Active preparation of repair service station

Abstract

Systems, methods, and / or computer program products predictively automate the configuration of modular service areas serving physical assets, maximize reuse of service areas, and optimize the time to serve multiple physical assets. A digital twinning model of physical assets is classified and arranged into a workflow of a service area, services performed by the physical assets reaching a service center are ordered, and the service area is prepared based on a type of requested service, an estimated time of arrival, and a similarity between classifications of different digital twinning of the physical assets. The method is based on the sequence of workflows, the arrival time of physical assets and overlapping among components, tools, machines and the like in each service area. The service center coordinates the robotic system to arrange service zones in a manner that minimizes latency between services, maximizes the number of physical assets maintained over a period of time, and reduces rearrangement of service zones between services provided to different physical assets.

Description

BACKGROUND OF THE DISCLOSURE

[0001] The present disclosure generally relates to the fields of artificial intelligence (AI) and digital twin technology. More specifically, AI and digital twins are used to classify maintenance profiles of physical assets, automate the layout of service centers responsible for maintaining or repairing physical assets, optimize the layout and workflow of service areas to maximize the services received by physical assets while minimizing waiting times.

[0002] A digital twin is a virtual representation of a physical object, system, or other asset. The digital twin tracks changes to the physical object, system, or other asset across the lifespan of the object and records those changes as they occur within the physical object. A digital twin is a complex virtual model that is an exact counterpart of a physical asset existing in the real space. Sensors and Internet of Things (IoT) devices connected to the physical asset typically collect data in real time. The collected data can then be mapped to the virtual model of the digital twin. Any individual with access to the digital twin can see real-time information about the physical asset operating in the real world without having to physically be present and view the physical asset while it is operating. Users, such as engineers, can use the digital twin not only to understand how the physical asset behaves but also to predict how the physical asset may behave in the future using data collected from sensors, IoT devices, and other sources of data and information that are being gathered. In addition, the digital twin can utilize information that helps manufacturers of physical assets understand how customers continue to use the product after the purchaser has bought the physical asset to assist the manufacturers and providers of the physical asset.

[0003] A classification algorithm generally can refer to a function that weights input features in a way that outputs a separation of two or more classes and then makes a decision based on the results of all classifiers. Classifier training can be performed to identify the weights and functions that provide the most accurate and optimal separation between data classes. Linear discriminant analysis is the most basic classifier, which identifies a linear weighting of multi-factor data as the mean that maximizes the distance between the means of two classes. However, for many data sets, the relative separation between classes is not well depicted by a single line. Artificial neural networks and random decision forests are recent computational methods that generate more complex partitions between classes. SUMMARY OF THE INVENTION

[0004] Embodiments of the present disclosure relate to a computer-implemented method, an associated computer system, and a computer program product that predictively automate the configuration of modular service areas for the repair or maintenance of physical assets and maximize the reuse of modular service areas for multiple physical assets. The computer-implemented method includes: receiving, by a processor, service requests from a plurality of physical assets, the locations of the physical assets, and the estimated arrival times of each of the physical assets at the service site, the service requests requesting the performance of services on the physical assets at the service site; analyzing, by the processor, digital twin models corresponding to each of the plurality of physical assets; creating, by the processor, a maintenance profile for each type of physical asset, the maintenance profile describing the classification of the services for the corresponding physical asset, the corresponding physical asset including one or more machines, tools, or components required to perform the services on the physical asset; creating, by the processor, one or more modular service areas within the service site based on commonalities between the maintenance profiles of the physical assets and the one or more machines, tools, or components required to perform the services, the modular service areas including at least one of the one or more machines, the tools, and the components required to perform the services on the physical assets; and instructing, by the processor, a robotic system located within the service site to create or modify the modular service areas by positioning the machines, the tools, and the components for each of the services to be performed on the physical assets within the modular service areas before the estimated arrival times of each of the physical assets at the service site. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The drawings included in the present disclosure are incorporated into the specification and form a part of the specification. The drawings illustrate embodiments of the present disclosure and, together with the specification, explain the principles of the present disclosure. The drawings are illustrative of only certain embodiments and do not limit the present disclosure.

[0006] Figure 1 A block diagram depicting an embodiment of a computer system and its components on which the embodiments described herein can be implemented in accordance with the present disclosure.

[0007] Figure 2 Depicts Figure 1 an extended block diagram of a computing system environment in which the computer system is configured to operate in a network environment (including a cloud environment) and perform the methods described herein in accordance with the present disclosure.

[0008] Figure 3Depicts a functional block diagram describing an embodiment of a computing environment according to the present disclosure, the computing environment being for predictively automating the configuration of a modular service area for repairing and maintaining multiple physical assets, while maximizing the reuse of the modular service area, minimizing wait times, and optimizing the total time for performing services.

[0009] Figure 4 Depicts a block diagram of an exemplary embodiment of a vehicle repair service that executes program code according to the present disclosure, the program code enabling the predictively automated configuration of a modular service area for repairing and maintaining physical assets.

[0010] Figure 5 Depicts a flowchart of an embodiment of a computer-implemented method according to the present disclosure for predictively automating the configuration of a modular service area for repairing and maintaining multiple physical assets.

[0011] Figure 6 Depicts a flowchart according to the present disclosure that depicts an embodiment of a computer-implemented method for maximizing the reuse of a modular service area to repair or maintain physical assets and optimizing the total service time for repairing or maintaining physical assets within the modular service area. Detailed Description

[0012] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that when used in this specification, the terms "comprises" and / or "comprising" specify the presence of the stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0013] All structural, material, acts, and equivalents of the corresponding structures, materials, acts, and functional elements of all parts or steps in the following claims are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the present disclosure. The selected and described embodiments are intended to best explain the principles of the present disclosure, practical applications, and to enable others of ordinary skill in the art to understand the present disclosure with various modifications suitable for the particular purposes contemplated.

[0014] Overview

[0015] Physical assets, such as motor vehicles, machines, equipment, or other devices (collectively referred to herein as "physical assets"), may wear out over time, require maintenance to prevent breakage or failure and / or require periodic repairs to fix physical assets that no longer work as intended or have become inoperable. Generally, physical assets that may require maintenance or repair can be taken to a service center with appropriate tools, parts, machines, equipment, and / or know-how to perform a repair process or maintenance on the physical asset. Different types of repairs and services can be requested for different types of physical assets that can be repaired or maintained within the service center. The service center can be equipped with different types of machines, tools, equipment, parts, or components, etc., to provide a wide range of different services to different types of physical assets. Tools, machinery, equipment, etc. can be used or applied to similar types of physical assets and / or physical assets that may have the same or similar repair and / or maintenance processes. Generally, when arranging services through a service center, physical assets such as vehicles, equipment, or machines can create a service log and send the data collected within the log to the service center, book appointments, and / or participate in various roadside or mobile repairs. Information about the asset, location data, and appointments can be ingested at the service center to identify the type of service required, spare parts that need to be on hand to perform the repair or maintenance service, and the machines or tools that may be required to perform the requested service. Appropriate parts, tools, machines, etc. can be manually arranged by employees stationed at the service center within a specific location where the repair is scheduled to take place.

