PROACTIVE PREPARATION OF A REPAIR SERVICE SITE

Abstract

Systems, methods, and / or computer program products that predictively automate configurations of modular service zones for providing a service to physical assets, maximize the reuse of one or more service zones, and optimize the time to provide a service to a plurality of physical assets. Digital twin models of physical assets are classified and arranged into workflows for the service zones, determining the order of services performed on physical assets arriving at service centers, and preparing service zones based on the type of requested services, the estimated arrival time, and similarities between classifications of different digital twins of physical assets.Based on workflow sequences, arrival times of physical assets, and overlaps between parts, tools, machines, etc. within different service zones, the service center coordinates robotic systems to arrange service zones in a manner that minimizes waiting time between services, maximizes the number of physical assets repaired within a given period, and reduces the reordering of service zones between services provided for different physical assets.

Description

BACKGROUND

[0001] This disclosure generally relates to the field of artificial intelligence (AI) and digital twin technology. More specifically, it relates to the use of AI and digital twins to classify maintenance profiles of physical assets, thereby automating the layout of service centers responsible for the maintenance or repair of physical assets, and optimizing the layout and workflow in the service zone to maximize the number of physical assets receiving services and minimize waiting time.

[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 throughout the object's lifetime, recording the changes as they occur within the physical object. Digital twins are a complex virtual model that represents an exact counterpart of the physical asset that exists in the real world. Sensors and Internet of Things (IoT) devices connected to the physical asset collect data, often in real time. The collected data can then be mapped to the digital twin's virtual model.Anyone with access to the digital twin can view real-time information about the physical asset in the real world without having to be physically present and see the physical asset in operation. Users such as engineers can use the digital twin to not only understand how the physical asset is performing, but also to predict how the physical asset might perform in the future, using data collected from sensors, IoT devices, and other sources of data and information. Furthermore, digital twins can assist manufacturers and asset suppliers with information that helps the manufacturer understand how customers continue to use the products after the buyers have purchased the physical asset.

[0003] A classification algorithm can generally refer to a function that weights input features in such a way that the output separates two or more classes, and then makes decisions based on the results of all classifiers. Classifier training can be performed to determine weights and functions that provide the most accurate and best separation between data classes. Linear discriminant analysis is the most basic classifier, which uses linear weighting of data with multiple factors as a means of maximizing the distance between the means of the two classes. However, for many datasets, the relative separation between classes is not well delineated by a single line.Artificial neural networks and random decision trees are a newer computational approach that produces more complex divisions between classes. SUMMARY

[0004] Embodiments of the present disclosure relate to a computer-implemented method, an associated computer system, and computer program products for predictively automating the configuration of modular service zones for repairing or maintaining physical assets and maximizing the reuse of a modular service zone for a plurality of physical assets. The computer-implemented method comprises: receiving, by a processor, service requests from a plurality of physical assets requesting the performance of services on physical assets at the service location, a position of the physical assets, and an estimated time of arrival for each of the physical assets at the service location;Analyzing, by the Processor, a digital twin model corresponding to each of the plurality of physical assets; Creating, by the Processor, a maintenance profile for each type of physical asset that describes classifications of services for a corresponding physical asset, including one or more machines, tools, or parts required to perform the services on the physical asset;Based on maintenance profiles of the physical assets and commonalities between the one or more machines, tools, or parts required to perform the services, the processor creating one or more modular service zones within the service location that include at least one of the one or more machines, tools, and parts required to perform the services on the physical asset; and the processor instructing a robotic system positioned within the service location to create or modify the modular service zone by positioning the machines, tools, and parts for performing each of the services on the physical assets within the modular service zone prior to the estimated arrival time of each of the physical assets at the service location. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The drawings included in this disclosure are incorporated in and constitute a part of the description. The drawings illustrate embodiments of the present disclosure and, together with the description, explain the principles of the disclosure. The drawings merely illustrate certain embodiments and do not limit the disclosure. Fig. 1 depicts a block diagram illustrating one embodiment of a computer system and its components on which embodiments described herein may be implemented in accordance with the present disclosure. Fig. Figure 2 shows a block diagram illustrating an extension of the data processing system environment from Fig. 1, wherein the computer systems are configured to operate in a network environment (including a cloud environment) and perform methods described herein in accordance with the present disclosure. Fig. 3 illustrates a functional block diagram describing one embodiment of a computing environment for predictively automating the configuration of modular service zones for repairing and maintaining a variety of physical assets while maximizing the reuse of modular service zones, minimizing wait time, and optimizing the overall time for performing the services in accordance with the present disclosure. Fig. 4 illustrates a block diagram of an exemplary embodiment of a vehicle repair service executing program code that enables predictive automated configuration of modular service zones for repairing and maintaining physical assets in accordance with the present disclosure. Fig. 5 illustrates a flowchart describing one embodiment of a computer-executed method for predictively automating the configuration of modular service zones for repairing and maintaining a plurality of physical assets in accordance with the present disclosure. Fig. 6 illustrates a flowchart illustrating one embodiment of a computer-executed method for maximizing the reuse of modular service zones for repairing or maintaining physical assets and for optimizing the total time for providing the repairs or maintenance of the physical assets within the modular service zones in accordance with the present disclosure. DETAILED DESCRIPTION

[0006] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The use of the singular form "a," "an," or "the" herein is intended to include the plural forms as well, unless the context clearly indicates otherwise. It is further understood that the terms "comprises" and / or "comprising," when used in this specification, indicate the presence of 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.

[0007] The corresponding structures, materials, steps, and equivalents of all means or steps and functional elements, if any, present in the following claims are to be understood as including any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure is presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosure in the form set forth. Many changes and modifications will be apparent to those skilled in the art without departing from the scope of the disclosure.The embodiments chosen and described serve to best explain the principles of the disclosure and the practical applications and to enable others skilled in the art to understand the disclosure for various embodiments with various modifications as are suited to a particular use contemplated. OVERVIEW

[0008] Physical assets such as motor vehicles, machines, units, or other devices (collectively referred to herein as "physical assets") may wear out over time, require maintenance to prevent breakage or malfunction, and / or may need to be repaired from time to time to repair a physical asset that no longer functions as intended or has become inoperable. Often, physical assets that may require maintenance or repair can be taken to a service center that has the appropriate tools, parts, machinery, equipment, and / or expertise to perform repair procedures or maintenance on the physical asset.Different types of repairs and services may be requested for different types of physical assets that can be repaired or maintained at a service center. Service centers may be equipped with various types of machines, tools, equipment, parts, or components, etc., to provide a wide range of different services for the different types of physical assets. Tools, machines, equipment, etc. may be used or applied to similar types of physical assets and / or physical assets that may have the same or similar procedures for repair and / or maintenance.Generally, when a service is contracted with a service center, a physical asset such as a vehicle, unit, or machine can create a service log and send the collected data within the log to the service center, book an appointment, and / or deal with various roadside assistance or mobile repairs. Information about the asset, location data, and the appointment can be recorded at the service center to determine the type of services required, the spare parts that must be on hand to perform the repair or maintenance service, and the machines or tools that may be required to perform the requested service. The relevant parts, tools, machines, etc.can be manually ordered by employees assigned to the service center at a specific location where the repairs are scheduled to take place in order to perform the requested service.

