A system and method for detecting and handling mechanical emergencies in a network.

Abstract

The present invention provides an efficient and reliable system for identifying machine emergency types among 3GPP networks / devices such as LTE, 5G, 6G, etc., and enriching emergency messages with appropriate data such as location, machine ID, plant ID, etc. The system may further provide mechanisms for identifying appropriate emergency servers capable of handling that type of emergency, and redirecting the emergency message with the enriched data to one or more servers. The system may further enable architecture systems that may be necessary to handle such emergencies in a time-efficient manner.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to detecting emergencies within a network. More particularly, the present disclosure relates to systems and methods for detecting machine emergencies and enabling appropriate handling of the emergencies.

Background Art

[0002] The following description of the prior art is intended to provide background information related to the field of the present disclosure. This section may include some aspects of the prior art that may be related to various features of the present disclosure. However, it should be understood that this section is used only to enhance the reader's understanding of the present disclosure and is not an admission of the prior art.

[0003] The Internet of Things (IoT) refers to a network of physical objects / "things" embedded with sensors, software, and other technologies for the purpose of connecting with other devices and systems over the internet and exchanging data. Things have evolved through a combination of multiple technologies, real-time analytics, machine learning, ubiquitous computing, commodity sensors, and embedded systems. Embedded systems in traditional fields, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of Things. In the consumer market, IoT technology is almost synonymous with products associated with the concept of a “smart home,” including devices and home appliances (lighting fixtures, thermostats, home security systems and cameras, and other household appliances) that support one or more common ecosystems and can be controlled via ecosystem-related devices such as smartphones and smart speakers. IoT can also be used in healthcare systems. Several significant concerns exist regarding the dangers of IoT growth, particularly in the areas of privacy and security, and as a result, industrial and governmental movements to address these concerns have been initiated, including the development of international standards.

[0004] The Industrial Internet of Things (IIoT) refers to the extension and use of the Internet of Things (IoT) in industrial sectors and applications. With a focus on machine-to-machine (M2M) communication, big data, and machine learning, IIoT enables industries and businesses to have better efficiency and reliability in their operations. IIoT encompasses industrial applications including robotics, medical devices, and software-defined production processes. IIoT goes beyond the scope of networking between ordinary consumer devices and physical devices typically associated with IoT. What makes this clear is the intersection of information technology (IT) and operational technology (OT). OT refers to networking between operational processes and industrial control systems (ICS), including human-machine interfaces (HMI), supervisory, control, and data acquisition (SCADA) systems, distributed control systems (DCS), and programmable logic controllers (PLC).

[0005] Such systems can greatly help industries grow by providing universally continuous data and help make better decisions overall. They can also help automatically provide data about the health of the overall system. For example, in a manufacturing plant, inserted sensors could help notify us of things like whether there is a hazardous gas leak using a smoke sensor, or if there is a water leak or machine overload requiring a shutdown. Such situations are commonly referred to as mechanical emergencies, and real-time emergency situations are automatically transmitted across the system. Such data travels via cellular networks (on next-generation cellular networks including 2G, 3G, 4G, or NB-IoT as technologies) and reaches emergency servers, SCADA servers, or remote control servers via the internet. Such emergency sessions are the most basic and critical services offered by telecommunications networks. They require priority action over normal sessions, which is achieved through QoS and resource management techniques. However, existing 3GPP® systems or solutions only provide priority action for publicly initiated emergency communications, while they do not offer special action for mission-critical calls or typical mechanical emergency types in IoT or IIoT.

[0006] Handling emergencies within a 3GPP® network has historically been intended to handle human emergencies. The nature of handling human emergencies is very different from that of handling machine emergencies. Existing emergency definitions and handling mechanisms in 3GPP® address human emergencies but not machine emergencies. The introduction of IoT and especially IIoT (Industrial IoT) exposes numerous types of emergencies that need to be handled differently from human emergencies. Furthermore, it should be noted that having a unique identity for subscriptions, such as SIM / USIM / eSIM / Soft SIM, is even more important, especially in machine-type communications that do not involve human intervention. Therefore, SIM-less operation in situations that would be acceptable in a normal 3GPP® system if it were not a machine-type is simply impossible.

[0007] Therefore, in this field, there is a need to provide systems and methods that can identify different types of mechanical emergencies and propose mechanisms for dealing with such emergencies. [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] Some of the objectives of this disclosure that at least one embodiment of this specification satisfies are listed herein below.

[0009] The purpose of this disclosure is to provide a system and method for facilitating SIM-less emergency attachment to mechanical devices.

[0010] The purpose of this disclosure is to provide a system and method covering an emergency attachment configuration for mechanical devices.

[0011] The purpose of this disclosure is to provide a system and method for identifying the type of mechanical emergency within a 3GPP® network / device (covering LTE, 5G, and 6G) ​​and for enhancing emergency messages with appropriate data such as location, machine ID, and plant ID.

[0012] The purpose of this disclosure is to provide a system and method for identifying an appropriate emergency server capable of handling that type of emergency and for redirecting emergency messages, along with enhanced data, to the appropriate server. [Means for solving the problem]

[0013] This section is provided to provide a simplified overview of some of the objects and aspects of the present disclosure, which are further described below in embodiments for carrying out the invention. This summary is not intended to identify the main features or scope of the claimed subject matter.

[0014] In one embodiment, the Disclosure provides a system for detecting and handling mechanical emergencies within a network. The system may include one or more first computing devices, one or more base stations, open radio access network (O-RAN) radios (RUs), and one or more processors operably coupled to a plurality of nodes. One or more first computing devices, one or more base stations, O-RAN RUs, and a plurality of nodes may be operably coupled to a network. One or more processors may be coupled to memory for storing instructions, and when executed by one or more processors, the instructions cause the system to receive a first set of data packets relating to a plurality of messages from one or more first computing devices in the network, a second set of data packets relating to a plurality of messages from one or more base stations in the network, a third set of data packets relating to a plurality of messages from an O-RAN RU in the network, and a fourth set of data packets relating to a plurality of messages from a plurality of nodes in the network. The system may be further configured to extract sets of attributes from first, second, third, and fourth sets of received data packets by using a machine learning (ML) engine associated with one or more processors, the sets of attributes relating to one or more first computing devices, one or more base stations, O-RAN-RUs, and one or more emergencies associated with multiple nodes. The system may be further configured by the ML engine to classify one or more emergencies into predetermined sets of classes based on a predetermined set of instructions.

[0015] In one embodiment, the ML engine may be configured to process and handle one or more emergencies based on a set of instructions associated with each predetermined class.

[0016] In one embodiment, the ML engine may be further configured to define a set of instructions for each of the predetermined classes in order to handle pre-mapped industry verticals.

[0017] In one embodiment, the ML engine may be further configured to respond to multiple real-time machine-type emergencies occurring within one or more IoT applications.

