Claims:What is claimed is:
1. A computer system comprising:
one or more processors (206); and
a memory (204) coupled to the one or more processors (206), the memory (204) for storing a plurality of instructions, wherein the plurality of instructions being executed by the one or more processors (206), wherein the one or more processors (206) enables a method for quality control of a digital facility (102) based on machine learning, the method comprising:
connecting, at a quality control system (110), a plurality of elements (106) associated with a plurality of regions (104) of the digital facility (102);
allocating, at the quality control system (110), a unique identity to each of the plurality of elements (106), wherein the unique identity being allocated based on a predefined pattern;
receiving, at the quality control system (110), a first set of data associated with each of the plurality of regions (104) of the digital facility (102), wherein the first set of data comprises of a plurality of architectural data;
collecting, at the quality control system (110), a second set of data associated with a plurality of micro descriptors (108), wherein each of the plurality of micro descriptors (108) being associated with one or more of the plurality of elements (106);
processing, at the quality control system (110), the second set of data to discover a plurality of patterns, wherein the processing being done based on attribute of the second set of data, wherein each of the plurality of patterns being associated with a characteristic attribute of one or more of the plurality of element (106);
predicting, at the quality control system (110), one or more issues associated with one or more of the plurality of elements (106), wherein the prediction being enabled with the facilitation of machine learning, wherein the prediction being done in real time;
assigning, at the quality control system (110), one or more high severity issue to the one or more severity issues, wherein the one or more high severity issue being assigned based on second set of data and machine learning;
storing, at the quality control system (110), a plurality of sets of information associated with the digital facility (102), wherein the plurality of sets of information being stored in a plurality of matrices, wherein the plurality of sets of information being stored in a database of quality control system (110);
updating, at the quality control system (110), the plurality of patterns associated with the plurality of elements (106) of the digital facility (102), wherein the plurality of patterns being updated in the database of quality control system (110);
recommending, at the quality control system (110), a plurality of optimum characteristic parameters to each of the plurality of elements (106), wherein the plurality of optimum characteristic parameters being recommended to ensure quality of each of the plurality of elements (106); and
notifying, at the quality control system (110), one or more manpower associated with the digital facility (102).
2. The computer system as recited in claim 1, wherein the plurality of architectural sources comprises a facility manager, a digital camera, a digital blueprint, a communication device, one or more graphical sensors and a satellite image.
3. The computer system as recited in claim 1, wherein the plurality of elements (106) comprises a plurality of electrical appliances, a plurality of furniture, a plurality of sanitary fittings, a plurality of structural fittings, a plurality of cutleries and a plurality of washroom fittings.

4. The computer system as recited in claim 1, wherein the one or more issue comprises fault in one or more of the plurality of electrical appliance, fault in one or more of the plurality of furniture, fault in one or more of the plurality of sanitary fittings, fault in one or more of the plurality of structural fittings, fault in one or more of the plurality of cutleries and fault in one or more of the plurality of washroom fittings.

5. The computer system as recited in claim 1, further comprising upgrading, at the quality control system (110), the first set of data, the second set of data, the one or more issues and the one or more high severity issue, wherein the updating being done in real time.

6. The computer system as recited in claim 1, further comprising preventing, at the quality control system (110), booking of one or more of the plurality of regions (104) of the digital facility (102), wherein prevention being done with the facilitation of the one or more high severity issue and machine learning, wherein the prevention being done in real time.

7. The computer system as recited in claim 1, further comprising forecasting, at the quality control system (110), a time to resolve the one or more issues in order to maintain quality of the digital facility (102), wherein the forecasting being done based on machine learning.

8. The computer system as recited in claim 1, wherein the unique identity differentiates each of the plurality of element (106) of the digital facility (102), wherein the plurality of micro descriptors (108) being coupled with the unique identity.
9. The computer system as recited in claim 1, wherein the quality control system (110) is connected to a server (114) with the facilitation of a communication network (114).

10. The computer system as recited in claim 1, wherein the plurality of micro descriptors (108) provides data of a plurality of characteristic attributes of plurality of elements (106).
, Description:SYSTEM FOR QUALITY CONTROL OF A FACILITY BASED ON MACHINE LEARNING
TECHNICAL FIELD
[0001] The present disclosure relates to a field of quality control and management. More specifically, the present disclosure relates to a method for quality control of a facility based on machine learning.
BACKGROUND
[0002] Service industry has taken a major leap with the huge increase in number of people constantly travelling from one place to another. People are in constant need for a place to stay overnight or for a few days. Typically, people stay in various hotels which match their needs and comfort ability factor. These hotels have been operating in an automated fashion since very long. Nowadays, there are innumerable software systems for managing hotel operations in real time. Further, these hotels continuously try to evolve and understand their customer needs in order to be more efficient. Maintenance of quality of the hotels is a big task for hotel owners. In addition, managing the quality of the facility needs time and money. Managing quality of the facility includes a regular audit of the facility, solving multiple major issues related to the facility and monitoring a large number of tasks related to the audit. Maintaining the quality of a facility requires one or more devices in order to collect information and perform certain number of tasks. The devices use wired connection in order to keep themselves connected to the main server and perform its task. Using the wired connection has a downside as a single loose wire will affect the performance of the system and the devices does not provide information in real time to keep track on the quality of the facility. As an example, the feedback provided by the customer is used to make changes which are applied after the visitor has left the facility which decrease the quality of service of the facility. Therefore, the wired connections do not provide efficient way of communicating information from devices and are not reliable. Further, information collected is stored separately for each device and does not prove to be useful for keeping track on the overall quality of the facility. In order to check all the aspects of the quality by using all the devices working together with each other, there is a need for a new system which overcomes the above-stated disadvantages.
