Remote workforce monitoring based on geofencing, location tracking, and machine learning

Abstract

The present invention relates to a method 700 for remote workforce monitoring is disclosed The method 700 includes receiving workforce data 118 associated with at least one worker of a plurality of workers 114 at a work site 116. The workforce data 118 associated with the at least one worker may be transmitted to a client device 102. Location data 120 associated with the at least one worker may be identified based on the workforce data 118. An activity to be assigned to the at least one worker at the work site 116 during a particular time instance may be automatically determined based on the workforce data 118 and the location data 120. The activity may be indicative of an industrial activity to be carried out by the at least one worker at the work site 116.

Description

[0001] REMOTE WORKFORCE MONITORING BASED ON GEOFENCING, LOCATION TRACKING, AND MACHINE LEARNING

[0002] FIELD OF THE INVENTION

[0003] The embodiments discussed in the present disclosure are related to the field of asset tracking systems and methods, and more particularly relates to remote workforce monitoring based on geofencing, location tracking, and machine learning techniques.

[0004] BACKGROUND OF THE INVENTION

[0005] With technical advancements in the field of asset tracking and monitoring, numerous machine learning (ML) models are being developed and used for workforce monitoring and various other applications. In recent years, there has been a considerable surge in pervasive issue of monitoring activity of employees at industrial sites that are currently at the core of mainstream approaches to remote employee surveillance. The failure in monitoring the activity of employees at industrial sites is caused due to negligence or unavailability of the employees at a particular time instance in carrying out industrial activity properly at the industrial sites, or inattentive attitude of employer in coordinating communication with the employees, thereby affecting work productivity of the workforce and failure to ensure managing allotment of the industrial activity to the employees for a particular industrial site. There have been many techniques developed in recent past for the monitoring of the activity of the employees at industrial sites. For example, one of the techniques involve communication between the employee and the employer using a wireless communication device like work-issued devices or software applications for management of the allotment of the industrial activity to the employees, and tracking of the productivity data of the employees. However, a major drawback of this technique is inability of the wireless communication device to efficiently and continuously monitor status of the industrial activity to be carried out by the employees. Additionally, the productivity data of the workforce is not effectively monitored which leads to failure in managing the allotment of the industrial activity to the employees for the particular site at a particular time instance.

[0006] The subject matter claimed in the present disclosure is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, thisbackground is only provided to illustrate one example technology area where some embodiments described in the present disclosure may be practiced.

[0007] Limitations and disadvantages of conventional and traditional approaches will become apparent to one skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.

[0008] OBJECTS AND FEATURES OF THE INVENTION

[0009] Accordingly, several objects and advantages of the present invention are to provide a system which incorporates use of a machine learning (ML) model trained on employee data to determine status of industrial activity associated with the employee. Further, the system incorporates use of a ML camera configured to automatically generate a plurality of images associated with the status of the industrial activity allotted to the employee, based on the application of the ML model to monitor the status of the industrial activity on a real-time basis. It is a further object of the present invention to provide the system which incorporates use of an accelerometer to detect type of the activity carried out by the employee at a particular work site. It is another object and feature of the present invention to provide the system which incorporates use of a geofencing sensor to determine a virtual boundary around the work site. It is yet another objective and feature of the present invention to provide the system which incorporates use of battery configured to provide electrical power to an Internet of Things (loT) device of the system. It is a further objective and feature of the present invention to provide the system which incorporates use of a charging circuit configured to regulate charging voltage and charging current provided to the battery for operation of the loT device. Another objective and feature of the present invention is to provide the system which incorporates use of a power management circuit configured to convert alternating current (AC) power received by the loT device into a direct current (DC) power, to provide an optimized DC power for operation of the loT device. Yet another objective and feature of the present invention is to provide the system which incorporates use of a protection circuit configured to protect the power management circuit from any kind of electrical failure. Likewise, another object and feature of the present invention is to provide the system which incorporates use of a pressure sensor configured to detect pressure and temperature corresponding to the loT device, during carrying out of the industrial activity by theemployee. Further objects and features of the present invention will become apparent from a consideration of the drawings and ensuing description.

[0010] SUMMARY OF THE INVENTION

[0011] According to one aspect of an embodiment, a system may be provided for remote workforce monitoring to perform a set of operations which may include receiving workforce data associated with at least one worker from a plurality of workers working at a work site. The set of operations may further include transmitting the workforce data to a client device. The set of operations may further include identifying a location data associated with the at least one worker based on the workforce data. The set of operations may further include automatically determining an activity to be assigned to the at least one worker at the work site during a particular time instance, based on the workforce data and the location data. The activity may be indicative of an industrial activity to be carried out by the at least one worker at the work site.

[0012] According to another aspect of the embodiment, a method for the remote workforce monitoring may include the set of operations which may include receiving the workforce data associated with the at least one worker from the plurality of workers working at the work site. The set of operations may further include transmitting the workforce data to a client device. The set of operations may further include identifying the location data associated with the at least one worker based on the workforce data. The set of operations may further include automatically determining the activity to be assigned to the at least one worker at the work site during the particular time instance, based on the workforce data and the location data. The activity may be indicative of the industrial activity to be carried out by the at least one worker at the work site.

[0013] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. The objects and advantages of the embodiments will be realized and achieved at least by the elements, features, and combinations particularly pointed out in the claims.

[0014] Both the foregoing general description and the following detailed description are given as examples and are explanatory and are not restrictive of the invention, as claimed.BRIEF DESCRIPTION OF DRAWINGS

[0015] The inventive concepts are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. Example embodiments will be described and explained with additional specificity and detail through the use of accompanying drawings, in which:

[0016] FIG. 1 is a diagram representing an exemplary network environment related to remote workforce monitoring, in accordance with an embodiment of the disclosure;

[0017] FIG. 2 A is a block diagram that illustrates an exemplary loT device of FIG. 1 for remote workforce monitoring, in accordance with an embodiment of the disclosure;

[0018] FIG. 2B is a block diagram that illustrates an exemplary processing circuitry of FIG. 2 A for remote workforce monitoring, in accordance with an embodiment of the disclosure;

[0019] FIG. 3 is a diagram that illustrates an exemplary execution pipeline for remote workforce monitoring, in accordance with an embodiment of the disclosure;

[0020] FIG. 4A is a diagram that illustrates an exemplary scenario for data transfer and exchange, in accordance with one embodiment of the disclosure;

[0021] FIG. 4B is a diagram that illustrates an exemplary scenario for data transfer and exchange, in accordance with another embodiment of the disclosure;

[0022] FIG. 5A is a diagram that illustrates an exemplary scenario for generation of report associated with monitoring of activity status, in accordance with one embodiment of the disclosure;

[0023] FIG. 5B is a diagram that illustrates an exemplary scenario for generation of report associated with monitoring of activity status, in accordance with another embodiment of the disclosure;

[0024] FIG. 6A is a diagram that illustrates an example scenario for detection of status of activity as a first activity status, in accordance with an embodiment of the disclosure;

[0025] FIG. 6B is a diagram that illustrates an example scenario for detection of status of activity as a second activity status, in accordance with an embodiment of the disclosure; and

[0026] FIG. 7 is a flowchart that illustrates exemplary operations for remote workforce monitoring, in accordance with an embodiment of the disclosure.

[0027] DETAILED DESCRIPTION OF THE INVENTION

[0028] Some embodiments described in the present disclosure may relate to methods and systems for facilitation of remote workforce monitoring. In the present disclosure, workforce data associatedwith at least one worker of a plurality of workers working at a work site may be received. The workforce data may be transmitted to a client device. Thereafter, a location data associated with the at least one worker may be identified, based on the workforce data. Based on the workforce data and the location data, an activity to be assigned to the at least one worker at the work site during a particular time instance may be automatically determined. The activity may be indicative of an industrial activity to be carried out by the at least one worker at the work site. A machine learning (ML) model may be further applied on the workforce data and the location data associated with the at least one worker. The ML model may be trained on workforce parameters corresponding to the workforce data and location parameters corresponding to the location data associated with the at least one worker. The workforce parameters may correspond to at least one of an activity log, or a process identification (ID), or an event log, or a worker efficiency, or a worker safety associated with the at least worker. The location parameters may correspond to a geolocation information associated with the at least one worker. A plurality of images associated with a status of the activity assigned to the at least worker may be automatically generated, based on the application of the ML model on the workforce data and the location data.

[0029] The technological field of remote monitoring of workforce may be improved by configuring an Internet of Things (loT) device to train a machine learning (ML) model on the workforce parameters and the location parameters. The loT device may receive, through an application server, the workforce data associated with at least one worker of a plurality of workers working at a work site. The loT device may transmit, through the application server, the workforce data to a client device. The loT device may identify, through a global positioning system (GPS) tracking device, a location data associated with the at least one worker based on the workforce data. The loT device may automatically determine, through the application server, an activity to be assigned to the at least one worker at the work site during a particular time instance, based on the workforce data and the location data. The activity may be indicative of the industrial activity to be carried out by the at least one worker at the work site. Thereafter, the loT device may apply the ML model on the workforce data and the location data associated with the at least worker. The ML model may be trained on workforce parameters corresponding to the workforce data and location parameters corresponding to the location data associated with the at least one worker. The loT device may automatically generate, through an ML camera, a plurality of images associated with a status of theactivity assigned to the at least one worker based on the application of the ML model, for monitoring of the status of the activity on a real-time basis.

