System for registering unknown objects into a device management system and method therefor

Abstract

Embodiments of the present disclosure provide a system (200) and method (300) for registering an unknown object into a device management system. The method includes receiving (302), by a processor (204), one or more object data related to an object. The object is unknown to the device management system. The method further includes extracting (304) one or more object identifiers from the one or more object data to identify one or more objects; and transmitting (306) the one or more object identifiers to an external source to identify refined information about the object. The object is classified using the refined information. The method further includes registering (308) the object to the device management system according to the classification of the object.

Description

Technical Field

[0001] This disclosure generally relates to the field of fleet management (FLM) systems. More specifically, this disclosure relates to systems and methods for registering unknown objects into an equipment management system. Background Technology

[0002] Companies, especially utility companies, need comprehensive tracking of assets and inventory, including the company's equipment, gear, vehicles, etc. (referred to as a fleet). Systems that provide a fleet overview are called Fleet Management (FLM) systems. FLM systems are designed to monitor and manage the fleet, allowing users to track specific assets through detailed information such as asset manufacturer, serial number, and / or asset type. Modern systems also provide location and status updates for each asset.

[0003] Ideally, new devices are seamlessly registered in the FLM system, capturing unique identifying details. Most FLM systems struggle to register devices / products from unknown brands or lacking standard identifiers such as barcodes. Decoding and parsing barcodes can also lead to registration failures. Therefore, registration of unknown brands or devices is often impossible, or a manual registration process can be performed. However, manual registration is error-prone and time-consuming. Since only branded devices / products are registered in the FLM system, manual registration of unknown devices or devices lacking standard identifiers can result in incomplete registration within the FLM system.

[0004] Therefore, current FLM systems may not be able to provide a comprehensive view of the entire fleet, especially when dealing with unbranded or non-standard assets (referred to as unknown objects). Summary of the Invention

[0005] Therefore, there is a need for an improved system and method for registering unknown objects into a device management system.

[0006] Therefore, the purpose of this disclosure is to provide a system and method for registering unknown objects into an equipment management system, which mitigates, alleviates, or eliminates all or at least some of the aforementioned disadvantages of currently known solutions.

[0007] This and other objectives are achieved by the systems and methods defined in the appended claims. In this context, the term "exemplary" should be understood as being used as an instance, example, or illustration.

[0008] According to a first aspect of this disclosure, a system for registering unknown objects to a device management system is provided. The system includes a processor and a memory. The memory is arranged to store a plurality of instructions to be executed by the processor. The processor is arranged to receive one or more object data related to an object. The object is unknown to the device management system. The processor is also arranged to extract one or more object identifiers from the one or more object data to identify the one or more objects. The processor is further arranged to transmit the one or more object identifiers to an external source to identify refined information about the object, wherein the refined information is used to classify the object. The processor is also arranged to register the object with the device management system according to the object's classification.

[0009] Advantageously, embodiments of this document provide a system in which unknown objects are registered to a device management system without a manual registration process. In this system, object identifiers of objects unknown to the device management system are extracted. These object identifiers are extracted by applying an artificial intelligence (AI)-based image recognition method. Furthermore, the object identifiers are transmitted to an external source to identify refined information about the objects. This refined information aids in the classification of the objects. Therefore, this system provides an automated and efficient method for registering objects initially unknown to the device management system.

[0010] In some embodiments, the object includes power grid equipment.

[0011] In some embodiments, one or more object data entries include one or more images of the object.

[0012] In some embodiments, one or more object identifiers include at least one of the following: text element, serial number, information about the manufacture of the object, device type when the object is a device, color of the object, context information associated with the object, and labels on one or more objects.

[0013] In some embodiments, one or more object identifiers are extracted by applying an artificial intelligence (AI) based image recognition method to one or more object identifiers.

[0014] Advantageously, the proposed embodiments provide an automated method by applying an artificial intelligence (AI)-based image recognition method to extract the object identifier of the object.

