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Title:
AIR TREATMENT APPARATUS, SENSOR ARRANGEMENT AND OPERATING METHOD
Document Type and Number:
WIPO Patent Application WO/2018/041637
Kind Code:
A1
Abstract:
The present disclosure relates to an air treatment apparatus (10) comprising an inlet (18) for inlet air, an outlet (20) for outlet air, an air treatment module (22) disposed between the inlet (18) and the outlet (20), the air treatment module (22) comprising a ventilating unit arranged to generate an air flow from the inlet (18) to the outlet (20), and an air treatment unit (26, 28) arranged to apply a purifying treatment to the air flow, a control unit (52) arranged to control the air treatment module (22), and a sensor unit (54) operatively coupled with the control unit (52), wherein the sensor unit (54) comprises an air quality sensor (14) arranged to detect a first air quality indicative property and to signal a characterizing first air quality value to the control unit (52), wherein the control unit (52) is arranged to derive a second air quality value, based on the first air quality value and on augmenting information that is indicative of a second air quality indicative property, and wherein the control unit (52) is arranged to operate the air treatment module (22) dependent on the first air quality value provided by the air quality sensor (14) and on the second air quality value. The present disclosure relates to a distributed air quality sensing arrangement (60) and to a method of operating an air treatment apparatus (10).

Inventors:
LOU, Xiaojun (High Tech Campus 5, 5656 AE Eindhoven, 5656 AE, NL)
KELLY, Declan Patrick (High Tech Campus 5, 5656 AE Eindhoven, 5656 AE, NL)
TAN, Jingwei (High Tech Campus 5, 5656 AE Eindhoven, 5656 AE, NL)
ZHOU, Xiaoming (High Tech Campus 5, 5656 AE Eindhoven, 5656 AE, NL)
Application Number:
EP2017/070805
Publication Date:
March 08, 2018
Filing Date:
August 17, 2017
Export Citation:
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Assignee:
KONINKLIJKE PHILIPS N.V. (High Tech Campus 5, 5656 AE Eindhoven, 5656 AE, NL)
International Classes:
F24F11/00
Domestic Patent References:
WO2015175304A12015-11-19
WO2014084832A22014-06-05
Foreign References:
EP0752085A11997-01-08
US20150032264A12015-01-29
US6494940B12002-12-17
US20130174646A12013-07-11
US20130038470A12013-02-14
Attorney, Agent or Firm:
STEENBEEK, Leonardus Johannes et al. (Philips International B.V. – Intellectual Property & Standards High Tech Campus 5, 5656 AE Eindhoven, 5656 AE, NL)
Download PDF:
Claims:
CLAIMS:

1. An air treatment apparatus (10) comprising:

an inlet (18) for inlet air,

an outlet (20) for outlet air,

an air treatment module (22) disposed between the inlet (18) and the outlet (20), the air treatment module (22) comprising a ventilating unit arranged to generate an air flow from the inlet (18) to the outlet (20), and an air treatment unit (26, 28) arranged to apply a purifying treatment to the air flow,

a control unit (52) arranged to control the air treatment module (22), and a sensor unit (54) operatively coupled with the control unit (52), wherein the sensor unit (54) comprises an air quality sensor (14) arranged to detect a first air quality indicative property relating to a first air contaminant and to signal a characterizing first air quality value based on said first air quality indicative property to the control unit (52),

wherein the control unit (52) is arranged to derive a second air quality value relating to a second air contaminant based on the first air quality value and on augmenting information that is indicative of a second air quality indicative property, and

wherein the control unit (52) is arranged to operate the air treatment module (22) dependent on the first air quality value provided by the air quality sensor (14) and on the second air quality value.

2. The air treatment apparatus (10) as claimed in claim 1, wherein the air quality sensor (14) is a physical entity arranged at the air treatment apparatus (10), and wherein the control unit (52) is arranged to compute the second air quality value in the absence of a physical sensor for the second air quality property.

3. The air treatment apparatus (10) as claimed in claim 1, wherein the control unit (52) is arranged to implement a virtual air quality sensor that substitutes a physical sensor for the second air quality property.

4. The air treatment apparatus (10) as claimed in claim 1, wherein the air quality sensor (14) is arranged as a particulate matter sensor arranged to detect a particulate matter indicative property and to signal a characterizing particulate matter value to the control unit (52) based on which the second air quality value is computed, and wherein the control unit (52) is arranged to operate the air treatment module (22) dependent on the particulate matter value provided by the air quality sensor (14) and on the second air quality value.

5. The air treatment apparatus (10) as claimed in claim 1, wherein the control unit (52) is arranged to derive a characterizing VOC value, based on the first air quality value and on augmenting information that is indicative of a VOC indicative property, and wherein the control unit (52) is arranged to operate the air treatment module (22) dependent on the first air quality value provided by the air quality sensor (14) and on the VOC value.

6. The air treatment apparatus (10) as claimed in claim 1, wherein the control unit (52) is arranged to process auxiliary augmenting information for deriving the second air quality value, the auxiliary augmenting information being selected from the group consisting of: timing information, weather information, seasonal information, wind information, time of day information, temperature information, humidity information, sound information, positional information, and combinations thereof.

7. The air treatment apparatus (10) as claimed in claim 1, wherein the

augmenting information is obtained from at least one remote sensor unit (70) comprising a remote air quality sensor (74) arranged to detect a second air quality indicative property and to provide a characterizing second air quality value.

8. The air treatment apparatus (10) as claimed in claim 1, wherein the control unit (52) is arranged to communicate with a network providing server processing capabilities, and to obtain, via the network, augmenting information from a server that is supplied with a characterizing second air quality value from at least one remote augmenting air quality sensor (74).

9. The air treatment apparatus (10) as claimed in claim 1, wherein the control unit (52) is arranged to derive the second air quality value based on a modeled correlation between the first air quality value and the second air quality value, and wherein the modeled correlation utilizes the augmenting information.

10. A distributed air quality sensing arrangement (60) comprising:

- an air treatment apparatus (10) as claimed in claim 1,

at least one augmenting air quality sensor (74) that is remote from the air treatment apparatus (10), wherein the at least one augmenting air quality sensor (74) is arranged to detect a second air quality indicative property and to signal a characterizing second air quality value, and

- a communication service (80) through which the air treatment apparatus (10) is supplied with augmenting information that is indicative of the second air quality value.

11. The sensing arrangement (60) as claimed in claim 10, wherein a correlation between the first air quality value and the second air quality value is modeled, wherein a change of the second air quality value detected by the at least one augmenting air quality sensor (74) is reflected in the augmenting information based on which the air treatment apparatus (10) is operated.

12. The sensing arrangement (60) as claimed in claim 10, wherein the air treatment apparatus (10) is a first type air treatment apparatus, wherein the at least one augmenting air quality sensor (74) is provided at an augmented air treatment apparatus (66) that is arranged as a second type air treatment apparatus (66), wherein the at least one augmenting air quality sensor (14) of the second type air treatment apparatus is utilized to supply the first type air treatment apparatus (10) with the augmenting information.

