Claims:WE CLAIM:
1. A system (100) for remote laboratory management using a collaborative and a time-sharing experiential learning model comprising:
a remote laboratory data collection subsystem (110) configured to collect remote laboratory data associated with a remote laboratory from a database (115);
a remote laboratory data analysis subsystem (120) operatively coupled to the remote laboratory data collection subsystem (110), wherein the remote laboratory data analysis subsystem (120) is configured to:
compute a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique;
analyse renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters;
a user interface (130) operatively coupled to the remote laboratory data analysis subsystem (120), wherein the user interface (130) is configured to:
display real-time status of the remote laboratory based on an analysed result;
predict an upcoming status of the remote laboratory by using a predictive analysis technique; and
notify a predicted upcoming status of the remote laboratory through one or more notification means.
2. The system (100) as claimed in claim 1, wherein the remote laboratory data comprises at least one of total number of remote laboratory, names of laboratory managers, number of remote laboratory equipment, names of the remote laboratory equipment, number of users using the remote laboratory or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the plurality of operational parameters comprises at least one of an effective utilisation rate of the remote laboratory, a flexibility for booking slots of the remote laboratory, peak period of utilisation of the remote laboratory, a time slot period for performing the remote experiment in the remote laboratory or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the remote laboratory data analysis subsystem (120) is configured to rent the remote laboratory to the at least one of the user when the effective utilisation rate of the remote laboratory is greater than 60 percent and the flexibility for booking slots of the remote laboratory is greater than 50 percent.
5. The system (100) as claimed in claim 1, wherein the at least one of the user comprises a remote laboratory renting organisation, an individual learner or a combination thereof.
6. The system (100) as claimed in claim 1, wherein the predictive analysis technique comprises at least one of a descriptive modelling technique, a decision modelling technique, a linear regression technique, a logistic regression technique or a combination thereof.
7. The system (100) as claimed in claim 1, further comprising a payment processing subsystem (140) operatively coupled to the remote laboratory data analysis subsystem (120), wherein the payment processing subsystem (140) is configured to enable the at least one of the user to make a payment for renting the remote laboratory through a payment gateway.
8. The system as claimed in claim 1, further comprising a remote laboratory user management subsystem (150) operatively coupled to the remote laboratory data collection subsystem (110) and the user interface (130), wherein the remote laboratory user management subsystem (150) is configured to manage a functionality associated with the remote laboratory by using the collaborative and the time sharing experiential learning model.
9. A method (300) comprising:
collecting, by a remote laboratory data collection subsystem, remote laboratory data associated with a remote laboratory from a database (310);
computing, by a remote laboratory data analysis subsystem, a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique (320);
analysing, by the remote laboratory data analysis subsystem, renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters (330);
displaying, by a user interface, real-time status of the remote laboratory based on an analysed result (340);
predicting, by the user interface, an upcoming status of the remote laboratory by using a predictive analysis technique (350); and
notifying, by the user interface, a predicted upcoming status of the remote laboratory through one or more notification means (360).
10. The method (300) as claimed in claim 1, further comprising enabling, by a payment processing subsystem, the at least one of the user to make a payment for renting the remote laboratory through a payment gateway.

Dated this 03rd day of October 2019


Vidya Bhaskar Singh Nandiyal
Patent Agent (IN/PA-2912)
Agent for applicant

, Description:BACKGROUND
[0001] Embodiments of the present disclosure relate to a management system and more particularly to a system and a method for remote laboratory management using a collaborative and a time-sharing experiential learning model.
[0002] Collaborative learning is an educational approach for teaching and learning in which one or more individuals learn or attempt to learn something together. The collaborative learning involves the one or more individuals working together to solve a problem, complete a task, or create a product. The one or more individuals engaged in the collaborative learning capitalize on one another's resources and assistances to enhance problem-solving skills, critical thinking and a retention ability of one or more concepts. Generally, the collaborative learning is utilised by the one or more individuals for distance learning courses. In case of the distance learning courses, one or more universities, colleges and educational institutes use remote laboratories to reinforce learning and provide the one or more individuals an opportunity to map a theory learnt in a classroom to hands-on experience. The remote laboratories provide access to the one or more individuals throughout a day and allow one or more individuals to remotely access the experiment or a laboratory based on a remote laboratory management. Various such systems are available which manages the remote laboratory to provide remote access to the one or more individuals.