[0009] In embodiments of the present disclosure, it is recognized that initiating the retrieval of spare parts and manually arranging the various pieces of equipment, machines, tools, and parts needed to perform one or more services at the service center may be a slow or time-consuming process.Arranging the spare parts, tools, equipment, and machinery required to perform a service may require a significant amount of effort and coordination, and may require diverting one or more service center personnel from actual service in other areas of the service center to ensure that a service zone within the service center is properly prepared to perform a service on the next physical asset scheduled for repair, maintenance, or other service in a timely manner.The process of arranging machines, tools, parts, components, or other accessories to perform services can be further complicated by the number of different types of services that can be scheduled through the service center and by numerous differences between physical assets, which may require rearranging a service zone each time a new physical asset arrives for maintenance.Therefore, there is a need to predictively automate configurations of modular service zones for repairing or maintaining physical assets, maximizing the reuse of the modular service zones during the performance of services for a plurality of different types of physical assets in order to minimize service times and maximize the number of physical assets for which a service can be provided.

[0010] Embodiments of the present disclosure leverage the use of AI-enabled systems and digital twin models to predictively automate configurations of modular service zones equipped to repair and / or maintain physical assets within a service center. The automated service zone configurations maximize the reuse of the modular service zones to provide service to one or more different types of physical assets and limit the number of service zone rearrangements between the different types of physical assets for which service is provided. Physical asset embodiments can create a service request for a specific physical asset.The request can be sent to a repair service or other type of application or program that can proactively evaluate a digital twin model and system data corresponding to the physical asset. The repair service can identify the types of services (such as maintenance or repairs) that may be required, as well as an estimated completion time for the services, along with any tools, machines, spare parts, components, or any other accessories that may be needed to perform the identified services. Based on the analysis performed by the repair service, the repair service can identify a service center capable of performing services on the physical asset.A portion of the service center, referred to herein as a modular service zone (or "service zone" for short), may further be identified as an acceptable location within the service center where services may be performed. Each identified service zone may already be equipped with the parts, machinery, tools, and / or components and previously arranged accordingly to perform the services. Alternatively, the modular service zone may be a location within the service center where every single part, tool, device, and / or other component may be present in a reasonable manner for arrangement, but may require some reconfiguration to be readily equipped with the tools, parts, and machinery to perform a service.

[0011] Embodiments of an AI-enabled repair service application or program may apply one or more classification algorithms to the services identified as applicable to physical assets submitting service requests. The classification of services applicable to the physical asset may indicate the type of spare parts, machinery, tools, or other components that can be assembled or positioned within the modular service zone prior to the physical asset's arrival at the service center to perform the service. The repair service or application that classifies services for the physical assets may associate the services with the appropriate parts, equipment, tools, components, etc., as part of creating a maintenance profile for each physical asset.Maintenance profiles can be compared by the repair service to identify commonalities between different services scheduled for application to one or more physical assets. Based on the commonalities between the different assignments for each service, as well as estimated arrival times of the incoming physical assets at the service center, the repair service can coordinate and sequence a workflow that schedules the incoming physical assets receiving the different services for one or more different service zones in an order that optimizes the total time required to provide a service to the majority of physical assets receiving services.

[0012] Based on the workflow sequence and taking into account the arrival times of the physical assets requiring service and the overlaps between the parts, tools, machines, etc. required for the physical assets assigned to different service zones, the repair service can coordinate robotic systems positioned within the service center by instructing the robotic systems to configure, arrange, or rearrange one or more service zones according to the workflow. Using configurations and arrangements by robotic systems, the next physical asset to be received for service within a designated service zone can be predicted, and tools, parts, equipment, machines, components, etc. can be allocated.in anticipation of the next physical asset within the service zone. By pre-placing tools, machines, equipment, and other components within service zones, wait times between services performed within a service zone are minimized and the number of physical assets that can be serviced within a period of time is maximized. In addition, by optimizing the workflow for scheduling services on physical assets that share a certain threshold of commonality between the tools, machines, equipment, etc., the repair service can reduce the amount of reordering required in a service zone between the order in which different physical assets arrive in the scheduled service zone for a service.

[0013] For example, the workflow created by the repair service may schedule physical assets to be serviced within the same service zone if the physical assets are the same type of physical asset and / or the physical assets being repaired consecutively have similar service characteristics within their maintenance profile, thereby allowing the services applied to one or more physical assets within the same service zone to use the same types of tools, machines, equipment or know-how to perform the service, without requiring rearrangement of the service zone by one or more robotic systems between the performance of services on the different physical assets.After completing a service on a first physical asset and pending the arrival of the next physical asset in the modular service zone, the robotic system may add to the service zone any additional tools, parts, machines, or other components that may not have been applicable to the first physical asset and simultaneously remove any tools, machines, equipment, or other components that are not applicable to the services applied to the next physical asset scheduled to arrive for service, based on the workflow prepared by the repair service application that coordinates the services for each modular service zone. DATA PROCESSING SYSTEM

[0014] Various aspects of the present disclosure are described by flowcharts, flowcharts, block diagrams of computer systems, and / or block diagrams of machine logic included in embodiments of computer program products (CPPs). With respect to any flowchart, the operations may (depending on the associated technology) be performed in a different order than shown in the flowchart. For example, two operations shown in consecutive blocks of a flowchart may be performed in reverse order, as a single integrated step, concurrently, or in an at least partially temporally overlapping manner.A computer program product embodiment ("CPP embodiment") is a term used in the present disclosure that can describe any set of one or more storage media (also called "media") that are collectively included in a set of one or more storage units. The storage media may collectively comprise machine-readable code corresponding to instructions and / or data for performing computer operations. A "storage unit" can refer to any physical hardware or device that can retain and store instructions for use by a computer processor.Without limitation, the computer-readable storage medium may include an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, and / or any suitable combination thereof. Some known types of storage devices that comprise media mentioned herein include a floppy disk, a hard disk, 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), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device (such as punched cards or pits / ridges formed on a larger surface of a storage disk), or any suitable combination thereof.A computer-readable storage medium should not be construed as storing transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses carried through a fiber optic cable, electrical signals transmitted through a wire, and / or other transmission media. As will be appreciated by those skilled in the art, data is commonly moved at certain times during the normal operation of a storage device, such as during access, defragmentation, or garbage collection, but this does not mean that the storage device is volatile, since the data is non-volatile while stored.

[0015] Fig. 1 illustrates a block diagram describing one embodiment of a data processing system 101 within a data processing environment, which may be a simplified example of a data processing device (i.e., a physical bare-metal system and / or a virtual system) capable of performing the data processing operations described herein. The data processing system 101 may be representative of the one or more data processing systems or devices implemented in accordance with the embodiments of the present disclosure and described in further detail below. It should be understood that Fig. 1 merely provides an illustration of an implementation of a data processing system 101 and does not impose any limitations on the environments in which various embodiments may be implemented. In general, the Fig. The components illustrated in Figure 1 represent an electronic unit, either physical or virtualized, capable of executing machine-readable program instructions.

[0016] Embodiments of the data processing system 101 may take the form of a desktop computer, a laptop computer, a tablet computer, a smartphone, a smartwatch or other wearable computer, a mainframe computer, a server, a quantum computer, a non-conventional computing system such as an autonomous vehicle or a household appliance, or any other form of computer or mobile device now known or developed in the future that is capable of executing an application 150, accessing a network 102, or querying a database such as a remote database 130. The performance of a computer-executed method executed by a data processing system 101 may be distributed across multiple computers and / or multiple locations.The data processing system 101 may be located as part of a cloud network, even if it is in . Fig. 1 to 2 is not shown within a cloud. Furthermore, the data processing system 101 does not have to be located in a cloud network unless explicitly stated otherwise.