13. A method of operating an air treatment apparatus (10), the method comprising the following steps:

providing an air quality sensor (14) that is arranged to detect a first air quality indicative property relating to a first air contaminant and to signal a characterizing first air quality value based on said first air quality indicative property to a control unit (52),

detecting, by means of the air quality sensor (14), the first air quality indicative property, and signaling the characterizing first air quality value to the control unit (52), obtaining augmenting information that is indicative of a second air quality indicative property,

deriving a second air quality value relating to a second air contaminant based on the first air quality value and on the augmenting information, and

operating the air treatment apparatus (10) dependent on the first air quality value provided by the air quality sensor (14) and on the second air quality value.

14. The method as claimed in claim 13, wherein the first air quality value is a particulate matter value, wherein the second air quality value is a virtual VOC value, wherein the second air quality value is established on the basis of modeled correlation between the first air quality value and the second air quality value, and wherein the modeled correlation utilizes the augmenting information.

15. A computer program comprising program code means for causing a computing device to carry out the steps of the method as claimed in claim 13 when said computer program is carried out on a computing device.

Description:
Air treatment apparatus, sensor arrangement and operating method

FIELD OF THE INVENTION

The present disclosure relates to an air treatment apparatus and to a distributed air quality sensing arrangement. The present disclosure further relates to a method of operating an air treatment apparatus and to a corresponding computer program.

In some specific embodiments, the present invention relates to home appliances that are arranged for treatment of ambient air in buildings, so as to improve a sense of well-being of the present residents. More particularly, the disclosure relates to improvements in air treatment apparatuses, particularly air purifying apparatuses, and in related operation methods that enhance a purifying performance.

In a more general context, the present disclosure relates to improvements in home automation and building automation, with the main focus on air purifying, particularly indoor air purifying.

Further, in some specific embodiments, the disclosure relates to a distributed air quality sensing arrangement that may be used for augmenting a sensory basis for operation of an air treatment apparatus.

BACKGROUND OF THE INVENTION

US 6,494,940 Bl discloses an air purifier comprising a housing supporting an air inlet, an air outlet and an air flow passage interconnecting said air inlet and said air outlet, a blower assembly supported within said housing for forcing air through said air flow passage from said air inlet to said air outlet, a treatment light source disposed in said air flow passage and positioned proximate said air outlet, a filter arrangement disposed in said air flow passage, and an outlet grille supported by said housing proximate said air outlet, said outlet grille permeable to air.

Air treatment apparatuses may be used in housing areas, but also in working areas, including offices, workshops, shops, etc. An air purifying apparatus is a device which is arranged to remove small particles and gaseous contaminants from the ambient air in a room. These devices are commonly considered as being beneficial to allergy sufferers and asthmatics. They may be also helpful in reducing or eliminating second-hand tobacco smoke, for instance, and similar small particle contaminants. Further fields of application may be envisaged.

Those appliances may be regarded as domestic appliances that improve the quality of the room air in buildings. Air purifying apparatuses may utilize, for instance, a set of filters to clean the room air. Further, air quality sensors may be provided. A ventilating unit may be provided that generates an air flow through the appliance. Regarding the purifying procedure, apart from filtering, further techniques may be utilized, for instance UV irradiators, thermodynamic sterilization, ozone generators, ionizers, etc.

Indoor air purification is an important topic for human health because nowadays people generally spend more than 80% of their time in houses, offices, and cars. Indoor air pollutants mainly comprise three groups: particulate matter (PM), volatile organic compounds (so-called VOCs), and microorganisms. Exposure to VOCs may cause adverse health effects like irritation of the eyes, skin and respiratory tract, and also may lead to more serious diseases including cancer and leukemia.

Generally, air treatment apparatuses may be provided with sensor equipment.

A respective sensor unit may include at least one particulate matter (PM) sensor. While PM sensors are often an inherent component of air treatment apparatuses, VOC sensors not so often incorporated since they are relatively complex and costly. It is generally desirable to incorporate further sensors in the air treatment apparatus, particularly, but not limited thereto, VOC sensors that detect VOCs and monitor a VOC concentration. Further supplemental sensors may be envisaged that may augment the air purifying performance and enable an on- demand smart operation of the air treatment apparatus.

However, additional sensory equipment, particularly VOC sensors, is relatively costly and therefore would increase the retail price of the air treatment apparatuses which is not accepted by a large fraction of potential customers.

Several attempts have been made to add further sensory features to air treatment apparatuses. A simple approach involves setting up a straightforward correlation between an available measurement parameter/quantity and desired characteristics of an unavailable measurement parameter/quantity. Hence, virtual measurements parameter may be obtained, assuming that a well-known and considerably steady correlation is present, for instance a directly or inversely proportional relation between the available measurement parameter and the unavailable measurement parameter.

In this context, further reference is made to US 2013/0174646 Al and to US 2013/0038470 Al . US 2013/0174646 Al discloses an air quality monitoring system, comprising a memory arranged to store instructions, and a processor, communicatively coupled to the memory, that is arranged to execute the instructions to perform computing operations, comprising monitoring air quality of a residential house to establish a data point with respect to an air pollutant extant within the residential house, and broadcasting the data point to a server.

US 2013/0038470 Al discloses an air quality monitoring device comprising a sensor bay comprising multiple sensors to monitor air quality modalities and generate corresponding monitored air quality data, a processor in communication with the multiple sensors to receive and process the monitored air quality data generated from the multiple sensors, a display unit in communication with the processor to display the processed air quality data, and a data communication unit to transmit the processed air quality data to a server.

However, it has been observed that in practice often no simple correlation between two involved measurement parameters is present. Rather, further influencing parameters may be involved and have to be considered which often renders simple correlation-based approaches inefficient, if not impracticable.

Hence, there is still room for improvement in air treatment apparatuses and operating methods therefor. SUMMARY OF THE INVENTION

It is an object of the present disclosure to provide an air treatment apparatus that is arranged to be controlled in a smart fashion, dependent on a detected pollution level, wherein a control unit of the air treatment apparatus is supplied with at least a first type of air quality information, and with augmenting sensory information including at least a second type of air quality information.

Preferably, the air treatment apparatus is provided with sensory equipment substantially designed for the first type of air quality information, wherein the second type of air quality information is obtained from augmenting information that is signaled to the control unit.

There is a certain need for an augmented air treatment apparatus that can be manufactured in a cost-efficient fashion, in spite of providing enhanced performance including enhanced features and operation modes. Preferably, the air treatment apparatus is arranged as a smart device that is operable in an on-demand (auto-mode operation) fashion which enables a power efficient operation and ensures a desired air quality level. Preferably, the air treatment apparatus is operable dependent on a PM level and a VOC level, at least in a mediate fashion.

Further, is an object of the present disclosure to provide a distributed air quality sensing arrangement that facilitates operating an air treatment apparatus. Preferably, the sensing arrangement contributes to a smart control of air treatment apparatus by providing augmenting sensory information that may be used by a control unit of the air treatment apparatus, even though the air treatment apparatus is not necessarily equipped with respective (integrated) sensory equipment.

The invention is defined by the claims.