[0018] In one embodiment, the ML engine may be further configured to define, manage, and handle one or more new emergency types associated with one or more first computing devices, one or more base stations, O-RAN-RUs, and multiple nodes.

[0019] In one embodiment, the ML engine may be further configured to define and classify one or more new emergency types as new classes of emergencies.

[0020] In one embodiment, the ML engine may be further configured to automatically or manually handle each new emergency type based on a predetermined set of classifications of one or more new emergency types and instructions associated with those classifications.

[0021] In one embodiment, the initial bits of the first set of data packets, the second set of data packets, the third set of data packets, and the fourth set of data packets indicate the emergency type, while the remainder of the first set of data packets, the second set of data packets, the third set of data packets, and the fourth set of data packets indicate packet forwarding after the establishment of the machine assembly.

[0022] In one embodiment, the ML engine may be further configured to receive newly defined quality of service to handle one or more emergencies, which are shared within a service request cause, to configure and map newly identified QoS profiles, and / or interface to identify the emergency type and required signaling identifiers to inform the network of the correct type of QoS profile in order to properly handle, identify and apply the emergency.

[0023] In one embodiment, the ML engine may be further configured to collect carefully gathered data, store it in a cloud-based data lake, process it, and extract actionable insights.

[0024] In one embodiment, the Disclosure provides a user device (UE) for detecting and handling mechanical emergencies within a network. The UE may include one or more base stations, open radio access network (O-RAN) radios (RUs), and one or more processors operably coupled to a plurality of nodes. The one or more base stations, O-RAN RUs, and the plurality of nodes may be operably coupled to a network. The one or more processors may be coupled to memory for storing instructions, which, when executed by the one or more processors, cause the UE to receive a first set of data packets relating to a plurality of messages from one or more first computing devices in the network, a second set of data packets relating to a plurality of messages from one or more base stations in the network, a third set of data packets relating to a plurality of messages from O-RAN RUs in the network, and a fourth set of data packets relating to a plurality of messages from a plurality of nodes in the network. The UE may be further configured to extract sets of attributes from first, second, third, and fourth sets of received data packets by using a machine learning (ML) engine associated with one or more processors, the sets of attributes relating to one or more first computing devices, one or more base stations, O-RAN-RUs, and one or more emergencies associated with multiple nodes. The UE may be further configured by the ML engine to classify one or more emergencies into predetermined sets of classes based on a predetermined set of instructions.

[0025] In one embodiment, the Disclosure provides a method for detecting and handling a mechanical emergency in a network. The method may include the step of receiving a first set of data packets relating to a plurality of messages from one or more first computing devices in the network by one or more processors. The one or more processors may be operably coupled to one or more first computing devices, one or more base stations, open radio access network (O-RAN) radios (RUs), and a plurality of nodes. The one or more first computing devices, one or more base stations, O-RAN RUs, and a plurality of nodes may be operably coupled to a network. Furthermore, the one or more processors may be coupled to memory for storing instructions. The method may also include the steps of receiving a second set of data packets relating to a plurality of messages from one or more base stations in the network by one or more processors; receiving a third set of data packets relating to a plurality of messages from open radio access network (O-RAN) radios (RUs) in the network by one or more processors; and receiving a fourth set of data packets relating to a plurality of messages from a plurality of nodes in the network by one or more processors. The method may further include the step of extracting sets of attributes from first, second, third, and fourth sets of received data packets by using a machine learning (ML) engine associated with one or more processors, the sets of attributes relating to one or more emergencies associated with one or more first computing devices, one or more base stations, O-RAN-RUs, and multiple nodes. Furthermore, the method may further include the step of classifying one or more emergencies into predetermined sets of classes based on a predetermined set of instructions, using the ML engine.

[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the methods and systems of the present disclosure, with like reference numerals referring to the same parts throughout different drawings. The components of the drawings are not necessarily to scale, and instead, emphasis has been placed on clearly showing the principles of the present invention. Some of the drawings use block diagrams to show components and do not represent the internal circuits of each component. It will be understood by those skilled in the art that the invention of such drawings includes the invention of electrical components, electronic components, or circuits generally used to implement such components.

Brief Description of the Drawings

[0027] [Figure 1] A diagram showing an exemplary network architecture in which or by which the presented system of the present disclosure can be implemented according to an embodiment of the present disclosure. [[ID=**10]] [Figure 2A] A diagram showing an exemplary representation of the presented system for detecting and handling machine emergencies within a network according to an embodiment of the present disclosure. [Figure 2B] A diagram showing an exemplary representation of the presented method for detecting and handling machine emergencies within a network according to an embodiment of the present disclosure. [Figure 3A] A diagram showing an exemplary representation of an existing system network architecture and message flow according to an embodiment of the present disclosure. [Figure 3B] A diagram showing an exemplary representation of an existing system network architecture and message flow according to an embodiment of the present disclosure. [Figure 4] A diagram showing an exemplary representation of the presented network system architecture according to an embodiment of the present disclosure. [Figure 5A] A diagram showing an example of an exemplary call flow for sharing emergency types across a system according to an embodiment of the present disclosure. [Figure 5B] Note: There seems to be a formatting issue with line ID 10 where it is shown as '**10' in the original text. It's not clear if this is intentional or an error. I've translated it as it is presented. If it's an error, please correct the original text for a more accurate translation.This figure shows an exemplary call flow example for sharing emergency types across systems, according to one embodiment of the present disclosure. [Figure 5C] This figure shows an exemplary call flow example for sharing emergency types across systems, according to one embodiment of the present disclosure. [Figure 6] This figure shows an exemplary NB-IOT system according to one embodiment of the present disclosure. [Figure 7A] This figure shows an example of an alternative call flow for how a machine emergency is forwarded over a cellular network on the IMS, according to one embodiment of the present disclosure. [Figure 7B] This figure shows an example of an alternative call flow for how a machine emergency is forwarded over a cellular network on the IMS, according to one embodiment of the present disclosure. [Figure 8] This figure shows an exemplary computer system according to embodiments of the present disclosure, in which embodiments of the present invention may be utilized, or thereby. [Modes for carrying out the invention]

[0028] The above will become clearer by understanding a more detailed description of the present invention.

[0029] In the following description, various specific details are provided for illustrative purposes to give a full understanding of the embodiments of this disclosure. However, it will be apparent that embodiments of this disclosure can be carried out without these specific details. Some of the features described herein may be used independently of each other or in any combination with other features. No individual feature may necessarily address all of the problems described above, or only some of the problems described above. Some of the problems described above may not necessarily be fully addressed by any of the features described herein.

[0030] The subsequent description provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of this disclosure. Rather, the subsequent description of exemplary embodiments will provide a description effective for implementing the exemplary embodiments to those skilled in the art. It should be understood that various modifications may be made in the function and arrangement of the elements without departing from the spirit and scope of the invention described.