[0017] A set 110 of processors comprises one or more computer processors of any type now known or developed in the future. Processing circuitry 120 may be distributed across multiple packaged devices. For example, multiple matched integrated circuit chips. Multiple processor threads and / or multiple processor cores may be implemented in the processing circuitry 120. A cache 121 may refer to memory located on the processor chip package(s) and / or used for data or code that may be made available for rapid access by the threads or cores running on the set 110 of processors. The cache 121 may be organized to be arranged in multiple levels, depending on their relative proximity to the processing circuitry 120.Alternatively, some or all of the cache 121 for the set 110 of processors may be located off-chip. In some computing environments, the set 110 of processors may be configured to work with qubits and perform quantum computing.

[0018] Computer-readable program instructions may be loaded into data processing system 101 to cause the set 110 of processors of data processing system 101 to perform a series of operational steps, thereby implementing a computer-executed method. Execution of the instructions may instantiate the flowchart and / or plain-text descriptions of computer-executed methods included in this specification (collectively, "the inventive methods"). The computer-readable program instructions may be stored on various types of computer-readable storage media, such as cache 121 and the other storage media discussed herein.The program instructions and associated data may be retrieved by the set 110 of processors to control and direct the performance of the inventive methods. In the data processing environments of . Fig. 1 to 2, at least some of the instructions for performing the inventive methods may be stored in a persistent memory 113, a volatile memory 112, and / or the cache 121, as application(s) 150 comprising one or more running processes, services, programs, and installed components thereof. For example, program instructions, processes, services, and installed components thereof may comprise a repair service 307 comprising components such as a profile module 401, a service zone optimization module 403, and / or a reporting module 409, as well as subcomponents thereof, as shown in Fig. 4 shown.

[0019] A data transmission network 111 may refer to signal transmission paths that may enable the various components of the data processing system 101 to exchange data with one another. For example, the data transmission network 111 may provide for electronic data transmission between the set 110 of processors, the volatile memory 112, the persistent storage 113, a set 114 of peripherals, and / or a network module 115. The data transmission network 111 may consist of switches and / or electrically conductive paths, such as the switches and electrically conductive paths that make up buses, bridges, physical input / output ports, and the like. Other types of signal transmission paths may also be used, such as fiber optic data transmission paths and / or wireless data transmission paths.

[0020] Volatile memory 112 may refer to any type of volatile memory now known or developed in the future, and may be characterized by random access, but this is not required unless explicitly stated. Examples include dynamic types of random access memory (RAM) or static types of RAM. In data processing system 101, volatile memory 112 is located in a single package and may be located within data processing system 101, but alternatively or additionally, volatile memory 112 may be distributed across multiple packaged devices and / or may be external to data processing system 101.The application 150, along with all programs, processes, services, and installed components thereof described herein, may be stored in the volatile memory 112 and / or the persistent memory 113 for execution and / or access by one or more of the respective sets 110 of processors of the data processing system 101.

[0021] Persistent memory 113 may be any form of non-volatile computer memory that may be known or developed in the future. The non-volatility of this memory means that the stored data can be retained regardless of whether power is applied to the data processing system 101 and / or the persistent memory 113 directly. Persistent memory 113 may be read-only memory (ROM), although at least a portion of persistent memory 113 may allow data to be written, erased, and / or rewritten. Some forms of persistent memory 113 include magnetic disks, semiconductor memory devices, hard disk drives, flash-based memory, erasable read-only memory (EPROM), and semiconductor storage devices.The operating system 122 can take various forms, such as various well-known proprietary operating systems or open-source operating systems of the Portable Operating System Interface type that employ a kernel.

[0022] The set 114 of peripheral units includes one or more peripheral units connected to the data processing system 101. For example, via an input / output (I / O) interface. Data transmission connections between the peripheral units and the other components of the data processing system 101 can be implemented using various methods. For example, through connections using Bluetooth, Near-Field Communication (NFC), wired connections or cables (such as Universal Serial Bus (USB) cables), pluggable connections (such as SD (Secure Digital) cards), connections established by local data transmission networks, and / or wide area networks such as the Internet.In various embodiments, the set 123 of UI units may include components such as a display screen, a speaker, a microphone, wearable units (such as glasses, headsets, and smartwatches), a keyboard, a mouse, a printer, a touchpad, game controllers, and haptic feedback units. Memory 124 may include external storage such as an external hard drive or pluggable storage such as an SD card. Memory 124 may be persistent and / or volatile. In some embodiments, memory 124 may take the form of a quantum computing memory unit for storing data in the form of qubits.In some embodiments, networks of computing systems 101 may utilize pooled computing and components that act as a single pool of seamless resources when accessed over a network by one or more computing systems 101. For example, a storage area network (SAN) shared by multiple geographically distributed computing systems 101 or network-attached storage (NAS) applications. A set 125 of IoT sensors may consist of sensors that can be used in Internet of Things applications. For example, a sensor may be a temperature sensor, a motion sensor, an infrared sensor, or any other known type of sensor.

[0023] The network module 115 may include a collection of computer software, hardware, and / or firmware that enables the data processing system 101 to exchange data with other computer systems over a computer network 102, such as a LAN or WAN. The network module 115 may include hardware such as modems or Wi-Fi signal transceivers, software for packetizing and / or unpacking data for transmission over a data transmission network, and / or web browser software for exchanging data over the Internet. In some embodiments, network control functions and network forwarding functions of the network module 115 are performed on the same physical hardware device.In other embodiments (e.g., embodiments using software-defined networks (SDN)), the control functions and forwarding functions of network module 115 may be performed on physically separate units, such that the control functions manage multiple different network hardware units. Computer-readable program instructions for performing the inventive methods may typically be downloaded to data processing system 101 from an external computer or external storage device via a network adapter card or network interface included in network module 115.

[0024] Fig. 2 illustrates a data processing environment 200, which is an extension of the data processing environment 100 of Fig. 1 operating as part of a network. In addition to the data processing system 101, the data processing environment 200 may include a data processing network 102, such as a wide area network (WAN) (or other type of computer network), over which the data processing system 101 is connected to an end-user device (EUD) 103, a remote server 104, a public cloud 105, and / or a private cloud 106.In this embodiment, the data processing system 101 comprises the set 110 of processors (including the processing circuitry 120 and the cache 121), the data transmission network 111, the volatile memory 112, the persistent storage 113 (including the operating system 122 and the application(s) 150, as indicated above), the set 114 of peripheral devices (including the set 123 of user interface (UI) devices, the memory 124, and the set 125 of IoT (Internet of Things) sensors), and the network module 115. The remote server 104 comprises the remote database 130. The public cloud 105 comprises a gateway 140, a cloud orchestration module 141, a set 142 of physical host machines, a set 143 of virtual machines, and / or a set 144 of containers.

[0025] Network 102 may consist of wired or wireless connections. For example, connections may consist of computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switching units, gateway computers, and / or edge servers. Network 102 may be described as any wide area network (e.g., the Internet) capable of transmitting computer data over non-local distances using any technology for transmitting computer data now known or developed in the future. In some embodiments, the WAN may be replaced and / or supplemented by local area networks (LANs) configured to exchange data between devices located within a local area, such as a Wi-Fi network.Other network types that may be used to connect the various computer systems 101, the end-user devices 103, the remote servers 104, the private cloud 106, and / or the public cloud 105 may include wireless local area networks (WLANs), home area networks (HANs), backbone networks (BBNs), peer-to-peer networks (P2P), campus networks, enterprise networks, the Internet, single-tenant or multi-tenant cloud computing networks, the public switched telephone network (PSTN), and any other network or network topology known to one of ordinary skill in the art for interconnecting the data processing systems 101.

