CN117433136A - Air treatment device, sensor arrangement and method of operation - Google Patents

Abstract

The present disclosure relates to an air treatment device (10) comprising an inlet (18) for inlet air, an outlet (20) for outlet air, an air treatment module (22) arranged between the inlet (18) and the outlet (20), a control unit (52) and a sensor unit (54) operatively coupled with the control unit (52), the air treatment module (22) comprising a ventilation unit and an air treatment unit (26, 28), the ventilation unit being arranged to generate an air flow from the inlet (18) to the outlet (20); the air handling unit is arranged to apply a cleaning treatment to the air flow, the control unit (52) is arranged to control the air handling module (22), wherein the sensor unit (54) comprises an air quality sensor (14), the air quality sensor (14) is arranged to detect a first air quality indication characteristic and to signal a characteristic 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 based on enhancement information indicative of the second air quality indication characteristic; and wherein the control unit (52) is arranged to operate the air treatment module (22) in dependence of a first air quality value provided by the air quality sensor (14) and in dependence of a second air quality value. The present disclosure relates to a distributed air quality sensing apparatus (60) and a method of operating an air treatment device (10).

Description

Air handling equipment, sensor devices and methods of operation

This application is a divisional application of the Chinese patent application titled "Air treatment equipment, sensor device and operating method" with application number 201780054177.1 and the filing date being August 17, 2017.

Technical field

The present disclosure relates to air treatment equipment and to distributed air quality sensing devices. The present disclosure also relates to methods of operating air treatment equipment and corresponding computer programs.

In some specific embodiments, the present invention relates to household appliances arranged to treat ambient air in a building in order to improve the well-being of current residents. More specifically, the present disclosure relates to improvements in air treatment equipment, particularly air purification equipment, and related operating methods that improve purification performance.

In a more general context, the present disclosure relates to improvements in home automation and building automation, focusing primarily on air purification, particularly indoor air purification.

Furthermore, in some specific embodiments, the present disclosure relates to a distributed air quality sensing device that can be used to enhance the sensing basis for operating an air treatment device.

Background technique

US 6,494,940 B1 discloses an air purifier, including a housing supporting an air inlet, an air outlet and an air flow channel connecting the air inlet and the air outlet, and a blower assembly supported in the housing for forcing Air flows from the air inlet to the air outlet through the air flow channel, a processing light source disposed in the air flow channel and close to the air outlet, a filter device disposed in the air flow channel, and An outlet grille is supported by the housing adjacent the air outlet, the outlet grille being permeable to air.

Air handling equipment can be used in residential areas as well as in work areas, including offices, workshops, shops, etc. An air purification device is a device used to remove small particles and gaseous pollutants from the ambient air in a room. These devices are often considered beneficial for allergy sufferers and asthma sufferers. They may also help reduce or eliminate secondhand tobacco smoke, for example, and similar small particle pollutants. Other areas of application are conceivable.

These appliances can be considered household appliances that improve indoor air quality in buildings. An air purification device can clean indoor air using, for example, a set of filters. Additionally, air quality sensors can be provided. A ventilation unit may be provided which generates air movement through the appliance. Regarding the purification procedure, in addition to filtration, other technologies can be used, such as UV irradiators, thermodynamic sterilizers, ozone generators, ionizers, etc.

Indoor air purification is an important topic for human health because people today typically spend more than 80% of their time in homes, offices, and cars. Indoor air pollutants mainly include three groups: particulate matter (PM), volatile organic compounds (so-called VOCs) and microorganisms. Exposure to VOCs can have adverse health effects, such as eye, skin, and respiratory tract irritation, and can lead to more serious illnesses, including cancer and leukemia.

Typically, air treatment equipment can be provided with sensor equipment. The corresponding sensor unit may include at least one particulate matter (PM) sensor. While PM sensors are often an inherent component of air handling equipment, VOC sensors are not often incorporated because they are relatively complex and expensive. It is often desirable to incorporate other sensors in air handling equipment, particularly but not limited to VOC sensors that detect VOCs and monitor VOC concentrations. Other complementary sensors can be envisioned that could enhance air purification performance and enable on-demand intelligent operation of air handling equipment.

However, additional sensing equipment (especially VOC sensors) is relatively expensive and therefore increases the retail price of air treatment equipment, which is unacceptable to most potential customers.

