objects to be determined in the two-dimensional design canvas, and the layout design of multiple warehouse objects to be performed.

[0057] In one example, the layout design process is linked to the user's warehouse layout operations. For instance, the warehouse layout operation instructs the user to drag and select the warehouse objects corresponding to three sets of shelves (i.e., shelf objects), the warehouse objects corresponding to three main conveyor lines, and the warehouse object corresponding to one elevator in sequence. Further, based on the warehouse layout operation, all warehouse objects are placed in preset positions on a two-dimensional design canvas to achieve the layout design of multiple warehouse objects.

[0058] In this embodiment of the disclosure, in the result of the layout design, the two-dimensional layout module 210 can traverse all warehouse objects and extract at least one of the business attributes of each warehouse object, such as boundary information, logistics direction information, input / output port information and type information, to perform a first connectivity verification between warehouse objects.

[0059] In one example, based on the type information of the warehouse object, it can be determined that the conveyor line and the elevator can be connected, and thus the first connectability between the warehouse object of the conveyor line and the warehouse object of the elevator is determined to be "connection allowed"; based on the type information of the warehouse object, it can be determined that the shelf and the ordinary aisle cannot be connected, and thus the first connectability between the shelf object and the warehouse object of the ordinary aisle is determined to be "connection not allowed".

[0060] In one example, based on the boundary information and logistics direction information of a warehouse object, other warehouse objects that are adjacent to the warehouse object and have a matching orientation can be filtered out, thereby determining the first connectivity between warehouse objects.

[0061] In one example, based on input and output port information, it is possible to verify whether the port types of multiple repository objects support docking, thereby determining the first connectivity between the repository objects.

[0062] In this embodiment of the disclosure, the first connectability can be considered as whether connections between repository objects are allowed.

[0063] In this embodiment of the disclosure, the two-dimensional layout module 210 can determine multiple warehouse objects that are "allowed to connect" as an object adsorption combination based on the first connectability of each warehouse object. For example, if the first connectability between the warehouse object of the conveyor line and the warehouse object of the elevator is "allowed to connect," then the warehouse object of the conveyor line and the warehouse object of the elevator can be in one object adsorption combination. In this example, each object adsorption combination can contain multiple warehouse objects with potential adsorption relationships, and different object adsorption combinations do not interfere with each other.

[0064] Furthermore, for each object snapping combination, the two-dimensional layout module 210 can determine the alignment position of the object snapping combination by utilizing the main axis direction, boundary center, and distance between each warehouse object within the object snapping combination.

[0065] In one example, this disclosure uses the parallelism of the main axes of multiple warehouse objects (e.g., two warehouse objects) (which can be determined by the main axis direction of each warehouse object) as a first constraint to calibrate the rotation angle between the multiple warehouse objects so that the warehouse objects of strip-shaped equipment such as conveyor lines and tracks are collinearly aligned.

[0066] Subsequently, based on the bounding box information of each warehouse object, this disclosure calculates the boundary center of each boundary in the warehouse object by using the extreme values ​​of each dimension in the bounding box information. In one example, this boundary center belongs to the boundary information of the warehouse object.

[0067] Furthermore, using the boundary center of each warehouse object as a second constraint, and combining it with a preset adsorption distance threshold, the alignment position verification of the object adsorption combination is completed: if the distance between warehouse objects is greater than the adsorption distance threshold, then it is not necessary to calculate the alignment position of multiple warehouse objects, i.e., the alignment position of the object adsorption combination (at this time, it can be considered that multiple warehouse objects in the object adsorption combination do not need to be adsorbed); if the distance between warehouse objects is less than or equal to the adsorption distance threshold, using the boundary center of the warehouse object as the alignment reference, the warehouse objects in the object adsorption combination are first coarsely aligned by the relative offset vector of the two boundary centers, and then the moving warehouse object is pulled closer to the reference warehouse object; finally, the coordinates of the predefined docking ports (such as input and output ports) in each warehouse object are extracted, and the moving warehouse object is translated to coincide with the corresponding docking port coordinates of the reference warehouse object to determine the alignment position of the object adsorption combination.

[0068] In one example, the relative offset vector is a two-dimensional planar vector generated based on the real-time coordinate differences of corresponding feature points (such as boundary centers) of multiple warehouse objects. The starting point of this relative offset vector points to the feature point (such as the boundary center) of the moving warehouse object, and the ending point points to the feature point (such as the boundary center) of the reference warehouse object.

[0069] In this embodiment of the disclosure, the two-dimensional layout module 210, in response to the alignment position of the object snapping group, snaps together multiple warehouse objects to which the objects are snapped. Subsequently, based on the snapping results for the warehouse objects, snapping information of the warehouse objects is generated. For example, the snapping information includes the geometric layout and explicit connection information of the warehouse objects after snapping, wherein the geometric layout may be data such as the spatial position, orientation, and port coordinates of the warehouse objects after snapping; the explicit connection information may be information such as port information indicating the connection relationship between a warehouse object and other warehouse objects, and allowed passage directions.

[0070] By adopting the above method, warehouse objects with connection relationships are automatically snapped up based on the business attributes of warehouse objects. This makes the two-dimensional semantic layout have candidate links for the simulation process, reducing errors such as parameter mismatch and path omission caused by subsequent manual link connection. This provides a data foundation for subsequent parametric model conversion, cargo transportation task simulation and other work, and improves the accuracy and efficiency of automated warehouse design.

[0071] The following content details how to determine the parametric model.

[0072] In some implementations, based on a two-dimensional semantic layout, a predefined three-dimensional transformation protocol is used to convert each repository object into a corresponding parameterized model, including: Based on two-dimensional semantic layout, a predefined three-dimensional transformation protocol is used to determine the generation parameters of each warehouse object and the parameter relationships between warehouse objects; the three-dimensional transformation protocol includes parameter linkage adaptation strategies between different warehouse objects and positional relationships between different warehouse objects; By utilizing the generated parameters and parameter relationships, each repository object is converted into a corresponding parameterized model.

[0073] In this embodiment of the disclosure, based on a two-dimensional semantic layout and relying on a predefined three-dimensional conversion protocol 220, the disclosure completes the automated batch generation of two-dimensional warehouse objects into three-dimensional parametric models.

