Field of the Invention

The present invention relates to smart home measuring and control systems, in particular to measuring systems for sensor-based measuring of sensor equipped smart home perils by corresponding measuring parameters and smart home control systems reacting on the measured sensory measuring parameter values. More particularly, the invention relates to smart home measuring and/or control devices or systems comprising a plurality of measuring sensors or measuring devices enabled to capture and trace measuring parameters and reacting on the measured parameter vales by controlling (e.g. turn on/off or activate or steer or operate) a variety of connected electronic devices, electronic alarm systems or other electronically operated systems or devices, as connected automated systems, and the like. In general, it relates to automated measuring and signaling systems and methods for providing peril measures in the context of smart homes and occurrences and impacts of peril events, such as natural catastrophes perils e.g. earthquake, windstorm, flood and/or weather perils; property perils e.g. perils related to neighborhood, family composition and/or building structure; life-related recognizable patterns e.g. perils related to routine, activity/inactivity, expected events and/or changes/forecast; home perils e.g. fire, water, wind, theft, cyber perils etc.

Background of the Invention

Smart home or smart house technology and home automation are already wide spread in the art, where the terms smart home or home automation denote building automation and/or monitoring of properties or homes. A home automation system is typically enabled to monitor and/or control home attributes such as lighting, climate, entertainment systems, and appliances. It can also include home security such as access control and alarm systems. When connected with the Internet, home devices are an important constituent of the Internet of Things ("loT"). A home automation system typically connects controlled devices to a central hub or "gateway". The user interface for control of the system can e.g. use wall-mounted terminals, tablet or desktop computers, a mobile phone application, or a Web interface that may also be accessible off-site through the Internet. However, there are issues with the current state of home automation including a lack of standardized security measures and deprecation of older devices without backwards compatibility. Home automation has high potential for sharing data between trusted individuals for personal security and can comprise energy saving measures with a positive environmental impact.

In the technical field, smart homes typically constitute a branch of automation and ubiquitous computing that involves incorporating smartness into dwellings for comfort, healthcare, safety, security, and energy conservation. Remote monitoring systems are common components of smart homes, which use telecommunication and web technologies to provide remote home control and support patients remotely from specialized assistance centers. Smart homes may offer a better quality of life by introducing automated appliance control and assistive services. The smart home technology is able to optimize user comfort by using context awareness and predefined constraints based on the conditions of the home environment. A user can control home appliances and devices remotely, which enables him or her to execute tasks before arriving home. Ambient intelligence systems, which monitor smart homes, sometimes optimize the household's electricity usage. Smart homes enhance traditional security and safety mechanisms by using intelligent monitoring and access control.

The most recent smart home technologies also include wearable and implantable devices and assistive robots. Location awareness is an important prerequisite to create an intelligent environment in smart homes and allow the system to react on life patterns of different users of a property. Several taxonomies of the location detection system are known in the location detection techniques. The properties of location detection systems may be classified according to physical position, symbolic location, and absolute and relative measurements. Issues related to accuracy, precision, measurement scale, and cost can also be incorporated in the different location systems. The smart home systems may be enabled to object tracking as well as tracking people.

Thus, smart home technology comprises the application of ubiquitous or pervasive computing or other technological environment. As such, the smart home technology is one part of developing a smart city. However, in addition to basic controls of home appliances and lamps, it is also an important issue in a smart home to perform controls corresponding to different property users, in particular family members, because different property users generally have different requirements. However, when multiple different events are detected, the systems are typically not able to identify the relationship between the multiple different events and actively give a suggestion to deal with the related events together, so that e.g. the performance or security of the smart home could be further improved.

To cover possible perils, homeowner and personal property insurance exists providing coverage and protection against damage to the home and personal property owner by the policyholder, respectively. There are many potential sources of damage to homes and personal property, some of which can be detected far enough in advance to take an action that may mitigate or prevent damage from occurring. Currently, many appliances and other goods are capable of communicating information about their operation via mesh networks as part of the "internet of things." However, there is no way to aggregate and analyze all of this communicated data to measure individual perils associated with a property, and further manage and reduce the risks for a loss impact associated with risk-transfer-related events.

