DIGITAL TWIN SIMULATION FOR TRANSPORTATION

A processor may receive first object data associated with a first object to be transported. The processor may receive vehicle data associated with one or more potential vehicles for transportation of the first object. The processor may receive context data associated with a context for the transportation of the first object. The processor may simulate the transportation of the first object utilizing each of the one or more potential vehicles using digital twin simulation. The processor may select a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation.

BACKGROUND

The present disclosure relates generally to the field of digital twin simulation, and more specifically to digital twin simulation of transportation of an object.

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

SUMMARY

Embodiments of the present disclosure include a method, computer program product, and system for digital twin simulation of transportation of an object.

A processor may receive first object data associated with a first object to be transported. The processor may receive vehicle data associated with one or more potential vehicles for transportation of the first object. The processor may receive context data associated with a context for the transportation of the first object. The processor may simulate the transportation of the first object utilizing each of the one or more potential vehicles using digital twin simulation. The processor may select a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to the field of digital twin simulation, and more specifically to digital twin simulation of transportation of an object. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.

In some embodiments, a processor may receive object data associated with an object to be transported. In some embodiments, the object data may relate to factors associated with the object related to the transportation of the object. In some embodiments, the object data may be associated with the size of the object, the weight of the object, the shape of the object, or the dimensions of the object. In some embodiments, the object data may be associated with the materials from which the object is made (e.g., fragile or temperature sensitive materials). In some embodiments, the object data may relate to aspects of the packaging or container for the object that may affect the outcome or process of transportation of the object, including packaging/container size, packaging/container weight, packaging/container materials (e.g., waterproof, water absorbent, rigid, flexible, etc.), packaging/container dimensions, etc. In some embodiments, the object data may be received specifically for each object. In some embodiments, the object data may be obtained from historical information stored in a database (e.g., historical information obtained from previous transportation of objects or standard package types). In some embodiments, the object data may include information regarding the route the object is to be transported, including origin and destination locations, conditions likely to be encountered on potential routes between the origin and destination (e.g., inclement weather, constraints associated with potential routes between the origin and destination (e.g., narrow roads), etc.

In some embodiments, the processor may receive vehicle data associated with one or more potential vehicles for transportation of the object. In some embodiments, the vehicle data may relate to the type of vehicle used to transport the object from its origin to its destination. As non-limiting examples, the vehicle may include a motorcycle, bicycle, drone, truck, three-wheeled motor vehicle, an autonomous vehicle, a boat, or any other device suitable for movement over land, air, or water and conveyance of the object from along the route, or portions of the route, from the origin to destination point.

In some embodiments, the vehicle data may include vehicle specific information regarding the operation, repair, or maintenance of the vehicle. For example, the vehicle data may include information regarding the tire pressure of a vehicle, motor/transmission oil levels of the vehicle (e.g., low transmission oil may make mountainous travel difficult), damage to the vehicle (e.g., a damaged flatbed may carry less weight), repair status of the vehicle (e.g., utilization of a replacement tire may permit the vehicle to carry less weight), fuel economy of the vehicle (e.g., an effect of performance/maintenance of vehicle), carrying capacity of the vehicle, road conditions required for travel (e.g., truck requires wider road), dimensions of vehicle (e.g., height of the vehicle which may relate to overpasses, width of the vehicle which may affect its ability to travel on narrow roads). In some embodiments, the vehicle data may be based on historical information (e.g., historical maintenance information for a category/type of vehicles, etc.).

In some embodiments, the processor may receive context data associated with the context for the transportation of the object. In some embodiments, the context data may relate to background conditions that may be encountered while transporting the object along the route from the origin to the destination. In some embodiments, the context data may relate to the route to be traveled and may include: traffic conditions (e.g., crawling traffic, jammed traffic, speed of traffic flow, volume of traffic, traffic flow patterns, availability of pathways for flow of traffic) and road conditions (e.g., obstructed roads, dirt roads, paved roads, number of traffic lights on a road, state of operation of traffic lights, potholes, low bridges, weight limits, flooding, etc.). In some embodiments, the context data may relate to weather or environmental conditions (e.g., rain, snow, hail, high winds, flooding, high tide, etc.). In some embodiments, the context data may be based on historical information, including historical traffic conditions, historical contextual situations (e.g., bad roads, low weight tolerant bridges, unpredictable rain/flash flooding during a particular time of year), etc.