SUMMARY
[0003] In one aspect, the present disclosure provides a computer system. The computer system includes one or more processors and a memory. The memory is coupled to the one or more processor. The memory stores a plurality of instructions. The plurality of instructions is executed by the one or more processors. The one or more processors enable a method for quality control of a digital facility based on machine learning. The method includes connecting at a quality control system a plurality of elements associated with a plurality of region of the digital facility. The method includes allocating at the quality control system a unique identity to each of the plurality of element. The unique identity is allocated based on a predefined pattern. The method includes receiving at the quality control system a first set of data associated with each of the plurality of regions of the digital facility. The first set of data comprises of a plurality of architectural data. The method includes collecting at the quality control system a second set of data associated with a plurality of micro descriptors. Each of the plurality of micro descriptors is associated with one or more of the plurality of elements. The method includes processing at the quality control system the second set of data to discover a plurality of patterns. The processing is done based on attribute of the second set of data. Each of the plurality of patterns are associated with a characteristic attribute of one or more of the plurality of elements. The method includes predicting at the quality control system one or more issues associated with one or more of the plurality of elements. The prediction is enabled with the facilitation of machine learning. The prediction is done in real time. The method includes assigning at the quality control system one or more high severity issue to the one or more severity issues. The one or more high severity issue is assigned based on second set of data and machine learning. The method includes storing, at the quality control system a plurality of sets of information associated with the digital facility. The plurality of sets of information is stored in a plurality of matrices. The plurality of sets of information is stored in a database of quality control system. The method includes updating at the quality control system the plurality of patterns associated with the plurality of elements of the digital facility. The plurality of patterns is updated in the database of quality control system. The method includes recommending at the quality control system a plurality of optimum characteristic parameters to each of the plurality of elements. The plurality of optimum characteristic parameters is recommended to ensure quality of each of the plurality of elements. The method includes notifying at the quality control system one or more manpower associated with the digital facility.
OBJECT OF THE DISCLOSURE
[0004] A Primary object of the present disclosure is to provide a method for quality control of a digital facility.
[0005] Another object of the present disclosure is to provide the method for quality control of a facility with the facilitation of machine learning.
[0006] Yet another object of the present disclosure is to retrieve data of a plurality of belongings of the digital facility from a plurality of micro descriptors.
[0007] Yet another object of the present disclosure is to retrieve data from a plurality of sources to predict occurrence of one or more issues.
[0008] Yet another object of the present disclosure is to digitize the method of quality control of a digital facility.
[0009] Yet another object of the present disclosure is to enable accurate predictions of the one or more issues and time to resolve the one or more issues.

BRIEF DESCRIPTION OF THE FIGURES
[0010] FIG. 1A illustrates a block diagram of a digital facility, in accordance with various embodiments of the present disclosure;
[0011] FIG. 1B illustrates a block diagram of a digital facility associated with a quality control system, in accordance with various embodiments of the present disclosure; and
[0012] FIG. 2 illustrates a block diagram of a computing device in accordance with various embodiments of the present disclosure.
DETAILED DESCRIPTION
[0013] FIG. 1A illustrates a block diagram 100 of a digital facility 102, in accordance with various embodiments of the present disclosure. FIG. 1B illustrates another block diagram 100 of a digital facility 102 associated with a quality control system 110, in accordance with various embodiments of the present disclosure. The digital facility 102 is an accommodation for human beings or pets to live or stay for a period of time. The digital facility 102 is a hotel providing accommodation, meals, and hospitality services for guest and visitors on a short-term or long-term basis. In an embodiment of the present disclosure, the digital facility 102 is a space for conducting seminars, conferences, meetings, social gatherings, family functions, events, and the like. In another embodiment of the present disclosure, the digital facility 100 is a hospital providing health care services to human beings or animals. In yet another embodiment of the present disclosure, the digital facility 102 is a temporary or permanent residence of one or more human beings. In yet another embodiment of the present disclosure, the digital facility is an educational institution. In yet another embodiment of the present disclosure, the digital facility 102 may be a guest house providing accommodation facility to guests or visitors. In yet another embodiment of the present disclosure, the digital facility 102 is a military base operated by or for the military. In yet another embodiment of the present disclosure, the digital facility 102 is an old age home or any other social institution of the like. In yet another embodiment of the present disclosure, the digital facility 102 is an office or a financial institution of the like. In yet another embodiment of the present disclosure, the digital facility 102 is a lodge, boarding house or a supermarket. In yet another embodiment of the present disclosure, the digital facility 102 includes, but is not limited to production area of a factory or an industry. In yet another embodiment of the present disclosure, the digital facility 102 includes, but is not limited to supermarket, cinema hall, railway station, bus station and the like. In yet another embodiment of the present disclosure, the digital facility 102 is government undertaking.
