A method and apparatus for constructing a simulation model

Abstract

The application discloses a simulation model construction method and device, and relates to the technical field of warehouse logistics. A specific embodiment of the method comprises the following steps: acquiring structural information of a logistics network used for constructing a simulation model, wherein the structural information indicates logistics nodes and lines contained in the logistics network; processing the structural information of the logistics network to generate line network information and logistics node layout information conforming to a data input model; generating a network simulation model containing the logistics nodes based on the line network information; generating a node simulation model corresponding to the logistics nodes based on the logistics node layout information; and calling the network simulation model and / or the node simulation model to perform simulation optimization on the network simulation model and / or the node simulation model. The embodiment realizes flexible decoupling and fusion of a network simulation model and a node simulation model corresponding to the logistics network, and improves the universality and expandability of the simulation model.

Description

Technical Field

[0001] This invention relates to the field of warehousing and logistics technology, and in particular to a method and apparatus for constructing a simulation model. Background Technology

[0002] By establishing simulation models of actual warehousing and logistics networks, it is possible to conduct reasonable evaluations of logistics system planning schemes in advance, and to find more optimized solutions for implementation without having to implement them in the actual logistics system. This reduces implementation risks and lowers experimental costs, thus simulation modeling technology has broad application prospects in logistics scenarios.

[0003] In the process of realizing this invention, the inventors discovered that existing simulation model construction technologies have at least the following problems: whenever any modeling object, such as the logistics network or logistics nodes, changes, a new simulation model needs to be built, which makes the development workload of simulation models in the same scenario large and highly repetitive, and the simulation model has poor scalability and cannot be used for other similar logistics systems; simulation models can only be built for the logistics network when logistics nodes such as warehouses and stations are simplified, or simulation models can be built independently for logistics nodes, but the route network in the logistics network and the internal information of logistics nodes cannot be integrated, which affects the accuracy of the simulation model. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and apparatus for constructing a simulation model, which can simultaneously construct a network simulation model and a node simulation model, thereby achieving flexible integration and decoupling of the network simulation model and the node simulation model, and improving the versatility and scalability of the simulation model.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for constructing a simulation model is provided, comprising:

[0006] Obtain structural information of the logistics network used to construct the simulation model, the structural information indicating the logistics nodes and routes included in the logistics network;

[0007] The structural information of the logistics network is processed to generate route network information and logistics node layout information that conform to the data input model;

[0008] Based on the route network information, a network simulation model containing the logistics nodes is generated; based on the logistics node layout information, a node simulation model corresponding to the logistics nodes is generated.

[0009] The network simulation model and / or the node simulation model are invoked to perform simulation optimization on the network simulation model and / or the node simulation model.

[0010] Optionally, the route network information indicates the node identifier, node type, node location information, and whether the node has an independent simulation identifier corresponding to the logistics node;

[0011] The logistics node layout information indicates the storage units inside the logistics node and the location information of the storage units.

[0012] Optionally, generating a node simulation model corresponding to the logistics node based on the logistics node layout information includes:

[0013] Determine whether the logistics node has an independent simulation identifier based on the route network information;

[0014] If the logistics node has an independent simulation identifier, generate a node simulation model corresponding to the logistics node.

[0015] Optionally, the data input model also indicates the order information, resource efficiency information, and parameter information to which the simulation model is applicable;

[0016] The order information indicates the corresponding logistics node for generating the order, the target logistics node, the order placement time, the item information contained in the order, and the storage unit where the items are located.

[0017] The resource efficiency information indicates the resource type, resource operation efficiency, and resource capacity;

[0018] The parameter information indicates the amount of resources and the resource scheduling strategy.

[0019] Optionally, the network simulation model and / or the node simulation model can be optimized by adjusting any one or more of the order information, resource efficiency information, and parameter information that conform to the data input model.

[0020] Optionally, during the simulation optimization of the network simulation model and / or the node simulation model, simulation orders are generated based on at least one of the following methods:

[0021] Generating simulated orders based on historical orders;

[0022] Simulated orders are generated based on preset item categories, quantities, and wave information.