[0026] End-user device 103 may include any computing device that can be used and / or controlled by an end user (e.g., a customer of a company operating data processing system 101) and may take any of the forms discussed above in connection with data processing system 101. EUD 103 may receive helpful and useful data from the operations of data processing system 101. For example, in a hypothetical case where data processing system 101 is configured to provide a recommendation to an end user, that recommendation may be transmitted from network module 115 of data processing system 101 to EUD 103 over network 102. In this example, EUD 103 may display or otherwise present the recommendation to an end user.In some embodiments, the EUD 103 may be a client device, such as a thin client, a thick client, a mobile computing device such as a smartphone, a mainframe computer, a desktop computer, and so on.

[0027] The remote server 104 may be any computing system that provides at least some data and / or functionality to the computing system 101. The remote server 104 may be controlled and used by the same entity that operates the computing system 101. The remote server 104 represents the machine(s) that collect and store helpful and useful data for use by other computers, such as the computing system 101. For example, in a hypothetical case where the computing system 101 is configured and programmed to provide a recommendation based on historical data, the historical data may be provided to the computing system 101 from the remote database 130 of the remote server 104.

[0028] The public cloud 105 may be any computer system available for use by multiple entities and provide the 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 may be performed by the computer hardware and / or software of the cloud orchestration module 141. The computing resources provided by the public cloud 105 may be implemented by virtual computing environments running on various computers that constitute and / or are available to the computers of the set 142 of physical host machines and / or the entirety of physical computers in the public cloud 105.The virtual computing environments (VCEs) may take the form of virtual machines from the set 143 of virtual machines and / or containers from the set 144 of containers. It should be noted that these VCEs may be stored as images and transferred among and between the various physical machine hosts, either as an image or after instantiation of the VCE. The cloud orchestration module 141 manages the transfer and storage of images, provisions new instantiations of VCEs, and manages active instantiations of VCE deployments. The gateway 140 is a collection of computer software, hardware, and firmware that enables the public cloud 105 to exchange data over the network 102.

[0029] VCEs can be stored as "images." A new active instance 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 the existence of multiple isolated user-space instances, called containers. These isolated user-space instances can behave like physical computers from the perspective of the applications 150 running on them. An application 150 running on an operating system 122 can utilize all of that computer's resources, such as attached devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities.The applications 150 running within a container of the set 144 of containers can only use the contents of the container and the units assigned to the container, a feature that can be referred to as containerization.

[0030] Private cloud 106 may be similar to public cloud 105, except that the computing resources may be available only to a single enterprise. While private cloud 106 is depicted as communicating with network 102 (such as the Internet), in other embodiments, a private cloud 106 may be completely disconnected from the Internet and accessible only via a local / private network. A hybrid cloud may refer to a combination of multiple clouds of different types (e.g., private, community, or public cloud types), and the plurality of clouds may be implemented or operated by different providers.Each of the multiple clouds remains a separate and distinct entity, but the larger hybrid cloud architecture is interconnected by standardized or proprietary technology that enables orchestration, management, and / or data / application portability between the multiple individual clouds. In this embodiment, public cloud 105 and private cloud 106 may both be part of a larger hybrid cloud environment. SYSTEM THAT PREDICTIVELY AUTOMATES THE CONFIGURATION OF A MODULAR SERVICE ZONE

[0031] It will be readily understood that the present components, as generally described herein and illustrated in the figures, may be arranged and implemented in a wide variety of different configurations. Accordingly, the following detailed description of the embodiments of at least one method, apparatus, non-transitory computer-readable medium, and system as illustrated in the accompanying figures is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments.

[0032] The present features, structures, or characteristics described in this specification may be combined or removed in one or more embodiments in any suitable manner. For example, the use of the phrases "exemplary embodiments," "some embodiments," or other similar language in this specification refers to the fact that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment. Accordingly, not every occurrence of the phrases "exemplary embodiments," "in some embodiments," "in other embodiments," or other similar language in this specification necessarily refers to the same group of embodiments of the invention, and the described features, structures, or characteristics may be combined or removed from one or more embodiments.Features may be combined or removed in any suitable manner in one or more embodiments. Furthermore, in the figures, any connection between elements may enable one-way and / or two-way data transmission, even if the connection depicted is a one-way or two-way arrow. Also, each entity depicted in the drawings may be a different entity. For example, where a mobile entity is shown sending data, a wired entity could also be used to send the data.

[0033] With reference to the drawings, Fig. 3 depicts an embodiment of a computing environment 300 illustrating a system capable of predictively automating configurations of modular service zones 309 within a service center that are scheduled to receive one or more physical assets 301. The modular service zones 309 may be described as an area within the service center that can be configured, arranged, and / or rearranged (as needed) to provide one or more services, such as repairs or maintenance of the physical assets 301.Embodiments of the computing environment 300 may optimize the scheduling and coordination of services provided to the physical assets 301 in a manner that reduces the total time required to provide services to all physical assets 301 receiving service at a service center within a period of time, while maximizing the number of physical assets 301 receiving service within the period of time and minimizing the number of reconfigurations in each of the service zones 309 as each new physical asset 301 arrives for service.As illustrated, the computing environment 300 may include one or more physical assets 301, a repair service 307, a digital twin repository 313, and one or more robotic systems 311 that may be positioned within or between the service zones 309 and may be mobile. Embodiments of the one or more physical assets 301, the repair service 307, the one or more robotic systems 311, and the digital twin repository may be communicable with each other via a communications network 102.

[0034] In embodiments, physical assets 301 may be any type of physical entity, machine or device, device, hardware, etc., that may be capable of connecting to and exchanging data over network 102. For example, in this exemplary embodiment, a physical asset 301 may be a vehicle, including (but not limited to) autonomous or semi-autonomous motor vehicles, aircraft, locomotives, turbines, boats, ships, etc. In other embodiments, physical assets 301 may include entities, machines, or devices such as medical machines or equipment, oil and gas-powered assets, mobile data transmission devices such as smartphones, household appliances, or other smart devices.In some embodiments, the physical asset 301 may be equipped with or tracked by one or more types of sensors and / or IoT devices onboard the physical asset 301. In other embodiments, the sensors and / or IoT devices monitoring the physical asset 301 may be positioned in or around the environment of the physical asset 301. The sensors and IoT devices, whether onboard the physical asset 301 or positioned within the surrounding environment, may measure one or more functions and the condition of the physical asset 301 and be responsible for collecting data describing the current condition of the physical asset 301 (i.e., in real time).The collected data describing the physical asset 301 may be stored as part of a system log and may include position data describing the physical location of the physical asset 301.

[0035] The data collected by the sensors, IoT devices, and / or other data sources describing the physical asset 301 may be stored as system data 303. The system data 303 collected by the plurality of onboard or nearby sensors and IoT devices of the physical asset 301 may be used to build a digital twin model of the physical asset 301. Embodiments of the digital twin model may represent a virtual representation of the physical asset 301 in the current state of the physical asset based on the system data 303 collected by the systems of the physical asset 301. Furthermore, the digital twin model may be updated concurrently (or nearly concurrently) as the system data 303 changes over time to reflect changes in the current state of the physical asset 301 (i.e.,, as measured by the sensors, IoT devices, etc.) to virtually reflect the changes within the digital twin model. Digital twin models may be organized and stored locally within the persistent storage 113, which may be located onboard the physical asset 301, or in some embodiments, the digital twin models of the physical asset 301 may be stored on a network-accessible storage device, such as the digital twin repository.