In a first aspect of the present disclosure there is provided an air treatment apparatus, the apparatus comprising:

an inlet for inlet air,

an outlet for outlet air,

an air treatment module disposed between the inlet and the outlet, the air treatment module comprising a ventilating unit arranged to generate an air flow from the inlet to the outlet, and an air treatment unit arranged to apply a purifying treatment to the air flow, a control unit arranged to control the air treatment module, and a sensor unit operatively coupled with the control unit,

wherein the sensor unit comprises an air quality sensor arranged to detect a first air quality indicative property relating to a first air contaminant and to signal a characterizing first air quality value based on said first air quality indicative property to the control unit,

wherein the control unit is arranged to derive a second air quality value relating to a second air contaminant based on the first air quality value and on augmenting information that is indicative of a second air quality indicative property, and

wherein the control unit is arranged to operate the air treatment module dependent on the first air quality value provided by the air quality sensor and on the second air quality value.

This aspect in based on the insight that the air treatment apparatus, in spite of not being equipped with a (cost-increasing) sensor that is arranged to detect a second air quality indicative property, may use augmenting information and, as a result, may be controlled (as if a respective second air quality sensor was provided) dependent on the second air quality indicative property which greatly improves the controllability and the overall air treatment performance, particularly the air purifying performance. In other words, a "virtual" sensor for the second air quality indicative property is provided which enlarges the field of application and provides further control options. The second air quality value that is derived by the control unit is computed dependent on the first air quality value. However, also the augmenting information has an influence on the computation of the second air quality value. Hence, the second air quality value is not just obtained on the basis of an assumed (simple) correlation between the first air quality value and the second air quality value but with further consideration of the augmenting

information. As a result, a more precise and reliable estimate or even a forecast of the second air quality value may be provided.

The augmenting information may be provided by remote devices and/or remote services. In an exemplary, non- limiting example, the air treatment apparatus is arranged to communicate with further (remote) air treatment apparatuses having augmented sensory capabilities, involving a physically present sensor for the second air quality indicative property. Further, remote sensors may be used that do not form part of an air treatment apparatus. Communication with further augmenting sensors may be performed in a direct and/or mediate fashion. Mediate communication may involve a server or service that is interposed between the air treatment apparatus and the remote air treatment apparatus(es) and/or remote sensor(s).

Further, the augmenting information may be supplemental information which is, in at least some respect, related to the second air quality indicative property. This may for instance involve timing information, weather information, positional information, etc.

Respective sensory equipment and/or indicating equipment may be arranged at the apparatus, or may be remotely arranged, involving that the respective information is signaled to the control unit.

In certain embodiments, the air treatment apparatus comprises a communication interface, wherein the control unit is arranged to receive the augmenting information via the communication interface from remote information sources. Preferably, the communication interface is a wireless interface. Via the communication interface, the apparatus may connect to a network, e.g. to a wireless network. Hence, the control unit may communicate with remote devices, involving a server and/or other air treatment apparatuses. Further, a user may control the air treatment apparatus by means of a remote computing device, such as a mobile phone, a mobile computer, a tablet computer, a home automation user terminal, etc. The ventilating unit generates an air flow through the air treatment module of the appliance. The air treatment module may comprise at least one air filter that is arranged to purify an air flow passing therethrough. The air treatment module may further comprise additional air purifying units, e.g. treatment UV light sources, treatment ozone sources, etc.

In an exemplary embodiment of the air treatment apparatus, the air quality sensor is a physical entity arranged at the air treatment apparatus, wherein the control unit is arranged to compute the second air quality value in the absence of a physical sensor for the second air quality property.

In a further exemplary embodiment of the air treatment apparatus, the control unit is arranged to implement a virtual air quality sensor that substitutes a physical sensor for the second air quality property. Hence, the apparatus may be operated as if it was provided as well with a physically present sensor for the second air quality indicative property.

In a further exemplary embodiment of the air treatment apparatus, the air quality sensor is arranged as a particulate matter (PM) sensor arranged to detect a particulate matter indicative property and to signal a characterizing particulate matter value to the control unit based on which the second air quality value is computed, wherein the control unit is arranged to operate the air treatment module dependent on the particulate matter value provided by the air quality sensor and on the second air quality value.

In indoor air treatment apparatuses, generally the presence, composition and/or concentration of PM in ambient air may be important variables for the control of the air purifying procedure. For instance, a so-called PM2.5 concentration may be detected and used to activate, deactivate and to control the air treatment module. Hence, the air quality sensor for the first air quality property may be arranged as a PM2.5 concentration sensor. As used herein, PM2.5 shall refer to particles which pass through a size-selective inlet with a 50 % efficiency cut-off at 2.5 μιη (micrometer) aerodynamic diameter. For definition purposes, and not for limiting the scope, reference is made to ISO 7708: 1995 "Air quality - Particle size fraction definitions for health-related sampling".

Further, in some exemplary embodiments, a PM10 concentration may be a value of interest. As used herein, PM10 shall refer to particles which pass through a size- selective inlet with a 50 % efficiency cut-off at 10 μιη (micrometer) aerodynamic diameter.

In a further exemplary embodiment of the air treatment apparatus, the control unit is arranged to derive a characterizing VOC (volatile organic compounds) value, based on the first air quality value and on augmenting information that is indicative of a VOC indicative property, wherein the control unit is arranged to operate the air treatment module dependent on the first air quality value provided by the air quality sensor and on the VOC value.

Generally, detecting and/or monitoring the presence, composition and/or concentration of VOC or TVOC (total volatile organic compounds) is desirable but requires rather complex and costly sensory equipment. Different measurement principles are utilized. This may even involve mass spectrometer sensors and further sophisticated sensory equipment. Therefore, it is desirable to supply the control unit with augmenting information that is indicative of or related to a VOC presence/concentration, in spite of incorporating a respective sensor at the apparatus.

There are various definitions for the term VOC. As used herein, for definition purposes, and not for limiting the scope of the disclosure, VOC is any organic compound having an initial boiling point less than or equal to 250 °C (482 °F) measured at a standard atmospheric pressure of 101.3 kPa. Further reference is made in this context to ISO 16000- 6:2011 "Determination of volatile organic compounds in indoor and test chamber air [...]" and to EN 13999-2:2013 " Adhesives - Short term method for measuring the emission properties of low-solvent or solvent-free adhesives after application - Part 2: Determination of volatile organic compounds".

In the context of air treatment, particularly indoor air treatment, also the term total volatile organic compounds (TVOC) is frequently used. Further reference in this context is made to Australian Government, Department of the Environment, National Pollutant Inventory (NPI): "Volatile Organic Compound definition and information" Version 2.7 - September 2009. Accordingly, total volatile organic compounds may be generally defined as any organic compound that participates in atmospheric photochemical reactions.

In a further exemplary embodiment of the air treatment apparatus, the control unit is arranged to process auxiliary augmenting information for deriving the second air quality value, the auxiliary augmenting information being selected from the group consisting of: timing information, weather information, seasonal information, wind information, time of day information, temperature information, humidity information, sound information, positional information, and combinations thereof.