[0015] In some embodiments, the external source includes at least one of the following: a website, a soft copy of at least one of a PDF or Word document, other image documents, and user input.

[0016] In some embodiments, one or more additional details are identified through a large language model (LLM), where the LLM maps one or more object identifiers to information present in an external source to generate refined information.

[0017] Advantageously, the proposed system provides a large language model (LLM) that maps object identifiers to information present in external sources to generate refined information. This refined information helps the system classify objects and identify them in the correct categories.

[0018] In some embodiments, the refinement information includes at least one of the following: the characteristics of the object, the model and type of the object, and the source from which the refinement information was generated.

[0019] In some embodiments, the processor is configured to store information about the source of refined information about an object.

[0020] In some embodiments, the processor is configured to receive user requests for replacement devices for existing devices to be identified in the device management system. The processor is also configured to extract one or more device identifiers from one or more pieces of input data associated with the existing device. The processor is further configured to compare one or more pieces of input data with at least one of: one or more object identifiers, refined information about the registered device, and information about the source. The processor is also configured to suggest replacement devices for existing devices based on the search results.

[0021] Advantageously, the proposed system provides recommendations for replacing certain devices. The LLM module of the device management system uses multiple information sources, such as information about the device's model and functionality, to suggest replacements.

[0022] According to a second aspect of this disclosure, a computer-implemented method is provided for registering unknown objects to a device management system. The method includes receiving, via a processor, one or more object data related to the object. The object is unknown to the device management system. The method further includes extracting one or more object identifiers from the one or more object data entries via the processor to identify the one or more objects. The method further includes transmitting the one or more object identifiers to an external source to identify refined information about the object, wherein the refined information is used to classify the object. The method further includes registering the object with the device management system based on the object's classification.

[0023] Advantageously, embodiments of this document provide a method for registering devices unknown to a device management system without any manual registration process. The method includes extracting object identifiers for objects unknown to the device management system. The object identifiers are extracted by applying an artificial intelligence (AI)-based image recognition method. Furthermore, the object identifiers are transmitted to an external source to identify refined information about the objects. This refined information aids in classifying the objects. Therefore, this method provides an automated and efficient way to register objects initially unknown to the device management system.

[0024] In some embodiments, one or more object data entries include one or more images or videos of the object.

[0025] In some embodiments, one or more object identifiers include at least one of the following: text element, serial number, information about the manufacture of the object, device type when the object is a device, color of the object, context information associated with the object, and labels on one or more objects.

[0026] In some embodiments, one or more object identifiers are extracted by applying an artificial intelligence (AI) based image recognition method to one or more object identifiers.

[0027] Advantageously, the proposed embodiments provide an automated method by applying an artificial intelligence (AI)-based image recognition method to extract the object identifier of the object.

[0028] In some embodiments, the external source includes at least one of the following: a website, a soft copy of at least one of the documents in PDF or Word format, other image documents, schematic diagrams, construction drawings, a parts list, a bill of materials (BOM), and user input.

[0029] In some embodiments, one or more additional details are identified through a Large Language Model (LLM), where the LLM maps one or more identifiers to information present in an external source to generate refined information.

[0030] Advantageously, the proposed method uses a large language model (LLM) to map object identifiers to information present in external sources to generate refined information. This refined information helps the system classify objects and identify them in the correct categories.

[0031] In some embodiments, the refinement information includes at least one of the following: the characteristics of the object, the model and type of the object, and the source from which the refinement information was generated.

[0032] In some embodiments, the method includes storing information about the source of refined information about the object.

[0033] In some embodiments, the method includes receiving a user request for a replacement device for an existing device to be identified in a device management system. The method also includes extracting one or more device identifiers from one or more pieces of input data associated with the existing device via a processor. The method further includes comparing one or more pieces of input data with at least one of: one or more object identifiers, refined information about the registered device, and information about its origin. The method also includes suggesting a replacement device based on the search to replace the existing device.