[0036] Embodiments of the physical asset 301 may self-assess the current status and / or condition of the physical asset 301. The self-assessment(s) of a physical asset 301 may identify one or more problems, malfunctions, errors, or other needs of the physical asset 301 and determine whether the physical asset 301 requires maintenance or repair, or whether other services are required to maintain optimal operation and / or return the physical asset 301 to an optimal operating condition.The self-assessment by one or more systems of the physical asset 301 may occur periodically at a regularly scheduled time interval, it may be triggered in response to changes in the current state of the physical asset 301, it may be manually selected for performance by a user, owner, or manager of the physical asset 301, it may be performed in response to detectable faults, non-functional features or functions of the physical asset, and / or upon detection of features of the physical asset 301 that are not functioning optimally.

[0037] The self-assessment of a physical asset 301 may be performed by reviewing or testing systems of the physical asset 301 and / or by analyzing the system data 303 collected by the IoT devices and sensors of the physical asset 301. An AI-enabled algorithm associated with the physical asset 301 may be trained to detect potential problems with the physical asset 301 that may require repair or can be mitigated through maintenance. Results of the self-assessment of the physical asset 301 may be recorded in a system data log file 303. If a self-assessment of the physical asset 301 or a manual assessment by a user, owner, manager, etc.of the physical asset 301 that repairs or maintenance should be further investigated or performed, the physical asset 301 may document potential problems or needs of the physical asset 301, including maintenance and / or repair work identified by the results of the self-assessment, as part of service request data 305. The physical assets 301 may create a service request and transmit the service request along with the system data 303, the service request data 305, and / or the digital twin model to a repair service 307.

[0038] Embodiments of the repair service 307 may be part of an application 150 or program accessible to the physical asset 301 via the network 102. The repair service 307 may operate as an instance in a service provider's cloud network or in any other type of network described herein. Embodiments of the repair service 307 may perform functions and processes associated with evaluating digital twin models of the physical assets 301 and associated system data 303 to determine the types of services to be provided to the physical assets 301, as well as any tools, equipment, machines, parts, accessories, and / or components that may be known or used to perform the types of services determined for the physical asset 301.Furthermore, based on the identified types of services, embodiments of the repair service may organize a workflow and scheduling for the physical assets 301 at a suitable service center capable of executing the services, optimize the layout of the service zones 309 within a selected service center, and schedule the execution of services for the physical assets 301 with the service center according to the created workflow. Scheduling the execution of one or more services with a service center may be performed according to an optimal workflow created by the repair service based on the overlap of commonalities between the physical assets 301 requesting services and capable of being assigned to the same service center.The schedule for performing services according to the workflow may be arranged to minimize waiting times for the services to be completed, to minimize rearrangement of the service zones 309 between the performance of services for different physical assets 301, and to maximize the number of physical assets 301 that receive services within a service zone 309 during a selected period of time.

[0039] With reference to the drawing of the Fig. 4, embodiments of the repair service 307 may include one or more modules or subcomponents responsible for implementing one or more specific processes, tasks, functions, or features of the repair service 307. The term "module" may refer to a hardware module or a software module, or a module may be a combination of hardware and software resources. A module (whether hardware, software, or a combination thereof) may be configured to implement or perform one or more specific tasks, routines, and / or functions. Embodiments of hardware-based modules may include self-contained components such as chipsets, specialized circuitry, the sets 110 of processors, the one or more units of volatile memory 112 and / or persistent storage 113.A software-based module may be part of an application 150, a program code, or associated with a program code comprising a set of specific programmed instructions loaded into the volatile memory 112 or the persistent memory 113. In the exemplary embodiment of FIG. Fig. 4, the repair service 307 may include a profile module 401, a service zone optimization module 403, and / or a reporting module 409.

[0040] Embodiments of the profile module 401 may perform tasks, functions, and / or processes of the repair service 307 directed toward creating a maintenance profile for each physical asset 301 connected to the repair service 307 via the network 102. For example, the connection to the repair service 307 may be accomplished by transmitting a service request, a digital twin model, the system data 303, and / or the service request data 305 to the repair 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 conjunction with the system data 303 and / or the service request data 305 transmitted along with the digital twin model, determine what types of repairs, maintenance, or other services can be (or should be) provided for the physical asset 301, as well as any associated machinery, tools, spare parts, components, or other accessories known to be used to perform 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 types of services identified in connection with the physical asset 301 and the corresponding types of equipment, machinery, parts, components, or other accessories used to implement the services associated with the maintenance profile of each physical asset 301. Comparisons between maintenance profiles of the physical assets 301 may enable comparisons between requirements for performing services on the physical assets 301 and / or classifications of the maintenance profiles of the physical assets 301 to identify commonalities between the various services that may be performed.

[0041] Embodiments of the profile module 401 may apply one or more classification algorithms to the maintenance profiles created by the profile module 401 using the digital twin model for each physical asset 301. Applying one or more classification algorithms to the maintenance profiles of the plurality of physical assets 301 may enable the profile module 401 to identify commonalities between the services applicable to each of the physical assets 301, including commonalities between the types of machines, tools, equipment, components, or other accessories that may be associated with each service applicable to a physical asset 301.For example, if a profile module 401 classifies digital twin models for a plurality of vehicles (which may or may not be autonomous), these vehicles may have multiple possible services in their maintenance profile that share commonalities despite different vehicle types or makes. For example, if two vehicles require tire-related services, such as rotating and balancing a tire for a first vehicle and changing a tire for the second vehicle, the profile module 401 may determine the types of tools, machines, equipment, and parts that the tire rotation service and the tire changing service being performed may have in common.The profile module 401 can identify commonalities between the tire rotation service and the tire replacement service, such as the need to use a lift, a pneumatic or manual wrench, lug nuts, a wheel balancer, etc., allowing the repair service 307 to identify a service center capable of performing both the tire rotation service and the tire replacement service. If the arrival times at the service center are within a threshold period, the repair service can schedule the tire services for the first physical asset and the second physical asset sequentially within a workflow, allowing a service zone 309 to allocate the tools, equipment, machines, components, etc.that are common to both services, while minimizing the number of changes in the service zone 309 between performing services on both physical assets 301.

[0042] Embodiments of the service zone optimization module 403 may perform functions, tasks, and processes of the repair service 307 associated with identifying service centers capable of performing services for the physical assets 301 and, based on the classifications of the maintenance profiles for each of the physical assets 301, the location of the physical assets 301, and the expected arrival times of the physical assets 301 at a designated service center, creating a workflow that implements a sequence of services within one or more service zones 309 of each service center.In addition, the service zone optimization module 403 can also implement the created workflows within the service center by instructing the robotic systems 311 positioned within the service zone 309 to prepare the service zones 309 according to the sequence of services to be performed on one or more physical assets 301 and scheduled for the service center. For each incoming service request and the associated services identified and classified via the profile module 401, embodiments of the service zone optimization module 403 can determine whether an existing service zone 309 within a service center can handle the services for the physical asset's service request.If the existing service zones 309 are not currently equipped to perform the services within the workflow, it is determined in which service zones of a service center the performance of the services can be assumed with a minimal number of modifications to an existing service zone 309. For example, determining service zones 309 that may be acceptable after a minimal number of modifications or rearrangements of the tools, equipment, components, and other accessories that may be present within the determined service zone 309 to provide one or more of the services determined by the profile module 401.