[0030] The disclosed approach may offer several advantages. Real-time tracking of the activity assigned to the plurality of workers and ability to log a large number of work records corresponding to the plurality of workers may be achieved using techniques like location tracking and machine learning techniques. Due to detection of type of the activity carried out by the at least one worker at the work site, a virtual boundary around the work site may be determined using techniques like geofencing. Reporting of industrial events based on the workforce data may be facilitated using techniques like machine learning (ML) model training. The ML model may be trained on the workforce parameters corresponding to the workforce data and the location parameters corresponding to the location data associated with the at least one worker. Due to the reporting of industrial events, information associated with details of the work site and the details associated with event log in records of each of the plurality of workers may be extracted using the ML technique. Generation of images associated with a status of the activity assigned to the at least one worker based on the application of the ML model may be achieved using techniques like movement detection. The movement detection technique may be used to track and monitor the status of the activity on a real-time basis. The proposed technique involves generation of fault report associated with the activity carried out by the at least one worker or more than one worker using techniques like the location tracking technique. The fault report may correspond to device identification (ID), altitude level, pressure level, and device connected status associated with the at least worker or the plurality of workers. Further, the proposed technique may involve sending notifications with respect to the status of the activity carried out by the plurality of workers on a real-time basis using techniques like interactive machine learning (ML) technique. Further, the proposed technique may involve sending Short Messaging Service (SMS) and email alerts corresponding to the change in the status of the activity carried out by the at least one worker or the plurality of workers. Additionally, this approach may be used across diverse applications such as industrial power plant operation, multiple devices monitoring, or device registration updating and maintenance applications. Embodiments of the present disclosure are explained with reference to the accompanying drawings.FIG. 1 is a block diagram that illustrates an example network environment related to remote workforce monitoring, arranged in accordance with at least one embodiment described in the present disclosure. With reference to FIG. 1, there is shown an environment 100. The environment 100 may include a client device 102, an loT device 104, an application server 106, a GPS tracking device 108, a vision language model (VLM) 110, a camera 112, a database 122, and a communication network 124. Further, the client device 102 and the loT device 104 may be communicatively coupled to the application server 106, via the communication network 124. The environment may further include a plurality of workers 114 working at a worksite 116. In FIG. 1, there is further shown a workforce data 118 and a location data 120 associated with the plurality of workers 114 that may be stored in the database 122.

[0031] The client device 102 may be an electronic device used by an office manager to handle monitoring and management of the industrial activity to be carried out by the plurality of workers 114. The client device 102 may further include suitable logic, circuitry, interfaces, and / or code that may be configured to receive the workforce data 118 and the location data 120 associated with the at least one worker of the plurality of workers 114 from the loT device 104. The client device 102 may further receive email alerts corresponding to the change in the status of the activity carried out by the at least one worker or the plurality of workers on a real-time basis. The client device 102 may further receive alerts related with change in the status of the activity to be assigned to the at least one worker due to change in operational conditions or weather conditions. Examples of the client device 102 may include, but are not limited to, a computing device, a smartphone, a cellular phone, a mobile phone, a mainframe machine, a server, a computer workstation, a machine learning device (enabled with or hosting, for example, a computing resource, a memory resource, and a networking resource), and / or a consumer electronic (CE) device.

[0032] The loT device 104 may include suitable logic, circuitry, interfaces and / or code that may be configured to receive the workforce data 118 and the location data 120 associated with the at least one worker of the plurality of workers 114 from the application server 106. The loT device 104 may further transmit the workforce data 118 from the application server 106 to the client device 102. The loT device 104 may further identify, through the GPS tracking device 108, the location data 120 associated with the at least one worker based on the workforce data 118. The loT device 104 may further automatically determine, through the application server 106, an activity to beassigned to the at least one worker at the work site 116 during a particular time instance based on the workforce data 118 and the location data 120. The activity may be indicative of the industrial activity to be carried out by the at least one worker at the work site 116. Also, the loT device 104 may measure, through an altimeter (shown in FIG. 2B), altitude of the at least one worker at the work site 116 based on a ground height of the at least one worker. The loT device 104 may further detect, through an accelerometer (shown in FIG. 2B), type of activity carried out by the at least one worker at the work site 116. The loT device 104 may further determine, through a geofencing sensor (shown in FIG. 2B), a virtual boundary around the work site 116.

[0033] The loT device 104 may further trigger, through the application server 106, a response specifying the status of the activity carried out by the at least one worker at the work site 116, based on the virtual boundary. The loT device 104 may further detect, through the application server 106, a first response (shown in FIG. 6A) specifying the status of the activity as the at least one worker entering in a danger area around the work site 116. The loT device 104 may further trigger, through the application server 106, an alarm to alert the at least one worker at the work site 116 by way of sending an alert notification by the client device 102 to the loT device 104. The loT device 104 may further detect, through the application server 106, a second response (shown in FIG. 6B) specifying the status of the activity as the at least one worker not present for carrying out the industrial activity at the work site 116. The loT device 104 may further trigger, through the application server 106, an alarm by way of receiving an alert notification by the client device 102.

[0034] In an embodiment, the loT device 104 may control a display device (e.g., a display device 206A of FIG. 2A). The display device 206A may be communicatively coupled to the loT device 104 or may be a standalone device configured to render the workforce data 118 associated with the at least one worker from the plurality of workers 114 working at the work site 116. The display device 206 A may be further configured to render the location data 120 associated with the at least one worker from the plurality of workers 114. Examples of the loT device 104 may include, but not limited to, a computing device, a smartphone, a mainframe machine, a server, a consumer electronic (CE) device, a computer workstation, and / or a device with a graph-processing capability (such as, a device with a set of graphic processor units (GPU)).In one or more embodiments, the loT device 104 may comprise the vision language model (VLM) 110 for generation of image data associated with the status of the activity carried out by the at least worker at the work site 116. The VLM 110 may be trained on workforce parameters corresponding to the workforce data 118 and the location parameters corresponding to the location data 120 associated with the at least one worker. The loT device 104 may further automatically generate, through a camera 112, a plurality of images associated with the status of the activity assigned to the at least one worker based on the application of the VLM 110, to monitor the status of the activity on a real-time basis.

[0035] The workforce parameters 118 may corresponds to at least one of: an activity log, or a process identification (ID), or an event log, or a worker efficiency, or a worker safety associated with the at least one worker. The locations parameters may correspond to a geolocation information associated with the at least one worker.

[0036] In some embodiments, the loT device 104 may extract, through the application server 106, the workforce data 118 and the location data 120 associated with the at least one worker. Further, the loT device 104 may determine, through the application server 106, the at least one worker or more than one worker from the plurality of workers 114 at the work site 116 required to carry out the activity during the particular time instance. Further, the loT device 104 may generate, through the application server 106, a fault report (shown in FIG. 5 A) associated with the activity carried out by the at least one worker more than one worker.

[0037] The application server 106 may include suitable logic, circuitry, and interfaces, and / or code that may be configured to receive the workforce data 118 associated with the at least one worker from the plurality of workers 114 working at the work site 116. The application server 106 may further transmit the workforce data 118 to the client device 102. Thereafter, the application server 106 may identify, through the global positioning system (GPS) tracking device 108, the location data 120 associated with the at least one worker based on the workforce data 118. Further, the application server 106 may automatically determine the activity to be assigned to the at least one worker at the work site 116 during the particular time instance, based on the workforce data 118 and the location data 120. The activity may be indicative of the industrial activity to be carried out by the at least one worker at the work site 116.The application server 106 may be implemented as a cloud server and may execute operations through web applications, cloud applications, Hypertext transfer protocol (HTTP) requests, repository operations, file transfer, and the like. Other example implementations of the application server 106 may include, but are not limited to, a database server, a file server, a web server, a media server, a mainframe server, a machine learning server (enabled with or hosting, for example, a computing resource, a memory resource, and a networking resource), or a cloud computing server.

[0038] In at least one embodiment, the application server 106 may be implemented as a plurality of distributed cloud-based resources by use of several technologies that are well known to those ordinary skilled in the art. A person with ordinary skill in the art will understand that the scope of the disclosure may not be limited to the implementation of the application server 106 and the client device 102, as two separate entities. In certain embodiments, the functionalities of the application server 106 can be incorporated in its entirety or at least partially in the client device 102 without a departure from the scope of the disclosure. In certain embodiments, the application server 106 may host the database 122. Alternatively, the application server 106 may be separate from the database 122 and may be communicatively coupled to the database 122.

[0039] The GPS tracking device 108 may be configured to identify the identify the location data 120 associated with the at least one worker based on the workforce data 118. The GPS tracking device 108 may comprise a GPS receiver that utilizes signals to calculate the location of the at least one worker or the plurality of workers 114. The GPS tracking device 108 may be further configured to record the location of the at least one worker or the plurality of workers 114 at regular time intervals. The GPS tracking device 108 may transmit the location data 120 to the application server 106 over the communication network 124. The GPS tracking device 108 may be attached to a vehicle, a worker, or any other asset. The GPS tracking device 108 may be further configured to monitor the location of the at least one worker or the plurality of workers 114. In one or more embodiments, the GPS tracking device 108 may utilize GPS technology to determine geolocation of the loT device 104, in order to track the location of the loT device 104. The geolocation may be a geolocation data used to identify the location of the loT device 104. The geolocation may identify the location of the loT device 104 using the GPS tracking device 108, or cell phone towers, or wireless fidelity (WiFi) access points, or Internet Protocol (IP) addresses, or a combination of these.The vision language model (VLM) 110 may include suitable logic, circuitry, interfaces, and / or code that may be a language model that may be configured to be applied on the workforce data 118 and the location data 120 associated with the at least one worker. The VLM 110 may receive the workforce data 118 associated with the at least one worker from the plurality of workers 114 working at the work site 116. The VLM 110 may further receive the extract the location data 120 associated with the at least one worker based on the workforce data 118. The VLM 110 may further detect the activity to be assigned to the at least one worker at the work site 116 during the particular time instance based on the workforce data 118 and the location data 120. The VLM 110 may further automatically generate, through the camera 112, the plurality of images associated with the status of the activity of the assigned to the at least one worker based on the application of the VLM 110, to monitor the status of the activity on the real-time basis.

