DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
METHOD AND SYSTEM FOR PREDICTING BACKUP POWER TIME OF AT LEAST ONE NETWORK COMPONENT
2. APPLICANT(S)
Name Nationality Address
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3. PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
[0001] The present invention relates to wireless communications, more particularly relates to a method and a system for predicting backup power time of at least one network component.
BACKGROUND OF THE INVENTION
[0002] Currently, network management techniques face numerous challenges in predicting and managing backup time for critical network components. Network operators struggle to accurately predict how long a backup power would last during power outages thereby risking service interruptions. Further, network operators assess backup power status manually which leads to delay in responding to power-related issues. This leads to delays in detecting power related issues and fixing the detected defects. The delays cause high downtime creating instability and service disruptions in the network. Furthermore, traditional network management systems lack the ability to predict future backup time based on historical power consumption data.
[0003] Hence, there is a need for a method and system that can predict future backup power time accurately using available network management system data. It ensures network stability and reduces the risk of service disruptions.
SUMMARY OF THE INVENTION
[0004] One or more embodiments of the present disclosure provide a method and a system predicting power backup time of at least one network component.
[0005] In one aspect of the present invention, the system for predicting backup power time of at least one network component is disclosed. The system includes a retrieving unit configured to retrieve data from the at least one network component. The system further includes a training unit configured to train an Artificial Intelligence/Machine Learning (AI/ML) model utilizing the retrieved data to identify trends and patterns associated with the backup power time of each of the at least one network component. The system further includes a predicting unit, configured to predict, the backup power time of at least one network component based on the identified trends and patterns. The system further includes a comparison unit, configured to, compare the predicted backup power time against the predefined threshold.
[0006] In an embodiment, the prediction of the backup power time of the at least one network component, the system comprises an initiating unit, configured to, initiate the one or more actions if the predicted power backup time is lesser than the predefined threshold.
[0007] In an embodiment, the predicted backup power time of the at least one network component is stored in a storage unit.
[0008] In an embodiment, wherein the system further comprises a preprocessing unit, configured to, pre-process, the retrieved data in order to utilize the pre-processed data for the training of the model.
[0009] In an embodiment, wherein the data relates to at least one of power consumption, battery status, and backup power of the at least one network component.
[0010] In an embodiment, the first set of data corresponds to historical data.
[0011] In an embodiment, the data is continuously and dynamically updated in real time.
[0012] In an embodiment, the predefined threshold is defined based on at least one of network traffic, power consumption, and battery health condition of each of the at least one network components.
[0013] In an embodiment, the one or more actions include transmitting notification to one or more User Equipment’s (UE) and initiating backup power management actions.
[0014] In another aspect of the present invention, the method for predicting backup power time of at least one network component, the method is disclosed. The method includes the step of retrieving, by one or more processors, data from the at least one network component. The method further includes the step of training, by the one or more processors, an Artificial Intelligence/Machine Learning (AI/ML) model utilizing the retrieved data to identify trends and patterns associated with the backup power time of each of the at least one network component. The method further includes the step of predicting, by the one or more processors, the backup power time of at least one network component based on the identified trends and patterns. The method further includes the step of comparing, by the one or more processors, the predicted backup power time against a predefined threshold.
[0015] In another aspect of invention, User Equipment (UE) is disclosed. The UE includes one or more primary processors communicatively coupled to one or more processors, the one or more primary processors coupled with a memory. The processor causes the UE to receive, the one or more actions include transmitting notification to one or more User Equipment’s (UE) and initiate backup power management actions.
[0016] In another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured to retrieve the data from the at least one network component. The processor is configured to train, an Artificial Intelligence/Machine Learning (AI/ML) model utilizing the retrieved data to identify trends and patterns associated with backup power time of each of the at least one network component. The processor is configured to predict, the power backup time of the at least one network component based on the identified trends and patterns. The processor is further configured to compare the predicted backup power time against the predefined threshold.
[0017] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0019] FIG. 1 is an exemplary block diagram of an environment for predicting backup power time of at least one network component, according to one or more embodiments of the present invention;
[0020] FIG. 2 is an exemplary block diagram of a system for predicting backup power time of at least one network component, according to one or more embodiments of the present invention;
[0021] FIG. 3 is a schematic representation of a workflow of the system of FIG. 1, according to the one or more embodiments of the present invention;
[0022] FIG. 4 is an exemplary block diagram of an architecture implemented in the system of the FIG.2, according to one or more embodiments of the present invention;
[0023] FIG. 5 is a signal flow diagram for predicting backup power time of at least one network component according to one or more embodiments of the present invention; and
[0024] FIG. 6 is a schematic representation of a method for predicting backup power time of at least one network component, according to one or more embodiments of the present invention.