[0014] The digital facility 102 includes a plurality of regions 104. The plurality of regions 104 facilitates to differentiate and specify various area sections of the digital facility 102. The plurality of regions 104 is differentiated to facilitate systematic identification of the plurality of regions 104 for a quality control system 110. The plurality of regions 104 collectively enables the digital facility 102. The plurality of regions 104 includes but is limited to a plurality of rooms, one or more common area, one or more restaurants, one or more parking, one or more kitchens, one or more gardens, one or more reception areas, one or more corridors, one or more stairs. In an embodiment of the present disclosure, the plurality of regions 104 includes any other regions. In another embodiment of the present disclosure, the plurality of regions 104 includes, but is not limited to shopping areas, cash counters, baggage counters, ticket counters, help desks, waiting areas, washrooms, sanitary areas, conference areas, and the like. In yet another embodiment of the present disclosure, the plurality of regions 104 includes, but is not limited to the meeting areas, event spaces, ground areas, fitness areas, gyms, swimming areas, classroom areas, dining areas and the like. In an embodiment of the present disclosure, the plurality of regions 104 may vary.
[0015] Each of the plurality of regions 104 includes a plurality of elements 106. The plurality of elements 106 is various components that enable structure, security, functionality and comfort of the plurality of regions 104. The plurality of elements 106 collectively enables each of the plurality of regions 104. In an embodiment of the present disclosure, the plurality of elements 106 may be of any other suitable form of the like. The plurality of elements 106 includes but is not limited to structural elements, functional elements, sanitary elements, security elements, entertainment elements, decorative elements and electrical elements. In an embodiment of the present disclosure, the plurality of elements 106 includes any other suitable elements of the like.
[0016] The plurality of elements 106 in each of the plurality of region 104 vary with the functionality or purpose of each of the plurality of region 104. For example, consider a region of the plurality of regions 104 to be a room. The structural elements of the room are walls of the room, floor of the room, ceiling of the room, windows of the room and doors of the room. In an embodiment of the present disclosure, the structural elements of the room include any other suitable elements of the like. The functional elements of the room are one or more bed, one or more chairs, one or more tables and one or more closets. In another embodiment of the present disclosure, the functional elements of the room include any other suitable elements of the like. The sanitary elements of the room are cleanliness, hygiene, one or more table cloths, one or more curtains, one or more linen and one or more pillows. In yet another embodiment of the present disclosure, the sanitary elements of the room include any other suitable elements of the like. The security elements of the room are one or more locks, one or more security camera, one or more security alarm, one or more fire extinguisher and a smart security system. In yet another embodiment of the present disclosure, the security elements of the room include any other suitable elements of the like. The entertainment elements of the room are one or more music systems, one or more musical instruments, one or more gaming systems, one or more communication devices, one or more computer devices, one or more smartphone devices and one or more printed reading materials. In yet another embodiment of the present disclosure, the entertainment elements of the room include any other suitable elements of the like. The decorative elements of the room are one or more flower bouquet, one or more painting, one or more indoor plant, one or more display piece and one or more work of art. In yet another embodiment of the present disclosure, the decorative elements of the room include any other suitable elements of the like. The electrical elements of the room are one or more lighting device, one or more refrigerators, one or more air conditioners, one or more fans, one or more water heaters, one or more switches, one or more electrical sockets and one or more televisions. In yet another embodiment of the present disclosure, the electrical elements of the room include any other suitable elements of the like.
[0017] The quality control system 110 allocates a unique identity to each of the plurality of elements 106. In general, a unique identity facilitates in differentiation and identification of each of the plurality of elements 106. The unique identity is allocated to each of the plurality of elements 106 based on a predefined pattern. The predefined pattern allocates similar identity to the plurality of elements 106 of a region of the plurality of regions 104. The predefined pattern allocates similar identity to similar characteristic elements of the plurality of elements 106. In an embodiment of the present disclosure, the predefined pattern allocates the unique identity with any other suitable manner of the like. The unique identity facilitates in integrations of a plurality of data associated with the plurality of elements 106. The unique identity enables systematic processing of the plurality of data associated with each of the plurality of elements 104. The unique identity facilitates in real time access to a particular data of the plurality of data associated with each of the plurality of elements 106. In an embodiment of the present disclosure, the unique identity is allocated on the basis of any other suitable distribution pattern of the like.
[0018] The quality control system 110 receives a first set of data associated with each of the plurality of regions 104 of the digital facility 102. The first set of data comprises of a plurality of architectural data. The first set of data is received is from a plurality of architectural data sources. The plurality of architectural data includes but is not limited to geographical data, images, videos, 3-D outlines, a master plan, laser scanned images, 360° camera images, blueprints of facility, engineering drawings and the like. In an embodiment of the present disclosure, the plurality of architectural data includes any other suitable data. The first set of data includes detailed architectural data of each of the plurality of region 104 of the digital facility 102. The first set of data includes detailed architectural data of the plurality of components 106 of each of the plurality of regions 104 of the digital facility 102. The quality control system 110 processes the first set of data to generate detailed visual representation of the digital facility 102 on a digital platform. In an embodiment of the present disclosure, the quality control system 110 processes the first set of data for any suitable purpose. In another embodiment of the present disclosure, the quality control system 110 stores the first set of data.