[0074] In the 3D conversion protocol 220, a parameter linkage adaptation strategy and positional relationships between different warehouse objects are predefined. This parameter linkage adaptation strategy can be used to determine the generation parameters of warehouse objects and the parameter relationships between warehouse objects, thereby determining the size of the parameterized model corresponding to the warehouse object.

[0075] For example, after the warehouse objects of the conveyor line and the hoist in the two-dimensional semantic layout are snapped together, according to the parameter linkage adaptation strategy in the three-dimensional conversion protocol 220, this disclosure can directly synchronize the width parameter of the warehouse object of the conveyor line to the effective passage width parameter of the corresponding entrance of the warehouse object of the hoist, and at the same time set the height parameters of the two warehouse objects to be equal, so that the docking port dimensions of the two subsequently generated parameterized models (i.e., the parameterized model corresponding to the conveyor line and the parameterized model corresponding to the hoist) match.

[0076] The 3D conversion protocol 220 may also include the positional relationships between different warehouse objects. These positional relationships can also be used to define the parametric relationships between warehouse objects, and then determine the positional relationships between the corresponding parametric models of the warehouse objects based on these parametric relationships. In one example, the 3D conversion protocol 220 can constrain the positional relationships between different warehouse objects, and thus constrain the positional relationships between the corresponding parametric models of the warehouse objects. For example, if there are two adjacent parametric models of conveyor lines, the 3D conversion protocol 220 can define the positional relationship between these two parametric models as "perfectly aligned".

[0077] In this embodiment of the disclosure, the generated parameters can be a set of three-dimensional instantiated attributes of the warehouse object. That is, the generated parameters can include the attributes of the parameterized model. For example, for the parameterized model of the conveyor line, the generated parameters can include length, width, support leg height, etc.; for the parameterized model of the shelf, the generated parameters can include the number of rows, columns, layers, length, width and height of a single storage location, overall dimensions, etc.

[0078] In this embodiment of the disclosure, the parameter relationship is a set of cross-object linkage constraint rules between multiple different warehouse objects, which can be determined by the parameter linkage adaptation strategy and positional relationship derivation in the 3D conversion protocol 220, and is used to adapt the parameterized models of different warehouse objects.

[0079] For example, parameter relationships can include dimensional linkage relationships, such as the width of the conveyor connection end being equal to the passage width of the elevator entrance, and the total depth of the rack automatically matching the total length of the corresponding stacker crane track; parameter relationships can also include positional constraint relationships, such as the rack and stacker crane track being parallel throughout and the spacing being fixed to an engineering safety value; parameter relationships can also include state synchronization relationships, such as the conveyor surface height of all conveyor lines in a continuous conveyor chain being equal, and the height of the storage location in the rack corresponding one-to-one with the stopping height of the elevator loading platform.

[0080] Furthermore, this disclosure can utilize generation parameters and parameter relationships to convert each repository object into a corresponding parameterized model.

[0081] In one example, this disclosure can utilize 3D conversion protocol 220 to match a corresponding 3D model template for each warehouse object. For example, the 3D model template corresponding to the warehouse object can be determined by the type information of the warehouse object and the type information (or identifier) ​​of the 3D model template. Next, this disclosure can directly write the generation parameters of the warehouse object into the 3D model template to generate a 3D model instance corresponding to the warehouse object. Subsequently, according to the parameter relationships of the warehouse object, the size, position, and other attributes of the 3D model instance are automatically adjusted, and finally, the parameterized model of the warehouse object is output.

[0082] Using the above method, this disclosure relies on a predefined 3D conversion protocol to complete the automatic generation process of parametric models. It eliminates the need for manual dragging and stitching of 3D models and individual calibration of cross-device parameters and spatial positions, reducing engineering errors such as size mismatch, docking misalignment, and omission of connection relationships that are prone to occur in manual modeling, thereby improving the efficiency and accuracy of automated warehouse design.

[0083] In some implementations, after converting each repository object into its corresponding parameterized model, the process further includes: The business attributes and adsorption information of the warehouse object are associated and saved with the parameterized model, and the corresponding association identifier of the parameterized model is determined.

[0084] In this embodiment of the disclosure, after converting each warehouse object into a corresponding parameterized model, the business attributes (such as the bounding box information, warehouse layer number, boundary information, logistics direction information, type information, and input / output port information) and adsorption information of the warehouse object can be mapped and associated with the corresponding parameterized model, and a globally unique association identifier can be generated for each parameterized model in a synchronized manner.

[0085] In some implementations, the process of determining the upstream and downstream topological relationships between different parameterized models further includes: Based on the association identifiers corresponding to the parameterized model, extract the adsorption information and business attributes of the warehouse object.

[0086] In this embodiment of the disclosure, relying on the association identifier of the aforementioned generated parameterized model, a bidirectional traceability channel between the parameterized model and the business attributes of the corresponding warehouse object can be directly established. In one example, this disclosure can retrieve the corresponding parameterized model through the association identifier, and then reverse-index to the business attributes and adsorption information of the warehouse object corresponding to the parameterized model. Based on the business attributes and adsorption information, the upstream and downstream topological relationships between parameterized models can be determined.

[0087] Using the above method, this disclosure achieves bidirectional data binding between warehouse objects and parameterized models through association identifiers. This eliminates the need for manual secondary recording of equipment attributes and manual marking of connection relationships in the 3D scene, reducing problems such as attribute omissions and loss of adsorption information that are prone to occur during cross-dimensional data transmission. In the subsequent derivation of upstream and downstream topology relationships, the adsorption information and business attributes of warehouse objects can be quickly retrieved directly through association identifiers, improving the efficiency and accuracy of automated warehouse design.

[0088] The following content details how upstream and downstream topological relationships are determined.

[0089] In some implementations, the upstream and downstream topological relationships between parameterized models are determined based on the adsorption information and business attributes of warehouse objects, including: Based on the adsorption information, candidate links between parameterized models are determined; this adsorption information includes the geometric layout and explicit connection information after the warehouse objects are adsorbed. Determine the second connectivity between parameterized models based on at least one of the following: bounding box information of warehouse objects, number of warehouse layers, boundary information, logistics direction information, type information, and input / output port information; Based on the second connectivity, valid connections between parameterized models are filtered from candidate links; Based on the valid connections, determine the upstream and downstream topological relationships between parameterized models.