[0016] Embodiments of the present disclosure recognize that initiating the retrieval of spare parts and manually arranging the different equipment, machines, tools, and parts required to perform one or more repairs at a service center can be a slow or time-consuming process. Arranging the spare parts, tools, equipment, and machines required for a service may require a sufficient amount of effort, coordination, and may require one or more service center employees to be diverted from other areas of the service center to provide actual services in order to ensure that the service area within the service center is properly prepared to service the next physical asset scheduled for repair, maintenance, or other services in a timely manner. The process of arranging the machines, tools, parts, components, or other accessories used to manage the service may be further complicated by the number of different types of services that the service center may schedule and the many differences between physical assets, which may require the service area to be reconfigured each time a new physical asset arrives for repair. Accordingly, there is a need to predictively automate the configuration of a modular service area for repairing or maintaining physical assets in order to maximize the reuse of the modular service area during the repair of multiple different types of physical assets, thereby minimizing service times and maximizing the number of physical assets that can be serviced.

[0017] Embodiments of the present disclosure utilize the use of AI-enabled systems and digital twin models to predictively automate the configuration of modular service areas that are equipped to service physical assets within a repair and / or maintenance service center. The automatic configuration of the service areas maximizes the reuse of the modular service areas to service one or more different types of physical assets and limits the number of reconfigurations of the service areas between different types of assets being serviced. Embodiments of the physical assets can create a repair request for a particular physical asset. The request can be sent to a repair service or other type of application or program, which can actively evaluate the digital twin model and system data corresponding to the physical asset. The repair service can identify the type of service that may be required (such as maintenance or repair), the estimated timing to complete the service, and any tools, machines, spare parts, components, or any other accessories that may be required for the performance of the identified service. Based on the analysis performed by the repair service, the repair service can identify a service center that is capable of performing the service on the physical asset. A portion of the service center (referred to herein as a modular service area (or simply "service area")) can be further identified as an acceptable location within the service center where the service can be provided. Each identified service area may already be equipped and preconfigured with parts, machines, tools, and / or components for performing repairs. Alternatively, the modular service area can be a location within the service center that can reasonably be made available for each of the parts, tools, equipment, and / or other components to be arranged therein, but may require at least a certain amount of reconfiguration to be easily equipped with tools, parts, and machines to perform the repairs.

[0018] Embodiments of an AI-enabled repair service application or program can apply one or more classification algorithms to the services identified as applicable to a physical asset for which a service request is submitted. The classification of the services applicable to the physical asset can indicate the type of spare parts, machines, tools, or other components that can be assembled or positioned within the modular service area prior to the physical asset arriving at the service center for performing the service. The repair service or application that classifies the services for the physical asset can map the services to the corresponding parts, equipment, tools, components, etc., as part of creating a maintenance profile for each physical asset. The repair service can compare the maintenance profiles to identify commonalities between different services that are planned to be applied to one or more physical assets. Based on the commonalities between the different mappings for each service and the estimated arrival time of the incoming physical assets to the service center, the repair service can coordinate and arrange a workflow that schedules the incoming physical assets receiving various services to one or more different service areas in an order that optimizes the total repair time of the multiple physical assets receiving the services.

[0019] Based on the workflow, taking into account the arrival time of the physical assets to be repaired and the overlap between the parts, tools, machines, etc. required for the physical assets to be allocated to each service area, the repair service can coordinate the robot systems located within the service center by instructing the robot systems to configure, arrange, or rearrange one or more of the service areas according to the workflow. The configuration and arrangement of the robot systems can be expected to be scheduled to receive the next physical asset to be serviced within the designated service area, and tools, parts, equipment, machines, components, etc. can be pre-placed within the service area when expecting the next physical asset. Pre-placing tools, machinery, equipment, and other parts within the service area minimizes the waiting time between the services performed within the service area and maximizes the number of physical assets that can be serviced over a period of time. In addition, by optimizing the workflow to schedule the servicing of physical assets that have a threshold amount of commonality between tools, machines, equipment, etc., the repair service can reduce the amount of rearrangement required for the service area between the sequences of different physicals arriving at the scheduled service area for servicing.

[0020] For example, the workflow generated by the repair service can schedule the physical assets to be repaired within the same service area, where the physical assets are of the same type of asset, and / or the assets to be repaired sequentially share similar repair characteristics within their maintenance profiles, thus allowing the services applied to one or more physical assets within the same service area to be performed using the same type of tools, machines, equipment, or know-how without having to rearrange the service area by one or more robot systems between performing services on different assets. When the service applied to the first physical asset is completed and the next physical asset is expected to arrive at the modular service area, the robot system can add additional tools, parts, machines, or other parts that may not have been applied to the first physical asset to the service area, while also removing any tools, machines, equipment, or other parts that are not applicable to the service to be applied to the next physical asset, which set of next physical assets arrives at the service based on the workflow prepared by the repair service that coordinates the services of each modular service area.

[0021] Computing system

[0022] Aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems, and / or block diagrams of machine logic included in computer program product (CPP) embodiments. With respect to any flowchart (depending on the technology involved), operations may be performed in an order different from the order shown in the flowchart. For example, two operations shown in consecutive flowchart blocks may be performed in reverse order, as a single integrated step, simultaneously, or in a manner that at least partially overlaps in time. The term "computer program product embodiment" ("CPP embodiment") as used in the present disclosure may describe any collection of one or more storage media (or "media") that are jointly included in a set of one or more storage devices. The storage media may jointly include machine-readable code corresponding to instructions and / or data for performing computer operations. A "storage device" may refer to any tangible hardware or device that can hold and store instructions for use by a computer processor. By way of non-limitation, computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, mechanical storage media, and / or any combination thereof. Some known types of storage devices that include the media cited herein may include magnetic disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded devices such as punched cards or pits / lands formed in the major surface of a disc, or any suitable combination thereof. Computer-readable storage media should not be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, optical pulses propagating through an optical fiber cable, electrical signals communicated through wires, and / or other transmission media. As will be understood by those skilled in the art, during the normal operation of a storage device, such as during access, defragmentation, or garbage collection, data is typically moved at some occasional points in time, but this does not render the storage device transitory because the data is not transitory when it is stored.

[0023] Figure 1 A block diagram is shown depicting an embodiment of a computing system 101 within a computing environment, which may be a simplified example of a computing device (i.e., a physical bare-metal system and / or a virtual system) capable of performing the computing operations described herein. The computing system 101 may represent one or more computing systems or devices implemented in accordance with embodiments of the present disclosure and described in further detail below. It should be understood that Figure 1 only an illustration of one implementation of the computing system 101 is provided, and no limitation is implied regarding the environment in which different embodiments may be implemented. Generally speaking, Figure 1The components shown in [Figure] can represent physical or virtualized electronic devices capable of executing machine-readable program instructions.

[0024] Embodiments of computing system 101 can take the form of: desktop computers, laptop computers, tablet computers, smart phones, smart watches or other wearable computers, mainframe computers, servers, quantum computers, non-traditional computer systems (such as autonomous vehicles or household appliances), or any other form of computer or mobile device now known or developed in the future that is capable of running application 150, accessing network 102, or querying a database (such as remote database 130). The execution of computer-implemented methods performed by computing system 101 can be distributed among multiple computers and / or among multiple locations. Computing system 101 can be positioned as part of a cloud network, even if it is not shown within the cloud in [Figure]. Additionally, computing system 101 does not need to be in a cloud network to any extent that can be positively indicated. Figure 1-2 In [Figure], it is not shown within the cloud. Additionally, computing system 101 does not need to be in a cloud network to any extent that can be positively indicated.

[0025] Processor set 110 includes one or more computer processors of any type now known or developed in the future. Processing circuitry 120 can be distributed across multiple packages. For example, multiple coordinated integrated circuit chips. Processing circuitry 120 can implement multiple processor threads and / or multiple processor cores. Cache 121 can refer to memory located on the (one or more) processor chip packages and / or can be used for data or code that can be made available for rapid access by threads or cores running on processor set 110. Cache 121 memory can be organized into multiple levels based on its relative proximity to processing circuitry 120. Alternatively, some or all of the cache 121 in processor set 110 can be located "off-chip". In some computing environments, processor set 110 can be designed to work with qubits and perform quantum computing.

