Method of creating operator digital profile and related uses

Abstract

To provide a method and system for performing operation optimization through virtual simulation.SOLUTION: A method disclosed herein comprises: causing a processor to generate operator digital profiles associated with a plurality of operators, perform virtual simulation using the operator digital profiles as input to a first model, and generate performance prediction as output from the first model; and performing operation optimization based on the performance prediction.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure generally relates to methods and systems for performing work optimization through virtual simulation.

Background Art

[0002] In manufacturing and production processes involving a certain level of operator activity, quantitative information regarding performance, efficiency, throughput, and work quality are difficult to obtain and predict. However, information such as detailed operator capabilities for a particular task, learning curves for new tasks, task times for each operation, and work quality are important information used when designing, monitoring, analyzing, and improving manufacturing processes. When designing a new production line or modifying an existing one, the performance and throughput of each operation play an important role in ensuring the proper flow of the manufacturing process. The throughput and performance of machines and / or automated processes can be obtained relatively easily. However, on the other hand, the performance and throughput of manual work are relatively difficult to obtain.

Summary of the Invention

Problems to be Solved by the Invention

[0003] In related art, methods for generating performance and throughput through estimation have been disclosed. However, the information obtained through estimated performance may rather be inaccurate, and its application tends to lead to incorrect results.

[0004] In related art, methods for generating performance and throughput through physical measurement in an inspection environment have been disclosed. However, the preparation of the inspection environment is not only time-consuming but also costly.

Means for Solving the Problems

[0005] Aspects of the present disclosure involve innovative methods for performing work optimization through virtual simulation. The method may include generating, by a processor, worker digital profiles associated with a plurality of workers; performing, by the processor, a virtual simulation using the worker digital profiles as input to a first model; generating, by the processor, a performance prediction as output from the first model; and performing work optimization based on the performance prediction.

[0006] Aspects of the present disclosure involve innovative non-transitory computer-readable media storing instructions for performing work optimization through virtual simulation. The instructions may include generating worker digital profiles associated with a plurality of workers; performing a virtual simulation using the worker digital profiles as input to a first model; generating a performance prediction as output from the first model; and performing work optimization based on the performance prediction.

[0007] Aspects of the present disclosure involve innovative server systems for performing work optimization through virtual simulation. The system may include generating, by a processor, worker digital profiles associated with a plurality of workers; performing, by the processor, a virtual simulation using the worker digital profiles as input to a first model; generating, by the processor, a performance prediction as output from the first model; and performing work optimization based on the performance prediction.

[0008] Aspects of the present disclosure involve an innovative system for performing work optimization through virtual simulation. The system may include means for generating worker digital profiles associated with a plurality of workers, means for using the worker digital profiles to perform a virtual simulation as an input to a first model, means for generating a performance prediction as an output from the first model, and means for performing work optimization based on the performance prediction.

[0009] Hereinafter, a general architecture for implementing various features of the present disclosure will be described with reference to the drawings. The drawings and the associated description are provided to illustrate exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. Throughout the drawings, reference numerals are reused to indicate the correspondence between the elements being referenced.

Brief Description of the Drawings

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[0011] The following detailed description provides details of the figures and exemplary embodiments of the present application. Reference numerals and descriptions of overlapping elements between the figures are omitted for clarity. The terms used throughout the description are provided as examples and are not intended to be limiting. For example, the use of the term "automated" can include fully automated or semi-automated embodiments, including user or administrator control over a particular mode of implementation, depending on the desired embodiments of those skilled in the art practicing the embodiments of the present application. The selection can be performed by the user via a user interface or other input means, or can be implemented via a desired algorithm. Exemplary embodiments as described herein can be utilized either alone or in combination, and the functions of those exemplary embodiments can be implemented.

[0012] Exemplary embodiments provide a novel method for measuring and modeling worker capabilities in various classifications and performing a simulation (virtual simulation) of a task / job based on the measurements. Once the worker characteristics are measured / modeled, a digital profile or digital twin of that worker is then generated. Using this digital profile, the performance of the worker / individual can be simulated for new tasks (e.g., tasks that the individual has never performed before). This digital profile can be used to map the worker's skill level and can provide information to assist in assigning work and / or training.

