WO2024114340A1 - 多物流场景的数字孪生模型构建方法、装置、设备及介质 - Google Patents
多物流场景的数字孪生模型构建方法、装置、设备及介质 Download PDFInfo
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Definitions
- the present application relates to the field of digital twin technology, and in particular to a method, device, equipment and medium for constructing a digital twin model of multiple logistics scenarios.
- digital twins As an important enabling method for achieving digital transformation and promoting intelligent upgrading, digital twins have always attracted attention from all walks of life and have moved from theoretical research to practical application. Digital twins are driven by multi-dimensional virtual models and fused data, and through virtual-real closed-loop interaction, they can realize actual functional services and application needs such as monitoring, simulation, prediction, and optimization.
- the construction of digital twin models is a prerequisite for the implementation of digital twin applications.
- the construction of digital twin models is to make full use of physical models, sensor updates, operation history and other data, and integrate multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes.
- the embodiments of the present application provide a method, device, equipment and medium for constructing a digital twin model of multiple logistics scenarios, which are used to solve the technical problem of high cost of constructing a digital twin model of multiple logistics scenarios in the prior art.
- an embodiment of the present application provides a method for constructing a digital twin model of multiple logistics scenarios, which includes: constructing a read-write data interface, wherein the read-write data interface includes a first interface and a second interface, the first interface is used to obtain logistics entity data corresponding to the logistics entity in the target logistics scenario, and the second interface is used to obtain an event network between multiple logistics entities in the target logistics scenario; obtaining multiple logistics entity data corresponding to multiple logistics entities in each logistics scenario in the multiple logistics scenarios according to the first interface, and obtaining a first event network between multiple logistics entities in the logistics scenario according to the second interface; constructing a digital twin model in the logistics scenario according to the multiple logistics entity data and the first event network; based on the digital twin framework of multiple logistics scenarios, deploying the digital twin model in each logistics scenario in the multiple logistics scenarios to the network nodes of the logistics scenarios; obtaining a second event network between the logistics entities in the multiple logistics scenarios according to the second interface; connecting the digital twin models on multiple network nodes corresponding to the multiple logistics scenarios according to the
- the method for constructing a digital twin model of multiple logistics scenarios also includes: obtaining the constraints and objective functions of the multiple logistics scenarios; and constructing a digital twin framework of the multiple logistics scenarios based on the constraints and objective functions of the multiple logistics scenarios.
- a digital twin model for a logistics scenario is constructed based on multiple logistics entity data and a first event network, including: constructing multiple intelligent entities for a logistics scenario based on multiple logistics entity data; and constructing a digital twin model for a logistics scenario based on the first event network and multiple intelligent entities.
- multiple intelligent entities of the logistics scene are constructed based on multiple logistics entity data, including: constructing ontology models corresponding to multiple logistics entities in the logistics scene based on multiple logistics entity data; digitally mapping the ontology models corresponding to multiple logistics entities in the logistics scene to obtain intelligent entities corresponding to multiple logistics entities in the logistics scene.
- a digital twin model for a logistics scenario is constructed based on the first event network and multiple intelligent agents, including: configuring multiple intelligent agents of the logistics scenario in the digital twin framework of the logistics scenario to obtain multiple configured intelligent agents; connecting the configured multiple intelligent agents according to the first event network to obtain the digital twin model for the logistics scenario.
- the method for constructing the digital twin models of the multiple logistics scenarios also includes: obtaining event parameters of the logistics scenarios; inputting the event parameters into the digital twin model under the logistics scenarios to obtain a first analysis result under the logistics scenarios; inputting the first analysis results corresponding to the multiple logistics scenarios into the digital twin model under the multiple logistics scenarios to obtain a second analysis result under the multiple logistics scenarios.
- the method for constructing the digital twin models of the multiple logistics scenarios also includes: calling the second interface to obtain the third event network between multiple logistics entities under the multiple logistics scenarios; and updating the digital twin models of the multiple logistics scenarios according to the third event network.
- an embodiment of the present application provides a digital twin model construction device for multiple logistics scenarios, which includes: a first construction unit, used to construct a read-write data interface, wherein the read-write data interface includes a first interface and a second interface, the first interface is used to obtain logistics entity data corresponding to the logistics entity in the target logistics scenario, and the second interface is used to obtain the event network between multiple logistics entities in the target logistics scenario; a first acquisition unit, used to obtain multiple logistics entity data corresponding to multiple logistics entities in each logistics scenario in the multiple logistics scenarios according to the first interface, and obtain the first event network between multiple logistics entities in the logistics scenario according to the second interface; a second construction unit, used to construct a digital twin model in the logistics scenario based on the multiple logistics entity data and the first event network; a deployment unit, used to deploy the digital twin model in each logistics scenario in the multiple logistics scenarios to the network node of the logistics scenario based on the digital twin framework of multiple logistics scenarios; a second acquisition unit, used to obtain the second event network between the logistics entities in the multiple logistics
- an embodiment of the present application further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for constructing a digital twin model of multiple logistics scenarios as described in the first aspect above is implemented.
- an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the method for constructing a digital twin model of multiple logistics scenarios described in the first aspect above.
- a read-write data interface is pre-constructed, and multiple logistics entity data of each logistics scenario in multiple logistics scenarios are obtained through the first interface of the read-write data interface, and the first event network between multiple logistics entities in the logistics scenario is obtained through the second interface of the read-write data interface. Then, a digital twin model of the logistics scenario is constructed through the multiple logistics entity data and the first event network.
- the digital twin model of each logistics scenario in the multiple logistics scenarios is deployed to the corresponding network node, and the digital twin models corresponding to the multiple logistics scenarios are connected according to the second event network between the logistics entities of the multiple logistics scenarios to obtain a digital twin model of multiple logistics scenarios with a distributed structure.
- FIG1 is an application scenario diagram of a method for constructing a digital twin model for multiple logistics scenarios provided in an embodiment of the present application.
- Figure 2 is a flow chart of a method for constructing a digital twin model for multiple logistics scenarios provided in an embodiment of the present application.
- Figure 3 is a flow chart of a method for constructing a digital twin model for multiple logistics scenarios provided in another embodiment of the present application.
- Figure 4 is a flow chart of a method for constructing a digital twin model for multiple logistics scenarios provided in another embodiment of the present application.
- Figure 5 is a flow chart of a method for constructing a digital twin model for multiple logistics scenarios provided in another embodiment of the present application.
- Figure 6 is a flow chart of a method for constructing a digital twin model for multiple logistics scenarios provided in another embodiment of the present application.
- Figure 7 is a flow chart of a method for constructing a digital twin model for multiple logistics scenarios provided in another embodiment of the present application.
- Figure 8 is a flow chart of a method for constructing a digital twin model for multiple logistics scenarios provided in another embodiment of the present application.
- Figure 9 is a structural schematic diagram of a digital twin model building device for multiple logistics scenarios provided in one embodiment of the present application.
- FIG10 is a schematic block diagram of a computer device provided in accordance with an embodiment of the present application.
- the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105.
- the network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.
- the network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.
- Terminal devices 101, 102, 103 Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc.
- Terminal devices 101, 102, 103 can be installed with various communication client applications, such as web browser applications, search applications, instant messaging tools, etc.
- the terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.
- the server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .
- the digital twin model construction method for multiple logistics scenarios provided in the embodiments of the present application is generally executed by a server, and accordingly, the digital twin model construction device for multiple logistics scenarios is generally configured in the server.
- the digital twin model construction method for multiple logistics scenarios can be executed by a terminal device, or executed interactively by a terminal device and a server.
- terminal devices, networks and servers in Figure 1 is only illustrative. Any number of terminal devices, networks and servers may be provided according to implementation requirements.
- Figure 2 shows a specific implementation method of a digital twin model construction method for multiple logistics scenarios.
- the method of the present application is not limited to the process sequence shown in Figure 2.
- the method for constructing a digital twin model of multiple logistics scenarios includes the following steps S110 to S160.
- the read-write data interface can be used to obtain logistics scene data.
- the logistics scene data may include logistics entity data corresponding to the logistics entity in the logistics scene and the event network between multiple logistics entities in the logistics scene.
- the read-write data interface includes a first interface and a second interface, the first interface is used to read and write logistics entity data in multiple logistics scenes to realize the construction of the ontology model of the logistics entity, and the second interface is used to read and write the event network between each logistics entity in a single logistics scene, and read and write the event network between each logistics entity in multiple logistics scenes to realize the intelligence of the logistics entity.
- the read-write data interface is a functional encapsulation for realizing the read and write operations on the data, which can be a Java interface.
- the read-write data interface is used to perform read and write operations in the database storing the data required for building the digital twin model of multiple logistics scenes to obtain the corresponding data, and then the construction of the digital twin model under multiple logistics scenes can be realized.
- the method for constructing a digital twin model of multiple logistics scenarios further includes steps S210 and S220 .
- S220 Construct a digital twin framework for multiple logistics scenarios based on the constraints and objective functions of the multiple logistics scenarios.
- each node in the digital twin framework of multiple logistics scenarios is each logistics scenario in the multiple logistics scenarios.
- the multiple logistics scenarios are independent of each other.
- the multiple logistics scenarios can be multi-level transfer field logistics scenarios, or they can be multi-logistics scenarios composed of multiple independent outlets in the same area.
- each first-level transfer field and each second-level transfer field can be used as a logistics scenario in the multi-logistics scenario, that is, each first-level transfer field and each second-level transfer field can be used as a node in the digital twin framework of the multi-logistics scenario;
- each outlet in the same area can be used as a logistics scenario in the multi-logistics scenario, that is, each outlet in the same area can be used as a node in the digital twin framework of the multi-logistics scenario, where the area in the same area can be a province, city or county.
- the objective function is a function used to generate the optimal decision for multiple logistics scenarios.
- Constraints are restrictions that affect the decision-making plan in multiple logistics scenarios.
- a constraint condition can be that one outlet can transport goods to multiple customers and one customer can only correspond to one outlet.
- the outlet transporting goods to each customer constitutes a logistics scenario, and the cost of the outlet transporting goods to each customer can be used as a variable function in the objective function of the multiple logistics scenario.
- the variable functions in the objective function are independent of each other, so a variable function in the objective function of the multiple logistics scenario can be used as the objective function of a single logistics scenario.
- the logistics entity data of the logistics scene and the first event network between the logistics entities are the elements that constitute the digital twin model of the logistics scene, wherein the logistics entity can be understood as multiple elements in the logistics scene, for example, the logistics entity can be the real objects such as the transport personnel, sorting personnel, goods, and transportation tools in the logistics scene.
- the logistics entity data is the element for constructing the ontology model of the corresponding logistics entity, such as the size and attributes of the logistics entity.
- the first event network is a mapping of the association relationship between the logistics entities in a logistics scene. The association relationship between the logistics entities determines the interaction rules between the logistics entities. For example, if the goods need to be delivered to the customer before 3 pm, it constitutes a network event between logistics entities.
- the digital twin model in the logistics scenario can map the logistics scenario to the digital world through digital expression to realize the interaction between the logistics entities in the logistics scenario
- the first interface and the second interface can be called respectively to obtain multiple logistics entity data of each logistics scenario in multiple logistics scenarios and the first event network between the logistics entities in the logistics scenario, and then the digital twin model of each logistics scenario in multiple logistics scenarios can be constructed.
- step S130 includes steps S131 and S132 .
- S132 Construct a digital twin model for a logistics scenario based on the first event network and multiple intelligent agents.
- an intelligent agent refers to an entity that can intelligently interact with other logistics entities in a logistics scenario.
- the intelligent agent includes a static intelligent agent and a dynamic intelligent agent.
- a static intelligent agent is an intelligent agent without motion characteristics, such as a warehouse
- a dynamic intelligent agent is an intelligent agent with motion characteristics, such as a sorter.
- the ontology model of the intelligent agent can be constructed.
- the ontology model of the intelligent agent needs to be developed intelligently, such as adding new modules to the product, writing firmware, debugging, testing, etc., so as to obtain the intelligent agent in the logistics scenario.
- step S131 includes steps S1311 and S1312 .
- the corresponding parameters can be input into the simulation software (such as Anylogic, Flexsim, etc.) to obtain the ontology model of the corresponding intelligent agent.
- the ontology model can be used to characterize the logistics entity in the logistics scene. It is difficult to characterize that the logistics entity can interact intelligently with other logistics entities in the logistics scene. Therefore, it is necessary to digitally map the ontology model to transform the ontology model of the intelligent agent, such as configuring a connection layer plug-in that can interact with each ontology model, and then the intelligent agent corresponding to the logistics entity can be obtained.
- the connection layer plug-in is a component that can process the interactive data between each ontology model based on the input and output requirements of the ontology model and output it in the form of message/interface call.
- step S132 includes steps S1321 and S1322 .
- the digital twin framework of each logistics scene in multiple logistics scenes can also be constructed through the constraints and objective functions of the corresponding logistics scenes.
- one of the logistics scenes in multiple logistics scenes is used as the target logistics scene.
- the digital twin model under the target logistics scene it is only necessary to determine the digital twin framework of the target logistics scene according to the constraints and objective functions of the target logistics scene, and configure all the agents of the target logistics scene in its digital twin framework.
- the association interface that can interact between the agents in the target logistics scene is constructed through the first event network between the logistics entities corresponding to each agent, and the agents are connected according to the association interface, so as to obtain the digital twin model under the target logistics scene.
- the association interface is composed of an input interface and an output interface. The construction of the input interface and the output interface can be developed according to the rules for interaction between agents in the target logistics scene.
- the digital twin model of each logistics scenario in the multiple logistics scenarios is deployed to the network node of the logistics scenario.
- the digital twin framework of multiple logistics scenarios is a distributed architecture.
- Each node on the digital twin framework of multiple logistics scenarios is a logistics scenario in the multiple logistics scenarios.
- One logistics scenario corresponds to a digital twin model. Therefore, when deploying the digital twin models in multiple logistics scenarios, the digital twin models in each logistics scenario can be deployed in a distributed manner.
- the digital twin model in a single logistics scenario The model only needs to run on one network node, which can solve the technical problem of high cost when digital twin models of multiple logistics scenarios run in a centralized manner.
- the second event network is a mapping of the association relationship between logistics entities in two logistics scenarios.
- the corresponding database can be read to obtain the second event network between the logistics entities in each logistics scenario.