[0026] Computer-readable program instructions can be loaded onto computing system 101 to cause the processor set 110 of computing system 101 to execute a series of operational steps to implement a computer-implemented method. The execution of the instructions can instantiate the methods specified in the flowcharts included in this specification and / or the narrative description of the computer-implemented method (collectively referred to as "the inventive method"). Computer-readable program instructions can be stored in various types of computer-readable storage media, such as cache 121 and other storage media discussed herein. The program instructions and associated data can be accessed by processor set 110 to control and direct the execution of the inventive method. In Figures 1 to 2In the computing environment, at least some of the instructions for performing the method of the present invention can be stored in the permanent storage device 113, volatile memory 112, and / or cache 121 as an application 150 that includes one or more running processes, services, programs, and their installed components. For example, program instructions, processes, services, and their installed components can include a maintenance service 307, and the maintenance service 307 includes components such as a configuration file module 401, a service area optimization module 403, and / or a reporting module 409, as well as their sub-components, such as Figure 4 as shown.

[0027] The communication structure 111 can refer to a signal conduction path that allows the various components of the computing system 101 to communicate with each other. For example, the communication structure 111 can provide electronic communication between the processor set 110, volatile memory 112, permanent storage device 113, peripheral device set 114, and / or network module 115. The communication structure 111 can be made of switches and / or conductive paths, such as switches and conductive paths that make up a bus, bridge, physical input / output port, etc. Other types of signal communication paths can be used, such as fiber optic communication paths and / or wireless communication paths.

[0028] The volatile memory 112 can refer to any type of volatile memory known now or developed in the future and can be characterized by random access, but this is not required unless affirmatively indicated. Examples include dynamic type random access memory (RAM) or static type RAM. In the computing system 101, the volatile memory 112 is located in a single package and can be inside the computing system 101, but alternatively or additionally, the volatile memory 112 can be distributed across multiple packages and / or located externally relative to the computing system 101. The application 150, along with any (one or more) programs, processes, services, and their installed components described herein, can be stored in the volatile memory 112 and / or the permanent storage device 113 for execution and / or access by one or more of the corresponding processor sets 110 of the computing system 101.

[0029] The permanent storage device 113 can be any form of non-volatile storage device for a computer that is currently known or developed in the future. The non-volatility of the storage device means that the stored data can be maintained regardless of whether power is supplied to the computing system 101 and / or directly to the permanent storage device 113. The permanent storage device 113 can be a read-only memory (ROM). However, at least a portion of the permanent storage device 113 can allow data to be written, deleted, and / or rewritten. Some forms of the permanent storage device 113 can include magnetic disks, solid-state storage devices, hard disk drives, flash-based memories, erasable programmable read-only memories (EPROMs), and semiconductor storage devices. The operating system 122 can take several forms, such as various known proprietary operating systems with kernels or operating systems of the open-source portable operating system interface type.

[0030] The set of peripheral devices 114 includes one or more peripheral devices connected to the computing system 101. For example, via an input / output (I / O) interface. The data communication connection between the peripheral devices and other components of the computing system 101 can be implemented using various methods. For example, by using Bluetooth, near field communication (NFC), wired connections or cables (such as universal serial bus (USB)-type cables), plug-in connections (such as secure digital (SD) cards), connections via a local area network and / or a wide area network (such as the Internet). In various embodiments, the set of UI devices 123 can include components such as display screens, speakers, microphones, wearable devices (such as goggles, headphones, and smartwatches), keyboards, mice, printers, touchpads, game controllers, and haptic feedback devices. The storage device 124 can include an external storage device (such as an external hard disk drive) or a plug-in storage device (such as an SD card). The storage device 124 can be permanent and / or volatile. In some embodiments, the storage device 124 can take the form of a quantum computing storage device for storing data in the form of qubits. In some embodiments, the network of the computing system 101 can utilize clustered computing and components that act as a single pool of seamless resources when accessed by one or more computing systems 101 via the network. For example, a storage area network (SAN) shared by multiple geographically distributed computer systems 101 or network-attached storage (NAS) applications. The set of IoT sensors 125 can consist of sensors that can be used in Internet of Things applications. For example, the sensors can be temperature sensors, motion sensors, infrared sensors, or any other type of known sensor type.

[0031] The network module 115 may include a collection of computer software, hardware, and / or firmware that allows the computing system 101 to communicate with other computer systems via a computer network 102 such as a LAN or WAN. The network module 115 may include hardware (such as a modem or a Wi-Fi signal transceiver), software for packetizing and / or depacketizing data for communication network transmission, and / or web browser software for transmitting data over the network. In some embodiments, the network control function and the network forwarding function of the network module 115 are executed on the same physical hardware device. In other embodiments (e.g., embodiments utilizing software-defined networking (SDN)), the control function and the forwarding function of the network module 115 may be executed on physically separate devices such that the control function manages several different network hardware devices. The computer-readable program instructions for performing the methods of the present invention may generally be downloaded to the computing system 101 from an external computer or an external storage device via a network adapter card or a network interface included in the network module 115.

[0032] Figure 2 depicts a computing environment 200, which may be an extension of the Figure 1 computing environment 100 operating as part of a network. In addition to the computing system 101, the computing environment 200 may further include a computing network 102, such as a wide area network (WAN) (or another type of computer network), that connects the computing system 101 to end-user devices (EUDs) 103, remote servers 104, public cloud 105, and / or private cloud 106. In this embodiment, the computing system 101 includes a set of processors 110 (including processing circuitry 120 and cache 121), a communication fabric 111, volatile memory 112, a permanent storage device 113 (including an operating system 122 and applications 150, as described above), a set of peripherals 114 (including a user interface (UI), a set of devices 123, a storage device 124, a set of Internet of Things (IoT) sensors 125), and a network module 115. The remote server 104 includes a remote database 130. The public cloud 105 includes a gateway 140, a cloud orchestration module 141, a set of host physical machines 142, a set of virtual machines 143, and / or a set of containers 144.

[0033] Network 102 can include wired or wireless connections. For example, the connections can include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. Network 102 can be described as any wide area network (e.g., the Internet) capable of transmitting computer data over non-local distances via any technology now known or to be developed for transmitting computer data. In some embodiments, the WAN can be replaced and / or supplemented by a local area network (LAN) (such as a Wi-Fi network) designed to transmit data between devices located in a local area. Other types of networks that can be used to interconnect the various computer systems 101, end-user devices 103, remote servers 104, private cloud 106, and / or public cloud 105 can include wireless local area networks (WLANs), home area networks (HANs), backbone networks (BBNs), peer-to-peer networks (P2Ps), campus networks, enterprise networks, the Internet, single-tenant or multi-tenant cloud computing networks, public switched telephone networks (PSTNs), and any other network or network topology known to those skilled in the art for interconnecting computing systems 101.

[0034] End-user device 103 can include any computer device that can be used and / or controlled by an end user (e.g., a customer of an enterprise operating computing system 101) and can take any form discussed above in connection with computing system 101. EUD 103 can receive helpful and useful data from the operation of computing system 101. For example, in the hypothetical case where computing system 101 is designed to provide recommendations to an end user, the recommendation can be transmitted from the network module 115 of computing system 101 to EUD 103 via network 102. In this example, EUD 103 can display or otherwise present the recommendation to the end user. In some embodiments, EUD 103 can be a client device such as a thin client, a fat client, a mobile computing device (such as a smart phone, a mainframe computer, a desktop computer, etc.).