[0013] Figure 1 shows an exemplary process flow 100 for generating an operator digital profile according to an exemplary embodiment. The preparation and characterization of the operator digital profile are performed by the process flow 100. In order to characterize the capabilities of the operator / individual, several standard tests need to be conducted. At step S102, quantitative measurements of the operator capabilities of the operator / individual are obtained through a set of standard tests. These standard tests may involve, but are not limited to, classifications such as muscle strength, speed, balance, coordination, accuracy rate, agility, reaction time, and sensation. To perform the standard tests, multiple sensors such as fixed or wearable cameras, wearable sensors, health monitoring devices, etc. are applied to obtain information / results (quantitative measurements) such as pressure, force, angle, speed, time, completion status, quality inspection, temperature, geometry, etc.

[0014] At step S104, these measurements are provided as inputs to an artificial intelligence (AI) / machine learning (ML) model to characterize and parameterize the operator capabilities in various classifications. The AI / ML model may include, but is not limited to, a convolutional neural network (CNN), a recurrent neural network (RNN), a deep RNN (DRNN), a Q-learning network (QN), a deep Q-learning network (DQN), linear regression, logistic regression, decision trees, k-nearest neighbor methods, etc. The RNN may include long short-term memory (LSTM).

[0015] Next, the process proceeds to step S106, where the worker / personal worker digital profile is generated as the output of the AI / ML model. This worker digital profile may include quantitative measurements of worker capabilities in various classifications. In some exemplary embodiments, each classification may further include sub-classifications. For example, the classification of "muscular strength" may have sub-classifications for different muscle groups and sub-classifications for performance in different types of muscle strength-related tasks such as lifting, horizontal propagation, and arm / trunk angles. In alternative exemplary embodiments, a rule-based model may be utilized instead of the AI / ML model when characterizing and parameterizing worker capabilities and generating the worker digital profile. At step S108, the generated worker digital profile is stored in a data storage, which may be local memory or centralized storage on the cloud.

[0016] Figure 2 shows an exemplary process flow 200 for applying a worker digital profile according to an exemplary embodiment. This process starts at step S202 where information associated with a new job / task is received. Such information may include a job / task description and quantitative job / task parameters. At step S204, an artificial intelligence (AI) / machine learning (ML) model receives and analyzes the information associated with the new job / task and the worker digital profile as derived in FIG. 1. The AI / ML model is trained using past worker digital profiles and may include, but is not limited to, convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep RNNs (DRNNs), Q-learning networks (QNs), deep Q-learning networks (DQNs), linear regression, logistic regression, decision trees, k-nearest neighbor methods, etc. The RNN may include long short-term memory (LSTM). The AI / ML model analyzes the information associated with the new job / task (e.g., job content) and simulates worker performance using the information provided from the worker digital profile (through virtual simulation).

[0017] In step S206, the performance prediction is generated as the output from the AI / ML model. This performance prediction may include information such as the ability of the worker / individual to perform the work (e.g., can or cannot), the estimated training time of the training for the work, the estimated task completion time, the estimated performance metrics (e.g., speed, quality, etc.), the estimated fatigue level, the work progress, etc., but is not limited thereto. These prediction results may be stored in the data storage.

[0018] In step S208, the generated performance prediction is used when performing various optimizations and business operations. Such operations may include, but are not limited to, work cycle time estimation in manufacturing, simulation of production line performance for line balancing / reconfiguration, performance simulation for production line design, worker skill mapping, training performance, video game design and customization, etc. In an alternative exemplary embodiment, a rule-based model may be utilized instead of the AI / ML model when performing the performance prediction.

[0019] FIG. 3 shows an exemplary system configuration 300 according to an exemplary embodiment. As shown in FIG. 3, the system configuration 300 may include components such as a standard inspection module 302, a sensing module 304, a data collection module 306, a data storage module 308, a worker characterization module 310, a worker digital profile module 312, a simulation module 314, a work prediction module 316, etc. The standard inspection module 302, the sensing module 304, the data collection module 306, the data storage module 308, and the worker characterization module 310 together form the worker digital profile generation process described in FIG. 1. The simulation module 314 and the work prediction module 316 together form the worker digital profile application process described in FIG. 2.

[0020] The standard inspection module 302 stores a set of standard inspections for various classifications performed by an operator / individual. The standard inspection module 302 can be physically or wirelessly connected to several sensors in order to quantify the operator capabilities of the operator / individual in several classifications. The sensing module 304 receives sensor data (raw data) generated from the sensors.