Several attempts have been made to add further sensing features to air handling equipment. A simple approach involves establishing a direct correlation between the desired characteristics of an available measurement parameter/quantity and an unavailable measurement parameter/quantity. Therefore, virtual measurement parameters can be obtained assuming that there is a well-known and fairly stable correlation, such as a direct or inverse relationship between an obtainable measurement parameter and an unobtainable measurement parameter.

In this regard, further reference is made to US2013/0174646 A1 and US2013/0038470A1. US2013/0174646A1 discloses an air quality monitoring system, including: a memory arranged to store instructions; and a processor communicatively coupled to the memory and arranged to execute instructions to complete computing operations, including detecting the air quality of a house, thereby Establish data points related to air pollutants present in the residence and broadcast the data points to the server.

EP 0 752 085 A1 discloses an indoor air quality sensor for controlling ventilation in a building area, and a method for achieving such control. The sensor includes a first sensor for sensing a selected leading indicator of contaminants in the area and providing an output signal representative of the indicator, and at least one additional sensor for sensing a selected leading indicator of contaminants in the area. detecting at least one selected secondary indicator of contaminants in the area and providing an output signal representative of the secondary indicator.

US2013/0038470A1 discloses an air quality monitoring device, which includes a sensor rack. The sensor rack includes multiple sensors for monitoring air quality patterns and generating corresponding air quality monitoring data. The processor communicates with the multiple sensors to receive and processing air quality monitoring data generated by multiple sensors; a display unit communicating with the processor for displaying the processed air quality data; and a data communication unit used for sending the processed air quality data to the server.

However, it has been found that in practice there is often no simple correlation between the two measured parameters involved. Instead, other influencing parameters may be involved and must be taken into account, which often makes processing based on simple correlations inefficient, if not infeasible.

Therefore, there is still room for improvement in air handling equipment and its operating methods.

Contents of the invention

It is an object of the present disclosure to provide an air treatment device arranged to be controlled in an intelligent manner in dependence on detected pollution levels, wherein a control unit of the air treatment device is supplied with at least a first type of air quality information, and comprising at least Enhanced sensing information of the second type of air quality information.

The air treatment device is provided with sensing equipment essentially designed for a first type of air quality information, wherein the second type of air quality information is obtained from enhanced information that is signaled to the control unit.

While enhanced performance including enhanced features and modes of operation are provided, there remains a need to be able to cost-effectively manufacture enhanced air handling equipment. Preferably, the air treatment device is arranged as an intelligent device, which can operate in an on-demand (automatic mode operation) manner, which enables energy-saving operation and ensures that the desired air quality level is achieved. Preferably, the air treatment equipment may operate, at least in an indirect manner, dependent on PM levels and VOC levels.

Furthermore, it is an object of the present disclosure to provide a distributed air quality sensing device that facilitates operation of air treatment equipment. Preferably, this sensing device facilitates intelligent control of the air treatment device by providing enhanced sensing information that can be used by the control unit of the air treatment device, even if the air treatment device is not necessarily equipped with corresponding (integrated) sensing equipment.

The invention is defined by the independent claims.

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

- a control unit arranged to control the air handling module, and

- a sensor unit operatively coupled to the control unit,

wherein the sensor unit includes an air quality sensor arranged to detect a first air quality indicative characteristic and to signal a characteristic first air quality value based on said first air quality indicative characteristic to the control unit, the first air quality Indicative characteristics are related to the first air pollutant,

wherein the control unit is arranged to derive a second air quality value related to the second air pollutant based on the first air quality value and based on enhanced information indicative of the second air quality indicating characteristic, and in dependence on a first air quality value provided by the air quality sensor. an air quality value and operating the air treatment module in dependence on a second air quality value, and wherein the air quality sensor is a physical entity arranged at the air treatment device, and the control unit is arranged to detect the second air quality when not A second air quality value is calculated in the absence of a physical sensor indicating characteristics.

This aspect is based on the insight that, although not equipped with a (increased cost) sensor arranged to detect the second air quality indicative characteristic, the air treatment device can use the enhanced information and can therefore be operated in reliance on the second air quality indicative characteristic. control (as if a corresponding second air quality sensor were provided), which greatly improves controllability and overall air handling performance, especially air purification performance.