- the second event network can simulate the interaction between the logistics entities of the two logistics scenarios, so the association interface that can interact between the intelligent entities in the two logistics scenarios is constructed through the second event network, and the corresponding intelligent entities between the logistics entities of the two logistics scenarios are connected according to the association interface.
- the connection between the digital twin models on each network node can be realized, and then the digital twin models of each logistics scenario can communicate with each other, thereby constructing a digital twin model in multiple logistics scenarios with a distributed structure.
- the method for constructing a digital twin model of multiple logistics scenarios further includes steps S170 , S180 , and S190 .
- the event parameter can be a factor that affects the operation result of the logistics scenario.
- the event parameter can be obtained according to the implementation status of each logistics entity in the digital twin model under multiple logistics scenarios.
- the first analysis result is the optimal solution of the objective function of each logistics scenario
- the second analysis result is the optimal solution of the objective function of the multiple logistics scenarios.
- the event parameters are input into the digital twin model under the corresponding logistics scenario, and the first analysis result affecting each logistics scenario can be obtained.
- the first analysis result under each logistics scenario is input into the digital twin model under the multiple logistics scenario as a variable in the objective function of the multiple logistics scenario to obtain the influence of the event parameters on the multiple logistics scenarios, so as to provide the optimal decision for the optimization of personnel and equipment under the multiple logistics scenarios.
- the method for constructing a digital twin model of multiple logistics scenarios further includes steps S310 and S320 .
- the third event network is the association information that increases or decreases between logistics entities in multiple logistics scenarios after the digital twin model of multiple logistics scenarios is constructed.
- the third event network between the logistics entities corresponding to each intelligent agent in the multiple logistics scenarios can be obtained in real time through the second interface, that is, the current association information between the logistics entities.
- the digital twin model in the multiple logistics scenarios can be updated, so that the digital twin model in each logistics scenario is more matched with the corresponding logistics scenario.
- a read-write data interface for obtaining logistics scenario data
- the read-write data interface includes a first interface and a second interface
- the first interface is used to obtain logistics entity data of the logistics scenario
- the second interface is used to obtain the event network between multiple logistics entities in the logistics scenario
- multiple logistics entity data of each logistics scenario in multiple logistics scenarios are obtained according to the first interface
- the first event network between multiple logistics entities in the logistics scenario is obtained according to the second interface
- a digital twin model under the logistics scenario is constructed according to multiple logistics entity data and the first event network
- the digital twin model under each logistics scenario in the multiple logistics scenarios is deployed to the network node of the logistics scenario
- the second event network between the logistics entities of the multiple logistics scenarios is obtained according to the second interface
- the digital twin models on the multiple network nodes corresponding to the multiple logistics scenarios are connected according to the second event network to obtain the digital twin model under the multiple logistics scenarios.
- An embodiment of the present application also provides a device for constructing a digital twin model for multiple logistics scenarios, which is used to execute any embodiment of the aforementioned method for constructing a digital twin model for multiple logistics scenarios.
- Figure 9 is a structural schematic diagram of a digital twin model building device for multiple logistics scenarios provided in an embodiment of the present application.
- the digital twin model construction device for multiple logistics scenarios includes: a first construction unit 110 , a first acquisition unit 120 , a second construction unit 130 , a deployment unit 140 , a second acquisition unit 150 and a first connection unit 160 .
- the first construction unit 110 is used to construct a read-write data interface, wherein the read-write data interface includes a first interface and a second interface, the first interface is used to obtain logistics entity data corresponding to the logistics entity in the target logistics scenario, and the second interface is used to obtain the event network between multiple logistics entities in the target logistics scenario.
- the digital twin model construction device for multiple logistics scenarios also includes a third acquisition unit and a third construction unit.
- the third acquisition unit is used to obtain the constraints and objective functions of multiple logistics scenarios; the third construction unit is used to construct a digital twin framework of multiple logistics scenarios based on the constraints and objective functions of multiple logistics scenarios.
- the first acquisition unit 120 is used to acquire multiple logistics entity data corresponding to multiple logistics entities in each logistics scene in multiple logistics scenes according to the first interface, and to acquire a first event network between multiple logistics entities in the logistics scene according to the second interface.
- the second construction unit 130 is used to construct a digital twin model in a logistics scenario according to multiple logistics entity data and the first event network.
- the second building unit 130 includes a fourth building unit and a fifth building unit.
- the fourth construction unit is used to construct multiple intelligences of logistics scenarios based on multiple logistics entity data; the fifth construction unit is used to construct a digital twin model under the logistics scenario based on the first event network and multiple intelligent entities.
- the fourth construction unit includes: a sixth construction unit and a mapping unit.
- the sixth construction unit is used to construct ontology models corresponding to multiple logistics entities in the logistics scene based on multiple logistics entity data; the mapping unit is used to digitally map the ontology models corresponding to multiple logistics entities in the logistics scene to obtain intelligent entities corresponding to multiple logistics entities in the logistics scene.
- the fifth building unit includes: a configuration unit and a second connecting unit.
- a configuration unit is used to configure multiple intelligent agents in a logistics scenario in a digital twin framework of the logistics scenario to obtain multiple configured intelligent agents; a second connection unit is used to connect the configured multiple intelligent agents according to a first event network to obtain a digital twin model in the logistics scenario.
- the deployment unit 140 is used to deploy the digital twin model of each logistics scenario in the multiple logistics scenarios to the network nodes of the logistics scenarios based on the digital twin framework of multiple logistics scenarios.
- the second acquisition unit 150 is used to acquire a second event network between logistics entities in multiple logistics scenarios according to the second interface.
- the first connection unit 160 is used to connect the digital twin models on multiple network nodes corresponding to multiple logistics scenarios according to the second event network to obtain the digital twin models under the multiple logistics scenarios.
- the digital twin model construction device for multiple logistics scenarios also includes a fourth acquisition unit, a first input unit and a second input unit.
- the fourth acquisition unit is used to obtain event parameters of the logistics scenario; the first input unit is used to input the event parameters into the digital twin model under the logistics scenario to obtain the first analysis result under the logistics scenario; the second input unit is used to input the first analysis results corresponding to multiple logistics scenarios into the digital twin model under multiple logistics scenarios to obtain the second analysis results under multiple logistics scenarios.
- the digital twin model building device for multiple logistics scenarios also includes a fifth acquisition unit and an update unit.
- a fifth acquisition unit configured to call the second interface to acquire a third event network between multiple logistics entities in a multi-logistics scenario
- An updating unit is used to update the digital twin model of multiple logistics scenarios according to the third event network.
- the digital twin model construction device for multiple logistics scenarios provided in the embodiment of the present application is used to execute the above-mentioned digital twin model construction method for multiple logistics scenarios, which can solve the bottleneck of digital twin model construction for multiple logistics scenarios and reduce the construction cost of digital twin models for multiple logistics scenarios.
- FIG. 10 is a schematic block diagram of a computer device provided in an embodiment of the present application.
- the computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a storage medium 503 and an internal memory 504 .
- the storage medium 503 can store an operating system 5031 and a computer program 5032.
- the processor 502 can execute a method for constructing a digital twin model of multiple logistics scenarios.
- the processor 502 is used to provide computing and control capabilities to support the operation of the entire device 500.
- the internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503.
- the processor 502 can execute a method for constructing a digital twin model of multiple logistics scenarios.
