Claims:
WE CLAIM:
1. An integration platform (100) for creating a risk profile of a living space, said integration platform (100) comprising:
o a repository (104) configured to store a set of pre-determined analysis rules, a set of pre-determined segregation rules, a set of pre-determined profile creating rules, and a plurality of pre-determined rating tables in a one-to-one correspondence with a first set of parameters and a second set of parameters;
o a data processor (106) configured to receive a set of activity data from a plurality of sensor nodes (102) deployed within said living space, and further configured to cooperate with said repository (104) to analyse said activity data to generate an analysis result, said data processor (106) further configured to segregate said analysis result based on said first set of parameters, said second set of parameters and said pre-determined segregation rules to generate a segregation result;
o a rating module (108) configured to cooperate with said data processor (106) and said repository (104) to extract said ratings based on said segregation result from said respective rating tables; and
o a risk profile creator (110) configured to cooperate with said rating module (108) and said repository (104) to create a risk profile of said living space based on said ratings and said pre-determined profile creating rules.
2. The integration platform (100) as claimed in claim 1, wherein said sensor nodes (102) include at least one sensor, said appliances, at least one actuating device, at least one alerting device and at least one IoT (Internet of Things) based devices.
3. The integration platform (100) as claimed in claim 1, wherein said activity data is selected from the group consisting of usage of said appliances and a record of servicing and damages to said appliances.
4. The integration platform (100) as claimed in claim 1, wherein said first set of parameters include a frequency of servicing of a plurality of appliances and a frequency of damages to said appliances, and said second set of parameters include a pattern of switching on and off of said appliance, frequency of alerts generated by said living space and a time period for which said applications are functioning.
5. The integration platform (100) as claimed in claim 1, wherein said data processor (106) includes:
• a data analyser (112) configured to receive said activity data from said sensor nodes (102), and further configured to cooperate with said repository (104) to analyse said activity data based on said pre-determined analysis rules, said data analyser (112) further configured to generate said analysis result; and
• a data segregator (114) configured to cooperate with said data analyser (112) and said repository (104) to segregate said analysis result based on said first set of parameters, said second set of parameters and said pre-determined segregation rules to generate a segregation result,
wherein said data analyser (112) and said data segregator (114) are implemented using one or more processor(s).
6. The integration platform (100) as claimed in claim 1, wherein said rating module (108) includes:
• a memory (116) configured to store
o a first rating table, associated with said parameter related to frequency of servicing, having a list of criteria for servicing and said ratings corresponding to said criteria;
o a second rating table, associated with said parameter related to frequency of damages, having a list of criteria for damages and said ratings corresponding to said criteria;
o a third rating table, associated with said parameter related to patterns, having a list of criteria for patterns and said ratings corresponding to said criteria;
o a fourth rating table, associated with said parameter related to frequency of alerts, having a list of criteria for alert and said ratings corresponding to said criteria; and
o a fifth rating table, associated with said parameter related to time period, having a list of criteria for time period and said ratings corresponding to said criteria,
• a first crawler and extractor (118) configured to cooperate with said data segregator (114) and said memory (116) to crawl said first rating table and extract said rating based on said segregation result;
• a second crawler and extractor (120) configured to cooperate with said data segregator (114) and said memory (116) to crawl said second rating table and extract said rating based on said segregation result;
• a third crawler and extractor (122) configured to cooperate with said data segregator (114) and said memory (116) to crawl said third rating table and extract said rating based on said segregation result;
• a fourth crawler and extractor (124) configured to cooperate with said data segregator (114) and said memory (116) to crawl said fourth rating table and extract said rating based on said segregation result; and
• a fifth crawler and extractor (126) configured to cooperate with said data segregator (114) and said memory (116) to crawl said fifth rating table and extract said rating based on said segregation result,
wherein said first crawler and extractor (118), said second crawler and extractor (120), said third crawler and extractor (122), said fourth crawler and extractor (124) and said fifth crawler and extractor (126) are implemented using one or more processor(s).