[0031] The present invention provides an efficient and reliable system for identifying machine emergency types within 3GPP® networks / devices such as LTE, 5G, and 6G, and for enhancing emergency messages with appropriate data such as location, machine ID, and plant ID. The system may further provide a mechanism for identifying appropriate emergency servers capable of handling that type of emergency and for redirecting emergency messages with the enhanced data to one or more servers.

[0032] Referring to Figure 1, Figure 1 shows an exemplary network architecture (100) (also referred to as network architecture (100)) according to one embodiment of the present disclosure, in which the machine emergency detection system (110) (or simply referred to as system (110)) of the present disclosure may be implemented. As shown in the figure, an exemplary network architecture (100) equipped with (110) may be communicatively coupled to a plurality of first computing devices (102-1, 102-2, 102-3...102-N) (synonymously referred to as base stations (104-1, 104-2,...104-N), individually referred to as base stations (104), and collectively referred to as base stations (104)) through a second computing device (104-1, 104-2, 104-3, ...104-N) (synonymously referred to as user equipment (102-1, 102-2, 102-3...102-N), individually referred to as user equipment (UE) (102), and collectively referred to as UE (102)) and may further be operationally coupled to base station (104) via a third computing device (synonymously referred to as open radio access network radio (108)). The system (110) may be further connected in a communicative manner to one or more fourth computing devices (114) (synonymously referred to as nodes (114)).

[0033] In one embodiment, the system (110) may be equipped with a machine learning engine (ML) (214) which can cause the system to receive a set of messages relating to a relevant emergency by receiving a first set of data packets relating to multiple messages from one or more first computing devices (102) in the network (106), a second set of data packets relating to multiple messages from one or more base stations (104) in the network (106), a third set of data packets relating to multiple messages from an O-RAN RU (108) in the network (106), and a fourth set of data packets relating to multiple messages from multiple nodes (114) in the network (106). The system (110) may use the ML engine (214) to extract sets of attributes from first, second, third, and fourth sets of received data packets, the sets of attributes relating to one or more emergencies related to one or more first computing devices (102), one or more base stations (104), O-RAN-RU (108), and multiple nodes (114), and the system (110) may classify one or more emergencies into predetermined sets of classes based on a predetermined set of instructions using the ML engine (214). For example, one or more emergencies could be any mechanical emergencies related to defects or other malfunctions occurring in a machine.

[0034] In one embodiment, the ML engine (214) is configured to process and handle one or more emergencies based on a set of instructions associated with each predetermined class.

[0035] For example, mechanical emergencies can be defined to cover specific industries such as Industry 4.0, railways, and power grids. The new classification of emergency types addresses the various real-time mechanical emergencies that occur in IoT applications.

[0036] In an exemplary embodiment, the system (110) may define new emergency types and be configured to handle such emergencies within a cellular network. For example, a new emergency attachment type within the network may allow certain types of machines to have different clauses, even when a SIM card is unavailable.

[0037] In another exemplary embodiment, a new type of emergency may be defined and classified as a new class of mechanical emergency. For example, an emergency in one or more sensors and actuators may be classified as a Class A emergency, an emergency in a machine may be classified as a Class B emergency, an emergency in a production line may be classified as a Class C emergency, an emergency in a plant may be classified as a Class D emergency, and so on. The handling of each of the above emergency types varies based on the type of emergency. Class A and B emergencies may be handled "automatically" through appropriate responses. For Class C and D emergencies, human intervention is visualized in a different manner than the procedures for handling Class C and D type mechanical emergencies.

[0038] In one embodiment, a Class A machine emergency at the machine level may be when data from sensors embedded within individual machines indicating that something may be malfunctioning in the machine is claimed as a machine-level emergency. In one embodiment, sensors in a lathe machine for detecting wear and breakage, correcting thermal deformation, and precisely positioning a cutting edge provide data indicating that the machine needs to be attended to for the aforementioned issues. Similarly, in a second embodiment, sensors for detecting the efficiency of individual robots with respect to operating speed showing reduced accuracy or negative values ​​may be classified as a Class A machine emergency type, as an issue with the machine requiring inspection. One or more sensors having a processor and software for integrating data from other sensors in the machine integrate data from multiple sensors in the machine to formulate an emergency message.

[0039] In one embodiment, a Class B machine emergency in a production line may be a plurality of machines that typically define the production line, and the production line may be, for example, an automotive painting line where a plurality of individual machines are assigned jobs to complete defined jobs. We define emergency types to indicate problems relating to a production line, and in the production line, a plurality of machines and sensors within the plurality of machines are configured, and data from the sensors embedded within the production line that support the function are integrated by one of the sensors, which acts as a master sensor and helps to integrate the data from the sensors within the production line to detect whether an emergency exists. An emergency may be defined in the master sensor as a correlation table that defines thresholds for sensor data from individual sensors to define a production line emergency.

[0040] In one embodiment of the present invention, a Class C mechanical emergency in a factory work area could be multiple production lines, which typically define the factory work area and which are responsible for, for example, the complete production / assembly of an automobile engine. We define emergency types to indicate problems relating to the factory work area, and in the work area, multiple machines and sensors within the multiple machines are configured, and data from sensors embedded within the multiple production lines that support the function are integrated by one of the sensors, which acts as a master sensor and helps to integrate the data from the sensors in the multiple production lines to detect whether an emergency exists. The emergency is defined in the master sensor as a correlation table that defines thresholds for sensor data from individual sensors to define a production line emergency.

[0041] In one embodiment of the present invention, a Class D mechanical emergency at the factory level could be in a multi-factory work area that typically defines a factory, which is responsible for, for example, the complete production of automobile manufacturing. We define an emergency type to indicate a problem relating to the factory itself, and in the factory itself, a multi-factory consists of a multi-factory and sensors within the multi-factory, and data from sensors embedded in the multi-factory production lines that support the function is integrated by one of the sensors, which acts as a master sensor and helps to integrate the data from the sensors in the multi-factory production lines to detect whether an emergency exists. The emergency is defined in the master sensor as a correlation table that defines thresholds for sensor data from individual sensors to define a production line emergency. The sensors may include a multi-factory,

[0042] In one embodiment, emergency access may be requested by a first computing device (102) (also called user equipment 102 or UE102) via a set of instructions that may be part of a service request IE to send part of the attach / power distribution unit (PDU) establishment / correction procedure.

[0043] In one exemplary embodiment, a predetermined structure may be sent during the PDU establishment procedure.

[0044] In another embodiment, initial bits may be sent on cause establishment to indicate the emergency type and the remainder of the data portion of packet forwarding after PDU establishment.