In other words, a "virtual" sensor is provided for the second air quality indicating characteristic, which expands the field of application and provides further control options. The second air quality value derived by the control unit is calculated as a function of the first air quality value. However, the enhanced information also has an impact on the calculation of the second air quality value. Therefore, the second air quality value is not obtained based only on a hypothetical (simple) correlation between the first air quality value and the second air quality value, but further takes into account the enhanced information. As a result, a more accurate and reliable estimate or even prediction of the second air quality value can be provided.

Enhanced information may be provided by remote devices and/or remote services. In an illustrative, non-limiting example, the air treatment device is arranged to communicate with other (remote) air treatment devices having enhanced sensing capabilities involving a physically present sensor for the second air quality indicative characteristic. Additionally, remote sensors that do not form part of the air handling equipment may be used. Communication with other augmented sensors may be performed in a direct and/or indirect manner. Indirect communication may involve a server or service interposed between the air treatment device and the remote air treatment device(s) and/or the remote sensor(s).

Additionally, the enhanced information may be supplementary information that is related, at least in some respects, to the second air quality indicative characteristic. This may involve, for example, timing information, weather information, location information, etc. The corresponding sensing equipment and/or indicating equipment may be arranged at the device, or may be arranged remotely, including signaling the corresponding information to the control unit.

In certain embodiments, the air treatment device includes a communication interface, wherein the control unit is arranged to receive the enhanced information from the remote information source via the communication interface. Preferably, the communication interface is a wireless interface. The device may be connected to a network via a communication interface, for example to a wireless network. Thus, the control unit can communicate with remote devices, involving servers and/or other air treatment equipment. In addition, users can control air treatment equipment with the help of remote computing devices, such as mobile phones, mobile computers, tablets, home automation user terminals, etc.

The ventilation unit generates air flow through the air handling module of the appliance. The air treatment module may include at least one air filter arranged to clean air flow therethrough. The air treatment module may also include additional air purification units, for example, processing ultraviolet light sources, processing ozone sources, etc.

The air quality sensor is a physical entity arranged at the air treatment device, wherein the control unit is arranged to calculate the second air quality value without the physical sensor for the second air quality property.

In another exemplary embodiment of the air treatment device, the control unit is arranged to implement a virtual air quality sensor, which replaces the physical sensor for the second air quality property. The device may therefore be operated as if it were also provided with a physically present sensor for the second air quality indicating characteristic.

In a further exemplary embodiment of the air treatment device, the air quality sensor is arranged as a particulate matter (PM) sensor, which is arranged to detect particulate matter indicative properties and to signal a characteristic particulate matter value to the control unit, the control unit being based on the characteristic particulate matter value A second air quality value is calculated, wherein the control unit is arranged to operate the air treatment module in dependence on the particulate matter value provided by the air quality sensor and in dependence on the second air quality value.

In indoor air treatment equipment, generally the presence, composition and/or concentration of PM in ambient air may be important variables in controlling the air purification process. For example, so-called PM _2.5 concentrations can be detected and used to activate, deactivate and control air treatment modules. Therefore, the air quality sensor for the first air quality property may be arranged as a PM _2.5 concentration sensor. As used herein, PM _2.5 shall refer to particles with size-selective inlet having a 50% efficiency cutoff at 2.5 μm (microns) aerodynamic diameter. For definition purposes, but not to limit scope, reference is made to ISO7708:1995 "Air quality-Particle size fraction definitions for health-related sampling".

Additionally, in some exemplary embodiments, PM ₁₀ concentration may be a value of interest. As used herein, PM ₁₀ shall refer to particles with size-selective inlet having a 50% efficiency cutoff at 10 μm (microns) aerodynamic diameter.

In a further exemplary embodiment of the air treatment device, the control unit is arranged to derive a characteristic VOC (volatile organic compound) value based on the first air quality value and based on enhanced information indicative of a VOC indicating characteristic, wherein the control unit is arranged The air treatment module is operated in dependence on the first air quality value provided by the air quality sensor and in dependence on the VOC value.

Often, it is desirable to detect and/or monitor the presence, composition and/or concentration of VOCs or TVOCs (Total Volatile Organic Compounds), but requires rather complex and expensive sensing equipment. Use different measuring principles. This may even involve mass spectrometer sensors and more sophisticated sensing equipment. Therefore, despite the incorporation of corresponding sensors at the device, it is desirable to provide the control unit with indication or enhanced information regarding the presence/concentration of VOCs.