[0003] Conventionally, the system available for the remote laboratory management includes monitoring and time-sharing of the remote laboratories across multiple organizations or individual learners. However, such systems are unable to inform the organisations or the individual learners regarding availability of the remote laboratory to perform the remote experiments in a collaborate manner. Also, incapability of providing the information of the remote laboratory to the multiple organisations or the individual learners leads to mismanagement in utilisation of the expensive laboratory equipment as the physical facilities are impossible to keep open for the multiple organisations or the individual learners for a longer period of time. Moreover, the multiple organisations or the individual learners faces difficulty in affording the expensive laboratory equipment from a remote location due to unavailability and nonappearance of display of the information of the remote laboratory.
[0004] Hence, there is a need for an improved system and a method for remote laboratory management using a collaborative and a time-sharing experiential learning model in order to address the aforementioned issues.
BRIEF DESCRIPTION
[0005] In accordance with an embodiment of the present disclosure, a system for remote laboratory management using a collaborative and a time-sharing experiential learning model is disclosed. The system includes a remote laboratory data collection subsystem configured to collect remote laboratory data associated with a remote laboratory from a database. The system also includes a remote laboratory data analysis subsystem operatively coupled to the remote laboratory data collection subsystem. The remote laboratory data analysis subsystem is configured to compute a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique. The remote laboratory data analysis subsystem is also configured to analyse renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters. The system also includes a user interface operatively coupled to the remote laboratory data analysis subsystem. The user interface is configured to display real-time status of the remote laboratory based on an analysed result. The user interface is also configured to predict an upcoming status of the remote laboratory by using a predictive analysis technique. The user interface is also configured to notify a predicted upcoming status of the remote laboratory through one or more notification means.
[0006] In accordance with another embodiment of the present disclosure, a method for remote laboratory management using a collaborative and a time-sharing experiential learning model is disclosed. The method includes collecting, by a remote laboratory data collection subsystem, remote laboratory data associated with a remote laboratory from a database. The method also includes computing, by a remote laboratory data analysis subsystem, a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique. The method also includes analysing, by the remote laboratory data analysis subsystem, renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters. The method also includes displaying, by a user interface, real-time status of the remote laboratory based on an analysed result. The method also includes predicting, by the user interface, an upcoming status of the remote laboratory by using a predictive analysis technique. The method also includes notifying, by the user interface, a predicted upcoming status of the remote laboratory through one or more notification means.
[0007] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0008] FIG. 1 is a block diagram of a system for remote laboratory management using a collaborative and a time-sharing experiential learning model in accordance with an embodiment of the present disclosure;
[0009] FIG. 2 depicts a schematic representation of a remote laboratory renting architecture of a system for remote laboratory management using a collaborative and a time-sharing experiential learning model of FIG.1 in accordance with an embodiment of the present disclosure;

[0010] FIG. 3 is a schematic representation of an exemplary embodiment of a system for remote laboratory management using a collaborative and a time-sharing experiential learning model of FIG. 1 in accordance with the embodiment of the present disclosure;
[0011] FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;
[0012] FIG. 5 is a flow chart representing the steps involved in a method for remote laboratory management using a collaborative and a time-sharing experiential learning model of FIG. 1 in accordance with the embodiment of the present disclosure.
[0013] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0014] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0015] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0017] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0018] Embodiments of the present disclosure relate to a system and a method for remote laboratory management using a collaborative and a time-sharing experiential learning model. The system includes a remote laboratory data collection subsystem configured to collect remote laboratory data associated with a remote laboratory from a database. The system also includes a remote laboratory data analysis subsystem operatively coupled to the remote laboratory data collection subsystem. The remote laboratory data analysis subsystem is configured to compute a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique. The remote laboratory data analysis subsystem is also configured to analyse renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters. The system also includes a user interface operatively coupled to the remote laboratory data analysis subsystem. The user interface is configured to display real-time status of the remote laboratory based on an analysed result. The user interface is also configured to predict an upcoming status of the remote laboratory by using a predictive analysis technique. The user interface is also configured to notify a predicted upcoming status of the remote laboratory through one or more notification means.
[0019] FIG. 1 is a block diagram of a system (100) for remote laboratory management using a collaborative and a time-sharing experiential learning model in accordance with an embodiment of the present disclosure. The system (100) includes a remote laboratory data collection subsystem (110) configured to collect remote laboratory data associated with a remote laboratory from a database (115). As used herein, the term ‘remote laboratory’ is defined as a laboratory of a remote laboratory owner organisation for performing one or more experiments by an individual. Similarly, the term ‘remote laboratory owner organisation’ is defined as an educational institution which owns a laboratory with one or more expensive and functional equipment. In one embodiment, the remote laboratory data may include at least one of total number of remote laboratory, names of laboratory managers, number of remote laboratory equipment, names of the remote laboratory equipment, number of users using the remote laboratory or a combination thereof.