[0090] In this embodiment of the disclosure, after determining the adsorption information and business attributes of the corresponding warehouse object based on the association identifier of the parameterized model, the upstream and downstream topological relationships of the parameterized model can be derived based on the adsorption information and business attributes.

[0091] In one example, this disclosure can determine candidate links between parameterized models based on adsorption information. This adsorption information includes the geometric layout and explicit connectivity information of the warehouse objects after adsorption.

[0092] Among them, the geometric layout can represent the spatial data of the warehouse object after it has been snapped up, which can include data such as spatial location, orientation, and port coordinates; the explicit connection information can represent the port information and allowed passage directions that indicate the connection relationship between the warehouse object and other warehouse objects.

[0093] In one example, if the geometric layout and explicit connection information in the adsorption information indicate the existence of other warehouse objects connected to the warehouse object corresponding to the adsorption information, and there is a link between the parameterized model corresponding to the warehouse object (i.e. the warehouse object corresponding to the adsorption information) and the parameterized model corresponding to the other warehouse object that can be used for cargo transportation, the link can be considered as a candidate link.

[0094] In one example, this disclosure can determine a second connectivity between parameterized models based on the business attributes of warehouse objects (such as bounding box information, number of warehouse layers, boundary information, logistics direction information, type information, and input / output port information).

[0095] Specifically, this disclosure determines whether different types of parametric models (such as conveyor lines, platforms, tracks, elevators, vehicle passages, etc.) are within intersecting or adjacent compliant spatial intervals based on the bounding box information of the warehouse objects corresponding to the parametric models, thus excluding abnormal pairings that result in spatial misalignment due to parameter linkage adjustments after snapping to warehouse objects. Subsequently, by combining the number of warehouse layers, boundary information, logistics direction information, type information, and input / output port information, it determines whether the parametric models corresponding to the warehouse objects are allowed to be connected, and then determines the second connectability between parametric models based on the judgment result.

[0096] For example, cross-level conveyor equipment with different warehouse levels is not allowed to establish direct planar connections; logistics output ports must be connected in the same direction as the input ports of downstream equipment; and different types of equipment are only allowed to establish connections under predefined pairing rules in the protocol. When the parameterized models meet the above connection conditions, this disclosure defines the second connectability between parameterized models as a "valid connection".

[0097] Furthermore, for multiple parameterized models with a second connectivity of "connection valid", this disclosure can identify candidate links between parameterized models as valid connections.

[0098] In one example, this disclosure can read the logistics direction information and input / output port information corresponding to each parameterized model involved in each valid connection, and automatically determine the upstream and downstream topological relationships between the parameterized models. For example, the parameterized model where the logistics output port is located is marked as the upstream node of the topology, the parameterized model where the logistics input port is located is marked as the downstream node of the topology, and a directed topological edge with a clear flow direction attribute is generated for each valid connection. This directed topological edge can be regarded as the upstream and downstream topological relationship between the parameterized models.

[0099] Using the above method, this disclosure determines candidate links through the adsorption information of warehouse objects and uses multi-dimensional business attributes to jointly verify the second connectivity between parameterized models, thereby automatically filtering valid connections. Finally, it generates upstream and downstream topological relationships between parameterized models based on valid connections. This avoids the traditional method of manually marking topological edges and calibrating equipment connection relationships one by one in the design of automated warehouses. It reduces problems such as missing connection relationships, incorrect flow direction marking, and illegal cross-layer connections that are easy to occur in the process of manually constructing topologies, thereby improving the accuracy and efficiency of automated warehouse design.

[0100] The following content details the simulation process for the target automated warehouse.

[0101] In some implementations, after identifying the target automated warehouse, the process further includes: Based on multiple parametric models and upstream and downstream topological relationships in the target automated warehouse, the starting point and ending point of the cargo transportation task are determined. Simulate the cargo transportation task based on at least one of the starting point and ending point, the initial cargo in the cargo transportation task, the inbound and outbound configuration information, and the transportation network. Based on the simulation results, a display animation for the cargo transportation task is determined.

[0102] In the embodiments disclosed herein, such as Figure 2 As shown, using simulation module 230, the business attributes corresponding to each parametric model in the target automated warehouse, as well as the upstream and downstream topological relationships between the parametric models, are traversed to identify the initial start point and initial end point of the cargo transportation task. The initial start point can be the inbound entrance, temporary storage platform, or vacant storage location; the initial end point can be the outbound entrance, target storage location, or sorting platform.

[0103] Subsequently, the simulation module 230, based on the directed connectivity verification of the upstream and downstream topological relationships, determines whether there is a valid connected logistics path between the initial starting point and the initial ending point, and eliminates paths without valid topological relationships, thereby determining the starting point and ending point of the cargo transportation task. The starting point and ending point are located on the valid passage of the target automated warehouse and are adapted to the logistics operation rules of the target automated warehouse.

[0104] Furthermore, the simulation module 230 can plan at least one passageway for the cargo transportation task within the target automated warehouse based on the starting and ending points and utilizing the transportation network. In one example, the transportation network can be determined by the shelving objects; details regarding the transportation network will be discussed later.

[0105] Subsequently, by examining the cargo attributes (such as cargo size, weight, and type) of the initial cargo in the cargo transportation task, the system verifies whether the transportation equipment corresponding to the parameterized model on the passage path matches the initial cargo. Abnormal paths with insufficient compatibility between the transportation equipment and the initial cargo are then filtered out from all passage paths. For example, if the initial cargo in a cargo transportation task is a large pallet measuring 1200mm*1200mm, and a certain passage path is found to have an effective passage width of only 1000mm, which is insufficient to support such a large pallet, then this passage path is identified as an abnormal path with insufficient compatibility and is thus eliminated.