[0040] The VLM 110 may be trained on vast amounts of textual descriptions and images, enabling the VLM 110 to perform a wide range of natural language processing tasks, such as translation, summarization, image generation, and text generation. The VLM 110 may analyze the textual description defining the activity performed by the at least one worker and generate the images associated with the activity performed by the at least one worker, based on the analyzed textual description. The VLM 110 may be trained on a large dataset of question-answer pairs to interpret human language, or other types of complex data. In certain instances, the dataset may be particular to the workforce data 118 and the location data 120 associated with the at least one worker or the plurality of workers 114.

[0041] In an embodiment, the VLM 110 may be type of an artificial intelligence (Al) system (also referred to as an artificial deep neural network) configured to process and understand multiple types of data modalities, such as text, images, audio, 3D data, and video, simultaneously. The VLM 110 may extend the capabilities of traditional large language models (LLMs) by integrating various forms of data, enabling a more comprehensive understanding and generation of information across different media types. In some embodiments, the VLM 110 may be a large language model, such as a transformer-based decoder-only model, an encoder-decoder model that uses transformers), or a model that uses neural networks other than transformers. In these or other embodiments, the VLM 110 may include multiple encoders specialized for processing different modalities of data (such asimage and text). The VLM 110 may include a fusion mechanism to integrate outputs from various encoders.

[0042] Techniques like cross-attention mechanisms or multimodal transformers may be used to combine the different data types (such as text and image of a prompt) into a unified representation. In some embodiments, the VLM 110 may include decoders to generate outputs in various modalities. For example, a text decoder generates textual responses, while an image encoder may create visual content. During training, the VLM 110 may use large-scale datasets that include paired data from multiple modalities (e.g., image-caption pairs, video with subtitles). The training may involve techniques like supervised learning, reinforcement learning with human feedback (RLHF), and fine-tuning to ensure that the model performs well across different tasks. For applications like text generation and image understanding or video understanding, the VLM 110 may include specialized heads that may be fine-tuned for specific tasks.

[0043] The VLM 110 may correspond to at least one of a multimodal large language model (MLLM), a vision large language model (VLLM), an image captioning model, or a three-dimensional large language model (3D-LLM).

[0044] As an artificial deep neural network, the VLM model 110 may be referred to as a computational network or a system of artificial neurons, arranged in a plurality of layers, as nodes that may be configured to receive the workforce data 118 associated with the at least worker or the plurality of workers 114. The plurality of layers of the neural network may include an input layer, one or more hidden layers, and an output layer. Each layer of the plurality of layers may include one or more nodes (or artificial neurons, represented by circles, for example). Outputs of all nodes in the input layer may be coupled to at least one node of hidden layer(s). Similarly, inputs of each hidden layer may be coupled to outputs of at least one node in other layers of the neural network. Outputs of each hidden layer may be coupled to inputs of at least one node in other layers of the neural network. Node(s) in the final layer may receive inputs from at least one hidden layer to output a result. The number of layers and the number of nodes in each layer may be determined from hyperparameters of the neural network. Such hyper-parameters may be set before, while training, or after training the neural network on a training dataset.Each node of the VLM 110 may correspond to a variable function (e.g., a sigmoid function or a rectified linear unit) with a set of parameters, tunable during training of the network. The set of parameters may include, for example, a weight parameter, a regularization parameter, and the like. Each node may use the variable function to compute an output based on one or more inputs from nodes in other layer(s) (e.g., previous layer(s)) of the neural network. All or some of the nodes of the neural network may correspond to same or a different variable function.

[0045] In training of the VLM 110, one or more parameters of each node of the neural network may be updated based on whether an output of the final layer for a given input (from the training dataset) matches a correct result based on a loss function for the neural network. The above process may be repeated for same or a different input until minima of loss function may be achieved and a training error may be minimized. Several methods for training are known in art, for example, gradient descent, stochastic gradient descent, batch gradient descent, gradient boost, meta-heuristics, and the like.

[0046] The VLM 110 may include electronic data, which may be implemented as, for example, a software component of an application executable on the client device 102 or the loT device 104. The VLM 110 may rely on libraries, external scripts, or other logic / instructions for execution by a processing device. The VLM 110 may include code and routines configured to enable a computing device to perform one or more operations. Additionally, or alternatively, the VLM 110 may be implemented using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (LPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, the neural network may be implemented using a combination of hardware and software.

[0047] In an embodiment, the VLM 110 may be a scalable deep-learning model comprising an encoder model, a decoder model, and a set of convolution neural network layers. The VLM 110 may correspond to at least one of a Convolutional Neural Network Model (CNN) or a Deep Reinforcement Learning Model. The deep reinforcement learning model or scalable deep-learning model may take un-processed dataset such as, an unprocessed workforce data indicative of the plurality of texts associated with the at least one worker or the plurality of workers 114. The encoder model of the present disclosure may receive the workforce data 118 associated with the atleast worker or the plurality of workers 114. The decoder model may extract the location data 120 associated with the at least one worker or the plurality of workers 114 based on the workforce data 118. The VLM 110 may be trained based on the workforce data 118 and the location data 120, and configured to automatically generate, through the camera 112, the plurality of images associated with the status of the activity assigned to the at least one worker or the plurality of workers based on the application of the VLM 110, to monitor the status of the activity on the real-time basis. Based on the application of the VLM 110 to automatically generate the plurality of images, the encoder model may determine a compressed feature vector associated with the plurality of images. An encoded version (i.e., the compressed feature vector) of the received plurality of images may be transmitted to the decoder model. The decoder model may reconstruct an input dataset such as, an image dataset back from the encoded version. Thus, the decoder model may decompress the compressed feature vector associated with the image dataset. Each of the set of convolution neural network layers may perform a dot product between two matrices. Herein, a first matrix also known as a kernel, may include a set of learnable parameters and a second matrix may be a portion of a receptive field associated with the corresponding convolution neural network layer. In an embodiment, a kernel size associated with each of the set of convolution neural network layers may be even. That is, the kernel size may be “2”, “4”, “6”, “8”, and so on.

[0048] In an embodiment, the scalable deep-learning model may be a deep learning machine learning (ML) model or the VLM 110. The VLM 110 may be trained to identify a relationship between inputs, such as, features in the image dataset, and output labels, such as, the workforce data 118 and the location data 110. The VLM 110 may be defined by its hyper-parameters, for example, number of weights, cost function, input size, number of layers, and the like. The parameters of the VLM 110 may be tuned and weights may be updated so as to move towards global minima of a cost function for the VLM 110. After several epochs of the training on the workforce data 118 and the location data 120 in the training dataset, the VLM 110 may be trained to output, through the camera 112, the plurality of images associated with the status of the activity assigned to the at least one worker or the plurality of workers 114 at the work site 116.

[0049] The VLM 110 may include electronic data, which may be implemented as, for example, a software component of an application executable on the loT device 104. The VLM 110 may rely on libraries, external scripts, or other logic / instructions for execution by a processing device. The VLM 110may include code and routines configured to enable a computing device, such as the loT device 104 to perform one or more operations such as, the reception of the workforce data 118, the transmission of the 118 workforce data to the client device 102, the reception of the location data 110, and the automatic determination of the activity to be assigned to the at least worker at the work site 116 during a particular time instance based on the workforce data 118 and the location data 120. Additionally, or alternatively, the VLM 110 may be implemented using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, the neural network may be implemented using a combination of hardware and software.

[0050] The camera 112 may be operatively enabled by the VLM 110 to automatically generate the plurality of images associated with the status of the activity assigned to the at least worker or the plurality of workers 114, based on the application of the VLM 110 on the workforce data 118 and the location data 120, to monitor the status of the activity on the real-time basis.

[0051] The database 122 may include suitable logic, interfaces, and / or code that may be configured to store the workforce data 118 associated with the at least one worker or the plurality of workers 114. The database 122 may further store the workforce parameters corresponding to the workforce data 118. The workforce parameters may corresponds to at least one of the activity log, the process ID, the event log, the worker efficiency, or the worker safety associated with the at least one worker. The database 122 may further store the location data 120 associated with the at least one worker or the plurality of workers 114. The database 122 may further store the location parameters corresponding to the location data 120. The location parameters may corresponds to the geolocation information associated with the at least one worker or the plurality of workers 114. The database 122 may be derived from data of a relational or non-relational database, or a set of comma-separated values (csv) files in conventional or big-data storage. The database 122 may be stored or cached on a device, such as a server (e.g., the application server 106) of the client device 102. The device storing the database 122 may be configured to receive a query for the workforce data 118 or the location data 120 from the client device 102 or the application server 106. In response, the device of the database 122 may be configured to retrieve and provide the queried workforce data118 or the queried location data 120 to the client device 102 or the application server 106, based on the received query.

[0052] In some embodiments, the database 122 may be hosted on a plurality of servers stored at the same or different locations. The operations of the database 122 may be executed using hardware including a processor, a microprocessor (e.g., to perform or control performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some other instances, the database 122 may be implemented using software.

[0053] A person with ordinary skill in the art will understand that the scope of the disclosure may not be limited to the implementation of the application server (or the client device 102 or the loT device 104) and the database 122 as two separate entities. In certain embodiments, the functionalities of the database 122 can be incorporated in its entirety or at least partially in the application server 106 9or the client device 102), without a departure from the scope of the disclosure.