It has been observed that further auxiliary information may be available that is directly or mediately indicative of the second air quality indicative property, at least in some respect related thereto. By way of example, the auxiliary augmenting information in accordance with this embodiment may be used to adjust a correlation model between the first air quality value and the second air quality value. By way of example, it has been observed that an actual time of day (e.g. working hours, rush hour, and home time) may influence a proportion/correlation of the first air quality value and the second air quality value. As a result, a multi-dimensional correlation (correlation map/correlation matrix) may be established, taking into account not only the first air quality value and a simple/static relationship, but also further augmenting influencing factors.

Generally, the augmenting information may involve event-based information spanning a short-time horizon and mid-term to long-term information spanning a

considerably long-time horizon. Further, the augmenting information may involve sudden changes and slow changes of a signal.

In an exemplary embodiment, diversified augmenting information is used for the derivation of the second air quality value. Multi-faceted information may involve more than one potentially indicative value, including auxiliary information values.

In a further exemplary embodiment of the air treatment apparatus, the augmenting information is obtained from at least one remote sensor unit comprising an air quality sensor arranged to detect a second air quality indicative property and to provide a characterizing second air quality value.

The remote sensor unit may be referred to as second sensor unit. Consequently, the air quality sensor of the remote sensor unit may be referred to as second air quality sensor. Further, the sensor unit of the air treatment apparatus in accordance with major aspects of the present disclosure may be referred to as first sensor unit, and the air quality sensor that is arranged to detect the first air quality indicative property may be referred to as first air quality sensor, primarily for distinctive purposes.

The remote sensor unit may be arranged at a remote air treatment apparatus. Primarily for distinctive purposes, the air treatment apparatus that is provided with the remote/second sensor unit may be referred to as second air treatment apparatus.

Consequently, the air treatment apparatus that is not provided with the remote/second sensor unit may be referred to as first air treatment apparatus.

In the alternative or in addition, a remote sensor unit may be present which is arranged separate from the (first) air treatment apparatus. For instance, administrative or public air quality sensor units may provide at least a fraction of the augmenting information, and may be equipped with sensory equipment for the second air quality indicative property.

In a further exemplary embodiment of the air treatment apparatus, the augmenting information is obtained from a sensor grid comprising a plurality of remote air quality sensors that are arranged to detect at least one second air quality indicative property and to provide a characterizing second air quality value. Hence, a distributed measurement setting may be utilized. The sensor grid may comprise sensors of the same type or sensors of different types. Hence, the augmenting information may be even further diversified. Some of the air quality sensors may be arranged to sense auxiliary augmenting information.

In a further exemplary embodiment of the air treatment apparatus, the control unit is arranged to communicate with a network providing server processing capabilities, and to obtain, via the network, augmenting information from a server that is supplied with a characterizing second air quality value from at least one remote augmenting air quality sensor.

Hence, a centralized or de-centralized service may be provided. The server may gather augmenting information from a variety of sensors and may process the gathered data. Assuming that a respective connection between the control unit and the server is present, certain processing and control tasks may be assigned to the server. This may involve, at least in some embodiments, the computation of the second air quality value, based on the first air quality value and the augmenting information.

In a further exemplary embodiment of the air treatment apparatus, the control unit is arranged to derive the second air quality value based on a modeled correlation between the first air quality value and the second air quality value, wherein the modeled correlation utilizes the augmenting information.

The modeled correlation may take the form of a correlation matrix, a correlation map, etc. The modeled correlation may incorporate multiple auxiliary augmenting information values (e.g., time, temperature, humidity, etc.) that may have an influence on the correlation between the first air quality value and the second air value.

The generation of the modeled correlation may involve probability considerations and calculations, big data analysis, forecasts, etc. The modeled correlation may be adaptive, involving an adaption of the correlation in response to actual events, detected deviations of actual values from set values, etc.

In a further aspect of the present disclosure, a distributed air quality sensing arrangement is presented, the sensing arrangement comprising:

- an air treatment apparatus in accordance with at least one embodiment as disrobed herein,

at least one augmenting air quality sensor that is remote from the air treatment apparatus, wherein the at least one augmenting air quality sensor is arranged to detect a second air quality indicative property and to signal a characterizing second air quality value, and

a communication service through which the air treatment apparatus is supplied with augmenting information that is indicative of the second air quality value.

In an exemplary embodiment of the sensing arrangement, a correlation between the first air quality value and the second air quality value is modeled, wherein a change of the second air quality value detected by the at least one augmenting air quality sensor is reflected in the augmenting information based on which the air treatment apparatus is operated.

In a further exemplary embodiment of the sensing arrangement, the air treatment apparatus is a first type air treatment apparatus, wherein the at least one augmenting air quality sensor is provided at an augmented air treatment apparatus that is arranged as a second type air treatment apparatus, wherein the at least one augmenting air quality sensor of the second type air treatment apparatus is utilized to supply the first type air treatment apparatus with the augmenting information.

Needless to say, in a more general context, the at least one augmenting air quality sensor may be provided at a remote sensor unit that is not necessarily arranged at an additional air treatment apparatus. Rather, so-called stand-alone sensor units may be used to this end.

In yet a further aspect of the present disclosure, a method of operating an air treatment apparatus is presented, the method comprising the following steps:

providing an air quality sensor that is arranged to detect a first air quality indicative property relating to a first air contaminant and to signal a characterizing first air quality value based on said first air quality indicative property to a control unit,

- detecting, by means of the air quality sensor, the first air quality indicative property, and signaling the characterizing first air quality value to the control unit,

obtaining augmenting information that is indicative of a second air quality indicative property,

deriving a second air quality value relating to a second air contaminant based on the first air quality value and on the augmenting information, and

operating the air treatment module dependent on the first air quality value provided by the air quality sensor and on the second air quality value.

In an exemplary embodiment of the operating method, the first air quality value is a particulate matter value, wherein the second air quality value is a virtual VOC value, wherein the second air quality value is established on the basis of modeled correlation between the first air quality value and the second air quality value, and wherein the modeled correlation utilizes the augmenting information.

As already indicated, the present disclosure is not limited to the first air quality value being a particulate matter value and the second air quality value being a virtual VOC value. Presence, concentration, composition and characteristics of further substances including, but not limited to, ultra-fine particles (UFP), relative humidity (RH), temperature (T), carbon dioxide (C0 2 ), etc. may be addressed as well. Further, particular pollutants that belong to the group of VOCs and/or PM may be addressed in isolation, such as, for instance, formaldehyde, chlorofluorocarbon, benzene, styrene, limonene, methylene chloride, etc.

In yet a further aspect of the present disclosure there is presented a computer program comprising program code means for causing a computing device to carry out the steps of the method in accordance with at least one embodiment as described herein, when said computer program is carried out on a computing device.

As used herein, the term "computing device" may stand for a large variety of processing devices. In other words, also mobile devices having a considerable computing capacity can be referred to as computing device, even though they provide less processing power resources than standard "computers". Needless to say, such a "computing device" can be a part of an air treatment device and/or system. Furthermore, the term "computing device" may also refer to a distributed computing arrangement which may involve or make use of computing capacity provided in a cloud environment. The term "computing device" may also relate to control devices in general that are capable of processing data.