[0034] Advantageously, the proposed method provides recommendations for replacing certain devices. The LLM module of the device management system uses multiple information sources, such as information about the device's model and functionality, to suggest replacements.

[0035] Other advantages may be apparent to those skilled in the art. Certain embodiments may have some or all of the advantages described. Attached Figure Description

[0036] The above will become apparent from the following more detailed description of exemplary embodiments, as illustrated in the accompanying drawings, wherein the same reference numerals refer to the same parts in different views. The drawings are not necessarily drawn to scale and are intended to illustrate exemplary embodiments.

[0037] Figure 1 A wireless communication system according to some embodiments is disclosed; Figure 2 A schematic block diagram of an example system for registering unknown objects to a device management system, according to some embodiments, is shown. Figure 3 A flowchart illustrating example steps of a method for registering an unknown object to a device management system according to some embodiments is shown; Figure 4 An example system diagram is shown for registering unknown objects to a device management system, according to some embodiments; Figure 5 An example flowchart illustrating example steps for registering an unknown object to a device management system, according to some embodiments, is shown. Figure 6 An example flowchart illustrating example steps for suggesting a replacement device in a system according to some embodiments is shown; and Figure 7 An example computing environment according to some embodiments is shown. Detailed Implementation

[0038] The aspects of this disclosure will be described more fully below with reference to the accompanying drawings. However, the systems and methods disclosed herein can be implemented in many different forms and should not be construed as limited to the aspects set forth herein. The same numerals in the accompanying drawings consistently refer to the same elements.

[0039] The terminology used herein is for the purpose of describing specific aspects of this disclosure only and is not intended to limit the invention. It should be emphasized that the term "comprising / including" as used herein is used to specify the presence of the stated feature, integer, step, or component, but does not exclude the presence or addition of one or more other features, integers, steps, components, or groups thereof. Unless the context clearly indicates otherwise, the singular forms "a," "an," and "the" as used herein also include the plural forms.

[0040] Embodiments of this disclosure will be described and illustrated more fully below with reference to the accompanying drawings. However, the solutions disclosed herein can be implemented in many different forms and should not be construed as limited to the examples described herein.

[0041] In the following description of exemplary embodiments, the same reference numerals denote the same or similar components.

[0042] In existing technologies, most fleet management (FLM) systems struggle to register equipment / products from unknown brands or those lacking standard identification such as barcodes. Decoding and parsing barcodes can also lead to registration failures. Manual registration processes to address these issues are error-prone and time-consuming. Therefore, existing FLM systems may not provide a comprehensive view of the entire fleet, especially when dealing with unbranded or non-standard assets (referred to as unknown objects).

[0043] Compared to existing technologies, the embodiments described herein provide a system and method for registering unknown objects to an equipment management system. An equipment management system can also refer to a fleet management (FLM) system.

[0044] Figure 1 An example wireless communication system (100) is disclosed. While the subject matter described herein can be implemented in any suitable system using any appropriate components, the examples disclosed herein are described in relation to wireless communication systems / wireless networks, such as... Figure 1 Example wireless communication system (100) described in the figure.

[0045] The wireless communication system (100) may include or interface with any type of communication, telecommunications, data, cellular, and / or radio network or other similar system. The wireless communication system (100) may be configured to operate according to a specific standard or other type of predefined procedural rules. Therefore, the wireless communication system (100) may implement communication standards such as, but not limited to, GSM, UMTS, LTE, and / or other suitable 2G, 3G, 4G, or 5G standards, WLAN standards (such as the IEEE 802.11 standard), and / or any other suitable wireless communication standard (such as WiMax, Bluetooth, Z-Wave, and / or ZigBee standards).

[0046] For simplicity, such as Figure 1 As shown, the wireless communication system (100) includes a system (2000), a network node (104), and a network (106). The system (2000) and the network node (104) operate together to provide wireless connectivity in the wireless communication system (100). The network (106) may include one or more backhaul networks, core networks, IP networks, public switched telephone networks (PSTN), packet data networks, optical networks, wide area networks (WAN), local area networks (LAN), wireless local area networks (WLAN), wired networks, wireless networks, metropolitan area networks (MANs), and other networks to enable communication between devices (e.g., wireless devices and network nodes).