[0035] Remote server 104 can be any computing system that provides at least some data and / or functionality to computing system 101. Remote server 104 can be controlled and used by the same entity that operates computing system 101. Remote server 104 represents the (multiple) machines that collect and store helpful and useful data for use by other computers such as computing system 101. For example, in the hypothetical case where computing system 101 is designed and programmed to provide recommendations based on historical data, the historical data can be provided to computing system 101 from the remote database 130 of remote server 104.

[0036] The public cloud 105 can be any computing system available for use by multiple entities, which provides on-demand availability of computer system resources and / or other computing capabilities including data storage (cloud storage) and computing power, without direct active management by the user. The direct and active management of the computing resources of the public cloud 105 can be performed by the computer hardware and / or software of the cloud orchestration module 141. The computing resources provided by the public cloud 105 can be implemented by a virtual computing environment that runs on various computers of the computer that makes up the host physical machine set 142, and / or runs within the public cloud 105 and / or over the range of physical computers available for the public cloud 105. The virtual computing environment (VCE) can take the form of virtual machines from the virtual machine set 143 and / or containers from the container set 144. It should be understood that these VCEs can be stored as images and can be transferred among and between various physical machine hosts as images or after the instantiation of the VCE. The cloud orchestration module 141 manages the transmission and storage of the images, deploys new instantiations of the VCE, and manages the active instantiations of the VCE deployment. The gateway 140 is a collection of computer software, hardware, and firmware that allows the public cloud 105 to communicate over the network 102.

[0037] The VCE can be stored as an "image". New active instances of the VCE can be instantiated from the image. Two types of VCEs can include virtual machines and containers. A container is a VCE that uses operating system-level virtualization, where the kernel allows multiple isolated user space instances called containers to exist. From the perspective of the application 150 running in these isolated user space instances, these isolated user space instances can behave like physical computers. The application 150 running on the operating system 122 can utilize all the resources of the computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. The application 150 running within the containers of the container set 144 can only use the contents of the container and the devices allocated to the container, which is a feature that can be called containerization.

[0038] The private cloud 106 can be similar to the public cloud 105, except that the computing resources may only be available for use by a single enterprise. Although the private cloud 106 is depicted as communicating with a network 102 (such as the Internet), in other embodiments, the private cloud 106 can be completely disconnected from the Internet and only accessible through a local / private network. A hybrid cloud can refer to a combination of multiple clouds of different types (e.g., private cloud, community cloud, or public cloud type), and the multiple clouds can be implemented or operated by different providers. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technologies that enable orchestration, management, and / or data / application portability between the multiple constituent clouds. In this embodiment, both the public cloud 105 and the private cloud 106 can be part of a larger hybrid cloud environment.

[0039] System Predictive Automated Configuration of Modular Service Areas

[0040] It will be readily understood that the instant components, as generally described and illustrated in the accompanying drawings herein, can be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of embodiments of at least one of a method, apparatus, non-transitory computer-readable medium, and system, as represented in the drawings, is not intended to limit the scope of the present application as claimed, but is merely representative of selected embodiments.

[0041] The instant features, structures, or characteristics described throughout this specification may be combined or removed in any suitable manner in one or more embodiments. For example, the use of the phrases "example embodiments", "some embodiments", or other similar language throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment. Thus, the appearances of the phrases "example embodiments", "in some embodiments", "in other embodiments", or other similar language throughout this specification do not necessarily all refer to the same set of embodiments, and the described features, structures, or characteristics may be combined or removed in any suitable manner in one or more embodiments. Additionally, in the drawings, any connection between elements can allow for one-way and / or two-way communication, even if the depicted connection is a one-way or two-way arrow. Moreover, any device depicted in the drawings can be a different device. For example, if a mobile device is shown as sending information, a wired device can also be used to send information.

[0042] Referring to the accompanying drawings, Figure 3Depicts an embodiment of a computing environment 300 that illustrates a system capable of predictively automating the configuration of modular service areas 309 within a service center that is scheduled to receive one or more physical assets 301. The modular service areas 309 can be described as areas within the service center that can be configured, arranged, and / or reconfigured (as needed) to provide one or more services, such as repair or maintenance of the physical assets 301. Embodiments of the computing environment 300 can optimize the scheduling and coordination of services provided to the physical assets 301 in such a way that: reduces the total amount of time used to service all of the physical assets 301 that are received at the service center over a period of time, while maximizing the number of physical assets 301 that are serviced during the period of time, and minimizes the number of times each of the service areas 309 is reconfigured when each new physical asset 301 arrives for service. As shown, the computing environment 300 can include one or more physical assets 301, repair services 307, a digital twin repository 313, and one or more robotic systems 311 that can be positioned within or between the service areas 309 and can move within or between the service areas 309. Embodiments of the physical assets 301, repair services 307, robotic systems 311, and digital twin repository can communicate with each other via a computing network 102.

[0043] Embodiments of the physical assets 301 can be any type of physical device, machine, or apparatus, equipment, hardware, etc. that can be capable of connecting and transmitting data over the network 102. For example, in an exemplary embodiment, the physical asset 301 can be a vehicle, including (but not limited to) autonomous or semi-autonomous cars, airplanes, locomotives, turbines, boats, ships, etc. In other embodiments, the physical asset 301 can include devices, machinery, or apparatuses, such as medical machinery or equipment, oil and gas energy equipment, mobile communication devices (such as smart phones), household appliances, or other smart devices. In some embodiments, the physical asset 301 can be equipped with or tracked by one or more types of sensors and / or IoT devices on the physical asset 301. In other embodiments, the sensors and / or IoT devices that monitor the physical asset 301 can be positioned in or around the environment surrounding the physical asset 301. The sensors and IoT devices, whether on the physical asset 301 or located within the surrounding environment, can measure one or more functions and health of the physical asset 301 and can be responsible for collecting data (i.e., in real time) that describes the current state of the physical asset 301. The data collected that describes the physical asset 301 can be stored as part of a system log and can include location data that describes the physical location of the physical asset 301.

[0044] Data describing physical asset 301 collected from sensors, IoT devices, and / or other data sources can be stored as system data 303. System data 303 collected from multiple on-board sensors or nearby sensors and IoT devices of physical asset 301 can be used to build a digital twin model of physical asset 301. An embodiment of the digital twin model can depict a virtual representation of physical asset 301 in its current state based on system data 303 that has been collected by the systems of physical asset 301. Additionally, when system data 303 changes over time to reflect changes in the current state of physical asset 301 (i.e., measured by sensors, IoT devices, etc.), the digital twin model can be updated simultaneously (or near simultaneously) to virtually reflect the changes within the digital twin model. The digital twin model can be organized and stored locally within persistent storage 113, which can be on physical asset 301, or in some embodiments, the digital twin model of physical asset 301 can be stored on a network-accessible storage device, such as digital twin repository 313.

[0045] An embodiment of physical asset 301 can self-assess the current state and / or condition of physical asset 301. The self-assessment of physical asset 301 can detect one or more problems, faults, errors, or other needs of physical asset 301, and can determine whether physical asset 301 requires maintenance, repair, or other services in order to maintain optimal operation and / or to return physical asset 301 to an optimal operating state. The self-assessment performed by one or more systems of physical asset 301 can occur periodically at regularly scheduled time intervals, can be triggered in response to changes in the current state of physical asset 301, in response to detectable errors, non-operational characteristics, or functions of the physical asset, and / or can be manually selected by a user, owner, or administrator of physical asset 301 when it is detected that a characteristic of physical asset 301 is not operating in an optimal manner.