- the network interface 505 is used for network communication, such as providing transmission of data information, etc. It can be understood by those skilled in the art that the structure shown in FIG. 10 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the device 500 to which the solution of the present application is applied.
- the specific device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
- the processor 502 is used to run the computer program 5032 stored in the memory to achieve the following functions: construct a read-write data interface, wherein the read-write data interface includes a first interface and a second interface, the first interface is used to obtain logistics entity data corresponding to the logistics entity in the target logistics scenario, and the second interface is used to obtain the event network between multiple logistics entities in the target logistics scenario; obtain multiple logistics entity data corresponding to multiple logistics entities in each logistics scenario in multiple logistics scenarios according to the first interface, and obtain the first event network between multiple logistics entities in the logistics scenario according to the second interface; construct a digital twin model under the logistics scenario according to the multiple logistics entity data and the first event network; based on the digital twin framework of multiple logistics scenarios, deploy the digital twin model under each logistics scenario in the multiple logistics scenarios to the network nodes of the logistics scenarios; obtain the second event network between the logistics entities in the multiple logistics scenarios according to the second interface; connect the digital twin models on multiple network nodes corresponding to the multiple logistics scenarios according to the second event network to obtain the digital twin model under the multiple logistics
- the embodiment of the device 500 shown in FIG. 10 does not constitute a limitation on the specific configuration of the device 500.
- the device 500 may include more or fewer components than shown, or combine certain components, or arrange the components differently.
- the device 500 may only include a memory and a processor 502. In such an embodiment, the structure and function of the memory and the processor 502 are consistent with the embodiment shown in FIG. 10 and will not be described in detail here.
- the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors 502, digital signal processors 502 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
- DSP digital signal processors 502
- ASIC application-specific integrated circuits
- FPGA field-programmable gate arrays
- the general-purpose processor 502 may be a microprocessor 502 or the processor 502 may also be any conventional processor 502, etc.
- a computer storage medium may be a non-volatile computer-readable storage medium or a volatile storage medium.
- the storage medium stores a computer program 5032, wherein the computer program 5032 implements the following steps when executed by the processor 502: construct a read-write data interface, wherein the read-write data interface includes a first interface and a second interface, the first interface is used to obtain logistics entity data corresponding to the logistics entity in the target logistics scene, and the second interface is used to obtain the event network between multiple logistics entities in the target logistics scene; obtain multiple logistics entity data corresponding to multiple logistics entities in each logistics scene in multiple logistics scenes according to the first interface, and obtain the first event network between multiple logistics entities in the logistics scene according to the second interface; construct a digital twin model in the logistics scene according to the multiple logistics entity data and the first event network; based on the digital twin framework of multiple logistics scenes, deploy the digital twin model in each logistics scene in the multiple logistics scenes to the network node of the logistics scene; obtain the second event network between the logistics entities
- the disclosed devices, apparatuses and methods can be implemented in other ways.
- the device embodiments described above are only schematic.