[0047] In one example, a network node (104) refers to equipment capable of, configured to, arranged to, and / or operable to communicate directly or indirectly with other network nodes or equipment in the system (200) and / or wireless communication system (100) to enable and / or provide wireless access to the system (200) and / or perform other functions (e.g., management) in the wireless communication system. Examples of network nodes (104) may include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs), new radio NRs, nodes (gNBs), etc.). BSs can be classified according to the coverage they provide (or different transmit power levels) and may also be referred to as femtoBSs, picoBSs, microBSs, macroBSs. A BS can be a relay node or a relay donor node for control relays.

[0048] The system (200) means a device that is capable of, configured to, arranged to, and / or operable to communicate wirelessly with network nodes (104) and / or other wireless devices.

[0049] In some examples, the system (200) may include one or more of the following: computing devices, wireless devices, ultra-low power wireless devices, Internet of Things (IoT) devices, etc.

[0050] Examples of computing devices may include, but are not limited to, smartphones, mobile phones, cell phones, Voice over Internet Protocol (VoIP) phones, wireless local loop phones, desktop computers, personal digital assistants (PDAs), wireless cameras, game consoles or devices, wearable terminal devices, wireless endpoints, mobile stations, tablets, laptops, laptop embedded devices (LEEs), laptop mounted devices (LMEs), smart devices, wireless customer premises equipment (CPEs), and in-vehicle wireless terminal equipment.

[0051] It should be understood that the system (200) is not limited to the wireless devices described above. The system (200) can be extended to other wireless devices of different categories or types that provide different services, while supporting, for example, enhanced mobile broadband (eMBB), massive machine-type communications (MTC), ultra-reliable low-latency communications (URLLC), time-sensitive networks (TSN), etc.

[0052] In the wireless communication system (100), network nodes (104) and the system (200) are connected via a 3GPP 5G core network, where specific network services and operations are provided through software components called network functions (NFs). The wireless communication system (100) carries large-scale applications.

[0053] In one embodiment, Figure 2 A schematic block diagram illustrating an example system (200) for registering unknown objects to an equipment management system (not shown in the figure) is disclosed. Objects may include, but are not limited to, electrical and electromechanical equipment, energy processing equipment (such as transportation, control, management, power generation, distribution, security, operation, etc.), and utility communication assets.

[0054] like Figure 2 As shown, the system (200) also includes one or more modules. The one or more modules may include a memory (202), a processor (204), a control circuit (206), and a driver (208). The control circuit (206) may be adapted to control other modules. The memory (202), processor (204), driver (208), and control circuit (206) may be operatively connected to each other.

[0055] The memory (202) is arranged to store multiple instructions to be executed by the processor (204). The processor (204) is arranged to receive one or more object data related to an object. The object is unknown to the device management system. After receiving one or more object data, the processor (204) extracts one or more object identifiers from the object data. The extraction of one or more object identifiers identifies one or more objects. The one or more object identifiers are transmitted by the processor (204) to an external source (not shown) to identify refined information about the object. This refined information is used to classify the object. The object can be classified as any type of equipment. In one example, the object can be classified as "Equipment Electrical Network" or "Utility". The processor (204) then registers the object with the device management system according to the object's classification. In one example, general functions of objects such as protecting IEDs or circuit breaker equipment or transformers, as well as similar information about the object, are registered in the device management system. In addition, object information can also be extracted from existing construction drawings and schematic diagrams.

[0056] The control circuit (206) may be adapted to control the steps executed by the processor (204). For example, the control circuit (206) may be adapted to extract one or more object identifiers from one or more object data, extract one or more object identifiers, transmit one or more object identifiers to an external source, etc.