[0045] In one exemplary embodiment, the system (110) may configure a mechanism for handling emergencies via newly defined quality of service (QoS) profiles, which are enforced by network-based emergency types shared within the service request causes. In another embodiment, an application may use a modem interface (AT command) to inform the modem and network of the emergency type to configure and map a newly identified QoS profile and / or handle the emergency appropriately. The AT command may interface with the emergency type and the necessary signaling identifiers to inform the network of the correct type of QoS profile to identify and apply.

[0046] In one embodiment, the system (110) may be, but is not limited to, a system-on-a-chip (SoC) system. In another embodiment, the on-site data capture, storage, matching, processing, decision-making, and operational logic may be coded using a microservices architecture (MSA), but is not limited to that. Multiple microservices may be containerized and event-based to support portability.

[0047] In one embodiment, the network architecture (100) may be modular and flexible to adapt to any kind of change within the system (110).

[0048] In one embodiment, the system (110) may be remotely monitored, and the data, applications, and physical security of the system (110) may be fully ensured. In one embodiment, data may be carefully collected, stored in a cloud-based data lake, processed, and used to extract actionable insights. Thus, a form of predictive maintenance may be achieved.

[0049] In one exemplary embodiment, the communication network (106) may include, but not limited to, one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, send, switch, process, or a combination thereof, one or more messages, packets, signals, waves, voltage or current levels, or several combinations thereof. The network may include, but not limited to, one or more wireless networks, wired networks, the Internet, intranets, public networks, private networks, packet-switched networks, circuit-switched networks, ad-hoc networks, infrastructure networks, public switched telephone networks (PSTNs), cable networks, cellular networks, satellite networks, optical fiber networks, or several combinations thereof.

[0050] In another exemplary embodiment, a centralized server (112) may be included in the architecture (100). The centralized server (112) may include, but is not limited to, standalone servers, server blades, server racks, banks of servers, server farms, hardware supporting part of a cloud service or system, home servers, hardware running virtual servers, one or more processors that run code to function as servers, one or more machines that perform server-side functions described herein, at least some of the above, or one or more of several combinations thereof.

[0051] In one embodiment, one or more first computing devices (102) and one or more mobile devices (not shown in Figure 1) may communicate with the system (110) via a set of executable instructions present on any operating system, including, but not limited to, Android®, iOS®, Kai OS®, etc. In one embodiment, one or more first computing devices (102) and one or more mobile devices may include, but not limited to, any electrical, electronic, electromechanical equipment such as a mobile phone, smartphone, virtual reality (VR) device, augmented reality (AR) device, laptop, general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, or one or more combinations of the above devices, and the computing devices may include, but not limited to, one or more embedded or external equipment including visual aids such as cameras, auditory aids, microphones, keyboards, input devices that receive user input, such as touchpads, touch-enabled screens, and electronic pens, receiving devices for receiving any auditory or visual signals within any range of frequencies, and transmitting devices that can transmit any auditory or visual signals within any range of frequencies. Please understand that one or more first computing devices (102) and one or more mobile devices are not limited to the devices described above, and various other devices may be used. A smart computing device may be one of the suitable systems for storing data and other private / confidential information.

[0052] Referring to Figure 1, Figure 2 shows an exemplary representation of a data receiver module (110) for facilitating real-time event data feeding according to one embodiment of the present disclosure. In one embodiment, the system (110) may include one or more processors (202). One or more processors (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuits, and / or any device that processes data based on arithmetic instructions. Among other capabilities, one or more processors (202) may be configured to fetch and execute computer-readable instructions stored in the memory (204) of the data receiver module (110). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-temporary computer-readable storage medium that can be fetched and executed to generate or share data packets over a network service. The memory (204) may include any non-temporary storage device, including, for example, volatile memory such as RAM or non-volatile memory such as EPROM, flash memory.

[0053] In one embodiment, the system (110) may include an interface 206. Interface 206 may include interfaces to various interfaces, such as data input and output devices called I / O devices, storage devices, etc. Interface 206 may facilitate communication of the system (110). Interface 206 may also provide a communication path to one or more components of the data receiver module (110). Examples of such components include, but are not limited to, a processing engine 208 and a database 210.

[0054] The processing engine (208) may be implemented as a combination of hardware and programming (e.g., programmable instructions) to implement one or more functions of the processing engine (208). In the examples described herein, such a combination of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine (208) may be processor-executable instructions stored on a non-temporary machine-readable storage medium, and the hardware for the processing engine (208) may include processing resources (e.g., one or more processors) to execute such instructions. In this example, the machine-readable storage medium may store instructions that implement the processing engine (208) when executed by the processing resources. In such an example, the system (110) may include a machine-readable storage medium that stores instructions and processing resources for executing the instructions, or the machine-readable storage medium may be isolated but accessible to the system (110) and the processing resources. In other examples, the processing engine (208) may be implemented by electronic circuits.

[0055] The processing engine (208) may include one or more engines selected from among the data acquisition engine (212), the machine learning (ML) engine (214), and other engines (216).

[0056] Figure 2B shows an exemplary representation of a presented method (250) for detecting and handling a mechanical emergency within a network (106) according to one embodiment of the present disclosure. As shown in the figure, the method (250) may include the step in 252 of receiving a first set of data packets relating to a plurality of messages from one or more first computing devices (102) within the network (106) by one or more processors (202). For example, the plurality of messages may include information about one or more first computing devices, such as user equipment (UE), including any handheld device, telephone, laptop, pager, mobile radio, smartphone, tablet, etc.

[0057] The method may also include the step in 254 of receiving a second set of data packets relating to multiple messages from one or more first base stations (104) in the network by one or more processors (202). For example, multiple messages may contain information about one or more base stations, such as macrocells, microcells, and picocells.

[0058] Furthermore, in 256, the method may include the step of receiving a third set of data packets relating to a plurality of messages from an Open Radio Access Network (O-RAN) radio (RU) (108) in the network by one or more processors (202). For example, the plurality of messages may contain information about the open network.

[0059] The method may include, in 258, a fourth set of data packets relating to multiple messages from multiple nodes (114) in the network, received by one or more processors (202). For example, the multiple messages may include information about multiple nodes, such as gNB nodes, data communication nodes, physical network nodes including data communication equipment or devices between data terminal equipment (DTE) and data transmission circuits, the Internet network, LANs and wide area networks, telecommunications networks, and cable systems.

[0060] The method may further include the step in 260 of extracting sets of attributes from first, second, third, and fourth sets of received data packets by a machine learning (ML) engine (214) associated with one or more processors (202), the sets of attributes relating to one or more emergencies associated with one or more first computing devices (102), one or more base stations (104), O-RAN-RU (108), and multiple nodes (114). For example, the system (110) may be configured to sense any kind of anomaly or emergency among one or more first computing devices (102), one or more base stations (104), O-RAN-RU (108), and multiple nodes (114).