The term VOC has various definitions. As used herein, for definitional purposes and not to limit the scope of this disclosure, a 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 ISO16000-6:2011 “Determination of volatile organic compounds in indoor and test chamber air[…]” and EN13999-2:2013 “Adhesives-Shortterm 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, especially indoor air treatment, the term total volatile organic compounds (TVOC) is often used. Further reference is made in this context to the Australian Government Department of the Environment's National Pollutant Inventory (NPI): "VolatileOrganic Compound definition and information" version 2.7 - September 2009. Therefore, total volatile organic compounds can generally be defined as any organic compound that participates in atmospheric photochemical reactions.

In a further exemplary embodiment of the air treatment device, the control unit is arranged to process auxiliary enhanced information for deriving the second air quality value, the auxiliary enhanced 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, location information and combinations thereof.

It has been observed that, at least in certain aspects related thereto, other auxiliary information can be obtained that directly or indirectly indicates the second air quality indicating characteristic. For example, the auxiliary enhancement information according to this embodiment may be used to adjust a correlation model between the first air quality value and the second air quality value. For example, it has been observed that the actual time of day (eg work hours, peak hours and home times) may affect the ratio/correlation of the first and second air quality values. As a result, multi-dimensional correlations (correlation diagrams/correlation matrices) can be established, taking into account not only the first air quality value and simple/static relationships, but also further considering enhanced influencing factors.

Typically, enhanced information can involve event-based information spanning short time frames as well as mid- to long-term information spanning considerably longer time frames. Furthermore, enhanced information may involve both sudden and slow changes in the signal.

In an exemplary embodiment, diverse enhanced information is used to derive the second air quality value. Multifaceted information may involve more than one potential indicator value, including auxiliary information values.

In another exemplary embodiment of the air treatment device, the enhanced information is obtained from at least one remote sensor unit comprising an air quality sensor arranged to detect a second air quality indicative characteristic and provide a characteristic second air quality value .

The remote sensor unit may be referred to as the second sensor unit. Therefore, the air quality sensor of the remote sensor unit may be called a second air quality sensor. Furthermore, the sensor unit of the air treatment apparatus according to the main aspect of the present disclosure may be referred to as a first sensor unit, and the air quality sensor arranged to detect the first air quality indicating characteristic may be referred to as a first air quality sensor, which Mainly for special purposes.

The remote sensor unit may be arranged at the remote air treatment device. Mainly for special purposes, the air treatment device provided with the remote/second sensor unit may be called a second air treatment device. Therefore, an air treatment device that is not provided with a remote/second sensor unit may be referred to as a first air treatment device.

Alternatively or additionally, there may be a remote sensor unit arranged separately from the (first) air treatment device. For example, an administrative or public air quality sensor unit may provide at least a portion of the enhanced information and may be equipped with sensing equipment for a second air quality indicative characteristic.

In another exemplary embodiment of the air treatment device, the enhanced information is obtained from a sensor network including a plurality of remote air quality sensors arranged to detect at least one second air quality indicative characteristic and provide a characteristic first 2. Air quality value. Therefore, a distributed measurement setup can be utilized. A sensor network may include sensors of the same type or sensors of different types. Therefore, enhanced information can be even further diversified. Some air quality sensors may be arranged to sense auxiliary enhanced information.

In another exemplary embodiment of the air treatment device, the control unit is arranged to communicate with a network providing processing capabilities of the server and to obtain the enhancement information via the network from the server supplied with the enhanced air quality sensor from the at least one remote Characteristic second air quality value.

Therefore, centralized or decentralized services can be provided. The server can collect augmented information from various sensors and can process the collected data. Certain processing and control tasks can be assigned to the server, assuming there is a corresponding connection between the control unit and the server. In at least some embodiments, this may include calculating a second air quality value based on the first air quality value and the enhanced information.

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

Modeled correlations can take the form of correlation matrices, correlation diagrams, etc. The modeled association may include multiple auxiliary enhanced information values (eg, time, temperature, humidity, etc.) that may affect the association between the first air quality value and the second air value.

The generation of modeled associations can involve probabilistic considerations and calculations, big data analysis, predictions, etc. Modeled correlations may be adaptive, involving adaptability of the correlation in response to actual events, detected deviations of actual values from set values, etc.

In another aspect of the present disclosure, a distributed air quality sensing device is proposed, the sensing device including:

- an air treatment device according to at least one embodiment as disclosed herein,

- at least one enhanced air quality sensor remote from the air treatment device, wherein said at least one enhanced air quality sensor is arranged to detect a second air quality indicative characteristic and to emit a signal characteristic of a second air quality value, and

- a communication service by which the air treatment device is supplied with enhanced information indicating the second air quality value.