[0025] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0026] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0027] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0028] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0029] FIG. 1 illustrates an exemplary block diagram of an environment 100 for predicting power backup time of at least one network component, according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 110, a server 115, a network 105 and a system 120 communicably coupled to each other for predicting backup power time of at least one network component. The network components include but are not limited to routers, switches, servers, firewalls, network interface cards, telecommunication equipment, access points and network storage devices. The backup power refers to reserve power supply used to maintain the functioning of the network components. The backup power time refers to the remaining duration that a network component can function on its backup power before the backup power is exhausted. The backup power time indicates how long the network component remains operational during the power outrage or under conditions when the primary power source is unavailable.
[0030] As per the illustrated embodiment and for the purpose of description and illustration, the UE 110 includes, but not limited to, a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 110 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 110a, the second UE 110b, and the third UE 110c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 110”.
[0031] In an embodiment, the UE 110 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as a smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0032] The environment 100 includes the server 115 accessible via the network 105. The server 115 may include, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0033] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0034] The network 105 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 105 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0035] The environment 100 further includes the system 120 communicably coupled to the server 115 and the UE 110 via the network 105. The system 120 is configured to predict backup power time of at least one network component. As per one or more embodiments, the system 120 is adapted to be embedded within the server 115 or embedded as an individual entity.
[0036] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0037] FIG. 2 is an exemplary block diagram of the system 120 for predicting backup power time of at least one network component, according to one or more embodiments of the present invention.
[0038] As per the illustrated embodiment, the system 120 includes one or more processors 205, a memory 210, a user interface 215, and a database 220. For the purpose of description and explanation, the description will be explained with respect to one processor 205 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 120 may include more than one processor 205 as per the requirement of the network 105. The one or more processors 205, hereinafter referred to as the processor 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0039] As per the illustrated embodiment, the processor 205 is configured to fetch and execute computer-readable instructions stored in the memory 210. The memory 210 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 210 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0040] In an embodiment, the user interface 215 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 215 facilitates communication of the system 120. In one embodiment, the user interface 215 provides a communication pathway for one or more components of the system 120. Examples of such components include, but are not limited to, the UE 110 and the database 220.
[0041] The storage unit 220 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 220 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0042] In order for the system 120 to predict backup power time of at least one network component, the processor 205 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a retrieving unit 225, a preprocessing unit 230, a training unit 235, a predicting unit 240, a comparison unit 245 and an initiating unit 250 communicably coupled to each other for predicting backup power time of at least one network component.
[0043] In one embodiment, the one or more modules includes, but not limited to, the retrieving unit 225, the preprocessing unit 230, the training unit 235,the predicting unit 240, the comparison unit 245, and the initiating unit 250 can be used in combination or interchangeably predicting backup power time of at least one network component.
[0044] The retrieving unit 225, the preprocessing unit 230, the training unit 235, the predicting unit 240, the comparison unit 245, and the initiating unit 250 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 202. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 210 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry.
[0045] In one embodiment, the retrieving unit 225 is configured to retrieve the data from the at least one network component. The data retrieved by the retrieving unit 225 includes at least one of the power consumption, battery status and backup power of the at least one network component. The data provides information related to the amount of time that a backup power source can sustain when the primary power source is unavailable, which is called the backup power time. The network components include but are not limited to routers, switches, servers, uninterruptible power supply systems, gateways, firewalls, access points and storage devices. The data retrieved is raw data. The raw data contains at least one redundant, irrelevant, non-uniform, insignificant and null values with significant data. The power related data from one or more components is recorded more than the required number of times becomes a redundant information for the present embodiment. The one or more power related data, for example, battery charge percentage ranges from 0 to 100 and power consumption in Watts are on the same scale. The different types of parameters are measured in different measuring units. For instance, voltage in Volt, current in Amperes, temperature in Celsius and energy in Joules, and state of health and state of charge in percentage. On training a model with inconsistent, incomplete, irrelevant and redundant data, the model gives outputs which are unreliable and erroneous. Therefore, the raw data is not suitable for the model training. The data devoid of inconsistent, incomplete, irrelevant and redundant values is suitable for model training. The raw data is preprocessed by at least one of data cleaning, data normalization and conversion of the data which is appropriate for model training... The data cleaning involves identifying and correcting or removing errors and inconsistencies in the raw data to ensure that the dataset is accurate and reliable. The data normalization involves scaling or adjusting the values of numerical data so that they fit within a standard range or distribution. The data conversion refers to the process of transforming raw data into a format that is structured, standardized, and suitable for machine learning model training. The preprocessing includes but is not limited to, removing inconsistent, incomplete, irrelevant and redundant values and aggregating the data relevant to the backup power time to train the model. The preprocessed data allows for the easier understanding of retrieved data.