[0043] Embodiments of the service zone optimization module 403 may create a workflow for scheduling the various incoming physical assets 301 arriving at the service center and organizing the order in which the physical assets 301 are assigned to one or more service zones 309 for performing the requested services, based on the services received and the commonalities between the types of machines, tools, parts, components, or other accessories associated with the service, as indicated by the maintenance profile of the physical asset 301.As part of the workflow creation and scheduling process, the service zone optimization module 403 may calculate and consider an estimated arrival time for each incoming physical asset 301 at the service center. The service zone optimization module 403 may use location data provided to the repair service 307 as part of the system data 303 to estimate arrival times. Additionally, in some embodiments, when creating a workflow and assigning the physical assets 301 to a service zone, the service zone optimization module 403 may further consider the time required to prepare a service zone 309 for the equipment, tools, parts, components, etc.to configure or rearrange to provide the requested services, as well as the time required to receive various parts or components that may not be immediately accessible within the service center or are scheduled for use in another service zone 309 within the service center. For example, time constraints may be considered when planning physical assets and creating the workflow to accommodate a service center that must obtain parts or tools from another location or from a parts supplier to perform the service.

[0044] Embodiments of the service zone optimization module 403 may generate a workflow with optimal scheduling and schedule the performance of services at the service center within the service zones 309 according to the optimal scheduling of the generated workflow. The workflow may include instructions and / or events for assigning the physical assets 301 to one or more service zones 309 in a sequential order, as well as steps for rearranging or configuring each of the designated service zones 309 with the tools, equipment, machines, components, ingredients, etc.associated with services performed on a corresponding physical asset 301 before the physical asset 301 arrives in the designated service zone 309, as well as instructions or steps for reconfiguring or rearranging the service zones 309 after services on a physical asset 301 have been completed in anticipation of the arrival of the next physical asset 301 within the service zone 309.The workflow generated by the service zone optimization module 403 may schedule the sequential order of assignments of physical assets to the service zones 309 to optimize the schedule for completing services, minimize the time associated with reordering a service zone 309, minimize wait times between service completions, and maximize the number of physical assets 301 that can receive services within the service zones 309 of the service center.For example, by establishing an order of the physical assets 301 assigned to the same service zone 309 when the maintenance profiles of the physical assets 301 assigned to the same service zone 309 have a threshold number of commonalities between the types of machines, tools, equipment, parts, components or other accessories applied during the implementation of the services.By sequentially scheduling physical asset services applied to different physical assets with a common number of commonalities above a threshold in the same service zones 309, a minimal number of changes to the service zone configuration may be required between service performance, thereby maximizing the number of physical assets 301 that can receive services via a service zone within a given period of time.

[0045] Embodiments of the service zone optimization module 403 may implement the created workflow to configure and sequence services within a service center into one or more designated service zones 309 by issuing one or more instructions to a robotic system 311 positioned within the service center. The robotic systems 311 may be instructed how to configure and sequence each of the service zones 309 in anticipation of the arrival of the physical assets 301 at the service center and / or in anticipation of the next physical asset 301 being placed within a service zone 309 to receive one or more services.The robot systems 311 can move autonomously through the service center and locate each of the designated service zones 309 based on the instructions provided to the robot systems 311 in accordance with the workflow established by the repair service 307. The robot systems 311 can move throughout the service center, pick up tools, move equipment, collect parts and / or components, or other accessories for providing services within the service zones 309, and place the picked up tools, equipment, parts, etc., within the designated service zone 309.Ensure that the appropriate tools, equipment, and parts for performing the services on the physical asset 301 are present and located within a reasonable distance from the persons or machines responsible for implementing the service within the service zone 309. After completing services for a first, sequenced asset within one or more service zones 309, the robotic systems 311 may, in accordance with the workflow and scheduling of services, rearrange the service zone 309 as instructed, including adding additional equipment, tools, and parts for providing services to the next physical asset in the workflow sequence within the service zone 309.The robotic system 311 may also remove equipment, tools, components, and / or unused parts from the service zone 309 that are not part of the service requirement for performing services on the physical asset that will next be serviced in the service zone 309. Tools, equipment, parts, and other components removed from one service zone 309 may be moved by the robotic system 311 to another service zone 309 and / or placed in a neutral staging or storage area outside of the various service zones 309 established within the service center.

[0046] Embodiments of the service zone optimization module 403 may include additional components that may assist the service zone optimization module 403 in creating an optimized workflow and sequence of services provided for one or more physical assets 301. For example, in the embodiment of the service zone optimization module 403, Fig. 4, a service zone visualization control routine 405 and / or a service zone placement module 407 may be present. Embodiments of the service zone visualization control routine 405 may perform tasks, functions, and processes that enable the repair service 307 to visualize the layout of the service centers, the spaces available for creating service zones within the service center, and the locations of the various pieces of equipment, tools, and machines within each of the available service centers. A service zone placement module 407 may utilize the service zone visualization control routine 405 to simulate workflows created by the service zone optimization module and ultimately determine the optimal workflow to be implemented.Using the service zone placement module 407 in conjunction with the service zone control routine 405, embodiments of the repair service 307 can simulate various workflow variations by simulating the allocation of machines, equipment tools, parts, etc., and a sequence of the physical assets 301 in various service zones 309 represented by the control routine.The service zone placement module 407 may calculate and determine the optimal schedule for assigning the incoming physical assets 301 to the various service zones 309 and the scheduled sequence that can be predicted to result in the maximum total number of physical assets 301 to be serviced within a threshold period, the minimum number of reconfigurations of a service zone 309 between the scheduled physical assets 301 to be sequentially serviced within the same service zone 309, and the minimum waiting time for receiving services.Based on the simulated workflow that leads to the optimal results, the optimal workflow can be selected by the service zone optimization module 403 and instructions for implementing the optimal workflow can be transmitted to the robot system(s) 311 positioned within the service center.

[0047] Embodiments of the repair service 307 may include a reporting module 409. The reporting module 409 may perform functions, processes, and / or tasks of the repair service 307 that may be directed to outputting data transmissions and reporting information to physical assets 301 that submit requests to the repair service 307. For example, the reporting module 409 may report back to the physical asset 301 and its users or owners the type of services scheduled for the physical asset 301, the location of the service center that will perform the services, an estimated date and time the services will be performed, and any additional recommended services the user may want to schedule for the physical asset 301. Embodiments of the reporting module 409 may further exchange data with the service center.For example, the reporting module 409 may confirm scheduled service appointments or cancellations of services for each of the physical assets 301, as well as confirm arrivals of the physical assets 301 at the service center. Output from the reporting module 409 may be in any form of electronic data transmission that may be sent over the network 102. For example, reports, notifications, and other messages sent by the reporting module 409 may be in the form of an email, a push notification, a text message, a notification, an alert, or any other known type of electronic data transmission format or delivery system. Method for predictive automation of configurations of a modular service zone

[0048] The drawings from Fig. 5 to 6 illustrate embodiments of methods 500, 600 for predictively automating configurations of a modular service zone located within a service center to provide one or more services to a plurality of physical assets 301 and optimizing the workflow of the modular service zone 309 based on the commonalities between maintenance profiles of the physical assets 301 in order to reuse service zones 309 with minimal changes, to provide a service to a maximum number of physical assets 301 within a period of time, and to minimize a total time for providing a service to a plurality of the physical assets 301. The embodiments of the methods 500, 600 may be implemented according to the methods described above in Fig. 1 to 4 and are described in this application. Those skilled in the art should recognize that the steps of the Fig. 5 to 6 may be performed in a different order than shown and that not all steps described herein need be performed.

