Method and system for pre-preparing a service site

Abstract

A system and method are provided that predictively automates the configuration of modular service zones that service physical assets, maximizes service zone reuse, and optimizes the time to service multiple physical assets. Digital twin models of physical assets are classified and arranged into workflows for service zones, sequencing services to be performed on physical assets arriving at a service center and provisioning service zones based on the type of service requested, estimated arrival time, and similarity between different digital twin classifications of the physical assets. Based on the workflow sequence, physical asset arrival times, and overlap between parts, tools, machines, etc. within various service zones, the service center coordinates robotic systems to arrange service zones in a manner that minimizes wait times between services, maximizes the number of physical assets repaired within a period, and reduces re-dispatch of service zones between services provided to different physical assets.

Description

[Background technology]

[0001] The present disclosure relates generally to the field of artificial intelligence (AI) and digital twin technologies, and more particularly to the use of AI and digital twins to classify the maintenance profile of physical assets, automate the dispatch of service centers responsible for maintaining and repairing the physical assets, and optimize the dispatch or workflow of service zones to maximize the service the physical assets receive while minimizing wait times.

[0002] A digital twin is a virtual representation of a physical object, system, or other asset. It is a mirror image of a real-world environment, often replicating it in a virtual space. The term "digital twin" refers to a "digital twin." A digital twin tracks changes to a physical object, system, or other asset throughout its lifetime and records changes as they occur within the physical object. A digital twin is a complex virtual model that closely corresponds to a physical asset 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. Any individual with access to the digital twin can view real-time information about the physical asset operating in the real world without being physically present to view the physical asset while operating. Users, such as engineers, can use the collected data and information from sensors, IoT devices, and other data sources to understand how the physical asset is operating and to predict how the physical asset may operate in the future. Digital twins can also assist manufacturers and providers of physical assets with information that helps manufacturers understand how customers continue to use their products after a buyer purchases the physical asset.

[0003] A classification algorithm may generally refer to a function that weights input features in such a way that the output falls into two or more classes, and then makes a decision based on the results of all the classifiers. Classifier training can be performed to identify the weights and functions that provide the most accurate and best division between classes of data. Linear discriminant analysis is the most basic classifier, identifying a linear weighting of multifactor data as a means of maximizing the distance between the means of two classes. However, in many datasets, the relative division between classes is not well distinguished by a single line. Artificial neural networks and random decision forests are more recent computational techniques that generate more complex divisions between classes. Summary of the Invention

[0004] Embodiments of the present disclosure relate to computer-implemented methods, associated computer systems, and computer program products for predictively automating the configuration of modular service zones to repair or maintain physical assets and maximize reuse of modular service zones for multiple physical assets. The computer-implemented method includes receiving, by a processor, service requests from a plurality of physical assets requesting performance of a service on the physical assets at a service site, the locations of the physical assets, and an estimated time of arrival for each of the physical assets at the service site; 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 a classification of services for the corresponding physical asset, including one or more machines, tools, or parts required to perform the service on the physical asset; creating, by the processor, one or more modular service zones within the service site that include at least one of the one or more machines, tools, and parts required to perform the service on the physical assets based on commonalities between the physical asset's maintenance profile and the one or more machines, tools, or parts required to perform the service; and instructing, by the processor, a robotic system located within the service site to create or modify the modular service zone by positioning the machines, tools, and parts to perform each of the services on the physical assets within the modular service zone prior to the estimated time of arrival of each of the physical assets at the service site. [Brief explanation of the drawings]

[0005] The drawings included in this disclosure are incorporated in and form a part of this specification. The drawings illustrate embodiments of the disclosure and, together with the description, explain the principles of the disclosure. The drawings are merely illustrative of particular embodiments and are not intended to limit the disclosure.

[0006] [Figure 1]1 illustrates a block diagram of an exemplary embodiment of a computer system and its components according to the present disclosure, on which the embodiments described herein may be implemented.

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

[0008] [Figure 3] FIG. 1 illustrates a functional block diagram describing an embodiment of a computing environment for predictively automating the configuration of modular service zones for repair and maintenance of multiple physical assets while maximizing modular service zone reuse, minimizing wait times, and optimizing total time to perform service, in accordance with the present disclosure.

[0009] [Figure 4] FIG. 1 illustrates a block diagram of an exemplary embodiment of a vehicle repair service executing program code that enables predictively automating the configuration of modular service zones for repairing and maintaining physical assets in accordance with the present disclosure.

[0010] [Figure 5] 1 illustrates a flow diagram describing an embodiment of a computer-implemented method for predictively automating the configuration of modular service zones for repair and maintenance of multiple physical assets, according to the present disclosure.

[0011] [Figure 6] FIG. 1 illustrates a flow diagram illustrating an embodiment of a computer-implemented method for maximizing reuse of modular service zones for repairing or maintaining physical assets and optimizing the total service time for repairing or maintaining physical assets within the modular service zones, in accordance with the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It is further understood that as used herein, the terms "including" and / or "comprising" indicate the presence of stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0013] Corresponding structure, materials, acts, and equivalents of all means- or step-plus-function elements in the claims below are intended to include any structure, material, or acts for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure is for purposes of illustration and description and is not intended to be exhaustive or to limit the disclosure to the form disclosed. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the present disclosure. The selected and described embodiments are intended to best explain the principles and practical applications of the disclosure and to enable others skilled in the art to understand the disclosure for various embodiments with various modifications suitable for the particular uses contemplated.

[0014] Overview

[0015] Physical assets such as automotive vehicles, machines, devices, or other equipment (generally referred to herein as "physical assets") wear out over time, require maintenance to prevent breakdowns or malfunctions, and / or may occasionally require repairs to remedy physical assets that no longer function as intended or have become inoperable. Often, physical assets that may require maintenance or repair may be brought to a service center that has the appropriate tools, parts, machinery, equipment, and / or know-how to perform repair procedures or maintenance on the physical asset. Different types of repairs and services may be required for different types of physical assets that are repaired or maintained within the service center. The service center may be equipped with different types of machines, tools, equipment, parts, components, etc. to provide a wide range of different services for different types of physical assets. The tools, machinery, equipment, etc. may 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 procedures. Typically, when service is arranged at a service center, a physical asset, such as a vehicle, device, or machine, creates a service log, transmits the collected data in the log to the service center, schedules an appointment, and / or performs various roadside or mobile repairs. Asset information, location data, along with the appointment, may be captured at the service center to identify the type of service required, the spare parts that need to be on hand to perform the repair or maintenance service, and any machines or tools that may be needed to perform the requested service. The appropriate parts, tools, machines, etc. may be manually arranged by personnel located at the service center within the specific location where the repair is scheduled to be performed.