[0021] The data collection module 306 receives sensor data (raw data) from the sensing module 304 and performs data preprocessing on the received sensor data. Data preprocessing may include cleaning, transforming, and integrating the sensor data to prepare the sensor data for subsequent processing steps. The preprocessed data is then stored in the data storage module 308 and retrieved for processing.

[0022] The operator characterization module 310 analyzes the preprocessed data to generate an operator digital profile, which is then stored in the operator digital profile module 312. In some exemplary embodiments, the data storage module 308 and the operator digital profile module 312 are part of a data storage (not shown) to the system configuration 300. In some exemplary embodiments, at least one of the data storage module 308 or the operator digital profile module 312 exists on cloud storage.

[0023] The operator digital profile application process is initiated through the reception of new work / task information in the simulation module 314. The simulation module 314 then performs a work prediction by analyzing the received new work / task information and the operator digital profile as obtained from the operator digital profile module 312. The generated work prediction is then output to the work prediction module 316, retrieved and further processed for various applications / uses.

[0024] In some exemplary embodiments, any verification module 318 may be included as part of the system configuration 300. The verification module 318 may perform enhancement of the worker digital profile and may include two sub-modules, namely a verification data collection module 320 and a verification data analysis module 322. Data regarding the physical performance of work / tasks by a worker / individual is collected using the verification data collection module 320 and can be used by the verification data analysis module 322 when performing data analysis. The verification data analysis module 322 compares the actual physical capabilities for the work / tasks performed by the worker with the predicted output from the work prediction module 316 in order to optimize / improve the relevant worker digital profile stored in the worker digital profile module 312. A quantitative measurement of the worker's ability is updated based on this comparison and can be used to further improve the accuracy of the results generated by the simulation module 314 and the work prediction module 316 in subsequent simulations.

[0025] First Embodiment - Line Balancing

[0026] Variations in throughput are common across production lines in the automotive manufacturing process. When such variations are detected, the manufacturer then performs line balancing across the various stations of the production line to address the variations and improve throughput. Line balancing involves the identification of slow or problematic stations and the reconfiguration of work between different stations.

[0027] Figure 4 shows a conventional line balancing process 400. The detailed task information 402 of various processes is typically measured manually at each workstation and analyzed manually to identify problems in the production / manufacturing line. Such detailed task information 402 typically includes the time taken for each task, target / standard work instructions, performance quality, etc. Based on this analysis, new line configurations / line recommendations 404 are proposed by experienced engineers and implemented on-site. A reconfiguration trail 406 for documenting configuration changes is always generated when the line configuration is updated. Then, manual measurements are performed to obtain updated detailed task information 402 in order to evaluate the modified line configuration. This balancing process can be repeated multiple times to further improve the production / manufacturing outcome. This process itself can be time-consuming and costly due to the labor involved as well as production downtime from the configuration implementation mode.

[0028] Figure 5 shows an exemplary line balancing process 500 according to an exemplary embodiment. The line balancing process 500 achieves higher efficiency, cost reduction, and minimization of obstacles to the production / manufacturing process compared to the conventional line balancing process. First, the detailed task information 502 is generated by performing automated data collection and analysis, which will be described in more detail below in connection with Figure 6. The line configuration module 504 encompasses the details of the configuration of the production / manufacturing line. The ontology database module 506 includes 4M ontology data (operator data 508, machine data 510, material data 512, and method data 514). Together, the detailed task information 502, the line configuration information of the line configuration module 504, and the 4M ontology data of the ontology database module 506 are combined and input into the line configurator module 516.

[0029] The line configurator module 516 analyzes the current performance and generates a proposed configuration based on the ontology data along with physical constraints. The generated configuration is then sent as input to the work simulation module 518. The work simulation module 518 executes the simulation function of the simulation module 314 in FIG. 3, and obtains the configuration generated by the line configurator module 516 and the digitized worker ability data (worker digital profile 524 of the relevant operator of the production line) from the worker digital profile module 312 when executing the simulation. Next, the simulated performance of the configuration is output to the line optimizer 520, and the line optimizer 520 compares the performance results and removes candidates based on the results (for example, performance below the performance threshold). The remaining candidates are then sent back to the line configurator module 516 along with updated constraints and results for additional configuration updates. The process consisting of configuration generation, simulation, and optimization can be repeated until one or a predetermined number of candidate configurations are generated as line recommendations 522. If a single candidate configuration is generated, that configuration is implemented. If a predetermined number of candidates are generated, the user selects the configuration to implement.