The machine-based measuring of measure factors of perils, or forecast of such factors, i.e. of occurrence probabilities for events associated with a property or home and causing loss impacts, e.g. occurring property perils or risks, is technically difficult to be realized, inter alia, because of their long-tail nature and their susceptibility to measuring and parametrizing quantitatively the multitude of impact factors and to capturing temporal time developments and parameter fluctuations. Automation of measuring, prediction and modeling of property perils and risk accumulation is especially challenging as there is typically limited historic individual loss data available, and new peril events with new characteristics keep emerging. It is therefore important to reduce the reliance on historic data by using forward-looking modelling (FLM) techniques. It is to be noted that FLM techniques can typically not trivially be applied to a specific technical problem, thus going beyond traditional data analysis and predictive modeling approaches and techniques by acknowledging a structured cause-effect chain. Property risk driven systems have been developed and used as forecasting systems based on risk values. Such prior art systems are able to predictively and quantitatively measure expected losses starting from a set of peril scenarios, i.e. parametrization of such scenarios, assessing the impact of key risk factors, and evaluate the effect of risk transfer parameters and conditions. The results of such forecast systems can be back tested against actually measured loss data wherever available and relevant. With peril-triggered products, such as risk-transfer, this is not the case. The coverage of the probably occurring event impact must be set/assessed in advance. If the actual occurring (not the forecasted occurring) of peril events and associated impact (inter alia: the associated loss) is greater than the cover or risk mitigation measures, as e.g. the amount of transferred resources, typically premiums collected, then a risk transfer or insurance system's operability will be corrupted. A precise, reliable, forward-looking and reproducible peril/risk measurement, prediction and assessment is therefore vital to all risk-triggered systems and processes. Hence, the ability to forecast and set assumptions for the expected losses is critical to the operation. The present invention was developed inter alia for measuring, optimized triggering, identifying, assessing, forward-looking modeling and measuring of property risk driven exposures and to give the technical basics to provide a fully automated pricing device for property/home exposure comprising self-adapting and self-optimizing means based upon varying property/home risk drivers.

Summary of the Invention

It is an object of the invention to provide and measure a safety score for a household, including a breakdown into various perils and covered categories. It should be based a technical representation of a home (Home Object) or property including its contents and should be able to return a safety score including breakdowns into individual perils and covers. Further it should allow for systematic capturing, measuring, quantifying, and forward-looking generating of appropriate peril and peril accumulation measures. It is also an object of the invention to provide an automated property peril/risk driven system for automated optimization and adaption in signaling generation by triggering risk exposure of insurance-covered objects. In particular, it is an object of the present invention to provide a system which is better able to capture the external and/or internal factors that affect property/home exposure, while keeping the used trigger techniques transparent. Moreover, the system should be better able to capture how and where risk is transferred, which will create a more efficient and correct use of risk and loss drivers in property risk-transfer technology systems. Furthermore, it is an object of the invention to provide an adaptive pricing tool for insurance products based upon home/property exposure. This approach differs from traditional ones in that the prior art methods rely on underwriting experts to hypothesize the most important characteristics and key factors from the environment and property that impact property/home peril exposure. Thus, the present invention should be enabled, inter alia, to detect and alleviate the foregoing risks, such as the risk of home damage, personal property damage, insurance claims, and/or other risks.

According to the present invention, these objects are achieved particularly through the features of the independent claims. In addition, further advantageous embodiments follow from the dependent claims and the description.