[0106] Next, combining the inbound / outbound strategies such as work cycle time and inbound / outbound priority in the inbound / outbound configuration information, the system drives various types of parametric models, including conveyors, elevators, and stacker cranes, to operate collaboratively according to the topological flow. This fully simulates the entire process of the initial goods starting from the origin, passing through all intermediate nodes (i.e., the parametric models between the origin and destination) along the travel path, and finally arriving at the destination. In one example, the entire process of the goods transportation task (i.e., the simulation result of the goods transportation task) can be considered as a display animation of the goods transportation task.

[0107] Using the above method, this disclosure automatically locates task points through parametric models and upstream and downstream topological relationships, and drives logistics simulation with multi-dimensional elements. Finally, it automatically generates a display animation of cargo transportation tasks, eliminating the need for manual marking of task points and configuration of simulation operation rules for each model. This reduces problems such as path incompatibility and equipment compatibility conflicts that are easy to occur when manually specifying task points, and improves the efficiency and accuracy of automated warehouse simulation.

[0108] In this embodiment of the disclosure, for a stacker crane automated warehouse scenario, the disclosure focuses on the full-process parametric design capability of independent racks. For example, users can complete the setting of plan view parameters for rack objects, such as the layout and positioning of the left and right racks, aisle width settings, storage location depth definitions, and rack support style selection, in the plan view parameter editing interface; then switch to the elevation view parameter editing interface to configure elevation view parameters for rack objects, such as the number of storage location columns, total number of layers, single layer height, total number of storage locations, and storage capacity of storage locations. After the plan view parameters and elevation view parameters are determined, this disclosure can automatically generate rack objects carrying bounding box information and a complete goods matrix.

[0109] The following content details how shelf objects and non-shelf objects are determined.

[0110] In some embodiments, this disclosure also includes: Identify shelf objects and non-shelf objects, where the warehouse object includes at least one of shelf objects and non-shelf objects.

[0111] In this embodiment of the disclosure, the shelf object can be considered as a warehouse object for the shelf, which is a component in an automated warehouse that carries goods for static storage. A shelf can include multiple storage compartments, each of which can be located in a storage location, and each storage location can correspond to a storage location code.

[0112] In this embodiment of the disclosure, non-shelf objects can be considered as warehouse objects for non-shelf equipment, which can undertake dynamic logistics operation functions such as cargo transfer, docking, lifting, and passage, such as conveyor lines, elevators, stacker crane tracks, cargo transfer platforms, elevators, and automatic sorting equipment.

[0113] In one example, the warehouse objects contained in the two-dimensional semantic layout may include at least one of shelf objects and non-shelf objects.

[0114] In some implementations, determining shelf objects and non-shelf objects includes: Based on the user-input floor plan and elevation view parameters, determine the shelving object; and, Based on the generated template corresponding to the non-shelf object, the non-shelf object is determined using the template parameters input by the user.

[0115] In one example, the plan view parameters include at least one of the following: shelf layout parameters, aisle width, and shelf depth; Elevation view parameters include at least one of the following: number of columns of the shelving, number of shelves of the shelving, height of the shelving, width of each column of the shelving, and number of storage locations.

[0116] In this embodiment of the disclosure, the shelf layout parameters can refer to the layout rules such as the horizontal placement position of the shelves, the number of rows, and the overall horizontal extension length, which determine the area occupied by the shelves in the warehouse plane.

[0117] Aisle width can refer to the net width of the passageway between racks, such as the passage size to match equipment like stacker cranes, so that non-rack equipment can move back and forth normally in the aisle.

[0118] Shelf depth can refer to the longitudinal dimension of the shelf along the pallet fork entry direction.

[0119] In this embodiment of the disclosure, the number of columns of the shelf can refer to the total number of shelves along the horizontal direction; the number of layers of the shelf can refer to the total number of shelves along the vertical direction; the height of the shelf can refer to the total vertical height of the shelf from the ground to the top; the width of each column in the shelf can refer to the horizontal net width of each shelf position, and the width of each column in the shelf can be different; the number of shelves can be the total number of shelves calculated based on the number of columns and layers.

[0120] In one example, this disclosure can determine shelf objects based on plan view parameters and elevation view parameters; at the same time, it can determine non-shelf objects based on the generated template corresponding to the non-shelf objects and using the template parameters input by the user.

[0121] In some implementations, the shelf object is determined based on user-inputted plan view parameters and elevation view parameters, including: Determine the index value of each storage location based on the floor plan and elevation view parameters input by the user. Based on at least one of the index value, size information, location information, and orientation information of each storage location, determine the bounding box information of each storage location and the storage location matrix containing multiple storage locations; The shelf objects are determined by using the location matrix, the bounding box information of each location, and the location attributes.

[0122] In one example, this disclosure combines plan view parameters and elevation view parameters to automatically complete the index definition of all storage locations (i.e., determine the index value of each storage location), and then generates a shelf object. The specific implementation logic is as follows: This disclosure can read user-set plan view parameters (including shelf layout parameters, aisle width, and shelf depth) and elevation view parameters (including the number of shelves, the number of shelves, the height of the shelves, the column width of each shelf, and the number of storage locations), and then determine the index value of each storage location based on the plan view parameters and elevation view parameters.

[0123] For example, based on predefined indexing rules that integrate shelf location, number of layers, number of rows, number of columns, and depth, a globally unique index value is assigned to each storage location. For instance, based on plan view parameters and elevation view parameters, the index value of a storage location is determined to be: left shelf - 2nd layer - 3rd row - 5th column - 1st depth. The term "left-side shelf" can correspond to the shelf layout parameters and aisle width in the plan view parameters, clearly indicating that the storage location belongs to the shelf group on the left side of the aisle, distinguishing it from the shelf group on the right side. In one example, this disclosure can mark the aisle width in this field of the index value (i.e., the shelf location field). "Second level" can correspond to the "number of shelf levels" and "shelf height" in the elevation view parameters, indicating that the storage location is on the second shelf level counted upwards from the ground reference. "Third row" can correspond to the "shelf layout parameters" in the plan view parameters, indicating that the storage location is located in the third row of the shelf. "Fifth column" can correspond to the "number of columns of the shelf" and "column width of each column in the shelf" in the elevation view parameters, indicating that the storage location is located in the fifth column of the shelf. In one example, this disclosure can mark the column width in this field of the index value (i.e., the column number field). "First deep position" can correspond to the "shelf depth" in the plan view parameters, indicating that the storage location is in the outermost deep position along the stacker crane forklift entry direction (single-deep shelf without stacked storage locations).