[0019] The first set of data includes but is not limited to the architectural data associated with digital facility 102. The first set of data includes detailed information of arrangement and physical characteristics the plurality of elements 106 of each of the plurality of region 104. The first set of data includes interior data of the plurality of region 104. multimedia data of the plurality of regions 104, 3-D view of the plurality of regions 104 and the like. In an embodiment of the present disclosure, the first set of data includes any other suitable data of the like. In an embodiment of the present disclosure, the architectural information includes but is not be limited to building architecture, ambience architecture of the digital facility 102, satellite view of the digital facility 102, and the like. In another embodiment of the present disclosure, architectural data includes any other suitable architectural data of the like.
[0020] The quality control system 110 processes the first set of data to generate a digital replica of the digital facility 102. The digital replica is identical virtual representation of the digital facility 102 on digital platform. The digital replica is identical virtual representation of each of the plurality of regions 104 of the digital facility. The digital replica is identical visual representation of each of the plurality of elements 106 of each of the plurality of region 104 of the digital facility 102. The digital replica is an identical visual multimedia representation of the digital facility 102. The digital replica is an identical visual multimedia representation of each of the plurality of region 104 of the digital facility 102. The digital replica is identical visual multimedia representation of the plurality of elements 106 of each of the plurality of regions 104 of the digital facility 102. The digital replica is a digital 3-D model of the digital facility 102. In an embodiment of the present disclosure, the digital replica is of any other suitable form of the like.
[0021] In addition, the quality control system 110 splits the digital replicas into one or more digital replicas. The one or more digital replicas correspond to a digital replica for each region of the plurality of regions 104 of the digital facility 102. The quality control enables the one or more digital with the facilitation of the first set of data. The quality control system 110 enables one or more digital replicas of each of the plurality of regions 104 for detailed information of the plurality of elements 106. The one or more digital replicas are identical virtual representation of each of the plurality of region 104 of the digital facility 102. The one or more digital replicas represent in complete detail the plurality of elements 106 of each of the plurality of regions 104. The one or more digital replicas identically represent shape, size, location orientation and the like of the plurality of elements 106 of each of the plurality of region 104. In an embodiment of the present disclosure, the one or more digital replicas collectively enable the digital replica of the digital facility 102. In another embodiment of the present disclosure, the one or more digital replicas represent any other suitable component of the digital facility 102.
[0022] The quality control system 110 associates and represents the unique identity of each of the plurality of elements 106 in the digital replica. The digital replica of the digital facility 102 visually represents the unique identity of each of the plurality of elements 106. The unique identity facilitates in differentiation and identification of each of the plurality of elements 106. The unique identity is allocated to each of the plurality of elements 106 based on a predefined pattern. The association of unique identity of each of the plurality of elements 106 with the corresponding digital replica facilitates in quality control and management of the digital facility 102. The unique identity of each of the plurality of elements 106 integrates data collection and data processing of data for the purpose of quality control and management. The unique identity of each of the plurality of elements 106 facilitates in preferential and subjective optimizations.
[0023] The quality control system 110 includes a plurality of micro descriptors 108. The plurality of micro descriptors 108 provides data of a plurality of characteristic attributes of each of the plurality of elements 106 of the digital facility 102. The plurality of micro descriptors 108 receive and provide data of a plurality of characteristic attributes of each of the plurality of elements 106 of the digital facility 102. The plurality of micro descriptors 108 are designed to retrieve accurate data of the plurality of characteristic attributes of the plurality of elements 106. The plurality of micro descriptors 108 are designed to provide accurate data of the plurality of characteristic attributes of the plurality of elements 106. In general, micro descriptors provide data of elementary attributes, characteristic features, hygiene condition, physical states, current condition and the like of elements associated with micro descriptors. In an embodiment of the present disclosure, the plurality of micro descriptors 108 provides any other suitable data. Each of the plurality of micro descriptors 108 is associated with one or more of the plurality of elements 106. Each of the plurality of elements 106 is associated with one or more of the plurality of micro descriptors 108. Each of the plurality of micro descriptor 110 is associated with similar one or more elements of the plurality of elements 106.
[0024] For example, a plurality of air conditioners in a region is associated with a first micro descriptor. In another example, a plurality of water valves in a region is associated with a second micro descriptor. The quality control system 110 employs the plurality of micro descriptors 108 to retrieve data of each of the plurality of element 106. The plurality of micro descriptors 108 provide data of elementary attributes, characteristic features, physical state, operational parameters, current condition and the like of the plurality of elements 106. In an embodiment of the present disclosure, the plurality of micro descriptors 108 provides data of any other suitable parameters of the plurality of elements 106.