- the division of the units is only a logical function division. There may be other division methods in actual implementation. Units with the same function may also be combined into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
- the device embodiments shown or discussed may be implemented in other ways.
- the mutual coupling or direct coupling or communication connection discussed herein may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.
- the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.
- each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
- the above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
- the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium.
- the technical solution of the present application is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product.
- the computer software product is stored in a storage medium, including several instructions for enabling a device 500 (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.
- the aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), magnetic disks or optical disks.
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Abstract
本申请公开了一种多物流场景的数字孪生模型构建方法、装置、设备及介质。该方法包括:构建第一接口和第二接口;根据第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络;根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型;基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至对应的网络节点上;根据第二接口获取多物流场景的物流实体之间的第二事件网络;根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。
Description
本申请涉及数字孪生技术领域,尤其涉及一种多物流场景的数字孪生模型构建方法、装置、设备及介质。
发明背景
数字孪生作为实现数字化转型和促进智能化升级的重要使能途径,一直备受各行各业关注,已从理论研究走向了实际应用阶段。数字孪生是以多维虚拟模型和融合数据双驱动,通过虚实闭环交互,来实现监控、仿真、预测、优化等实际功能服务和应用需求,其中数字孪生模型的构建是实现数字孪生落地应用的前提。数字孪生模型的构建是充分利用物理模型、传感器更新、运行历史等数据,集成多学科、多物理量、多尺度、多概率的仿真过程。
目前,在采用仿真软件构建多物流场景的数字孪生模型时,由于需要大量的设备和决策等数据,同时也需要对海量数据进行集中处理,导致需要增加一个超级计算机来保证模型高效率的构建,极大的增加了模型的构建成本。
发明内容
本申请实施例提供了一种多物流场景的数字孪生模型构建方法、装置、设备及介质,用于解决现有技术中多物流场景的数字孪生模型构建成本较高的技术问题。
第一方面,本申请实施例提供了一种多物流场景的数字孪生模型构建方法,其包括:构建读写数据接口,其中,读写数据接口包括第一接口和第二接口,第一接口用于获取目标物流场景中物流实体对应的物流实体数据,第二接口用于获取目标物流场景中多个物流实体之间的事件网络;根据第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络;根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型;基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至物流场景的网络节点上;根据第二接口获取多物流场景的物流实体之间的第二事件网络;根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。
进一步的,在构建读写数据接口之前,该多物流场景的数字孪生模型构建方法还包括:获取多物流场景的约束条件和目标函数;根据多物流场景的约束条件以及目标函数构建多物流场景的数字孪生框架。
进一步的,根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型,包括:根据多个物流实体数据构建物流场景的多个智能体;根据第一事件网络、多个智能体构建物流场景下的数字孪生模型。
更进一步的,根据多个物流实体数据构建物流场景的多个智能体,包括:根据多个物流实体数据构建物流场景中多个物流实体分别对应的本体模型;将物流场景中多个物流实体分别对应的本体模型进行数字化映射,得到物流场景中多个物流实体分别对应的智能体。
更进一步的,根据第一事件网络、多个智能体构建物流场景下的数字孪生模型,包括:将物流场景的多个智能体在物流场景的数字孪生框架中进行配置,得到配置后的多个智能体;根据第一事件网络连接配置后的多个智能体,以得到物流场景下的数字孪生模型。
进一步的,在根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型之后,该多物流场景的数字孪生模型构建方法还包括:获取物流场景的事件参数;将事件参数输入至物流场景下的数字孪生模型中,得到物流场景下的第一分析结果;将多物流场景分别对应的第一分析结果输入至多物流场景下的数字孪生模型中,得到多物流场景下的第二分析结果。
进一步的,在根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型之后,该多物流场景的数字孪生模型构建方法还包括:调用第二接口以获取多物流场景下多个物流实体之间的第三事件网络;根据第三事件网络对多物流场景的数字孪生模型进行更新。
第二方面,本申请实施例提供了一种多物流场景的数字孪生模型构建装置,其包括:第一构建单元,用于构建读写数据接口,其中,读写数据接口包括第一接口和第二接口,第一接口用于获取目标物流场景中物流实体对应的物流实体数据,第二接口用于获取目标物流场景中多个物流实体之间的事件网络;第一获取单元,用于根据第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络;第二构建单元,用于根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型;部署单元,用于基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至物流场景的网络节点上;第二获取单元,用于根据第二接口获取多物流场景的物流实体之间的第二事件网络;第
一连接单元,用于根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。
第三方面,本申请实施例又提供了一种计算机设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,其中,处理器执行计算机程序时实现如上述第一方面所述的多物流场景的数字孪生模型构建方法。
第四方面,本申请实施例还提供了一种计算机可读存储介质,其中计算机可读存储介质存储有计算机程序,计算机程序当被处理器执行时使处理器执行上述第一方面所述的多物流场景的数字孪生模型构建方法。
在本申请实施例提供的多物流场景的数字孪生模型构建方法中,通过预先构建读写数据接口,并通过读写数据接口的第一接口获取多个物流场景中每个物流场景的多个物流实体数据以及通过读写数据接口的第二接口获取物流场景中多个物流实体之间的第一事件网络,然后通过多个物流实体数据、第一事件网络构建物流场景的数字孪生模型,最后通过多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至对应的网络节点上,并根据多物流场景的物流实体之间的第二事件网络将多物流场景分别对应的数字孪生模型进行连接,以得到具有分布式结构的多物流场景的数字孪生模型,如此解决了多物流场景的数字孪生模型构建的瓶颈,同时降低了多物流场景的数字孪生模型的构建成本。
附图简要说明
为了更清楚地说明本申请实施例中的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1为本申请一实施例提供的多物流场景的数字孪生模型构建方法的应用场景图。
图2为本申请一实施例提供的多物流场景的数字孪生模型构建方法的流程示意图。
图3为本申请另一实施例提供的多物流场景的数字孪生模型构建方法的流程示意图。
图4为本申请另一实施例提供的多物流场景的数字孪生模型构建方法的流程示意图。
图5为本申请另一实施例提供的多物流场景的数字孪生模型构建方法的流程示意图。
图6为本申请另一实施例提供的多物流场景的数字孪生模型构建方法的流程示意图。
图7为本申请另一实施例提供的多物流场景的数字孪生模型构建方法的流程示意图。
图8为本申请另一实施例提供的多物流场景的数字孪生模型构建方法的流程示意图。
