Dynamic change of operating parameters of a machine

Abstract

Embodiments are provided for changing operating parameters of machines in a multi-machine environment. Embodiments can include receiving IoT feeds from one or more IoT devices and data related to activity in the multi-machine environment. Embodiments can also include identifying a health condition of one or more machines. Embodiments can also include identifying a time range in which one or more maintenance actions can be performed in response to determining that at least one of the one or more machines requires the one or more maintenance actions. Embodiments can also include updating a movement path of one or more service robots in the multi-machine environment. Embodiments can also include deploying the one or more service robots to perform the one or more maintenance actions. Embodiments can also include adjusting one or more operating parameters of the one or more machines.

Description

Background Technology

[0001] This invention generally relates to the field of computing, and more particularly to a system for changing the operating parameters of machines in a multi-machine environment.

[0002] Various machines perform a variety of activities in industrial environments. Sometimes, these machines may require maintenance after performing these activities. For example, a machine may need cleaning after manufacturing work products. Regular cleaning helps remove dirt, dust, debris, and other contaminants that can accumulate on the machine's surfaces and components. Additionally, cleaning can improve the machine's lifespan and overall functionality. Machine maintenance can be performed manually and / or by automated systems. Summary of the Invention

[0003] According to one embodiment, a method, computer system, and computer program product are provided for changing operating parameters of machines in a multi-machine environment. The embodiment may include receiving IoT feeds from one or more IoT devices and data relating to activities in the multi-machine environment. The embodiment may also include identifying the health status of one or more machines performing activities based on the IoT feeds. The embodiment may further include identifying a time range in which the one or more maintenance actions can be performed in response to determining, based on the identified health status, that at least one of the one or more machines requires one or more maintenance actions. The embodiment may further include updating the movement paths of one or more service robots in the multi-machine environment based on the identified time range. The embodiment may further include deploying one or more service robots to perform one or more maintenance actions according to the updated movement paths. The embodiment may further include adjusting one or more operating parameters of one or more machines within a threshold distance of the deployed one or more service robots. Attached Figure Description

[0004] These and other objects, features, and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is taken in conjunction with the accompanying drawings. For clarity, the drawings are provided to assist those skilled in the art in understanding the invention in conjunction with the detailed description; various features in the drawings are not to scale. In the drawings:

[0005] Figure 1 An exemplary computing environment according to at least one embodiment is shown.

[0006] Figure 2 An operation flowchart is shown for changing the operating parameters of a machine in a multi-machine environment during the operation parameter change process, according to at least one embodiment.

[0007] Figure 3 This is an exemplary diagram illustrating a service robot performing maintenance actions on a machine according to at least one embodiment. Detailed Implementation

[0008] This document discloses detailed embodiments of the claimed structures and methods; however, it is to be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods, which can be implemented in various forms. The invention can be implemented in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Details of well-known features and techniques may be omitted in the description to avoid unnecessarily obscuring the presented embodiments.

[0009] It should be understood that, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” also include plural referents. Thus, for example, unless the context clearly indicates otherwise, reference to “component surface” includes reference to one or more such surfaces.

[0010] Embodiments of the present invention relate to the field of computing, and more specifically to a system for changing operating parameters of machines in a multi-machine environment. The exemplary embodiments described below provide a system, method, and program product for determining, based on the health status of one or more machines, whether at least one of the one or more machines requires one or more maintenance actions, and accordingly adjusting one or more operating parameters of the at least one machine requiring one or more maintenance actions within a threshold distance of one or more deployed service robots. Therefore, this embodiment has the capability to improve industrial technology by dynamically changing the operating parameters of machines.

[0011] As mentioned earlier, various machines perform a variety of activities in industrial environments. Sometimes, these machines may require maintenance after performing these activities. For example, a machine may need cleaning after manufacturing work products. Regular cleaning helps remove dirt, dust, debris, and other contaminants that can accumulate on the machine's surfaces and components. Additionally, cleaning can improve the machine's lifespan and overall functionality. Machine maintenance can be performed manually and / or by automated systems. Over time, machines can become contaminated with debris during various activities. For example, airborne dust particles can accumulate on the machine. This problem is typically addressed by deploying floor cleaning robots to sweep the area to be cleaned. However, sweeping the area to be cleaned does not change the machine's operating parameters.

[0012] Therefore, it is necessary to set up a system in the appropriate location to change the operating parameters of the machine that needs maintenance.