[0016] Embodiments of the present disclosure recognize that initiating the retrieval of spare parts and manually arranging the different equipment, machines, tools, and parts needed to perform one or more services at a service center can be a slow or time-consuming process. Arranging the spare parts, tools, equipment, and machines needed to perform a service can require a significant amount of labor, coordination, and may require pulling one or more service center employees away from providing the actual service in other areas of the service center to ensure that a service zone within the service center is properly prepared in a timely manner to service the next physical asset scheduled for repair, maintenance, or other service. The process of arranging machines, tools, parts, components, or other accessories to perform a service can be further complicated by the number of different types of services scheduled by a service center and the many variations among physical assets that may require rearranging the service zone each time a new physical asset arrives for service. Therefore, to minimize service time and maximize the number of physical assets serviced, there is a need to predictively automate the configuration of modular service zones to repair or maintain physical assets and maximize the reuse of modular service zones during the servicing of multiple different types of physical assets.

[0017] Embodiments of the present disclosure leverage the use of AI-enabled systems and digital twin models to predictively automate the configuration of modular service zones within a service center equipped for the repair and / or maintenance of physical assets. The automated configuration of the service zones maximizes the reuse of the modular service zones to service one or more different types of physical assets and limits the number of redeployments to the service zones while the different types of assets are serviced. An embodiment of a physical asset may create a service request for a particular physical asset. The request may be sent to a repair service or other type of application or program that may proactively evaluate the digital twin model and system data corresponding to the physical asset. The repair service may identify the type of service required (e.g., maintenance or repair), an estimated timing for completing the service, along with any tools, machinery, spare parts, components, or any other accessories that may be needed to perform the service identification. Based on the analysis performed by the repair service, the repair service may identify service centers capable of performing the service on the physical asset. Portions of the service centers, referred to herein as modular service zones (or simply "service zones"), may be further identified as acceptable locations within the service center where the service is to be provided. Each identified service zone may already be equipped and previously dispatched with parts, machines, tools, and / or components to perform the service. Alternatively, a modular service zone may be a location within a service center that may have each of the parts, tools, equipment, and / or other components reasonably available for dispatch therein, but may require at least some amount of reconfiguration to easily be equipped with the tools, parts, and machines to perform the service.

[0018] An embodiment of an AI-enabled repair service application or program may apply one or more classification algorithms to services identified as applicable to a physical asset submitting a service request. The classification of services applicable to a physical asset may indicate the type of spare part, machine, tool, or other component that may be assembled or positioned within a modular service zone before the physical asset arrives at a service center to perform the service. A repair service or application that classifies services for a physical asset may map the services to corresponding parts, equipment, tools, components, etc. as part of creating a maintenance profile for each physical asset. The maintenance profiles may be compared by the repair service to identify commonalities between different services scheduled to be applied to one or more physical assets. Based on the commonalities between the different mappings for each service and the estimated arrival times of physical assets entering the service center, the repair service may coordinate and arrange a workflow to schedule physical assets entering one or more different service zones to receive various services in an order that optimizes the total service time for the multiple physical assets being serviced.

[0019] Based on the workflow sequence, taking into account the arrival times of physical assets requiring service and the overlap between parts, tools, machines, etc. required for the physical assets to be assigned to various service zones, the repair service may coordinate robotic systems located within the service center by instructing the robotic systems to configure, arrange, or rearrange one or more of the service zones according to the workflow. The configuration and arrangement by the robotic systems may anticipate the next physical asset scheduled to receive service within a specified service zone and may pre-position tools, parts, equipment, machinery, components, etc. within the service zone in anticipation of the next physical asset. Pre-positioning tools, machinery, equipment, and other components within the service zone minimizes wait times between services performed within the service zone and maximizes the number of physical assets serviced within a period of time. Additionally, by optimizing the workflow for scheduling physical asset service having a threshold amount of commonality among tools, machinery, equipment, etc., the repair service may reduce the amount of rearrangement required for the service zone between a series of different physical assets arriving at the service zone scheduled for service.

[0020] For example, a workflow generated by a repair service may schedule physical assets to be serviced within the same service zone, where the physical assets are the same type of asset and / or the assets to be repaired sequentially share similar service characteristics in their maintenance profiles, allowing services applied to one or more physical assets within the same service zone to be performed using the same type of tools, machines, equipment, or know-how without having to re-arrange the service zones by one or more robotic systems between performance of services on different assets. Upon completion of the service applied to a first physical asset, and in anticipation of the arrival of the next physical asset in the modular service zone, the robotic system may add additional tools, parts, machines, or other components to the service zone that may not have been applied to the first physical asset, and remove any tools, machines, equipment, or other components that do not apply to the service applied to the next set of physical assets arriving for service, based on a workflow prepared by the repair service application that coordinates the service for each modular service zone.

[0021] Computing Systems

[0022] Various aspects of the present disclosure are described through text, flowcharts, block diagrams of computer systems, and / or block diagrams of machine logic included in computer program product (CPP) embodiments. For any flowchart, operations may be executed in a different order than that shown in the flowchart (depending on the technology involved). For example, two operations shown in successive flowchart blocks may be executed in the reverse order, as a single integrated step, simultaneously, or in an at least partially time-overlapping manner. A computer program product embodiment (CPP embodiment) is a term used in this disclosure that may describe any set of one or more storage media (or "media") collectively contained in one or more storage devices. The storage media may collectively contain machine-readable code corresponding to instructions and / or data for performing computer operations. A "storage device" may refer to any tangible hardware or device capable of holding and storing instructions for use by a computer processor. Computer-readable storage media may include, without limitation, electronic, magnetic, optical, electromagnetic, semiconductor, mechanical storage media, and / or any combination thereof. Some known types of storage devices, including the media referred to herein, may include diskettes, 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 disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits / lands formed on a major surface of a disk), or any suitable combination thereof. A computer-readable storage medium should not be considered storage in the form of a transitory signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses through fiber optic cables, electrical signals communicated through wires, and / or other transmission media.As will be appreciated by those skilled in the art, this does not present the storage device as temporary, as data is typically moved at some occasional time during normal operation of the storage device, such as during access, defragmentation, or garbage collection, but the data is not temporary while it is stored.

[0023] 1 illustrates a block diagram describing 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. Computing system 101 may represent one or more computing systems or devices, described in more detail below, implemented in accordance with embodiments of the present disclosure. It should be understood that FIG. 1 provides only an illustration of one implementation of computing system 101 and does not imply any limitation regarding the environment in which different embodiments may be implemented. In general, the components illustrated in FIG. 1 may represent physical or virtualized electronic devices capable of executing machine-readable program instructions.