[0046] The self - assessment of the physical asset 301 can be performed by scanning or testing the systems of the physical asset 301 and / or analyzing the system data 303 collected by the IoT devices and sensors of the physical asset 301. An AI - enabled algorithm connected to the physical asset 301 can be trained to identify potential problems of the physical asset 301 that may require repair or can be mitigated through maintenance. The results of the self - assessment of the physical asset 301 can be logged in the log file of the system data 303. If, during the self - assessment of the physical asset 301 or a manual assessment by a user, owner, administrator, etc. of the physical asset 301, it is determined that further exploration or repair or maintenance should be carried out, the physical asset 301 can record the potential problems or requirements of the physical asset 301, including the maintenance and / or repair identified by the output of the self - assessment, as part of the service demand data 305. The physical asset 301 can generate a service request and send the service request together with the system data 303, service demand data 305, and / or digital twin model to the repair service 307.

[0047] An embodiment of the repair service 307 can be part of an application 150 or program accessible to the physical asset 301 via the network 102. The repair service 307 can run as an instance on the cloud network of the service provider or any other type of network described herein. An embodiment of the repair service 307 can perform functions and processes associated with evaluating the digital twin model of the physical asset 301 and the associated system data 303 to identify the type of service to be provided for the physical asset 301 and any tools, equipment, machines, parts, accessories, and / or components that may be known or used for the type of service identified for the physical asset 301. Additionally, based on the type of service identified, an embodiment of the repair service can organize the workflow and scheduling of the physical asset 301 at a suitable service center capable of performing the execution of the service, optimize the layout of the service area 309 within the selected service center, and schedule the execution of the service for the physical asset 301 using the service center according to the generated workflow. Scheduling the execution of one or more services using the service center can be performed according to the best workflow generated by the repair service based on the overlap of commonalities between the physical assets 301 requesting the service and can be assigned to the same service center. Scheduling the service according to the workflow can be arranged in a way that minimizes the waiting time for completing the service, minimizes the rearrangement of the service area 309 between the executions of services for different physical assets 301, and maximizes the number of physical assets 301 receiving service within the service area 309 during a selected time period.

[0048] Reference Figure 4In the accompanying drawings, embodiments of the maintenance service 307 may include one or more modules or sub-components responsible for implementing one or more specific processes, tasks, functions, or features of the maintenance service 307. The term "module" may refer to a hardware module, a software module, or the module may be a combination of hardware and software resources. A module (whether hardware, software, or a combination thereof) may be designed to implement or execute one or more specific tasks, routines, and / or functions. Embodiments of hardware-based modules may include self-contained components such as a chipset, a dedicated circuit, a set of processors 110, one or more volatile memory 112 devices, and / or a permanent storage device 113. Software-based modules may be an application 150, a part of program code, or linked to program code that includes a set of specific programming instructions loaded into the volatile memory 112 or the permanent storage device 113. In Figure 4 an exemplary embodiment, the maintenance service 307 may include a profile module 401, a service area optimization module 403, and / or a reporting module 409.

[0049] Embodiments of the profile module 401 may perform the tasks, functions, and / or processes of the maintenance service 307 that are directed to creating a maintenance profile for each physical asset 301 that interfaces with the maintenance service 307 via the network 102. For example, interfacing with the maintenance service 307 may be performed by submitting a service request, a digital twin model, system data 303, and / or service requirement data 305 to the maintenance service 307 for analysis and scheduling of one or more services. Embodiments of the profile module 401 may evaluate the digital twin model of the physical asset 301 and, in combination with the system data 303 and / or service requirement data 305 submitted with the digital twin model, identify what type of repair, maintenance, or other service can be provided (or should be provided) for the physical asset 301, and any associated machines, tools, spare parts, components, or other accessories known to be used for the services available for the physical asset 301. As part of the maintenance profile created by the profile module 401, embodiments of the profile module 401 may create a mapping between the type of service identified in association with the physical asset 301 and the corresponding type of equipment, machines, parts, components, or other accessories used to implement the service associated with the maintenance profile of each physical asset 301. A comparison between the maintenance profiles of the physical assets 301 may allow a comparison between the requirements for the services of the physical assets 301 and / or the classification of the maintenance profiles of the physical assets 301 to determine the commonalities between the different services that can be performed.

[0050] An embodiment of the profile module 401 can apply one or more classification algorithms to maintenance profiles created by the profile module 401 using digital twin models of each physical asset 301. Applying one or more classification algorithms to the maintenance profiles of multiple physical assets 301 can allow the profile module 401 to identify commonalities between services applicable to each of the physical assets 301, including commonalities between the types of machines, tools, equipment, components, or other attachments that can be mapped to each of the services applicable to the physical assets 301. For example, if the profile module 401 is classifying digital twin models of multiple vehicles (which can be autonomous or non-autonomous), these vehicles may have several potential services within their maintenance profiles, and although the vehicles are of different types or brands, there are commonalities between them. For example, if two vehicles require tire-related services, such as tire rotation and balancing for the first vehicle and tire replacement for the second vehicle, the profile module 401 can determine the types of tools, machines, equipment, and components that may be common between the tire rotation service and the tire replacement service being performed. The profile module 401 can identify commonalities between the tire rotation service and the tire replacement service, such as the need to use a vehicle lift, pneumatic or manual wrenches, lug nuts, a wheel balancer, etc., thereby allowing the repair service 307 to identify service centers capable of performing both the tire rotation service and the tire replacement service, and if the arrival time at the service center will be within a threshold time period, the repair service can continuously schedule the execution of the tire services for the first physical asset and the second physical asset within the workflow, thereby allowing the service area 309 to maintain the tools, equipment, machines, components, etc. common between the two services while minimizing the amount of change to the service area 309 between performing services on the two physical assets 301.

[0051] An embodiment of the service area optimization module 403 can perform functions, tasks, and processes associated with identifying maintenance services 307 associated with service centers capable of servicing physical assets 301, and create a workflow for implementing a service sequence within one or more service areas 309 at each service center based on the classification of the maintenance profiles of each of the physical assets 301, the location of the physical assets 301, and the expected arrival time of the physical assets 301 at a designated service center. Additionally, the service area optimization module 403 can also implement the generated workflow within the service center by instructing a robotic system 311 located within the service area 309 to prepare the service area 309 according to the service sequence to be performed on one or more physical assets 301 scheduled at the service center. For each incoming service request, associated service, identified and classified via the profile module 401, an embodiment of the service area optimization module 403 can identify whether the existing service area 309 within the service center can accommodate the service for the service request for the physical asset. If the existing service area 309 is not currently equipped to accommodate the service within the workflow, identify which service areas of the service center can accommodate the execution of the service with a minimum number of modifications to the existing service area 309. For example, identify a service area 309 that can be accepted after making a minimum number of changes or rearrangements to the tools, equipment, components, and other accessories that may be present within the identified service area 309 in order to provide one or more of the services identified by the profile module 401.

[0052] An embodiment of the service area optimization module 403 can create a workflow that is used to schedule the various incoming physical assets 301 arriving at the service center and organize the order of allocating the physical assets 301 to one or more service areas 309 to perform the requested service based on the received service and the commonality between the types of machines, tools, parts, components, or other attachments associated with the service indicated by the maintenance profile of the physical assets 301. As part of the workflow generation and scheduling process, the service area optimization module 403 can calculate and consider the estimated arrival time of each incoming physical asset 301 at the service center. The service area optimization module 403 can use the location data provided as part of the system data 303 to the repair service 307 to estimate the arrival time. Additionally, in some embodiments, the service area optimization module 403 can further consider the amount of time to configure or rearrange the service area 309 with equipment, tools, parts, components, etc. to provide the requested service, as well as the timing for receiving various parts or components that may not be immediately accessible within the service center or may be scheduled to be used at another service area 309 within the service center when creating the workflow and allocating the physical assets 301 to the service area. For example, time constraints can be considered when scheduling physical assets and creating a workflow to accommodate a service center that needs to obtain parts or tools from another location or from a parts vendor in order to perform the service.