According to the present invention, the abovementioned objects are particularly achieved by the measuring and control system measuring and providing a multitude of peril measures associated with a property, in particular with sensory monitored properties as smart homes, in that the measuring and control system comprises at least one hardware controller and a plurality of sensory devices, the property being populated with the sensory devices and the at least one hardware controller being in communication with the plurality of sensory devices, the sensory devices transmitting sensory signals to the hardware controller, in that the measuring and control system comprises a digital raw layer for capturing raw sampling signals comprising at least sensory data of the sensory devices and physical condition data measuring real world physical conditions related to the property wherein the measuring and control system comprises a signal conditioning circuitry for processing the captured raw sampling signals into convertible digital values and wherein the digital raw layer provides secure retrieval of the measured and/or captured convertible digital values, in that the measuring and control system comprises a calibration layer with an abstract digital representation of the property, the calibration layer comprising a first calibration structure modelling static properties parameters related to the property based on the abstract digital representation and the captured digital values of the digital raw layer, a second calibration structure modeling dynamic properties based on the abstract digital representation and the captured digital values of the digital raw layer, and a third calibration structure modeling lifestyle behavior and characteristics of the users of the property based on the abstract digital representation and the captured digital values of the digital raw layer, and in that the control system comprises a peril indexing layer splitting the perils to be measured into various individual perils comprising at least natural catastrophes and/or property perils and/or life pattern perils and/or home perils, and generating an individual safety score measure for each of the individual perils, the individual safety score measures providing indexing measures measuring the respective physical peril linked to the property. The captured raw sampling signals can e.g. comprise at least sensory data transmitted to the hardware controller from the sensory devices associated with the property and/or property condition data and/or perils- related condition data and/or natural catastrophe related sensory data and/or weather/air related sensory data and/or crime related monitoring data, wherein the raw sampling signals measure real world physical conditions related to the property and wherein the measuring and control system comprises a signal conditioning circuitry for processing the captured raw sampling signals into convertible digital values and providing secure retrieval of digital values. The captured raw sampling signals can further e.g. comprise measuring parameters measuring the occurrence of natural catastrophes events at least comprising earthquake measuring parameters and/or windstorm measuring parameters and/or flood measuring parameters and/or weather measuring parameters. The captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of property perils events in dependence to neighborhood characteristics parameters and/or family composition characteristics parameters and/or building structure characteristics parameters. The captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of perils associated with life patterns in dependence to routines characteristics parameters and/or activity/inactivity parameters and/or expected social event parameters and/or social change/forecast parameters. The captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of home perils, the home perils being at least comprising measuring parameters related to fire perils and/or measuring parameters related to water perils and/or measuring parameters related to wind perils and/or measuring parameters related to theft perils. Finally, the captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of smart home perils, the smart home perils being at least related to sensor-related perils and/or actuator-related perils and/or cyber perils.

In an embodiment variant, the measuring and control system can e.g. comprise at least one processor for receiving over the communication of the hardware controller, by the at least one processor, a plurality of communications sent from a plurality of Internet-of-Things (lo T) devices on a network, the loT-devices comprise at least the plurality of sensory devices, wherein each communication describe operations of a respective loT device, wherein, by the at least one processor, based on the plurality of communications, a model structure of an operational state for at least one of the plurality of loT-devices is determined, wherein, by the at least one processor, an operational deviation of the at least one loT-device compared to the model structure is measured, and wherein, in response, an adaption of a raw sampling measuring parameter of the digital raw layer based on the operational deviation of the at least one loT device is conducted. The presence of a new loT device can e.g. be automatically detected and added to the network, wherein, by the at least one processor, the model structure is modified based on the presence of the new loT device on the network.

In another embodiment variant, the individual safety score measures of the peril indexing layer can e.g. be dynamically adapted by means of the measuring and control system.

In a further embodiment variant, the peril indexing layer can e.g. comprise representation and/or monitoring means grouping individual safety score measures and alerts monitoring, wherein the monitoring at least comprises environmental perils related individual safety score measures and alerts and/or property perils related individual safety score measures and alerts and/or life-style associated perils related individual safety score measures and/or cyber-perils related individual safety score measures and alerts.

In an embodiment variant, the raw sampling signals can e.g. comprise at least condition data capturing risk-transfer conditions related to peril coverage parameters and/or peril exclusion parameters and/or deductibles parameters and/or claim parameters and/or time period of coverage parameters. For example, by means of the at least one processor, the operational deviation of the at least one loT device is compared to the model structure of an operational state, and, in response, at least one cost parameter value comprised in the peril coverage parameters is determined based on the operational deviation of the at least one loT device, wherein the at least one cost parameter value relates to monetary premium parameters for conducting a risktransfer for the peril coverage. By means of the at least one processor, a risk-transfer policy can e.g. be automatically based on the cost parameter value and a premium parameter value to be transferred in response to the operational deviation. Further, by means of the at least one processor, a signal can e.g. be transmitted to transfer a value related to the premium parameter value to an account that is associated with the coverage.