[0057] The driver (208) may be adapted to process one or more object data related to an object received by the system (200).

[0058] In some embodiments, the object includes power grid equipment.

[0059] In some embodiments, one or more object data entries include one or more images of the object.

[0060] In some embodiments, one or more object identifiers include at least one of the following: text element, serial number, information about the manufacture of the object, device type when the object is a device, color of the object, context information associated with the object, and labels on one or more objects.

[0061] In some embodiments, one or more object identifiers are extracted by applying an artificial intelligence (AI)-based image recognition method to one or more object identifiers. Example AI algorithms may include various variants of convolutional neural networks (R-CNN, Fast R-CNN), YOLO, etc., and the method of object identifier extraction depends on the performance of the AI ​​algorithm.

[0062] Advantageously, the proposed embodiments provide an automated method by applying an artificial intelligence (AI)-based image recognition method to extract the object identifier of the object.

[0063] In some embodiments, the external source includes at least one of the following: a website, a soft copy of at least one of the documents in PDF or Word format, other image documents, schematic diagrams, construction drawings, a parts list, a bill of materials (BOM), and user input.

[0064] In some embodiments, one or more additional details are identified using a Large Language Model (LLM), where the LLM maps one or more object identifiers to information present in an external source to generate refined information. Furthermore, users can interact with the LLM module to provide additional information, such as narrowing the search space, requesting additional input images, etc.

[0065] Advantageously, the proposed system provides an LLM to map object identifiers to information present in external sources to generate refined information. This refined information helps the system classify objects and identify them in the correct categories.

[0066] In some embodiments, the refinement information includes at least one of the following: the characteristics of the object, the model and type of the object, and the source from which the refinement information was generated.

[0067] In some embodiments, the processor is configured to store information about the source of refined information about an object.

[0068] Figure 3 A flowchart illustrating example steps of a method (300) for registering an unknown object to a device management system is provided. The order in which the method (300) is described should not be construed as limiting, and the method (300) or alternative methods can be implemented by combining any number of the described method blocks in any order. Furthermore, individual blocks may be removed from the method (300) without departing from the spirit and scope of the embodiments described herein.

[0069] In step (302), method (300) includes receiving one or more object data related to an object. The object is unknown to the device management system. The request is received via processor (204).

[0070] In step (304), the method (300) includes extracting one or more object identifiers from one or more object data to identify one or more objects. The extraction is performed by a processor (204).

[0071] In step (306), method (300) includes transmitting one or more object identifiers to an external source to identify refined information about the objects. The refined information is used to classify the objects.

[0072] In step 308, method 300 includes registering the object with the device management system according to the object's classification.

[0073] Advantageously, embodiments of this document provide a method (300) for registering unknown objects or devices to a device management system without requiring any manual registration process. The method (300) includes extracting object identifiers for objects unknown to the device management system. The object identifiers are extracted by applying an artificial intelligence (AI)-based image recognition method. Furthermore, the object identifiers are transmitted to an external source to identify refined information about the objects. This refined information aids in classifying the objects. Therefore, the method (300) provides an automated and efficient method for registering objects initially unknown to the device management system.

[0074] In some embodiments, one or more object data entries include one or more images or videos of the object.

[0075] In some embodiments, one or more object identifiers include at least one of the following: text element, serial number, information about the manufacture of the object, device type when the object is a device, color of the object, context information associated with the object, and labels on one or more objects.

[0076] In some embodiments, one or more object identifiers are extracted by applying an artificial intelligence (AI) based image recognition method to one or more object identifiers.

[0077] Advantageously, the proposed embodiments provide an automated method by applying an artificial intelligence (AI)-based image recognition method to extract the object identifier of the object.

[0078] In some embodiments, the external source includes at least one of the following: a website, a soft copy of at least one of the documents in PDF or Word format, other image documents, schematic diagrams, construction drawings, a parts list, a bill of materials (BOM), and user input.