[0020] The system (100) also includes a remote laboratory data analysis subsystem (120) operatively coupled to the remote laboratory data collection subsystem (110). The remote laboratory data analysis subsystem (120) is configured to compute a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique. In one embodiment, the plurality of operational parameters may include at least one of an effective utilisation rate of the remote laboratory, a flexibility for booking slots of the remote laboratory, peak period of utilisation of the remote laboratory, a time slot period for performing the remote experiment in the remote laboratory or a combination thereof. As used herein, the term ‘effective utilisation rate of the remote laboratory’ is defined as productive time for which the remote experiment is utilized by professors or students. The effective utilisation rate is computed based on a below-mentioned equation (1) which is represented as follows:
Effective Utilisation rate = Total time the remote equipment (RE) is utilized for experimenting / Total time the RE is made available …. (1)
[0021] Similarly, the term ‘flexibility for booking slots of the remote laboratory’ is defined as an amount of flexibility provided to the professors or students to book their corresponding slots of the remote laboratory to perform the remote experiment. The flexibility for booking the slots of the remote laboratory is computed based on a below mentioned equation (2), which is depicted as follows:
Flexibility to book the slots for student =
Total number of slots of experimenting to be done / Total number of slots provided …… (2)
[0022] Again, the term ‘peak period of utilisation of the remote laboratory’ is defined as a peak duration when the remote laboratory is most frequently used. The peak period of the utilisation of the remote laboratory is the period when the effective utilization rate is more than 80%. In such period the examinations are scheduled to provide a way to distribute the peak period of utilization across the organizations to effectively increase the net utilization. The term ‘time slot period for performing the remote experiment in the remote laboratory’ is defined as a duration of the time for which a particular slot is allocated to the student for experimenting on a specific remote experiment.
[0023] The remote laboratory data analysis subsystem (120) is also configured to analyse renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters. The remote laboratory is rented to the at least one of the user when the effective utilisation rate of the remote laboratory is greater than 60 percent and the flexibility for booking slots of the remote laboratory is greater than 50 percent. In one embodiment, the at least one of the user may include a remote laboratory renting organisation, an individual learner or a combination thereof.
[0024] The system (100) also includes a user interface (130) operatively coupled to the remote laboratory data analysis subsystem (120). The user interface (130) is configured to display real-time status of the remote laboratory based on an analysed result. In one embodiment, the real-time status of the remote laboratory may include at least one of an availability of the remote equipment (RE), a connectivity status of the remote equipment, an organisation using the RE, a username using the RE, a name of a laboratory manager, analytics of the RE or a combination thereof.
[0025] Whenever the connectivity status is red, a notification through at least one notification means is sent to an administrator, a sub-administrator of a remote laboratory owner or a remote laboratory tenant organization, a lab manager or an individual learner who have booked the slot. In one embodiment, the administrator or the laboratory manager may add and delete any of the remote equipment to be monitored. When the remote equipment is on-boarded for management, the master server model constantly monitors the remote equipment and sends the status and notifications to the dashboard to be displayed.
[0026] The user interface (130) is also configured to predict an upcoming status of the remote laboratory by using a predictive analysis technique. As used herein, the term ‘upcoming status’ is defined as a future condition which is predicted and suggested to the at least one of the user for proactive decision making. The upcoming predicted status helps the at least one of the user to pro-actively take a decision for further action in advance so that the at least one of the user may effectively plan their time and the support overhead is reduced. In one embodiment, the upcoming status of the remote laboratory may include at least one of a non-availability of the remote equipment, information about subsequent organisation using the remote laboratory, duration of booking of the remote laboratory, upcoming availability of the remote laboratory or a combination thereof. In some embodiment, the predictive analysis technique may include at least one of a descriptive modelling technique, a decision modelling technique, a linear regression technique, a logistic regression technique or a combination thereof.
[0027] The user interface (130) is also configured to notify a predicted upcoming status of the remote laboratory through one or more notification means. In some embodiment, the one or more notifications may include at least an email, a short message service (SMS), an alarm or a push notification.