[0053] An embodiment of the service area optimization module 403 may generate a workflow with optimal timing and schedule the execution of services at service centers within the service area 309 according to the optimal timing of the generated workflow. In anticipation of the next physical asset 301 arriving within the service area 309, the workflow may include instructions and / or events for allocating the physical asset 301 to one or more service areas 309 in an ordered sequence, steps for reconfiguring or arranging each designated service area 309 with tools, equipment, machines, components, parts, etc. mapped to the services to be presented on the corresponding physical asset 301 before the physical asset 301 arrives at the designated service area 309, and instructions or steps for reconfiguring or arranging the service area 309 after the service is completed on the physical asset 301. The workflow generated by the service area optimization module 403 may schedule the order of physical asset assignment to the service area 309 in a manner that optimizes the timing of service completion, minimizes the timing associated with reconfiguring the service area 309, minimizes the waiting time between service completions, and maximizes the number of physical assets 301 that may be receiving service within the service area 309 of the service center. For example, when there is a threshold amount of commonality between the types of machines, tools, equipment, parts, components, or other attachments utilized during the implementation of the services for the physical assets 301 assigned to the same service area 309, the order of the physical assets 301 assigned to the same service area 309 is sorted. By sequentially scheduling the physical asset services applied to different physical assets with a threshold amount of shared commonality to the same service area 309, a minimum number of changes to the configuration of the service area may be required between the executions of the services, thereby maximizing the amount of physical assets 301 that may receive service through the service area over a period of time.

[0054] An embodiment of the service area optimization module 403 may implement the generated workflow to configure and arrange the service sequence within the service center at one or more designated service areas 309 by outputting one or more instructions to a robotic system 311 located within the service center. In the case where a physical asset 301 is expected to arrive at the service center and / or the next physical asset 301 is expected to be placed within the service area 309 to receive one or more services, the robotic system 311 may be instructed on how to configure and arrange each service area 309. The robotic system 311 may move autonomously throughout the service center and arrange each of the designated service areas 309 based on instructions provided to the robotic system 311 that are consistent with the workflow generated by the repair service 307. The robotic system 311 may move throughout the service center, collect tools, move equipment, collect parts and / or components or other accessories for providing services within the service area 309 and place the collected tools, equipment, parts, etc. within the designated service area 309. Ensure that the appropriate tools, equipment, and parts for performing services on the physical asset 301 are present and within a reasonable distance of the individual or machine responsible for implementing the service within the service area 309. When ending the service for the first sorted asset within one or more service areas 309, the robotic system 311 may rearrange the service area 309 as instructed in accordance with the workflow and scheduling of the service, including adding additional equipment, tools, and parts for servicing the next asset in the workflow sequence within the service area 309. The robotic system 311 may also remove from the service area 309 equipment, tools, components, and / or unused parts that are not part of the service requirements for servicing the physical asset being repaired next within the service area 309. The tools, equipment, parts, and other components removed from the service area 309 may be taken by the robotic system 311 to another service area 309 and / or placed in a neutral staging or storage area outside of each of the service areas 309 established within the service center.

[0055] Embodiments of the service area optimization module 403 may include additional components that may assist the service area optimization module 403 in generating an optimized workflow and sequencing the services provided to one or more physical assets 301. For example, in Figure 4In an embodiment of the service area optimization module 403, the service area visualization engine 405 and / or the service area layout module 407 may be available. An embodiment of the service area visualization engine 405 may perform tasks, functions, and processes that allow the maintenance service 307 to visualize the layout of the service center, the space available for creating service areas within the service center, and the locations of various equipment, tools, and machines within each of the available service centers. The service area layout module 407 may utilize the service area visualization engine 405 to simulate the workflows created by the service area optimization module and ultimately identify the best workflow to implement. By using the service area layout module 407 in combination with the service area visualization engine 405, an embodiment of the maintenance service 307 may simulate various permutations of the workflow by simulating the assignment of machines, equipment tools, components, etc., and the physical assets 301 to the respective service areas 309 represented by the visualization engine. The service area layout module 407 may calculate and identify the best schedule and the scheduled order for assigning incoming physical assets 301 to different service areas 309, and the scheduled order may be predicted to result in the maximum total number of physical assets 301 being serviced within a threshold time period, the minimum number of reconfigurations of the service areas 309 between the scheduled physical assets 301 being serviced in sequence within the same service area 309, and the minimum amount of waiting time for receiving service. Based on the simulated workflow that achieves the best results, the best workflow may be selected by the service area optimization module 403, and instructions for implementing the best workflow may be sent to the (multiple) robotic systems 311 located within the service center.

[0056] An embodiment of the maintenance service 307 may include a reporting module 409. The reporting module 409 may perform functions, processes, and / or tasks of the maintenance service 307 that may output communication and reporting information for the physical asset 301 that submitted a request to the maintenance service 307. For example, the reporting module 409 may report back to the physical asset 301 and its user or owner the type of service scheduled for the physical asset 301, the location of the service center where the service is being performed, the estimated date and time of the service in progress, and any additional recommended services that the user may wish to schedule for the physical asset 301. An embodiment of the reporting module 409 may also communicate with the service center. For example, the reporting module 409 may confirm the scheduled service appointment or cancellation of service for each physical asset 301, and confirm the arrival of the physical asset 301 at the service center. The output from the reporting module 409 may be any form of electronic communication that may be sent over the network 102. For example, reports, notifications, and other messages sent from the reporting module 409 may be in the form of email, push messages, text messages, notifications, alerts, or any other known type of electronic communication format or delivery system.

[0057] Method for Predictive Automated Configuration of Modular Service Areas

[0058] Figures 5 to 6 The figures represent embodiments of methods 500, 600 for predictively and automatically arranging the configuration of modular service areas within a service center for providing one or more services to a plurality of physical assets 301, and optimizing the workflow of the modular service areas 309 based on commonalities between the maintenance profiles of the physical assets 301, so as to reuse the service areas 309 with minimal changes, serve the maximum number of physical assets 301 over a period of time, and minimize the total amount of time to serve the plurality of physical assets 301. Embodiments of methods 500, 600 may be implemented according to the computing systems and examples depicted above Figures 1 to 4 and have been described throughout this application. Those skilled in the art should recognize that Figures 5 to 6 the steps of methods 500, 600 described herein may be performed in an order different from the presented order, and it may not be necessary to perform all of the steps described herein.

[0059] by Figure 5 An embodiment of method 500 described may begin at step 501. During step 501, IoT devices and / or sensors connected to or located in the surrounding environment of the physical asset 301 collect system data 303 of the physical asset 301. Using the collected system data 303, a digital twin model reflecting the current state of the physical asset 301 is created or updated. In step 503, the physical asset 301 may perform a self-assessment. Based on the self-assessment of the physical asset 301, it is determined whether one or more services should be performed on the physical asset, taking into account the current state of the physical asset 301 as well as the measurements and outputs of the IoT devices and sensors. For example, based on sensor data, it may be determined that the physical asset is not operating optimally, or it may be determined that an error is identified during the self-assessment, and thus one or more maintenance services or repairs may be required to correct the error. If, in step 503, the system data 303 indicates that one or more services should be performed on the physical asset 301, method 500 may proceed to step 505; otherwise, method 500 may return to step 501, where the IoT devices and / or sensors may continue to collect system data 303 and keep the digital twin model up-to-date with the latest performance data of the physical asset 301. In response to identifying that one or more services may be required on the physical asset 301, a computing system on the physical asset 301 or a computing system connected to the physical asset 301 may send a service request to a repair service or application (such as repair service 307), which may be hosted on a public cloud or a private cloud and / or any other type of network 102.