[0013] According to at least one embodiment, a computer-based method, computer system, and computer program product are provided for changing operating parameters of machines in a multi-machine environment. The method includes receiving IoT feeds from one or more IoT devices and data related to activities in the multi-machine environment; identifying the health status of one or more machines performing the activities based on the IoT feeds; determining, based on the identified health status, whether at least one of the one or more machines requires one or more maintenance actions; identifying a time range during which the one or more maintenance actions can be performed in response to determining that at least one machine requires one or more maintenance actions; updating the movement paths of one or more service robots in the multi-machine environment based on the identified time range; deploying one or more service robots to perform one or more maintenance actions according to the updated movement paths; and adjusting one or more operating parameters of the one or more machines within a threshold distance of the deployed one or more service robots. This embodiment has the advantage of dynamically changing the operating parameters of machines requiring maintenance.

[0014] According to at least one embodiment, deploying one or more service robots to perform one or more maintenance actions may further include having the one or more service robots bypass one or more idle machines within a threshold distance that do not require one or more maintenance actions, and predicting an updated timeframe for the one or more service robots used for deployment to return to one or more idle machines to perform one or more maintenance actions. This embodiment has the advantage of ensuring that machines will receive maintenance at the appropriate time.

[0015] According to at least one embodiment, identifying the time range within which one or more maintenance actions can be performed may further include, in response to determining that at least one machine requires multiple maintenance actions from multiple service robots, identifying a sequence in which multiple service robots will be deployed to perform multiple maintenance actions. This embodiment has the advantage of optimizing the use of multiple service robots.

[0016] According to at least one embodiment, the identified sequence may include multiple service robots simultaneously performing corresponding maintenance actions on at least one machine. This embodiment has the advantage of reducing the total time spent providing maintenance to machines that require multiple maintenance actions from different service robots.

[0017] According to at least one embodiment, adjusting one or more operating parameters of one or more machines within a threshold distance may further include, in response to determining that at least one machine is moving, moving at least one machine toward one or more service robots deployed in a multi-machine environment. This embodiment has the advantage of optimizing the time required for machine reception of maintenance.

[0018] According to at least one embodiment, adjusting one or more operating parameters of one or more machines within a threshold distance may further include causing at least one machine to open a material collection tray in response to determining that waste material has been collected within the threshold limit. This embodiment has the advantage of reducing productivity losses from machine waste material spillage.

[0019] According to at least one embodiment, the adjusted operating parameter may be the stopping of the electric motor. Stopping the electric motor has the advantage of proactively preparing the machine for maintenance. According to at least one embodiment, the adjusted operating parameter may be a reduction in the electric motor's revolutions per minute (RPM). Reducing the RPM has the advantage of reducing the power consumption of the machine requiring maintenance. According to at least one embodiment, the adjusted operating parameter may be a reduction in the cutting speed of at least one machine. Reducing the cutting speed has the advantage of reducing wear on the machine requiring maintenance.

[0020] Various aspects of this disclosure are described by narrative text, flowcharts, block diagrams of computer systems, and / or block diagrams of machine logic included in embodiments of a computer program product (CPP). Regarding any flowchart, depending on the technology involved, operations may be performed in a different order than that shown in a given flowchart. For example, again according to the technology involved, two operations shown in consecutive flowchart blocks may be performed in reverse order, as a single integrated step, simultaneously, or in a manner that at least partially overlaps in time.

[0021] Computer Program Product Embodiment (“CPP Embodiment” or “CPP”) is a term used in this disclosure to describe any collection of one or more storage media (also referred to as “media”) collectively included in a collection of one or more storage devices, the collection of one or more storage devices collectively including machine-readable code corresponding to instructions and / or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device capable of holding and storing instructions used by a computer processor. Without limitation, a computer-readable storage medium can be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these media include: magnetic disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, mechanical encoding devices (such as punch cards or pits / platforms formed in the main surface of the disk), or any suitable combination of the foregoing. As used in this disclosure, computer-readable storage media should not be construed as storing transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides, optical pulses through fiber optic cables, electrical signals transmitted through wires, and / or other transmission media. As those skilled in the art will understand, data is typically moved at certain incidental points in time during normal operation of the storage device, such as during access, defragmentation, or garbage collection; however, this does not render the storage device transient, as the data is not transient when it is stored.

[0022] The exemplary embodiments described below provide a system, method, and program product for determining, based on the health status of one or more machines, whether at least one of the one or more machines requires one or more maintenance actions, and accordingly adjusting one or more operating parameters of the at least one machine requiring one or more maintenance actions within a threshold distance of one or more deployed service robots.