图9为本申请一实施例提供的多物流场景的数字孪生模型构建装置的结构示意图。
图10为本申请一实施例提供的计算机设备的示意性框图。
实施本发明的方式
请参阅图1,系统架构100可以包括终端设备101、102、103,网络104和服务器105。网络104用以在终端设备101、102、103和服务器105之间提供通信链路的介质。网络104可以包括各种连接类型,例如有线、无线通信链路或者光纤电缆等等。
用户可以使用终端设备101、102、103通过网络104与服务器105交互,以接收或发送消息等。终端设备101、102、103上可以安装有各种通讯客户端应用,例如网页浏览器应用、搜索类应用、即时通信工具等。
终端设备101、102、103可以是具有显示屏并且支持网页浏览的各种电子设备,包括但不限于智能手机、平板电脑、膝上型便携计算机和台式计算机等等。
服务器105可以是提供各种服务的服务器,例如对终端设备101、102、103上显示的页面提供支持的后台服务器。
需要说明的是,本申请实施例所提供的多物流场景的数字孪生模型构建方法一般由服务器执行,相应地,多物流场景的数字孪生模型构建装置一般配置于服务器中。可选地,在其他实施例中,多物流场景的数字孪生模型构建方法可由终端设备执行,或由终端设备和服务器交互执行。
应该理解,图1中的终端设备、网络和服务器的数目仅仅是示意性的。根据实现需要,可以具有任意数目的终端设备、网络和服务器。
下面对所述的多物流场景的数字孪生模型构建方法进行详细说明。
请参阅图2,图2示出了多物流场景的数字孪生模型构建方法的具体实施方式。
需注意的是,若有实质上相同的结果,本申请的方法并不以图2所示的流程顺序为限。如图2所示,多物流场景的数字孪生模型构建方法包括以下步骤S110~S160。
S110、构建读写数据接口,其中,读写数据接口包括第一接口和第二接口,第一接口用于获取目标物流场景中物流实体对应的物流实体数据,第二接口用于获取目标物流场景中多个物流实体之间的事件网络。
在本实施例中,读写数据接口可用于获取物流场景数据。物流场景数据可包括物流场景中物流实体对应的物流实体数据以及物流场景中多个物流实体之间的事件网络。例如,读写数据接口包括第一接口和第二接口,第一接口用于对多物流场景中物流实体数据进行读写以实现物流实体的本体模型的构建,第二接口用于对单个物流场景中各物流实体之间的事件网络进行读写,以及对多物流场景中各物流实体之间的事件网络进行读写,以实现物流实体的智能化。其中,读写数据接口为用于实现对数据进行读写操作的功能封装,其可以为Jave接口。在完成读写数据接口的构建后,通过读写数据接口在存储有构建多物流场景的数字孪生模型所需数据的数据库中进行读写操作,以获取相应的数据,进而便可以实现对多物流场景下的数字孪生模型的构建。
在另一实施例中,如图3所示,在步骤S110之前,该多物流场景的数字孪生模型构建方法还包括步骤S210和S220。
S210、获取多物流场景的约束条件和目标函数;
S220、根据多物流场景的约束条件以及目标函数构建多物流场景的数字孪生框架。
在本实施例中,多物流场景的数字孪生框架中的每个节点为多物流场景中每个物流场景,多物流场景相互独立,多物流场景可以为多级中转场物流场景,也可以为同一区域下的多个独立网点构成的多物流场景。当多物流场景为多级中转场物流场景时,每个一级中转场、每个二级中转场均可作为多物流场景中的一个物流场景,即每个一级中转场、每个二级中转场均可作为多物流场景的数字孪生框架中的一个节点;当多物流场景为同一区域下的多个独立网点构成的多物流场景时,同一区域下的每个网点均可作为多物流场景中的一个物流场景,即同一区域下的每个网点均可作为多物流场景的数字孪生框架中的一个节点,其中,同一区域中的区域可以为省、市或县。
目标函数为用于生成多物流场景的最优决策的函数。约束条件为在多物流场景下影响决策方案的限制条件,如约束条件可以为一个网点可以向多个客户运输货物且一个客户只能对应一个网点。此时网点向每个客户运输货物构成一个物流场景,网点向每个客户运输货物的成本则可以作为多物流场景的目标函数中的一个变量函数,而目标函数中的变量函数均相互独立,故多物流场景的目标函数中的一个变量函数可以作为单一物流场景的目标函数。在确定多物流场景的约束条
件以及目标函数后,便可以将多物流场景分解成多个独立的物流场景,进而便可以构建出多物流场景下的数字孪生框架。
以同一区域下的多个独立网点构成的多物流场景为例,多物流场景中每个物流场景对应的目标函数可以为f(z)=f(x)+f(y),其中,f(x)为人力成本函数,f(y)为网点租赁成本函数,此时多物流场景中每个物流场景对应的目标函数可以作为多物流场景的目标函数中的一个变量函数,从而便可以得到多物流场景的目标函数为f(u)=f(z1)+f(z2)+f(z3)…+f(zn)。
S120、根据第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络。
在一实施例中,物流场景的物流实体数据以及各物流实体之间的第一事件网络是构成物流场景的数字孪生模型的要素,其中,物流实体可以理解为物流场景下的多个要素,比如物流实体可以是物流场景下的运输人员、分拣人员、货物、运输工具等真实存在的实物。物流实体数据为构建对应物流实体的本体模型的要素,如物流实体的尺寸、属性等。第一事件网络为一个物流场景中各物流实体之间关联关系的映射,各物流实体之间的关联关系决定了各物流实体之间的交互规则,如货物需要在下午三点之前派送至客户,便构成了一个物流实体之间的网络事件。
S130、根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型。
由于物流场景下的数字孪生模型可以将物流场景通过数字化表达映射至数字世界中,以实现物流场景中各物流实体之间的交互,故在构建物流场景的数字孪生模型的过程中,可以分别调用第一接口、第二接口以获取多物流场景中每个物流场景的多个物流实体数据以及物流场景中各物流实体之间的第一事件网络,进而便可以构建出多物流场景中每个物流场景下的数字孪生模型。
在另一实施例中,如图4所示,步骤S130包括步骤S131和S132。
S131、根据多个物流实体数据构建物流场景的多个智能体;
S132、根据第一事件网络、多个智能体构建物流场景下的数字孪生模型。
在本实施例中,智能体是指在物流场景中可以与其他物流实体进行智能交互的实体,智能体包括静态智能体和动态智能体,其中,静态智能体为不具备运动特性的智能体,如仓库,动态智能体为具备运动特性的智能体,如分拣员。在获取到智能体对应的物流实体数据后,便可以构建出智能体的本体模型,此时智能体的本体模型需进行智能化开发,如为产品添加新的模组、写固件、调试、测试等等,进而得到物流场景下的智能体。
在另一实施例中,如图5所示,步骤S131包括步骤S1311和S1312。
S1311、根据多个物流实体数据构建多个物流实体分别对应的本体模型;
S1312、将多个物流实体分别对应的本体模型进行数字化映射,得到多个物流实体分别对应的智能体。
具体的,在采用物流实体数据生成对应智能体的本体模型时,可以在仿真软件(如Anylogic、Flexsim等)中输入对应的参数,便可以得到对应智能体的本体模型。而此时本体模型可以用于表征物流场景中的物流实体,其难以表征物流实体可以在物流场景中与其他物流实体进行智能交互,故需要将本体模型进行数字化映射以对智能体的本体模型进行改造,如对各本体模型配置可以进行交互的连接层插件,进而便可以得到物流实体对应的智能体。其中,连接层插件为能够基于本体模型的输入输出需求,将各本体模型之间的交互数据进行加工并以消息/接口调用的方式进行输出的组件。
在另一实施例中,如图6所示,步骤S132包括步骤S1321和S1322。
S1321、将物流场景的多个智能体在物流场景的数字孪生框架中进行配置,得到配置后的多个智能体;
S1322、根据第一事件网络连接配置后的多个智能体,以得到物流场景下的数字孪生模型。
其中,多物流场景中每个物流场景的数字孪生框架同样也可以通过对应物流场景的约束条件以及目标函数构建得到。具体的,分别以多个物流场景中的一个物流场景作为目标物流场景,当对目标物流场景下的数字孪生模型进行构建时,只需根据目标物流场景的约束条件以及目标函数确定目标物流场景的数字孪生框架,并将目标物流场景的所有智能体在其数字孪生框架中进行配置。智能体配置完成后,通过各智能体所对应的物流实体之间的第一事件网络构建目标物流场景中智能体之间能进行交互的关联接口,并根据关联接口将各智能体进行连接,进而便可以得到目标物流场景下的数字孪生模型。其中,关联接口由输入接口和输出接口构成,输入接口以及输出接口的构建可以根据目标物流场景中智能体之间进行交互的规则开发得到。
S140、基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至物流场景的网络节点上。
在本实施例中,多物流场景的数字孪生框架为分布式架构,多物流场景的数字孪生框架上的每个节点为多物流场景中的一个物流场景,一个物流场景对应有一个数字孪生模型,故对多物流场景下的数字孪生模型在进行部署时,可以分别将每个物流场景下的数字孪生模型进行分布式部署,单个物流场景下的数字孪生
模型只需在一个网络节点上进行运行,进而可以解决多物流场景的数字孪生模型集中运行时成本较高的技术问题。
S150、根据第二接口获取多物流场景的物流实体之间的第二事件网络。
具体的,第二事件网络为两个物流场景的物流实体之间关联关系的映射,通过调用第二接口,并执行相应的指令,便可以对对应的数据库进行读取,以获取各物流场景的各物流实体之间的第二事件网络。
S160、根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。
在本实施例中,第二事件网络可以模拟出两个物流场景的物流实体之间的交互,故通过第二事件网络构建两个物流场景中智能体之间能进行交互的关联接口,并根据关联接口以实现将两个物流场景的物流实体之间对应的智能体进行连接。如此,便可以实现各网络节点上的数字孪生模型之间的连接,进而便可以实现各物流场景的数字孪生模型之间相互通信,从而构建出具有分布式结构的多物流场景下的数字孪生模型。