[0054] The communication network 124 may include various communication media through which the loT device 104 and the application server 106 may communicate with one another. The communication network 124 may be one of a wired connection or a wireless connection. Examples of the communication network 124 may include, but are not limited to, the Internet, a cloud network, Cellular or Wireless Mobile Network (such as Long-Term Evolution and 5thGeneration (5G) New Radio (NR)), satellite communication system (using, for example, low earth orbit satellites), a Wireless Fidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local Area Network (LAN), or a Metropolitan Area Network (MAN). Various devices in the network environment 100 may be configured to connect to the communication network 124 in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but not limited to, at least one of a Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Zig Bee, EDGE, IEEE 802.11, Light Fidelity (Li-Fi), 802.16, IEEE 802.11g, multi-hop communication, wireless access point (AP), device to device communication, cellular communication protocols, and Bluetooth (BT) communication protocols.In operation, the loT device 104 may be configured to receive, through the application server 106, the workforce data 118 associated with the at least one worker from the plurality of workers 114 at the work site 116. The loT device 104 may be further configured to receive the workforce parameters corresponding to the workforce data 118. The workforce parameters may corresponds to at least one of an activity log, or a process ID, or an event log, or a worker efficiency, or a worker safety associated with the at least one worker. The reception of the workforce data 118 is described further, for example, with reference to FIG. 4A, FIG. 6 A, and FIG. 6B.

[0055] The loT device 104 may be configured to transmit, through the application server 106, the workforce data 118 to the client device 102. The transmission of the workforce data 118 is described further, for example, with reference to FIG. 4B, FIG. 6A, and FIG. 6B.

[0056] The loT device 104 may be configured to identify, through the GPS tracking device 108, the location data 120 associated with the at least one worker based on the workforce data 118. The loT device 104 may be further configured to identify the location parameters corresponding to the location data 118. The location parameters may corresponds to a geolocation information associated with the at least one worker. The reception of the location data 120 is described further, for example, with reference to FIG. 4A, FIG. 6A, and FIG. 6B.

[0057] The loT device 104 may be configured to automatically determine, through the application server 106, the activity to be assigned to the at least one worker at the work site 116 during the particular time instance based on the workforce data 118 and the location data 120. The activity may be indicative of the industrial activity to be carried out by the at least one worker at the work site 116. The loT device 104 may further apply the VLM 110 on the workforce data 118 and the location data 120 associated with the at least one worker. The VLM 110 may be trained on the workforce parameters corresponding to the workforce data 118 and location parameters corresponding to the location data 120 associated with the at least one worker. The loT device may further automatically determine, through the camera 112, the plurality of images associated with the status of the activity assigned to the a least one worker based on the application of the VLM 110, to monitor the status of the activity on the real-time basis. The automatic determination of the activity to be assigned to the at least one worker at the work site 116 is described further, for example, with reference to FIG.

[0058] 4A, FIG. 5A, FIG. 5B, FIG. 6A, and FIG. 6B.Modifications, additions, or omissions may be made to FIG. 1 without departing from the scope of the present disclosure. For example, the environment 100 may include more or fewer elements than those illustrated and described in the present disclosure. For instance, in some embodiments, the environment 100 may include the client device 102 or the loT device 104, but not the database 122. In addition, in some embodiments, the functionality of each of the database 122 may be incorporated into the client device 102 or the loT device 104, without a deviation from the scope of the disclosure.

[0059] FIG. 2 A is a block diagram that illustrates an exemplary loT device of FIG. 1 for remote workforce monitoring, in accordance with at least one embodiment described in the present disclosure. FIG.

[0060] 2A is explained in conjunction with elements from FIG. 1. With reference to FIG. 2A, there is shown a block diagram 200A of the loT device 104. The loT device 102 may include a memory 202, the VLM 110, a processing circuitry 204, an input / output (I / O) device 206, and a network interface 208. The I / O device 206 may include a display device 206A. The memory 202 may include the workforce data 118 and the location data 120. The memory 202 may store the dataset associated with the plurality of images associated with the status of the activity to be assigned to the at least one worker or the plurality of workers 114.

[0061] The memory 202 may include suitable logic, circuitry, interfaces, and / or code that may be configured to store one or more instructions to be executed by the processing circuitry 204. The one or more instructions stored in the memory 202 may be configured to execute the different operations of the processing circuitry 204 (and / or the loT device 104). Examples of implementation of the memory 202 may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and / or a Secure Digital (SD) card.

[0062] The processing circuitry 204 may include suitable logic, circuitry, and / or interfaces that may be configured to execute program instructions associated with different operations to be executed by the loT device 104. The operations may include, but not limited to, workforce data reception, workforce data transmission, location data identification, and automatic activity determination. The processing circuitry 204 may include any suitable special-purpose or general-purpose computer, computing entity, or processing device, including various computer hardware or software modules,and may be configured to execute instructions stored on any applicable computer-readable storage media. For example, the processing circuitry 204 may include a microprocessor, a microcontroller, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a Field-Programmable Gate Array (FPGA), or any other digital or analog circuitry configured to interpret and / or to execute program instructions and / or to process data.

[0063] Although illustrated as a single processor in FIG. 2, the processing circuitry 204 may include any number of processors configured to, individually or collectively, perform or direct performance of any number of operations of the loT device 104, as described in the present disclosure. Additionally, one or more processors may be present on one or more different loT devices, such as different servers.

[0064] In some embodiments, the processing circuitry 204 may be configured to interpret and / or execute program instructions and / or process data stored in the memory 202. The processing circuitry 204 may include one or more processing units, which may be implemented as a separate processor. The one or more processing units may be implemented as an integrated processor or a cluster of processors that perform the functions of the one or more specialized processing units, collectively. In some embodiments, the processing circuitry 204 may fetch program instructions from the VLM 110 and load the program instructions in the memory 204. After the program instructions are loaded into the memory 202, the processing circuitry 204 may execute the program instructions. The processing circuitry 204 may be implemented based on a number of processor technologies known in the art. Examples of implementations of the processing circuitry 204 may be an X86-based processor, a Graphics Processing Unit (GPU), a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CISC) processor, a co-processor, a microcontroller, a control circuit, and / or a combination thereof.

[0065] The I / O device 206 may include suitable logic, circuitry, interfaces, and / or code that may be configured to receive the workforce data 118 and the location data 120 associated with the at least one worker or the plurality of workers 114. For example, the I / O device 206 may receive the workforce parameters corresponding to the workforce data 118 and the location parameters corresponding to the location data 120. The VO device 206 may be further configured to providean output in response to the reception of the workforce data 118 and the location data 120. For example, the output may correspond to the activity to be assigned to the at least one worker or the plurality of workers 114 based on the workforce data 118 and the location data 120, or the plurality of images associated with the status of the activity. The I / O device 206 may include various input and output devices, which may be configured to communicate with the processing circuitry 204 and other components, such as the network interface 208. The I / O device 206 may further include the display device 206A. Examples of the I / O device 206 may include, but are not limited to, a display (e.g., a touch screen), a keyboard, a mouse, a joystick, a microphone, or a speaker. The I / O device 206 may be within the loT device 104 or outside of the loT device 104.

[0066] The display device 206 A may include suitable logic, circuitry, interfaces, and / or code that may be configured to display the workforce data 118, the location data 120, the activity to be assigned to the at least one worker or the plurality of workers 114, and the plurality of images associated with the assigned activity. The display device 210 may be a touch screen which may enable the user (e.g., the at least one worker or the plurality of workers 114) to provide user-inputs via the display device 206A. The touch screen may be at least one of a resistive touch screen, a capacitive touch screen, or a thermal touch screen. The display device 210 may be realized through several known technologies such as, but not limited to, at least one of a Liquid Crystal Display (LCD) display, a Light Emitting Diode (LED) display, a plasma display, or an Organic LED (OLED) display technology, or other display devices. In accordance with an embodiment, the display device 210 may refer to a display screen of a head mounted device (HMD), a smart-glass device, a see-through display, a projection-based display, an electro-chromic display, or a transparent display.

[0067] The network interface 208 may include suitable logic, circuitry, interfaces, and / or code that may be configured to facilitate communication between the processing circuitry 204 (i.e., the loT device 104) and the server 106, via the communication network 124. The network interface 208 may be implemented by use of various known technologies to support wired or wireless communication of the loT device 104 with the communication network 124. The network interface 208 may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, or a local buffer circuitry.The network interface 208 may be configured to communicate via wireless communication with networks, such as the Internet, an Intranet, or a wireless network, such as a cellular telephone network, a wireless local area network (LAN), and a metropolitan area network (MAN). The wireless communication may be configured to use one or more of a plurality of communication standards, protocols and technologies, such as Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), Long Term Evolution (LIE), 5thGeneration (5G) New Radio (NR), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11g or IEEE 802.1 In), voice over Internet Protocol (VoIP), light fidelity (Li-Fi), Worldwide Interoperability for Microwave Access (Wi-MAX), a protocol for email, instant messaging, and a Short Message Service (SMS).

[0068] In certain embodiments, the loT device 104 may be divided into a front-end subsystem and a backend subsystem. The front-end subsystem may be solely configured to receive requests / instructions from a user device, one or more of third-party servers, web servers, client machine, and the backend subsystem. These requests may be communicated back to the backend subsystem, which may be configured to act upon theses requests. For example, in case the loT device 104 is in communication with multiple servers, few of the servers may be front-end servers configured to relay the requests / instructions to remaining servers associated with the backend subsystem.