[0030] Based on the above, the line balancing process 500 does not require actual trial and error to be performed by the worker / operator. In addition, the use of the worker digital profile enables the generation of more accurate and reliable predictions and recommendations than conventional methods. For example, a task that requires 10 seconds for operator A to execute may require 15 seconds for operator B to execute. The worker digital profile can identify such differences in operator capabilities and enable the generation of predictions that take such differences into account.

[0031] FIG. 6 shows an exemplary FIG. 600 for performing automated detailed task information collection according to an exemplary embodiment. During the production process, position data may be obtained from a camera and a Light Detection and Ranging (LiDAR) sensor 602, while other production data (e.g., work errors, etc.) may be obtained using other sensors / sensing devices such as a sensor glove 604 (i.e., a wearable sensing device). The camera and LiDAR sensor 602 generate an image / video from the camera and LiDAR data from the LiDAR sensor. Then, data analysis (AI / ML, image recognition, feature extraction, etc.) is performed on the data to generate operator and / or object position data 606 (e.g., identifier, timestamp, orientation of the object (x, y, z), etc.). Then, the position data 606 is analyzed (e.g., applying AI / ML, feature extraction) to identify a work location 608 (e.g., tool rack, task site, installation location, primary work area, etc.). Then, by analyzing the work location 608, primary work data 610 is derived.

[0032] The sensor glove 604 generates other production data, and then this other production data is processed (e.g., pattern recognition, sampling, data post-processing, etc.) to generate motion data 612 (e.g., force, pressure, sound, etc.). Then, the motion data 612 is analyzed (e.g., applying AI / ML, data post-processing, etc.) to generate detailed work data 614 (e.g., pickup instrument / tool, drilling, installation tool, etc.). Then, by analyzing the detailed work data 614, detailed individual work data 616 is derived.

[0033] Then, the primary work data 610 and the detailed individual work data 616 are analyzed and combined to generate detailed task information 502. The collected detailed task information can be used to perform work variation analysis, line balancing, work quality verification, work verification, design / redesign of the production line, training, etc.

[0034] Second Embodiment - Production Line Design and Virtual Commissioning

[0035] FIG. 7 shows an exemplary diagram 700 for performing a production line design, according to an exemplary embodiment. A new production line design (new design 702) is proposed and provided to a production simulation module 704 that performs a function similar to the simulation module 314 of FIG. 3. In addition to the new design 702, the production simulation module 704 also receives a worker digital profile 706 generated by the worker digital profile module 312 when performing a production simulation. The production simulation module 704 then generates a performance prediction 708, which is then used to determine whether to implement the new design 702 or to generate additional recommendations using a design optimization module 710. If recommendations are generated by the design optimization module 710, those recommendations are applied as an updated new design 702, and additional simulations are performed based on the updated design.

[0036] Third Embodiment - Game Design

[0037] The worker digital profile can also be used in the design / creation of virtual characters in games or any metaverse-based software. Players can participate in a set or subset of the inspections performed by the standard inspection module 302 of FIG. 3 when generating their digital profiles. The worker digital profile can then be utilized to enhance the in-game reality representation of the player and the player's abilities.

[0038] Fourth Embodiment - Skill Mapping and Training

[0039] The generation of the worker digital profile naturally provides criteria for the operator's skills and abilities. Additional insights can be generated to recommend training materials and track the progress of training.

[0040] The above exemplary embodiments can have various benefits and advantages. For example, it is an unconventional way to improve product line configuration through the generation of an operator digital profile when performing virtual simulations based on the operator digital profile to map the exact skill level of the operator and optimize the line configuration. When generating an operator digital profile that uniquely characterizes and parameterizes the capabilities of employees / operators, which was not achievable in the related art, measurement values derived from various sensors and inspections are provided as inputs to the AI / ML model. The implementation of the operator digital profile in training and simulation helps to quantify the performance of the operator / employee, reduce costs, and minimize obstacles to the actual production / manufacturing process.