In an exemplary embodiment of the sensing device, a correlation between a first air quality value and a second air quality value is modeled, wherein changes in the second air quality value detected by the at least one enhanced air quality sensor are reflected in the enhanced information, the air handling equipment is operated based on the enhanced information.

In another exemplary embodiment of the sensing device, the air treatment device is an air treatment device of a first type, wherein at least one enhanced air quality sensor is provided at the enhanced air treatment device, the enhanced air treatment device being arranged as a second A type air treatment device, wherein at least one enhanced air quality sensor of the second type air treatment device is used to provide the first type air treatment device with enhanced information.

It goes without saying that, in a more general context, at least one enhanced air quality sensor can be provided at a remote sensor unit which does not have to be arranged at a further air treatment device. Instead, so-called independent sensor units can be used for this purpose.

In yet another aspect of the present disclosure, a method of operating an air treatment device is proposed, which method includes the following steps:

- providing an air quality sensor arranged to detect a first air quality indicative characteristic and to signal a characteristic first air quality value based on the first air quality indicative characteristic to the control unit, the first air quality indicative characteristic being associated with the first air pollution related to things,

- detecting the first air quality indicating characteristic by means of the air quality sensor and signaling the characteristic first air quality value to the control unit,

- Obtain enhanced information indicating a second air quality indication characteristic,

- based on the first air quality value and based on the enhanced information, without a physical sensor for detecting the second air pollutant, deriving a second air quality value related to the second air pollutant, and

- operating the air treatment module in dependence on the first air quality value provided by the air quality sensor and in dependence 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, and wherein the second air quality value is based on a relationship between the first air quality value and the second air quality value. A modeled association is established, and the modeled association uses augmented information.

As already noted, the present disclosure is not limited to having the first air quality value be a particulate matter value and the second air quality value be a virtual VOC value. The presence, concentration, composition and characteristics of other substances may also be addressed, including but not limited to ultrafine particles (UFP), relative humidity (RH), temperature (T), carbon dioxide ( _CO2 ), etc. In addition, specific pollutants belonging to the VOCs and/or PM groups can be treated individually, such as formaldehyde, chlorofluorocarbons, benzene, styrene, limonene, methylene chloride, etc.

In yet another aspect of the present disclosure, a computer program is provided, comprising program code means for, when the computer program is executed on a computing device, causing the computing device to perform at least one implementation in accordance with the invention. Example method steps.

As used herein, the term "computing device" can represent a variety of processing devices. In other words, mobile devices with considerable computing power may also be called computing devices, even though they provide less processing power resources than a standard "computer." It goes without saying that such a "computing device" can be part of an air treatment device and/or system. Additionally, the term "computing device" may also refer to distributed computing devices that may involve or utilize computing capabilities provided in a cloud environment. The term "computing device" may also refer to a control device generally capable of processing data.

In an exemplary embodiment, the computer program executes at least partially on a mobile computing device, particularly a mobile phone, a mobile computer, and/or a tablet computer. Preferably, the mobile computing device is arranged to be operatively coupled with the air treatment device and a remote service (eg a server). The server may be arranged to aggregate and process enhanced information obtained from one or more enhanced air quality sensors of the distributed air quality sensing device.

Preferred embodiments of the disclosure are defined in the dependent claims. It is to be understood that the claimed method and the claimed computer program may have similar preferred embodiments to the claimed device/system and are as defined in the dependent device claims.

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 picture

Figure 1 is a perspective view of an air treatment device arranged as an air purification device;

Figure 2 shows another perspective view of the device of Figure 1 in a partially exploded state;

Figure 3 shows a perspective rear end top view of the device of Figures 1 and 2 with the outlet cover arranged as a top grille partially removed from the top end of the housing of the device;

Figure 4 shows a simplified schematic block diagram of the internal components of the device according to Figures 1 to 3;

Figure 5 is a graph illustrating schematic indoor PM _2.5 and TVOC concentration measurements;

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

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

Figure 8 shows a schematic block diagram of an exemplary layout of an underlying control algorithm;

Figure 9 shows a schematic block diagram of exemplary layouts and modes of operation of air handling equipment;

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

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

Figure 12 shows a schematic block diagram schematically illustrating several steps and aspects of an embodiment of an operating method in accordance with the present disclosure.