[0046] Upon preprocessing the retrieved data from at least one network component, the training unit 235 is configured to train the Artificial Intelligence/Machine Learning model utilizing the retrieved data. The AI/ML model refers to a computational algorithm which is designed to perform tasks that require human intelligence. The model is trained using data so that the model learns from data for functions including but not limited to making predictions, decisions or identifying patterns without being programmed for each specific task. The training of a model can be based on at least one of supervised learning, unsupervised learning, deep learning and reinforcement learning. Upon training by the training unit 235, the model identifies the trends and patterns associated with the backup power time of each of the at least one network component. The trends and patterns associated with the backup power time include at least one of threshold values or range of values of the one or more power consumption, battery status and backup power of the at least one network component. The predefined threshold is at least one of defined by the user. In an alternate embodiment, the model auto learns the predefined threshold in the course of time. For example, the model learns that the backup power time is reduced if the value or range of values of at least one of the power consumption, battery status and backup power of the at least one network component exceeds or fall short of the threshold value or range of values. For example, when a greater number of devices are connected or the router is handling high traffic, the power consumption in the network 105 is higher and the backup time is less. Similarly, for instance, when the battery status is 80% and power consumption is at 50 Watts, the router has approximately 4 hours of backup power remaining. The calculation of these trends and patterns in backup power time is predefined by the user or learnt by the model in due course of time.
[0047] Upon training the model to identify the trends and patterns associated with the backup power time of each of the at least one network component, the predicting unit 240 is configured to predict the backup power time of at least one network component. In an embodiment, the system 120 receives data pertaining to the backup power time of each of the at least one network component in real time. The real time data is continuously and dynamically updated data. The data received pertaining to the backup power time of each of the at least one network component in in the previous data transmissions are historic data. The trends and patterns of the historic data are utilized by the predicting unit 240 for making real time prediction for the backup power time of at least one network component. The predicted backup power time is the predicted amount of time a network component continues to operate on its available backup power before it runs out...In an embodiment, the prediction is performed by comparing using the comparison unit 240 the backup power time against the predefined threshold. The predefined threshold is defined based on at least one of the network traffic, power consumption, and battery health condition of at least one network component. The predefined threshold is at least one of defined by the user or learnt by the model during the training. For example, consider a scenario in which the backup power time is to be provided by a power supply unit, such as, but not limited to, a battery for the network component, such as, the router. In this regard, the system 120 initially retrieves data pertaining to power consumption of each of the router. The data may be one of, but not limited to, power consumed by the battery, percentage of available charge of the battery, health of the battery, temperature of the battery, network traffic at one or more timepoints, and a combination thereof. Further, the data thus retrieved is retrieved for one or more time points over an interval of time. The interval of time may be decided by a network operator.
[0048] Utilizing the retrieved data, the model is configured to identify trends and patterns associated with the backup power time of the router. Accordingly, the model identifies trends and patterns associated with the backup power time of the router at different time points over the interval of time. Based on the identification of the trends and the patterns, the training unit 235 is further configured to analyze each of the identified trends and patterns. More specifically, in an embodiment, the training unit recognizes the network traffic and the corresponding power consumption of the battery. Subsequently, the predicting unit 240 is configured to predict the power back up of the router for the respective network traffic and the corresponding power consumed by the battery. The predicted back up power is stored in a storage unit 220.
[0049] Further, during operation of the router, the comparison unit 245 is configured to compare the predefined threshold with the predicted power back time. The predefined threshold of the router is defined by the network operator. The predefined threshold is defined based on the network traffic, the power consumption, and the health of the battery of the router. The predicted back up power corresponding to the network condition and the power consumption is retrieved from the data repository. The comparing unit thereafter compares the predicted backup power against the predefined threshold. If the predicted backup power time is less than the predefined threshold, the initiating unit is configured to initiate one or more actions.