[0023] Optionally, the network simulation model and the node simulation model can be generated using any of the following simulation platforms: FlexSim, AnyLogic, or Simio.

[0024] To achieve the above objectives, according to another aspect of the present invention, a simulation modeling apparatus is provided, comprising: a structural information acquisition module, a structural information processing module, a simulation model construction module, and a simulation model optimization module; wherein,

[0025] The structural information acquisition module is used to acquire structural information of the logistics network used to construct the simulation model. The structural information indicates the logistics nodes and routes included in the logistics network.

[0026] The structural information processing module is used to process the structural information of the logistics network to generate route network information and logistics node layout information that conform to the data input model.

[0027] The simulation model construction module is used to generate a network simulation model containing the logistics nodes based on the route network information; and to generate a node simulation model corresponding to the logistics nodes based on the logistics node layout information.

[0028] The simulation model optimization module is used to call the network simulation model and / or the node simulation model to perform simulation optimization on the network simulation model and / or the node simulation model.

[0029] Optionally, the route network information indicates the node identifier, node type, node location information, and whether the node has an independent simulation identifier corresponding to the logistics node;

[0030] The logistics node layout information indicates the storage units inside the logistics node and the location information of the storage units.

[0031] Optionally, generating a node simulation model corresponding to the logistics node based on the logistics node layout information includes:

[0032] Determine whether the logistics node has an independent simulation identifier based on the route network information;

[0033] If the logistics node has an independent simulation identifier, generate a node simulation model corresponding to the logistics node.

[0034] Optionally, the data input model also indicates the order information, resource efficiency information, and parameter information to which the simulation model is applicable;

[0035] The order information indicates the corresponding logistics node for generating the order, the target logistics node, the order placement time, the item information contained in the order, and the storage unit where the items are located.

[0036] The resource efficiency information indicates the resource type, resource operation efficiency, and resource capacity;

[0037] The parameter information indicates the amount of resources and the resource scheduling strategy.

[0038] Optionally, the simulation model optimization module is used to optimize the network simulation model and / or the node simulation model by adjusting any one or more of the order information, resource efficiency information, and parameter information that conform to the data input model.

[0039] Optionally, during the simulation optimization process of the network simulation model and / or the node simulation model, the simulation model optimization module is used to generate simulation orders based on at least one of the following methods:

[0040] Generating simulated orders based on historical orders;

[0041] Simulated orders are generated based on preset item categories, quantities, and wave information.

[0042] Optionally, the simulation model building module is used to generate the network simulation model and the node simulation model using any of the following simulation platforms: FlexSim, AnyLogic, and Simio.

[0043] To achieve the above objectives, according to another aspect of the present invention, an electronic device for constructing a simulation model is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described above for constructing a simulation model.

[0044] To achieve the above objectives, according to another aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described above for constructing a simulation model.

[0045] One embodiment of the above invention has the following advantages or beneficial effects: By unifying the data structure or data format used to construct the simulation model through the data input model, the standardization of simulation input data is achieved. Different simulation models can be constructed for different application scenarios or the same scenario simply by adjusting the input data, thereby improving the versatility and scalability of the simulation model construction method. At the same time, by generating network simulation models and node simulation models respectively for the layout information of logistics networks and logistics nodes, and by calling the network simulation models and / or node simulation models, the network simulation models and node simulation models are flexibly decoupled and integrated, thereby realizing the flexible decoupling and integration of the route network and logistics nodes in the logistics network, improving the accuracy and applicability of the simulation model.

[0046] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0047] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0048] Figure 1 This is a schematic diagram of the main flow of the simulation model construction method according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of a network simulation model according to an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of a node simulation model according to an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the main modules of a simulation model construction device according to an embodiment of the present invention;

[0052] Figure 5 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied;

[0053] Figure 6 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0054] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0055] Figure 1 This is a schematic diagram of the main flow of the network simulation model construction method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the specific steps that can be included in constructing this simulation model are as follows:

[0056] Step S101: Obtain the structural information of the logistics network used to construct the simulation model. The structural information indicates the logistics nodes and routes included in the logistics network.