[0023] refer to Figure 1An exemplary computing environment 100 according to at least one embodiment is depicted. The computing environment 100 includes examples of environments for executing at least some computer code involved in performing the methods of the present invention, such as parameter changing program 150. In addition to block 150, the computing environment 100 includes, for example, a computer 101, a wide area network (WAN) 102, an end-user equipment (EUD) 103, a remote server 104, a public cloud 105, and a private cloud 106. In this embodiment, the computer 101 includes a processor group 110 (including processing circuitry 120 and cache 121), communication infrastructure 111, volatile memory 112, persistent storage device 113 (including operating system 122 and block 200, as described above), a peripheral device group 114 (including a user interface (UI) device group 123, storage device 124, and Internet of Things (IoT) sensor group 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 coordination module 141, a host physical unit 142, a virtual machine group 143, and a container set 144.

[0024] Computer 101 can take the form of a desktop computer, laptop computer, tablet computer, smartphone, smartwatch or other wearable computer, mainframe computer, quantum computer, or any other form of computer or mobile device now known or to be developed in the future capable of running programs, accessing networks, or querying databases such as remote database 130. As is well known in the field of computer technology, and depending on the technology, the performance of a computer-implemented method can be distributed across multiple computers and / or multiple locations. On the other hand, in this presentation of computing environment 100, the detailed discussion focuses on a single computer, specifically computer 101, to keep the presentation as simple as possible. Computer 101 can reside in the cloud, even... Figure 1 It is not shown in the cloud. On the other hand, computer 101 is not required to be in the cloud except to the extent that can be definitively indicated.

[0025] Processor group 110 includes one or more computer processors of any type now known or to be developed in the future. Processing circuitry 120 may be distributed across multiple packages, such as multiple cooperating integrated circuit chips. Processing circuitry 120 may implement multiple processor threads and / or multiple processor cores. Cache 121 is memory located within the processor chip package and is typically used for data or code that should be readily accessible by the threads or cores running on processor group 110. Cache memory is typically organized into multiple levels based on its relative proximity to the processing circuitry. Alternatively, some or all of the cache in the processor group may be located “off-chip.” In some computing environments, processor group 110 may be designed to work with qubits and perform quantum computing.

[0026] Computer-readable program instructions are typically loaded onto computer 101 to cause the processor assembly 110 of computer 101 to perform a series of operational steps to implement a computer-implemented method, such that the instructions thus executed instantiate the method specified in the flowchart and / or the narrative description of the computer-implemented method included in this document (collectively, the “method of the invention”). These computer-readable program instructions are stored in various types of computer-readable storage media, such as cache 121 and other storage media discussed below. The processor assembly 110 accesses the program instructions and associated data to control and direct the execution of the method of the invention. In computing environment 100, at least some of the instructions for performing the method of the invention may be stored in persistent storage device 113 in block 200.

[0027] Communication structure 111 is a signal transmission path that allows various components of computer 101 to communicate with each other. Typically, this structure consists of switches and conductive paths, such as switches and conductive paths that form buses, bridges, physical input / output ports, etc. Other types of signal communication paths can be used, such as fiber optic communication paths and / or wireless communication paths.

[0028] Volatile memory 112 is any type of volatile memory now known or to be developed in the future. Examples include dynamic random access memory (RAM) or static RAM. Typically, volatile memory 112 is characterized by random access, but this is not necessary unless explicitly indicated. In computer 101, volatile memory 112 is located in a single package and is internal to computer 101; however, alternatively or additionally, volatile memory 112 may be distributed across multiple packages and / or located externally relative to computer 101.

[0029] The persistent storage device 113 is any form of non-volatile memory for a computer, now known or to be developed in the future. The non-volatility of this storage device means that the stored data is retained regardless of whether power is supplied to the computer 101 and / or directly to the persistent storage device 113. The persistent storage device 113 may be a read-only memory (ROM), but typically at least a portion of the persistent storage device 113 allows for data writing, data deletion, and data rewriting. Some familiar forms of persistent storage devices 113 include hard disks and solid-state storage devices. The operating system 122 may take several forms, such as various known proprietary operating systems or operating systems employing an open-source portable operating system interface type with a kernel. The code included in block 150 generally includes at least some of the computer code involved in performing the methods of the present invention.

[0030] Peripheral device group 114 includes a collection of peripheral devices for computer 101. Data communication connections between peripheral devices 114 and other components of computer 101 can be implemented in various ways, such as Bluetooth connectivity, near field communication (NFC) connectivity, connections made by cables (such as Universal Serial Bus (USB) type cables), plug-in connections (e.g., secure digital (SD) cards), connections made via local area communication networks, and even connections made via wide area networks such as the Internet. In various embodiments, UI device group 123 may include components such as displays, speakers, microphones, wearable devices (such as goggles and smartwatches), keyboards, mice, printers, touchpads, game controllers, and haptic devices. Storage device 124 is an external storage device, such as an external hard drive, or a pluggable storage device, such as an SD card. Storage device 124 can be permanent and / or volatile. In some embodiments, storage device 124 may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computer 101 requires substantial storage (e.g., where computer 101 locally stores and manages a large database), this storage device can be provided by a peripheral storage device designed to store very large amounts of data, such as a storage area network (SAN) shared by multiple geographically distributed computers. The IoT sensor assembly 125 comprises sensors that can be used in IoT applications. For example, one sensor could be a thermometer, while another could be a motion detector. The peripheral device group 114 may also include industrial machines, service robots, drones, and / or any other devices used to perform labor-related tasks.