[0061] Furthermore, the method may further include the step of classifying one or more emergencies into a predetermined set of classes based on a predetermined set of instructions, by an ML engine (214). For example, the system (110) may identify a machine emergency type within a 3GPP® network / device (covering LTE, 5G, and 6G), enhance the emergency message with appropriate data such as location, machine ID, and plant ID, identify an appropriate emergency server capable of handling that type of emergency, and redirect the emergency message to the appropriate server with the enhanced data.

[0062] Figures 3A and 3B illustrate exemplary representations of existing network system architectures and message flows according to one embodiment of the present disclosure. As shown in the figures, in one embodiment, Figure 3A shows that existing emergency definitions and handling mechanisms in 3GPP® address human emergencies but not mechanical emergencies. The introduction of IoT and, in particular, IIoT (Industrial IoT) exposes numerous types of emergencies that need to be handled differently from human emergencies. Existing networks may include Long-Term Evolution 306 (LTE306), Mobility Management Entity (302) or MME (302), Home Subscriber Server (304) or HSS (304), Serving Gateway (GW) (308), PDN Gateway (310), Service Call Session Control Function 312 (S-CSCF312), Proxy CSCF314 (P-CSCF314), Media Gateway (MGW) (318), Media Gateway Control Function (MGCF) (320), Public Switched Telephone Network (PSTN) (322), Location Search Function (LRF) (324), Emergency (E)-CSCF (316), and Public Safety Answering Point (PSAP) (326). For example, a UE using LTE (306) may request an emergency attach to preferentially connect to an Emergency Access Point Name (APN). The signal is sent by S-CSCF(312) for IP Multimedia System (IMS) emergency registration. P-CSCF(314) detects the emergency request-uniform resource identifier (R-URI) and forwards the guidance to E-CSCF(316). E-CSCF(316) queries the UE location and forwards it to the appropriate PSAP(326). The user plane may be established for voice via MGW(318) and MGCF(320). PSAP(326) may also look up the UE location via LRF(324) to expedite emergency services to the correct location address.

[0063] Figure 3B shows an exemplary representation of an existing emergency call sequence between UE(352) and PSAP(326). For example, UE(352) detects and initiates an emergency call, assigning the location and SIP INVITE(Emergency) to P-CSCF(314). P-CSCF(314) retrieves the emergency number or URN. P-CSCF(314) again sends the SIP INVITE(Emergency) to the SIP Option Location / Routing Request, which is then approved by E-CSCF(316) and LRF(324). E-CSCF(316) updates the R-URI with an Emergency Service Query Key (ESQK) and then sends the SIP INVITE to PSAP(326). PSAP(326) requests the location from LRF(324), and LRF(324) responds with the location details.

[0064] For example, notification of an emergency service to the network is done by proceeding with normal emergency type information - Establishment Cause as part of a service request. Emergency access may be requested by a UE (User Equipment) or mobile device via Establishment_Cause IE, which is part of a service request IE that includes part of the attach / PDU establishment / correction procedure. For example, emergency access may be requested on 5G NR: Establishment_Cause ::= ENUMERATED { emergency, highPriorityAccess, mt-Access, mo-Signalling, Data, mo-VoiceCall, mo-VideoCall, mo-SMS, mps-priorityAccess,mcsPriorityAccess, spare6, spare5, spare4, spare3, spare2, spare1} It can be given by.

[0065] As shown, there is no way to transmit any other emergency types that may arise from industrial IoT or machine types to the network within current cellular systems. Therefore, in current systems, the network always assigns a default QoS profile to machine emergency data and treats them as normal internet data, always on a best-effort basis.

[0066] To address such machine emergency requirements, we propose a new established cause IE for handling such machine emergency scenarios.

[0067] Figure 4 shows an exemplary representation of the presented network system architecture according to one embodiment of the present disclosure. As shown in the diagram, the presented network may include a Long-Term Evolution 306 (LTE306), a Mobility Management Entity (302) or MME (302), a Home Subscriber Server (304) or HSS (304), a Serving Gateway (GW) (308), a PDN Gateway (310), a Service Call Session Control Function 312 (S-CSCF312), a Proxy CSCF314 (P-CSCF314), a Media Gateway (MGW) (318), a Media Gateway Control Function (MGCF) (320), a Public Switched Telephone Network (PSTN) (322), a Location Search Function (LRF) (324), an Emergency (E)-CSCF (316), and a Machine Emergency Response Server (MERS) (402) which may be communicably coupled to a Machine Emergency Response (MERS) handler (402) that may reside within an IMS entity.

[0068] In some embodiments of communicating machine emergencies, one possible method is to use IMS to share recorded voice messages / SMS to a predetermined number that may belong to primary / secondary responders. To communicate the same application, MERS(402) may use the AT interface to send AT commands to a modem to share a predetermined voice message to a predetermined number. This predetermined message or predetermined number may be part of an image in a device in memory storage (ROM) or may be configured by an application / emergency server via http / TCP / MQTT messages.

[0069] In an exemplary embodiment, at least three mechanisms for communicating the type of mechanical emergency may be provided by MERS(402). In an exemplary embodiment, the preliminary values ​​in the existing EstablishmentCause IE are EstablishmentCause ::= ENUMERATED { emergency, highPriorityAccess, mt-Access, mo-Signalling, Data, mo-VoiceCall, mo-VideoCall, mo-SMS, mps-priorityAccess,mcsPriorityAccess, ClassA, ClassB, ClassC, ClassD, spare2, spare1} It can be used as given by.

[0070] In another exemplary embodiment, a new information element identifier (IEI) may be defined. For example, EstablishmentCause ::= ENUMERATED { ClassA, ClassB, ClassC, ClassD, spare3, spare2, spare1}

[0071] In another exemplary embodiment, a fixed bitstream may be used to identify the type of machine emergency. For example, EstablishmentCause-ME ::= {11100000} In the above equation, - 8-bit representation :: - 8th bit - Class A - 7th bit - Class B - 6th bit - Class C - 5th bit - Class D - 4th bit - 1st bit - Spare.

[0072] In one exemplary embodiment, schemas for multiple emergencies may be used. Apart from the above IEI defined for communicating machine emergency types, the following structure is: EmergencyType::={ EmergencyClass::={Class A, Class B, Class C, Class D,..}, LocationInfo:={Lat, Long}, <Cell ID> , <Type::={Alert-0, Warning-1, Message-2}, <identifier>::={IMSI / TMSI / P-TMSI>}, <Data::={…..}} It can be given by.

[0073] The defined structure can be shared across the network in at least two possible ways, such as by initially sending the defined structure itself during the PDU establishment procedure, and then sending initial bits on the establishment cause to indicate the emergency type and the rest of the data portion of the packet forwarding after the PDU establishment.