[0079] In some embodiments, one or more additional details are identified through a Large Language Model (LLM), where the LLM maps one or more identifiers to information present in an external source to generate refined information.

[0080] Advantageously, the proposed method (300) uses a large language model (LLM) to map object identifiers to information existing in external sources to generate refined information. This refined information helps the system classify objects and identify them in the correct categories.

[0081] In some embodiments, the refinement information includes at least one of the following: the characteristics of the object, the model and type of the object, and the source from which the refinement information was generated.

[0082] In some embodiments, the method includes storing information about the source of refined information about the object.

[0083] In some embodiments, the method (300) includes receiving a user request for a replacement device for an existing device to be identified in a device management system. The method (300) also includes extracting one or more device identifiers from one or more pieces of input data associated with the existing device via a processor. The method (300) further includes comparing one or more pieces of input data with at least one of: one or more object identifiers, refined information about the registered device, and information about its origin. The method (300) also includes suggesting replacement devices based on search results for replacing the existing device.

[0084] Advantageously, the proposed method provides recommendations for replacing certain devices. The LLM module of the device management system uses multiple information sources, such as information about the device's model and functionality, to suggest replacements.

[0085] Alternatively, the additional details of method (300) are similar to those of system (200), and therefore will not be repeated for the sake of brevity.

[0086] Figure 4 An example system diagram (400) is shown for registering unknown objects to a device management system according to some embodiments. Figure 4As shown, a user uses a handheld device (404) to capture one or more images of an object (402). The object includes power grid equipment. One or more images are processed locally on the handheld device (404) or passed to the FLM system module. The processing steps apply AI image recognition methods to extract one or more object identifiers from one or more images and labels related to the target equipment. One or more object identifiers may include a set of text elements, possibly consisting of labels, serial numbers, manufacturers, equipment types, colors, and contextual information. The user may provide his / her confirmation before processing the object identifiers that the image processing task has identified the correct equipment category. In one example, this set of text elements is then provided as input to the LLM module of the equipment management system (also known as the FLM system). The FLM module processes the object identifiers, such as text elements, and uses other data sources, such as online and offline documents, manuals, and datasheets, to identify refined information about the object. The refined information may include, but is not limited to, the equipment capabilities and model of the device or object. The refined information also includes the sources used to derive it (reference manuals, documents). To obtain the refined information, the LLM module may interact with the user and obtain additional information as needed (e.g., narrowing the search space (406), requesting additional input images). Once the object's information is refined, the object is registered in the device management system or FLM system. Information about the source of the refined information about the object is also stored in the FLM system (408).

[0087] Figure 5 Example flowchart 500 illustrates example steps for registering an unknown object to a device management system according to some embodiments. After receiving one or more object data (512) associated with the object, as... Figure 5 As shown, in step (502), object data is provided to the image recognition module (514). One or more object data include one or more images of the object.

[0088] In step (504), one or more object identifiers (516) are extracted from the image of the object by the image recognition module (504). The one or more object identifiers (516) include at least one of the following: text elements, serial numbers, information about the manufacture of the object, device type when the object is a device, color of the object, context information associated with the object, and labels on one or more objects.

[0089] In step (506), one or more object identifiers (516) are provided to the LLM module (518) for object classification. Additionally, in step (506), the user can interact with the LLM module (518) or provide additional information about the objects to the LLM module (518). Furthermore, in step (506), the user can manually provide the LLM module (518) with online / offline documents or data tables about the objects to obtain refined information about the objects.

[0090] In step 508, the LLM module (518) provides refinement information (522) to the fleet management equipment database (524) for object registration. The refinement information (522) may include information about the capabilities of the object or equipment model and references to manuals.