[0031] Network module 115 is a collection of computer software, hardware, and firmware that allows computer 101 to communicate with other computers via WAN 102. Network module 115 may include hardware such as a modem or Wi-Fi transceiver, software for packetizing and / or depacketizing data transmitted over the communication network, and / or web browser software for transmitting data over the Internet. In some embodiments, the network control and network forwarding functions of 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 network module 115 are performed on physically separate devices, such that the control function manages several different network hardware devices. Computer-readable program instructions for performing the methods of the present invention can typically be downloaded to computer 101 from an external computer or external storage device via a network adapter card or network interface included in network module 115.

[0032] WAN 102 is any wide area network (e.g., the Internet) capable of transmitting computer data over non-local distances using any technology known now or developed in the future for transmitting computer data. In some embodiments, a WAN may be replaced by and / or supplemented by a local area network (LAN), which is designed to transmit data between devices located in a local area, such as a Wi-Fi network. WAN 102 and / or LAN typically include computer hardware such as copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and edge servers.

[0033] End User Equipment (EUD) 103 is any computer system used and controlled by an end user (e.g., a customer of the enterprise operating computer 101) and can take any of the forms discussed above in conjunction with computer 101. EUD 103 typically receives helpful and useful data from the operation of computer 101. For example, assuming computer 101 is designed to provide recommendations to the end user, these recommendations are typically transmitted to EUD 103 from network module 115 of computer 101 via WAN 102. In this way, EUD 103 can display or otherwise present the recommendations to the end user. In some embodiments, EUD 103 can be a client device, such as a thin client, a heavy client, a mainframe computer, a desktop computer, etc.

[0034] Remote server 104 is any computer system that provides at least some data and / or functionality to computer 101. Remote server 104 can be controlled and used by the same entity operating computer 101. Remote server 104 represents a machine that collects and stores helpful and useful data used by other computers such as computer 101. For example, if computer 101 is designed and programmed to provide recommendations based on historical data, that historical data can be provided to computer 101 from a remote database 130 of remote server 104.

[0035] Public cloud 105 is any computer system that can be used by multiple entities, providing on-demand availability of computer system resources and / or other computing capabilities (especially data storage (cloud storage) and computing power) without direct active management by users. Cloud computing typically leverages resource sharing to achieve scalability consistency and economy. The direct and active management of the computing resources of public cloud 105 is performed by the computer hardware and / or software of cloud coordination module 141. The computing resources provided by public cloud 105 are typically implemented by virtual computing environments running on various computers constituting host physical machine group 142, which is the entire domain of physical computers in and / or available to public cloud 105. Virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine group 143 and / or containers from container group 144. It should be understood that these VCEs can be stored as images and can be transferred between various physical machine hosts as images or after the VCEs are instantiated. Cloud coordination module 141 manages the transfer and storage of images, deploys new instantiations of VCEs, and manages the active instantiation of VCE deployments. Gateway 140 is a collection of computer software, hardware, and firmware that allow public cloud 105 to communicate via WAN 102.

[0036] Now, we will provide some further explanation of Virtualized Computing Environments (VCEs). A VCE can be stored as an "image." New active instances of a VCE can be instantiated from this image. Two common types of VCEs are virtual machines and containers. A container is a VCE that uses operating system-level virtualization. This refers to an operating system feature where the kernel allows multiple isolated user-space instances, called containers, to exist. From the perspective of the programs running within them, these isolated user-space instances typically appear as actual computers. Computer programs running on a regular operating system can utilize all the resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running within a container can only use the contents of the container and the devices allocated to the container; this is a characteristic known as containerization.

[0037] Private cloud 106 is similar to public cloud 105, except that computing resources are available only to a single enterprise. While private cloud 106 is depicted as communicating with WAN 102, in other embodiments, private cloud 106 may be completely disconnected from the Internet and accessible only via a local / private network. A hybrid cloud is a combination of multiple clouds of different types (e.g., private, community, or public cloud types) typically implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardization or proprietary technology that enables coordination, management, and / or data / application portability across the multiple component clouds. In this embodiment, public cloud 105 and private cloud 106 are both part of a larger hybrid cloud.