在另一实施例中,如图7所示,在步骤S160之后,该多物流场景的数字孪生模型构建方法还包括步骤S170、S180和S190。
S170、获取物流场景的事件参数;
S180、将事件参数输入至物流场景下的数字孪生模型中,得到物流场景下的第一分析结果;
S190、将多物流场景分别对应的第一分析结果输入至多物流场景下的数字孪生模型中,得到多物流场景下的第二分析结果。
在本实施例中,事件参数可以为影响物流场景运行结果的要素,事件参数可根据多物流场景下的数字孪生模型中各个物流实体的实施状态得到,第一分析结果为每个物流场景的目标函数的最优解,第二分析结果为多物流场景的目标函数的最优解。以由同一等级的多中转场构成的多物流场景为例,每个中转场均可作为一个物流场景,当每个中转场需向其他中转场同时运输一批货物时,此时每个物流场景下的可调配的运输工具、人员、向其他中转场运输货物的数量以及期限等可以作为每个物流场景的事件参数。在得到每个物流场景的事件参数后,将事件参数输入至对应物流场景下的数字孪生模型中,便可以得到影响每个物流场景的第一分析结果。然后将每个物流场景下的第一分析结果作为多物流场景的目标函数中的变量输入至多物流场景下的数字孪生模型中,以得到事件参数对多物流场景的影响,进而便可以为多物流场景下的人员设备优化提供最优决策。
在另一实施例中,如图8所示,在步骤S160之后,该多物流场景的数字孪生模型构建方法还包括步骤S310和S320。
S310、调用第二接口以获取多物流场景下多个物流实体之间的第三事件网络;
S320、根据第三事件网络对多物流场景的数字孪生模型进行更新。
具体的,第三事件网络为构建多物流场景的数字孪生模型后多物流场景下各物流实体之间增加或减少的关联信息。在多物流场景的数字孪生模型构建完成后,可以通过第二接口以实时获取多物流场景下的各智能体所对应物流实体之间的第三事件网络,即各物流实体之间当前的关联信息。通过重新构建各物流实体对应的智能体之间进行交互的关联接口,以实现对多物流场景下的数字孪生模型的更新,进而使得各物流场景下的数字孪生模型与其对应的物流场景更加匹配。
在本申请实施例所提供的多物流场景的数字孪生模型构建方法中,通过构建用于获取物流场景数据的读写数据接口,其中,读写数据接口包括第一接口和第二接口,第一接口用于获取物流场景的物流实体数据,第二接口用于获取物流场景中多个物流实体之间的事件网络;根据第一接口获取多个物流场景中每个物流场景的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络;根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型;基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至物流场景的网络节点上;根据第二接口获取多物流场景的物流实体之间的第二事件网络;根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。本申请无需在构建多物流场景的数字孪生模型的过程中进行额外的硬件配置,极大的降低了多物流场景的数字孪生模型的构建成本,同时还可以实时控制物流场景硬件设施,以降低场地运营成本。
本申请实施例还提供了一种多物流场景的数字孪生模型构建装置,该装置用于执行前述多物流场景的数字孪生模型构建方法的任一实施例。
具体地,请参阅图9,图9是本申请实施例提供的多物流场景的数字孪生模型构建装置的结构示意图。
如图9所示,多物流场景的数字孪生模型构建装置包括:第一构建单元110、第一获取单元120、第二构建单元130、部署单元140、第二获取单元150和第一连接单元160。
第一构建单元110,用于构建读写数据接口,其中,读写数据接口包括第一接口和第二接口,第一接口用于获取目标物流场景中物流实体对应的物流实体数据,第二接口用于获取目标物流场景中多个物流实体之间的事件网络。
在本申请其他实施例中,多物流场景的数字孪生模型构建装置还包括第三获取单元和第三构建单元。
第三获取单元,用于获取多物流场景的约束条件和目标函数;第三构建单元,用于根据多物流场景的约束条件以及目标函数构建多物流场景的数字孪生框架。
第一获取单元120,用于根据第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络。
第二构建单元130,用于根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型。
在本申请其他实施例中,第二构建单元130包括第四构建单元和第五构建单元。
第四构建单元,用于根据多个物流实体数据构建物流场景的多个智能;第五构建单元,用于根据第一事件网络、多个智能体构建物流场景下的数字孪生模型。
在本申请其他实施例中,第四构建单元包括:第六构建单元和映射单元。
第六构建单元,用于根据多个物流实体数据构建物流场景中多个物流实体分别对应的本体模型;映射单元,用于将物流场景中多个物流实体分别对应的本体模型进行数字化映射,得到物流场景中多个物流实体分别对应的智能体。
在本申请其他实施例中,第五构建单元包括:配置单元和第二连接单元。
配置单元,用于将物流场景的多个智能体在物流场景的数字孪生框架中进行配置,得到配置后的多个智能体;第二连接单元,用于根据第一事件网络连接配置后的多个智能体,以得到物流场景下的数字孪生模型。
部署单元140,用于基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至物流场景的网络节点上。
第二获取单元150,用于根据第二接口获取多物流场景的物流实体之间的第二事件网络。
第一连接单元160,用于根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。
在本申请其他实施例中,多物流场景的数字孪生模型构建装置还包括第四获取单元、第一输入单元和第二输入单元。
第四获取单元,用于获取物流场景的事件参数;第一输入单元,用于将事件参数输入至物流场景下的数字孪生模型中,得到物流场景下的第一分析结果;第二输入单元,用于将多物流场景分别对应的第一分析结果输入至多物流场景下的数字孪生模型中,得到多物流场景下的第二分析结果。
在本申请其他实施例中,多物流场景的数字孪生模型构建装置还包括第五获取单元和更新单元。
第五获取单元,用于调用第二接口以获取多物流场景下多个物流实体之间的第三事件网络;
更新单元,用于根据第三事件网络对多物流场景的数字孪生模型进行更新。
本申请实施例所提供的多物流场景的数字孪生模型构建装置用于执行上述多物流场景的数字孪生模型构建方法,可以解决多物流场景的数字孪生模型构建的瓶颈,同时降低多物流场景的数字孪生模型的构建成本。
请参阅图10,图10是本申请实施例提供的计算机设备的示意性框图。
参阅图10,该计算机设备500包括通过系统总线501连接的处理器502、存储器和网络接口505,其中,存储器可以包括存储介质503和内存储器504。
该存储介质503可存储操作系统5031和计算机程序5032。该计算机程序5032被执行时,可使得处理器502执行多物流场景的数字孪生模型构建方法。
该处理器502用于提供计算和控制能力,支撑整个设备500的运行。
该内存储器504为非易失性存储介质503中的计算机程序5032的运行提供环境,该计算机程序5032被处理器502执行时,可使得处理器502执行多物流场景的数字孪生模型构建方法。
该网络接口505用于进行网络通信,如提供数据信息的传输等。本领域技术人员可以理解,图10中示出的结构,仅仅是与本申请方案相关的部分结构的框图,并不构成对本申请方案所应用于其上的设备500的限定。具体的设备500可以包括比图中所示更多或更少的部件,或者组合某些部件,或者具有不同的部件布置。
其中,处理器502用于运行存储在存储器中的计算机程序5032,以实现如下功能:构建读写数据接口,其中,读写数据接口包括第一接口和第二接口,第一接口用于获取目标物流场景中物流实体对应的物流实体数据,第二接口用于获取目标物流场景中多个物流实体之间的事件网络;根据第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络;根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型;基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至物流场景的网络节点上;根据第二接口获取多物流场景的物流实体之间的第二事件网络;根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。
本领域技术人员可以理解,图10中示出的设备500的实施例并不构成对设备500具体构成的限定,在其他实施例中,设备500可以包括比图示更多或更少的部件,或者组合某些部件,或者不同的部件布置。例如,在一些实施例中,设
备500可以仅包括存储器及处理器502,在这样的实施例中,存储器及处理器502的结构及功能与图10所示实施例一致,在此不再赘述。
应当理解,在本申请实施例中,处理器502可以是中央处理单元(Central Processing Unit,CPU),该处理器502还可以是其他通用处理器502、数字信号处理器502(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现成可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。其中,通用处理器502可以是微处理器502或者该处理器502也可以是任何常规的处理器502等。
在本申请的另一实施例中提供计算机存储介质。该存储介质可以为非易失性的计算机可读存储介质,也可以是易失性的存储介质。该存储介质存储有计算机程序5032,其中计算机程序5032被处理器502执行时实现以下步骤:构建读写数据接口,其中,读写数据接口包括第一接口和第二接口,第一接口用于获取目标物流场景中物流实体对应的物流实体数据,第二接口用于获取目标物流场景中多个物流实体之间的事件网络;根据第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据第二接口获取物流场景中多个物流实体之间的第一事件网络;根据多个物流实体数据、第一事件网络构建物流场景下的数字孪生模型;基于多物流场景的数字孪生框架,将多物流场景中每个物流场景下的数字孪生模型部署至物流场景的网络节点上;根据第二接口获取多物流场景的物流实体之间的第二事件网络;根据第二事件网络连接多物流场景对应的多个网络节点上的数字孪生模型,以得到多物流场景下的数字孪生模型。