[0124] In one example, in addition to the five fields of the index value mentioned above, this disclosure can also incorporate the numerical identifier of the storage location into the index value based on the "number of storage locations" parameter in the elevation view. The numerical identifier of the storage location can be represented as 45 / 300, which means that the storage location is the 45th storage location out of 300 storage locations.

[0125] Furthermore, this disclosure uses the index value of each storage location, combined with storage location size information (such as the length, width and height of the storage location), storage location location information (such as the position coordinates of each vertex of the storage location in three-dimensional space) and storage location orientation information (such as the orientation of the storage location), to solve the three-dimensional bounding box of the storage location, that is, to calculate the bounding box information of the storage location.

[0126] For example, using the world coordinate origin of the entire shelf as a reference, the minimum and maximum coordinates of the three-dimensional bounding box of the storage location are calculated using index values ​​to determine the bounding box information of that storage location. Subsequently, all storage locations whose bounding box information has been calculated are arranged in order according to the dimensions of shelf location, layer number, row number, column number, and depth in the index values ​​to generate a storage location matrix, which contains multiple storage locations.

[0127] Furthermore, in one example, this disclosure can utilize a location matrix, as well as the bounding box information and location attributes of each location, to determine the shelf object.

[0128] The storage location attributes can include at least one of the following: storage location operating status, storability attribute, and accessibility attribute. The storage location operating status can indicate different operating states such as the storage location is currently idle, occupied, or locked due to fault; the storability attribute can define the types of goods that the storage location can carry, the maximum load, and the pallet compatibility rules; the accessibility attribute can define whether the docking port of the storage location allows stacker crane operations for picking and placing goods.

[0129] In this example, this disclosure uses a location matrix as the data skeleton of a shelf object. By traversing each location in the location matrix, the bounding box information of each location is associated with the corresponding location in the matrix. Furthermore, the location attributes of each location (i.e., location permission status, storability attributes, and accessibility attributes) are synchronously mounted to the corresponding location. Based on the bounding box information of each location, the spatial boundaries of each location are encapsulated. The final generated shelf object can include multiple locations with clearly defined location attributes. It is understood that through the above process, the final generated shelf object can include the business attributes of the shelf object.

[0130] Using the above method, this disclosure automatically generates a location index and calculates the bounding box information of the location based on the joint configuration of plan view parameters and elevation view parameters, and finally automatically generates a complete shelf object. The entire process does not require designers to edit each storage cell in three-dimensional space directly, which reduces the problems of chaotic location numbering, mismatched spatial coordinates, and low efficiency of batch modification of attributes in traditional three-dimensional manual modeling. It provides a data foundation for subsequent automated warehouse design and improves the efficiency and accuracy of automated warehouse design.

[0131] In one example, this disclosure can quickly generate non-shelf objects with complete attributes and compliant structure based on the non-shelf object generation template and user-defined input template parameters. The specific implementation logic may include: Based on the type of the non-shelf object, the corresponding generation template is automatically matched. For example, the generation templates for different types of non-shelf objects contain a common framework such as the standard shape structure, docking port position, and basic attribute fields of the corresponding equipment. Subsequently, this disclosure can read template parameters, such as the length and width of the conveyor line, the lifting stroke of the elevator, the external dimensions of the connecting platform, and the extension length of the track, and then write the template parameters into the corresponding attribute fields of the generation template to complete the size adaptation and attribute assignment of the generation template. Finally, the non-shelf object is generated according to the template parameters for non-shelf objects in the generation template. In this example, the non-shelf object may contain the business attributes of the non-shelf object.

[0132] Using the above method, this disclosure utilizes a template for generating non-shelf objects combined with template parameters to automatically generate non-shelf objects. This can adapt to the creation requirements of non-shelf logistics equipment in automated warehouses, such as conveyor lines, elevators, connecting platforms, tracks, and vehicle aisles. Designers are not required to manually draw scattered equipment components, which improves the generation efficiency of non-shelf objects and thus improves the design efficiency of automated warehouses.

[0133] The following details the relevant information about the transportation network.

[0134] In some embodiments, this disclosure also includes: Generate a transport network for goods to pass through based on the shelf objects. The transport network includes at least one of the following functions: Determine the initial cargo transportation sequence in a cargo transportation task; Indicates the storage location where the initial goods need to be transported; Indicates the location from which the initial goods need to be transported; Provide transportation navigation services for cargo transportation tasks.

[0135] In this embodiment, the location matrix in the shelf object is first traversed. Based on the location attributes of each location in the matrix, all locations marked as accessible (referred to as accessible locations) are selected. Then, a transportation network is generated by combining this with the cargo transportation task. For example, the docking ports of accessible locations are connected and bound to the stacker crane's running path nodes to complete the transportation network. In one example, the transportation network may include multiple transportation routes for transporting goods, which can be associated with the requirements of the cargo transportation task.

[0136] In this embodiment of the disclosure, the transportation network can support at least one of four types of functions: (1) Based on the weight of each running path node in the transportation network, the transportation order of multiple initial goods is automatically sorted to reduce stacker congestion and conflict when multiple goods transportation tasks are carried out in parallel. (2) Directly associate the storage locations marked as free and available in the storage location matrix, indicating the storage locations into which the initial goods need to be transported; (3) Directly bind the locations marked as occupied in the location matrix to locate the locations where the initial goods need to be transported out; (4) Automatically plan the optimal route for cargo transportation tasks and provide real-time transportation navigation services for the entire cargo transportation task.

[0137] Using the above method, this disclosure generates a transportation network that allows goods to pass through the shelf object. It can reuse the native structured data built into the shelf object, such as the location matrix, passability attributes, and bounding box information. There is no need to manually draw network nodes and define passage paths segment by segment. This reduces problems that are easy to occur when building transportation networks manually in the traditional way, such as node omissions, path conflicts with shelf physical space, and misalignment of storage locations. At the same time, the generated transportation network includes multiple functions and can be directly applied to the automated warehouse simulation process, improving the efficiency and accuracy of automated warehouse simulation.