[0028] In a specific embodiment, the system (100) further includes a payment processing subsystem (not shown in FIG.1) operatively coupled to the remote laboratory data analysis subsystem (120). The payment processing subsystem is configured to enable the at least one of the user to make a payment for renting the remote laboratory through a payment gateway based on a generated invoice. In such embodiment, one or more types of the payment gateways may include at least one of hosted payment gateways, self-hosted payment gateways, non-hosted payment gateways, local bank integration, direct payment gateways, platform-based payment gateway solutions or a combination thereof.
[0029] In a preferred embodiment, the system (100) further includes a remote laboratory user management subsystem (not shown in FIG. 1) operatively coupled to the remote laboratory data collection subsystem (110), the remote laboratory data analysis subsystem (120) and the user interface (130). In such embodiment, the remote laboratory user management subsystem is configured to manage a functionality associated with the remote laboratory by using the collaborative and the time-sharing experiential learning model. In one embodiment, the functionality associated with the remote laboratory may include at least one of controlling one or more remote laboratory users, managing remote experiments by adding new experiments or editing existing experiments, publishing a relevant content associated with the remote experiments, evaluating remote experiment results, viewing analytics of usage and performance of the one or more remote laboratory users. The remote laboratory user management subsystem also enables the remote laboratory manager to allow the management of one or more remote cameras of the remote laboratories using third party camera and live streaming management information system.
[0030] In some embodiment, the remote laboratory user management subsystem enables a remote laboratory manager to make use of one or more inherent features of the experiential learning model such as providing a secure system behind demilitarized zone, improving reliability using availability, providing a cost-effective and convenient solution. The remote laboratory user management subsystem also enables the remote laboratory manager to pro-actively take a decision based on computation of the plurality of operational parameters and idle time to pro-actively suggest further action to the students in advance, so that the students may effectively plan their time and the support overhead is reduced. The remote laboratory manger upon computation of the plurality of operational parameters enables renting of the remote laboratory or a specific remote experiment to a remote laboratory renting organization based on a remote laboratory renting architecture (135) as shown in FIG. 2.
[0031] FIG. 2 depicts a schematic representation of a remote laboratory renting architecture of a system (100) for remote laboratory management using a collaborative and a time-sharing experiential learning model of FIG. 1 in accordance with the embodiment of the present disclosure. In one embodiment, the remote laboratory owner organization may be a premier institute with high funding which has acquired one or more expensive remote experiment equipment. The time when the one or more expensive remote experiment equipment is unutilized may be provided and multiplexed between the students of different remote laboratory renting organizations.
[0032] FIG. 3 is a schematic representation of an exemplary embodiment of a system (100) for remote laboratory management using a collaborative and a time-sharing experiential learning model of FIG. 1 in accordance with the embodiment of the present disclosure. The system (100) is essential for management of the remote laboratory. The management of the remote laboratory across various remote locations becomes difficult as managing multiple remote equipment across multiple universities and their professors/students is a very complex and a time-consuming problem. So, the system (100) helps in managing the remote laboratory and enables maximum utilisation of the remote laboratory among different users. For example, let us assume a student from a student workstation (105) wants to utilise the experiential learning model using a communication network (108) to obtain an access of the remote laboratory (109) for performing a remote experiment. Here, the communication network (108) may include an internet connection. The access for the remote laboratory (109) is obtained by the student only when the remote laboratory (109) is available. In order to check the availability of the remote laboratory (109), remote laboratory data associated with the remote laboratory (109) is collected by a remote laboratory data collection subsystem (110) from a database (115). Here, the remote laboratory data may include at least one of total number of remote laboratory, names of laboratory managers, number of remote laboratory equipment, names of the remote laboratory equipment, number of users using the remote laboratory or a combination thereof. The database (115) may include a cloud-based platform which may be hosted in a remote server.
[0033] Once, the remote laboratory data is collected, a plurality of operational parameters is computed by a remote laboratory data analysis subsystem (120) by using a laboratory handling computation technique. For example, the plurality of operational parameters may include at least one of an effective utilisation rate of the remote laboratory, a flexibility for booking slots of the remote laboratory, peak period of utilisation of the remote laboratory, a time slot period for performing the remote experiment in the remote laboratory or a combination thereof. Here, the laboratory handling computation technique helps is computing the effective utilisation rate based on computation of total time the remote equipment is utilised for experimenting and total time the remote equipment is made available. Similarly, the flexibility for booking the slots is computed based on total number of slots of experimenting to be done and total number of slots provided for performing the remote experiment. Again, the peak period of utilisation of the remote laboratory as well as the time slot period is computed by using the laboratory handling computation technique.