[0050] Upon comparing the backup power time against the predefined threshold, the initiating unit 250 is configured to initiate one or more actions. If the backup power time exceeds the predefined threshold, then there is sufficient for the power requirements of the network components. Then no actions are initiated. The one or more actions are initiated if the backup power time is lesser than the predefined threshold. The one or more actions are purported to initiate backup power management actions. The one or more actions include transmitting notifications to one or more UE 110. The notifications include at least one of warning of the backup power time, and detailed description of the reduced backup power time and recommendations to improve the backup power time of at least one network components. In the previous example, the predefined threshold backup power time is 3 hours. If the battery has degraded further and the router is able to run only 2 hours, the predicted backup power time is insufficient when compared to the predefined threshold. Therefore, one or more actions are initiated. The initiating unit 250 initiates a notification to the UE 110 of the network operators, indicating the presence of reduced backup power time of at least one network component. The user or network operators adopt proactive steps according to the received notifications, so that the network stability is ensured and downtime during power outages is minimized. After receiving the notifications via the UE 110, the users or network operators take steps including but not limited to reducing unnecessary network services, disabling non-essential ports, rerouting traffic from overloaded router to other routers having more available power and activating cloud-based backup infrastructure. In an alternate embodiment, the steps taken to maintain the availability of power in the network 105 are performed in complete automation without manual intervention ensuring network reliability and operational continuity with negligible downtime during power failures as compared to manually performed steps.
[0051] In an embodiment, the historic data which is the data pertaining to the backup power time of network components in the previous times, identified trends and patterns from the historic data and the predefined thresholds of backup power time of at least one of the network components, are stored in the storage unit 220. Further, the predicted backup power time of the at least one network component, outputs of the execution of models, actions initiated, and results of actions are stored in a storage unit 220. The data stored in the storage unit 220 is utilized by the model to continuously learn and enhance the predictive model so that greater accuracy is achieved over time.
[0052] FIG. 3 describes a preferred embodiment of the system 120 of FIG. 2, according to various embodiments of the present invention. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the first UE 110a and the system 120 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0053] As mentioned earlier in FIG. 1, each of the first UE 110a, the second UE 110b, and the third UE 110c may include an external storage device, a bus, a main memory, a read-only memory, a mass storage device, communication port(s), and a processor. The exemplary embodiment as illustrated in FIG. 3 will be explained with respect to the first UE 110a without deviating from the scope of the present disclosure and the limiting the scope of the present disclosure. The first UE 110a includes one or more primary processors 305 communicably coupled to the one or more processors 205 of the system 120.
[0054] The one or more primary processors 305 are coupled with a memory 210 storing instructions which are executed by the one or more primary processors 305. Execution of the stored instructions by the one or more primary processors 305 enables the first UE 110a to receive, the one or more actions include transmitting notification to one or more User Equipment’s (UE) 110 and initiate backup power management actions.
[0055] As mentioned earlier in FIG. 2, the one or more processors 202 of the system 120 is configured to predict backup power time of at least one network component. As per the illustrated embodiment, the system 120 includes the one or more processors 205, the memory 210, the user interface 215, and the database 220. The operations and functions of the one or more processors 205, the memory 210, the user interface 215, and the database 220 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0056] Further, the processor 205 includes the retrieving unit 225, the preprocessing unit 230, the training unit 235, the predicting unit 240, the comparison unit 245, and the initiating unit 250. The operations and functions of the retrieving unit 225, the preprocessing unit 230, the training unit 235, the predicting unit 240, the comparison unit 245 and the initiating unit 250 are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 120 in FIG. 3, should be read with the description provided for the system 120 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0057] FIG. 4 is an exemplary block diagram of an architecture implemented in the system of the FIG.2, according to one or more embodiments of the present invention.
[0058] The architecture 400 includes alarm sources 405, a Network Management System (NMS) 410, an Artificial Intelligence/ Machine Learning (AI/ML) unit 415, data lake 425, workflow manager 435 and a user interface 215. The AI/ML unit 415 includes a preprocessing unit 230, and an algorithm execution unit 420.
[0059] In an embodiment, the AI/ ML unit 415 receives at least one of alarm data and counter data from one or more alarm sources 405 via the NMS 410. The alarm data refers to specific alarm events where immediate intervention is required to prevent network downtime. The counter data refers to quantitative or status-based data received from one or more network components which are used to track and monitor the power consumption in the present invention. The alarm sources 405 include one or more network components and devices. The NMS 410 is a centralized software platform. The NMS 410 collects, monitors, analyzes and manages the data from the one or more network components and devices. The network components include but are not limited to routers, switches, servers, firewalls, network interface cards, telecommunication equipment, access points and network storage devices. The data received pertains to at least one of the power consumption, battery status, and backup power of the at least one network component. For example, alarm data including but not limited to battery charge below 20% and counter data like current power consumption 150 Watts, are sent to the AI/ML unit 415 via the NMS 410. The received data through the NMS 410 is raw data comprising at least one of non-uniform, redundant and irrelevant along with significant values. The raw data is not suitable for training a model. If the model is trained on redundant and insignificant values the output of the execution of the model becomes not reliable. Therefore, the raw data is converted into standardized format with uniform format and without irrelevant and redundant values. The raw data is converted into standardized format using the preprocessing unit 230. The preprocessing by the preprocessing unit 230 includes not limited to data definition, data normalization or data cleaning. The redundant and null values are removed using the preprocessing unit 230. The preprocessed data is utilized to train the model.