[0060] In step 505, the cloud-hosted repair service 307 can receive a service request from the physical asset 301. Along with the service request, additional information about the physical asset 301 can be provided, including the system data 303 of the physical asset 301 and / or the digital twin model of the physical asset 301 for which the service is requested. In step 507, based on the current state of the digital twin model and the system data 303, the digital twin model of the physical asset 301 is analyzed by the repair service 307 for a suitable service applicable to the physical asset. The repair service 307 can identify one or more services, including repairs and / or maintenance applicable to the physical asset 301, in order to keep the asset operating at optimal performance and / or to fix persistent errors and performance issues experienced or exemplified by the digital twin model.

[0061] In step 509, an instance of the repair service 307 can identify the types of machines, tools, spare parts, and / or other components associated with the identified repairs, maintenance, or other services based on the analysis of the digital twin model. In step 511, each type of service applicable to the physical asset 301 can be classified by the repair service 307. The maintenance profile can include multiple potentially available services for each physical asset 301 using the repair service 307, thereby allowing comparisons between physical assets 301 based on the maintenance profile. Comparisons between the physical asset 301 maintenance profiles can identify commonalities between the services of each physical asset 301, including (but not limited to) commonalities between the types of machines, tools, equipment, components, parts, or other accessories required for repairs, maintenance, or other services. By identifying commonalities between the services available for each of the physical assets 301, the repair service 307 can schedule and coordinate physical asset services into the assigned modular service areas 309, whereby the scheduled services with similar type requirements for machines, tools, parts, knowledge, and other components can be sequentially performed within the same service area 309, within adjacent service areas 309, and / or within nearby service areas 309. Allowing common tools, machines, equipment, and other parts to be easily shared between scheduled services and / or between service areas 309 reduces the amount of reconfiguration or rearrangement of the service areas 309.

[0062] In step 513, the maintenance service 307 can select a service center location that is either known to be equipped with machines, tools, parts, etc. for performing one or more services on the physical asset 301, or a service center that can receive and configure itself with one or more machines, tools, parts, components, etc. for servicing the physical asset 301 either before the physical asset 301 arrives at the service center or within a threshold amount of time after the physical asset 301 arrives at the service center. In step 515, it is determined whether the modular service area 309 within the selected service center has been configured with an arrangement of machines, tools, parts, and other components required to perform services on the physical asset 301. If the modular service area 309 within the selected service center has been configured, the method 500 can proceed to step 517. In step 517 of the method 500, the incoming physical asset 301 expected to arrive at the service center is scheduled to receive service at the service center and is assigned to the previously equipped and configured modular service area 309. Conversely, if the existing service area within the selected service center has not been previously configured in a manner that meets the needs of performing services on the incoming physical asset 301 expected to arrive at the service center, the method 500 can proceed to step 519.

[0063] During step 519, the maintenance service 307 can send instructions to one or more robotic systems 311 located within the selected service center. The instructions sent can direct the robotic systems 311 to create a new modular service area 309 within the service center or modify the existing service area 309 such that the existing service area can perform one or more of the requested services. The robotic systems 311 within the service center can be instructed to retrieve and / or arrange one or more machines, tools, parts, accessories, or other components within the designated service area 309 that are consistent with the performance of the requested service. The robotic systems can be instructed to prepare the service area in advance according to the instructions sent either before the physical asset 301 arrives at the service center, or when the ongoing service within the designated service area 309 is completed and / or before a predetermined time for starting the requested service within the service area 309. The robotic systems 311 that prepare the modular service area 309 can further arrange the service area 309 by removing any unnecessary components or instruments present. For example, removing any machines, tools, components, and / or parts that may be present within the service area but may not be required for servicing the next incoming physical asset 301 assigned to the service area and / or any subsequent physical assets scheduled to be serviced within the same service area 309 at a later point in time.

[0064] By Figure 6An embodiment of the described method 600 can begin at step 601. During step 601, multiple physical assets 301 can send their respective digital twin models of the physical assets 301 to a cloud network or server running an instance of the maintenance service 307. In step 603, the maintenance service 307 identifies, within the maintenance profile of each physical asset 301 that submitted a digital twin model, the location of the physical asset 301, the type of service applicable to the physical asset 301, and any type of tool, equipment, part, component, or other accessory that may be required to perform a service on the physical asset 301. In step 605, the maintenance service 307 can classify the maintenance profiles of the multiple physical assets 301 using one or more classification algorithms. The classification of each maintenance profile of the physical asset 301 can identify the different degrees of commonality between the types of services that can be applicable to each physical asset, and can group services with a commonality threshold level into the same or nearby classifications.

[0065] In step 607, the maintenance service 307 can generate a mapping between the type of service applicable to each of the physical assets 301 and the equipment, parts, machines, tools, components, accessories, etc. that the service center may need to perform each service that may be requested for the physical assets 301. In step 609, based on the commonality of the classifications between servicing different physical assets 301, the maintenance service 307 identifies the types of maintenance or service equipment required to perform various services on the physical assets. In step 611, it is determined whether the maintenance service 307 has received one or more service requests from one or more of the physical assets 301. If one or more service requests have not been received, the method 600 can return to step 601, whereby new or updated digital twin models can be provided to the maintenance service 307 over time. Otherwise, if one or more service requests are received in step 611, the method 600 can proceed to step 613.

[0066] During step 613, the repair service can proactively create modular service areas 309 within the selected service center tasked with servicing the physical assets 301, based on a classification pattern of commonalities between the maintenance profiles of the physical assets 301 and the number of physical assets 301 within each classified maintenance profile. The repair service 307 can optimize the workflow for each service area 309 to minimize repair time, changes between service areas 309 for physical asset services, and wait times between services for the physical assets 301. In step 615, it is determined whether the modular service areas 309 visualized by the repair service 307 optimize the total service time for completing services on multiple physical assets 301, which may be scheduled to be serviced within one or more service areas 309. If the total service time is not optimized, method 600 can return to step 613 and further optimize the service areas 309 based on the classification of the maintenance profiles and commonalities between them, and simulate potential changes to the workflow to achieve optimization. Otherwise, if the service areas 309 visualized by the repair service 307 do optimize the total service time for completing services on all physical assets 301 scheduled at the service center and assigned to one or more service areas 309, the method can proceed to step 617.

[0067] During step 617, one or more service areas 309 can be implemented within the service center according to the workflow generated by the repair service 307. The repair service can use one or more robotic systems 311 located within the service center to indicate the layout of the service areas 309. Instructions sent to the robotic systems 311 can indicate the layout of the service areas 309 based on the visualization and optimized workflow of the service areas 309 provided by the repair service 307, resulting in an optimized total service time for completing services on the physical assets 301. In step 619, feedback data describing the actual repair, maintenance, or other services provided to each physical asset is captured and the feedback data can be fed back to the repair service 307 to improve the classification model and optimization and / or workflow scheduling of the service areas 309.