In some embodiments, the processor may simulate the transportation of the object utilizing each of the one or more potential vehicles using digital twin simulation. In some embodiments, the processor may retrieve a digital twin simulation for a type of vehicle stored in a repository of simulations. In some embodiments, the processor may generate a new digital twin simulation of the vehicle. In some embodiments, the digital twin may simulate the overall condition of the vehicle to provide a simulation of the outcome and conditions associated with transportation of an object from its origin to its destination (e.g., fuel economy of the vehicle during successful travel over the route, halted travel along the route resulting from a breakdown in the vehicle, damage to the object resulting from route conditions, the capabilities of/features of (e.g., type of suspension) the vehicle used for transportation, etc.). In some embodiments, the digital twin may simulate transportation from origin to destination based on location, object details, and background context associated with the transportation (e.g., poor/moderate condition vehicle may not travel long distance without incidence). In some embodiments, the processor may receive information about the outcome of the simulated transportation, including the timeline for delivery, fuel usage, damage or wear to vehicle, status of the transportation task (e.g., successful completion vs. breakdown along the route).

In some embodiments, the processor may select a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation. In some embodiments, the optimization factor may relate to the timeline of transportation (e.g., fastest), costs associated with the transportation (e.g., fuel, toll, wear and tear on the vehicle), environmental factors (e.g., least fuel consumption), other delivery factors (e.g., use of the same vehicle for multiple deliveries along the same or similar transportation route). In some embodiments, the optimization factor may be selectable from (e.g., by a user or by the processor): transportation cost, transportation time, damage (e.g., from wear and tear) to the vehicle, distance to be traveled, number of objects to be transported along the same route or portion of a route (e.g., lump packages to same locality in one delivery truck to minimize number of miles driven), etc.

In some embodiments, the processor may generate the optimization factor. In some embodiments, generating the optimization factor may include analyzing the digital twin simulation for one or more transportation impacts. In some embodiments, the processor may select at least one of the one or more transportation impacts on which to base the optimization factor. In some embodiments, one or more transportation impacts may include the timeline of transportation of the first object, costs associated with the transportation of the first object, environmental factors associated with the transportation of the first object, damage to vehicles, distance to be traveled, number of objects to be transported along the same route or portion of a route, other delivery factors (e.g., use of fewest vehicles), etc.

In some embodiments, the processor may send a command to a processor associated with the selected vehicle. In some embodiments, the processor may, based on the command, schedule transportation of the object. In some embodiments, the command may be sent to a processor associated with an autonomous vehicle that controls the timing and route of travel of the autonomous vehicle. In some embodiments, the command may be sent to a processor of a device that runs scheduling software that stores information regarding upcoming tasks (e.g., transportation routes and objects), the time of the upcoming tasks, the amount of time required for the completion of the task, reminders regarding the upcoming scheduled task, etc.

In some embodiments, the processor may select a second vehicle of the one or more potential vehicles based on an optimization of the optimization factor associated with a digital twin simulation of the second vehicle transporting the first object. In some embodiments, the processor may determine a first transportation route for the first vehicle and a second transportation route for the second vehicle for the transportation of the object based, at least in part, on the optimization of the optimization factor. In some embodiments, more than one vehicle may be used to transport the object along one or more portions of the transportation route between the origin and destination. In some embodiments, the processor may select the additional vehicles (e.g., second or more) based on optimization of the optimization factor used to select the first vehicle. In some embodiments, the processor may also determine the route that the first vehicle is to transport the object and the route that the second vehicle is to transport the vehicle. In some embodiments, the routes or portions of the route (for transportation of the object by the first and second vehicles) may also selected based on an optimization of the optimization factor. In some embodiments, a command may be sent to a processor associated with the first vehicle and the second vehicle to schedule transportation of the object.