[0069] Modifications, additions, or omissions may be made to the example loT device 104 without departing from the scope of the present disclosure. For example, in some embodiments, the example loT device 104 may include any number of other components that may not be explicitly illustrated or described for the sake of brevity.

[0070] FIG. 2B is a block diagram that illustrates an exemplary processing circuitry of FIG. 2 A for remote workforce monitoring, in accordance with at least one embodiment described in the present disclosure. FIG. 2B is explained in conjunction with elements from FIG. 1 and FIG. 2A. With reference to FIG. 2B, there is shown a block diagram 200B of the processing circuitry 204. The processing circuitry 204 may include the memory 202, a transceiver 202B, digital input and output (IO) pins 204B, an altimeter 210, an accelerometer 212, a geofencing sensor 214, a battery 216, acharging circuit 218, a power management circuit 220, a protection circuit 222, a pressure sensor 224, a switch 226, and a plurality of light emitting diodes (LEDs) 228.

[0071] The transceiver 202B may comprise a transmitter and a receiver. The transmitter may be configured to transmit the workforce data 118 and the location data 120 to the client device 102. The receiver may be configured to receive the workforce data 118 and the location data 120 from the application server 106 or the database 122. The digital IO pins 204B may be positioned within the transceiver 202B to send and receive the workforce data 118 and the location data 120 between the processing circuitry 204 and external devices. The altimeter 210 may measure altitude of the at least one worker at the work site 116 based on a ground height of the at least one worker. The accelerometer 212 may detect type of the activity carried out by the at least one worker at the work site 116. The geofencing sensor 214 may determine a virtual boundary around the work site 116. In some embodiments, a response may be triggered by the application server 106 to specify the status of the activity carried out by the at least one worker at the work site 116, based on the virtual boundary. The response may be the first response specifying the status of the industrial activity as the at least one worker entering in a danger area around the work site 116. Further, the response may be a second response specifying the status of the activity as the at least one worker not present for carrying out the industrial activity at the work site 116.

[0072] In one embodiment, the processing circuitry 204 may comprise the battery 216 connected with the charging circuit 218, the power management circuit 220, and the protection circuit 222. The battery 216 may be configured to provide electrical power to the loT device 104. The charging circuit 218 may be configured to regulate charging voltage and charging current of the battery 218 for smooth operation of the loT device 104. The power management circuit 220 may be configured to convert alternating current (AC) power into direct current (DC) power, to provide an optimized DC power required for facilitating continuous operation of the loT device 104 in a smooth manner even in an event of battery discharging condition or low power of the battery 216. The protection circuit 222 may be configured to protect the power management circuit 220 from any kind of electrical failure like battery short circuited due to overcharging or battery heating.

[0073] In another embodiment, the processing circuitry 204 may comprise a pressure sensor 224 configured to detect pressure level and temperature level associated with the loT device 104, duringcarrying out the activity by the at least one worker. The pressure sensor 224 may further comprise a switch 226 having a plurality of light emitting diodes (LEDs) 228. The switch 226 may be activated by the processing circuitry 204 to trigger emission of different lights by each of the plurality of LEDs 228 in an event of detection of the first response or the second response specifying the status of the activity carried out by the at least one worker.

[0074] Modifications, additions, or omissions may be made to the example processing circuitry 204 without departing from the scope of the present disclosure. For example, in some embodiments, the example processing circuitry 204 may include any number of other components that may not be explicitly illustrated or described for the sake of brevity.

[0075] FIG. 3 is a diagram that illustrates an exemplary execution pipeline for remote workforce monitoring, in accordance with an embodiment of the disclosure. FIG. 3 is explained in conjunction with elements from FIG. 1, FIG. 2 A, and FIG. 2B. With reference to FIG. 3, there is shown an exemplary processing pipeline 300. The execution pipeline 300 may a sequence of operations that may be executed by the processing circuitry 204 of the loT device 104 of FIG. 1 for remote monitoring of the workforce.

[0076] The execution pipeline 300 includes an operation for workforce data reception 302, an operation for workforce data transmission 304, an operation for location data identification 306, and an operation for automatic activity determination 308. Though only the operations associated with the workforce data 118 and the location data 120 is shown in FIG. 3, the scope of the disclosure may not be so limited. There may be operations associated with other data inputs received or identified by the processing circuitry 204, without departure from the scope of the disclosure.

[0077] At 302, an operation for workforce data reception may be executed. The processing circuitry 204 may be configured to receive the workforce data 118 associated with the at least one worker from the plurality of workers 114 working at the work site 116. In one or more embodiments, the processing circuitry 204 may be configured to receive the workforce parameters corresponding to the workforce data 118. Herein, the workforce parameters may corresponds to at least one of the activity log, or the process ID, or the event log, or the worker efficiency, or the worker safetyassociated with the at least one worker. An exemplary implementation of an electronic UI for receiving the workforce data 118 is provided, for example, in FIG. 6A and FIG. 6B.

[0078] In one instance, the processing circuitry 204 may be further configured to measure, through the altimeter 210, the altitude of the at least one worker at the work site 116 based on the ground height of the at least one worker. The processing circuitry 204 may be further configured to detect, through the accelerometer 212, type of the activity carried out by the at least one worker based on the workforce data 118.

[0079] In another instance, the processing circuitry 204 may be further configured to extract, through the application server 106, the workforce data 118 associated with the at least one worker. The processing circuitry 204 may be further configured to determine, through the application server 106, the at least one worker or more than one worker from the plurality of workers 114 at the work site 116 required to carry out the activity at the work site 116.

[0080] At 304, an operation for workforce data transmission may be executed. The processing circuitry 204 may be configured to transmit the workforce data 118 to the client device 102. An exemplary implementation of an electronic UI for transmitting the workforce data 118 to the client device 102 is provided, for example, in FIG. 6 A and FIG. 6B.

[0081] At 306, an operation for location data identification may be executed. The processing circuitry 204 may be configured to identify the location data 120 associated with the at least one worker based on the workforce data 118. In one or more embodiments, the processing circuitry 204 may be configured to identify the location parameters corresponding to the location data 120. Herein, the location parameters may corresponds to the geolocation information associated with the at least one worker. An exemplary implementation of an electronic UI for identifying the location data 120 is provided, for example, in FIG. 6 A and FIG. 6B.

[0082] In first instance, the processing circuitry 204 may be further configured to determine, through the application server 106, the at least one worker or more than one worker from the plurality of workers 114 at the work site 116 required to carry out the activity at the work site 116 during the particular time instance based on the location data 120. The processing circuitry 204 may be furtherconfigured to determine, through the geofencing sensor 214, the virtual boundary around the work site 116.

[0083] In second instance, the processing circuitry 204 may be further configured to trigger, through the application server 106, a response specifying the status of the activity carried out by the at least one worker at the work site 116, based on the virtual boundary. The processing circuitry 204 may be further configured to detect, through the application server 106, the first response specifying the status of the activity as the at least one worker entering in a danger area around the work site 116. The processing circuitry 204 may be further configured to trigger, through the application server 106, an alarm to alert the at least one worker at the work site 116 by sending an alert notification to the loT device 104 by the client device 102. The detection of the first response and triggering of the alarm based on the first response are described further, for example, in FIG. 6A.

[0084] In third instance, the processing circuitry 204 may be further configured to detect, through the application server 106, the second response specifying the status of the activity as the at least one worker not present for carrying out the industrial activity at the work site 116. The processing circuitry 204 may be further configured to trigger, through the application server 106, an alarm by way of receiving an alert notification by the client device 102. The detection of the second response and triggering of the alarm based on the second response are described further, for example, in FIG. 6B.

[0085] At 308, an operation for automatic activity determination may be executed. The processing circuitry 204 may be configured to automatically determine, through the application server 106, the activity to be assigned to the at least one worker at the work site 116 during the particular time instance based on the workforce data 118 and the location data 120. The activity may be indicative of the industrial activity to be carried out by the at least one worker at the work site 116. An exemplary implementation of an electronic UI for automatically determining the activity to be assigned to the at least one worker at the work site 116 during the particular time instance is provided, for example, at FIG. 5 A, FIG. 5B, and FIG. 6B.

[0086] In one instance, the processing circuitry 204 may be further configured to apply the vision language model (VLM) 110 on the workforce data 118 and the location data 120 associated with the at leastone worker. The VLM 110 may be trained on the workforce parameters corresponding to the workforce data 118 and the location parameters corresponding to the location data 120 associated with the at least one worker. The processing circuitry 204 may be further configured to automatically generate, through the camera 112, the plurality of images associated with the status of the activity assigned to the at least one worker based on the application of the VLM 110, to monitor the status of the activity on the real-time basis.

[0087] In another instance, the processing circuitry 204 may be further configured to extract, through the application server 106, the workforce data 118 and the location data 120 associated with the at least one worker. The processing circuitry 204 may be further configured to determine, through the application server 106, the at least one worker or more than one worker from the plurality of workers 114 at the work site 116 required to carry out the activity during the particular time instance. The processing circuitry 204 may be further configured to generate, through the application server 106, a fault report (shown in FIG. 5 A and FIG. 5B) associated with the activity carried out by the at least one worker or more than one worker. The determination of one or more than one worker at the work site 116 required to carry out the activity during the particular time instance, and generation of the fault report are described further, for example, in FIG. 5A and FIG.

[0088] 5B.