[0060] Upon preprocessing the retrieved data, the model is trained in the algorithm execution unit 420. The data pertaining to the power consumption, battery status, and backup power of the at least one network component. The model identifies the trends and patterns related to the backup power time of each of the at least one network component. The backup power time is the amount of time that a network continues to operate using the backup power supply during the power outages, other disruptions to the primary power source. The trends and patterns correspond to the threshold values or range of values pertaining to at least one of but not limited to the power consumption, battery status and backup power of the at least one network components. Whenever the power consumption exceeds or falls short of the threshold value or range of values in at least one of but not limited to power consumption, battery status and backup power, then the power back up time is affected. For instance, when there is high traffic in the network, there is high consumption of power, which leads to reduced backup power time.
[0061] Upon training a model, the algorithm execution unit 420 is further configured to allow the model to predict the backup power time of at least one network component. The AI/ML unit 415 is further configured to receive data pertaining to at least one of the power consumption, battery status and backup power in real time. The real time data is continuously, and dynamically updated data received by the AI/ML unit 415. The model predicts the backup power time for the real time data. The predicted backup power time of the at least one network component is the forecast of how long a backup power system continue to supply power in the during at least one of the failure of power or when the primary power source is unavailable. The prediction is performed based on the identified trends and patterns in the historic data. The historic data is the data retrieved by the AI/ML unit 415 previously before the given moment. The predicted backup power time is stored in the data lake 425.
[0062] Further, the algorithm execution unit 420 compares the predicted backup power time against the predefined threshold, by utilizing the model. If the predicted backup power time is less than the predefined threshold, then there is sufficient amount of power to be consumed by the network components. Whenever the predicted backup power time exceeds the predefined threshold, then the power available is inadequate with respect to the power requirements of at least one network component. Then the workflow manager 430 coordinates the execution of initiating one or more actions. The one or more actions include but are not limited to, transmitting notifications to the user interface 215, initiating backup power management. On receipt of notifications of the inadequacy of the power, the network operator adopts proactive steps including but not limited to reducing unnecessary network services, disabling non-essential ports, rerouting traffic from overloaded router to other routers having more available power and activating cloud-based backup infrastructure. The workflow manager 430 speeds up decision making and ensures consistency in how backup power issues are handled. In an alternate embodiment, adjustment of the network power consumption is facilitated through automation.
[0063] FIG. 5 is a signal flow diagram for predicting backup power time of at least one network component, according to one or more embodiments of the present invention.
[0064] At step 505, the data from at least one network component is retrieved via the NMS 410. The data integrated into the system 120 includes at least one of the power consumption, battery status and backup power of the at least one network component. The network components include at least one of routers, switches, server, The data integrated is useful to find the backup power time for at least one network component.
[0065] At step 510, the data which is retrieved is preprocessed by the preprocessing unit 230. The data retrieved is raw data. The raw data contains data, having at least one of non-uniform formatted, redundant and irrelevant data along with significant data. The raw data is not suitable for training a model. The model if trained on the raw data tends to give unreliable output while executing the model. Therefore, the raw data is transformed into standardized data using the preprocessing unit 230.
[0066] At step 515, the preprocessed data is utilized for algorithm execution. On execution, the model identifies the trends and patterns associated with the backup power time of each of the at least one network component. The trends and patterns refer to the threshold value or range of values pertaining to at least one of the power consumption, battery status and backup power of the at least one network component. The data received in the past and the learnt trends and patterns of the past received data is the historic data. The model further receives the real time data pertaining to at least one of the power consumption, battery status and backup power of the at least one network component in real time which is continuously and dynamically updated in real time. On execution of the model in the real time, the model predicts the backup power time of at least one network component. The prediction is performed by the model based on the identified trends and patterns.
[0067] At step 520, the predicted backup power time is stored in the data lake 425. Further the learnt trends and patterns of at least one of the power consumption, battery status and backup power of the at least one network component along with the predefined thresholds are stored in the data lake 425. The stored data is utilized to predict the backup power time in the forthcoming times.