[0091] In some embodiments, the processor (204) (such as...) Figure 2 (As shown) is also arranged for use in the recommendation system (200) for replacement devices. Figure 6 An example flowchart illustrating example steps for recommending a replacement device in a system (200) according to some embodiments is shown. A processor (204) is configured to receive a user request for a replacement device for an existing device to be identified in a device management system. Upon receiving the user request for a replacement device, the processor (204) extracts one or more device identifiers from one or more pieces of input data associated with the existing device. The processor (204) then compares one or more pieces of input data with at least one of the following: one or more object identifiers, refined information about the registered device, and information about the source or external source. The processor (204) then recommends a replacement device for replacing the existing device based on the search.

[0092] After receiving a user request for a replacement device, such as Figure 6 As shown, in step 602, the processor (204) extracts one or more device identifiers (610) from one or more input data. In one example, the one or more device identifiers (610) may include information about the capabilities of an object or device model and references to a manual. In one example, the information (610) is extracted from a fleet management device database (608).

[0093] In step 604, one or more device identifiers (610) are provided as input data to the Large Language Model (LLM) module (612). Additionally, in step 604, customer requirements (614) and information and data tables (616) for replacement devices may be provided to the LLM module (612). One or more pieces of input data are compared with at least one of the following: one or more object identifiers, refined information about the registered device, and information about the source.

[0094] In step 606, the LLM module (612) can replace (618) the existing device based on the search suggestion.

[0095] Advantageously, the proposed system provides recommendations for replacing certain devices. The LLM module of the device management system uses multiple information sources, such as information about the device's model and functionality, to suggest replacements.

[0096] Figure 7 The implementation is shown as follows Figure 2 and Figure 3 The example computing environment (700) shown is for a system (200) and method (300) used to register unknown objects to a device management system. (See example...) Figure 5 As shown, the computing environment (700) includes at least one data processing module (706), which is equipped with a control module (702) and an arithmetic logic unit (ALU) (704), multiple networked devices (708) and multiple input / output I / O devices (710), a memory (712), and a storage device (714). The data processing module (706) can be responsible for implementing the following respectively: Figure 2 and Figure 3 The systems and methods described above. For example, in some embodiments, the data processing module (706) is equivalent to the one described above. Figure 2 and Figure 3 The control circuit of the described platform. The data processing module (506) is capable of executing software instructions stored in the memory (712). The data processing module (706) receives commands from the control module (702) to perform its processing. In addition, it calculates any logical and arithmetic operations (704) involved in the execution of instructions with the help of the ALU.

[0097] A computer program may be loaded into a data processing module (706), which may, for example, be included in an electronic device (such as a platform). When loaded into the data processing module (706), the computer program may be stored in a memory (712) associated with or contained within the data processing module. According to some embodiments, when loaded into and run by the data processing module (706), the computer program may, for example, [follow certain parameters]. Figure 2 and Figure 3 The method steps may be performed using any of the methods shown or otherwise described herein.

[0098] The entire computing environment (700) can consist of multiple homogeneous and / or heterogeneous cores, multiple CPUs of different types, special media, and other accelerators. Furthermore, multiple data processing modules 606 can reside on a single chip or multiple chips.

[0099] The algorithm, consisting of the instructions and code required for its implementation, is stored in memory (712) or storage device (714) or both. During execution, the instructions can be retrieved from the corresponding memory (712) and / or storage device (714) and executed by the data processing module (706).

[0100] In any hardware implementation, various networking devices (708) or external I / O devices (710) can be connected to the computing environment to support implementation via networking devices (704) and I / O devices (710).

[0101] The embodiments disclosed herein can be implemented by at least one software program that runs on at least one hardware device and performs network management functions to control the element. Figure 7 The elements shown include blocks that may be at least one of hardware devices or a combination of hardware devices and software modules.

[0102] The above description of the specific embodiments will fully reveal the general nature of the embodiments herein, enabling others to easily modify and / or adapt these specific embodiments to various applications by applying present knowledge without departing from the general concepts. Therefore, such adaptations and modifications should and are intended to be understood within the meaning and scope of equivalents of the disclosed embodiments. It should be understood that the wording or terminology used herein is for descriptive purposes and not for limitation. Therefore, although the embodiments herein are described according to preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modifications within the scope of this disclosure.