[0038] According to this embodiment, the parameter changing program 150 may be a program capable of receiving IoT feeds from one or more IoT devices and data related to activities in a multi-machine environment, determining, based on the health status of one or more machines, whether at least one of the one or more machines requires one or more maintenance actions, and adjusting one or more operating parameters of the at least one machine requiring one or more maintenance actions within a threshold distance of one or more deployed service robots. Furthermore, although described in computer 101, the parameter changing program 150 may be stored individually or in any combination in and / or executed by end-user device 103, remote server 104, public cloud 105, and private cloud 106. Reference will be made below. Figure 2 The method of changing parameters will be explained in more detail below. It is understood that the examples described below are not limiting, and the parameters used in the examples may differ in embodiments of the invention.

[0039] Now for reference Figure 2 According to at least one embodiment, an operation flowchart for changing the operating parameters of a machine in a multi-machine environment during an operation parameter change process 200 is described. At 202, the parameter change procedure 150 receives IoT feeds from one or more IoT devices and data related to activities in the multi-machine environment.

[0040] According to at least one embodiment, the IoT feed can be a video feed captured by cameras and / or drones in a multi-machine environment. For example, the cameras and / or drones can capture dust particles in the air or on the ground. In another example, the cameras and / or drones can capture oil or coolant on the ground near one or more machines. According to at least one other embodiment, the IoT feed can be a sensor feed captured by IoT sensor group 125. The sensors in IoT sensor group 125 can be included in one or more machines. Examples of sensors in IoT sensor group 125 may include, but are not limited to, temperature sensors, oil pressure sensors, light sensors, and / or speed sensors. For example, a temperature sensor can capture the internal temperature of one or more machines. In another example, a speed sensor can capture the RPM of the motors of one or more machines.

[0041] Data related to an activity may include the type of activity to be performed in a multi-machine environment. Examples of activities may include, but are not limited to, assembling objects in a manufacturing facility, 3D printing objects, cutting metal objects, removing objects in a disaster recovery area, and transporting objects from one location to another (e.g., moving automotive parts from an assembly line to a loading dock). Data related to an activity may also include one or more objects associated with the activity. Examples of objects may include, but are not limited to, vehicles, equipment on an assembly line, building materials, and / or any object capable of moving from a source to a destination (i.e., from one location to another). Data related to an activity may also include the time required to complete the activity. For example, an activity might take two hours to complete.

[0042] Then, at 204, parameter changing procedure 150 identifies the health status of one or more machines performing the activity. Health status is identified based on IoT feeds. To assess health status, parameter changing procedure 150 may access a database containing information related to the normal operating status of one or more machines, such as remote database 130.

[0043] For example, the database might specify that the machine's internal temperature should not exceed 150°F. Continuing with this example, a health condition can be identified as poor when the machine's internal temperature exceeds 150°F. In another example, the database might specify that the machine's oil pressure should be between 25 and 65 pounds per square inch (PSI). Continuing with this example, a health condition can be identified as poor when the oil pressure drops below 25 PSI or rises above 65 PSI. In yet another example, the database might specify that there should be no fluid on the floor within five feet of the machine. Continuing with this example, a health condition can be identified as poor when coolant and / or oil accumulate on the floor within five feet of the machine.

[0044] Next, at 206, parameter change procedure 150 determines whether at least one of the one or more machines requires one or more maintenance actions. This determination is based on the identified health condition. Examples of maintenance actions may include, but are not limited to, lubricating at least one machine, applying oil and / or coolant to at least one machine, cleaning at least one machine, and / or collecting waste from at least one machine. As described above with respect to step 204, at least one machine may require one or more maintenance actions when its health condition is identified as poor.

[0045] For example, when the internal temperature of a machine exceeds 150°F, the health condition can be identified as poor, and the maintenance action could be applying coolant to at least one machine. In another example, when the oil pressure drops below 25 PSI or rises above 65 PSI, the health condition can be identified as poor, and the maintenance action could be applying oil to at least one machine. In yet another example, when coolant and / or oil accumulates on the floor within five feet of at least one machine, the health condition can be identified as poor, and the maintenance action could be sweeping the floor near at least one machine and replenishing coolant and / or oil to at least one machine. In a further example, when waste material exceeding a threshold limit is collected, the health condition can be identified as poor, and the maintenance action could be removing the waste material.

[0046] In response to determining that at least one machine requires one or more maintenance actions (step 206, "Yes" branch), the operating parameter change process 200 proceeds to step 208 to identify the time range within which one or more maintenance actions can be performed. In response to determining that at least one machine does not require one or more maintenance actions (step 206, "No" branch), the operating parameter change process 200 ends.