[0034] Upon computation of the plurality of operational parameters, an analysis of a renting tenure of the remote laboratory (109) with the at least one of the user to enable the access of the remote laboratory (109) is also done by the remote laboratory data analysis subsystem (120). For example, here the at least one of the user refers to the student from the student workstation (105). The remote laboratory is rented to the at least one of the user based on an analysed result, wherein the analysed result depicts that if the effective utilisation rate of the remote laboratory is greater than 60 percent and the flexibility for booking the slots of the remote laboratory is greater than 50 percent, then the remote laboratory along with the remote equipment may be rented to the student for a predefined time interval.
[0035] Also, a real-time status of the remote laboratory (109) is displayed on a user interface (130) of an electronic device associated with the at least one of the user such as the student. For example, the user interface may include a dashboard (135) of the electronic device. Here, the electronic device may include a laptop. So, the real-time status such as at least one of an availability of the remote equipment (RE), a connectivity status of the remote equipment, an organisation using the RE, a username using the RE, a name of a laboratory manager, analytics of the RE or a combination thereof is depicted on the dashboard. For example, the connectivity status shows an active connection of the remote equipment of the remote laboratory (109) with the communication network (108). If the connectivity status is ‘red’, then it indicates that connectivity of the remote equipment is lost and a notification is sent to an administrator of a remote laboratory organisation, a sub-administrator of a remote laboratory owner organisation or a remote laboratory tenant organisation, a lab manager of a remote laboratory organisation or the student. Similarly, if the connectivity status is green, then it indicates that connection of the remote equipment is established with the communication network.
[0036] Further, once the real-time status is displayed, the student may book the available slots or rent the remote laboratory (109) for performing the remote experiment. Payment for the renting tenure of the remote laboratory (109) is provided by a payment processing subsystem (140), wherein the payment processing subsystem (140), enables the at least one of the user to make a payment for renting the remote laboratory through a payment gateway based on a generated invoice. For example, the student may make the payment using one or more types of the payment gateways which may include at least one of hosted payment gateways, self-hosted payment gateways, non-hosted payment gateways, local bank integration, direct payment gateways, platform-based payment gateway solutions or a combination thereof.
[0037] In addition to, the user interface (130) also predicts an upcoming status of the remote laboratory (109) by using a predictive analysis technique. The upcoming status helps the at least one of the user such as the student to pro-actively take a decision for further action in advance so that the at least one of the user may effectively plan their time and the support overhead is reduced in future. For example, the upcoming status may include at least one of a non-availability of the remote equipment, information about subsequent organisation using the remote laboratory, duration of booking of the remote laboratory, upcoming availability of the remote laboratory or a combination thereof. Here, the predictive analysis technique may include at least one of a descriptive modelling technique, a decision modelling technique, a linear regression technique, a logistic regression technique or a combination thereof.
[0038] Also, the user interface (130) notifies a predicted upcoming status of the remote laboratory through a notification means, wherein the notification means may include at least an email sent to the student. Here, the notification alerts the student about the availability of the booking slots of the remote laboratory experiment to perform the experiment. The real-time status and real-time analytics of the uptime of all the remote equipment and their usage helps the student in improved decision making for collaborative experiential learning as well as helps in better management of the remote laboratory (109).
[0039] FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (200) includes processor(s) (230), and memory (210) operatively coupled to the bus (220).
[0040] The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0041] The memory (210) includes a plurality of subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) is substantially similar to the system (100) of FIG.1. The memory (210) has following subsystems: a remote laboratory data collection subsystem (110), a remote laboratory data analysis subsystem (120), a user interface (130).
[0042] The remote laboratory data collection subsystem (110) is configured to collect remote laboratory data associated with a remote laboratory from a database. The remote laboratory data analysis subsystem (120) is also configured to compute a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique. The remote laboratory data analysis subsystem (120) is also configured to analyse renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters. The user interface (130) is configured to display real-time status of the remote laboratory based on an analysed result. The user interface (130) is also configured to predict an upcoming status of the remote laboratory by using a predictive analysis technique. The user interface (130) is also configured to notify a predicted upcoming status of the remote laboratory through one or more notification means.