[0025] The plurality of micro descriptors 108 provides data of elementary attributes of each of the plurality of element 106. The elementary attributes includes but is not limited to shape, size, color, cleanliness, texture and motion. In an embodiment of the present disclosure, the plurality of micro descriptors 108 provides data of any other suitable elementary attributes of the like. The plurality of micro descriptors 108 provides data of characteristic features, physical state, operational parameter and current condition. The plurality of micro descriptors 108 are couples with the unique identity of each of the plurality of elements 106. Each of the plurality of micro descriptor 108 provides data of one or more of the plurality of elements 106 coupled with the unique identity. The quality control system 110 processes and stores data of each of the plurality of elements 106 coupled with unique identity. The unique identity of each of the plurality of elements 106 facilitates the quality control system 110 in differentiation and identification. The quality control system 110 processes and stores the data provided by the plurality of micro descriptors 108. The quality control system 110 controls and monitors each of the plurality of micro descriptors 108. The quality control system 110 governs and coordinates the operational performance of the plurality of micro descriptors 108. The quality control system 110 monitors the operations of the plurality of micro descriptors 108 in real time. The quality control system 110 manipulates different configurations of each of the plurality of micro descriptors 108 to receive desired characteristic data of the plurality of elements 106. In an embodiment of the present disclosure, the quality control system 110 controls any other suitable parameter of the plurality
[0026] In an embodiment of the present disclosure, each of the plurality of micro descriptors 108 is associated with a plurality of sensors and an embedded electronic system. The plurality of sensors sense and provide data of one or more of the plurality of characteristic attributes of each of the plurality of elements 106. The embedded electronic system is suitably designed to receive significant data of the plurality of attribute of the plurality of elements 106 with the facilitation of the plurality of sensors. Each of the plurality of micro descriptors 108 is associated with similar one or more elements of the plurality of elements 106. The embedded system and the plurality of sensors are configured to receive data of similar one or more elements of the plurality of elements 106. For example, a plurality of light bulbs in a region is associated with a micro descriptor with electrical sensors and a suitable embedded system. In another embodiment of the present disclosure, one or more of the plurality of micro descriptors 108 are associated with smart sensors with embedded computers systems. In yet another embodiment of the present disclosure, the plurality of micro descriptors 108 provides attribute data of the plurality of elements 106 with the facilitation of human feedback. In yet another embodiment of the present disclosure, the plurality of micro descriptors 108 directly receives human feedback about the plurality of elements 106 of the digital facility 102. In yet another embodiment of the present disclosure, the plurality of micro descriptors 108 receives human feedback about the plurality of elements 106 of the digital facility 102 with the facilitation of plurality of communication devices. In yet another embodiment of the present disclosure, the plurality of micro descriptors 108 receives human feedback about the plurality of elements 106 of the digital facility 102 with the facilitation of any other suitable device of the like. In yet another embodiment of the present disclosure, the plurality of micro descriptors 108 are digital descriptors specially designed for each of the plurality of elements 106. In yet another embodiment of the present disclosure, the plurality of micro descriptors are associated with sensors, embedded systems, computer system, connected devices, human feedback and the like. In yet another embodiment of the present disclosure, the plurality of micro descriptors 108 provides attribute data of the plurality of elements 106 with any other suitable mechanism of the like. In yet another embodiment of the present disclosure, the plurality of micro descriptors 108 is communication devices. The human feedback is received from a plurality of individuals associated with the digital facility 102. The human feedback may be received with the facilitation of plurality of communication devices.
[0027] The quality control system 110 collects a second set of data. The quality control system 110 collects the second set of data from the plurality of micro descriptors 108. The quality control system 110 collects the second set of data to ensure quality control of the digital facility 102. The quality control system 110 processes the second set of data. The quality control system 110 process the second set of data with the facilitation of machine learning. The processing of second set of data enables efficient and effective quality control and management of the digital facility 102. The processing of second set of data includes differentiating the plurality of characteristic attributes of each of the plurality of elements 106. The differentiation of the plurality of characteristic attributes facilitates in qualitative and quantitative analysis of the plurality of characteristic attributes. The differentiation of the plurality of characteristic attributes facilitates in monitoring of each of the plurality of attribute of the plurality of elements 106. The differentiation of the plurality of attribute facilitates in comparison of one or more of the plurality of attribute of similar one or more elements of the plurality of elements 106. In an embodiment of the present disclosure, the differentiation of the plurality of characteristic attributes facilitates in any other suitable processing of the like.
[0028] The second set of data includes a plurality of sets audit data. The quality control system 110 includes an audit process. The audit process is done at regular intervals on the digital facility 102. The audit process is done for each of the plurality of region 104 of the digital facility 102. The audit process facilitates in maintaining quality of the digital facility 102. The audit process is done with the facilitation of one or more quality assurance manager. In an embodiment of the present disclosure, the audit process is done with the facilitation of one or more visitor of the digital facility. In another embodiment of the present disclosure, audit process is done with the facilitation of any other suitable mechanism of the like. The audits process provides the plurality of sets of audit data to associate with one or more of the plurality of elements 106 of the digital facility 102. The plurality of sets of audit data is a constituent element of the second set of data. In an embodiment of the present disclosure the plurality of sets of audit data is provided directly to the quality control system. In another embodiment of the present disclosure, the plurality of sets of data is associated with the quality control system 110 with the facilitation of any other suitable mechanism of the like.