In some embodiments, the processor may receive second object data. In some embodiments, the processor may simulate transportation of the second object. In some embodiments, the simulation may be based on the combination of the constraints associated with the first object data and the second object data. In some embodiments, the processor may select the first vehicle based on a combined optimization factor, where the combined optimization factor combines a set of constraints associated with the first object and a set of constraints associated with the second object. In some embodiments, the constraints associated with the first and/or second object may include: factors associated with the first object related to the transportation of the object, factors associated with the second object related to the transportation of the object, delivery time, delivery location for the first and/or second object, conditions needed for delivery based on both objects (e.g., refrigeration, high care for fragile items), the size of the first object and/or the second object, the weight of the first object and/or the second object, the shape of the first object and/or the second object, or the dimensions of the first object and/or the second object, the materials from which the first object and/or the second object are made, characteristics of the packaging or container for the first object and/or the second object that may affect the outcome or process of transportation of the first object and/or the second object, packaging/container dimensions, the route the first object is to be transported, the route the second object is to be transported, etc.

In some embodiments, the combined optimization factor may include the timeline of transportation of the first object and/or the second object, costs associated with the transportation of the first object and/or the second object, environmental factors associated with the transportation of the first object and/or the second object, damage to vehicles, distance to be traveled, number of objects to be transported along the same route or portion of a route, other delivery factors (e.g., use of fewest vehicles), etc.

In some embodiments, the processor may further determine a first transportation route for the first object and a second transportation route for the second object based, at least in part, on the optimization of the optimization factor. For example, the processor may determine a combined route that passes through the origin location for the first object, the destination location for the first object, the origin location for the second object, and the destination location for the second object. The combined route may include overlapping transportation routes for the first object and the second object which results in a reduction in costs associated with the transportation of the first object and the second object.

Referring now toFIG.1, a block diagram of a system100for digital twin simulation of transportation of an object is illustrated. System100includes user devices102A-B, vehicle computing devices104A-B, and a system device106. The user devices102A-B and vehicle computing devices104A-B are configured to be in communication with the system device106. The system device106includes a database108, a digital twin simulation machine110, and a transportation controller112. In some embodiments, the user devices102A-B, vehicle computing devices104A-B, and a system device106may be any devices that contain a processor configured to perform one or more of the functions or steps described in this disclosure.

In some embodiments, first object data associated with a first object to be transported is received from the user device102A by the system device106. The system device106also receives vehicle data associated with one or more potential vehicles for transportation of the object and context data associated with a context for the transportation of the object. The first object data, vehicle data, and context data may be based, at least in part, on historical data stored in database108. The first object data, vehicle data, and context data are used by the digital twin simulation machine110of the system device106to simulate the transportation of the first object utilizing each of the one or more potential vehicles. The transportation controller112of the system device106is used to select a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation.

In some embodiments, the transportation controller112sends a command to a processor associated with the selected vehicle (e.g., to vehicle computing device104A) and schedules, based on the command, transportation of the first object.

In some embodiments, the transportation controller112may select a second vehicle of the one or more potential vehicles (e.g., to a vehicle in communication with vehicle computing device104B) based on an optimization of the optimization factor associated with a digital twin simulation of the second vehicle transporting the first object. In some embodiments, the transportation controller112may determine a first transportation route for the first vehicle and a second transportation route for the second vehicle for the transportation of the first object based, at least in part, on the optimization of the optimization factor.

In some embodiments, the transportation controller112may generate the optimization factor. In some embodiments, the transportation controller112may analyze the digital twin simulation for one or more transportation impacts and select at least one of the one or more transportation impacts on which to base the optimization factor.

In some embodiments, the system device106may receiving second object data from user device102B. In some embodiments, the digital twin simulation machine110may be used to simulate transportation of the second object, wherein the simulation is based on the combination of the constraints associated with the first object data and the second object data. In some embodiments, the transportation controller112may select the first vehicle for transportation of the first object and the second object based on a combined optimization factor. In some embodiments, the combined optimization factor combines a set of constraints associated with the first object and a set of constraints associated with the second object. In some embodiments, the transportation controller112may determine a first transportation route for the first object and a second transportation route for the second object based, at least in part, on the optimization of the optimization factor.