In an exemplary embodiment, the computer program is, at least in part, executed on a mobile computing appliance, particularly a mobile phone, a mobile computer and/or a tablet computer. Preferably, the mobile computing appliance is arranged to be operatively coupled with the air treatment apparatus and with a remote service, such as a server. The server may be arranged to agglomerate and process augmenting information obtained from one or more augmenting air quality sensors of the distributed air quality sensing arrangement.

Preferred embodiments of the disclosure are defined in the dependent claims.

It should be understood that the claimed method and the claimed computer program can have similar preferred embodiments as the claimed apparatus/system and as defined in the dependent device claims. BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. In the following drawings

Fig. 1 shows a perspective view of an air treatment apparatus that is arranged as an air purifying apparatus;

Fig. 2 shows a further perspective view of the apparatus of Fig. 1 in a partially exploded state;

Fig. 3 shows a perspective rear end top view of the apparatus of Fig. 1 and Fig. 2, wherein an outlet cover that is arranged as a top grille is partially removed from a top end of a housing of the apparatus;

Fig. 4 shows a simplified schematic block representation of internal components of the apparatus in accordance with the arrangement of Fig. 1 to 3;

Fig. 5 is a chart illustrating exemplary indoor PM2.5 and TVOC concentration measurement values;

Fig. 6 shows a schematic block representation of an exemplary layout of a system in accordance with the present disclosure;

Fig. 7 is a schematic illustration of an exemplary embodiment of an algorithm for establishing a correlation model between a first and a second air quality parameter;

Fig. 8 shows a schematic block illustration of an exemplary layout of an underlying control algorithm;

Fig. 9 shows a schematic block illustration of an exemplary layout and operation mode of an air treatment apparatus;

Fig. 10 illustrates, by means of a schematic block representation, several aspects of an exemplary embodiment of a data matching operation in accordance with the present disclosure;

Fig. 11 illustrates, by means of a schematic block representation, several aspects of another exemplary embodiment of a data matching operation in accordance with the present disclosure; and

Fig. 12 shows a schematic block diagram exemplary illustrating several steps and aspects of an embodiment of an operating method in accordance with the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

Fig. 1 shows a perspective view of an air treatment apparatus that is designated by reference numeral 10. The apparatus 10 is arranged as an air purifying apparatus. Fig. 2 shows a corresponding partially exploded view of the apparatus 10, wherein the views of Fig. 1 and Fig. 2 use a similar view orientation but different scale ratios.

The apparatus 10 comprises a main housing or overall housing 12. The housing 12, at least in accordance with the embodiment shown in Fig. 1 and Fig. 2, comprises a nearly rectangular or square-shaped base area and extends upwardly. Overall, the housing 12 of the apparatus 10 defines a basically cuboid shape. Needless to say, at least slightly curved (convexly or concavely curved) walls may be present. Further, rounded and/or chamfered edges may be present.

The apparatus 10 further comprises an air quality sensor 14, refer also to the perspective rear top view of Fig. 3. The air quality sensor 14 is arranged to detect an air property. The air quality sensor unit 14 may be capable of monitoring inlet air and/or outlet air. In accordance with certain embodiments, the air quality sensor unit 14 is arranged as a particular matter (PM) sensor that sensor a PM concentration.

The apparatus 10 further comprises a user interface 16 which may comprise appropriate controls, keys, switches, indicators, LEDs, displays, etc.

In accordance with the arrangement of the exemplary embodiment illustrated in connection with Fig. 1 and Fig. 2, the apparatus 10 comprises two opposite lateral inlets that are covered by inlet covers 18 which are arranged as grilles. Further, the apparatus 10 comprises an outlet cover 20 at a top side thereof, wherein the outlet cover 20 is arranged as a grille. The outlet cover 20 may be also referred to as top grille or outlet grille.

The air purifying apparatus 10 comprises an air treatment module 22 which may be arranged as an air purification module. The air treatment module 22 comprise filters 26, 28 that are assigned to an air treatment unit 30. As shown in Fig. 2, a first type of filters 26 and a second type of filters 28 may be present at the air treatment unit 30. For instance, the filter 26 may be arranged as a pre-filter. Further, the filter 28 may be arranged as a fine- filter. The filters 26, 28 are arranged to filter an inlet air flow that enters the apparatus 10 through the inlet covers 18. Hence, an inlet air flow is a basically lateral flow. Further, an outlet air flow is a basically upwardly directed flow. The air treatment unit 30 is, in a fluidic view, interposed between the inlet and the outlet of the apparatus 10.

Needless to say, there may be different operating principles for air treatment units which may involve, for instance, thermodynamic sterilization, ultraviolet irradiation, photocatalytic oxidation, high-efficiency particulate arresting (HEP A) filtering, ionizer purifiers, ozone generators, and combinations thereof.

The apparatus 10 further comprises a ventilating unit which is indicated in Fig. 2 by reference numeral 24. In accordance with the exemplary embodiment of Fig. 2, the ventilating unit 24 is arranged in an interior of the housing 12 between two opposite units of inlet filters 26, 28.

Fig. 3 shows a perspective rear top view of the arrangement of Fig. 1 and 2. The side of the housing 12 where the at least one air quality sensor of the air quality sensor unit 14 is arranged is opposite from the side of the housing 12 where the user controls 16 are arranged. However, this exemplary arrangement shall not be construed in a limiting sense.

Further reference is made to Fig. 4 showing an illustrative block diagram of components of an air treatment apparatus 10 that may be arranged in accordance with the embodiment shown in Fig. 1, 2 and 3.

As indicated above, the apparatus 10 comprises an air treatment module 22 that is provided with a treatment unit 30 that implements a filter arrangement that involves filters 26, 28. For instance, two opposite sets of filters 26, 28 may be provided at respective lateral ends of the housing 12 of the apparatus 10.

In a central portion of the housing 12, the ventilating unit 24 is arranged. The ventilating unit 24 comprises a ventilator 34 which is powered by a motor 36. An operation of the ventilator 34 is indicated by a curved arrow 36 in Fig. 4. By way of example, the ventilator 34 may be arranged as centrifugal ventilator. Accordingly, the ventilator 34 may be arranged to axially suck in inlet air and to blow out pressurized outlet air in a radial direction. In accordance with the arrangement of Fig. 4, the ventilator 34 is arranged to upwardly blow out pressurized air.

An inlet flow 42 passes a flow inlet 40 of the air treatment module 22 and enters the ventilator 34. The inlet flow 42 passes the respective filters 26, 28.

Preferably, the inlet flow 42 comprises two inlet flow components at opposite axial sides of the ventilator 34 which are associated with the two opposite sets of filters 26, 28, as shown in Fig. 2 and Fig. 4.