The presence of a new loT device can e.g. be automatically detected and added to the network, wherein, by the at least one processor, the model structure is modified based on the presence of the new loT device on the network. By means of the at least one processor, the risk-transfer policy can e.g. be automatically modified based on the presence of the new loT device on the network.

In another embodiment variant, the measuring and control system can e.g. comprise a machine learning (ML) module, wherein the individual safety score measure for the life pattern perils is generated the step of pattern recognition of life patterns associated with measuring parameters of the captured raw sampling signals measuring comprising routines characteristics parameters and/or activity/inactivity parameters and/or expected social event parameters and/or social change/forecast parameters, and linking said life patterns to an individual safety score value by means of the machine learning (ML) module. The machine learning (ML) module can e.g. comprise at least one artificial intelligence (Al) unit. Further, the pattern recognition of life patterns can e.g. comprise a first step of clustering measured life patterns and assigning a value to the individual safety score associated with the life pattern perils based on a second step of classifying the measured and recognized clusters.

The inventive measuring and monitoring system and method has, inter alia, the advantage, that it provides and measures a total home risk index measure, but also to measure a safety score for a household, including a breakdown into various perils and covered categories. The system relies on an technical representation of a home (Home Object) including its contents and returns a safety score measure including breakdowns into individual perils and covers. The systems inventive forward-looking model structure has further the advantage to allow for a most accurate measurement and prediction of future peril impact probabilities, for example, the characteristics of measured future losses, by reflecting the mechanics, building structures and processes that drive them by the technical structure of the invention. The system further allows to be validated and trained through an understanding of historical experience, which forms a subset of what the system's modelling can predict. This has also the advantage, that it technically allows the system's measurements and predictions to be applied in situations with and without relevant historical experience, which is not possible by the known prior art systems. The inventive measuring and forward-looking prediction structure also go beyond traditional smart home systems' approach by being based on a structured cause-effect chain depending on real world measuring parameters.

Brief Description of the Drawings

The present invention will be explained in more detail below relying on examples and with reference to these drawings in which:

Figure 1 shows a block diagram, schematically illustrating a measuring and control system, according to the invention, measuring a plurality of smart home perils by mutual calibrated measuring parameter values and providing a peril-based smart home control system. The system provides a safety score for a household, including a breakdown into various perils and covered categories. It is based on an abstract representation of a home (Home Object) including its contents and returns a safety score including breakdowns into individual perils and covers.

Figures 2 and 3 show block diagrams, schematically illustrating the basic structure comprising three main parts: the predicting digital home object structure, the properties indicator retrieval and the impact experience processing. The virtual home object structure allows to measure and forecast quantitative peril/risk measures and expected impact/loss measures from the physical property/asset/smart home using characteristic technical main elements, namely the digital home object structure with the simulation and synchronization means and the loT sensory providing the constant real-world streaming linkage and connection (c.f. figure 1 ).

Figure 4 shows a block diagram illustrating schematically an exemplary digital home object, which can be made available throughout the entire lifecycle of the real-world asset 31 /32 or object 33 and/or digital platform 1 through its structure, as shown in Fig. 4. Figure 4 shows the digital asset/object replica 48, the digital home object 47, the digital ecosystem replica 46, the digital peril/risk robot 45 and the safety index measuring engine 4 with its optional artificial intelligence 45 of a physical entity 3 in the inventive digital platform 1 .

Figure 5 and 6 show block diagrams illustrating schematically an exemplary structure comprising a digital raw layer, a calibration layer and a peril indexing layer providing together the digital smart home peril robot 45.

Detailed Description of the Preferred Embodiments

Figures 1-6 schematically illustrate an architecture for a possible implementation of an embodiment of the inventive index measuring system 1 for peril and safety score measurements of temporally or permanently evolving risk-based real- world measuring parameters. The measuring and control system 1 measures and provides a multitude of peril measures associated with a sensor equipped and sensor monitored property, in particular with sensory monitored properties as smart homes.