[0029] In an embodiment of the present disclosure, the second set of data includes demographical data associated with the digital facility 102. In another embodiment of the present disclosure, the second set of data includes weather data associated with the digital facility 102. In yet another embodiment of the present disclosure, the second set of data includes occupancy data associated with the digital facility 102. In yet another embodiment of the present disclosure, the second set of data includes demand data of the digital facility 102 for different quarter or seasons of year. In yet another embodiment of the present disclosure, the second set of data includes data received from external data sources. In yet another embodiment of the present disclosure the second set of data includes any other suitable data of the like.
[0030] The processing of second set of data includes analysis of the plurality of characteristic attributes of the plurality of elements 106. The processing of the second set 112 of data includes monitoring of the plurality of characteristic attributes of each of the plurality of elements 106. The processing of the second set of data includes comparison of one or more of the plurality of attribute of the plurality of elements 106. The processing of the second set of data includes storing of the plurality of characteristic attributes of each of the plurality of elements 106. The processing of the second set of data includes updating of the plurality of characteristic attributes of each of the plurality of elements 106. In an embodiment of the present disclosure, the processing of the second set of data includes any other suitable processing of the plurality of characteristic attributes of the each of the plurality of elements 106.
[0031] The quality control system 110 process the second set of data to discovers a plurality of patterns. Each of the plurality of patterns is associated with a characteristic attribute of the plurality of characteristic attributes of one or more of the plurality of elements 106. The quality control system 110 discovers the plurality of patterns with the facilitation of machine learning. In general, a pattern is a regular and intelligible form or sequence discernible in a way of occurring of an event. The quality control system 110 process the second set of data to discover the plurality of patterns in data of each of the plurality of characteristic attributes. For example, the quality control system 110 100 process data associated with an air conditioner to discover a pattern in occurrence of a defect in functionality of compressor under various operating conditions. The plurality of patterns is observed in data of each of the plurality of characteristic attribute of the plurality of elements 106. The quality control system 110 employ machine learning to discover the plurality of patterns in data of each of the plurality of characteristic attribute of the plurality of elements 106.
[0032] The quality control system 110 predicts one or more issues associated with one or more of the plurality of elements 106 of the digital facility 102. The quality control system 110 predicts one or more issues with the facilitation of machine learning. The quality control system 110 predicts the one or more issues with the facilitation of the second set of data. The one or more issues corresponds to deviation of one or more of the plurality of characteristic attribute of one or more elements of the plurality of elements 106 from ideal attributes or ideal parameters. The quality control system 110 establishes the standard data in form of the ideal attributes or ideal parameters with the facilitation of machine learning. The ideal attribute and ideal parameter refer to attributes or parameters of the plurality of elements that enable highest possible working efficiency of the plurality of elements. The plurality of patterns facilitates the quality control system 110 in predicting the one or more issues associated with one or more of the plurality of elements 106 of the digital facility 102. The quality control system 110 predicts the one or more issues in real time.
[0033] The quality control system 110 predicts one or more issues associated with the digital facility 102 with the facilitation a plurality of predictive model. In general, predictive model refers to a variety of statistical techniques to analyze current and historical data to make predictions about future events or otherwise unknown events. The quality control system 110 executes the plurality of predictive model with the facilitation of machine learning. Each of the plurality of predictive model facilitates in accurately predicting the one or more issues associated with one or more of the plurality of elements 106. Each of the plurality of model is improved and trained with the facilitation of machine learning. Each of the plurality of predictive model is designed for one or more of the plurality of characteristic attribute. Each predictive model is suitable for one or more of the plurality of characteristic attribute of similar category. The plurality of characteristic attributes is different and unrelated. The quality control system 110 enables accurate predictions with the facilitation of different predictive models for different characteristic attributes. For example, one or more issues might be associated with staff or manpower associated with a digital facility, one or more issues might be seasonal and one or more issues might be associated with weather condition around a facility. In general, factors influencing different prediction are different as a result different predictive model has different inputs in the form of plurality of characteristic attributes.
[0034] The quality control system 110 employ different predictive models for predicting the one or more issues associated with similar elements of the plurality of element 106. The quality control system 110 executes the plurality of predictive models with the facilitation of machine learning. In an embodiment of the present disclosure, the quality control system 110 executes the plurality of patterns with the facilitation of any other suitable mechanism. The quality control system 110 employs a plurality of predictive models. The plurality of predictive models employed by the quality control system 110 includes but is not limited to tree based ensemble models. In an embodiment of the present disclosure, the plurality of predictive models includes random forests method. In another embodiment of the present disclosure, the plurality of predictive models includes extreme gradient boosted trees method. In yet another embodiment of the present disclosure, the quality control system 110 employ combination of two or more predictive methods for making predictions. In yet another embodiment of the present disclosure, the plurality of predictive models includes any other suitable predictive model of the like.
[0035] The one or more issues include fault, problem or inefficiency of one or more of the plurality of electrical appliance. The one or more issues include fault or problem in one or more of the plurality of furniture. The one or more issues include fault or problem in one or more of the plurality of sanitary fittings. The one or more issues include fault or problem in one or more of the plurality of structural fittings. The one or more issues includes problem in manpower associated with the digital facility 102. The one or more issues include fault or problem in one or more of the plurality of cutleries. The one or more issues include fault or problem in one or more of the plurality of washroom fittings. In an embodiment of the present disclosure, the one or more issues include any other fault or problem of the like. The fault corresponds to structural fault, mechanical fault, electrical fault, positioning fault, design fault and the like. The problem includes bad hygienic condition, inadequate cleanliness, uncomfortable, unpleasant, unhealthy, and unprofessional. In an embodiment of the present disclosure, the problem includes any other situation of the like.