[0138] In some embodiments, this disclosure also includes: When a user's warehouse layout operation changes, new adsorption information and new business attributes for the warehouse object are determined based on the changed warehouse layout operation. Based on the new adsorption information and new business attributes, the parametric model is updated to determine the updated parametric model. The placement reference and orientation of the updated parametric model remain unchanged. The upstream and downstream topological relationships are updated based on the updated parametric model.

[0139] In this embodiment of the disclosure, when a user's warehouse layout operation changes, such as when the user performs warehouse layout change operations (i.e., changes in warehouse layout operation) such as adjusting the number of shelf rows, modifying the length of the conveyor line, or changing the position of mobile devices, the warehouse objects directly affected by this warehouse layout change operation can be determined. Based on the changed two-dimensional semantic layout, the new adsorption information and new business attributes for the warehouse objects can be automatically recalculated.

[0140] Subsequently, based on the new adsorption information and new business attributes, the parametric model corresponding to the warehouse object is updated in a targeted manner. The placement reference and orientation of the updated parametric model are retained, and the size, spatial boundary and other attributes related to the layout change of the parametric model are adjusted simultaneously.

[0141] In one example, the placement reference can include a positioning anchor point where the parametric model is fixed in three-dimensional space. For instance, for a parametric model of a warehouse object, the positioning anchor point could be located at the contact point between the parametric model and the ground, or at the center point of the contact surface between the parametric models.

[0142] Finally, this disclosure uses the updated parametric model to automatically re-verify the original upstream and downstream topology relationships, automatically disconnect failed topology edges caused by port misalignment, layer number mismatch, and unreachable paths due to changes in the two-dimensional semantic layout, and automatically complete new topology edges that conform to the connection rules based on new adsorption information and new business attributes, thereby outputting upstream and downstream topology relationships that adapt to the new two-dimensional semantic layout, i.e., the updated upstream and downstream topology relationships.

[0143] Using the above method, when users adjust the number of shelf rows, modify the length of conveyor lines, or change the position of mobile devices in the warehouse layout, there is no need to manually check related objects, rematch adsorption relationships, or correct topology edges segment by segment. The adsorption information and business attributes adapted to the new layout can be automatically generated. The model orientation update is completed while keeping the placement benchmark and orientation of the parametric model unchanged. This updates the upstream and downstream topology relationships of the parametric model, reducing problems such as topology omissions, port misalignments, and data inconsistencies that are prone to occur during manual correction, and improving the design iteration efficiency for automated warehouses.

[0144] Figure 3 This is a schematic flowchart illustrating the process of determining a parameterized model according to an embodiment of the present disclosure.

[0145] In the embodiments disclosed herein, such as Figure 3 As shown, the parameterized model disclosed herein is not determined by manually piecing together a model. Instead, the parameterized model is instantiated according to the data of the warehouse object, and the business attributes and adsorption information of the warehouse object are bound to the corresponding parameterized model.

[0146] The two-dimensional layout module 310 can determine a two-dimensional semantic layout, which can include multiple warehouse objects. Furthermore, this disclosure can input the warehouse objects into the conversion layer 320, and use the three-dimensional conversion protocol in the conversion layer 320 to convert the warehouse objects into corresponding parametric models.

[0147] In one example, the 3D conversion protocol can transform warehouse objects into parametric models of rack uprights, beams, storage locations, tracks, aisles, and lifting equipment (i.e., hoists) based on their hierarchical structure (e.g., rows, bays, cells, slots). Simultaneously, it associates the warehouse object's business attributes (e.g., bounding box information, number of warehouse layers, boundary information, logistics direction information, type information, and input / output port information) and snapping information with the parametric model. Thus, the generated parametric model can both display the physical appearance and serve as input data for cargo transportation task simulation.

[0148] The beneficial effects of this disclosure are as follows: (1) Replace 3D model editing with parameterized warehouse objects: When the warehouse object is created, it can contain business attributes such as bounding box information, number of warehouse layers, boundary information, logistics direction information, type information and input / output port information, which facilitates subsequent modeling and simulation calculation of the automated warehouse.

[0149] (2) The generation of parametric models has the ability to maintain business attributes: Unlike ordinary 3D model placement operations that only pursue visual consistency, this disclosure associates and saves the parametric model with the business attributes of the corresponding warehouse object, ensuring that the parametric model has business computability.

[0150] (3) Derivation of upstream and downstream topology relationships with low manual intervention: This disclosure can automatically generate upstream and downstream topology relationships for the parameterized model based on the adsorption information and business attributes of the warehouse object corresponding to the parameterized model, thereby reducing the workload of manual connection and reducing the risk of inconsistency in topology data after layout changes.

[0151] (4) The shelf object is generated by combining plan view parameters and elevation view parameters, which not only conforms to the two-dimensional parameter configuration operation habits familiar to designers, but also directly outputs the shelf object, addressable storage location matrix and transportation network required for simulation.

[0152] (5) This disclosure has two-dimensional semantic design capabilities. It can intuitively express the candidate links between warehouse objects through adsorption information from the two-dimensional design stage, so that the effective connection of the subsequent parameterized model can be generated directly from the design action without the need to build additional topological logic in the three-dimensional scene.

[0153] This disclosure also proposes a spatially generated automated warehouse design device. Figure 4 This is a structural schematic diagram of an automated warehouse design device 400 according to an embodiment of the present disclosure, comprising: The layout determination module 410 is used to determine a two-dimensional semantic layout in response to the user's warehouse layout operation. The two-dimensional semantic layout includes multiple warehouse objects and the adsorption information of the warehouse objects. The model conversion module 420 is used to convert each warehouse object into a corresponding parameterized model based on a two-dimensional semantic layout and using a predefined three-dimensional conversion protocol. At least one parameterized model is included in the model set. The topology determination module 430 is used to determine the upstream and downstream topological relationships between parameterized models based on the adsorption information and business attributes of warehouse objects. The warehouse determination module 440 is used to determine the target automated warehouse based on multiple parametric models and upstream and downstream topological relationships.

[0154] In some implementations, the business attributes include at least one of bounding box information, warehouse layer number, boundary information, logistics direction information, type information, and input / output port information.