The measuring and control system 1 comprises at least one hardware controller and a plurality of sensory devices, the property being populated with the sensory devices and the at least one hardware controller being in communication with the plurality of sensory devices, the sensory devices transmitting sensory signals to the hardware controller. The sensors can include smart sensors as the invisible network meshing sensory data together. The sensors can e.g. comprise movement sensors and/or light sensors and/or temperature sensors etc. The sensors can e.g. be operated by and transmit data using Zigbee, Z-Wave, Bluetooth, Wi-Fi and the like.. Further, the sensors may be connected wired or wireless and can e.g.. have a battery-powered or wired power supply. The smart home can e.g. be a hub-based smart home, using SmartThings, HomeKit, Google Assistant or Alexa etc. to tie the sensors together, where sensors can be associated with electronic control, triggering routines and home automations by sensing human presence. For the communication, loT can e.g. be used for the smart home protocols, where Internet of Things (loT) is a communication structure that connects devices and objects in order to harvest and process data produced by several edge devices. Industrial loT devices can e.g. be used for sensing, tracking, and controlling using low-power, wide area network (LP-WAN). The used LP- WAN technologies can e.g. comprise: Long range (LoRa), Sigfox, narrow-band loT (NB- loT), LTE-machine type communication (LTE-M), extended coverage global system for mobile communication (EC-GSM), random phase multiple access (RPMA), my-things (MIOTY), and DASH7. Wherein, LoRa, Sigfox, RPMA, MIOTY uses the unlicensed spectrum and others are based on cellular licensed spectrum. The proposed LP-WAN technologies have inter alia the advantage (i) that they are able to cover wide area providing long distance communication, and (ii) that devices consume very low power, (iii)of having a low data rate, and (iv) of providing low deployment cost. For realizing the inventive remote smart home (SH) monitoring system, the system can e.g. comprise smart loT sensors network to sense, transmission, and control the home environment, supplies, and instruments. The devices should consume low power and the network provider have to cover longer area to support remote monitoring. The existing short- range wireless technologies, mentioned above, such as ZigBee, RFID, Wi-Fi, and Bluetooth low energy (BLE) are not suitable for embodiment variants where long range communication is required even though they consume very low power. Moreover, the conventional cellular networks consume large amount of power for large coverage area and higher manufacturing cost. Therefore, to support remote control and monitoring of SH equipment among the other LP-WAN technologies LoRa can e.g. be suitable in terms of both long range and low power consumption as the channel throughput requirement is reasonably low for this system.

For the inventive system, e.g. am Al-based dataflow design for LoRa- enabled SH loT system can be used. A Sense HAT is e.g. an add-on board for a Raspberry Pi. The board allows to make measurements of temperature, humidity, pressure, and orientation, and to output information using a built-in LED matrix. Hence, this board can be used along with other gas, dust, stored energy, noise sensors, and other SH appliances to the network. These smart sensors can produce vast amount of data that are sent to cloud and data server through LoRa gateway for further processing with Al. As an embodiment variant, the physical layer LP-WAN technology can e.g. modulate the signal in sub-GHz Industrial, Scientific, and Medical (ISM) frequency band. A bidirectional communication can e.g. be provided by a special chirp spread spectrum technique. At the medium access control layer, an ALOHA scheme can e.g. be used that is combined with the LoRa physical layer. This enables multiple device to communicate simultaneously using different spreading factors. Therefore, no extra signaling overhead is required to hop any end users. End devices can e.g. be linked with the LoRa access point via a backhaul to network server that suppresses duplicate receptions, adapts radio access links, and forwards data to suitable application servers. An application server can then e.g. process the received data and perform as user requests to the server. The bandwidth requirement for this technology can e.g. be chosen quite flexible and can be varied in 7.8-500 kHz range. The battery lifetime can thus be more than 10 years. The embodiment variant with the LoRa can cover up to 15 km in suburban area and up to 5 km in urban area.