7. The integration platform (100) as claimed in claim 1, wherein said risk profile creator (110) includes:
• a first adder and multiplier (140) configured to cooperate with said first crawler and extractor (118) to add said extracted ratings, termed as first sum, and further configured to multiply said first sum with a pre-determined weight of said parameter related to frequency of servicing, stored in said repository (104), to generate a first score;
• a second adder and multiplier (142) configured to cooperate with said second crawler and extractor (120) to add said extracted ratings, termed as second sum, and further configured to multiply said second sum with a pre-determined weight of said parameter related to frequency of damages, stored in said repository (104), to generate a second score;
• a third adder and multiplier (144) configured to cooperate with said third crawler and extractor (122) to add said extracted ratings, termed as third sum, and further configured to multiply said third sum with a pre-determined weight of said parameter related to patterns, stored in said repository (104), to generate a third score;
• a fourth adder and multiplier (146) configured to cooperate with said fourth crawler and extractor (124) to add said extracted ratings, termed as fourth sum, and further configured to multiply said fourth sum with a pre-determined weight of said parameter related to frequency of alerts, stored in said repository (104), to generate a fourth score;
• a fifth adder and multiplier (148) configured to cooperate with said fifth crawler and extractor (126) to add said extracted ratings, termed as fifth sum, and further configured to multiply said fifth sum with a pre-determined weight of said parameter related to time period, stored in said repository (104), to generate a fifth score;
• a scoring calculator (128) configured to cooperate with said first adder and multiplier (140), said second adder and multiplier (142), said third adder and multiplier (144), said fourth adder and multiplier (146), and said fifth adder and multiplier (148) to calculate an average of said first score, said second score, said third score, said fourth score and said fifth score; and
• a profile calculator (130) configured to cooperate with said scoring calculator (128) to build a risk profile based on said average and said pre-determined profile building rules,
wherein said first adder and multiplier (140), said second adder and multiplier (142), said third adder and multiplier (144), said fourth adder and multiplier (146), said fifth adder and multiplier (148), and said scoring calculator (128) and said profile calculator (130) are implemented using one or more processor(s).
8. The integration platform (100) as claimed in claim 1, wherein said sensor nodes (102) and said integration platform (100) communicate via a communication module (132), and at least one user device (134) is communicatively coupled with said integration platform (100).
9. The integration platform (100) as claimed in claim 1, wherein said integration platform (100) further includes:
• a warning module (136) configured to send at least one notification to said user device (134) and to said sensor nodes (102) based on comparison of said analysis result with a pre-stored threshold value stored in said repository (104); and
• a suggestion module (138) configured to provide suggestions to said user device (134) on analysing health of said appliances based on said sensed data and a set of pre-determined suggesting rules stored in said repository (104),
wherein said warning module (136) and said suggesting module (138) are implemented using one or more processor(s).
10. A method for creating a risk profile of a living space, said method comprises the following steps:
• storing (202), by a repository (104), a set of pre-determined analysis rules, a set of pre-determined segregation rules, a set of pre-determined profile building rules, and a plurality of pre-determined rating tables in a one-to-one correspondence with a first set of parameters and a second set of parameters;
• receiving (204), by a plurality of sensor nodes (102) deployed within said living space, a set of activity data;
• analysing (206), by a data processor (106), said activity data to generate an analysis result;
• segregating (208), by said data processor (106), said analysis result based on said first set of parameters, said second set of parameters and said pre-determined segregation rules to generate a segregation result;
• extracting (210), by a rating module (108), said ratings based on said segregation result from said respective rating tables; and

• creating (212), by a risk profile creator (110), a risk profile of said living space based on said ratings and said pre-determined profile creating rules.
, Description:FIELD
The present invention relates to the field of an integration platform for creating a risk profile of a living space.
DEFINITIONS

As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.
Living space - The term ‘Living space’ hereinafter refers to a home, a house, a dormitory, a workspace, a room, a cluster of rooms or an apartment.
BACKGROUND
The insurance providers (property/casualty, liability, life, and health insurance), generally seek to minimize the risk of a living space for which it is important to prepare a risk profile of living space. The profile of the living space may include examining nature and level of threats at a living space, likelihood of occurrence of adverse effects due to the threats, level of disruption and costs associated with each type of threat, and effectiveness of controls in place to manage those threats. The risks at a living space may have immediate safety hazards or they could become apparent to the user later. Further, it is possible that, threats to a given living space potentially poses a risk to others in the neighborhood.
Further, for generating a profile of the living space it is essential to monitor various parameters at living spaces to perform analysis so as to detect hazardous situations and alert users in case of detection of hazardous situations. The users are unable to remotely control various living space devices to reduce the risk of potential hazards.
Therefore, there is a need of an integration platform for creating a risk profile of a living space that detects threats at a given living space and notifies the neighbors or other interested parties of the detected threats.

OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
An object of the present disclosure is to provide an integration platform for creating a risk profile of a living space.
An object of the present disclosure is to provide an integration platform that detect hazardous situations.
Another object of the present disclosure is to provide an integration platform that alert users in case of detection of hazardous situations.
Still another object of the present disclosure is to provide an integration platform that analyses health of appliances and provide predictive maintenance suggestions.
Yet another object of the present disclosure is to provide an integration platform that reduces losses through preventive monitoring and alerts.
An object of the present disclosure is to provide an integration platform that offers personalized service to users through better risk profiling.
Another object of the present disclosure is to provide an integration platform that identify and prioritize risks for action.
Yet another object of the present disclosure is to provide an integration platform that facilitate transformation and availability of data for analytics in order to deduce inference for taking corrective measures.
Still another object of the present disclosure is to provide an integration platform that helps companies to analyze data and differentiate legitimate and fraudulent users.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages an integration platform for creating a risk profile of a living space.
The integration platform for creating a risk profile of a living space comprises a repository, a data processor, a rating module and a risk profile creator.
The repository is configured to store a set of pre-determined analysis rules, a set of pre-determined segregation rules, a set of pre-determined profile creating rules, and a plurality of pre-determined rating tables in a one-to-one correspondence with a first set of parameters and a second set of parameters.
The data processor is configured to receive a set of activity data from a plurality of sensor nodes deployed within the living space, and further configured to cooperate with the repository to analyse the activity data to generate an analysis result. The data processor is further configured to segregate the analysis result based on the first set of parameters, the second set of parameters and the pre-determined segregation rules to generate a segregation result.
In an embodiment, the sensor nodes include at least one sensor, at least one appliance, at least one actuating device, at least one alerting device and at least one IoT (Internet of Things) based devices.
In another embodiment, the activity data is selected from the group consisting of usage of the appliances and a record of servicing and damages to the appliances.
In another embodiment, the first set of parameters include a frequency of servicing of a plurality of appliances and a frequency of damages to the appliances, and the second set of parameters include a pattern of switching on and off of the appliance, frequency of alerts generated by the living space and a time period for which the applications are functioning.
The data processor includes a data analyser and a data segregator.
The data analyser is configured to receive the activity data from the sensor nodes, and further configured to cooperate with the repository to analyse the activity data based on the pre-determined analysis rules, the data further configured to generate the analysis result.
The data segregator is configured to cooperate with the data analyser and the repository to segregate the analysis result based on the first set of parameters, the second set of parameters and the pre-determined segregation rules to generate a segregation result.
The data analyser and the data segregator are implemented using one or more processor(s).
The rating module is configured to cooperate with the data processor and the repository to extract the ratings based on the segregation result from the respective rating tables.
The rating module includes a memory, a first crawler and extractor, a second crawler and extractor, a third crawler and extractor, a fourth crawler and extractor, and a fifth crawler and extractor.
The memory is configured to store:
o a first rating table, associated with the parameter related to frequency of servicing, having a list of criteria for servicing and the ratings corresponding to the criteria;
o a second rating table, associated with the parameter related to frequency of damages, having a list of criteria for damages and the ratings corresponding to the criteria;
o a third rating table, associated with the parameter related to patterns, having a list of criteria for patterns and the ratings corresponding to the criteria;
o a fourth rating table, associated with the parameter related to frequency of alerts, having a list of criteria for alert and the ratings corresponding to the criteria; and
o a fifth rating table, associated with the parameter related to time period, having a list of criteria for time period and the ratings corresponding to the criteria,
The first crawler and extractor is configured to cooperate with the data segregator and the memory to crawl the first rating table and extract the rating based on the segregation result.
The second crawler and extractor is configured to cooperate with the data segregator and the memory to crawl the second rating table and extract the rating based on the segregation result;
The third crawler and extractor configured to cooperate with the data segregator and the memory to crawl the third rating table and extract the rating based on the segregation result;
The fourth crawler and configured to cooperate with the data segregator and the memory to crawl the fourth rating table and extract the rating based on the segregation result; and
The fifth crawler and extractor configured to cooperate with the data segregator and the memory to crawl the fifth rating table and extract the rating based on the segregation result,
The first crawler and extractor, second crawler and extractor, third crawler and extractor, fourth crawler and extractor and fifth crawler and extractor are implemented using one or more processor(s).