[0024] Embodiments of computing 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 non-traditional computer system such as a mainframe computer, a server, a quantum computer, an autonomous vehicle or a home appliance, or any other form of computer or mobile device now known or later developed that is capable of running application 150, accessing network 102, or querying a database such as remote database 130. Execution of computer-implemented methods performed by computing system 101 may be distributed among multiple computers and / or among multiple locations. Computing system 101 is deployed as part of a cloud network, but is not depicted in the cloud in FIGS. 1-2 . Additionally, computing system 101 need not be in a cloud network unless expressly indicated.

[0025] Processor set 110 includes one or more computer processors of any type now known or later developed. Processing circuitry 120 may be distributed across multiple packages, e.g., multiple coordinated integrated circuit chips. Processing circuitry 120 may implement multiple processor threads and / or multiple processor cores. Cache 121 may refer to memory located on the processor chip package and / or may be used for data or code that may be made available for fast access by threads or cores executing on processor set 110. Cache 121 memory may be organized into multiple levels depending on its relative proximity to processing circuitry 120. Alternatively, some or all of a processor set 110's cache 121 may be located "off-chip." In some computing environments, processor set 110 may be designed to operate on qubits and perform quantum computing.

[0026] Computer-readable program instructions may be loaded onto computing system 101 and cause processor set 110 of computing system 101 to perform a series of operational steps, thereby implementing a computer-implemented method. Execution of the instructions may instantiate the methods specified in the computer-implemented method flowcharts and / or textual descriptions contained herein (collectively referred to as "inventive methods"). The computer-readable program instructions may be stored in various types of computer-readable storage media, such as cache 121 and other storage media described herein. The program instructions and associated data may be accessed by processor set 110 to control and direct the execution of the inventive methods. In the computing environment of FIGS. 1-2, at least a portion of the instructions for executing the inventive methods may be stored in persistent storage 113, volatile memory 112, and / or cache 121 as an application 150, including one or more executing processes, services, programs, and their installed components. For example, the program instructions, processes, services, and their installed components may include repair services 307, including components such as profile module 401, service zone optimization module 403, and / or reporting module 409, and their subcomponents, as shown in FIG.

[0027] Communications fabric 111 may refer to signaling paths that may allow various components of computing system 101 to communicate with one another. For example, communications fabric 111 may provide electronic communications between processor set 110, volatile memory 112, persistent storage 113, peripheral device set 114, and / or network module 115. Communications fabric 111 may be made up of switches and / or conductive paths, such as switches and conductive paths that make up buses, bridges, physical input / output ports, and the like. Other types of signaling paths may be used, such as fiber optic communications paths and / or wireless communications paths.

[0028] Volatile memory 112 may refer to any type of volatile memory now known or later developed, and may be characterized by random access, although this is not required unless expressly indicated. Examples include dynamic random access memory (RAM) or static RAM. In computing system 101, volatile memory 112 may be located in a single package and internal to computing system 101, although alternatively or additionally, volatile memory 112 may be distributed across multiple packages and / or located external to computing system 101. Applications 150, along with any programs, processes, services, and installed components described herein, may be stored in volatile memory 112 and / or persistent storage 113 for execution and / or access by one or more of the respective processor sets 110 of computing system 101.

[0029] Persistent storage 113 may be any form of non-volatile storage for computers now known or later developed. The non-volatility of this storage means that stored data may be maintained regardless of whether power is supplied to computing system 101 and / or directly to persistent storage 113. Persistent storage 113 may be read-only memory (ROM). However, at least a portion of persistent storage 113 may allow data to be written, data to be deleted, and / or data to be rewritten. Some forms of persistent storage 113 may include magnetic disks, solid-state storage devices, hard drives, flash-based memory, erasable read-only memory (EPROM), and semiconductor storage devices. Operating system 122 may take multiple forms, including various known proprietary operating systems or open-source Portable Operating System Interface-based operating systems that may utilize a kernel.

[0030] The peripheral device set 114 includes one or more peripheral devices connected to the computing system 101, for example, via input / output (I / O) interfaces. Data communication connections between the peripheral devices and other components of the computing system 101 may 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)-type cables), pluggable connections (e.g., Secure Digital (SD) cards), connections over local area communication networks and / or wide area networks such as the Internet. In various embodiments, the UI device set 123 may include components such as display screens, speakers, microphones, wearable devices (such as goggles, headsets, and smartwatches), keyboards, mice, printers, touchpads, game controllers, and haptic feedback devices. The storage 124 may include external storage, such as an external hard drive, or insertable storage, such as an SD card. The storage 124 may be persistent and / or volatile. In some embodiments, the storage 124 may take the form of a quantum computing storage device for storing data in the form of qubits. In some embodiments, a network of computing systems 101 may utilize clustered computing and components that act as a single pool of seamless resources when accessed over the network by one or more computing systems 101. For example, a storage area network (SAN) shared by multiple geographically distributed computer systems 101 or a network-attached storage (NAS) application. The IoT sensor set 125 may consist of sensors used in Internet of Things applications. For example, the sensors may 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 enables the computing system 101 to communicate with other computer systems over a computer network 102, such as a LAN or WAN. The network module 115 may include hardware such as a modem or Wi-Fi signal transceiver, software for packetizing and / or de-packetizing data for communication network transmission, and / or web browser software for communicating data over the network. In some embodiments, the network control and network forwarding functions of the network module 115 are performed on the same physical hardware device. In other embodiments (e.g., embodiments utilizing software-defined networking (SDN)), the control and forwarding functions of the network module 115 may be performed on physically separate devices, such that the control function manages multiple different network hardware devices. Computer-readable program instructions for performing the methods of the invention may typically be downloaded to the computing system 101 from an external computer or external storage device through a network adapter card or network interface included in the network module 115.

[0032] 2 illustrates a computing environment 200, which may be an extension of the computing environment 100 of FIG. 1, operating as part of a network. In addition to the computing system 101, the computing environment 200 may 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 an end-user device (EUD) 103, a remote server 104, a public cloud 105, and / or a private cloud 106. In this embodiment, the computing system 101 includes a processor set 110 (including processing circuitry 120 and cache 121), a communications fabric 111, volatile memory 112, persistent storage 113 (including an operating system 122 and applications 150 as identified above), a peripheral device set 114 (including a user interface (UI), a device set 123, storage 124, and an Internet of Things (IoT) sensor set 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 may be comprised of wired or wireless connections. For example, connections may include copper transmission cables, optical fiber transmissions, wireless transmissions, and computer hardware such as routers, firewalls, switches, gateway computers, and / or edge servers. Network 102 may be described as any wide area network (e.g., the Internet) capable of communicating computer data over non-local distances by any now known or later developed technology for communicating computer data. In some embodiments, a WAN may be replaced and / or supplemented by a local area network (LAN) designed to communicate data between devices located in a local area, such as a Wi-Fi network. Other types of networks used to interconnect various computer systems 101, end user devices 103, remote servers 104, private clouds 106, and / or public clouds 105 may 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 by those skilled in the art for interconnecting computing systems 101.