The next task can e.g. be building an accurate machine learning structure by using those vast amount of loTs' data for predictive maintenance, enhanced security, voice controlled appliances etc., using such loT and big data platforms. Related to the specific embodiment variant, various tasks that can be performed additionally in the proposed Al powered SH system as e.g.: (i) Predictive maintenance, by using data analytics of service equipment possibility of failure or damage prediction. That can e.g. be used to mitigate unplanned damage; (ii) Maximum efficiency, as the Al-based loT system can learn house owners' behavior and automatically modify settings to provide residents with maximum comfort and efficiency. As a further embodiment variant, these systems can notify users about any vulnerable situations; (iii) Improving security, as the Al-based face and key recognition system helps to make door lock security in home automation; (iv) Enhancing services, as natural language processing based on Al can e.g. help the loT devices and home appliances to control with voices.

For the loT communication, for example, an loT stack can be used as the combination of four main components: the physical layer, the loT platform, the communication protocols/technologies and the application layer. Thus, this is different to the Open Systems Interconnection (OSI) layer model of the prior art being based on 7 layers. In an alternative embodiment variant, the architecture can be based on three main layers (instead of the 7 OSI layers): the perception layer, the network layer, and the application layer. Following these inventive approaches, the network layer is an important part for the inventive system in which several protocols and communication technologies are handled as well as the security and privacy aspects of each of the selected domains. Depending on the embodiment variant, there are different ways in which devices can be connected to each other on the wireless domain including e.g.: (i) Centralized or star: There is central node o hub which is the responsible one for managing communications with other nodes of the network and the outside: or (ii) Decentralized or fully connected: All nodes are connected to other network nodes. This kind of topology may not be efficient for embodiment variants where the network is bigger, because the communication effort grows exponential with the number of. As middle ground between the topologies above a mesh topology can e.g. be realized, in which several nodes are able to communicate with each other through the communication between intermediary nodes. According to the invention, the mesh network may include other topologies such as: (i) Ring: All nodes make a loop in which each node is connected to two nodes. The information goes through each node until it reaches the destination node: (ii) Bus: All nodes are connected to a backbone and communications and messages go through it in which all nodes receive all messages. If the backbone cable fails, all networks fail: and (iii) Line or point-to-point: This is the simplest network topology consisting of the connection between two endpoints.

The measuring and control system comprises a digital raw layer for capturing raw sampling signals comprising at least sensory data of the sensory devices and physical condition data measuring real world physical conditions related to the property wherein the measuring and control system comprises a signal conditioning circuitry for processing the captured raw sampling signals into convertible digital values and wherein the digital raw layer provides secure retrieval of the measured and/or captured convertible digital values,

The measuring and control system 1 comprises a calibration layer with an abstract digital representation 47 of the property. 20. The digital representation 47 of the property /smart home can be realized as a twinned digital home object 47 together with a twinned digital ecosystem 46 and a twinned digital peril representation 45. The calibration layer comprising a first calibration structure modelling static properties parameters related to the property based on the abstract digital representation and the captured digital values of the digital raw layer, a second calibration structure modeling dynamic properties based on the abstract digital representation and the captured digital values of the digital raw layer, and a third calibration structure modeling lifestyle behavior and characteristics of the users of the property based on the abstract digital representation and the captured digital values of the digital raw layer.

The control system 1 comprises a peril indexing layer splitting the perils to be measured into various individual perils comprising at least natural catastrophes and/or property perils and/or life pattern perils and/or home perils, and generating an individual safety score measure for each of the individual perils, the individual safety score measures providing indexing measures measuring the respective physical peril linked to the property. The captured raw sampling signals can e.g. comprise at least sensory data transmitted to the hardware controller from the sensory devices associated with the property and/or property condition data and/or perils-related condition data and/or natural catastrophe related sensory data and/or weather/air related sensory data and/or crime related monitoring data, wherein the raw sampling signals measure real world physical conditions related to the property and wherein the measuring and control system comprises a signal conditioning circuitry for processing the captured raw sampling signals into convertible digital values and providing secure retrieval of digital values.