The risk profile creator is configured to cooperate with the rating module and the repository to create a risk profile of the living space based on the ratings and the pre-determined profile creating rules.
The risk profile creator includes a first adder and multiplier, a second adder and multiplier, a third adder and multiplier, a fourth adder and multiplier, a fifth adder and multiplier, a scoring calculator and a profile calculator.
The first adder and multiplier is configured to cooperate with the first crawler and extractor to add the extracted ratings, termed as first sum, and is further configured to multiply the first sum with a pre-determined weight of the parameter related to frequency of servicing, stored in the repository, to generate a first score.
The second adder and multiplier configured to cooperate with the second crawler and extractor to add the extracted ratings, termed as second sum, and is further configured to multiply the second sum with a pre-determined weight of the parameter related to frequency of damages, stored in the repository, to generate a second score.
The third adder and multiplier is configured to cooperate with the third crawler and extractor to add the extracted ratings, termed as third sum, and is further configured to multiply the third sum with a pre-determined weight of the parameter related to patterns, stored in the repository, to generate a third score.
The fourth adder and multiplier is configured to cooperate with the fourth crawler and extractor to add the extracted ratings, termed as fourth sum, and is further configured to multiply the fourth sum with a pre-determined weight of the parameter related to frequency of alerts, stored in the repository, to generate a fourth score;
The fifth adder and multiplier is configured to cooperate with the fifth crawler and extractor to add the extracted ratings, termed as fifth sum, and is further configured to multiply the fifth sum with a pre-determined weight of the parameter related to time period, stored in the repository, to generate a fifth score.
The scoring calculator is configured to cooperate with the first adder and multiplier, the second adder and multiplier, the third adder and multiplier, the fourth adder and multiplier, and the fifth adder and multiplier to calculate an average of the first score, the second score, the third score, the fourth score and the fifth score.
The profile calculator is configured to cooperate with the scoring calculator to build a risk profile based on the average and the pre-determined profile building rules.
The first adder and multiplier, second adder and multiplier, third adder and multiplier, fourth adder and multiplier, fifth adder and multiplier, scoring calculator and profile calculator are implemented using one or more processor(s).
The integration platform further includes a warning module and a suggestion module.
The warning module is configured to send at least one notification to the user device and to the sensor based on comparison of the analysis result with a pre-stored threshold value stored in the repository.
The suggestion module is configured to provide suggestions to the user device on analysing health of the appliances based on the sensed data and a set of pre-determined suggesting rules stored in the repository.
The warning module and the suggesting module are implemented using one or more processor(s).
The sensor nodes and the integration platform communicate via a communication module, and at least one user device is communicatively coupled with the integration platform.
The present disclosure envisages a method for creating a risk profile of a living space.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
An integration platform for creating a risk profile of a living space of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a block diagram of an integration platform for creating a risk profile of a living space;
Figure 2a and 2b illustrate a flow diagram of a method of creating a risk profile of a living space;
Figure 3 illustrates an architecture diagram of the integration platform for creating a risk profile of a living space; and
Figure 4 illustrates the flow of the integration platform for creating a risk profile of a living space.

LIST OF REFERENCE NUMERALS
100 System
102 Sensor nodes
104 Repository
106 Data processor
108 Rating module
110 Risk profile creator
112 Data analyser
114 Data segregator
116 Memory
118 First crawler and extractor
120 Second crawler and extractor
122 Third crawler and extractor
124 Fourth crawler and extractor
126 Fifth crawler and extractor
128 Scoring Calculator
130 profile calculator
132 communication module
134 User device
136 warning module
138 suggestion module
140 First adder and multiplier
142 Second adder and multiplier
144 Third adder and multiplier
146 Fourth adder and multiplier
148 Fifth adder and multiplier
150 Other systems

DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, elements, components, and/or groups thereof.
An integration platform for creating a risk profile of a living space of the present disclosure, is described with reference to Figure 1 through Figure 4.
Referring to Figure 1, the integration platform for creating a risk profile of a living space (hereinafter referred as “platform”) (100) comprises a repository (104), a data processor (106), a rating module (108) and a risk profile creator (110).
The repository (104) is configured to store a set of pre-determined analysis rules, a set of pre-determined segregation rules, a set of pre-determined profile creating rules, and a plurality of pre-determined rating tables in a one-to-one correspondence with a first set of parameters and a second set of parameters.