[0041] FIG. 8 shows an exemplary computing environment having an exemplary computing device suitable for use in some exemplary embodiments. The computing device 805 in the computing environment 800 can include one or more processing units, cores, or processors 810, memory 815 (e.g., RAM, ROM, and / or the like), internal storage 820 (e.g., magnetic, optical, solid-state storage, and / or organic), and / or an I / O interface 825, any of which can be coupled on a communication mechanism or bus 830 for communicating information, or can be incorporated into the computing device 805. The I / O interface 825 is further configured to receive images from a camera or provide images to a projector or display, depending on the desired embodiment.

[0042] Computing device 805 can be communicatively coupled to an input / user interface 835 and an output device / interface 840. One or both of the input / user interface 835 and the output device / interface 840 can be a wired or wireless interface and can be removable. The input / user interface 835 can include any physical or virtual device, component, sensor, or interface (e.g., buttons, touch screen interface, keyboard, pointing / cursor control, microphone, camera, Braille, motion sensor, accelerometer, optical reader, and / or the like) that can be used to provide input. The output device / interface 840 can include a display, television, monitor, printer, speaker, Braille, or the like. In some exemplary embodiments, the input / user interface 835 and the output device / interface 840 can be incorporated with or physically coupled to the computing device 805. In other exemplary embodiments, other computing devices can function as or provide the functionality of the input / user interface 835 and the output device / interface 840 for the computing device 805.

[0043] Examples of computing device 805 can include, but are not limited to, highly mobile devices (e.g., smartphones, devices mounted on vehicles and other machines, devices held by persons or animals, and the like), mobile devices (e.g., tablets, notebooks, laptops, personal computers, portable televisions, radios, and the like), and devices not designed for mobility (e.g., desktop computers, other computers, information kiosks, televisions with one or more processors incorporated therein and / or one or more processors coupled thereto, radios, and the like).

[0044] Computing device 805 can be communicatively coupled to external storage 845 and network 850 (e.g., via I / O interface 825) for communication with any number of network-connected components, devices, and systems, including one or more computing devices of the same or different configurations. Computing device 805 or any connected computing device can function as, provide services as, or be referred to by the names of, a server, client, thin server, general-purpose machine, dedicated machine, or others.

[0045] I / O interface 825 can include wired and / or wireless interfaces that use any communication or I / O protocol or convention (e.g., Ethernet, 802.11x, Universal System Bus, WiMax, modem, cellular network protocol, and the like) for information communication to and / or from at least all connected components, devices, and networks in computing environment 800, but is not limited thereto. Network 850 can be any network or combination of networks (such as, for example, the Internet, local area network, wide area network, telephone network, cellular network, satellite network, and the like).

[0046] Computing device 805 can use and / or communicate using computer-usable media or computer-readable media, including transient media and non-transient media. Transient media includes transmission media (e.g., metal cables, optical fibers), signals, carrier waves, and the like. Non-transient media includes magnetic media (e.g., disks and tapes), optical media (e.g., CD ROM, digital video disk, Blu-ray (registered trademark) disk), solid state media (e.g., RAM, ROM, flash memory, solid state storage), and other non-volatile storage or memory.

[0047] Computing device 805 can be used to implement techniques, methods, applications, processes, or computer-executable instructions in some exemplary computing environments. The computer-executable instructions can be retrieved from a temporary medium, stored in a non-temporary medium, and retrieved therefrom. The executable instructions can be derived from one or more of any programming language, scripting language, and machine language (e.g., C, C++, C#, Java, Visual Basic, Python, Perl, JavaScript, etc.).

[0048] Processor 810 can execute under any operating system (OS) (not shown) in a native environment or a virtual environment. One or more applications can be deployed that include a logic unit 860, an application programming interface (API) unit 865, an input unit 870, an output unit 875, and an inter-unit communication mechanism 895 for inter-unit communication, communication with the OS, and communication with other applications (not shown). The units and elements described above can vary in design, function, configuration, or implementation and are not limited to the above description. Processor 810 can be in the form of a hardware processor such as a central processing unit (CPU), or can be a combination of hardware units and software units.