[0036] In addition, the quality control system 110 compares the second set of data with the standard data. The quality control system 110 compares the second set of data in real time. The quality control system 110 evaluates a deviation of the second set of data with the standards data in order to predict one or more issue associated with one or more of the plurality of elements 106 the digital facility 102. The quality control system 110 evaluates the second set of data in real time. The quality control system 110 evaluates the deviation between the second set of data and the standard data. The deviation facilitates the quality control system 110 to predict one or more issue associated with one or more of the plurality of elements 106 in the digital facility 102. The deviation facilitates in assigning a degree of severity to the one or more issue.
[0037] The quality control system 110 assigns one or more high severity issue to the one or more issues. The one or more high severity issues correspond to the one or more issues with serious consequence on the performance of one or more of the plurality of elements 106. The quality control system 110 assigns the one or more high severity issues to the one or more issues based on machine learning. The quality control system 110 assigns the one or more high severity issues to the one or more issues based on the second set of data. For example, the quality control system 110 predicted a fault in air conditioner, based on second set of data the fault in air conditioner becomes a reason for a negative feedback, based on this the fault in air conditioner becomes a high severity issue. The quality control system 110 assigns the one or more high severity issues based on the human feedback of the one or more issues. In an embodiment of the present disclosure, the quality control system 110 assigns one or more high severity issues based on time to resolve the one or more issues in the past. For example, one or more complex issues of the one or more issues may be high severity due to excess amount of time involved in resolving the one or more complex issues.
[0038] In addition, the quality control system 110 stores a plurality of sets of information associated with the digital facility 102. The plurality of sets of information includes the first set of data, the second set of data, the plurality of patterns, one or more issues, the one or more high severity issues and the like. In an embodiment of the present disclosure, the plurality of sets of information includes any other suitable information of the like. The plurality of sets of information being stored in a plurality of matrices. The plurality of matrices stores the plurality of sets of information in a systematic and ordered pattern. The plurality of sets of information being stored in a database of quality control system 110. The database of the quality control system 110 stores the plurality of sets of information for processing with the facilitation of machine learning. The quality control system 110 stores the plurality of sets of information in real time.
[0039] The quality control system 110 updates the plurality of patterns associated with the plurality of elements 106 of the digital facility 102. The plurality of patterns is updated in the database of the quality control system 110. The plurality of patterns is updated based on the stored plurality of sets of information and machine learning. The plurality of patterns is updated in real time. In an embodiment of the present disclosure, the plurality of patterns is updated with the facilitation of any other suitable mechanism of the like. The quality control system 110 continuously processes the plurality of sets of information to update the plurality of patterns.
[0040] In addition, the quality control system 110 notifies the one or more manpower associated with the digital facility 102. The quality control system illustrates a block diagram of a digital facility associated with a quality control system in accordance with various embodiments of the present disclosure notifies the manpower of the one or more issues and the one or more high severity issues. The one or more manpower refers to maintenance staff of the digital facility 102. In an embodiment of the present disclosure, the one or more manpower refers to managers of the digital facility 102. In another embodiment of the present disclosure, the one or more manpower refers to inspection staff of the digital facility 102. In yet another embodiment of the present disclosure, the one or more manpower refers to owner of the digital facility 102. In another embodiment of the present disclosure, the one or more manpower refers to any other suitable individual associated with the digital facility 102.
[0041] In addition, the quality control system 110 alerts the one or more manpower associated with the digital facility. The quality control system 110 alerts the one or more manpower to resolve the predicted one or more issue and the one or more high severity issues. The alerts are raised to resolve the issues to maintain the quality of the digital facility 102. The quality control system 110 alters the one or more manpower in real time. The one or more manpower includes but is not limited to the maintenance staff, quality manager, the secret auditor, and the like. The quality control system 110 alerts by sending notification to one or more portable communication device of the one or more manpower of the digital facility 102. In an embodiment of the present disclosure, the quality control system 110 alerts the one or more manpower by any other suitable notification mechanism of the like.
[0042] In addition, the quality control system 110 prevents the booking of a particular region of the plurality of regions 104 of the digital facility 102. The quality control system 110 analyzes the one or more high severity issues associated with each region of the plurality of regions 104. The analysis is done in order to identify if the number of one or more high severity issues being higher or lower than a predefined standard limit. The predefined standard limit is established with the facilitation of machine learning. In case, the degree of severity is higher than the predefined standard limit the identified region of the plurality of regions 104 is prevented from booking. The quality control system 110 prevents the booking of the identified region of the plurality of region 104 in real time. The quality control system 110 prevents booking of the identified region of the plurality of regions 104 until the degree of severity is reduced to meet the quality standard of the digital facility 102. In an embodiment of the present disclosure, the quality control system 110 prevents booking of the particular region of the plurality of regions 104 with the facilitation of any other suitable criteria of the like. In another embodiment of the present disclosure, the quality control system 110 prevents booking of the particular region of the plurality of regions 104 with the facilitation of any other suitable mechanism of the like.