Claims

1. A system (200) for registering unknown objects to a device management system, the system (200) comprising: Processor (204); as well as The memory (202) is arranged to store a plurality of instructions to be executed by the processor (204), wherein the processor (204) is arranged to: Receive one or more object data related to an object, wherein the object is unknown to the device management system; Extract one or more object identifiers from the one or more object data to identify one or more objects; The one or more object identifiers are transmitted to an external source to identify refined information about the objects, wherein the refined information is used to classify the objects; and Register the object with the device management system according to the object's classification.

2. The system (200) according to claim 1, wherein, The objects include power grid equipment.

3. The system (200) according to claim 1 or 2, wherein, The one or more object data entries include one or more images or videos of the object.

4. The system (200) according to any one of the preceding claims, wherein, The one or more object identifiers include at least one of the following: text element, serial number, information about the manufacture of the object, device type when the object is a device, color of the object, context information associated with the object, and label on the one or more objects.

5. The system (200) according to any one of the preceding claims, wherein, The one or more object identifiers are extracted by applying an artificial intelligence (AI) based image recognition method to the one or more object data.

6. The system (200) according to any one of the preceding claims, wherein, The external source includes at least one of the following: a website, a soft copy of at least one of the documents in PDF or Word format, other image documents, schematic diagrams, construction drawings, parts lists, bills of materials (BOM), and user input.

7. The system (200) according to any one of the preceding claims, wherein, One or more additional details of the one or more objects are identified by a large language model (LLM), wherein the LLM maps the one or more identifiers to information present in the external source to generate the refined information, and wherein the refined information includes at least one of the following: characteristics of the object, model and type of the object, and source from which the refined information is generated.

8. The system (200) according to any one of the preceding claims, wherein, The processor (204) is configured to: Receive user requests for replacement devices for existing equipment to be identified in the device management system; The processor (204) extracts one or more device identifiers from one or more input data associated with the existing device; The one or more input data entries are compared with at least one of the following: one or more object identifiers, refined information of the registered device, and information about the source; and The replacement device suggested by the search is used to replace the existing device.

9. A computer-implemented method (300) for registering an unknown object to a device management system, the method (300) comprising: The processor (204) receives (302) one or more object data related to an object, wherein the object is unknown to the device management system; The processor (204) extracts (304) one or more object identifiers from the one or more object data to identify one or more objects; The one or more object identifiers are transmitted (306) to an external source to identify refined information about the objects, wherein the refined information is used to classify the objects; and Register the object with the device management system according to the object's classification (308).

10. The method (300) according to claim 9, wherein, The one or more object data entries include one or more images or videos of the object.

11. The method (300) according to claim 9 or 10, wherein, The one or more object identifiers include at least one of the following: text element, serial number, information about the manufacture of the object, device type when the object is a device, color of the object, context information associated with the object, and label on the one or more objects.

12. The method (300) according to any one of claims 9 to 11, wherein, The one or more object identifiers are extracted by applying an artificial intelligence (AI) based image recognition method to the one or more object data.

13. The method (300) according to any one of claims 9 to 12, wherein, The external source includes at least one of the following: a website, a soft copy of at least one of the documents in PDF or Word format, other image documents, schematic diagrams, construction drawings, parts lists, bills of materials (BOM), and user input.

14. The method (300) according to any one of claims 9 to 13, wherein, One or more additional details of the one or more objects are identified through a Large Language Model (LLM), wherein the LLM maps the one or more identifiers to information present in the external source to generate the refined information.

15. The method (300) according to any one of claims 9 to 14, comprising: Receive user requests for replacement devices for existing equipment to be identified in the device management system; The processor (204) extracts one or more device identifiers from one or more input data associated with the existing device; The one or more input data entries are compared with at least one of the following: one or more object identifiers, refined information of the registered device, and information about the source; and The replacement device suggested by the search is used to replace the existing device.

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