[0049] In some exemplary embodiments, when the API unit 865 receives information or execution instructions, it can communicate them to one or more other units (e.g., the logic unit 860, the input unit 870, the output unit 875). In some examples, the logic unit 860 can control the information flow between units and, in some of the exemplary embodiments described above, can be configured to direct the services provided by the API unit 865, the input unit 870, and the output unit 875. For example, the flow of one or more processes or embodiments can be controlled by the logic unit 860, either alone or in conjunction with the API unit 865. The input unit 870 may be configured to obtain inputs for the calculations described in the exemplary embodiments, and the output unit 875 may be configured to provide outputs based on the calculations described in the exemplary embodiments.

[0050] As shown in FIGS. 1-3, the processor 810 can be configured to generate worker digital profiles associated with a plurality of workers. The processor 810 can also be configured to perform a virtual simulation using the worker digital profiles as inputs to a first model, as shown in FIGS. 1-3. The processor 810 can also be configured to generate a performance prediction as an output from the first model, as shown in FIGS. 1-3. The processor 810 can also be configured to perform work optimization based on the performance prediction, as shown in FIGS. 1-3.

[0051] As shown in FIG. 3, the processor 810 can also be further configured to perform data verification on the worker digital profiles based on the performance prediction. As shown in FIG. 3, the processor 810 can also be further configured to update the worker digital profiles based on the results of the data verification. As shown in FIG. 7, the processor 810 can also be further configured to generate a line configuration. As shown in FIG. 5, the processor 810 can be configured to receive task information, production line configuration information, and ontology information.

[0052] Some parts of the detailed description are presented by algorithms and symbolic representations of operations within a computer. The description of those algorithms and symbolic representations is a means used by those skilled in the data processing field to convey the essence of those innovations to other persons skilled in the art. An algorithm is a series of defined steps that lead to a desired final state or result. In an exemplary embodiment, the steps executed require physical operations of a physical quantity to achieve a physical result.

[0053] Unless otherwise specified, as is apparent from the description, throughout this specification, descriptions using terms such as "processing", "calculating", "computing", "determining", "displaying", or the like may include actions and processes of a computer system or other information processing device that manipulate data represented as a physical (electronic) quantity in registers and memories of the computer system and convert it into other data similarly represented as a physical quantity in memories or registers or other information storage devices, transmission devices, or display devices of the computer system.

[0054] Exemplary embodiments may further relate to an apparatus for performing operations herein. The apparatus may be specially constructed for the required purposes or may include one or more general-purpose computers selectively activated or reconfigured by one or more computer programs. Such computer programs may be stored on a computer-readable medium such as a computer-readable storage medium or a computer-readable signal medium. The computer-readable storage medium may include tangible media such as, but not limited to, optical disks, magnetic disks, read-only memory, random access memory, solid-state devices and drives, or any other type of tangible or non-transitory media suitable for storing electronic information. The computer-readable signal medium may include media such as carrier waves. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. The computer program may include a software-only implementation including instructions to perform the operations of the desired embodiments.

[0055] Various general-purpose systems may be used with the programs and modules according to the examples herein, or it may prove convenient as a result to construct more specialized apparatus for performing the steps of the desired method. Further, exemplary embodiments have not been described with reference to any particular programming language. It will be understood that various programming languages may be used to implement the teachings of the exemplary embodiments as described herein. The instructions of the programming language may be executed by one or more processing devices, such as, for example, a central processing unit (CPU), a processor, or a controller.

[0056] As is known in the art to which the present invention pertains, the operations described above can be performed by hardware, software, or some combination of software and hardware. While various aspects of the exemplary embodiments may be implemented using circuits and logic devices (hardware), other aspects may be implemented using instructions stored on a machine-readable medium (software) that, when executed by a processor, cause the processor to perform a method for implementing the present application. Further, some exemplary embodiments of the present application may be performed by hardware only, while other exemplary embodiments may be performed by software only. Additionally, the various functions described can be performed by a single unit or can be distributed across multiple components in any number of ways. When performed by software, the method may be executed by a processor, such as a general-purpose computer, based on instructions stored on a computer-readable medium. Optionally, the instructions can be stored on the medium in a compressed and / or encrypted format.

[0057] Furthermore, other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the teachings herein. The various aspects and / or components of the exemplary embodiments described may be used singly or in any combination. The specification and exemplary embodiments are intended to be considered as examples only, and the true scope and spirit of the present application are indicated by the following claims.