Detailed ways

FIG. 1 shows a perspective view of an air treatment device designated by reference numeral 10 . The device 10 is arranged as an air purification device. Figure 2 shows a corresponding partially exploded view of the device 10, with the views of Figures 1 and 2 using similar view directions but using different scales.

The device 10 includes a main or overall housing 12 . The housing 12 according to at least the embodiment shown in FIGS. 1 and 2 includes an approximately rectangular or square base region extending upwardly. In summary, the housing 12 of the device 10 defines a substantially cuboid shape. It goes without saying that at least slightly curved (convexly or concavely curved) walls may be present. Additionally, rounded and/or chamfered edges may be present.

The device 10 also includes an air quality sensor 14, see also Figure 3 for perspective rear end top view. The air quality sensor 14 is arranged to detect air characteristics. The air quality sensor unit 14 may be capable of monitoring inlet air and/or outlet air. According to certain embodiments, the air quality sensor unit 14 is arranged as a PM sensor that senses particulate matter (PM) concentration.

Device 10 also includes a user interface 16, which may include appropriate controls, keys, switches, indicators, LEDs, displays, and the like.

According to the arrangement of the exemplary embodiment shown in conjunction with Figures 1 and 2, the device 10 includes two opposing lateral inlets, which are covered by inlet covers 18 arranged as a grid. Furthermore, the device 10 includes an outlet cover 20 on its top side, wherein the outlet cover 20 is arranged as a grid. The outlet cover 20 may also be referred to as a top grill or outlet grill.

The air purification device 10 includes an air treatment module 22, which may be arranged as an air purification module. The air treatment module 22 includes filters 26 , 28 assigned to the air treatment unit 30 . As shown in FIG. 2 , a first type filter 26 and a second type filter 28 may be present in the air handling unit 30 . For example, 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 the inlet air flow entering the device 10 through the inlet cover 18 . Therefore, the inlet air flow is essentially transverse. Furthermore, the outlet air flow is a substantially upward flow. From a fluid perspective, the air handling unit 30 is located between the inlet and outlet of the device 10 .

It goes without saying that air handling units may have different operating principles, which may involve, for example, thermodynamic sterilization, UV radiation, photocatalytic oxidation, high-efficiency particulate capture (HEPA) filtration, ionizer purifiers, ozone generators and their combination.

The device 10 also includes a ventilation unit, which is designated with reference numeral 24 in FIG. 2 . According to the exemplary embodiment of FIG. 2 , the ventilation unit 24 is arranged inside the housing 12 between the units of two opposing inlet filters 26 and 28 .

Figure 3 shows a perspective rear end top view of the device of Figures 1 and 2. The side of the housing 12 on which the at least one air quality sensor of the air quality sensor unit 14 is arranged is opposite the side of the housing 12 on which the user controls 16 are arranged. However, this exemplary arrangement should not be interpreted in a limiting sense.

Referring further to FIG. 4 , an illustrative block diagram of the components of an air treatment device 10 that may be arranged in accordance with the embodiments shown in FIGS. 1 , 2 and 3 is shown.

As mentioned above, the device 10 includes an air treatment module 22 provided with a processing unit 30 implementing a filter arrangement involving filters 26 , 28 . For example, two opposing sets of filters 26, 28 may be provided on corresponding sides of the housing 12 of the device 10.

In the central part of the housing 12 a ventilation unit 24 is arranged. Ventilation unit 24 includes a ventilator 34 powered by a motor 36 . The operation of the ventilator 34 is indicated by the curved arrow 36 in FIG. 4 . For example, the ventilator 34 may be arranged as a centrifugal ventilator. Therefore, the ventilator 34 may be arranged to draw in inlet air axially and blow out pressurized outlet air radially. According to the arrangement of Figure 4, the ventilator 34 is arranged to blow pressurized air upwards.

The inlet airflow 42 passes through the airflow inlet 40 of the air handling module 22 and enters the ventilator 34 . The inlet air flow 42 passes through corresponding filters 26,28.

Preferably, the inlet airflow 42 includes two inlet airflow components at opposite axial sides of the ventilator 34 associated with two opposing sets of filters 26, 28, as shown in Figures 2 and 4.