[0068] At step 525, the initiating unit 250 is configured to initiate the one or more actions if the backup up time is less than the predefined threshold. For that, the predicted backup power time is compared against the predefined threshold. The predefined threshold is defined based on at least one of the network traffic, power consumption, and battery health condition of the at least one network component. If the predicted backup power time is less than the predefined threshold, then there is insufficient power for at least one of the network component to operate. The one or more actions include transmitting notifications and reporting the inadequate backup power time to the one or more UE 110. If the predicted backup power time is greater than the predefined threshold, then there is sufficient power for at least one of the network component to operate. Then no actions are initiated.
[0069] At step 530, the users or network operators on the receipt of the one or more notifications adopt steps to improve the power management so that the network is stable, and the network components have operational continuity without downtime. In an alternate embodiment, an automated adoption of steps, without manual intervention, to facilitate the power management is also present.
[0070] FIG. 6 is a flow diagram of a method 600 for predicting backup power time of at least one network component, according to one or more embodiments of the present invention. For the purpose of description, the method 600 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0071] At step 605, the method 600 includes the step of retrieving the data from the at least one network component. The data retrieved pertains to at least one of the power consumption, battery status and backup power of the at least one network component. The network components include but are not limited to routers, switches, servers, firewalls, network interface cards, telecommunication equipment, access points and network storage devices. The data retrieved is helpful to find the backup power time for at least one of the network component.
[0072] At step 610, the method 600 includes the step of training an AI/ML model utilizing the retrieved data. The model through training is able to identify the trends and patterns associated with the backup power time of each of the at least one network component. The trends and patterns refer to threshold values or range of values if exceeded or fallen short of, result in power failure and interruptions in operational continuity within the network 105. The threshold values or range of values pertain to at least one of backup power time, network traffic, power consumption, and battery health condition of the at least one network component.
[0073] At step 615, the method 600 includes the step of predicting the backup power time of at least one network component based on the identified trends and patterns. The predicted backup power time is stored in the data lake 425. The prediction is performed utilizing the model on the real time data. The data pertaining to at least one of the power consumption, battery status and backup power of the at least one network component received by the system 120 in real time is called real time data. Further, the real time data is the data which is continuously and dynamically updated in real time. The predicted backup power time is compared against the predefined threshold. The predefined threshold is prepared based on at least one of the network traffic, power consumption, and battery health condition of the at least one network component. The continuous real time monitoring and considering historic data pertaining to at least one of power consumption and battery status enables prompt detection of deviations from the predefined thresholds. If the predicted backup power time is greater than the predefined threshold, it is inferred that there is sufficient power available in the network 105 for operations. Then no actions are initiated. Whenever the predicted backup power time exceeds the predefined threshold, then it is inferred that the power available in the network 105 is insufficient to continue the network operations. Therefore, one or more actions are initiated to report the user or network operators regarding the inadequacy of power in the network 105. The user or network operators adopt proactive measures to tackle the deficiency in the availability of power in the network 105, including but not limited to disabling non-essential ports and rerouting traffic from overloaded router to other routers having more available power. In an alternate embodiment, the automated communication of power requirements and performance of proactive mitigative steps enable operational continuity in the network. The proactive actions taken manually or by automation ensure uninterrupted service delivery even during power outages, reduced downtime and cost savings for network operations.
[0074] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 205. The processor 205 is configured to retrieve the data from the at least one network component. The processor 205 is further configured to train an Artificial Intelligence/Machine Learning (AI/ML) model utilizing the retrieved data to identify trends and patterns associated with backup power time of each of the at least one network component. The processor 205 is further configured to predict the backup power time of the at least one network component based on the identified trends and patterns.
[0075] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-6) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0076] The present disclosure incorporates technical advancement in solving the problem of uncertain back up time for critical networks. Further, the present invention enhances the efficiency, reliability in managing the power requirements of network components. The present invention utilizes the historical data and offers insights into trends and patterns of power usage and corresponding potential issues. The present disclosure continuously monitors the power consumption and battery status in real time to take proactive steps guaranteeing network stability and reducing downtime. The present invention further automates the power management through prediction, automated alerts and in an alternate embodiment taking steps to arrange for the power requirements in the network without manual intervention. Furthermore, the present invention, by automating the prediction of backup power time of at least one network component and managing the power requirements, the present invention ensures uninterrupted service delivery even during the events of power outages, power failures or non-availability of primary power source.
[0077] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS

[0078] Environment- 100
[0079] Network- 105
[0080] User Equipment (UE)- 110
[0081] Server- 115
[0082] System -120
[0083] Processor- 205
[0084] Memory- 210
[0085] User interface- 215
[0086] Storage unit - 220
[0087] Retrieving unit-225
[0088] Preprocessing unit- 230
[0089] Training unit- 235
[0090] Predicting unit- 240
[0091] Comparison unit- 245
[0092] Initiating unit- 250
[0093] Primary Processor- 305
[0094] Primary Memory- 310
[0095] Alarm sources- 405.
[0096] Network Management System- 410
[0097] AI/ML unit- 415
[0098] Algorithm Execution unit- 420
[0099] Data Lake- 425
[00100] Workflow Manager- 430 ,CLAIMS:CLAIMS:
We Claim:
1. A method (600) for predicting backup power time of at least one network component, the method (600) comprising the steps of:
retrieving, by one or more processors (205), data from the at least one network component;
training, by the one or more processors (205), an Artificial Intelligence/Machine Learning (AI/ML) model utilizing the retrieved data to identify trends and patterns associated with the backup power time of each of the at least one network component;
predicting, by the one or more processors (205), the backup power time of at least one network component based on the identified trends and patterns; and
comparing, by the one or more processors (205), the predicted backup power time against a predefined threshold.