[0034] End-user device 103 may include any computing device used and / or controlled by an end user (e.g., a customer of the enterprise operating computing system 101) and may take any of the forms described above in connection with computing system 101. EUD 103 may receive useful and actionable data from the operation of computing system 101. For example, in a hypothetical case in which computing system 101 is designed to provide recommendations to the end user, the recommendations may be communicated over network 102 from network module 115 of computing system 101 to EUD 103. In this example, EUD 103 may display or otherwise present the recommendations to the end user. In some embodiments, 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 the like.

[0035] Remote server 104 may be any computing system that provides at least some data and / or functionality to computing system 101. Remote server 104 may be controlled and used by the same entity that operates computing system 101. Remote server 104 represents a machine that collects and stores useful and helpful 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 may be provided to computing system 101 from remote database 130 of remote server 104.

[0036] Public cloud 105 may be any computing system available for use by multiple entities that provides on-demand utilization of computer system resources and / or other computer capabilities, including data storage (cloud storage) and computing power, without direct, active management by users. Direct, active management of the computing resources of public cloud 105 may be performed by computer hardware and / or software of cloud orchestration module 141. The computing resources provided by public cloud 105 may be implemented by virtual computing environments running on various computers comprising host physical machine set 142 and / or the universe of physical computers in and / or available from public cloud 105. Virtual computing environments (VCEs) may take the form of virtual machines from virtual machine set 143 and / or containers from container set 144. It is understood that these VCEs may be stored as images and transferred among and between various physical machine hosts, either as images or after instantiation of the VCEs. Cloud orchestration module 141 manages the transfer and storage of images, deploys new instantiations of VCE, and manages active instantiations of VCE deployments. Gateway 140 is a collection of computer software, hardware, and firmware that enables public cloud 105 to communicate over network 102.

[0037] A VCE may be stored as an "image." A new active instance of a VCE may be instantiated from an image. Two types of VCEs may 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 may behave as a physical computer from the perspective of applications 150 running in them. Applications 150 running on operating system 122 may utilize all of the computer's resources, such as attached devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. Applications 150 running within a container in container set 144 may use only the contents of the container and the devices assigned to the container, a feature referred to as containerization.

[0038] A private cloud 106 may be similar to a public cloud 105, except that computing resources may be available only for use by a single enterprise. While the private cloud 106 is illustrated as communicating with a network 102 (e.g., the Internet), in other embodiments, the private cloud 106 may be completely disconnected from the Internet and accessible only through a local / private network. A hybrid cloud may refer to the configuration of multiple clouds of different types (e.g., private, community, or public cloud types), where the multiple clouds may be implemented or operated by different vendors. While each of the multiple clouds remains a separate, discrete entity, 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 may be part of a larger hybrid cloud environment.

[0039] System for predictively automating the configuration of modular service zones

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

[0041] Simple features, structures, or characteristics described throughout this specification may be combined or eliminated in any suitable manner in one or more embodiments. For example, the use of the phrases "exemplary embodiment," "some embodiments," or other similar language throughout this specification indicates that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment. Thus, the appearances of the phrases "exemplary embodiment," "some embodiments," "other embodiments," or other similar language throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined or eliminated in any suitable manner in one or more embodiments. Furthermore, in the figures, any connections between elements may allow for one-way and / or two-way communication, even if the illustrated connections are represented by one-way and / or two-way arrows. Also, any devices illustrated in the figures may be different devices. For example, when a mobile device is shown transmitting information, a wired device may also be used to transmit information.

[0042] 3 illustrates an embodiment of a computing environment 300, illustrating a system capable of predictively automating the configuration of modular service zones 309 within a service center scheduled to receive one or more physical assets 301. A modular service zone 309 may be described as an area within a service center that is configured, arranged, and / or re-arranged (as needed) to provide one or more services, such as repairs or maintenance, to a physical asset 301. An embodiment of computing environment 300 may optimize the scheduling and coordination of services provided to physical assets 301 in a manner that maximizes the number of physical assets 301 serviced within a period of time, while reducing the total amount of time it takes to service all physical assets 301 serviced within a period of time at the service center, and minimizes the number of reconfigurations for each of the service zones 309 as each new physical asset 301 arrives for service. As illustrated, computing environment 300 may include one or more physical assets 301, repair services 307, digital twin repository 313, and one or more robotic systems 311 that may be capable of being positioned or moved within or between service zones 309. Embodiments of physical assets 301, repair services 307, robotic systems 311, and digital twin repository may be arranged to communicate with one another via computing network 102.

[0043] An embodiment of physical asset 301 may be any type of physical device, machine, or apparatus, equipment, hardware, etc. that may be capable of connecting and communicating data over network 102. For example, in an exemplary embodiment, physical asset 301 may be a vehicle, including, but not limited to, an autonomous or semi-autonomous automobile, aircraft, train, turbine, boat, watercraft, etc. In other embodiments, physical asset 301 may include devices, machinery, or equipment, such as medical machinery or equipment, oil or gas powered equipment, mobile communication devices such as smartphones, home appliances, or other smart devices. In some embodiments, physical asset 301 is equipped with or tracked by one or more types of sensors and / or IoT devices mounted on physical asset 301. In other embodiments, sensors and / or IoT devices monitoring physical asset 301 may be located in or around the environment surrounding physical asset 301. Sensors and IoT devices, whether mounted on the physical asset 301 or located within the surrounding environment, may measure one or more functions and health of the physical asset 301 and may be responsible for collecting data describing the current (i.e., real-time) state of the physical asset 301. The collected data describing the physical asset 301 may be stored as part of a system log and may include location data describing the physical location of the physical asset 301.

[0044] Data describing the physical asset 301, collected from sensors, IoT devices, and / or other data sources, may be stored as system data 303. The system data 303 collected from multiple sensors and IoT devices on or near the physical asset 301 may be used to construct a digital twin model of the physical asset 301. An embodiment of the digital twin model may depict a virtual representation of the physical asset 301 in its current state based on the system data 303 collected by the physical asset's 301 systems. Additionally, 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 sensors, IoT devices, etc.), the digital twin model may be simultaneously (or near simultaneously) updated to virtually reflect the changes within the digital twin model. The digital twin model may be organized and stored locally in persistent storage 113 onboard the physical asset 301, or in some embodiments, the digital twin model of the physical asset 301 may be stored on a network-accessible storage device, such as digital twin repository 313.