On the outlet side of the ventilator 34 , the outlet airflow 48 escapes from the ventilator 34 radially through the airflow outlet 46 of the air handling module 22 toward the top grille (outlet cover 20 ). The outlet airflow 42 passes through the inner cover 32 (see also Figure 3).

Therefore, potentially polluted or dirty air from the environment enters the device 10 from its sides, with purified air escaping from the device 10 through the top side.

The device 10 also includes a control unit 52, which is represented in Figure 4 by a corresponding control block. Furthermore, a sensor unit 52 incorporating at least one sensor 14 is provided. In certain embodiments, device 10 also includes a communication interface 56, particularly a wireless communication interface. Through communication interface 56, appliance 10 may communicate with remote appliances, remote sensor units, remote services, and/or mobile computing devices involving smartphones, mobile computers, tablet devices, and the like. It will be understood that the remote control and/or smart home control terminal may be communicatively coupled with the device 10 via the communication interface 56 .

Figure 5 shows a distributed air quality sensing device 60. As schematically shown, an air treatment device 10 is provided including an air quality sensor 14 of a first type. However, in order that the device 10 can obtain further information related to the air quality, a so-called second type air quality sensor 74 is provided, which may be implemented in the remote air treatment device 66 or in a separate (eg stand-alone) sensor unit 70 realized in. Therefore, while not equipped with additional (increasing cost) sensors, the device may benefit from the addition of enhanced information provided by the second type air quality sensor 74. As a result, hypotheses or even predictions about the level of measured quantities processed by the second type air quality sensor 74 can be calculated. In other words, virtual sensors can be implemented in the device 10 .

By means of a network-based communication service 80 implementing at least one (virtual, distributed or discrete) server 82, information can be exchanged between the devices involved. In certain embodiments discussed herein, the first type of air quality sensor 14 is a particulate matter (PM) sensor and the air quality sensor 74 type is a volatile organic compound (VOC) sensor. Therefore, at the device 10, a "virtual" VOC sensor can be provided. The device 10 may operate on the second air quality value even if the device 10 is unable to sense this quantity directly (eg due to cost reasons).

Mobile computing device 86 may serve as an operating terminal for device 10 . Computing device 86 (eg, smartphone, tablet, home automation terminal, etc.) may be coupled to device 10 directly or indirectly (eg, via network-based communication service 80). Further, a computing device 86 may be (communicatively) interposed between device 10 and communication service 80 .

In the following, several aspects of the present disclosure will be described with particular reference to specific embodiments, which, however, are not to be construed in a limiting sense. Rather, specific features can be extracted from the overall context of the illustrative description below. Accordingly, specific features and embodiments may readily be pursued individually.

In the context of the present disclosure, a method for creating virtual VOC sensors based on big data analysis is proposed, which can provide home air purifiers with the ability to more effectively clean daily generated VOCs. For example, a relationship can be established between PM concentration (which can be enhanced by further information such as temperature and relative humidity) and VOCs during specific daily activity events. Therefore, VOCs can be predicted based on PM concentration, which can improve purifier performance, especially in automatic mode operation.

In a more general context, the method can also be applied to provide additional air quality information to other devices equipped with physical sensors. For example, an air quality sensing device containing only a PM sensor can be used to assess and predict gas pollution levels. (e.g. VOC value).

Air purifiers (also referred to as air treatment devices in this article) are widely used in many countries. Typically, air purifiers are used to clean indoor air pollutants, including particulate matter (PM), volatile organic compounds (VOC), bacteria, etc. In order to balance power consumption and air quality, almost all purifiers introduce automatic mode settings to clean indoor air more effectively. Typically, automatic mode operating status is based on air sensor measurements. For example, when high contaminant levels are detected, the purifier can operate in turbine mode with high fan speeds. Additionally, the purifier’s fan speed can be reduced when pollutant levels drop.

In daily life, most indoor air pollutants are generated by regular activity events, such as cooking, floor cleaning, smoking, dining, and ironing (referred to as events in this article). These activities may result in both PM and VOC pollutants. Most of the above regular activity events are reflected in changes in pollutant concentrations. Knowing temporal information and contaminant concentration, events can be detected. Furthermore, knowledge of the event can be used to derive corresponding pollutant concentrations (or changes thereof).

In addition, so-called connected air purification devices can be used. For example, a connected air purification device can be controlled remotely, which can involve using a software application through the connected device to obtain operating status and measurements indicative of air quality. Furthermore, it is possible to envisage separate sensor boxes comprising different sensors arranged to sense and track several indoor air pollution measured quantities.