[0057] In this context, a logistics network refers to a network structure consisting of routes that carry out the mission of logistics movement and nodes that carry out the mission of logistics cessation. For example, in a warehousing and logistics network, the logistics nodes include, but are not limited to, warehouses, distribution centers, delivery centers, and delivery stations.

[0058] Step S102: Process the structural information of the logistics network to generate route network information and logistics node layout information that conform to the data input model.

[0059] Among them, the data input model refers to the data structure that is pre-set according to actual needs, so as to standardize the format or structure of all data used for simulation modeling, and lay the foundation for the universality and scalability of the simulation model.

[0060] In one optional implementation, the route network information indicates the node identifier, node type, node location information, and whether the node has an independent simulation identifier for the logistics node. Specifically, as shown in Table 1 below,

[0061] Table 1 Example of Line Network Information

[0062]

[0063] Based on this, a network structure containing multiple logistics nodes can be generated using a Geographic Information System (GIS), and navigation paths between logistics nodes can be calculated based on the node location information, namely node longitude and node latitude.

[0064] In one optional implementation, the logistics node layout information indicates the storage units within the logistics node and their location information. Specifically, taking a warehouse node as an example, the warehouse node can further define the applicable data structures for the two types of logistics node layout information shown in Tables 2 and 3 below, depending on whether its storage areas are divided into storage locations.

[0065] Table 2 Example of Logistics Node Layout Information - Temporary Storage Entities

[0066] Simulation scheme name A simulation scheme name identifies a specific transportation network system. Node identifier Node name or node number Temporary number Unique identifier of temporary entity Temporary type Identify temporary storage purpose Temporary size The length, width, and maximum allowed stacking height of the temporary entity Temporary coordinates Corner coordinates of temporary entities

[0067] Temporary storage entities refer to any entity or storage area that has storage function but has not been divided into storage locations, such as receiving temporary storage area, shipping temporary storage area, express parcel sorting buffer zone, and pre-packaging temporary storage area.

[0068] Table 3 Example of Logistics Node Layout Information - Storage Entities

[0069] Simulation scheme name A simulation scheme name identifies a specific transportation network system. Node identifier Node name or node number Area code Unique identifier of the region where the storage entity is located Number of rows and columns The number of rows and columns of a storage class entity within the storage domain spacing Row spacing and column spacing of storage class entities within the storage domain coordinate Corner coordinates of the region aisle Record the rows and columns with channels storage unit Storage cell structure in storage entities Storage cell size The length, width, and maximum allowable stacking height of a storage cell

[0070] The storage entities include all storage entities that are divided into storage locations, such as pallet stacking areas and various types of shelving areas. Thus, based on the corner coordinates of the areas indicated by the layout information of the two types of logistics nodes—temporary storage entities and storage entities—as well as the storage unit size, row information, column information, row spacing, and column spacing, the horizontal and vertical coordinates of the storage unit or storage area in the simulation model can be determined, thereby generating a node simulation model containing the internal layout information of the node.

[0071] In one optional implementation, the data input model further indicates the order information, resource efficiency information, and parameter information applicable to the simulation model; wherein, the order information indicates the generating logistics node, target logistics node, order placement time, and item information contained in the order, as well as the storage unit where the items are located; the resource efficiency information indicates the resource type, resource operation efficiency, and resource capacity; and the parameter information indicates the resource quantity and resource scheduling strategy. See Tables 4, 5, and 6 below for details.

[0072] Table 4 Example of Order Information

[0073]

[0074]

[0075] In this way, standardized order information can drive simulation models to simulate the operation of real logistics networks or logistics nodes according to certain logic or processes.

[0076] Table 5 Example of resource efficiency information

[0077]

[0078] Among them, resources refer to the entities that the simulation model needs to complete a certain task. They are usually limited resources and have a certain operational efficiency, such as sorting personnel in warehouse operations, pallets for loading goods, and dispatchable transport vehicles in logistics networks.

[0079] Table 6 Example of parameter information

[0080] Simulation scheme name A simulation scheme name identifies a specific transportation network system. Parameter name Unique name identifier of parameters Parameter value Parameters of any type are recorded using a string type.