[0089] The disclosed approach may offer several advantages. Real-time tracking of the activity assigned to the plurality of workers and ability to log a large number of work records corresponding to the plurality of workers may be achieved using techniques like location tracking and machine learning techniques. Due to detection of type of the activity carried out by the at least one worker at the work site, a virtual boundary around the work site may be determined using techniques like geofencing. Reporting of industrial events based on the workforce data may be facilitated using techniques like machine learning (ML) model training. The ML model may be trained on the workforce parameters corresponding to the workforce data and the location parameters corresponding to the location data associated with the at least one worker. Due to the reporting of industrial events, information associated with details of the work site and the details associated with event log in records of each of the plurality of workers may be extracted using the ML technique. Generation of images associated with a status of the activity assigned to the at least one worker based on the application of the ML model may be achieved using techniques like movementdetection. The movement detection technique may be used to track and monitor the status of the activity on a real-time basis. The proposed technique involves generation of fault report associated with the activity carried out by the at least one worker or more than one worker using techniques like the location tracking technique. The fault report may correspond to device identification (ID), altitude level, pressure level, and device connected status associated with the at least worker or the plurality of workers. Further, the proposed technique may involve sending notifications with respect to the status of the activity carried out by the plurality of workers on a real-time basis using techniques like interactive machine learning (ML) technique. Further, the proposed technique may involve sending Short Messaging Service (SMS) and email alerts corresponding to the change in the status of the activity carried out by the at least one worker or the plurality of workers. Additionally, this approach may be used across diverse applications such as industrial power plant operation, multiple devices monitoring, or device registration updating and maintenance applications.

[0090] FIG. 4A is a diagram that illustrates an exemplary scenario for data transfer and exchange, in accordance with one embodiment of the disclosure. FIG. 4A is described in conjunction with elements from FIG. 1, FIG. 2A, FIG. 2B, and FIG. 3. With reference to FIG. 4A, there is shown an exemplary scenario 400A for data transfer and exchange. The exemplary scenario 400A may include the loT device 104, the application server 106, the database 122, a Cloud Application 402A, a Firmware Upgrade Over the Air (FOTA) Application 404A, a Main Application 406A, and a FOTA Image 408A. The loT device 104 may communicate with the application server 106 through the communication network 124.

[0091] The cloud application 402A of the application server 106 may be configured to receive the workforce data 118 and the location data 120 associated with the plurality of workers 114 from the main application 406A of the loT device 104. Further, the cloud application 402A may be configured to receive from the main application 406A, one or more events reported during carrying out the activity by the at least one worker at the work site 116 at the particular time instance. Further, the cloud application 402A may be configured to present, through the application server 106, the status as well as type of the activity performed by the at least one worker or more than one worker. In one or more embodiments, the main application 406A of the loT device 104 may transfer to or exchange with, the workforce data 118 and location data 120 detected by the GPS trackingdevice 108, to the client device 102 through the cloud application 402A of the application server 106. Further, the main application 406A may be configured to receive the plurality of images associated with the status of the activity assigned to the at least one worker or more than one worker. Further, the main application 406A may be configured to send the plurality of images associated with the status of the activity assigned to the one or more workers to the cloud application 402A.

[0092] In some embodiments, the main application 406A may be further configured to detect the type of activity carried out by the at least one worker at the work site 116. Based on the type of activity detected carried out at the work site 116, the virtual boundary may be formed around the work site 116. The main application 406A may further trigger the cloud application 402A of the application server 106 to generate a response specifying the status of the activity carried out by the at least one worker at the work site 116, based on the virtual boundary.

[0093] The FOTA application 404A of the application server 106 may allow the client device 102 and the loT device 104 to be updated wirelessly, over the communication network 124, with new firmware. The FOTA application 404A may further update the operating firmware of the client device 102 and the loT device 104. The FOTA application 404A may further be configured to provide FOTA updates in form of the FOTA image 408A displayed on the screen of the client device 102 and the loT device 104. The FOTA image 408A may be utilized by industry managers having their client devices and the at least one worker or more than one worker having their loT devices to obtain access for downloading the firmware updates in their respective devices. The firmware updates provided by the FOTA application 404A may be used to update operating system (OS), applications, configuration settings, or encryption keys associated with the client device 102. And the loT device 104. The term “firmware” as used herein may be the software that operates at the lowest level on the client device 102 or the loT device 104. The firmware may control the most basic functions like booting of the client device 102 or the loT device 104 and acts as the foundation for other software (by interacting directly with hardware like screens, batteries, and sensors) that runs on the client device 102 or the loT device 104. Further, the firmware may provide interfaces that the OS (e.g., Android, iOS, Windows, or Linux) of the client device 102 or the loT device 104 use to communicate with the hardware running on the client device 102 or the loT device 104. As the firmware is very fundamental to the operation of devices (e.g., the client device 102 or the loTdevice 104), the firmware is designed with very few bugs as possible to ensure a stable operation of the client device 102 or the loT device 104 after installation. This makes the regular and reliable deployment and installation of the firmware updates vital.

[0094] FIG. 4B is a diagram that illustrates an exemplary scenario for data transfer and exchange, in accordance with another embodiment of the disclosure. FIG. 4B is described in conjunction with elements from FIG. 1, FIG. 2A. FIG. 2B, FIG. 3, and FIG. 4A. With reference to FIG. 4B, there is shown an exemplary scenario 400B for data transfer and exchange. The exemplary scenario 400B may include the client device 102, the loT device 104, the cloud application 402A, a processing server 402B, a virtual machine (VM) 404B, and a user interface (UI) 406B. The processing server 402B may be configured to process requests associated with the reception of the workforce data 118 and the location data 120 associated with the at least one worker. The processing server 402B may be further configured to process requests associated with identification of the location data 120 based on the workforce data 118. The processing server 402B may be further configured to process requests associated with the receiving of the one or more events reported during carrying out the activity by the at least one worker at the work site 116 at the particular time instance. The processing server 402B may be further configured to process requests associated with detection and reception of the status as well as the type of the activity performed by the at least one worker or more than one worker at the work site 116.

[0095] In one instance, the processing server 402B may communicate with the client device 102 and the loT device 104 to perform processing of different requests such as reception of the workforce data 118, transmission of the workforce data 118, identification of the location data 120, and automatic determination of the activity. The activity may be the industrial activity assigned to the at least one worker or the plurality of workers 114 at the work site 116 during the particular time instance, based on the workforce data 118 and the location data 120.

[0096] In another instance, the processing server may be communicatively coupled with the VM 404B to create virtualized version of the loT device 104 on the user interface 406B of the client device 102. The virtualized version of loT device 104 enables display of the workforce data 118 data and the location data 120 on the user interface (UI) 406B of the client device 102. For example, the VM 404B may enable display of the workforce data 118 data and the location data 120 on the userinterface (UI) 406B of the client device 102, thereby allowing the industry manager to monitor effectively day to day industrial activities performed by the plurality of workers 114, user identification associated with the plurality of workers 114, status of the industrial activity assigned to each of the plurality of workers 114, work efficiency associated with each of the plurality of workers 114, location of each of the plurality of workers 114 at the work site 116, or safety parameters associated with each of the plurality of workers 114.

[0097] FIG. 5A is a diagram that illustrates an exemplary scenario for generation of report associated with monitoring of activity status, in accordance with one embodiment of the disclosure. FIG. 5A is described in conjunction with elements from FIG. 1, FIG. 2 A, FIG. 2B, FIG. 3, FIG. 4A, and FIG.

[0098] 4B. With reference to FIG. 5A, there is shown an exemplary scenario 500A for the generation of the report associated with monitoring of the status of the activity performed by the at least one worker. The exemplary scenario 500A may include the client device 102, the loT device 104, the application server 106, and the processing server 402B. The client device 102, the loT device 104, the application server 106, and the processing server 402B may be communicatively coupled with each other through the communications network 124.

[0099] The processing circuitry 204 of the loT device 104 may be configured to extract, through the application server 106, the workforce data 118 and the location data 120 associated with the at least one worker. The processing circuitry 204 of the loT device 104 may be further configured to determine, through the application server 106, the at least one worker or the more than one worker from the plurality of workers 114 at the work site 116 required to carry out the activity during the particular time instance. The processing circuitry 204 may be further configured to generate, through the application server 106, a fault report 504A associated with the activity carried out by the at least one worker or more than one worker.

[0100] With reference to FIG. 5A, there is further shown an electronic user interface (UI) 502A, which may be rendered by the client device 102 or the loT device 104 of FIG. 1 on the display device 206A based on user request, received via application software. The application software may correspond to, for example, a software development kit (SDK), a cloud server-based application, a web-based application, an operation system (OS)-based application / application suite, an enterprise application, or a mobile application for remote workforce monitoring. For example, the loT device104 may be configured to generate the fault report 504A associated with the activity carried out by the at least one worker or the plurality of workers 114. The fault report 504A may be displayed on the electronic UI 502A of the client device 102. For example, the electronic UI 502A may display the parameters associated with the activity and the type of fault detected while carrying out the activity by one or more workers. For instance, a fault may be detected during carrying out tower testing process by the one or more workers. The process ID is displayed as: “1296”, issue detected while performing the activity by workers is displayed as: “Tower Issue: Test”, and number of workers performing the activity is displayed as: “4” (total of four workers).

[0101] It should be noted that the electronic UI 502A is merely provided as an exemplary implementation of a user interface 406B of the client device 102 of FIG. 1 and should not be construed as limiting for the scope of the disclosure. The present disclosure may also be applicable to other modifications, deletions, or additions to the client device 102, without a deviation from the scope of the present disclosure.