[0074] In one exemplary embodiment, if the computing device does not support GPS / GNSS, the above-described structural device will share GNSS information regarding latitude / longitude. If the computing device does not have GPS / GNSS capability, the network may trigger a measurement report, fetch the device's location information and report it to the LRF / SMLC, and the emergency server via the LRF / SMLC may request location information (LoC info) via a predetermined API within the 3GPP® / ETSI TS, but is not limited to this.

[0075] Figure 5A shows an example of a call flow that illustrates how emergency types are shared across the system, thereby enabling the network to assign the correct QoS profile and thus resource management. As illustrated, SIB 1 broadcasts its ability to support machine type emergencies for several classes of machine type. UE502 (MTC device) reads the emergency support and the class (machine type) of UE502 that can connect to the IoT network. The machine type UE sends an RRCSetupRequest along with the established cause as a machine type emergency, and other attributes previously shown (the class of the emergency type, i.e., class A or class B or class C or class D).

[0076] In one exemplary embodiment, to indicate a machine-type emergency within a UE / modem, the system may use the application layer within the modem interface, i.e., the AT command interface, to communicate the emergency type, thereby the application layer may be used by the modem to communicate the message / IE to the network. The new AT commands used by the application residing in the final device to communicate the emergency type to the modem and to the network are highlighted in TABLE 1 below.

[0077] [Table 1]

[0078] For example, an AT command for sharing the machine emergency type along with its attributes to a modem sent by an application may use, but is not limited to, a defined value such as the Modem Obtaining application type information.

[0079] Another example is that AT commands can be used by applications to inform the modem of the type of machine emergency, and the defined values ​​are: <cmd>: "MEMERGENCYTYPE" - param1.. N indicates that the emergency type setting for IoT / IIoT devices is a configuration parameter defined by the network or application server. Param 1…N indicates the following types or attributes: a. Class A b. Class B c. Class C d. Class D e. Etc LocInfo CellID Type Identifier = {IMSI,…} <data> Etc <responsetype>: OK / ERROR It can be given by.

[0080] Figure 5B shows another example of a call flow that shares emergency types across the system, thereby enabling the network to assign the correct QoS profile and resource management.

[0081] When the modem (510) receives an emergency type from the application (508) via the AT command described above on the AT interface, the modem advances the emergency type on the previously defined IE, using the same part of the service request IE, which then sends part of the PDU establishment / correction procedure, including the attach (including the emergency attach). Once the network becomes aware of the emergency cause, the PCF / PCRF assigns the correct QCI / 5QI values ​​as defined in TABLE 2 below, or as defined in 3GPP TS23.501.

[0082] [Table 2]

[0083] In another embodiment, the system - Sending IMSI / TMSI / P-TMSI as part of the data sent by the device using the newly defined emergency type schema / structure (as described above), - Network triggering for the IDENTITY REQ procedure (IMSI as identifier) ​​as a mandatory procedure during registration / emergency registration. This could enable SIM-less operation.

[0084] Figure 5C shows another example of a call flow to illustrate the attachment procedure, including the IDENTITY procedure portion of a machine emergency scenario.

[0085] When the modem (510) receives an emergency type from the application (508) via the AT command described above on the AT interface, the modem proceeds with the attach (including the emergency attach procedure). When the network (512) becomes aware of the attach procedure, in one embodiment, the AMF (506) sends an IDENTITY request to the modem and receives a response from the modem (510). If the timer expires or the maximum number of retries is reached, the AMF (506) sends an attach failure message. In an alternative embodiment, the AMF sends an IDENTITY request to the modem (510) and receives a response from the modem (510). In one embodiment, the AMF sends an IDENTITY request to the modem and receives a response from the modem. Within a predetermined time, the modem (510) provides an attach acknowledgment and the attach procedure is performed on the application (508).

[0086] Figure 6 shows an exemplary NB-IOT system according to one embodiment of the present disclosure. As shown in the figure, in the case of an NB-IOT system, the emergency handler may reside in the MME as an emergency handler proxy, in the MTC-IWF (Interaction Function) (610), or in the Application Server (AS) (626), as shown in Figure 6. If the machine emergency handler (402) entity resides in the MME itself, the machine emergency handler (402) entity may, but is not limited to, handle Class A and Class B type emergencies by a fixed response closing a sensor type or device such as a machine associated with the MTC UE / SIM (616).

[0087] If a machine emergency handler (402) entity exists within IMS-IWF (602), that handler can handle machine emergencies of types A, B, C, and D. The T5b interface, from MME to IMS-IWF, is enhanced in this case to support the machine emergency types.

[0088] In one embodiment, Class C and Class D type emergencies may be handled by an Application Server (AS) (626), and the IMS-IWF needs to be aware that the call type is a mechanical emergency call type. In this case, the T5b interface between the MME and the IMS-IWF is enhanced with an emergency type indicator as shown in the “Emergency Schema”. The above examples covering IoT systems are merely illustrative, and it should be understood that the presented invention can be extended to other cellular architectures, such as 5G, 6G, or any variant of private network of the cellular technologies described above.

[0089] In another embodiment, the emergency handler (402) entity may reside outside the 5G network within the application server, in which case a new emergency interface may be defined via a Network Exposure Function (NEF) to reach the emergency handler and establish an appropriate data path between the machine and the server.

[0090] Figures 7A and 7B illustrate an example of an alternative call flow for how a machine emergency is forwarded over a cellular network on the IMS, according to one embodiment of the present disclosure. As shown, in one embodiment, when a machine-type emergency is of class C or class D type, the granularity of the emergency is much larger than just machine-type, in which case the emergency server initiates a data channel with an emergency device and simultaneously initiates a broadcast type with a number of other registered emergency responders.

[0091] In another exemplary embodiment, as shown in Figure 7B, when the type of a machine-type emergency is of class C or class D, the emergency handler can use a Cell Broadcast Center Function (CBCF) / Public Alarm System (PWS) to initiate broadcasting of the emergency data type. The emergency handler can also use any other broadcast / multicast mechanism to initiate broadcasting of the relevant data. When a class C or class D emergency is indicated, data is sought by the emergency server to discover the nature of the emergency, not only from that device BUT which is the host of devices across the plant / factory. This is also assisted by human responders which are part of a broadcast group.