[0045] An embodiment of physical asset 301 may self-assess the current status and / or condition of physical asset 301. Self-assessment of physical asset 301 may detect one or more problems, malfunctions, errors, or other needs of physical asset 301 and may determine whether physical asset 301 requires maintenance, repair, or other service to maintain optimal operation and / or return physical asset 301 to an optimal operating condition. Self-assessment by one or more systems of physical asset 301 may occur periodically at regularly scheduled time intervals, may be manually selected for execution by a user, owner, or manager of physical asset 301, may be triggered in response to a change in the current state of physical asset 301, in response to a detectable error, a non-operational feature or function of the physical asset, and / or in response to detection that a feature of physical asset 301 is not operating in an optimal manner.

[0046] The physical asset 301's self-assessment may be performed by scanning or testing the physical asset's 301 systems and / or analyzing system data 303 collected by the physical asset's 301 IoT devices and sensors. AI-enabled algorithms connected to the physical asset 301 may be trained to identify potential issues with the physical asset 301 that may need to be repaired or that can be mitigated by maintenance. The results of the physical asset's 301 self-assessment may be logged in a system data 303 log file. If the physical asset's 301 self-assessment or a manual assessment by the physical asset's 301 user, owner, manager, etc., determines that repairs or maintenance should be further sought or performed, the physical asset 301 may record the physical asset's 301 potential issues or needs, including the maintenance and / or repairs identified by the self-assessment output, as part of service requirements data 305. The physical asset 301 may generate a service request and submit the service request along with the system data 303, service requirements data 305, and / or digital twin model to a repair service 307.

[0047] An embodiment of repair service 307 may be part of application 150 or a program accessible to physical asset 301 via network 102. Repair service 307 may be running as an instance on a service provider's cloud network or any other type of network described herein. An embodiment of repair service 307 may perform functions and processes related to evaluating a digital twin model of physical asset 301 and associated systems data 303 to identify types of services to provide to physical asset 301 and any tools, equipment, machines, parts, accessories, and / or components known or used to perform the types of services identified for physical asset 301. Based on the identified types of services, an embodiment of repair service may orchestrate a workflow, schedule physical asset 301 at an appropriate service center capable of performing the service, optimize the allocation of service zones 309 within the selected service center, and schedule the performance of the service for physical asset 301 at the service center according to the generated workflow. Scheduling the performance of one or more services at a service center may be performed according to an optimal workflow generated by the repair service based on overlapping commonalities among physical assets 301 requesting service that are assignable to the same service center. The schedule for performing services according to the workflow may be arranged in a manner that minimizes wait times for completion of services, minimizes redistribution of service zones 309 between performance of services for different physical assets 301, and maximizes the number of physical assets 301 receiving service within a service zone 309 during a selected period of time.

[0048] Referring to the diagram of FIG. 4 , an embodiment of repair service 307 may include one or more modules or subcomponents responsible for implementing one or more specific processes, tasks, functions, or features of repair service 307. The term “module” may refer to a hardware module, 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 designed to implement or perform one or more specific tasks, routines, and / or functions. A hardware-based module embodiment may include a self-contained component such as a chipset, dedicated circuitry, a processor set 110, one or more volatile memory 112 devices, and / or persistent storage 113. A software-based module may be an application 150, a portion of program code, or linked to program code including a specific set of program instructions loaded into volatile memory 112 or persistent storage 113. In the exemplary embodiment of FIG. 4 , repair service 307 may include a profile module 401, a service zone optimization module 403, and / or a reporting module 409.

[0049] An embodiment of the profile module 401 may perform tasks, functions, and / or processes for the repair service 307 related to creating a maintenance profile for each physical asset 301 that interfaces with the repair service 307 over the network 102. For example, interfacing with the repair service 307 may occur by submitting a service request, a digital twin model, system data 303, and / or service requirements data 305 to the repair service 307 for analysis and scheduling of one or more services. An embodiment 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 service requirements data 305 submitted with the digital twin model, identify what types of repair, maintenance, or other services can (or should) be provided to the physical asset 301, and any associated machines, tools, spare parts, components, or other accessories known to be used to perform the available services for the physical asset 301. As part of the maintenance profiles created by the profile module 401, embodiments of the profile module 401 may create a mapping between the types of services identified in association with the physical assets 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 physical assets 301 may enable comparisons between the requirements for performing the services of the physical assets 301 and / or classifications of the maintenance profiles of the physical assets 301 to determine commonalities between different services performed.

[0050] An embodiment of the profile module 401 may apply one or more classification algorithms to the maintenance profile created by the profile module 401 using the digital twin model for each physical asset 301. The application of one or more classification algorithms to the maintenance profiles of multiple physical assets 301 may enable the profile module 401 to identify commonalities between services applicable to each of the physical assets 301, including commonalities between types of machinery, tools, equipment, components, or other accessories that map to each of the services applicable to the physical assets 301. For example, if the profile module 401 classifies digital twin models for multiple vehicles (which may or may not be autonomous), these vehicles may have multiple potential services within their maintenance profile that have commonality between them, despite being different types or brands of vehicles. 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 may determine the types of tools, machinery, equipment, and parts that may be common between the tire rotation and tire replacement services to be performed. The profile module 401 may identify commonalities between tire rotation services and tire replacement services, such as the need for the use of a vehicle lift, air pressure or hand wrenches, lug nuts, wheel balancers, etc., allowing the repair service 307 to identify a service center that can perform both tire rotation and tire replacement services, and if the service center's arrival time is within a certain threshold period, the repair service may schedule the performance of tire services on the first physical asset and the second physical asset consecutively within a workflow, allowing the service zone 309 to maintain tools, equipment, machinery, components, etc. that are common between the two services while minimizing the amount of changes to the service zone 309 between performing services on both physical assets 301.

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

[0052] An embodiment of the service zone optimization module 403 may create a workflow to schedule various incoming physical assets 301 arriving at a service center based on commonalities between the services received and the types of machines, tools, parts, components, or other accessories associated with the services as indicated by the maintenance profiles of the physical assets 301, and to orchestrate the order in which the physical assets 301 are assigned to one or more service zones 309 for performance of the requested services. As part of the workflow generation and scheduling process, the service zone optimization module 403 may calculate and consider an estimated arrival time for each incoming physical asset 301 arriving at the service center. The service zone optimization module 403 may estimate the arrival time using location data provided to the repair service 307 as part of the system data 303. Additionally, in some embodiments, the service zone optimization module 403 may further consider, when creating workflows and assigning physical assets 301 to service zones, the amount of time to configure or rearrange the service zone 309 with equipment, tools, parts, components, etc. to perform the requested service, and the timing of receiving various parts or components that may not be immediately accessible within the service center or may be scheduled for use in another service zone 309 within the service center. For example, for a service center that needs to obtain parts or tools from another location or from a parts supplier to perform a service, time constraints may be considered when scheduling physical assets and creating corresponding workflows.