The home automation respectively the property /smart home sensory can e.g. comprise (i) Heating, ventilation and air conditioning (HVAC) systems, e.g. a remote control of all home energy monitors over the internet incorporating a or more user interface; (ii) Lighting control system, i.e. a smart network that incorporates communication between various lighting system inputs and outputs, using one or more central computing devices; (iii) Occupancy-aware control system to sense the occupancy of the home using smart meters and environmental sensors like CO2 sensors, which can be integrated into the building automation system to trigger automatic responses for energy efficiency and building comfort applications; (iv) Appliance control and integration with the smart grid and a smart meter, taking advantage, for instance, of high solar panel output in the middle of the day to run washing machines; (v) Home robots and security, e.g. a household security system integrated with a home automation system can provide additional services such as remote surveillance of security cameras over the Internet, or access control and central locking of all perimeter doors and windows; (vi) Leak detection, smoke and CO detectors; (vii) Indoor positioning systems (IPS); (viii) Home automation for the elderly and disabled; (ix) Pet and/or baby care systems, for example tracking the pets and babies' movements and controlling pet access rights; (x) Air quality control systems. For example, Air Quality Egg is used by people at home to monitor the air quality and pollution level in the city and create a map of the pollution; (xi) Smart kitchen and connected cooking systems; (xii) Voice control devices like e.g. Amazon Alexa or Google Home used to control home appliances or systems etc. It is to be noted, that smart home devices are typically connected via a Wi-Fi network or other data-transmission network connected to the internet, which makes it vulnerable to hacking and other cyber risks.

The captured raw sampling signals can further e.g. comprise measuring parameters measuring the occurrence of natural catastrophes events at least comprising earthquake measuring parameters and/or windstorm measuring parameters and/or flood measuring parameters and/or weather measuring parameters. Occurring natural catastrophe events can e.g. be measured by means of measuring stations or sensors in loco and/or by satellite image processing and/or by other technical measuring processes. The measuring stations or sensors can e.g. be realized as part of the system 1 . The measured sensory data of the measuring devices can be transmitted via an appropriate data transmission network to the system 1 , comprising e.g. an electronic, control unit controller 16 for processing of the captured electronic data, and assigned to a historic set comprising event parameters for each assigned natural catastrophe event. To capture and measure the appropriate measured sensory data, the hardware controller or another control unit can e.g. comprise an event driven core aggregator with measuring data-driven triggers for triggering, capturing, and monitoring in the data flow pathway of the sensors and/or measuring devices of the risk-exposed and affected properties/smart homes. The sensors and/or measuring devices can, e.g., comprise technical measuring devices as e.g. seismometers or seismographs for measuring any ground motion, including seismic waves generated by earthquakes, volcanic eruptions, and other seismic sources, stream gauges in key locations across a specified region, measuring during times of flooding how high the water has risen above the gauges to determine flood levels, measuring devices for establishing wind strength, e.g. according to the Saffir-Simpson Scale, sensors for barometric pressure measurements and/or ocean temperature measurements, in particular the temperatures of ocean surface waters and thereby determining the direction a hurricane will travel and a potential hurricane's intensity (e.g., by means of floating buoys to determine the water temperature and radio transmissions back to a central system), and/or satellite image measurements estimating hurricane strength by comparing the images with physical characteristics of the hurricane.

The captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of property perils events in dependence to neighborhood characteristics parameters and/or family composition characteristics parameters and/or building structure characteristics parameters. The captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of perils associated with life patterns in dependence to routines characteristics parameters and/or activity/inactivity parameters and/or expected social event parameters and/or social change/forecast parameters. The captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of home perils, the home perils being at least comprising measuring parameters related to fire perils and/or measuring parameters related to water perils and/or measuring parameters related to wind perils and/or measuring parameters related to theft perils. Finally, the captured raw sampling signals can e.g. comprise measuring parameters measuring the occurrence of smart home perils, the smart home perils being at least related to sensor-related perils and/or actuator-related perils and/or cyber perils. The individual safety score measures of the peril indexing layer can e.g. be dynamically adapted by means of the measuring and control system 1 .