[0155] In some implementations, the layout determination module 410 is used for: In response to the user's warehouse layout operation, determine multiple warehouse objects and design the layout based on multiple warehouse objects; Based on the layout design results, and at least one of the boundary information, logistics direction information, input / output port information, and type information of each warehouse object, determine the first connectivity between warehouse objects; and determine the object adsorption combination based on the first connectivity. Based on the main axis direction, boundary center, and distance between multiple warehouse objects in the object snapping group, determine the alignment position of the object snapping group, and snap multiple warehouse objects in the object snapping group based on the alignment position; Based on the adsorption results, the adsorption information of the warehouse objects is determined, and based on multiple warehouse objects and the adsorption information, a two-dimensional semantic layout is determined.

[0156] In some implementations, the model conversion module 420 is used for: Based on two-dimensional semantic layout, a predefined three-dimensional transformation protocol is used to determine the generation parameters of each warehouse object and the parameter relationships between warehouse objects; the three-dimensional transformation protocol includes parameter linkage adaptation strategies between different warehouse objects and positional relationships between different warehouse objects; By utilizing the generated parameters and parameter relationships, each repository object is converted into a corresponding parameterized model.

[0157] In some implementations... Figure 5 This is a structural schematic diagram of an automated warehouse design device 500 according to an embodiment of the present disclosure, comprising: The association storage module 550 is used to associate and store the business attributes and adsorption information of the warehouse object with the parameterized model, and to determine the association identifier corresponding to the parameterized model.

[0158] In some embodiments, the automated warehouse design device 500 further includes: The information extraction module 560 is used to extract the adsorption information and business attributes of warehouse objects based on the association identifiers corresponding to the parameterized model.

[0159] In some implementations, the topology determination module 430 is used for: Based on the adsorption information, candidate links between parameterized models are determined; this adsorption information includes the geometric layout and explicit connection information after the warehouse objects are adsorbed. Determine the second connectivity between parameterized models based on at least one of the following: bounding box information of warehouse objects, number of warehouse layers, boundary information, logistics direction information, type information, and input / output port information; Based on the second connectivity, valid connections between parameterized models are filtered from candidate links; Based on the valid connections, determine the upstream and downstream topological relationships between parameterized models.

[0160] In some embodiments, the automated warehouse design device 500 further includes a warehouse simulation module 570, used for: Based on multiple parametric models and upstream and downstream topological relationships in the target automated warehouse, the starting point and ending point of the cargo transportation task are determined. Simulate the cargo transportation task based on at least one of the starting point and destination, the initial cargo in the cargo transportation task, the inbound and outbound configuration information, and the transportation network. Based on the simulation results, a display animation for the cargo transportation task is determined.

[0161] In some embodiments, the automated warehouse design device 500 further includes a warehouse object determination module 580, used for: Identify shelf objects and non-shelf objects, where the warehouse object includes at least one of shelf objects and non-shelf objects.

[0162] In some implementations, the warehouse object determination module 580 is used for: Based on the user-input floor plan and elevation view parameters, determine the shelving object; and, Based on the generated template corresponding to the non-shelf object, the non-shelf object is determined using the template parameters input by the user.

[0163] In some implementations, the warehouse object determination module 580 is used for: Determine the index value of each storage location based on the floor plan and elevation view parameters input by the user. Based on at least one of the index value, size information, location information, and orientation information of each storage location, determine the bounding box information of each storage location and the storage location matrix containing multiple storage locations; The shelf objects are determined by using the location matrix, the bounding box information of each location, and the location attributes.

[0164] In some embodiments, the automated warehouse design device 500 further includes a road network determination module 590, used for: Generate a transport network for goods to pass through based on the shelf objects. The transport network includes at least one of the following functions: Determine the initial cargo transportation sequence in a cargo transportation task; Indicates the storage location where the initial goods need to be transported; Indicates the location from which the initial goods need to be transported; Provide transportation navigation services for cargo transportation tasks.

[0165] In some implementations, the plan view parameters include at least one of the following: shelf layout parameters, aisle width, and shelf depth; Elevation view parameters include at least one of the following: number of columns of the shelving, number of shelves of the shelving, height of the shelving, width of each column of the shelving, and number of storage locations.

[0166] In some implementations, the topology determination module 430 is further configured to: When a user's warehouse layout operation changes, new adsorption information and new business attributes for the warehouse object are determined based on the changed warehouse layout operation. Based on the new adsorption information and new business attributes, the parametric model is updated to determine the updated parametric model. The placement reference and orientation of the updated parametric model remain unchanged. The upstream and downstream topological relationships are updated based on the updated parametric model.

[0167] The specific functions and examples of each module and submodule of the apparatus in this disclosure can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0168] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0169] Figure 6 This is a structural block diagram of an electronic device according to an embodiment of the present disclosure. Figure 6 As shown, the electronic device includes a memory 610 and a processor 620. The memory 610 stores a computer program that can run on the processor 620. There can be one or more memories 610 and processors 620. The memory 610 can store one or more computer programs, which, when executed by the electronic device, cause the electronic device to perform the methods provided in the above-described method embodiments. The electronic device may also include a communication interface 630 for communicating with external devices and performing data exchange and transmission.

[0170] If the memory 610, processor 620, and communication interface 630 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0171] Optionally, in a specific implementation, if the memory 610, processor 620, and communication interface 630 are integrated on a single chip, then the memory 610, processor 620, and communication interface 630 can communicate with each other through an internal interface.

[0172] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0173] Further, optionally, the aforementioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct RAMBUS RAM (DR RAM).

[0174] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions proposed according to the embodiments of this disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line, DSL) or wireless (e.g., infrared, Bluetooth, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)). It is worth noting that the computer-readable storage media mentioned in this disclosure can be non-volatile storage media; in other words, it can be non-transient storage media.

[0175] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program mentioned can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0176] In the description of the embodiments of this disclosure, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0177] In the description of the embodiments disclosed herein, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0178] In the description of embodiments of this disclosure, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0179] The above content is merely an exemplary embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A spatially generated automated warehouse design method, comprising: In response to the user's warehouse layout operation, a two-dimensional semantic layout is determined, which includes multiple warehouse objects and the adsorption information of the warehouse objects; Based on the two-dimensional semantic layout, each of the warehouse objects is converted into a corresponding parameterized model using a predefined three-dimensional transformation protocol, and at least one of the parameterized models is included in the model set. Based on the adsorption information and business attributes of the warehouse object, determine the upstream and downstream topological relationships between the parameterized models; The target automated warehouse is determined based on the multiple parametric models and the upstream and downstream topological relationships.