Description of Reference Numerals

[0058] 302 Standard Inspection Module 304 Sensing Module 306 Data Collection Module 308 Data Storage Module 310 Operator Characterization Module 312 Operator Digital Profile Module 314 Simulation Module 316 Work Prediction Module 320 Verification Data Collection Module 322 Verification Data Analysis Module 506 Ontology Database Module 516 Line Configurator Module 518 Work Simulation Module 520 Line Optimizer 805 Computing Device 810 Processor 815 Memory 820 Internal Storage 825 I / O Interface 835 Input / User Interface 840 Output Device / Interface 845 External Storage 850 Network 860 Logic Unit 865 API Unit 870 Input Unit 875 Output Unit

Claims

1. A method for performing work optimization through virtual simulation, comprising: generating, by a processor, worker digital profiles associated with a plurality of workers; executing, by the processor, a virtual simulation using the worker digital profiles as input to a first model; generating, by the processor, a performance prediction as output from the first model; and performing work optimization based on the performance prediction.

2. further comprising, by the processor, performing data verification on the worker digital profiles based on the performance prediction, wherein the processor is configured to perform data verification by comparing the performance prediction with actual performance data of actual work performed by the plurality of workers, the method according to claim 1.

3. further comprising, by the processor, updating the worker digital profiles based on the results of the data verification, the method according to claim 2.

4. further comprising generating a line configuration, wherein the first model generates the performance prediction by using, as input, the line configuration and the worker digital profiles, and wherein performing the work optimization includes optimizing the line configuration based on the performance prediction to generate and implement an optimized line configuration, the method according to claim 1.

5. further comprising receiving task information, production line configuration information, and ontology information, wherein the line configuration is generated based on the task information, the production line configuration information, and the ontology information, the method according to claim 4.

6. wherein the processor is configured to acquire quantitative measurements of the plurality of workers using a plurality of sensors; and generate the worker digital profiles by using the quantitative measurements of the plurality of workers as input to a second model for generating the worker digital profiles, the method according to claim 1.

7. wherein the worker digital profiles include quantitative measurements of worker capabilities in a plurality of classifications, the method according to claim 1.

8. The method according to claim 1, wherein the first model is trained using past worker digital profiles.

9. Using the worker digital profile to monitor the training progress of the plurality of workers; The method according to claim 1, further comprising providing training recommendations based on the monitored training progress.

10. The method according to claim 1, further comprising using the worker digital profile to design a virtual character for augmented reality representation.

11. A system for performing work optimization through virtual simulation, comprising: A plurality of sensors; A processor communicating with the plurality of sensors, the processor being configured to: Generate a worker digital profile associated with a plurality of workers by using the plurality of sensors; Perform a virtual simulation using the worker digital profile as an input to a first model; Generate a performance prediction as an output from the first model; Perform work optimization based on the performance prediction.

12. The processor is further configured to: Perform data verification on the worker digital profile based on the performance prediction; The system according to claim 11, wherein the processor performs data verification by comparing the performance prediction with actual performance data of actual work performed by the plurality of workers.

13. The processor is further configured to: Update the worker digital profile based on the result of the data verification.

14. The processor is further configured to: Generate a line configuration; The first model generates the performance prediction by using the line configuration and the worker digital profile as inputs; The system according to claim 11, wherein work optimization including line optimization is performed based on the performance prediction to generate and implement an optimized line configuration.

15. The processor is further configured to: further configured to receive task information, production line configuration information, and ontology information, The system according to claim 14, wherein the line configuration is generated based on the task information, the production line configuration information, and the ontology information.

16. The processor is configured to: obtain quantitative measurements of the plurality of workers using the plurality of sensors; and generate the worker digital profile by using the quantitative measurements of the plurality of workers as inputs to a second model for generating the worker digital profile. The system according to claim 11.

17. The system according to claim 11, wherein the worker digital profile includes quantitative measurements of worker capabilities in a plurality of classifications.

18. The system according to claim 11, wherein the first model is trained using past worker digital profiles.

19. The processor is further configured to: use the worker digital profile to monitor the training progress of the plurality of workers; and provide training recommendations based on the monitored training progress. The system according to claim 11.

20. The processor is further configured to: use the worker digital profile to design virtual characters for augmented reality representations. The system according to claim 11.

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