[0081] Parameters refer to the key data that the simulation system relies on when performing simulations. They can be instantiated according to actual needs to simulate the operation process of different logistics networks or logistics nodes, thereby comparing and optimizing different solutions, such as the number of sorting personnel, the number of transport vehicles, the number of pallets, vehicle departure time, and order interception time.

[0082] In addition, other data required for building simulation models can be standardized according to actual needs. For example, since the numbering rules inside actual logistics nodes vary, the corresponding numbers or identifiers of storage units also vary. To ensure the universality of the simulation system, the same set of numbering rules can be used to uniformly number the storage units in the simulation model, and the correspondence between the actual number of the storage unit and the logical number in the simulation model can be recorded.

[0083] Step S103: Based on the route network information, generate a network simulation model containing the logistics nodes; based on the logistics node layout information, generate a node simulation model corresponding to the logistics nodes.

[0084] Specifically, the network simulation model and the node simulation model can be generated using any of the following simulation platforms: FlexSim, AnyLogic, or Simio. Alternatively, other simulation platforms can be used, or the simulation model can be constructed directly using object-oriented programming languages ​​such as Java or Python.

[0085] For network simulation models, the network simulation model can be divided into four modules based on the actual logistics operation scheduling process: GIS map module, vehicle dispatch module, transportation process module, and performance statistics module. Specifically, taking a route network indicating warehouse A, distribution centers B1 and B2, and delivery stations C1, C2, and C2 as an example, the following steps illustrate the process: First, the route network information is input into the FlexSim software to generate a 3D logistics network containing six nodes: warehouse A, distribution centers B1 and B2, and delivery stations C1, C2, and C2. Each node is indicated by its corresponding node number, node type, and whether it can be simulated independently of the route network. Second, the GIS map module is called to calculate the navigation path between nodes based on the longitude and latitude of the corresponding nodes. To simplify the simulation model and improve simulation efficiency, the vehicles in the vehicle scheduling module are set to unlimited resources for use. For the transportation process module, the outbound shipment of goods is the trigger event, and the delivery of goods to the target logistics nodes such as delivery stations is the end event. The specific process is as follows:

[0086] (1) After the goods leave the warehouse, read the target logistics node in the corresponding order of the goods and check whether there are vehicles parked at the platform on the current route. If there are, execute the following step (1); if not, execute the following step (3).

[0087] (2) Load the goods onto the vehicle and check whether the vehicle is fully loaded or whether the latest departure time has been reached; if so, proceed with step (4) below; otherwise, do not proceed.

[0088] (3) When a freight vehicle is requested, it is determined whether there is an available platform. If there is, the vehicle stops at the platform; otherwise, the vehicle waits in line.

[0089] (4) When the vehicle departs, the navigation route calculated by the GIS map module is obtained based on the target logistics node and the current logistics node or origin node. The performance statistics module is called to calculate the transportation time based on the vehicle's moving speed and trigger the event of goods being delivered to the target logistics node.

[0090] (5) The vehicle is unloaded at the target logistics node, and the process ends.

[0091] For the node simulation model, it can be divided into four modules for modeling based on the warehouse operation process: layout control module, order processing module, operation logic module, and performance statistics module. Taking a logistics node layout indicating a temporary storage entity (pre-packaging temporary storage area A1) and two storage entities (shelves B1 and B2) as an example, the logistics node layout information can be input into the FlexSim software first. Next, the layout control module is called to determine the horizontal and vertical coordinates of the storage units or storage areas in the simulation model based on the corner coordinates of the pre-packaging temporary storage area A1, shelf B1, and shelf B2, as well as the storage unit size, row information, column information, row spacing, and column spacing, thus generating a 3D node simulation model containing entities such as A1, B1, and B2. The order processing module is then called to obtain the simulation orders. The operation logic module is called to set the warehouse operation mode, including but not limited to planting mode, picking mode, and zoning mode, and to process the simulation orders based on the set operation mode. Finally, the performance statistics module is called to calculate the operation time consumed during the simulation order processing.

[0092] More specifically, taking the sowing mode as an example of the set warehouse operation mode, the warehouse operation process is as follows:

[0093] (1) Summarize simulated orders by SKU.