[0092] [Table 3]

[0093] Figure 8 shows an exemplary computer system according to an embodiment of the present disclosure, in which or by which an embodiment of the present disclosure may be utilized. As shown in Figure 8, the computer system 800 may include an external storage device 810, a bus 820, main memory 830, read-only memory 840, mass storage device 850, a communication port 860, and a processor 870. It will be understood by those skilled in the art that the computer system may include two or more processors and communication ports. Examples of the processor 870 include, but are not limited to, an Intel® Itenium® or Itenium 2 processor, or an AMD® Opteron® or Athlon MP® processor, a Motorola® line processor, a FortiSOC® system-on-chip processor, or other further processors. The processor 870 may include various modules related to embodiments of the present invention. The communication port 860 may be an RS-232 port for use with a modem-based dial-up connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or any other existing or future port. The communication port 860 may be selected depending on the network, such as a local area network (LAN), a wide area network (WAN), or any network to which the computer system connects. The memory 830 may be random access memory (RAM) or any other dynamic storage device commonly known in the art. The read-only memory 840 may be any static storage device, for example, a programmable read-only memory (PROM) chip for storing static information, such as boot or BIOS instructions for the processor 870, but not limited to these. The mass storage 850 may be any current or future mass storage solution, which may be used to store information and / or instructions.Exemplary high-capacity storage solutions include, but are not limited to, parallel advanced technology attachment (PATA) or serial advanced technology attachment (SATA) hard disk drives or solid-state drives (internal or external, with, for example, Universal Serial Bus (USB) and / or FireWire interfaces) available from, for example, Seagate (e.g., Seagate Barracuda 782 family) or Hitachi (e.g., Hitachi Deskstar 8K800), one or more optical disks, for example, arrays of disks (e.g., SATA arrays) and redundant array of independent disks (RAID) storage available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc., and Enhance Technology, Inc.

[0094] Bus 820 connects the processor 870 to other memory, storage, and communication blocks in a communicative manner. Bus 820 may be a Peripheral Component Interconnect (PCI) / PCI Expansion (PCI-X) bus, a Small Computer System Interface (SCSI), or a USB bus for connecting expansion cards, drives, and other subsystems, as well as other buses such as a front-side bus (FSB) that connects the processor 870 to a software system.

[0095] Optionally, operator and management interfaces, such as displays, keyboards, and cursor control devices, may also be coupled to bus 820 to support direct interaction between the operator and the computer system. Other operator and management interfaces may be provided via network connections connected through communication port 860. The external storage device 810 may be any type of external hard drive, floppy drive, IOMEGA® Zip drive, compact disc read-only memory (CD-ROM), rewritable compact disc (CD-RW), or digital video disc read-only memory (DVD-ROM). The components described above are merely illustrative of various possibilities. The exemplary computer system described above does not limit the scope of this disclosure in any way.

[0096] Accordingly, this disclosure provides unique and efficient solutions for identifying different types of mechanical emergencies and presents mechanisms for dealing with such emergencies, including architectural systems that may be necessary to handle such emergencies efficiently in a timely manner.

[0097] While considerable emphasis has been placed in the preferred embodiments herein, it will be understood that many embodiments can be created without departing from the principles of the invention, and that many modifications can be made within the preferred embodiments. These and other modifications within the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, and it should be clearly understood that the foregoing descriptions are merely implemented as examples of the invention and do not limit it.

[0098] Benefits of this disclosure Some of the objectives of this disclosure that at least one embodiment of this specification satisfies are listed herein below.

[0099] This disclosure provides a system and method for facilitating SIM-less emergency attachment to mechanical devices.

[0100] This disclosure provides a system and method covering an embodiment of emergency attachment to a mechanical type device.

[0101] This disclosure provides a system and method for identifying types of mechanical emergencies within 3GPP® networks / devices (covering LTE, 5G, and 6G) ​​and for enhancing emergency messages with appropriate data such as location, machine ID, and plant ID.

[0102] This disclosure provides a system and method for identifying an appropriate emergency server capable of handling that type of emergency and for redirecting emergency messages, along with enhanced data, to the appropriate server. [Explanation of Symbols]

[0103] 100 Network Architectures 102 First computing device, user equipment (UE) 102-1 First Computing Device, UE 102-2 First Computing Device, UE 102-3 First computing device, UE 102-N First Computing Device, UE 104 Base station 104-1 Second computing device, base station 104-2 Second computing device, base station 104-3 Second computing device, base station 104-N Second computing device, base station 106 Network 108 Open Radio Access Network (O-RAN) Radios (RUs) 110 System, Data Receiver Module 112 Centralized Server 114. The fourth computing device, node Figure 200 202 processors 204 memory 206 Interfaces 208 Processing Engines 210 Databases 212 Data Acquisition Engines 214 Machine Learning Engines (ML) 216 Other engines Figure 300 302 Mobility Management Entity, MME 304 Home Subscriber Server, HSS 306 Long-Term Evolution, LTE 308 Serving Gateway, GW 310 PDN Gateway 312 Service Call Session Control Function, S-CSCF 314 Proxy CSCF, P-CSCF 316 Emergency (E)-CSCF 318 Media Gateway (MGW) 320 Media Gateway Control Function (MGCF) 322 Public Switched Telephone Network (PSTN) 324 Location Search Function (LRF) 326 Public Safety Response Points (PSAP) 352 UE 354 IP CAN 356 P-CSCF 402 Machine Emergency Response (MERS) Handler, Machine Emergency Response Server (MERS) 502 UE 504 NG RAN 506 AMF 508 Applications 510 Modem 512 Network 514 SMF 516 PCF 518 DN 602 IMS-IWF 604 SMC-SC GMSC / IW MSC 606 SME 608 MTC AAA 610 MTC-IWF (interaction function) 612 CDF / CGF 614 MSC 616 MTC UE / SIM 620 SGSN 622 GGSN / P-GW 624 SCS 626 Application Server (AS) 626-1 AS 628-1 AS 700A Figure Figure 700B 800 Computer Systems 810 External storage devices 820 Bus 830 Main Memory 840 Read-only memory 850 Mass Storage Devices 860 communication ports 870 processor< / responsetype> < / data> < / cmd> < / identifier>

Claims

1. A system (110) for detecting and handling mechanical emergencies within a network, The system includes one or more first computing devices (102), one or more base stations (104), an open radio access network (O-RAN) radio (RU) (108), and one or more processors (202) operably coupled to a plurality of nodes (114), wherein the one or more first computing devices (102), the one or more base stations (104), the O-RAN RU (108), and the plurality of nodes (114) are operably coupled to the network, and the one or more processors are coupled to a memory (204), the memory (204) stores instructions, and when the instructions are executed by the one or more processors (202), they are sent to the system (110), Receiving a first set of data packets relating to multiple messages from one or more first computing devices (102) in the network (106), Receiving a second set of data packets relating to multiple messages from one or more base stations (104) within the network (106), Receiving a third set of data packets related to multiple messages from the O-RAN RU (108) within the network (106), Receiving a fourth set of data packets relating to multiple messages from multiple nodes (114) within the network (106), The machine learning (ML) engine (214) is used to extract sets of attributes from the first, second, third, and fourth sets of received data packets, wherein the sets of attributes relate to one or more emergencies related to the one or more first computing devices (102), the one or more base stations (104), the O-RAN-RU (108), and the multiple nodes (114), and the ML engine relates to the one or more processors (202), A system (110) which causes the ML engine (214) to classify one or more emergencies into a predetermined set of classes based on a predetermined set of instructions.