[0047] Then, at 208, parameter changing procedure 150 identifies a time range within which one or more maintenance actions can be performed. The time range can be identified based on historical data related to the activity. As described above with respect to step 202, the activity-related data may include the time required to complete the activity and the downtime during the activity. For example, the activity might take two hours to complete. Historical data may also include time intervals between activities and the amount of time required to perform one or more maintenance actions. For example, the time interval between the first and second activities could be 30 minutes, and the amount of time required to perform one or more maintenance actions could be 20 minutes. Therefore, the identified time range can be a period of time during which at least one machine can be idle.

[0048] For example, when machines "A" and "B" are machines requiring one or more maintenance actions, and where applying oil and / or coolant to machine "A" takes five minutes and collecting waste material from machine "B" takes 15 minutes, the identified time range could be 30 minutes between the first and second activities. Continuing this example, where the 30 minutes between the first and second activities occur between 2:00 PM and 2:30 PM, the time range for applying oil and / or coolant to machine "A" could be from 2:00 PM to 2:05 PM, and the time range for collecting waste material from machine "B" could be from 2:10 PM to 2:25 PM.

[0049] According to at least one embodiment, in response to determining that at least one machine requires multiple maintenance actions from multiple service robots, a sequence of multiple service robots to be deployed to perform multiple maintenance actions can be identified, which will be described in further detail below with respect to step 210.

[0050] Next, at 210, parameter changing procedure 150 updates the movement paths of one or more service robots in the multi-machine environment. The movement paths are updated based on the identified time range. Examples of service robots may include, but are not limited to, cleaning robots, lubrication robots, and / or material collection robots. One or more service robots may be constantly moving in the multi-machine environment. One or more service robots may move on the ground (e.g., automated guided vehicles (AGVs)) and / or in the air (e.g., drones). For example, if machine "A" and machine "B" are machines requiring one or more maintenance actions, and the time range for applying oil and / or coolant to machine "A" may be from 2:00 PM to 2:05 PM, and the time range for collecting waste material from machine "B" may be from 2:10 PM to 2:25 PM, the updated movement path of one or more service robots may be that one or more service robots travel to machine "A," perform maintenance actions, and then travel to machine "B" to perform additional maintenance actions after completing the maintenance actions.

[0051] According to at least one embodiment, in cases where at least one machine requires multiple maintenance actions from multiple service robots, a sequence in which multiple service robots are deployed to perform multiple maintenance actions can be identified. For example, machine "A" may require oil and / or coolant as well as waste removal, and the addition of oil and / or coolant and the removal of waste may need to be performed by different service robots. Continuing this example, the identified sequence could be a lubrication robot applying oil and / or coolant, followed by a material collection robot removing waste.

[0052] According to at least one other embodiment, the identified sequence may include multiple service robots simultaneously performing corresponding maintenance actions on at least one machine. For example, a lubrication robot may apply oil and / or coolant to machine "A", while a material collection robot may simultaneously remove waste from machine "A".

[0053] Then, at 212, parameter change procedure 150 deploys one or more service robots to perform one or more maintenance actions. One or more service robots are deployed based on the updated movement path. Parameter change procedure 150 may send signals to one or more service robots to deploy one or more service robots to at least one machine. For example, if machine "A" and machine "B" are machines requiring one or more maintenance actions, and if the updated movement path of one or more service robots could be one or more service robots traveling to machine "A," performing a maintenance action, and then traveling to machine "B" to perform another maintenance action after completing the maintenance action, a signal may be sent to one or more service robots to deploy one or more service robots to machine "A," and subsequently to machine "B."

[0054] According to at least one embodiment, deploying one or more service robots to perform one or more maintenance actions may include having one or more service robots bypass one or more idle machines within a threshold distance that do not require one or more maintenance actions. During the identified time period, one or more idle machines may not have requested one or more maintenance actions. For example, in addition to machines "A" and "B", machine "C" may also be idle between the first and second activities. However, unlike machines "A" and "B", machine "C" may not require any maintenance actions. In this example, the deployed one or more service robots may bypass (e.g., skip) machine "C" and perform one or more maintenance actions on machines "A" and "B". An updated time period for the deployed one or more service robots to return to one or more idle robots to perform one or more maintenance actions can then be predicted. For example, if the identified time period is 30 minutes between the first and second activities, the updated time period could be 30 minutes between the second and third activities. Continuing with the example, where the 30 minutes between the second and third activities occur between 3:00 PM and 3:30 PM, the time range for updating machine "C" to perform one or more maintenance actions can be between 3:00 PM and 3:30 PM.