[0094] (2) Under each SKU, the simulated order is split into several work orders according to the pallet capacity.

[0095] (3) Each work order requests the assignment of a sorting personnel. Once the request is successful, the sorting personnel will begin sorting.

[0096] (4) Sorting personnel should first pick up goods from the receiving temporary storage area. The time is calculated by multiplying the number of items by the unit operation time delay.

[0097] (5) Before sorting personnel sow seeds in the next storage unit, they first determine whether the storage unit has working space (to avoid the working space being too crowded). If not, they wait in place. If so, they move to the corresponding storage unit.

[0098] (6) After the sorting personnel arrive at the storage unit, they look for a pallet to unload the goods. If not, a pallet replacement task is triggered for the shipping personnel to execute, and they wait in place. It is worth noting that when looking for a pallet, the personnel determine whether the pallet meets the conditions. They prioritize looking for a pallet whose pallet type matches the current pallet type. If no pallet is found, an empty pallet is used, and the type of the pallet is set according to the type of the first one placed on the pallet. If no pallet is found, a replacement pallet is required.

[0099] (7) After the sorting personnel find the pallet, they begin to unload the goods. The unloading time is determined by the product of the unloading quantity and the unit operation time delay. The unit operation time delay will be extended by a factor as the number of people simultaneously sorting in the storage unit increases.

[0100] (8) For each product unloaded, two judgments are made: whether the current pallet is full. If it is full, the palletizing operation of the current pallet is triggered to execute the following step (9); whether all orders in the current storage unit under this wave are completed. If so, the palletizing operation of all non-empty pallets in the operation of the storage unit is triggered to execute the following step (9).

[0101] (9) The pallet to be palletized requests a palletizing operator. After the request is successful, the pallet is moved to the shipping area.

[0102] (10) Personnel walking route: Call the navigation algorithm to plan the personnel walking path. It is worth noting that, in order to avoid deadlock that may be caused when multiple moving obstacles walk to the same point at the same time, it is preferable to set only the logic for avoiding fixed obstacles, and not to execute the collision avoidance logic between moving obstacles.

[0103] In one optional implementation, generating a node simulation model corresponding to the logistics node based on the logistics node layout information includes: determining whether the logistics node has an independent simulation identifier based on the route network information; and generating a node simulation model corresponding to the logistics node if the logistics node has an independent simulation identifier.

[0104] Taking a logistics network containing four logistics nodes A, B, C, and D as an example, if the route network information of the logistics network indicates that logistics nodes A and B have independent simulation identifiers, then corresponding node simulation models a and b can be generated based on the node layout information of logistics nodes A and B, respectively. In this way, the logistics nodes for independent simulation model construction can be flexibly selected through independent simulation identifiers, thereby achieving flexible decoupling and fusion of the network simulation model and the node simulation model.

[0105] Understandably, when logistics nodes in a logistics network change, such as requiring optimization of the internal layout of logistics node C, the simulation model for logistics node C can be constructed directly based on the existing network simulation model and node simulation models a and b. Only the independent simulation identifier corresponding to logistics node C in the route network information needs to be adjusted to complete the construction of the simulation model for logistics node C, without needing to repeatedly construct simulation models for the entire logistics network and other logistics nodes. This achieves the universality and scalability of the simulation model construction. Similarly, when the routes of the logistics network change, the simulation model can be constructed directly only for the adjusted route network, while continuing to reuse the existing node simulation models in the route network. This avoids the redundant development of node simulation models corresponding to the same logistics node, improving the efficiency of simulation model construction.

[0106] Step S104: Invoke the network simulation model and / or the node simulation model to perform simulation optimization on the network simulation model and / or the node simulation model.