[0102] FIG. 5B is a diagram that illustrates an exemplary scenario for generation of report associated with monitoring of activity status, in accordance with another embodiment of the disclosure. FIG. 5B is described in conjunction with elements from FIG. 1, FIG. 2A, FIG. 2B, FIG. 3, FIG. 4A, FIG. 4B, and FIG. 5A. With reference to FIG. 5B, there is shown an exemplary scenario 500B for the generation of the report associated with monitoring of the status of the activity performed by the at least one worker. The exemplary scenario 500B may include the client device 102, the loT device 104, the application server 106, and the processing server 402B. The client device 102, the loT device 104, the application server 106, and the processing server 402B may be communicatively coupled with each other through the communications network 124.

[0103] The processing circuitry 204 of the loT device 104 may be configured to extract, through the application server 106, the workforce data 118 and the location data 120 associated with the at least one worker. The processing circuitry 204 of the loT device 104 may be further configured to determine, through the application server 106, the at least one worker or the more than one worker from the plurality of workers 114 at the work site 116 required to carry out the activity during the particular time instance. The processing circuitry 204 may be further configured to generate,through the application server 106, a fault report 504A associated with the activity carried out by the at least one worker or more than one worker.

[0104] With reference to FIG. 5B, there is further shown an electronic user interface (UI) 502B, which may be rendered by the client device 102 or the loT device 104 of FIG. 1 on the display device 206A based on user request, received via application software. The application software may correspond to, for example, a software development kit (SDK), a cloud server-based application, a web-based application, an operation system (OS)-based application / application suite, an enterprise application, or a mobile application for remote workforce monitoring. For example, the loT device 104 may be configured to generate the fault report 504B associated with the activity carried out by the at least one worker or the plurality of workers 114. The fault report 504B may be displayed on the electronic UI 502B of the client device 102. For example, the electronic UI 502B may display the parameters associated with the activity and the type of fault detected while carrying out the activity by one or more workers. For instance, a fault may be detected during carrying out clamp circuit testing process by the one or more workers at the tower. The process ID is displayed as: “4857”, issue detected while performing the activity by workers is displayed as: “Tower Issue: Circuit Testing”, and status of the activity performed by the one or more workers and associated with clamp circuit testing is displayed as: “Both clamps connected in the clamp circuit at the tower”.

[0105] It should be noted that the electronic UI 502B is merely provided as an exemplary implementation of a user interface 406B of the client device 102 of FIG. 1 and should not be construed as limiting for the scope of the disclosure. The present disclosure may also be applicable to other modifications, deletions, or additions to the client device 102, without a deviation from the scope of the present disclosure.

[0106] FIG. 6A is a diagram that illustrates an example scenario for detection of status of activity as a first activity status, in accordance with an embodiment of the disclosure. FIG. 6A is described in conjunction with elements from FIG. 1, FIG. 2A, FIG. 2B, FIG. 3, FIG. 4A, FIG. 4B, FIG. 5 A, and FIG. 5B. With reference to FIG. 6A, there is shown an exemplary scenario 600A for the detection of the status of the activity performed by the at least one worker as the first activity status. The exemplary scenario 600A may include the client device 102, the loT device 104, the applicationserver 106, and the processing server 402B. The client device 102, the loT device 104, the application server 106, and the processing server 402B may be communicatively coupled with each other through the communications network 124.

[0107] The processing circuitry 204 of the loT device 104 may be configured to detect, through the application server 106, a first response 604A specifying the status of the activity as the at least one worker entering in a danger area around the work site 116. The processing circuitry 204 may be further configured to trigger, through the application server 106, an alarm to alert the at least one worker at the worksite 116 by way of sending an alert notification by the client device 102 to the loT device 104.

[0108] With reference to FIG. 6A, there is further shown an electronic user interface (UI) 602A, which may be rendered by the client device 102 or the loT device 104 of FIG. 1 on the display device 206A based on user request, received via application software. The application software may correspond to, for example, a software development kit (SDK), a cloud server-based application, a web-based application, an operation system (OS)-based application / application suite, an enterprise application, or a mobile application for remote workforce monitoring. For example, the loT device 104 may be configured to detect the first response 604A specifying the status of the activity as the at least one worker entering in the danger area around the work site 116. The first response 604A may be received and displayed on the electronic UI 602A of the client device 102. For example, the electronic UI 602A may display the option for raising alarm to alert the at least one worker entering in the danger area around the work site 116. For instance, the first response 604A may be detected by displaying worker ID or employee ID as: “CB7842”. Type of the area where the at least one worker is entering is displayed as: “Industrial Area Type: Dangerous”. Based on this detected information, a notification of raising alarm with option of “YES” or “NO” is also displayed on the client device 102.

[0109] It should be noted that the electronic UI 602A is merely provided as an exemplary implementation of a user interface 406B of the client device 102 of FIG. 1 and should not be construed as limiting for the scope of the disclosure. The present disclosure may also be applicable to other modifications, deletions, or additions to the client device 102, without a deviation from the scope of the present disclosure.FIG. 6B is a diagram that illustrates an example scenario for detection of status of activity as a second activity status, in accordance with an embodiment of the disclosure. FIG. 6A is described in conjunction with elements from FIG. 1, FIG. 2A, FIG. 2B, FIG. 3, FIG. 4A, FIG. 4B, FIG. 5 A, FIG. 5B, and FIG. 6A. With reference to FIG. 6B, there is shown an exemplary scenario 600B for the detection of the status of the activity performed by the at least one worker as the second activity status. The exemplary scenario 600B may include the client device 102, the loT device 104, the application server 106, and the processing server 402B. The client device 102, the loT device 104, the application server 106, and the processing server 402B may be communicatively coupled with each other through the communications network 124.

[0110] The processing circuitry 204 of the loT device 104 may be configured to detect, through the application server 106, a second response 604B specifying the status of the activity as the at least one worker not present for carrying out the industrial activity at the work site 116. The processing circuitry 204 may be further configured to trigger, through the application server 106, an alarm by way of receiving an alert notification by the client device 102.

[0111] With reference to FIG. 6B, there is further shown an electronic user interface (UI) 602B, which may be rendered by the client device 102 or the loT device 104 of FIG. 1 on the display device 206A based on user request, received via application software. The application software may correspond to, for example, a software development kit (SDK), a cloud server-based application, a web-based application, an operation system (OS)-based application / application suite, an enterprise application, or a mobile application for remote workforce monitoring. For example, the loT device 104 may be configured to detect the second response 604B specifying the status of the activity as the at least one worker not present for carrying out the industrial activity at the work site 116. The second response 604B may be received and displayed on the electronic UI 602B of the client device 102. For example, the electronic UI 602B may display the option for receiving the alert notification by the client device 102 to alert the industry manager about the status of the activity and take necessary action. For instance, the second response 604B may be detected by displaying worker ID or employee ID as: “CB7842”. Attendance Status of the at least one worker is entering is displayed as: “Employee not present for carrying out rigger movement and clamps connection at tower-2”. This may imply that the at least one worker or employee who was supposed to carry out the rigger movement and clamps connection activity at second tower of the work site 116 is notpresent. Based on this detected information, an alert notification notifying the industry manager of receiving alarm notification with option of “YES” or “NO” is also displayed on the client device 102.

[0112] It should be noted that the electronic UI 602B is merely provided as an exemplary implementation of a user interface 406B of the client device 102 of FIG. 1 and should not be construed as limiting for the scope of the disclosure. The present disclosure may also be applicable to other modifications, deletions, or additions to the client device 102, without a deviation from the scope of the present disclosure.

[0113] FIG. 7 is a flowchart that illustrates exemplary operations for remote workforce monitoring, in accordance with an embodiment of the disclosure. FIG. 7 is described in conjunction with elements from FIG. 1, FIG. 2A, FIG. 2B, FIG. 3, FIG. 4A, FIG. 4B, FIG. 5 A, FIG. 5B, FIG. 6A, and FIG.

[0114] 6B. With reference to FIG. 7, there is shown a flowchart 700. The flowchart 700 may include operations from 702 to 710 and may be implemented by the loT device 104 of FIG. 1 or by the processing circuitry 204 of FIG. 2A. The flowchart 700 may start at 702 and proceed to 704.

[0115] At 704, the workforce data 118 associated with the at least one worker from the plurality of workers 114 working at the work site 116 may be received. The processing circuitry 204 may be configured to receive the workforce data 118 associated with the at least one worker from the plurality of workers 114 working at the work site 116. The reception of the workforce data 118 is described further, for example, in FIG. 3 (at 302 in FIG. 3).

[0116] At 706, the received workforce data 118 associated with the at least one worker may be transmitted. The processing circuitry 204 may be configured to transmit the received workforce data 118 associated with the at least one worker to the client device 102. The transmission of the received workforce data 118 is described further, for example, in FIG. 3 (at 304 in FIG. 3).

[0117] At 708, the location data 120 associated with the at least one worker from the plurality of workers 114 working at the work site 116 may be identified. The processing circuitry 204 may be configured to identify the location data 120 associated with the at least one worker from the plurality of workers 114 working at the work site 116, based on the workforce data 118. The identification ofthe location data 120 associated with the at least one worker is described further, for example, in FIG. 3 (at 306 in FIG. 3).

[0118] At 710, the activity to be assigned to the at least one worker at the work site 116 may be automatically determined. The processing circuitry 204 may be configured to automatically determine the activity to be assigned to the at least one worker at the work site 116 during the particular time instance, based on the workforce data 118 and the location data 120. The activity may be indicative of the industrial activity to be carried out by the at least worker at the work site 116. The automatic determination of the activity to be assigned to the at least one worker is described further, for example, in FIG. 3 (at 308 in FIG. 3). Control may pass to end.