The data processor (106) is configured to receive a set of activity data from a plurality of sensor nodes (102) deployed within the living space, and further configured to cooperate with the repository (104) to analyse the activity data to generate an analysis result. The data processor (106) is further configured to segregate the analysis result based on the first set of parameters, the second set of parameters and the pre-determined segregation rules to generate a segregation result.
In an embodiment, the sensor nodes (102) include at least one sensor, at least one appliance, at least one actuating device, at least one alerting device and at least one IoT (Internet of Things) based devices. In another embodiment, the sensors are selected from the group consisting of Water Flow Meter sensor, Flame and fire detection sensor, Temperature and Humidity sensor and Energy meter.
In another embodiment, the activity data is selected from the group consisting of usage of the appliances and a record of servicing and damages to the appliances.
In an embodiment, the activity data from the sensor nodes (102) helps in creation of risk profiling using the U-PASCAL framework which is defined as "User based patterns, alerts, service, claims and live connections".
In another embodiment, the first set of parameters include a frequency of servicing of a plurality of appliances and a frequency of damages to the appliances, and the second set of parameters include a pattern of switching on and off of the appliance, frequency of alerts generated by the living space and a time period for which the applications are functioning.
The data processor (106) includes a data analyser (112) and a data segregator (114).
The data analyser (112) is configured to receive the activity data from the sensor nodes (102), and further configured to cooperate with the repository (104) to analyse the activity data based on the pre-determined analysis rules, the data analyser (112) further configured to generate the analysis result.
The data segregator (114) is configured to cooperate with the data analyser (112) and the repository (104) to segregate the analysis result based on the first set of parameters, the second set of parameters and the pre-determined segregation rules to generate a segregation result.
The data analyser (112) and the data segregator (114) are implemented using one or more processor(s).
The rating module (108) is configured to cooperate with the data processor (106) and the repository (104) to extract the ratings based on the segregation result from the respective rating tables.
The rating module (108) includes a memory (116), a first crawler and extractor (118), a second crawler and extractor (120), a third crawler and extractor (122), a fourth crawler and extractor (124), and a fifth crawler and extractor (126).
The memory (116) is configured to store:
o a first rating table, associated with the parameter related to frequency of servicing, having a list of criteria for servicing and the ratings corresponding to the criteria;
o a second rating table, associated with the parameter related to frequency of damages, having a list of criteria for damages and the ratings corresponding to the criteria;
o a third rating table, associated with the parameter related to patterns, having a list of criteria for patterns and the ratings corresponding to the criteria;
o a fourth rating table, associated with the parameter related to frequency of alerts, having a list of criteria for alert and the ratings corresponding to the criteria; and
o a fifth rating table, associated with the parameter related to time period, having a list of criteria for time period and the ratings corresponding to the criteria,
The first crawler and extractor (118) is configured to cooperate with the data segregator (114) and the memory (116) to crawl the first rating table and extract the rating based on the segregation result.
The second crawler and extractor (120) is configured to cooperate with the data segregator (114) and the memory (116) to crawl the second rating table and extract the rating based on the segregation result;
The third crawler and extractor (122) configured to cooperate with the data segregator (114) and the memory (116) to crawl the third rating table and extract the rating based on the segregation result;
The fourth crawler and extractor (124) configured to cooperate with the data segregator (114) and the memory (116) to crawl the fourth rating table and extract the rating based on the segregation result; and
The fifth crawler and extractor (126) configured to cooperate with the data segregator (114) and the memory (116) to crawl the fifth rating table and extract the rating based on the segregation result,
The first crawler and extractor (118), second crawler and extractor (120), third crawler and extractor (122), fourth crawler and extractor (124) and fifth crawler and extractor (126) are implemented using one or more processor(s).
The risk profile creator (110) is configured to cooperate with the rating module (108) and the repository (104) to create a risk profile of the living space based on the ratings and the pre-determined profile creating rules.
The risk profile creator (110) includes a first adder and multiplier (140), a second adder and multiplier (142), a third adder and multiplier (144), a fourth adder and multiplier (146), a fifth adder and multiplier (148), a scoring calculator (128) and a profile calculator (130).
The first adder and multiplier (140) is configured to cooperate with the first crawler and extractor (118) to add the extracted ratings, termed as first sum, and is further configured to multiply the first sum with a pre-determined weight of the parameter related to frequency of servicing, stored in the repository (104), to generate a first score.