[0107] Specifically, taking the construction of a network simulation model for the logistics network and node simulation models a and b for logistics nodes A and B as an example, it can be used not only to simulate and optimize the number of transport vehicles, transport vehicle scheduling strategies, and transport routes by calling the network simulation model alone, but also to simulate and optimize the number of sorting personnel, operation models, and operation routes within logistics node A or logistics node B by calling node simulation model a or node simulation model b alone. Simultaneously, at least one of the network simulation model and node simulation models a and b can be called simultaneously to simulate and optimize transport routes and warehouse operation modes. In this way, flexible decoupling and fusion of the network simulation model and the node simulation model are achieved, that is, the fusion and decoupling of the route network and the internal layout information of the logistics nodes in the logistics network are realized, ensuring the accuracy and applicability of the simulation model.

[0108] In one optional implementation, the network simulation model and / or the node simulation model are optimized by adjusting any one or more of the order information, resource efficiency information, and parameter information that conform to the data input model.

[0109] Specifically, see Figure 2 The network simulation model 200 shown includes: a GIS map module 201, a vehicle dispatch module 202, a transportation process module 203, and a performance statistics module 204. The GIS map module 201 controls the routes or navigation paths between logistics nodes; the vehicle dispatch module 202 schedules vehicles used for logistics transportation between logistics nodes; the transportation process module 203 controls the flow of orders and even transportation vehicles within the logistics system; and the performance statistics module 204 calculates performance indicators such as operation time based on resource efficiency information and parameter information indicating the number of vehicles.

[0110] Based on this, by adjusting parameters such as the number of vehicles and the latest departure time input into the network simulation model, the vehicle dispatch module 202 can directly use the adjusted number of vehicles and latest departure time for vehicle scheduling. Following the operational logic of the transportation process module 203, it performs flow operations, thereby triggering the performance statistics module 204 to calculate preset performance indicators such as transportation time and average transportation time based on statistical data such as operation time. This allows for optimization of the logistics network based on these performance indicators. Furthermore, different navigation paths can be used based on the GIS map module 201 to optimize navigation routes; and different vehicle dispatch strategies can be used based on the vehicle dispatch module 202 to optimize overloaded vehicle dispatch strategies. In other words, by adjusting the input data relied upon by each module of the network simulation model, multi-dimensional simulation optimization of the network simulation model, including transportation routes and vehicle dispatch, can be achieved.

[0111] Further, see Figure 3 The node simulation model 300 shown includes: a layout control module 301, an order processing module 302, a work logic module 303, and a performance statistics module 304. The layout control module 304 controls the layout within the logistics node; the order processing module acquires simulated orders; the work logic module 303 controls the work mode of the simulation model; and the performance statistics module 304 calculates preset performance indicators. The work mode includes, but is not limited to, any combination of one or more modes such as sowing mode, picking mode, and zoning mode. The preset performance indicators include, but are not limited to, personnel utilization rate, cumulative personnel working time, cumulative personnel walking distance, average personnel walking distance, SKU work time, ineffective personnel time, and order timeliness.

[0112] Based on this, the operational efficiency of logistics nodes under different simulated orders can be simulated by adjusting the simulated orders input to the order processing module 302 in the node simulation model 300; the sorting mode can be optimized by adjusting the number of sorting personnel, operation mode, picking path, etc., input to the operation logic module; in addition, the layout information within the logistics nodes adopted by the simulation model can be adjusted through the layout control module 301 to optimize the layout scheme within the logistics nodes. In other words, by adjusting the input data on which each module of the node simulation model depends, optimization can be achieved in multiple dimensions such as the layout information within the nodes, operation mode, picking path, and resource allocation.

[0113] In one optional implementation, during the simulation optimization of the network simulation model and / or the node simulation model, simulation orders are generated based on at least one of the following methods: generating simulation orders based on historical orders; generating simulation orders based on preset item categories, item quantities, and wave information.

[0114] Based on the above embodiments, by unifying the data structure or format used to construct the simulation model through the data input model, the standardization of simulation input data is achieved. Different simulation models can be constructed for different application scenarios or the same scenario simply by adjusting the input data, thereby improving the versatility and scalability of the simulation model construction method. At the same time, by generating network simulation models and node simulation models for the layout information of logistics networks and logistics nodes respectively, and by calling the network simulation models and / or node simulation models, the network simulation models and node simulation models are flexibly decoupled and integrated, thereby realizing the flexible decoupling and integration of the route network and logistics nodes in the logistics network, improving the accuracy and applicability of the simulation model.