At the outlet side of the ventilator 34, an outlet flow 48 escapes radially from the ventilator 34 through a flow outlet 46 of the air treatment module 22 towards the top grille (outlet cover 20). The outlet flow 42 passes the inner cover 32 (refer also to Fig. 3). Hence, ambient potentially polluted or contaminated air enters the apparatus 10 at lateral sides thereof, wherein purified airs escapes from the apparatus 10 through a top side.

The apparatus 10 further comprises a control unit 52 which is indicated in Fig. 4 by a respective control block. Further, a sensor unit 52 is provided that incorporates the at least one sensor 14. In certain embodiments, the apparatus 10 further comprises a

communication interface 56, particularly a wireless communication interface. Through the communication interface 56, the appliance 10 may communicate with remote appliances, remote sensors units, a remote service, and/or mobile computing devices involving smart phones, mobile computers, tablets, etc. Needless to say, also remote controls and/or smart home control terminals may be communicatively coupled with the apparatus 10 via the communication interface 56.

Fig. 5 illustrates a distributed air quality sensing arrangement 60. As schematically shown, an air treatment apparatus 10 is provided that incorporates a first type air quality sensor 14. However, so as to make further information relating to air quality available to the apparatus 10, so-called second type air quality sensors 74 are provided which may be implemented in remote air treatment apparatuses 66, or in separate (e.g. stand-alone) sensor units 70. Hence, despite of not being equipped with additional (cost-increasing) sensors, the apparatus may profit from augmenting information provided by the second type air quality sensors 74. As a result, an assumption or even a forecast of a level of the measured quantity addressed by the second type air quality sensors 74 may be computed. In other words, a virtual sensor may be implemented in the apparatus 10.

Information is exchanged between involved devices by means of a network- based communication service 80 implementing at least one (virtual, distributed or discrete) server 82. In certain embodiments discussed herein, the first type air quality sensor 14 is a particulate matter (PM) sensor, whereas the type air quality sensor 74 is a volatile organic compound (VOC) sensor. Hence, at the apparatus 10, a "virtual" VOC sensor may be provided. The apparatus may be operates on the second air quality value, even though the apparatus 10 (e.g. for cost reasons) is not capable of directly sensing this quantity.

A mobile computing device 86 may be used as an operating terminal for the apparatus 10. The computing device 86 (e.g. a smart phone, a tablet computer, a home automation terminal, etc.) may be directly or mediately (e.g. via the network-based communication service 80) coupled with the apparatus 10. Further, the computing device 86 may be (communicatively) interposed between the apparatus 10 and the communication service 80.

In the following, several aspects of the present disclosure will be described with particular reference to particular embodiments which, however, shall not be understood in a limiting sense. Rather, particular features may be extracted from the overall context of the following exemplary description. Hence, particular features and embodiments may be readily pursued in isolation.

In the context of the present disclosure a method for creating a virtual VOC sensor based on big data analysis is presented, which may provide home-use air purifiers with the capability to clean daily generated VOC more effectively. For instance, a

relationship between PM concentration (which may be augmented by further information such as temperature and relative humidity) and VOC in specific daily activity events may be established. Hence, VOC can be predicted based on the PM concentration which may improve the purifiers' performance, especially in auto-mode working state.

In a more general context, this approach may also be applied to provide additional air quality information to further devices that are provided with physical sensors, e.g. a air quality sensing arrangements that only incorporate PM sensors may be used to assess and forecast a gas pollution level (e.g. VOC value).

Air purifiers (also referred to as air treatment apparatuses herein) are widely used in several countries. Generally, air purifiers are used to clean indoor air pollutants, including particulate matters (PM), volatile organic compounds (VOC), bacteria, and so on. In order to balance the power consumption and air quality, almost all purifiers incorporate an auto-mode setting in order to clean indoor air more efficiently. Usually, the auto mode working status is based on air sensor measurements. For instance, when a high pollutant level is detected, the purifier may be operated in turbo mode with high fan speed. Further, when the pollutant level goes down, the fan speed of the purifier may be decreased.

In daily life, a large part of indoor air pollutants are generated by regular activity events, like cooking, floor cleaning, smoking, dinning, ironing (herein referred to as events). These activities may cause both PM and VOC pollutants. Most of the above regular activity events are reflected in a change of the pollutant concentration. Having knowledge of time information and pollutant concentration, the events may be detected. In turn, having knowledge of an event may be used to derive a corresponding pollutant concentration (or a change thereof). Further, so-called connected air purifying apparatuses may be used. For instance, connected air purifying apparatuses may be remotely controlled which may involve that an operation state and air quality indicating measurements may be obtained through connected devices, using software applications. Further, so stand-alone sensor boxes may be envisaged that incorporate different sensors which are arranged to sense and track several indoor air pollution measurement quantities.

However, most of the readily available air purifiers on the market are only equipped with PM sensors, and, as a result, their auto mode working state may be controlled using PM measurements. Hence, there may be certain periods when the air purifiers cannot be aware of VOC pollution (due to their lack of respective sensory equipment) and maintain their standby working state, for instance. There is a coarse correlation between PM (PM2.5) and VOC concentration which, however, has proven to lack reliability and accuracy, at least temporarily. Fig. 6 shows a temporal chart of air quality data illustrating a relation between PM and VOC, based on a real test. Trace 100 represents a PM concentration. Trance 102 represents a VOC (rather TVOC) concentration. The time period covers an exemplary working day. A certain trend is present. When the PM concentration is rising, generally also the VOC concentration is rising. However, it can be further seen that the PM2.5 and TVOC curves 100, 102 are not always consistent. Especially after 17:00, TVOC still kept a high concentration for a time period but the PM2.5 concentration is low.

A direct way to solve this issue is to add a VOC sensor to an air purifier or to use a stand-alone sensor box to the monitor VOC level. But this involves several

disadvantages. On the one hand, there are extra costs of VOC sensor. The higher the desired accuracy of the VOC sensor, the higher are the costs involved. On the other hand, although most of available VOC sensors are sensitive to several gaseous components, some gaseous pollutants will be still missing in the measurement provided by such VOC sensors. Hence, even in the case that a VOC sensor would be provided at the air purifier, there may be some VOC components that are overlooked or ignored, due to the particular design of the VOC sensor.

In accordance with the present disclosure, there is provided an air purifier that is operable to automatically clean both PM2.5 and VOC contaminants, even if there is no physical VOC sensor arranged at or directly coupled with the appliance.

In accordance with a further aspect of the present disclosure, there is presented an approach that provides detailed knowledge about air quality, even in the absence of physical sensors for each of the measurement values of interest. This may involve, but is not limited to, VOC, C0 2 and NO values

In certain exemplary embodiments, additional augmenting air quality information may be provided, based on measurements performed by other (remote) sensors. For example, close to a person A, a PM2.5 sensor is provided. If for instance, person A starts smoking, then the sensor would show a respective signal (increased PM concentration). In the absence of a VOC sensor nearby, no respective data may be provided. However, in accordance with the present disclosure, a person B may be looked for which is in a similar situation/environment (e.g. also smoking indoors). Assuming that B is equipped with both a PM2.5 sensor and a VOC sensor nearby, then a correlation between PM and VOC may be detected. Hence, since both cases A and B are similar, the correlation of case B may be applied to case of. Hence, on the basis of the correlation and the actual PM value provided by the nearby sensor, a VOC estimate may be provided.