Claims

1. A computer-implemented method for predictively automating the configuration of modular service areas within a service site, the computer-implemented method comprises: receiving, by a processor, a service request from a plurality of physical assets, the location of the physical assets, and an estimated arrival time of each of the physical assets at the service site, the service request requesting the performance of a service on the physical assets at the service site; analyzing, by the processor, digital twin models corresponding to each of the plurality of physical assets; creating, by the processor, a maintenance profile for each type of physical asset, the maintenance profile describing the classification of the service for the corresponding physical asset, the corresponding physical asset including one or more machines, tools, or components required to perform the service on the physical asset; creating, by the processor, one or more modular service areas within the service site based on commonalities between the maintenance profiles of the physical assets and the one or more machines, tools, or components required to perform the service, the modular service areas including at least one of the one or more machines, the tools, and the components required to perform the service on the physical assets; and instructing, by the processor, a robotic system located within the service site to create or modify the modular service areas by positioning the machines, the tools, and the components for each of the services to be performed on the physical assets within the modular service areas before the estimated arrival time of each of the physical assets at the service site.

2. The computer-implemented method according to claim 1, wherein, creating the one or more modular service areas within the service site further comprises: creating, by the processor, a workflow configured to allocate the physical assets to the one or more modular service areas for performing the service in an order based on a combination of the estimated arrival time and minimizing the number of changes to the one or more modular service areas between the services provided to a first physical asset and subsequent physical assets scheduled by the workflow.

3. The computer-implemented method according to claim 2, wherein, the order of the workflow is configured to allocate the physical assets to the one or more modular service areas to further maximize the number of physical assets being provided the service at the service site.

4. The computer-implemented method according to claim 2, further comprises: modifying, by the processor, a first modular service area within the service site, wherein the first physical asset receives one or more services within the first modular service area, and adding or removing at least one or more of the machines, tools, or components lacking commonality between the maintenance profile of the first physical asset and the maintenance profile of the subsequent physical asset from the first modular service area before the subsequent physical asset within the first modular service area arrives.

5. The computer-implemented method according to claim 1, further Comprising: Optimizing, by the processor, the workflow to minimize the repair time of the physical assets, the changes to each of the physical assets, or the waiting time between the services provided to each of the physical assets assigned to the same modular service area.

6. The computer-implemented method according to claim 1, further Comprising: Capturing, by the processor, data describing the performance of the services within the one or more modular service areas; And Inputting, by the processor, the data into a classification model to provide feedback and continuous learning for improving the classification of the maintenance profiles assigned to each type of physical asset.

7. The computer-implemented method according to claim 1, further Comprising: Mapping, by the processor, one or more of the services requested by the plurality of physical assets to the one or more machines, tools, or components required to perform the services.

8. A computer system for predictively automating the configuration of modular service areas within a service site, Comprising: A processor; And A computer-readable storage medium coupled to the processor, wherein the computer-readable storage medium contains program instructions for executing a computer-implemented method via the processor, the computer-implemented method comprising: Receiving, by the processor, service requests from a plurality of physical assets, the locations of the physical assets, and the estimated arrival times of each of the physical assets at the service site, the service requests requesting the performance of services on the physical assets at the service site; Analyzing, by the processor, the digital twin models corresponding to each of the plurality of physical assets; Creating, by the processor, a maintenance profile for each type of physical asset, the maintenance profile describing the classification of the services for the corresponding physical asset, the corresponding physical asset including one or more machines, tools, or components required to perform the services on the physical asset; Creating, by the processor, one or more modular service areas within the service site based on the commonalities between the maintenance profiles of the physical assets and the one or more machines, tools, or components required to perform the services, the modular service areas including at least one of the one or more machines, the tools, and the components required to perform the services on the physical assets; and Instructing, by the processor, a robotic system located within the service site to create or modify the modular service areas by positioning the machines, the tools, and the components for each of the services to be performed on the physical assets within the modular service areas before the estimated arrival times of each of the physical assets at the service site.

9. The computer system according to claim 8, Wherein, Creating the one or more modular service areas within the service site further comprises: A workflow is created by the processor, the workflow being configured to allocate the physical assets to the one or more modular service areas for performing the services in an order based on a combination of the estimated arrival times and minimizing the number of changes to the one or more modular service areas between the services provided to a first physical asset and subsequent physical assets scheduled by the workflow.

10. The computer system according to claim 9, wherein, the order of the workflow is configured to allocate the physical assets to the one or more modular service areas to further maximize the number of physical assets being provided the services at the service site.

11. The computer system according to claim 9, further comprising: modifying, by the processor, a first modular service area within the service site, wherein the first physical asset receives one or more services within the first modular service area, and adding or removing at least one or more of the machines, tools, or components lacking commonality between the maintenance profile of the first physical asset and the maintenance profile of the subsequent physical asset from the first modular service area before the subsequent physical asset within the first modular service area arrives.

12. The computer system according to claim 8, further comprising: optimizing, by the processor, the workflow to minimize the repair time of the physical assets, the changes to each of the physical assets, or the waiting time between the services provided to each of the physical assets assigned to the same modular service area.

13. The computer system according to claim 8, further comprising: capturing, by the processor, data describing the performance of the services within the one or more modular service areas; and inputting, by the processor, the data into a classification model to provide feedback and continuous learning for improving the classification of the maintenance profiles assigned to each type of physical asset.

14. The computer system according to claim 8, further comprising: mapping, by the processor, one or more of the services requested by the plurality of physical assets to the one or more machines, tools, or components required to perform the services.

15. A computer program product for predictively automating the configuration of modular service areas within a service site, comprising: receiving, by a processor, service requests from a plurality of physical assets, the locations of the physical assets, and the estimated arrival times of each of the physical assets at the service site, the service requests requesting the performance of services on the physical assets at the service site; analyzing, by the processor, digital twin models corresponding to each of the plurality of physical assets; creating, by the processor, a maintenance profile for each type of physical asset, the maintenance profile describing the classification of the services for the corresponding physical asset, the corresponding physical asset including one or more machines, tools, or components required to perform the services on the physical asset; Based on the commonalities between the maintenance profile of the physical asset and the one or more machines, tools, or components required to perform the service, the processor creates one or more modular service areas within the service site, the modular service area including at least one of the one or more machines, the tools, and the components required to perform the service on the physical asset; and the processor instructs a robotic system located within the service site to create or modify the modular service area by positioning the machines, tools, and components for each service to be performed on the physical asset within the modular service area before an estimated arrival time of each physical asset at the service site.

16. The computer program product according to claim 15, wherein, creating the one or more modular service areas within the service site further includes: the processor creates a workflow configured to assign the physical assets to the one or more modular service areas for performing the service in an order based on a combination of the estimated arrival time and minimizing the number of changes to the one or more modular service areas between services provided to a first physical asset and subsequent physical assets scheduled by the workflow.

17. The computer program product according to claim 16, wherein, the order of the workflow is configured to further maximize the number of physical assets being provided the service at the service site when assigning the physical assets to the one or more modular service areas.

18. The computer program product according to claim 16, further comprising: the processor modifies a first modular service area within the service site, wherein the first physical asset receives one or more services within the first modular service area, and before a subsequent physical asset within the first modular service area arrives, adds or removes at least one or more of the machines, tools, or components lacking commonality between the maintenance profile of the first physical asset and the maintenance profile of the subsequent physical asset from the first modular service area.

19. The computer program product according to claim 16, further comprising: the processor optimizes the workflow to minimize the repair time of the physical assets, the changes to each physical asset, or the waiting time between services provided to each physical asset assigned to the same modular service area.

20. The computer program product according to claim 16, the processor captures data describing the performance of the services within the one or more modular service areas; and the processor inputs the data into a classification model to provide feedback and continuous learning for improving the classification of the maintenance profiles assigned to each type of physical asset.

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