[0115] See Figure 4 Based on the above embodiments, this embodiment of the invention provides a simulation model construction device 400, including: a structural information acquisition module 401, a structural information processing module 402, a simulation model construction module 403, and a simulation model optimization module 404; wherein,

[0116] The structural information acquisition module 401 is used to acquire structural information of the logistics network used to construct the simulation model, wherein the structural information indicates the logistics nodes and routes included in the logistics network.

[0117] The structural information processing module 402 is used to process the structural information of the logistics network to generate route network information and logistics node layout information that conform to the data input model.

[0118] The simulation model construction module 403 is used to generate a network simulation model containing the logistics nodes based on the route network information; and to generate a node simulation model corresponding to the logistics nodes based on the logistics node layout information.

[0119] The simulation model optimization module 404 is used to call the network simulation model and / or the node simulation model to perform simulation optimization on the network simulation model and / or the node simulation model.

[0120] In one optional implementation, the route network information indicates the node identifier, node type, node location information, and whether the node has an independent simulation identifier corresponding to the logistics node.

[0121] The logistics node layout information indicates the storage units inside the logistics node and the location information of the storage units.

[0122] In one optional implementation, generating a node simulation model corresponding to the logistics node based on the logistics node layout information includes:

[0123] Determine whether the logistics node has an independent simulation identifier based on the route network information;

[0124] If the logistics node has an independent simulation identifier, generate a node simulation model corresponding to the logistics node.

[0125] In one alternative implementation, the data input model further indicates the order information, resource efficiency information, and parameter information to which the simulation model applies;

[0126] The order information indicates the corresponding logistics node for generating the order, the target logistics node, the order placement time, the item information contained in the order, and the storage unit where the items are located.

[0127] The resource efficiency information indicates the resource type, resource operation efficiency, and resource capacity;

[0128] The parameter information indicates the amount of resources and the resource scheduling strategy.

[0129] In one optional implementation, the simulation model optimization module 404 is used to optimize the network simulation model and / or the node simulation model by adjusting any one or more of the order information, resource efficiency information, and parameter information that conform to the data input model.

[0130] In one optional implementation, during the simulation optimization of the network simulation model and / or the node simulation model, the simulation model optimization module 404 is used to generate simulation orders based on at least one of the following methods:

[0131] Generating simulated orders based on historical orders;

[0132] Simulated orders are generated based on preset item categories, quantities, and wave information.

[0133] In one optional implementation, the simulation model building module is used to generate the network simulation model and the node simulation model using any of the following simulation platforms: FlexSim, AnyLogic, and Simio.

[0134] Figure 5 An exemplary system architecture 500 is shown, which can be used to construct a simulation model according to embodiments of the present invention.

[0135] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, and 503, a network 504, and a server 505. Network 504 serves as the medium for providing communication links between terminal devices 501, 502, and 503 and server 505. Network 504 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0136] Users can use terminal devices 501, 502, and 503 to interact with server 505 via network 504 to receive or send messages, etc. Various communication client applications, such as web browser applications, can be installed on terminal devices 501, 502, and 503.

[0137] Terminal devices 501, 502, and 503 can be various electronic devices with displays that support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0138] Server 505 can be a server providing various services, such as a backend management server supporting shopping websites browsed by users using terminal devices 501, 502, and 503. The backend management server can analyze and process received data such as product information query requests, and feed back the optimization results of the simulation model to the terminal devices.

[0139] It should be noted that the simulation model construction method provided in the embodiments of the present invention is generally executed by the server 505, and correspondingly, the simulation model construction device is generally set in the server 505.

[0140] It should be understood that Figure 5 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0141] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing a terminal device of the present invention. Figure 6 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0142] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0143] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0144] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this invention.

[0145] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0147] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor can be described as including a structural information acquisition module, a structural information processing module, a simulation model construction module, and a simulation model optimization module. The names of these modules do not necessarily limit the module itself; for example, a sending unit can also be described as "a module that sends an image acquisition request to a connected server."