2. The system (110) according to claim 1, wherein the ML engine (214) is configured to process and handle one or more emergencies based on a set of instructions associated with each predetermined class.

3. The system (110) according to claim 2, wherein the ML engine (214) is further configured to define the set of instructions for each of the predetermined classes in order to handle pre-mapped industries.

4. The system (110) according to claim 2, wherein the ML engine (214) is further configured to respond to multiple real-time machine-type emergencies occurring in one or more IoT applications.

5. The aforementioned ML engine (214) is, To define one or more new emergency types related to the one or more first computing devices (102), the one or more base stations (104), the O-RAN RU (108), and the multiple nodes (114), The system (110) according to claim 2, further configured to manage and handle the one or more new emergency types within the network.

6. The aforementioned ML engine (214) is, The system (110) according to claim 2, further configured to define and classify the one or more new emergency types as new classes of emergencies.

7. The aforementioned ML engine (214) is, The system (110) according to claim 6, further configured to handle each of the new emergency types automatically or manually based on a predetermined set of classifications of the one or more new emergency types and instructions associated with those classifications.

8. The system (110) according to claim 1, wherein the initial bits of the first set of data packets, the second set of data packets, the third set of data packets, and the fourth set of data packets indicate an emergency type, and the remainder of the first set of data packets, the second set of data packets, the third set of data packets, and the fourth set of data packets indicate packet forwarding after the establishment of a machine assembly.

9. The aforementioned ML engine (214) is, Receiving a newly defined quality of service for handling one or more emergencies, wherein the one or more emergencies are shared among the service request causes, Configure and map the newly identified QoS profile and / or appropriately handle the emergency, The system (110) according to claim 6, further configured to interface with and identify the emergency type and the required signaling identifiers in order to inform the network of the correct type of QoS profile for identification and application.

10. The aforementioned ML engine (214) is, The system (110) according to claim 6, further configured to collect carefully gathered data, store it in a cloud-based data lake, process it, and extract actionable insights.

11. User equipment (UE) for detecting and handling mechanical emergencies within a network, wherein the UE is The system includes one or more base stations (104), an open radio access network (O-RAN) radio (RU) (108), and one or more processors (202) operably coupled to a plurality of nodes (114), wherein the one or more base stations (104), the O-RAN RU (108), and the plurality of nodes (114) are operably coupled to the network, and the one or more processors are coupled to a memory (204), the memory (204) stores instructions, and when the instructions are executed by the one or more processors (202), the system (110) receives Receiving a first set of data packets relating to multiple messages from the UE within the network (106), Receiving a second set of data packets relating to multiple messages from one or more base stations (104) within the network (106), Receiving a third set of data packets related to multiple messages from the O-RAN RU (108) within the network (106), Receiving a fourth set of data packets relating to multiple messages from multiple nodes (114) within the network (106), The machine learning (ML) engine (214) is used to extract sets of attributes from the first, second, third, and fourth sets of received data packets, wherein the sets of attributes relate to one or more emergencies related to the UE, one or more base stations (104), the O-RAN-RU (108), and the multiple nodes (114), and the ML engine relates to one or more processors (202), A user device (UE) that causes the ML engine (214) to classify one or more emergencies into a predetermined set of classes based on a predetermined set of instructions.

12. A method (250) for detecting and handling a mechanical emergency within a network (106), A step of receiving a first set of data packets relating to a plurality of messages from one or more first computing devices (102) in the network (106) with one or more processors (202), wherein the one or more processors (202) are operably coupled to the one or more first computing devices (102), one or more base stations (104), open radio access network (O-RAN) radios (RUs) (108), and a plurality of nodes (114), and the one or more first computing devices (102), the one or more base stations (104), the O-RAN RUs (108), and the plurality of nodes (114) are operably coupled to the network, and the one or more processors are coupled to a memory (204), and the memory (204) stores instructions to be executed by the one or more processors, The steps include receiving a second set of data packets related to multiple messages from one or more base stations (104) within the network (106) using one or more processors (202), The steps include receiving a third set of data packets related to multiple messages from the O-RAN RU (108) in the network (106) by one or more processors (202), The steps include receiving a fourth set of data packets related to multiple messages from multiple nodes (114) within the network (106) by one or more processors (202), A step of extracting sets of attributes from the first, second, third, and fourth sets of received data packets using the ML engine (214), wherein the sets of attributes relate to one or more emergencies relating to the one or more first computing devices (102), the one or more base stations (104), the O-RAN-RU (108), and the multiple nodes (114), A method (250) comprising the step of classifying one or more of the aforementioned emergencies into a predetermined set of classes based on a predetermined set of instructions by the ML engine (214).

13. The method according to claim 12, further comprising the step of processing and handling one or more emergencies by the ML engine (214) based on a set of instructions associated with each predetermined class.

14. The method according to claim 13, further comprising the step of defining the set of instructions for each of the predetermined classes by the ML engine (214) in order to handle pre-mapped industries.

15. The method according to claim 13, further comprising the step of having the ML engine (214) respond to multiple real-time machine-type emergencies occurring in one or more IoT applications.

16. The ML engine (214) defines one or more new emergency types related to the one or more first computing devices (102), the one or more base stations (104), the O-RAN RU (108), and the multiple nodes (114), The method according to claim 13, further comprising the step of managing and handling the one or more new emergency types within the network by the ML engine (214).

17. The method according to claim 16, further comprising the step of defining and classifying the one or more new emergency types as new classes of emergencies by the ML engine (214).

18. The method according to claim 17, further comprising the step of having the ML engine (214) handle each of the new emergency types automatically or manually, based on a predetermined set of classifications of the one or more new emergency types and instructions associated with the classifications.

19. The method according to claim 13, wherein the initial bits of the first set of data packets, the second set of data packets, the third set of data packets, and the fourth set of data packets indicate an emergency type, and the remainder of the first set of data packets, the second set of data packets, the third set of data packets, and the fourth set of data packets indicate packet forwarding after the establishment of a machine assembly.

20. A step of receiving a newly defined quality of service (QoS) for handling one or more emergencies by the ML engine (214), wherein the one or more emergencies are shared among the service request causes, The steps include configuring and mapping the newly identified QoS profile using the ML engine (214) and / or appropriately handling the emergency, The method according to claim 13, further comprising the step of the ML engine (214) interface to and identify the emergency type and the required signaling identifier in order to inform the network of the correct type of QoS profile to identify and apply.

21. The method according to claim 12, further comprising the steps of collecting carefully gathered data by the ML engine (214), storing it in a cloud-based data lake, processing it, and extracting actionable insights.

Images