2. The method (600) as claimed in claim 1, wherein the prediction of the power time of the at least one network component, the method comprises the steps of:
initiating, by the one or more processors (205), one or more actions if the predicted backup power time is lesser than the predefined threshold.

3. The method (600) as claimed in claim 1, wherein the predicted backup power time of the at least one network component is stored in a storage unit (220).

4. The method (600) as claimed in claim 1, wherein retrieving, by one or more processors (205), data from the at least one network component further includes pre-processing, by the one or more processors, the retrieved data in order to utilize the pre-processed data for training the model.

5. The method (600) as claimed in claim 1, wherein the data relates to at least one of power consumption, battery status, and backup power of the at least one network component;

6. The method (600) as claimed in claim 1 wherein the first set of data corresponds to historical data.

7. The method as claimed in claim 1 wherein the data is continuously and dynamically updated in real time.

8. The method (600) as claimed in claim 1, wherein the predefined threshold is defined based on at least one of network traffic, power consumption, and battery health condition of each of the at least one network components.

9. The method (600) as claimed in claim 2, wherein the one or more actions include transmitting notification to one or more User Equipment’s (UE) (110) and initiating backup power management actions.

10. A system (120) for, predicting backup power time of at least one network component, the system (120) comprising:
a retrieving unit (225), configured to, data from the at least one network component;
a training unit (235), configured to, train the Artificial Intelligence/Machine Learning (AI/ML) model utilizing the retrieved data to identify the trends and patterns associated with the backup power time of each of the at least one network component; and
a predicting unit (240), configured to, predict the backup power time of at least one network component based on the identified trends and patterns; and
a comparison unit (245), configured to, compare the predicted backup power time against the predefined threshold.

11. The system (120) as claimed in claim 10, wherein the prediction of the backup power time of at least one network component, the system (120) comprises:
an initiating unit (250), configured to, initiate the one or more actions if the predicted backup power time is lesser than the predefined threshold.

12. The system (120) as claimed in claim 10, wherein the predicted backup power time of the at least one network component is stored in a storage unit (220).

13. The system (120) as claimed in claim 10, further comprises a, pre-processing unit, configured to, pre-process, the retrieved data in order to utilize the pre-processed data for the training of the model.

14. The system (120) as claimed in claim 10, wherein the data relates to at least one of the power consumption, battery status, and backup power of the at least one network components;

15. The system (120) as claimed in claim 10, wherein the first set of data corresponds to historical data.

16. The system (120) as claimed in claim 10, wherein the data is continuously and dynamically updated in real time.

17. The system (120) as claimed in claim 10, wherein the predefined threshold is defined based on at least one of the network traffic, power consumption, and battery health condition of the at least one network component.

18. The system (120) as claimed in claim 10, wherein the one or more actions include transmitting notification to one or more User Equipment’s (UE) (110) and initiating backup power management actions.

19. A user equipment (110), comprising:
one or more primary processors (305) communicatively coupled to one or more processors (205), the one or more primary processors (305) coupled with a memory (210), wherein said memory (210) stores instructions which when executed by the one or more primary processors (305) causes the UE (110) to:
receive, the one or more actions include transmitting notification to one or more User Equipment’s (UE) (110);
initiate backup power management actions; and
wherein the one or more processors (205) is configured to perform the steps as claimed in claim 1.