[0148] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: acquiring structural information of a logistics network for constructing a simulation model, the structural information indicating the logistics nodes and routes included in the logistics network; processing the structural information of the logistics network to generate route network information and logistics node layout information conforming to a data input model; generating a network simulation model containing the logistics nodes based on the route network information; generating a node simulation model corresponding to the logistics nodes based on the logistics node layout information; and invoking the network simulation model and / or the node simulation model for simulation optimization.

[0149] According to the technical solution of this invention, by unifying the data structure or data format used to construct the simulation model through the data input model, the standardization of simulation input data is achieved. Different simulation models can be constructed for different application scenarios or the same scenario simply by adjusting the input data, thereby improving the versatility and scalability of the simulation model construction method. At the same time, by generating network simulation models and node simulation models respectively for the layout information of logistics networks and logistics nodes, and by calling the network simulation models and / or node simulation models, the network simulation models and node simulation models are flexibly decoupled and integrated, thereby realizing the flexible decoupling and integration of the route network and logistics nodes in the logistics network, improving the accuracy and applicability of the simulation model.

[0150] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for constructing a simulation model, characterized in that, include: Obtain structural information of the logistics network used to construct the simulation model, the structural information indicating the logistics nodes and routes included in the logistics network; The structural information of the logistics network is processed to generate route network information and logistics node layout information that conform to the data input model; the route network information indicates the node identifier, node type, node location information, and whether the node has an independent simulation identifier; the logistics node layout information indicates the storage unit inside the logistics node and the location information of the storage unit. Based on the route network information, a network simulation model containing the logistics nodes is generated; based on the logistics node layout information, a node simulation model corresponding to the logistics nodes is generated. The network simulation model and / or the node simulation model are invoked to perform simulation optimization on the network simulation model and / or the node simulation model.

2. The method for constructing a simulation model according to claim 1, characterized in that, The step of generating a node simulation model corresponding to the logistics node based on the logistics node layout information includes: Determine whether the logistics node has an independent simulation identifier based on the route network information; If the logistics node has an independent simulation identifier, generate a node simulation model corresponding to the logistics node.

3. The method for constructing a simulation model according to claim 1, characterized in that, The data input model also indicates the order information, resource efficiency information, and parameter information applicable to the simulation model; The order information indicates the corresponding logistics node for generating the order, the target logistics node, the order placement time, the item information contained in the order, and the storage unit where the items are located. The resource efficiency information indicates the resource type, resource operation efficiency, and resource capacity; The parameter information indicates the amount of resources and the resource scheduling strategy.

4. The method for constructing a simulation model according to claim 3, characterized in that, The network simulation model and / or the node simulation model are optimized by adjusting any one or more of the order information, resource efficiency information, and parameter information that conform to the data input model.

5. The method for constructing a simulation model according to claim 4, characterized in that, During the simulation optimization of the network simulation model and / or the node simulation model, simulation orders are generated based on at least one of the following methods: Generating simulated orders based on historical orders; Simulated orders are generated based on preset item categories, quantities, and wave information.

6. The method for constructing a simulation model according to claim 1, characterized in that, The network simulation model and the node simulation model can be generated using any of the following simulation platforms: FlexSim, AnyLogic, or Simio.

7. A device for constructing a simulation model, characterized in that, include: The system comprises a structural information acquisition module, a structural information processing module, a simulation model construction module, and a simulation model optimization module; among which, The structural information acquisition module is used to acquire structural information of the logistics network used to construct the simulation model. The structural information indicates the logistics nodes and routes included in the logistics network. The structural information processing module is used to process the structural information of the logistics network to generate route network information and logistics node layout information that conform to the data input model. The route network information indicates the node identifier, node type, node location information, and whether the node has an independent simulation identifier. The logistics node layout information indicates the storage unit inside the logistics node and the location information of the storage unit. The simulation model construction module is used to generate a network simulation model containing the logistics nodes based on the route network information; and to generate a node simulation model corresponding to the logistics nodes based on the logistics node layout information. The simulation model optimization module is used to call the network simulation model and / or the node simulation model to perform simulation optimization on the network simulation model and / or the node simulation model.

8. An electronic device for constructing simulation models, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

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