Exemplary embodiments and aspects of the disclosure involve, but are not limited to the following features:

1) A detection of specific daily activity events using time information, air sensors measurements and public web information, ambient sensors may be also used, where provided and available, like microphones, thermometers, humidity sensors, etc.

Manual input could be also included

2) Establishing a relationship between PM concentration and VOC level in specific daily activity events using big data analysis, including PM and VOC decreasing pattern when purifier is working. It is assumed that a certain number of VOC sensors is available to provide data. Further (remote) sensors types may be used. A relationship model or map may be established based on this data. Relationship building may involve a self-learning algorithm based on feedback, which may be provided via an online network service. Machine learning and data mining approaches may be used. The algorithm may be come more accurate and robots when more data is generated and correlated with the passage of time. As a further benefit, different types of gaseous pollutants may be discriminated and distinguished.

3) When certain activity events take place, a detection of the PM concentration may be used to assess and predict the VOC level based on the established

relationship. This is an option to create a virtual VOC sensor for devices that do not incorporate a real VOC sensor and/or similar sensors. 4) Having knowledge of the calculated predicted VOC level, the auto-mode algorithm of the air purifier may be improved to clean VOC more effectively, as well as PM. The auto-mode algorithm is arranged to consider different input values and levels. Hence, a smart enhanced (multi-dimensional) control of the air purifier is enabled.

In a more general context, exemplary aspects and embodiments of the present disclosure are based on the insight that some devices may have a complete set of sensors (e.g. PM2.5, UFP, TVOC, CO2, T, RH), for example, in a sensor box. Other devices (first type devices) may have a subset (e.g. PM2.5 and/or TVOC), for example, in an air purifier/air treatment apparatus. The sensors also could be in connected wearable devices.

Further, big data analysis of the data from the complete set of sensors (across a sufficiently large installed sample size), patterns are detected to indicate a correlation between the subset of the sensors and the complete set of sensors (due to the occurrence of similar events at the respective devices).

When the patterns are detected with a subset of the sensors, expected value of the other sensors can be inferred from the big data analysis, e.g. based on a correlation model or map. It is to be noted that in some cases this may lead to a range or set of possible values. Hence, for instance, a worst case estimate could be provided.

Based on the inferred values, the air purifier may be controlled to enhance the performance thereof, resulting in an improved removal of pollutants. The inferred values of the virtual sensor could be communicated to consumers via a user interface (at the purifier or at a separate computing device) and may be reflected, for instance, in an updated air quality index value or level.

The big data analysis may be based on one or more of:

- sensor measurements over time (to detect events),

sensor values (e.g. PM2.5) but also derived information such as particle size distribution,

location information involving nearby public air-related events, like burning, traffic jam and gas leak, and

- inputs from consumers through apps or other forms of UI.

The proposed virtual VOC sensor creating method is based on big data analysis. In the following, an exemplary layout of a respective system architecture will be described. To implement a system in accordance with the disclosure, a set (preferably a large number) of purifiers and/or sensor boxes or wearable sensors may to be connected, at least temporality via a network service. Hence, there is a control centre which is arranged to collect measurement data from the purifiers and sensor boxes, and which also may control the purifiers to update a built-in control algorithm.

Fig. 7 shows the system architecture of a system 118 in accordance with such an approach. A control entity/service 120 is provided that is provided with, or coupled to, a data base 122 and processing capabilities to execute an operating algorithm 124. The control service 120 may be at least partially provided by a network/cloud environment. In alternative embodiments, the control service 120 may be at least partially provided by a control unit of the purifier itself. Further, in alternative embodiments, the control service 120 may be at least partially provided by a computing terminal device (smart phone, tablet computer, etc.).

Distributed control using more that one control entity may be envisaged.

Sets of devices 128, 130, 132 are connected to the control entity/service 120. As indicated by respective arrows, information may be exchanged the involved devicesl28, 130, 132 and the control entity/service 120, including upload to an download from the control entity/service 120. Upload may involve a measurements upload. Download may involve a correlation algorithm and/or control algorithm download, including updates, etc.

The devices may involve a first set of devices 128 that are provided with limited sensory capacity, for instance with PM sensors only. The devices may involve a further set of devices 130 that are provided with enlarged sensory capacity, for instance with PM sensors and with VOC sensors of a first type. The devices may involve a further set of devices 130 that are provided with enlarged sensory capacity, for instance with PM sensors and with VOC sensors of a second type. As indicate above, a variety of pollutants that fall under the definition of VOCs is known. Hence, there may be different types of VOC sensors as well. As indicated by a dotted line between devices 130 and devices 132 in Fig. 7, there may be further sets of devices incorporating further sensor types.

In a defined area, e.g. a city area, sensor measurements from connected purifiers and sensor boxes are uploaded to the control centre. The measurements may include PM concentrations and different types of VOC levels, where available. The algorithm at the control entity/service 120 may be arranged to establish a relationship/correlation between the PM and VOC in the context of several activity events. As a result, an auto-mode control algorithm for the purifiers may be updated and sent to the purifiers remotely.

Fig. 8 shows a schematic illustration of an algorithm 140 that may be executed by in the control entity/service 120. There are two basic functions. A first function, designated by 142, relates to the detection of events, which uses augmenting information such as time/location 146, ambient sound/temperature 148, and air quality 150 as inputs. A further function, designated by 144, relates to correlation building, which uses augmenting information such as air quality 150, a purifier working status 152 and detected activity events 142 as inputs. The function 144 establishes and adapts a correlation model or map 152.

Hence, there may be two types of augmenting information, involving directly attributable air quality information and mediately attributable auxiliary augmenting information.

The algorithm 140 may be configured as an online algorithm permanently provided by an online service that may updated periodically. Further, after a calculation procedure, an updated version of the correlation map 152 may be generated. In an exemplary embodiment, the correlation map 152 may include a relationship between PM concentration and different types of VOC levels in different regular daily activity events.

When a new version of the correlation map 152 is generated, the purifiers may be operated in accordance with the auto-mode algorithm, based on the data provided through the network which can be any kind of network. In an exemplary embodiment, the algorithm module (control unit) of the purifier is programmable. Thus, in individual homes, the purifiers may be provided with enhanced knowledge to clean VOC more effectively, even when there is not enough information about indoor air quality directly obtained through nearby sensory equipment.

Fig. 9 is a schematic illustration of a layout and operation mode of a purifier in the context of the present disclosure, refer to the block 160. Purifiers in different settings

(different locations, different types, etc.) may be supplied with different information because of the presence, type, and accuracy of sensors. Hence, a robust auto-mode algorithm architecture is proposed that is preferably compatible to different levels of information. In Fig. 9, activity events detection is represented by block 162. Further, block 164 represents the utilized correlation model or map.

Even when there is for instance only time information (block 166) and PM concentration information (block 168), the purifier may also profit from the auto-mode algorithm as discussed herein to remove VOCs more effectively. The output of auto-mode algorithm is the target operating status of the purifier, which for instance determines the fan speed, block 170.