[0049] The Fig. The embodiment of the method 500 described in Figure 5 may begin at step 501. In step 501, IoT units and / or sensors connected to a physical asset 301 or positioned in the vicinity thereof collect the system data 303 of the physical asset 301. Using the collected system data 303, a digital twin model is created or updated that reflects the current state of the physical asset 301. In step 503, a physical asset 301 may perform a self-assessment. Based on the self-assessment of the physical asset 301, a decision is made as to 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 units and sensors.For example, based on sensor data, it may be determined that the physical asset is not functioning optimally or that errors are detected during self-assessment, so that one or more maintenance actions 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, the method 500 may proceed to step 505. Otherwise, the method 500 may return to step 501, where the IoT devices and / or sensors may continue to collect the 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 determining that one or more services may be needed for the physical asset 301, computing systems on board or connected to the physical asset 301 may submit a service request to a repair service or application, such as the repair service 307, which may be hosted in a public or private cloud and / or any other type of network 102.

[0050] In step 505, the cloud-hosted repair service 307 may receive a service request from a physical asset 301. Accompanying the service request may be additional information about the physical asset 301, including the system data 303 of the physical asset 301 and / or a digital twin model of the physical asset 301 requesting services. In step 507, the digital twin model of the physical asset 301 is analyzed by the repair service 307 based on the current status of the digital twin model and the system data 303 for suitable services that may be applicable to the physical asset.The repair service 307 may identify one or more services, including repairs and / or maintenance, applicable to the physical asset 301 so that the physical asset continues to operate at optimal performance, and / or may address ongoing faults and performance issues identified or highlighted by the digital twin model.

[0051] In step 509, the repair service instance 307 may determine, based on the analysis of the digital twin model, the types of machines, tools, spare parts, and / or other components associated with the identified repairs, maintenance, or other services. In step 511, each type of service applicable to the physical asset 301 may be classified by the repair service 307. A maintenance profile may include a plurality of potentially available services for each physical asset 301 that utilizes the repair service 307, enabling comparisons between physical assets 301 based on maintenance profiles.The comparisons between maintenance profiles of the physical assets 301 may reveal commonalities between the services for each of the physical assets 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 plan and coordinate services for physical assets in assigned modular service zones 309, whereby planned services requiring similar types of requirements for machinery, tools, parts, know-how, and other components can be performed sequentially within the same service zones 309, within adjacent service zones 309, and / or nearby service zones 309. This allows common tools, machinery, equipment, and other components to be readily shared between planned services and / or between service zones 309, thereby reducing the amount of reconfiguration or rearrangement of the service zones 309.

[0052] In step 513, the repair service 307 may select a service center facility that is either known to be equipped with the machines, tools, parts, etc., to perform one or more services on the physical asset 301, or a service center is capable of receiving and configuring itself with the one or more machines, tools, parts, components, etc., to perform services on the physical asset 301, either before the arrival time of the physical asset 301 at the service center or within a threshold time after the arrival of the physical asset 301 at the service center.In step 515, it is determined whether a modular service zone 309 within the selected service center is already configured with the arrangement of machines, tools, parts, and other components required to perform the services on the physical asset 301. If a modular service zone 309 within the selected service center is already configured, the method 500 may 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 the services at the service center and is assigned to the previously established and configured modular service zone 309.Conversely, the method 500 may proceed to step 519 if an existing service zone within the selected service center has not previously been configured in a manner that would meet the requirements for performing services on the incoming physical asset 301 expected to arrive at the service center.

[0053] During step 519, the repair service 307 may transmit instructions to one or more robotic systems 311 positioned within the selected service center. The transmitted instructions may instruct the robotic systems 311 to create a new modular service zone 309 within the service center or to modify an existing service zone 309 so that the existing service zone can perform one or more requested services. The robotic systems 311 within the service center may be instructed to retrieve and / or locate one or more machines, tools, parts, accessories, or other components associated with performing the requested services within the designated service zone 309.The robotic systems may be instructed to prepare the service zone according to the transmitted instructions in advance before the physical asset 301 arrives at the service center, or after completion of ongoing services within the designated service zone 309 and / or before a scheduled time for the start of the requested services within the service zone 309. The robotic systems 311 that prepare the modular service zone 309 may further arrange the service zone 309 by removing any unnecessary components or instruments that are present.For example, removing machines, tools, components, and / or parts that are located within the service zone but are not required to perform services for the next incoming physical asset 301 assigned to the service zone and / or any subsequent physical assets scheduled for service at a later time within the same service zone 309.

[0054] The Fig.The embodiment of the method 600 described in Figure 6 may begin at step 601. During step 601, a plurality of the physical assets 301 may transmit their respective digital twin models of the physical asset 301 to a server or cloud network running an instance of a repair service 307. In step 603, the repair service 307 determines, within the maintenance profile of each physical asset 301 transmitting a digital twin model, the location of the physical asset 301, the type of services applicable to the physical asset 301, and any types of tools, equipment, parts, components, or other accessories that may be required to perform the services on the physical asset 301.In step 605, the repair service 307 may classify the maintenance profiles of the majority of the physical assets 301 using one or more classification algorithms. Classifying each maintenance profile of the physical assets 301 may determine varying degrees of commonality between the types of services applicable to each physical asset and may group services with a threshold level of commonality into the same or nearby classifications.

[0055] In step 607, the repair service 307 may establish a mapping between the types of services applicable to each of the physical assets 301 and the equipment, parts, machines, tools, components, accessories, etc. required by the service center to perform each of the services that may be requested for the physical assets 301. In step 609, the repair service 307 determines, based on the classified commonalities between providing a service to various physical assets 301, the types of maintenance or service equipment required to perform various services on the physical assets. In step 611, it is determined whether one or more service requests from one or more physical assets 301 are received by the repair service 307.If one or more requests are not received, the method 600 may return to step 601, which may provide new or updated digital twin models to the repair service 307 over time. Otherwise, if one or more service requests are received in step 611, the method 600 may proceed to step 613.

[0056] During step 613, the repair service may proactively create modular service zones 309 within a selected service center tasked with providing services to the physical assets 301 based on classification patterns of commonalities between maintenance profiles of the physical assets 301 and the number of physical assets 301 within each classified maintenance profile. The repair service 307 may optimize the workflow for each service zone 309 to minimize repair times, changes in the service zone 309 between services on physical assets, and the wait time between services on physical assets 301.In step 615, it is determined whether the modular service zone 309 visualized by the repair service 307 optimizes the total service time for completing services on the majority of the physical assets 301 that can be scheduled to provide the service within one or more service zones 309. If the total service time is not optimized, the method 600 may return to step 613 and further optimize the service zone(s) 309 based on the classification of the maintenance profiles and the commonalities between them, and simulate possible workflow changes to achieve optimization.Otherwise, the method may proceed to step 617 if the service zones 309 visualized by the repair service 307 optimize the total service time for completing the services on all physical assets 301 planned in the service center and assigned to one or more service zones 309.

[0057] During step 617, one or more service zones 309 may be implemented within the service center according to the workflow created by the repair service 307. The repair service may instruct the layout of the service zones 309 using one or more of the robotic systems 311 positioned within the service center. Instructions transmitted to the robotic systems 311 may instruct the layout of the service zones 309 according to the optimized workflow and visualizations of the service zones 309 provided by the repair service 307, resulting in an optimized overall service time for completing the services on the physical assets 301.In step 619, feedback data describing the actual repairs, maintenance, or other services performed for each of the physical assets is collected and may be reported back to the repair service 307 to improve the classification models and optimization of the service zones 309 and / or the scheduling of work processes.