[0043] The quality control system 110 forecasts a time to resolve the one or more issue in order to maintain quality of the digital facility 102. The quality control system 110 forecasts the time to resolve the one or more issue with the facilitation of the second set of data. The quality control system 110 forecasts the time to resolve the one or more issue with the facilitation of machine learning. The forecasting is done by analyzing the previously stored data and the data received in real time to forecast the time to resolve the one or more issues. The time to resolve the one or more issues facilitates in assigning the one or more high severity issues. In case, an issue is not resolved in the forecasted time, the quality control system 110 assigns a high severity status to the unresolved issue. In an embodiment of the present disclosure, the time to resolve the one or more issues facilitates in any other suitable processing of the like.
[0044] The quality control system 110 upgrades the first set of data to accurately predict and forecast. The quality control system 110 upgrades the first set of data in real time. The quality control system 110 upgrades the second set of data to accurately predict and forecast. The quality control system upgrades the second set of data in real time. The quality control system 110 upgrades the one or more issues to ensure quality management of the digital facility 102. The quality control system 110 upgrades the one or more high severity issue to ensure quality control of the digital facility 102. The quality control system 110 upgrades the second set of data in real time. In an embodiment of the present disclosure, the quality control system 110 upgrades any other suitable data of the like.
[0045] The quality control system 110 is connected with the server 114 with the facilitation of the communication network 112. In an embodiment of the present disclosure, the communication network 112 enables the quality control system 110 to gain access to the internet for transmitting data to the server 114. Moreover, the communication network 112 provides a medium to transfer the data between the quality control system 110 and the server 114. The server 114 handles each operation and task performed by the quality control system 110. The server 114 stores one or more instructions for performing the various operations of the quality control system 110.
[0046] In an embodiment of the present disclosure, the type of communication network 112 is a wireless mobile network. In another embodiment of the present disclosure, the type of communication network 112 is a wired network with a finite bandwidth. In yet another embodiment of the present disclosure, the type of communication network 112 is a combination of the wireless and the wired network for the optimum throughput of data transmission. In yet another embodiment of the present disclosure, the type of communication network 112 network is an optical fiber high bandwidth network that enables a high data rate with negligible connection drops. The communication network 112 includes a set of channels. Each channel of the set of channels supports a finite bandwidth. Moreover, the finite bandwidth of each channel of the set of channels is based on capacity of the communication network 112.
[0047] The quality control system 110 is connected to the server 114. In general, the server 114 is a computer program or device that provides functionality for other programs or devices. The server 114 provides various functionalities, such as sharing data or resources among multiple clients, or performing computation for a client. However, those skilled in the art would appreciate that more number of quality control system 110 are connected to more number of servers 114. Furthermore, it may be noted that the server 114 includes a database. However, those skilled in the art would appreciate that more number of the server 114 includes more numbers of databases.
[0048] In an embodiment of the present disclosure, the quality control system 110 is located in the main server110. In another embodiment of the present disclosure, the quality control system 110 is associated with the server 114. In yet another embodiment of the resent disclosure, the quality control system 110 is a part of the server 114. The server 114 handles each operation and task performed by the quality control system 110. The server 114 stores one or more instructions for performing the various operations of the e-learning system 112. The main server 114 is located remotely from the one or more device 104.
[0049] The server 114 is associated with the administrator 116. In general, the administrator 116 manages the different components in the quality assurance 108. The administrator 116 coordinates the activities of the components involved in the quality control system 100. The administrator 116 is any person or individual who monitors the working of the quality control system 110 and the server 114 in real time. The administrator 116 monitors the working of the quality control system 110 and the server 114 through a communication device. The communication device includes the laptop, the desktop computer, the tablet, a personal digital assistant and the like.
[0050] FIG. 2 illustrates a block diagram of a computing device 200, in accordance with various embodiments of the present disclosure. The computing device 200 includes a bus 202 that directly or indirectly couples the following devices: memory 204, one or more processors 206, one or more presentation elements 208, one or more input/output (I/O) ports 210, one or more input/output elements 212, and an illustrative power supply 214. The bus 202 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 2 are shown with lines for the sake of clarity, in reality, delineating various elements is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. The inventors recognize that such is the nature of the art, and reiterate that the diagram of FIG. 2 is merely illustrative of an exemplary computing device 200 that can be used in connection with one or more embodiments of the present invention. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of FIG. 2 and reference to “computing device”.
[0051] The computing device 200 typically includes a variety of computer-readable media. The computer-readable media can be any available media that can be accessed by the computing device 200 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer storage media and communication media. The computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device 200. The communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.
[0052] Memory 204 includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory 204 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical-disc drives, etc. The computing device 200 includes one or more processors that read data from various entities such as memory 204 or I/O elements 212. The memory 204 stores a plurality of instructions. The plurality of instructions is executed by the one or more processors 206. The one or more processors 206 enable a method for quality control of the digital facility 102 based on machine learning. The one or more presentation elements 208 present data indications to a user or other device. Exemplary presentation elements include a display device, speaker, printing component, vibrating component, etc. The one or more I/O ports 210 allow the computing device 200 to be logically coupled to other devices including the one or more I/O elements 212, some of which may be built in. Illustrative elements include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.