[0055] Next, at 214, parameter changing procedure 150 adjusts one or more operating parameters of one or more machines within a threshold distance of the deployed one or more service robots. Examples of adjusted operating parameters may include, but are not limited to, stopping the motor, reducing the RPM of the motor, and / or reducing the cutting speed of at least one machine. When the deployed one or more service robots move toward at least one machine, at least one machine may change one or more operating parameters when the deployed one or more service robots are within the threshold distance of at least one machine. For example, the threshold distance may be 20 feet. Continuing this example, when the lubrication robot is within 20 feet of machine "A", "machine "A" may change one or more operating parameters.

[0056] According to at least one embodiment, adjusting one or more operating parameters of one or more machines within a threshold distance may include causing at least one machine to open a material collection tray in response to determining that waste material has been collected within a threshold limit. For example, the threshold limit may be 10 pounds of waste material. When the threshold limit is reached, one or more service robots, such as a material collection robot, may be deployed to remove the waste material from the material collection tray.

[0057] According to at least one other embodiment, adjusting one or more operating parameters of one or more machines within a threshold distance may further include moving at least one machine toward one or more service robots deployed in a multi-machine environment in response to determining that at least one machine is moving. In this embodiment, one or more service robots are capable of providing one or more maintenance actions to multiple machines simultaneously. For example, if the machines requiring one or more maintenance actions include machine "A", machine "B", and machine "C", and machines "A" and "B" are moving, machines "A" and "B" may move toward one or more service robots.

[0058] Now for reference Figure 3 Figure 300 illustrates an exemplary depiction of a service robot 302 performing maintenance actions on machines 304A, 304B, 306A, and 306B according to at least one embodiment. In Figure 300, the above-described... Figure 2The description outlines the updated movement path for deploying service robot 302. Arrows indicate the updated movement path of service robot 302. Machines "1", "2", "3", and "4" can be machines requiring one or more maintenance actions. A first plurality of machines 304A and 304B may require one or more maintenance actions from a single service robot 302. A second plurality of machines 306A and 306B may require one or more maintenance actions from multiple service robots 302. As shown in Figure 300, service robot 302 can simultaneously perform one or more maintenance actions on the second plurality of machines 306A and 306B.

[0059] According to at least one embodiment, service robot 302 may delay performing one or more maintenance actions on machines 304A, 304B, 306A, and 306B in response to determining that machines 304A, 304B, 306A, and 306B are operating to meet a Service Level Agreement (SLA) for product delivery. (As stated above regarding...) Figure 2 As described, one or more maintenance actions can be performed on machines 304A, 304B, 306A, and 306B that are operating in compliance with SLAs during the updated timeframe.

[0060] Understandable. Figure 2 and 3 This is merely an illustration of an implementation and does not imply any limitation on how different embodiments can be implemented. Many modifications can be made to the depicted environment based on design and implementation requirements.

[0061] Various embodiments of the invention have been described for illustrative purposes, but are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been chosen to best explain the principles of the embodiments, their practical application, or technical improvements to existing technologies on the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A computer-based method for changing operating parameters of machines in a multi-machine environment, the method comprising: Receives IoT feeds from one or more IoT devices, as well as data related to activities in a multi-machine environment; The health status of one or more machines performing the activity is identified based on the IoT feed; Based on the identified health status, determine whether at least one of the one or more machines requires one or more maintenance actions; In response to determining that the at least one machine requires the one or more maintenance actions, a time range in which the one or more maintenance actions can be performed is identified; The movement paths of one or more service robots in the multi-machine environment are updated based on the identified time range. Deploy the one or more service robots according to the updated movement path to perform the one or more maintenance actions; as well as Adjust one or more operating parameters of the one or more service robots within a threshold distance of the deployed one or more service robots.

2. The computer-based method according to claim 1, wherein, Deploying the one or more service robots to perform the one or more maintenance actions also includes: The one or more service robots bypass one or more idle machines within the threshold distance that do not require the one or more maintenance actions; and Predict the timeframe for the deployment of one or more service robots to return to the one or more idle machines to perform the one or more maintenance actions.

3. The computer-based method according to claim 1, wherein, Identifying the time range within which the one or more maintenance actions can be performed also includes: In response to determining that the at least one machine requires multiple maintenance actions from multiple service robots, a sequence of the multiple service robots to be deployed to perform the multiple maintenance actions is identified.

4. The computer-based method according to claim 3, wherein, The identified sequence includes the multiple service robots simultaneously performing corresponding maintenance actions on at least one machine.

5. The computer-based method according to claim 1, wherein, Adjusting the one or more operating parameters of the one or more machines within the threshold distance further includes: In response to determining that the at least one machine is moving, the at least one machine is moved toward one or more service robots deployed in the multi-machine environment.