[0053] An embodiment of the service zone optimization module 403 may generate a workflow having optimal timings and schedule the performance of services at a service center within a service zone 309 according to the optimal timings of the generated workflow. The workflow may include instructions and / or events to assign physical assets 301 to one or more service zones 309 in an ordered sequence, steps to rearrange or configure each of the designated service zones 309 with tools, equipment, machines, components, parts, etc. mapped to the services to be provided for the corresponding physical asset 301 prior to the arrival of the physical asset 301 in the designated service zone 309, and instructions or steps to reconfigure or rearrange the service zone 309 following completion of the services for the physical asset 301 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 sequence of assignment of physical assets to service zones 309 in a manner that optimizes the timing of completing services, minimizes the timing associated with redistributing service zones 309, minimizes wait times between completed services, and maximizes the number of physical assets 301 that can be serviced within a service center's service zone 309. For example, this may be done by sequencing the sequence of 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 utilized during the performance of the service. By consecutively scheduling physical asset services applied to different physical assets with a shared number of commonalities with the same service zone 309 that exceeds the threshold number, a minimal number of changes to the service zone configuration are required during the performance of the service, thereby maximizing the amount of physical assets 301 that can be serviced through the service zone within a period of time.

[0054] An embodiment of the service zone optimization module 403 may implement the generated workflow to configure and arrange a series of services within the service center in one or more designated service zones 309 by outputting one or more instructions to a robotic system 311 located within the service center. The robotic system 311 may be instructed on how to configure and arrange each of the service zones 309 in anticipation of the arrival of a physical asset 301 at the service center and / or in anticipation of the location within the service zone 309 of the next physical asset 301 to receive one or more services. The robotic system 311 may move autonomously throughout the service center and arrange each of the designated service zones 309 based on instructions provided to the robotic system 311 consistent with the workflow generated by the repair service 307. The robotic system 311 may move throughout the service center to collect tools, move equipment, collect parts and / or components or other accessories, and place the collected tools, equipment, parts, etc. within the designated service zones 309 to perform services within the service zones 309. Ensure that the appropriate tools, equipment, and parts to perform service on the physical assets 301 are present and within a reasonable distance of the individual or machine responsible for implementing the service within the service zone 309. Upon completion of service on the first-sequenced asset within one or more service zones 309, the robotic system 311 may rearrange the service zone 309 as commanded, consistent with the workflow and service schedule, including adding additional equipment, tools, and parts within the service zone 309 to provide service to the next asset in the workflow sequence. 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 needs to perform service on the next-to-be-serviced physical asset within the service zone 309.Tools, equipment, parts, and other components removed from a service zone 309 may be transported 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.

[0055] An embodiment of the service zone optimization module 403 may include additional components that may assist the service zone optimization module 403 in generating optimized workflows and sequencing the services provided to one or more physical assets 301. For example, in the embodiment of the service zone optimization module 403 of FIG. 4, a service zone visualization engine 405 and / or a service zone placement module 407 may be available. An embodiment of the service zone visualization engine 405 may perform tasks, functions, and processes that enable the repair service 307 to visualize the layout of service centers, the space available for creating service zones within the service centers, and the location of various equipment, tools, and machinery at each of the available service centers. The service zone placement module 407 may utilize the service zone visualization engine 405 to simulate the workflows created by the service zone optimization module and ultimately identify the optimal workflow to implement. Using the service zone placement module 407 in conjunction with the service zone visualization engine 405, an embodiment of the repair service 307 can simulate various permutations of workflows by simulating the sequential assignment of machines, equipment tools, parts, etc., and physical assets 301 to the various service zones 309 represented by the virtualization engine. The service zone placement module 407 can calculate and identify optimal schedules for assigning incoming physical assets 301 to different service zones 309, scheduled sequences predicted to result in the greatest total number of physical assets 301 serviced within a threshold period, the minimum number of service zone 309 reconfigurations between consecutively scheduled physical assets 301 serviced within the same service zone 309, and the minimum amount of wait time to receive service. Based on the simulated workflows that achieve optimal results, the optimal workflow is selected by the service zone optimization module 403, and instructions for implementing the optimal workflow can be sent to a robotic system 311 located within the service center.

[0056] An embodiment of repair service 307 may include a reporting module 409. Reporting module 409 may perform functions, processes, and / or tasks of repair service 307 that may relate to outputting communications and reporting information to physical assets 301 that submit requests to repair service 307. For example, reporting module 409 may report to physical assets 301 and their users or owners the type of service scheduled for physical asset 301, the location of the service center that will perform the service, the estimated date and time the service will be performed, and any additional recommended services that the user may want to schedule for physical asset 301. An embodiment of reporting module 409 may also communicate with the service center. For example, reporting module 409 may confirm scheduled service appointments or cancellations of service for each of physical assets 301 and confirm the arrival of physical asset 301 at the service center. Output from reporting module 409 may be any form of electronic communication transmitted over network 102. For example, reports, notifications, and other messages sent from the reporting module 409 may be in the form of emails, push messages, text messages, notifications, alerts, or any other known type of electronic communication format or delivery system.

[0057] Method for predictively automating the configuration of modular service zones - Patents.com

[0058] 5-6 depict embodiments of methods 500, 600 for predictively automating the configuration of modular service zones arranged within a service center to provide one or more services to multiple physical assets 301 and optimize the workflow of the modular service zones 309 based on commonalities among the maintenance profiles of the physical assets 301 so as to reuse the service zones 309 with minimal changes, service a maximum number of physical assets 301 within a time period, and minimize the total amount of time to service the multiple physical assets 301. Embodiments of methods 500, 600 may be implemented in accordance with the computing systems and examples illustrated in FIGS. 1-4 above and described throughout this application. Those skilled in the art should recognize that the steps of methods 500, 600 described in FIGS. 5-6 may be performed in a different order than presented, and not all steps described herein may be required to be performed.