The second adder and multiplier (142) is configured to cooperate with the second crawler and extractor (120) to add the extracted ratings, termed as second sum, and is further configured to multiply the second sum with a pre-determined weight of the parameter related to frequency of damages, stored in the repository (104), to generate a second score.
The third adder and multiplier (144) is configured to cooperate with the third crawler and extractor (122) to add the extracted ratings, termed as third sum, and is further configured to multiply the third sum with a pre-determined weight of the parameter related to patterns, stored in the repository (104), to generate a third score.
The fourth adder and multiplier (146) is configured to cooperate with the fourth crawler and extractor (124) to add the extracted ratings, termed as fourth sum, and further configured to multiply the fourth sum with a pre-determined weight of the parameter related to frequency of alerts, stored in the repository (104), to generate a fourth score.
The fifth adder and multiplier (148) is configured to cooperate with the fifth crawler and extractor (126) to add the extracted ratings, termed as fifth sum, and is further configured to multiply the fifth sum with a pre-determined weight of the parameter related to time period, stored in the repository (104), to generate a fifth score.
The scoring calculator (128) is configured to cooperate with the first adder and multiplier (140), the second adder and multiplier (142), the third adder and multiplier (144), the fourth adder and multiplier (146), and the fifth adder and multiplier (148) to calculate an average of the first score, the second score, the third score, the fourth score and the fifth score.
The profile calculator (130) is configured to cooperate with the scoring calculator (128) to build a risk profile based on the average and the pre-determined profile building rules.
The first adder and multiplier (140), the second adder and multiplier (142), the third adder and multiplier (144), the fourth adder and multiplier (146), the fifth adder and multiplier (148), the scoring calculator (128) and the profile calculator (130) are implemented using one or more processor(s).
In an exemplary embodiment,

Where
S= Score
ai= weight of the parameter
pij= score of the parameter
The pre-determined weights of parameters are:
Servicing (a1) = 10
Damages (a2) = 30
Patterns (a3) = 20
Alerts (a4) = 20
Time period (a5) = 20
Table 1 to 5 denotes all the five parameters, criteria corresponding to each of the parameter and ratings corresponding to the criteria.
Parameters Rating
p1 Patterns - 10%
p11 How many times appliance are On/Off 2
p12 What duration of time appliance are on but not in used 2
p13 Energy Consumption - % of overall consumption 2
p14 Power Fluctuation 2
p15 Daily Usage - Running time on daily basis 2
Table 1
p2 Alerts - 30%
p21 Fault Alerts - Number of fault alerts 2
p22 Maintenance - related alerts 2
p23 Service - related alerts 2
p24 Number of fake alerts 2
p25 Emergency - related alerts 2
Table 2
p3 Service History - 20%
p31 Scheduled/On time - Due services completed on time 6
p32 Issue Based - number of incidents 2
p33 Seasonal - proactive measure-good 2
Table 3
p4 Claims - 20%
p41 Failure Claims - Number of claims submitted, OEM related claims? 4
p42 Issue Based - Number of claims submitted 3
p43 Value Claims - overall value claims 3
Table 4
p5 Time period - 20%
p51 Real time connectivity - Is connected all the time? 5
p52 Offline on daily basis - how much time offline? 3
p53 Data sending patterns - Anomaly detection? 2
Table 5
The sum of the ratings for each of the table is computed.
First sum computed for parameter p1=p11+p12+p13+p14+p15
Second sum computed for parameter p2= p21+p22+p23+p24+p25
Third sum computed for parameter p3= p31+p32+p33
Fourth sum computed for parameter p4= p41+p42+p43
Fifth sum computed for parameter p5= p51+p52+p53
For calculation of the score, pre-determined weight of the parameters is multiplied with the respective sums to calculate an average.
S=[(a1xp1) + (a2xp2) + (a3xp3) + (a4xp4) +(a5xp5)]/100
In an embodiment, the integration platform (100) further includes a warning module (136) and a suggestion module (138).
The warning module (136) is configured to send at least one notification to the user device (134) and to the sensor nodes (102) based on comparison of the analysis result with a pre-stored threshold value stored in the repository (104).
The suggestion module (138) is configured to provide suggestions to the user device (134) on analysing health of the appliances based on the sensed data and a set of pre-determined suggesting rules stored in the repository (104).
The warning module (136) and the suggesting module (138) are implemented using one or more processor(s).