[0043] FIG. 5 is a flow chart representing the steps involved in a method (300) for remote laboratory management using a collaborative and a time-sharing experiential learning model of FIG. 1 in accordance with the embodiment of the present disclosure. The method (300) includes collecting, by a remote laboratory data collection subsystem, remote laboratory data associated with a remote laboratory from a database in step 310. In one embodiment, collecting the remote laboratory data associated with the remote laboratory from the database may include collecting the remote laboratory data which may include at least one of total number of remote laboratory, names of laboratory managers, number of remote laboratory equipment, names of the remote laboratory equipment, number of users using the remote laboratory or a combination thereof.
[0044] The method (300) also includes computing, by a remote laboratory data analysis subsystem, a plurality of operational parameters associated with the remote laboratory based on collected remote laboratory data using a laboratory handling computation technique in step 320. In some embodiment, computing the plurality of operational parameters associated with the remote laboratory may include computing the plurality of operational parameters which may include at least one of an effective utilisation rate of the remote laboratory, a flexibility for booking slots of the remote laboratory, peak period of utilisation of the remote laboratory, a time slot period for performing the remote experiment in the remote laboratory or a combination thereof.
[0045] The method (300) also includes analysing, by the remote laboratory data analysis subsystem, renting tenure of the remote laboratory with at least one of a user to enable an access of the remote laboratory for performing a remote experiment based on a plurality of computed parameters in step 330. In one embodiment, analysing the renting tenure of the remote laboratory may include analysing the renting tenure of the remote laboratory with the at least one of the user based on the utilisation rate and the flexibility for booking slots of the remote laboratory. In such embodimnet, the remote laboratory may be rented for performing the remote experiments by the at least one of the user when the effective utilisation rate of the remote laboratory is greater than 60 percent and the flexibility for booking slots of the remote laboratory is greater than 50 percent. In some embodiment, the at least one of the user may include a remote laboratory renting organisation, an individual learner or a combination thereof.
[0046] The method (300) also includes displaying, by a user interface, real-time status of the remote laboratory based on an analysed result in step 340. In one embodiment, displaying the real-time status of the remote laboratory based on the analysed result may include displaying the real-time status which includes at least one of an availability of the remote equipment (RE), a connectivity status of the remote equipment, an organisation using the RE, a username using the RE, a name of a laboratory manager, analytics of the RE or a combination thereof.
[0047] The method (300) also includes predicting, by the user interface, an upcoming status of the remote laboratory by using a predictive analysis technique in step 350. In some embodiment, predicting the upcoming status of the remote laboratory may include predicting the upcoming status of the remote laboratory by using at least one of a descriptive modelling technique, a decision modelling technique, a linear regression technique, a logistic regression technique or a combination thereof.
[0048] The method (300) also includes notifying, by the user interface, a predicted upcoming status of the remote laboratory through one or more notification means in step 360. In one embodiment, notifying the predicted upcoming status of the remote laboratory may include notifying the predicted upcoming status to the at least one of the user through at least an email, a short message service (SMS), an alarm or a push notification.
[0049] In a specific embodiment, the method (300) further includes enabling, by a payment processing subsystem, the at least one of the user to make a payment for renting the remote laboratory through a payment gateway. In such embodiment, one or more types of the payment gateways may include at least one of hosted payment gateways, self-hosted payment gateways, non-hosted payment gateways, local bank integration, direct payment gateways, platform-based payment gateway solutions or a combination thereof.
[0050] In a preferred embodiment, the method (300) further includes managing, by a remote laboratory user management subsystem, functionality associated with the remote laboratory by using the collaborative and the time-sharing experiential learning model. In one embodiment, manging the functionality associated with the remote laboratory may include managing at least one of controlling one or more remote laboratory users, managing remote experiments by adding new experiments or editing existing experiments, publishing a relevant content associated with the remote experiments, evaluating remote experiment results, viewing analytics of usage and performance of the one or more remote laboratory users. In such embodiment, the one or more remote laboratory users may include at least one of student, a professor, an administrator, a lab instructor or a combination thereof.
[0051] Various embodiments of the present disclosure enable visualisation of the real-time status associated with the remote laboratory in the user interface which helps the at least one of the user in proper utilisation of the remote laboratory and one or more resources.
[0052] Moreover, the present disclosed analyses the plurality of operational parameters to display the real-time status or for providing suggestions to the at least one of the user.
[0053] Furthermore, also predicts and notifies the at least one of the user about the upcoming status which helps the at least one of the user in a planned and correct decision making for performing the remote experiments.
[0054] Also, the present disclosed system automatically disables the slots in the slot management system in the downtime which improves efficient utilisation of smooth running of the system, reducing confusion and support and effectively utilising the time.
[0055] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[0056] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0057] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.