6. The computer-based method according to claim 1, wherein, Adjusting the one or more operating parameters of the one or more machines within the threshold distance further includes: In response to determining that there is a collection threshold for waste material, the at least one machine opens the material collection tray.

7. The computer-based method according to claim 1, wherein, The adjusted operating parameters are selected from a group that includes stopping the motor, reducing the motor's revolutions per minute (RPM), and reducing the cutting speed of the at least one machine.

8. A computer system, the computer system comprising: The computer system comprises one or more processors, one or more computer-readable storage devices, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media, the program instructions being executable by at least one of the one or more processors via at least one of the one or more computer-readable storage devices, wherein the computer system is capable of performing a method comprising: Receives IoT feeds from one or more IoT devices, as well as data related to activities in a multi-machine environment; The health status of one or more machines performing the activity is identified based on the IoT feed; Based on the identified health status, determine whether at least one of the one or more machines requires one or more maintenance actions; In response to determining that the at least one machine requires the one or more maintenance actions, a time range in which the one or more maintenance actions can be performed is identified; The movement paths of one or more service robots in the multi-machine environment are updated based on the identified time range. Deploy the one or more service robots according to the updated movement path to perform the one or more maintenance actions; and Adjust one or more operating parameters of the one or more service robots within a threshold distance of the deployed one or more service robots.

9. The computer system according to claim 8, wherein, Deploying the one or more service robots to perform the one or more maintenance actions also includes: The one or more service robots bypass one or more idle machines within the threshold distance that do not require the one or more maintenance actions; and Predict the timeframe for the deployment of one or more service robots to return to the one or more idle machines to perform the one or more maintenance actions.

10. The computer system according to claim 8, wherein, Identifying the time range within which the one or more maintenance actions can be performed also includes: In response to determining that the at least one machine requires multiple maintenance actions from multiple service robots, a sequence of the multiple service robots to be deployed to perform the multiple maintenance actions is identified.

11. The computer system according to claim 10, wherein, The identified sequence includes the multiple service robots simultaneously performing corresponding maintenance actions on at least one machine.

12. The computer system according to claim 8, wherein, Adjusting the one or more operating parameters of the one or more machines within the threshold distance further includes: In response to determining that the at least one machine is moving, the at least one machine is moved toward one or more service robots deployed in the multi-machine environment.

13. The computer system according to claim 8, wherein, Adjusting the one or more operating parameters of the one or more machines within the threshold distance further includes: In response to determining that there is a collection threshold for waste material, the at least one machine opens the material collection tray.

14. The computer system according to claim 8, wherein, The adjusted operating parameters are selected from a group that includes stopping the motor, reducing the motor's revolutions per minute (RPM), and reducing the cutting speed of the at least one machine.

15. A computer program product, the computer program product comprising: One or more computer-readable tangible storage media and program instructions stored on at least one of the one or more computer-readable tangible storage media, the program instructions being executable by a processor, the processor being capable of performing a method comprising: Receives IoT feeds from one or more IoT devices, as well as data related to activities in a multi-machine environment; The health status of one or more machines performing the activity is identified based on the IoT feed; Based on the identified health status, determine whether at least one of the one or more machines requires one or more maintenance actions; In response to determining that the at least one machine requires the one or more maintenance actions, a time range in which the one or more maintenance actions can be performed is identified; The movement paths of one or more service robots in the multi-machine environment are updated based on the identified time range. Deploy the one or more service robots according to the updated movement path to perform the one or more maintenance actions; and Adjust one or more operating parameters of the one or more service robots within a threshold distance of the deployed one or more service robots.

16. The computer program product according to claim 15, wherein, Deploying the one or more service robots to perform the one or more maintenance actions also includes: The one or more service robots bypass one or more idle machines within the threshold distance that do not require the one or more maintenance actions; and Predict the timeframe for the deployment of one or more service robots to return to the one or more idle machines to perform the one or more maintenance actions.

17. The computer program product according to claim 15, wherein, Identifying the time range within which the one or more maintenance actions can be performed also includes: In response to determining that the at least one machine requires multiple maintenance actions from multiple service robots, a sequence of the multiple service robots to be deployed to perform the multiple maintenance actions is identified.

18. The computer program product according to claim 17, wherein, The identified sequence includes the multiple service robots simultaneously performing corresponding maintenance actions on at least one machine.

19. The computer program product according to claim 15, wherein, Adjusting the one or more operating parameters of the one or more machines within the threshold distance further includes: In response to determining that the at least one machine is moving, the at least one machine is moved toward one or more service robots deployed in the multi-machine environment.

20. The computer program product according to claim 15, wherein, Adjusting the one or more operating parameters of the one or more machines within the threshold distance further includes: In response to determining that there is a collection threshold for waste material, the at least one machine opens the material collection tray.

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