Referring now toFIG.2, illustrated is a flowchart of an exemplary method200for digital twin simulation of transportation of an object, in accordance with embodiments of the present disclosure. In some embodiments, a processor of a system may perform the operations of the method200. In some embodiments, method200begins at operation202. At operation202, the processor receives first object data associated with a first object to be transported. In some embodiments, method200proceeds to operation204, where the processor receives vehicle data associated with one or more potential vehicles for transportation of the first object. In some embodiments, method200proceeds to operation206. At operation206, the processor receives context data associated with a context for the transportation of the first object. In some embodiments, method200proceeds to operation208. At operation208, the processor simulates the transportation of the object utilizing each of the one or more potential vehicles using digital twin simulation. In some embodiments, method200proceeds to operation210. At operation210, the processor selects a first vehicle of the one or more potential vehicles based on an optimization of an optimization factor associated with an outcome of the digital twin simulation.

As discussed in more detail herein, it is contemplated that some or all of the operations of the method200may be performed in alternative orders or may not be performed at all; furthermore, multiple operations may occur at the same time or as an internal part of a larger process.

Characteristics are as follows:

Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of portion independence in that the consumer generally has no control or knowledge over the exact portion of the provided resources but may be able to specify portion at a higher level of abstraction (e.g., country, state, or datacenter).

Service Models are as follows:

Deployment Models are as follows:

FIG.3A, illustrated is a cloud computing environment310is depicted. As shown, cloud computing environment310includes one or more cloud computing nodes300with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone300A, desktop computer300B, laptop computer300C, and/or automobile computer system300N may communicate. Nodes300may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof.

This allows cloud computing environment310to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices300A-N shown inFIG.3Aare intended to be illustrative only and that computing nodes300and cloud computing environment310can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

FIG.3B, illustrated is a set of functional abstraction layers provided by cloud computing environment310(FIG.3A) is shown. It should be understood in advance that the components, layers, and functions shown inFIG.3Bare intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted below, the following layers and corresponding functions are provided.

Hardware and software layer315includes hardware and software components. Examples of hardware components include: mainframes302; RISC (Reduced Instruction Set Computer) architecture based servers304; servers306; blade servers308; storage devices311; and networks and networking components312. In some embodiments, software components include network application server software314and database software316.

Virtualization layer320provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers322; virtual storage324; virtual networks326, including virtual private networks; virtual applications and operating systems328; and virtual clients330.

Workloads layer360provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation362; software development and lifecycle management364; virtual classroom education delivery366; data analytics processing368; transaction processing370; and digital twin simulation of transportation of an object372.

FIG.4, illustrated is a high-level block diagram of an example computer system401that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer), in accordance with embodiments of the present disclosure. In some embodiments, the major components of the computer system401may comprise one or more CPUs402, a memory subsystem404, a terminal interface412, a storage interface416, an I/O (Input/Output) device interface414, and a network interface418, all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus403, an I/O bus408, and an I/O bus interface unit410.

The computer system401may contain one or more general-purpose programmable central processing units (CPUs)402A,402B,402C, and402D, herein generically referred to as the CPU402. In some embodiments, the computer system401may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system401may alternatively be a single CPU system. Each CPU402may execute instructions stored in the memory subsystem404and may include one or more levels of on-board cache.

One or more programs/utilities428, each having at least one set of program modules430may be stored in memory404. The programs/utilities428may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Programs428and/or program modules430generally perform the functions or methodologies of various embodiments.

Although the memory bus403is shown inFIG.4as a single bus structure providing a direct communication path among the CPUs402, the memory subsystem404, and the I/O bus interface410, the memory bus403may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface410and the I/O bus408are shown as single respective units, the computer system401may, in some embodiments, contain multiple I/O bus interface units410, multiple I/O buses408, or both. Further, while multiple I/O interface units are shown, which separate the I/O bus408from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses.

It is noted thatFIG.4is intended to depict the representative major components of an exemplary computer system401. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG.4, components other than or in addition to those shown inFIG.4may be present, and the number, type, and configuration of such components may vary.