2. The method according to claim 1, wherein, The business attributes include at least one of the following: bounding box information, number of warehouse layers, boundary information, logistics direction information, type information, and input / output port information.

3. The method according to claim 2, wherein, The process of determining a two-dimensional semantic layout in response to a user's warehouse layout operation includes: In response to the user's warehouse layout operation, determine the multiple warehouse objects and perform layout design based on the multiple warehouse objects; Based on the results of the layout design, and at least one of the boundary information, logistics direction information, input / output port information, and type information of each warehouse object, a first connectivity between the warehouse objects is determined; and based on the first connectivity, an object adsorption combination is determined. Based on the main axis direction, boundary center, and distance between multiple warehouse objects in the object adsorption combination, the alignment position of the object adsorption combination is determined, and multiple warehouse objects in the object adsorption combination are adsorbed based on the alignment position; Based on the adsorption results, the adsorption information of the warehouse objects is determined, and based on the multiple warehouse objects and the adsorption information, the two-dimensional semantic layout is determined.

4. The method according to claim 2, wherein, The process of converting each of the repository objects into a corresponding parameterized model based on the two-dimensional semantic layout and using a predefined three-dimensional transformation protocol includes: Based on the two-dimensional semantic layout, the generation parameters of each of the warehouse objects and the parameter relationships between the warehouse objects are determined using the predefined three-dimensional transformation protocol; the three-dimensional transformation protocol includes parameter linkage adaptation strategies between different warehouse objects and positional relationships between different warehouse objects; Using the generated parameters and the parameter relationships, each of the repository objects is converted into a corresponding parameterized model.

5. The method according to claim 4, further comprising, after converting each of the repository objects into a corresponding parameterized model: The business attributes and adsorption information of the warehouse object are associated and saved with the parameterized model, and the association identifier corresponding to the parameterized model is determined.

6. The method according to claim 5, further comprising, before determining the upstream and downstream topological relationships between different parameterized models: Based on the association identifier corresponding to the parameterized model, the adsorption information and business attributes of the warehouse object are extracted.

7. The method according to claim 2 or 6, wherein, The step of determining the upstream and downstream topological relationships between the parameterized models based on the adsorption information and business attributes of the warehouse object includes: Based on the adsorption information, candidate links between the parameterized models are determined; the adsorption information includes the geometric layout and explicit connection information after the warehouse object is adsorbed. The second connectivity between the parameterized models is determined based on at least one of the bounding box information, warehouse layer number, boundary information, logistics direction information, type information, and input / output port information of the warehouse object. Based on the second connectivity, valid connections between parameterized models are filtered from the candidate links; Based on the valid connections, the upstream and downstream topological relationships between the parameterized models are determined.

8. The method according to claim 2, further comprising, after determining the target automated warehouse: Based on the multiple parametric models and upstream and downstream topological relationships in the target automated warehouse, the starting point and ending point of the cargo transportation task are determined; The cargo transportation task is simulated based on at least one of the starting point and ending point, the initial cargo in the cargo transportation task, the inbound and outbound configuration information, and the transportation network. Based on the simulation results, the animation for displaying the cargo transportation task is determined.

9. The method according to claim 8, further comprising: Determine shelf objects and non-shelf objects, wherein the warehouse object includes at least one of the shelf objects and the non-shelf objects.

10. The method according to claim 9, wherein, The process of determining shelf objects and non-shelf objects includes: The shelf object is determined based on the plan view parameters and elevation view parameters input by the user; and, Based on the generated template corresponding to the non-shelf object, the non-shelf object is determined using the template parameters input by the user.

11. The method according to claim 10, wherein, Determining the shelf object based on the user-input plan view parameters and elevation view parameters includes: Determine the index value of each storage location based on the floor plan and elevation view parameters input by the user. Based on at least one of the index value, size information, location information, and orientation information of each storage location, determine the bounding box information of each storage location and a storage location matrix containing multiple storage locations; The shelf object is determined using the location matrix, the bounding box information of each location, and the location attributes.

12. The method according to claim 11, further comprising: A transport network for goods to pass through is generated based on the shelf object, the transport network including at least one of the following functions: Determine the initial cargo transportation sequence in the cargo transportation task; Indicates the storage location where the initial goods need to be transported; Indicates the location from which the initial goods need to be transported; Provide transportation navigation services for the aforementioned cargo transportation task.

13. The method according to claim 10 or 11, wherein, The plan view parameters include at least one of the following: shelf layout parameters, aisle width, and shelf depth; The elevation view parameters include at least one of the following: the number of columns of the shelving, the number of layers of the shelving, the height of the shelving, the width of each column in the shelving, and the number of storage locations.

14. The method according to claim 1, further comprising: If the user's warehouse layout operation changes, new adsorption information and new business attributes for the warehouse object are determined based on the changed warehouse layout operation. Based on the new adsorption information and the new business attributes, the parameterized model is updated to determine the updated parameterized model, while the placement reference and orientation of the updated parameterized model remain unchanged. The upstream and downstream topological relationships are updated based on the updated parameterized model.

15. A spatially generated automated warehouse design device, comprising: The layout determination module is used to determine a two-dimensional semantic layout in response to the user's warehouse layout operation. The two-dimensional semantic layout includes multiple warehouse objects and the adsorption information of the warehouse objects. The model conversion module is used to convert each of the warehouse objects into a corresponding parameterized model based on the two-dimensional semantic layout and using a predefined three-dimensional conversion protocol, wherein at least one of the parameterized models is included in the model set. The topology determination module is used to determine the upstream and downstream topological relationships between the parameterized models based on the adsorption information and business attributes of the warehouse object. The warehouse determination module is used to determine the target automated warehouse based on multiple parametric models and the upstream and downstream topological relationships.

16. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-14.

17. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-14.

18. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-14.

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