[0059] An embodiment of method 500 described by FIG. 5 may begin at step 501. During step 501, IoT devices and / or sensors connected to the physical asset 301 or located in the surrounding environment collect system data 303 for the physical asset 301. The collected system data 303 is used to create or update a digital twin model that reflects the current state of the physical asset 301. In step 503, the physical asset 301 may perform a self-assessment. Based on the self-assessment of the physical asset 301, and taking into account the current state of the physical asset 301 and measurements and outputs of the IoT devices and sensors, it is determined whether one or more services should be performed on the physical asset. For example, based on sensor data, a determination is made that the physical asset is not functioning optimally, or that errors were identified during the self-assessment and one or more maintenance services or repairs may be required to correct the errors. If, in step 503, the system data 303 indicates that one or more services should be performed on the physical asset 301, then 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 execution data for 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 or connected to the physical asset 301 may send a service request to a repair service or application, such as repair service 307, hosted on a public or private cloud and / or any other type of network 102.

[0060] At step 505, cloud-hosted repair service 307 may receive a service request from physical asset 301. The service request may be accompanied by additional information about the physical asset 301, including system data 303 for the physical asset 301 and / or a digital twin model of the physical asset 301 requesting service. At step 507, the digital twin model of the physical asset 301 is analyzed by repair service 307 for suitable services that may be applicable to the physical asset based on the current status of the digital twin model and system data 303. Repair service 307 may identify one or more services, including repairs and / or maintenance, that may be applicable to the physical asset 301 to keep the asset operating at optimal performance and / or may remedy ongoing errors and performance issues experienced or illustrated by the digital twin model.

[0061] In step 509, the repair service 307 instance may identify the type of machinery, tools, spare parts, and / or other components associated with the identified repair, maintenance, or other service based on analysis of the digital twin model. In step 511, each type of service applicable to the physical asset 301 may be categorized by the repair service 307. The maintenance profile may include multiple potentially available services for each physical asset 301 using the repair service 307, allowing comparisons to be made between physical assets 301 based on the maintenance profile. Comparisons between physical asset 301 maintenance profiles may identify commonalities between services for each of the physical assets 301, including, but not limited to, commonalities between the types of machinery, tools, equipment, components, parts, or other accessories required for the repair, maintenance, or other service. By identifying commonalities among the services available for each of the physical assets 301, repair services 307 can schedule and coordinate physical asset services to assigned modular service zones 309, such that scheduled services requiring similar types of needs for machinery, tools, parts, know-how, and other components can be performed consecutively within the same service zone 309, in adjacent service zones 309, and / or near service zones 309. Common tools, machinery, equipment, and other components can be easily shared between scheduled services and / or between service zones 309, reducing the amount of reconfiguration or re-dispatch of service zones 309.

[0062] In step 513, the repair service 307 may select a service center facility known to be either equipped with machines, tools, parts, etc. to complete one or more services on the physical asset 301, or the service center may configure itself to receive one or more machines, tools, parts, components, etc. to perform services on the physical asset 301 either prior to the arrival time of the physical asset 301 at the service center or within a threshold amount of time following the arrival of the physical asset 301 at the service center. In step 515, a determination is made whether a modular service zone 309 within the selected service center is already configured with arrangements of machines, tools, parts, and other components necessary to perform services on the physical asset 301. If a modular service zone 309 within the selected service center is already configured, method 500 may proceed to step 517. In step 517 of method 500, an incoming physical asset 301 expected to arrive at the service center is scheduled to receive service at the service center and assigned to a previously equipped and configured modular service zone 309. Conversely, if an existing service zone within the selected service center has not previously been configured in a manner that meets the needs for performing service on incoming physical assets 301 expected to arrive at the service center, method 500 may proceed to step 519.

[0063] During step 519, the repair service 307 may send instructions to one or more robotic systems 311 located within the selected service center. The sent 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 to enable it to perform one or more requested services. The robotic systems 311 within the service center may be instructed to acquire and / or arrange one or more machines, tools, parts, accessories, or other components within the designated service zone 309 consistent with performing the requested service. The robotic systems may be instructed to pre-arrange the service zone in accordance with the sent instructions prior to the arrival of the physical asset 301 at the service center, upon completion of an ongoing service within the designated service zone 309, and / or prior to the scheduled timing for initiating the requested service within the service zone 309. The robotic systems 311 preparing the modular service zone 309 may further arrange the service zone 309 by removing any unnecessary components or equipment present. For example, remove any machines, tools, components and / or parts that may be present within the service zone but may not be necessary to perform service on the next incoming physical asset 301 assigned to the service zone, and / or any subsequent physical assets scheduled to be serviced within the same service zone 309 at a later time.

[0064] An embodiment of method 600 described by FIG. 6 may begin at step 601. During step 601, multiple physical assets 301 may submit their respective digital twin models to a server or cloud network running an instance of repair service 307. In step 603, repair service 307 identifies, within a maintenance profile for each physical asset 301 submitting a digital twin model, the location of the physical asset 301, the types 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, repair service 307 may classify the maintenance profiles of the multiple physical assets 301 using one or more classification algorithms. The classification of each physical asset's 301's maintenance profile may identify different degrees of commonality among the types of services that may be applicable to each physical asset and may group services that have a threshold level of commonality into the same or similar classification.

[0065] In step 607, repair service 307 may generate a mapping between the types of services 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 of the requested services on the physical assets 301. In step 609, based on the categorized commonalities between servicing different physical assets 301, repair service 307 identifies the maintenance types or service equipment needed to perform various services on the physical assets. In step 611, it is determined whether one or more service requests are received by repair service 307 from one or more physical assets 301. If one or more service requests are not received, method 600 may return to step 601 so that new or updated digital twin models can be provided to repair service 307 over time. Otherwise, if one or more service requests are received in step 611, method 600 may proceed to step 613.

[0066] During step 613, the repair service may pre-create modular service zones 309 within the selected service center responsible for providing service to the physical assets 301 based on the classification patterns of the physical assets' 301 maintenance profiles and the commonalities between 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 time, modifications to the service zone 309 between physical asset services, and wait time between services of the physical assets 301. In step 615, a determination is made whether the modular service zones 309 visualized by the repair service 307 optimize the total service time to complete service for multiple physical assets 301 scheduled for service within one or more service zones 309. If the total service time is not optimized, method 600 may return to step 613 and further optimize the service zones 309 based on the classifications of the maintenance profiles and the commonalities therebetween, and may simulate potential modifications to the workflow to achieve optimization. Otherwise, if the service zone 309 visualized by the repair service 307 optimizes the total service time for completing service for all physical assets 301 scheduled at the service center and assigned to one or more service zones 309, the method may proceed to step 617.