The measuring and control system 1 can e.g. comprise at least one processor for receiving over the communication of the hardware controller, by the at least one processor, a plurality of communications sent from a plurality of Internet-of- Things (Io T) devices on a network, the loT-devices comprise at least the plurality of sensory devices, wherein each communication describe operations of a respective loT device, wherein, by the at least one processor, based on the plurality of communications, a model structure of an operational state for at least one of the plurality of loT-devices is determined, wherein, by the at least one processor, an operational deviation of the at least one loT-device compared to the model structure is measured, and wherein, in response, an adaption of a raw sampling measuring parameter of the digital raw layer based on the operational deviation of the at least one loT device is conducted. The presence of a new loT device can e.g. be automatically detected and added to the network, wherein, by the at least one processor, the model structure is modified based on the presence of the new loT device on the network.

The peril indexing layer can e.g. further comprise representation and/or monitoring means grouping individual safety score measures and alerts monitoring. The monitoring can at least comprise environmental perils related individual safety score measures and alerts and/or property perils related individual safety score measures and alerts and/or life-style associated perils related individual safety score measures and/or cyber-perils related individual safety score measures and alerts.

The raw sampling signals can e.g. comprise at least condition data capturing risk-transfer conditions related to peril coverage parameters and/or peril exclusion parameters and/or deductibles parameters and/or claim parameters and/or time period of coverage parameters. For example, by means of the at least one processor, the operational deviation of the at least one loT device is compared to the model structure of an operational state, and, in response, at least one cost parameter value comprised in the peril coverage parameters is determined based on the operational deviation of the at least one loT device, wherein the at least one cost parameter value relates to monetary premium parameters for conducting a risk-transfer for the peril coverage. By means of the at least one processor, a risk-transfer policy can e.g. be automatically based on the cost parameter value and a premium parameter value to be transferred in response to the operational deviation. Further, by means of the at least one processor, a signal can e.g. be transmitted to transfer a value related to the premium parameter value to an account that is associated with the coverage.

The presence of a new loT device can e.g. be automatically detected and added to the network, wherein, by the at least one processor, the model structure is modified based on the presence of the new loT device on the network. By means of the at least one processor, the risk-transfer policy can e.g. be automatically modified based on the presence of the new loT device on the network.

As a variant, the measuring and control system 1 can e.g. comprise a machine learning (ML) module, wherein the individual safety score measure for the life pattern perils is generated the step of pattern recognition of life patterns associated with measuring parameters of the captured raw sampling signals measuring comprising routines characteristics parameters and/or activity/inactivity parameters and/or expected social event parameters and/or social change/forecast parameters, and linking said life patterns to an individual safety score value by means of the machine learning (ML) module. The machine learning (ML) module can e.g. comprise at least one artificial intelligence (Al) unit. Further, the pattern recognition of life patterns can e.g. comprise a first step of clustering measured life patterns and assigning a value to the individual safety score associated with the life pattern perils based on a second step of classifying the measured and recognized clusters.

List of reference signs Measuring and control system for providing individual safety index for smart homes

10 Data Store

101 105 Modular Digital Assets/Objects Data Elements loT Sensory (input devices and sensors) Real-world Asset or Object (Smart Home)

31 Physical Asset

32 Intangible Asset

33 Living Object

331 Human Being

332 Animal

34 Subsystems of the Real-world Asset or Object

341 , 342, 343 34i Subsystems 1 i

35 Subsystems and Components of the Ecosystem

351 , 352, 353 35i Subsystems 1 i Digital Home Object Measuring Engine

41 Digital Intelligence Layer

41 1 Machine Learning

412 Neural Network

42 Property Parameters of Real-World Asset or Object

43 Status Parameters of Real-World Asset or Object

431 Structural Status Parameters

432 Operational Status Parameters

433 Environmental Status Parameters

44 Data Structures Representing States of Each of the Plurality of Subsystems of the Real-World Asset or Object

45 Digital Peril Robot

451 Simulation

452 Synchronization

453 Twin Linking: Sensory/Measuring/Data Acquisition

46 Digital Ecosystem Replica Layer

461 , 462, 463 46i Virtual Subsystems of Virtual Representation of Ecosystem

47 Digital Home Object

471 Simulation

472 Synchronization 473 Linking: Sensory/Measuring/Data Acquisition

48 Digital Asset/Object Replica Layer

481 , 482, 483 48i Virtual Subsystems of Real-World Smart Home Asset/Object Ecosystem - Environment - Interaction between Real-world Assets/Objects