The processor may be a general-purpose processor, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), and/or the like. The processor may be configured to retrieve data from and/or write data to the memory. The memory can be for example, a random access memory (RAM), a memory buffer, a hard drive, a database, an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), a read only memory (ROM), a flash memory, a hard disk, a floppy disk, cloud storage, and/or so forth.
The sensor nodes (102) and the integration platform (100) communicate via a communication module (132), and at least one user device (134) is communicatively coupled with the integration platform (100).
Figures 2a and 2b illustrate a flow diagram of a method for creating a risk profile of a living space.
The steps include:
• Step 202: storing (202), by a repository (104), a set of pre-determined analysis rules, a set of pre-determined segregation rules, a set of pre-determined profile building rules, and a plurality of pre-determined rating tables in a one-to-one correspondence with a first set of parameters and a second set of parameters;
• Step 204: receiving (204), by a plurality of sensor nodes (102) deployed within the living space, a set of activity data;
• Step 206: analysing (206), by a data processor (106), the activity data to generate an analysis result;
• Step 208: segregating (208), by the data processor (106), the analysis result based on the first set of parameters, the second set of parameters and the pre-determined segregation rules to generate a segregation result;
• Step 210: extracting (210), by a rating module (108), the ratings based on the segregation result from the respective rating tables; and
• Step 212: creating (212), by a risk profile creator (110), a risk profile of the living space based on the ratings and the pre-determined profile creating rules.

Referring to Figure 3, the integration platform (100) provides users with complete control, and notify users and authorities on detection of any unintended event, through the communication module (132). In an embodiment, the communication module (132) is selected from the group, but not limited to, BLE receiver, RFID reader, Wi-Fi receiver, cellular and LoRa gateway.
The integration platform (100) is capable of connecting and collecting data from multiple data sources/appliances and integrate it on a single integration platform (100) to facilitate transformation and availability of data for analytics on other systems (150) in order to deduce inference for taking corrective measures. In an embodiment, the other systems (150) is selected from the group, but not limited to, mobile applications, ERP, Third party system, analytics platform, dashboard and analytics, data center and web portals. In another embodiment, the integration platform (100) responds to hazardous situations like detecting gas, water or oil leakage by opening doors, shut down power supply, alert authorities and the like, and alert the owner of the house via SMS, or email.

The integration platform (100) will provide daily, weekly, monthly and yearly reports on the expenditure spent on the areas like energy consumption, water consumption and environmental data provided from the sensor nodes (102) programmed.

The integration platform (100) will also provide live usage of electricity and water so as to decrease the consumption according to the average maintenance. The lights equipped with a wireless smart plug will go dim in case of any intrusion. Moreover, the sensor nodes (102) detects bad weather, automatically retract the awning at a certain wind strength or amount of rainfall, gas leakage monitoring and reporting, smart geyser control and monitoring, remote monitoring of entry and exit points, temperature sensors for active fire monitoring and reporting, and fire alarms. Also, automatically sets the curtains to open in the morning and be drawn again at night and monitor humidity and adjust windows to ventilate the rooms. The integration platform (100) turns on the radio at scheduled time and play music when a user uses shower. The lights fade and the blinds close while watching movie. Further, the lights will automatically turn on or off when people enter or leave the house. The user controls the living space lightening using user device (134). The bathroom heating switches are switched on automatically in morning and the AC turns on/off automatically using motion sensors.

The flow for creating a risk profile of a living space (as referred to Figure 4) includes the steps of:
• Step 301: collection of data from the sensor nodes (102);
• Step 302 a and b: transfer of data to the integration platform (100) via communication module (132);
• Step 303: processing of data by the integration platform (100);
• Step 304: processed data available for other systems (150) for user to monitor and control devices, for creating the risk profile and alerts, and for dashboards, reports and other applications.

TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of an integration platform for creating a risk profile of a living space, which:
• detect hazardous situations;
• alert users in case of detection of hazardous situations;
• analyses health of appliances and provide predictive maintenance suggestions;
• reduces losses through preventive monitoring and alerts;
• offers personalized service to users through better risk profiling;
• identify and prioritize risks for action;
• facilitate transformation and availability of data for analytics in order to deduce inference for taking corrective measures; and
• helps companies to analyze data and differentiate legitimate and fraudulent users.
The foregoing disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, step, or group of elements, steps, but not the exclusion of any other element, or step, or group of elements, or steps.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.