[0067] During step 617, one or more service zones 309 may be implemented within the service center according to a workflow generated by the repair service 307. The repair service may command the dispatch of the service zones 309 using one or more robotic systems 311 located within the service center. The commands sent to the robotic systems 311 may command the dispatch of the service zones 309 according to the optimized workflow and visualization of the service zones 309 provided by the repair service 307, resulting in an optimization of the total service time for completing service of the physical assets 301. In step 619, feedback data describing the actual repair, maintenance, or other services provided to each of the physical assets may be captured and fed back to the repair service 307 to improve the classification model and the optimization of the schedule of the service zones 309 and / or workflow.

Claims

1. 1. A computer-implemented method for predictively automating configuration of modular service zones within a service site, comprising: receiving, by a processor, service requests from a plurality of physical assets requesting performance of services on the physical assets at the service site, locations of the physical assets, and estimated times of arrival for each of the physical assets at the service site; analyzing, by the processor, a digital twin model corresponding to each of the plurality of physical assets; creating, by said processor, a maintenance profile for each type of physical asset that describes a classification of said services for the corresponding physical asset, including one or more machines, tools, or parts required to perform said services on said physical asset; creating, by the processor, one or more modular service zones within the service site that include at least one of the one or more machines, tools, and parts needed to perform the service on the physical asset based on a commonality between the maintenance profile of the physical asset and the one or more machines, tools, or parts needed to perform the service; instructing, by the processor, a robotic system located within the service site to create or modify the modular service zone by positioning the machines, the tools, and the parts to perform 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 site.

1. A computer-implemented method comprising:

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

3. 3. The computer-implemented method of claim 2, wherein the workflow sequence is configured to allocate the physical assets to the one or more modular service zones in a manner that further maximizes the number of physical assets being serviced at the service site.

4. modifying, by the processor, a first modular service zone within the service site, wherein the first physical asset receives one or more services within the first modular service zone, and adding or removing at least the one or more of the machines, tools, or parts that lack commonality between maintenance profiles of the first physical asset and the subsequent physical asset prior to arrival of the subsequent physical asset within the first modular service zone; The computer-implemented method of claim 2 further comprising:

5. 2. The computer-implemented method of claim 1, further comprising optimizing, by the processor, the workflow to minimize repair times for the physical assets, changes to each of the physical assets, or wait times between the services provided to each of the physical assets assigned to the same modular service zone.

6. capturing, by said processor, data describing said execution of said services within said one or more modular service zones; inputting, by the processor, the data into a classification model to provide feedback and continuous learning to improve classification of maintenance profiles assigned to each type of physical asset. The computer-implemented method of claim 1 further comprising:

7. mapping, by the processor, one or more of the services required by the plurality of physical assets to the one or more machines, tools, or parts required to perform the service; The computer-implemented method of claim 1 further comprising:

8. 1. A computer system for predictively automating the configuration of modular service zones within a service site, comprising: processor; a computer-readable storage medium coupled to the processor; wherein the computer-readable storage medium is operable via the processor to: receiving, by the processor, service requests from a plurality of physical assets requesting performance of services on the physical assets at the service site, locations of the physical assets, and estimated times of arrival for each of the physical assets at the service site; analyzing, by the processor, a digital twin model corresponding to each of the plurality of physical assets; creating, by said processor, a maintenance profile for each type of physical asset that describes a classification of said services for the corresponding physical asset, including one or more machines, tools, or parts required to perform said services on said physical asset; creating, by the processor, one or more modular service zones within the service site that include at least one of the one or more machines, tools, and parts needed to perform the service on the physical asset based on a commonality between the maintenance profile of the physical asset and the one or more machines, tools, or parts needed to perform the service; instructing, by the processor, a robotic system located within the service site to create or modify the modular service zone by positioning the machines, the tools, and the parts to perform 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 site. comprising program instructions for performing a computer-implemented method comprising: Computer system.

9. Creating the one or more modular service zones within the service site further comprises: creating, by the processor, a workflow configured to assign the physical assets to the one or more modular service zones for performing the services in an order based on the combination of estimated arrival times, and minimizing the number of changes to the one or more modular service zones between services provided to a first physical asset and subsequent physical assets scheduled by the workflow; 9. The computer system of claim 8, comprising:

10. 10. The computer system of claim 9, wherein the workflow sequence is configured to allocate the physical assets to the one or more modular service zones to further maximize the number of physical assets being serviced at the service site.

11. modifying, by the processor, a first modular service zone within the service site, wherein the first physical asset receives one or more services within the first modular service zone, and adding or removing at least the one or more of the machines, tools, or parts that lack commonality between maintenance profiles of the first physical asset and the subsequent physical asset prior to arrival of the subsequent physical asset within the first modular service zone; The computer system of claim 9 further comprising:

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

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

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

15. 1. A computer program product for predictively automating the configuration of modular service zones within a service site, comprising: one or more computer readable storage media having computer readable program instructions stored thereon, the program instructions comprising: receiving, by a processor, service requests from a plurality of physical assets requesting performance of services on the physical assets at the service site, locations of the physical assets, and estimated times of arrival for each of the physical assets at the service site; analyzing, by the processor, a digital twin model corresponding to each of the plurality of physical assets; creating, by said processor, a maintenance profile for each type of physical asset that describes a classification of said services for the corresponding physical asset, including one or more machines, tools, or parts required to perform said services on said physical asset; creating, by the processor, one or more modular service zones within the service site that include at least one of the one or more machines, tools, and parts needed to perform the service on the physical asset based on a commonality between the maintenance profile of the physical asset and the one or more machines, tools, or parts needed to perform the service; instructing, by the processor, a robotic system located within the service site to create or modify the modular service zone by positioning the machines, the tools, and the parts to perform 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 site.

1. A computer program product for performing a computer-implemented method comprising:

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

17. 17. The computer program product of claim 16, wherein the workflow sequence is configured to allocate the physical assets to the one or more modular service zones to further maximize the number of physical assets being serviced at the service site.

18. modifying, by the processor, a first modular service zone within the service site, wherein the first physical asset receives one or more services within the first modular service zone, and adding or removing at least the one or more of the machines, tools, or parts that lack commonality between maintenance profiles of the first physical asset and the subsequent physical asset prior to arrival of the subsequent physical asset within the first modular service zone; 17. The computer program product of claim 16, further comprising:

19. 17. The computer program product of claim 16, further comprising optimizing, by the processor, the workflow to minimize repair time of the physical assets, changes to each of the physical assets, or wait time between the services provided to each of the physical assets assigned to the same modular service zone.

20. capturing, by the processor, data describing the execution of the service within the one or more modular service zones; inputting the data into a classification model by the processor to provide feedback and continuous learning to improve classification of maintenance profiles assigned to each type of physical asset; 17. A computer program product according to claim 16.

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