In an exemplary embodiment, pre-downloaded fixed data or algorithms may be used which to not necessarily require an instantly programmable purifier and real-time updating. Fig. 10 illustrates several aspects of an exemplary embodiment of a data matching operation in accordance with the present disclosure. A database is indicated by 180. The database 180 includes sampled records. The database 180 contains information relating to certain events e (el, e2, e3, ...). Each event may be assigned with further auxiliary attributes a (al, a2, a3, ...). Attributes may relate to auxiliary information such as time, location, presence of people, room ventilation, humidity, purifier status, outdoor PM values, sound, decoration time, etc. A respective value v may be assigned to the attributes a of the events e.

Reference numeral 182 indicates a processed dataset, refer also to reference numerals 184, 186, highlighting respective data portions. The dataset 182 represents a best match among the available data (PM values plus auxiliary events). Hence, the TVOC value provided by the database 180 for the dataset 182 may be used to control the air purifier that not provided with respective sensory equipment.

general, building an accurate and robust relationship between PM and

TVOC is challenging when there is no information obtained through direct measurements. However, if a sufficient level of related (auxiliary) augmenting information is gathered, the prediction of TVOC may be simplified.

So the "events" data may be recorded defined in a widespread and fine-grained fashion. The more data sets are provided, the more reliable the matching operation may be. For instance, the "events" and further information may be represented by a vector. The vector may include any factor that may (directly or mediately) indicate indoor air quality, involving, but not limited to: time, location of the sensor, presence of people, room ventilation, indoor humidity, outdoor PM, purifier status, ambient sound and decoration time.

Once the database is built and filled, real-time or nearly real-time TVOC assessment may be performed. The data matching procedure may be based on a minimum distance between real-time data sampling and database sampling to find the best matching record. Equation (1) shows the principle of minimum distance estimation, where R is the vector of real-time data (e.g. PM data), D is the vector set of the database, <i is a sampling of the database, and N is the dimension of the "event" vector.

I

(1) Similarly, also Fig. 11 illustrates several aspects of another exemplary embodiment of a data matching operation in accordance with the present disclosure, wherein the matching operation is based on event classification and on probability considerations.

In Fig. 11, reference numeral 190 designates the database. Reference numeral

192 indicates a processed dataset, refer also to reference numerals 200, 202, highlighting respective data portions. A block 194 represents a clustering algorithm. Event clusters 196 are shown in Fig. 11 in a vertical series. For each cluster, a relationship between PM and TVOC is built, reference numeral 198.

Subsequently, for a real-time or nearly real-time TVOC assessment without direct TVOC sensing equipment, the probabilities of being each event cluster are calculated (pi, p 2 , p n ). For instance, maximum likelihood probability considerations may be applied to the current event(s), and eventually to estimate or assess the TVOC value.

In this way, a set of probable TVOC values with probabilities may be computed, and not only an absolute prediction value. This data may be advantageously used to finally determine the on-demand auto-mode operation of the purifier.

For illustrative purposes, it is assumed that a prediction value set of three TVOC values is processed, based on the data available. There are in total three values augmented by respective probabilities, {Ti, pi}, {T 2 , p 2 } and {T 3 , p 3 } . Further, assumingly, the "imaginary" costs of using these values are ci, c 2 and c 3 , the benefits of using these values are bi, b 2 and b 3 . Benefits may represent, for instance, pollutant amount. The costs may represent, for instance, power consumption or operation noise.

For instance, the best choice calculated based on equation (2). If the expectation of total benefits is positive, the maximum probability prediction value is used. Otherwise, TVOC estimation is aborted.

The auto mode of the purifiers may still utilize the PM measurement. This may ensure that statistically no negative results will happen.

if ax (pi

{

(2) Fig. 12 shows a schematic block diagram exemplary illustrating several steps and aspects of an exemplary embodiment of an operating method in accordance with the present disclosure.

In a step 220 an air treatment apparatus is provided, the apparatus air quality sensor that is arranged to detect a first air quality indicative property and to signal a characterizing first air quality value.

In a step 222, a first air quality indicative property is sensed. A characterizing first air quality value resulting therefrom is signaled to a control unit.

In a further step 224, augmenting information is obtained that is indicative of a second air quality indicative property. Preferably, there is no nearby sensor for the second air quality indicative property. Rather, is virtual sensing procedure is proposed that replaces on- site sensors.

Augmenting information may be arranged from a communications network or cloud environment, step 226. This may include an online service provided via the internet or similar networks. Further, facility, building and/or home automation networks may be utilized.

Further, the network or cloud environment may be provided and/or operatively coupled with processing capacity, 230, remote sensor units, 232, and a database, 228, for records relating to air quality information. The remote sensor units may be provided with physical sensors that are capable of measuring the second air quality indicative property. Processing capacity may be provided by virtual or discrete servers.

The database may be supplied with sensor data from a plurality of air treatment apparatuses and, if any, distinct stand-alone sensor units. Further, public measurements and environmental measurement data may be used. Preferably, the database contains a large number a datasets involving values for the first air quality indicative property and the second air quality indicative property. Optionally, additional augmenting information may be provided which may be referred to as events information or auxiliary information.

A goal may be to establish a correlation, e.g. a correlation model or a correlation may, between the first air quality value and a second air quality value that results from the second air quality indicative property.

In a step 240, a second air quality value is inferred from the first air quality value and augmenting information. To this end, a (local) database 242 may be used. In the database, temporary data and/or a device-specific correlation map may be stored. Needless to say, also in the step 240, data provided from the communications network or cloud environment may be used and processed.

In a further step 244, the air treatment apparatus is operated based on the first air quality value which is provided by the air quality sensor and on the second air quality value which is inferred from the correlation model and the augmenting information.

In the following, further exemplary embodiments within the general context of the present disclosure are provided.

In an exemplary embodiment, an air sensing system is provided, comprising one or more physical sensors to detect properties of the air, a database containing patterns of air properties over time, wherein, based on the physical sensor data and the database, other properties of the air are inferred.

In a refined embodiment, the inferred values may indicate a range of values or a worst case estimate.

In a further refined embodiment, the inferred values include probabilities. In a further refined embodiment, the database includes other information such as season, time of day weather information, location and nearby air-related public events to refine the inferred values.

In a further refined embodiment, the database is stored in a cloud environment and updated based on analysis of sensor data from remote devices the incorporate

augmenting sensors and are therefore able to supply complete sets of data.

In a further refined embodiment, the database is regularly updated from the cloud.

Air treatment apparatuses in accordance with the present disclosure are able to be operated dependent on air properties for which no physical sensor is present. Rather, in accordance with the present disclosure, virtual sensing is enabled.

Embodiments of devices, systems and methods in accordance with the present disclosure may be used in the context of connected air purifiers to improve the performance of cleaning different types of pollutants, including VOC. Further, aspects and features of the present disclosure also may be used in air quality sensor box or wearable sensors to provide more information about air quality without the present of respective physical sensors nearby.

While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single element or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

Any reference signs in the claims should not be construed as limiting the scope.