[0119] Although the flowchart 700 is illustrated as discrete operations, such as 702, 704, 706, 708, and 710 the disclosure is not so limited. Accordingly, in certain embodiments, such discrete operations may be further divided into additional operations, combined into fewer operations, or eliminated, depending on the implementation without detracting from the essence of the disclosed embodiments.

[0120] Various embodiments of the disclosure may provide one or more non-transitory computer-readable storage medium configured to store instructions that, in response to being executed, cause a system (such as the example system 100 or the example loT device of the system 100) to perform a set of operations. The set of operations may include receiving, through an application server 106, workforce data associated with at least one worker of a plurality of workers 114 at a work site 116. The set of operations may further include transmitting, through the application server 106, the workforce data 118 to the client device 102. The set of operations may further include identifying, through a global positioning system (GPS) tracking device 108, a location data 120 associated with the at least one worker based on the workforce data 118. The set of operations may further include automatically determining, through the application server 106, an activity to be assigned to the at least one worker at the work site 116 during a particular time instance based on the workforce data 118 and the location data 120. The activity may be indicative of an industrial activity to be carried out by the at least one worker at the work site 116.The various actions, acts, blocks, steps, or the like in the flow diagrams may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

[0121] The present disclosure may also be positioned in a computer program product, which comprises all the features that enable the implementation of the methods described herein, and which when loaded in a computer program is able to carry out these methods. Computer program, in the present context, means any expression, in any language, code or notation, of a set of instructions intended to cause a system with information processing capability to perform a particular function either directly, or after either or both of the following: a) conversion to another language, code or notation; b) reproduction in a different material form.

[0122] The embodiments disclosed herein can be implemented using at least one software program running on at least one hardware device and performing network management functions to control the elements.

[0123] While the present disclosure is described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, any equivalents may be substituted without departure from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departure from its scope. Therefore, it is intended that the present disclosure is not limited to the embodiment disclosed, but that the present disclosure will include all embodiments that fall within the scope of the appended claims.

[0124]

Claims

Claims:

1. A system (100) for remote workforce monitoring, the system (100) comprising:a client device (102);an Internet of Things (loT) device (104) communicably coupled with the client device (102), characterized in that:a memory (202); anda processing circuitry (204) communicably coupled with the memory (202) and configured to:receive, through an application server (106), workforce data (118) associated with at least one worker of a plurality of workers (114) working at a work site (116); transmit, through the application server (106), the workforce data (118) to the client device (102);identify, through a global positioning system (GPS) tracking device (108), a location data (120) associated with the at least one worker based on the workforce data (118); andautomatically determine, through the application server (106), an activity to be assigned to the at least one worker at the work site (116) during a particular time instance based on the workforce data (118) and the location data (120), the activity indicative of an industrial activity to be carried out by the at least one worker at the work site (116).

2. The system (100) as claimed in claim 1 , wherein the processing circuitry (204) is further configured to:apply a vision language model (VLM) (110) on the workforce data (118) and the location data (120) associated with the at least one worker, wherein the VLM (110) is trained on workforce parameters corresponding to the workforce data (118) and location parameters corresponding to the location data (120) associated with the at least one worker; andautomatically generate, through a camera (112), a plurality of images associated with a status of the activity assigned to the at least one worker based on the application of the VLM (110), to monitor the status of the activity on a real-time basis.

3. The system (100) as claimed in claim 1, wherein the processing circuitry (204) is further configured to:measure, through an altimeter (210), altitude of the at least one worker at the work site (116) based on a ground height of the at least one worker.

54. The system (100) as claimed in claim 1 , wherein the processing circuitry (204) is further configured to:detect, through an accelerometer (212), type of the activity carried out by the at least one worker at the work site (116);10 determine, through a geofencing sensor (214), a virtual boundary around the work site (116); andtrigger, through the application server (106), a response specifying the status of the activity carried by the at least one worker at the work site (116), based on the virtual boundary.

155. The system ( 100) as claimed in claim 4, wherein the processing circuitry (204) is further configured to:detect, through the application server (106), a first response (604A) specifying the status of the activity as the at least one worker entering in a danger area around the work site (116); and trigger, through the application server (106), an alarm to alert the at least one worker at the 20 work site (116) by way of sending an alert notification by the client device (102) to the loT device (104).

6. The system (100) as claimed in claim 4, wherein the processing circuitry (204) is further configured to:25 detect, through the application server (106), a second response (604B) specifying the status of the activity as the at least one worker not present for carrying out the industrial activity at the work site (116); andtrigger, through the application server (106), an alarm by way of receiving an alert notification by the client device (102).

307. The system (100) as claimed in claim 1, wherein the system (100) comprises a battery (216) configured to provide electrical power to the loT device (104), and a charging circuit (218) toregulate charging voltage and charging current provided to the battery (216) for operation of the loT device (104).

8. The system (100) as claimed in claim 1, wherein the system (100) comprises a power management circuit (220) configured to convert alternating current (AC) power received by the loT device (104) into a direct current (DC) power, to provide an optimized DC power for operation of the loT device (104).

9. The system (100) as claimed in claim 8, wherein the system (100) comprises a protection circuit (222) configured to protect the power management circuit (220) from any kind of electrical failure.

10. The system (100) as claimed in claim 1, wherein the processing circuitry (204) is further configured to:extract, through the application server (106), the workforce data (118) and the location data (120) associated with the at least one worker;determine, through the application server (106), the at least one worker or more than one worker from the plurality of workers (114) at the work site (116) required to carry out the activity during the particular time instance; andgenerate, through the application server (106), a fault report (504A) associated with the activity carried out by the at least one worker or more than one worker.

11. The system (100) as claimed in claim 1, wherein the system (100) comprises a pressure sensor (224) configured to detect pressure and temperature associated with the loT device (104), during carrying out the activity by the at least one worker.

12. The system (100) as claimed in claim 11, wherein the pressure sensor (224) comprises a switch (226) having a plurality of light emitting diodes (LEDs) (228), and the switch (226) being activated to trigger emission of different lights by each of the plurality of LEDs (228) in an event of detection of a first response (604A) or a second response (604B).

13. The system (100) as claimed in claim 2, wherein the workforce parameters (118) corresponds to at least one of: an activity log, or a process identification (ID), or an event log, or a worker efficiency, or a worker safety associated with the at least one worker.

514. The system (100) as claimed in claim 1, wherein the location parameters corresponds to a geolocation information associated with the at least one worker.

15. A method (700), comprising:in an loT device (104) including a memory (202) and a processing circuitry (204), the 0 processing circuitry (204) configured to:receiving, through an application server (106), workforce data (118) associated with at least one worker of a plurality of workers (114) at a work site (116);transmitting, through the application server (106), the workforce data (118) to the client device (102);5 identifying, through a global positioning system (GPS) tracking device (108), a location data (120) associated with the at least one worker based on the workforce data (118); and automatically determining, through the application server (106), an activity to be assigned to the at least one worker at the work site (116) during a particular time instance based on the workforce data (118) and the location data (120), the activity indicative of an industrial activity to 0 be carried out by the at least one worker at the work site (116).

16. The method (700) as claimed in claim 15, further comprising:applying a VLM (110) on the workforce data (118) and the location data (120) associated with the at least one worker, wherein the VLM (110) is trained on workforce parameters 5 corresponding to the workforce data (118) and location parameters corresponding to the location data (120) associated with the at least one worker; andautomatically generating, through a camera (112), a plurality of images associated with a status of the activity assigned to the at least one worker based on the application of the VLM (110), to monitor the status of the activity on a real-time basis.

017. The method (700) as claimed in claim 15, further comprising:measuring, through an altimeter (212), altitude of the at least one worker at the work site (116) based on a ground height of the at least one worker.

18. The method (700) as claimed in claim 15, further comprising:detecting, through an accelerometer (214), to detect type of the activity carried out by the at least one worker at the work site (116);determining, through a geofencing sensor (216), a virtual boundary around the work site (116); andtriggering, through the application server (106), a response specifying the status of the activity carried by the at least one worker at the work site (116), based on the virtual boundary.

19. The method (700) as claimed in claim 15, further comprising:detecting, through the application server (106), a first response (604A) specifying the status of the activity as the at least one worker entering in a danger area around the work site (116); and triggering, through the application server (106), an alarm to alert the at least one worker at the work site (116) by way of sending an alert notification by the client device (102) to the loT device (104).

20. The method (700) as claimed in claim 15, further comprising:detecting, through the application server (106), a second response (604B) specifying the status of the activity as the at least one worker not present for carrying out the industrial activity at the work site (116); andtriggering, through the application server (106), an alarm by way of receiving an alert notification by the client device (102).

21. The method (700) as claimed in claim 15, further comprising:extracting, through the application server (106), the workforce data (118) and the location data (120) associated with the at least one worker;determining, through the application server (106), the at least one worker or more than one worker from the plurality of workers (114) required to carry out the activity at the work site (116) during the particular time instance; andgenerating, through the application server (106), a fault report (504A) associated with the activity carried out by the at least one worker or more than one worker.

22. An loT device (104) for remote workforce monitoring, the loT device (104) comprising:a memory (202); anda processing circuitry (204) communicably coupled with the memory (202) and configured for:receiving, through an application server (106), workforce data (118) associated with at least one worker of a plurality of workers (114) at a work site (116);transmitting, through the application server (106), the workforce data (118) to the client device (102);identifying, through a global positioning system (GPS) tracking device (108), a location data (120) associated with the at least one worker based on the workforce data (118); and automatically determining, through the application server (106), an activity to be assigned to the at least one worker at the work site (116) during a particular time instance based on the workforce data (118) and the location data (120), the activity indicative of an industrial activity to be carried out by the at least one worker at the work site (116).

Images