Claims

[1] A computer-implemented method for predictively automating configurations of a modular service zone within a service location, the computer-implemented method comprising: Receiving, by a processor, service requests from a plurality of physical assets requesting the performance of services on the physical assets at the service location, a position of the physical assets, and an estimated time of arrival for each of the physical assets at the service location; Analyzing, by the processor, a digital twin model corresponding to each of the plurality of physical assets; Creation by the Processor of a maintenance profile for each type of physical asset that describes classifications of services for a corresponding physical asset, including one or more machines, tools, or parts required to perform the services on the physical asset; Based on maintenance profiles of the physical assets and commonalities between the one or more machines, tools, or parts required to perform the services, the processor creating one or more modular service zones within the service location that include at least one of the one or more machines, tools, and parts required to perform the services on the physical asset; and Instructing, by the processor, a robotic system positioned within the service location to create or modify the modular service zone by positioning the machines, tools, and parts for performing each of the services on the physical assets within the modular service zone prior to the estimated arrival time of each of the physical assets at the service location. [2] The computer-implemented method of claim 1, wherein creating the one or more modular service zones within the service location further comprises: Creating, by the processor, a workflow configured to allocate the physical assets of the one or more modular service zones for performing the services in an order based on a combination of the estimated arrival time and a minimization of a number of changes to the one or more modular service zones between services provided for a first physical asset and a subsequent physical asset scheduled by the workflow. [3] The computer-implemented method of claim 2, wherein the sequence of operations is configured to allocate the physical assets to the one or more modular service zones, and further maximizes a number of physical assets for which services are provided at the service location. [4] A computer-implemented method according to claim 2, further comprising: Modifying, by the processor, a first modular service zone within the service location, wherein the first physical asset receives one or more services within the first modular service zone, and prior to arrival of the subsequent physical asset within the first modular service zone, adding or removing at least the one or more of the machines, tools, or parts that have no commonality between maintenance profiles of the first physical asset and the subsequent physical asset to or from the first modular service zone. [5] A computer-implemented method according to claim 1, further comprising: Optimizing the workflow by the processor to minimize the repair time of the physical assets, changes to each of the physical assets, or the waiting time between services provided to each of the physical assets assigned to the same modular service zone. [6] A computer-implemented method according to claim 1, further comprising: Collecting data describing the performance of the services within the one or more modular service zones by the processor; and Input of the data into classification models by the processor, providing feedback and continuous learning to improve the classification of maintenance profiles assigned to each type of physical asset. [7] A computer-implemented method according to claim 1, further comprising: Assigning, by the processor, one or more of the services requested by the plurality of physical assets to the one or more machines, tools, or parts required to perform the services. [8] A computer system for predictively automating configurations of a modular service zone within a service location, comprising: a processor; and a computer-readable storage medium connected to the processor, the computer-readable storage medium containing program instructions that execute, via the processor, a computer-executable method, comprising: Receiving, by the processor, service requests from a plurality of physical assets requesting the performance of services on the physical assets at the service location, a position of the physical assets, and an estimated time of arrival for each of the physical assets at the service location; Analyzing, by the processor, a digital twin model corresponding to each of the plurality of physical assets; Creation by the Processor of a maintenance profile for each type of physical asset that describes classifications of services for a corresponding physical asset, including one or more machines, tools, or parts required to perform the services on the physical asset; Based on maintenance profiles of the physical assets and commonalities between the one or more machines, tools, or parts required to perform the services, the processor creating one or more modular service zones within the service location that include at least one of the one or more machines, tools, and parts required to perform the services on the physical asset; and Instructing, by the processor, a robotic system positioned within the service location to create or modify the modular service zone by positioning the machines, tools, and parts for performing each of the services on the physical assets within the modular service zone prior to the estimated arrival time of each of the physical assets at the service location. [9] The computer system of claim 8, wherein creating the one or more modular service zones within the service location further comprises: Creating, by the processor, a workflow configured to allocate the physical assets of the one or more modular service zones for performing the services in an order based on a combination of the estimated arrival time and a minimization of a number of changes to the one or more modular service zones between services provided for a first physical asset and a subsequent physical asset scheduled by the workflow. [10] The computer system of claim 9, wherein the order of the workflow is configured to assign the physical assets to the one or more modular service zones, and further maximizes a number of physical assets for which the services are provided at the service location. [11] A computer system according to claim 9, further comprising: Modifying a first modular service zone within the service location by the processor, wherein the first physical asset receives one or more services within the first modular service zone, and prior to arrival of the subsequent physical asset within the first modular service zone, adding or removing at least one or more of the machines, tools, or parts that have no commonality between maintenance profiles of the first physical asset and the subsequent physical asset to or from the first modular service zone [12] A computer system according to claim 8, further comprising: Optimizing the workflow by the processor to minimize the repair time of the physical assets, changes to each of the physical assets, or the waiting time between services provided to each of the physical assets assigned to the same modular service zone. [13] A computer system according to claim 8, further comprising: Collecting data describing the performance of the services within the one or more modular service zones by the processor; and Input of the data into classification models by the processor, providing feedback and continuous learning to improve the classification of maintenance profiles assigned to each type of physical asset. [14] A computer system according to claim 8, further comprising: Assigning, by the processor, one or more of the services requested by the plurality of physical assets to the one or more machines, tools, or parts required to perform the services. [15] A computer program product for predictively automating configurations of a modular service zone within a service location, comprising: one or more computer-readable storage media having computer-readable program instructions stored thereon, the program instructions executing a method executed on a computer, comprising: Receiving, by a processor, service requests from a plurality of physical assets requesting the performance of services on the physical assets at the service location, a position of the physical assets, and an estimated time of arrival for each of the physical assets at the service location; Analyzing, by the processor, a digital twin model corresponding to each of the plurality of physical assets; Creation by the Processor of a maintenance profile for each type of physical asset that describes classifications of services for a corresponding physical asset, including one or more machines, tools, or parts required to perform the services on the physical asset; Based on maintenance profiles of the physical assets and commonalities between the one or more machines, tools, or parts required to perform the services, the processor creating one or more modular service zones within the service location that include at least one of the one or more machines, tools, and parts required to perform the services on the physical asset; and Instructing, by the processor, a robotic system positioned within the service location to create or modify the modular service zone by positioning the machines, tools, and parts for performing each of the services on the physical assets within the modular service zone prior to the estimated arrival time of each of the physical assets at the service location. [16] The computer program product of claim 15, wherein creating the one or more modular service zones within the service location further comprises: Creating, by the processor, a workflow configured to allocate the physical assets of the one or more modular service zones for performing the services in an order based on a combination of the estimated arrival time and a minimization of a number of changes to the one or more modular service zones between services provided for a first physical asset and a subsequent physical asset scheduled by the workflow. [17] The computer program product of claim 16, wherein the sequence of operations is configured to allocate the physical assets to the one or more modular service zones, and further maximizes a number of physical assets for which the services are provided at the service location. [18] The computer program product of claim 16, further comprising: Modifying, by the processor, a first modular service zone within the service location, wherein the first physical asset receives one or more services within the first modular service zone, and prior to arrival of the subsequent physical asset within the first modular service zone, adding or removing to or from the first modular service zone at least one or more of the machines, tools, or parts that have no commonality between maintenance profiles of the first physical asset and the subsequent physical asset. [19] The computer program product of claim 16, further comprising: Optimizing the workflow by the processor to minimize the repair time of the physical assets, changes to each of the physical assets, or the waiting time between services provided to each of the physical assets assigned to the same modular service zone. [20] The computer program product of claim 16, wherein the processor collects data describing the performance of the services within the one or more modular service zones; and the processor inputs the data into classification models, providing feedback and continuous learning to improve the classification of maintenance profiles assigned to each type of physical asset.

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