所属领域的技术人员可以清楚地了解到,为了描述的方便和简洁,上述描述的设备、装置和单元的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、计算机软件或者二者的结合来实现,为了清楚地说明硬件和软件的可互换性,在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。
在本申请所提供的几个实施例中,应该理解到,所揭露的设备、装置和方法,可以通过其它的方式实现。例如,以上所描述的装置实施例仅仅是示意性的,例如,所述单元的划分,仅仅为逻辑功能划分,实际实现时可以有另外的划分方式,也可以将具有相同功能的单元集合成一个单元,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。另外,所显示或讨
论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口、装置或单元的间接耦合或通信连接,也可以是电的,机械的或其它的形式连接。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本申请实施例方案的目的。
另外,在本申请各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个存储介质中。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分,或者该技术方案的全部或部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台设备500(可以是个人计算机,服务器,或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述,仅为本申请的具体实施方式,但本申请的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本申请揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本申请的保护范围之内。因此,本申请的保护范围应以权利要求的保护范围为准。
Claims (15)
- 一种多物流场景的数字孪生模型构建方法,其特征在于,包括:构建读写数据接口,其中,所述读写数据接口包括第一接口和第二接口,所述第一接口用于获取目标物流场景中物流实体对应的物流实体数据,所述第二接口用于获取所述目标物流场景中多个物流实体之间的事件网络;根据所述第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据所述第二接口获取所述物流场景中所述多个物流实体之间的第一事件网络;根据所述多个物流实体数据、所述第一事件网络构建所述物流场景下的数字孪生模型;基于所述多物流场景的数字孪生框架,将所述多物流场景中每个物流场景下的数字孪生模型部署至所述物流场景的网络节点上;根据所述第二接口获取所述多物流场景的物流实体之间的第二事件网络;根据所述第二事件网络连接所述多物流场景对应的多个网络节点上的数字孪生模型,以得到所述多物流场景下的数字孪生模型。
- 根据权利要求1所述的多物流场景的数字孪生模型构建方法,其特征在于,在所述构建读写数据接口之前,所述多物流场景的数字孪生模型构建方法还包括:获取所述多物流场景的约束条件和目标函数;根据所述多物流场景的约束条件以及目标函数构建所述多物流场景的数字孪生框架。
- 根据权利要求1或2所述的多物流场景的数字孪生模型构建方法,其特征在于,所述根据所述多个物流实体数据、所述第一事件网络构建所述物流场景下的数字孪生模型,包括:根据所述多个物流实体数据构建所述物流场景的多个智能体;根据所述第一事件网络、所述多个智能体构建所述物流场景下的数字孪生模型。
- 根据权利要求3所述的多物流场景的数字孪生模型构建方法,其特征在于,所述根据所述多个物流实体数据构建所述物流场景的多个智能体,包括:根据所述多个物流实体数据构建所述物流场景中所述多个物流实体分别对应的本体模型;将所述物流场景中所述多个物流实体分别对应的本体模型进行数字化映射,得到所述物流场景中所述多个物流实体分别对应的智能体。
- 根据权利要求3或4所述的多物流场景的数字孪生模型构建方法,其特征在于,所述根据所述第一事件网络、所述多个智能体构建所述物流场景下的数字孪生模型,包括:将所述物流场景的多个智能体在所述物流场景的数字孪生框架中进行配置,得到配置后的多个智能体;根据所述第一事件网络连接所述配置后的多个智能体,以得到所述物流场景下的数字孪生模型。
- 根据权利要求1至5中任一项所述的多物流场景的数字孪生模型构建方法,其特征在于,在所述根据所述第二事件网络连接所述多物流场景对应的多个网络节点上的数字孪生模型,以得到所述多物流场景下的数字孪生模型之后,所述多物流场景的数字孪生模型构建方法还包括:获取所述物流场景的事件参数;将所述事件参数输入至所述物流场景下的数字孪生模型中,得到所述物流场景下的第一分析结果;将所述多物流场景分别对应的第一分析结果输入至所述多物流场景下的数字孪生模型中,得到所述多物流场景下的第二分析结果。
- 根据权利要求1至6中任一项所述的多物流场景的数字孪生模型构建方法,其特征在于,在所述根据所述第二事件网络连接所述多物流场景对应的多个网络节点上的数字孪生模型,以得到所述多物流场景下的数字孪生模型之后,所述多物流场景的数字孪生模型构建方法还包括:调用所述第二接口以获取所述多物流场景下多个物流实体之间的第三事件网络;根据所述第三事件网络对所述多物流场景的数字孪生模型进行更新。
- 一种多物流场景的数字孪生模型构建装置,其特征在于,包括:第一构建单元,用于构建读写数据接口,其中,所述读写数据接口包括第一接口和第二接口,所述第一接口用于获取目标物流场景中物流实体对应的物流实体数据,所述第二接口用于获取所述目标物流场景中多个物流实体之间的事件网络;第一获取单元,用于根据所述第一接口获取多物流场景中每个物流场景的多个物流实体对应的多个物流实体数据,并根据所述第二接口获取所述物流场景中所述多个物流实体之间的第一事件网络;第二构建单元,用于根据所述多个物流实体数据、所述第一事件网络构建所述物流场景下的数字孪生模型;部署单元,用于基于所述多物流场景的数字孪生框架,将所述多物流场景中每个物流场景下的数字孪生模型部署至所述物流场景的网络节点上;第二获取单元,用于根据所述第二接口获取所述多物流场景的物流实体之间的第二事件网络;第一连接单元,用于根据所述第二事件网络连接所述多物流场景对应的多个网络节点上的数字孪生模型,以得到所述多物流场景下的数字孪生模型。
- 根据权利要求8所述的多物流场景的数字孪生模型构建装置,其特征在于,还包括:第三获取单元,用获取所述多物流场景的约束条件和目标函数;第三构建单元,用于根据所述多物流场景的约束条件以及目标函数构建所述多物流场景的数字孪生框架。
- 根据权利要求8或9所述的多物流场景的数字孪生模型构建装置,其特征在于,所述第二构建单元用于:根据所述多个物流实体数据构建所述物流场景的多个智能体;根据所述第一事件网络、所述多个智能体构建所述物流场景下的数字孪生模型。
- 根据权利要求10所述的多物流场景的数字孪生模型构建装置,其特征在于,所述第二构建单元用于:根据所述多个物流实体数据构建所述物流场景中所述多个物流实体分别对应的本体模型;将所述物流场景中所述多个物流实体分别对应的本体模型进行数字化映射,得到所述物流场景中所述多个物流实体分别对应的智能体。
- 根据权利要求10或11所述的多物流场景的数字孪生模型构建装置,其特征在于,所述第二构建单元用于:将所述物流场景的多个智能体在所述物流场景的数字孪生框架中进行配置,得到配置后的多个智能体;根据所述第一事件网络连接所述配置后的多个智能体,以得到所述物流场景下的数字孪生模型。
- 根据权利要求8至12中任一项所述的多物流场景的数字孪生模型构建装置,其特征在于,还包括:第四获取单元,用于获取所述物流场景的事件参数;第一输入单元,用于将所述事件参数输入至所述物流场景下的数字孪生模型中,得到所述物流场景下的第一分析结果;第二输入单元,用于将所述多物流场景分别对应的第一分析结果输入至所述多物流场景下的数字孪生模型中,得到所述多物流场景下的第二分析结果。
- 一种计算机设备,包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序,其特征在于,所述处理器执行所述计算机程序时实现如权利要求1至7中任一项所述的多物流场景的数字孪生模型构建方法。
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质存储有计算机程序,所述计算机程序当被处理器执行时使所述处理器执行如权利要求1至7任一项所述的多物流场景的数字孪生模型构建方法。
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US20220083976A1 (en) * | 2020-09-17 | 2022-03-17 | International Business Machines Corporation | Digital twin based supply chain routing |
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US20210294946A1 (en) * | 2020-03-19 | 2021-09-23 | Koninklijke Philips N.V. | Selecting and applying digital twin models |
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