However, most off-the-shelf air purifiers on the market are only equipped with PM sensors, and therefore, their automatic mode working status can be controlled using PM measurements. Thus, for example, there may be certain periods when air purifiers are not aware of VOC contamination (due to their lack of corresponding sensing equipment) and remain in their standby operating state. There is a rough correlation between PM (PM _2.5 ) and VOC concentrations, but it turns out that this correlation is, at least temporarily, lacking in reliability and accuracy. Figure 6 shows a time plot of air quality data showing the relationship between PM and VOC based on actual testing. Trace 100 represents PM concentration and trace 102 represents VOC (not TVOC) concentration. This time period covers an exemplary work day. There is a certain trend. When PM concentrations rise, typically VOC concentrations also rise. However, it can be further seen that the PM _2.5 and TVOC curves 100, 102 are not always consistent. Especially after 17:00, TVOC concentration remains high for a period of time, but PM _2.5 concentration is low.

The immediate solution to this problem is to add a VOC sensor to the air purifier or use a standalone sensor box to monitor VOC levels. But this involves several drawbacks. For one thing, VOC sensors cost extra. The higher the required accuracy of the VOC sensor, the higher the cost involved. On the other hand, although most available VOC sensors are sensitive to several gas components, some gaseous contaminants are still missing in the measurements provided by these VOC sensors. Therefore, even where a VOC sensor is provided at the air purifier, there may be some VOC components that are overlooked or disregarded due to the specific design of the VOC sensor.

According to the present disclosure, there is provided an air purifier that is operable to automatically clean both PM _2.5 and VOC pollutants even if no physical VOC sensor is disposed at the device or directly coupled to the appliance.

According to another aspect of the present disclosure, a method is provided that provides detailed knowledge about air quality even in the absence of physical sensors for each measurement of interest. This may involve but is not limited to VOC, _CO2 and NO values.

In certain exemplary embodiments, additional enhanced air quality information may be provided based on measurements performed by other (remote) sensors. For example, a PM _2.5 sensor is provided close to person A. For example, if person A starts smoking, the sensor will show a corresponding signal (increased PM concentration). In the absence of VOC sensors nearby, corresponding data may not be provided. However, according to the present disclosure, person B can be found in a similar situation/environment (eg, also smoking indoors). Assuming that B is equipped with a PM _2.5 sensor and a VOC sensor near B, the correlation between PM and VOC can be detected. Therefore, since both situations A and B are similar, the correlation of situation B can be applied to the situation. Therefore, based on this correlation and the actual PM values provided by nearby sensors, an estimate of VOC can be provided.

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

1) Use time information, air sensor measurements and public network information to detect specific daily activity events. When environmental sensors are provided and available, the environmental sensors can also be used, such as microphones, thermometers, humidity sensors, etc. Manual input can also be included.

2) Use big data analysis to establish the relationship between PM concentration and VOC levels in specific daily activity events, including PM and VOC reduction patterns when the purifier is working. It is assumed that a certain number of VOC sensors are available to provide data. Other (remote) sensor types can be used. A relational model or mapping can be built based on this data. Building relationships can involve feedback-based self-learning algorithms, which can be provided through online web services. Machine learning and data mining methods can be used. The algorithm can be more precise and robust when more data is generated and correlated over time. As a further benefit, different types of gaseous pollutants can be distinguished and differentiated.

3) When certain activity events occur, the detection of PM concentration can be used to evaluate and predict VOC levels based on the established relationship. This is an option to create virtual VOC sensors for devices that do not contain real VOC sensors and/or similar sensors.

4) Understanding the calculated predicted VOC levels can improve the air purifier’s automatic mode algorithm to more effectively clean VOCs as well as PM. The automatic mode algorithm is arranged to consider different input values and levels. Therefore, intelligent enhanced (multi-dimensional) control of the air purifier can be achieved.

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., PM _2.5 , UFP, TVOC, CO ₂ , T, RH), e.g., in a sensor box ). Other devices (first type devices) may have sub-collections (eg PM _2.5 and/or TVOC), for example in air purifiers/air treatment devices. Sensors can also be in connected wearable devices.

In addition, big data analysis of data from the complete sensor collection (spanning a sufficiently large installation sample size) detects patterns to indicate correlations between sub-collections of sensors and the complete sensor collection (due to similarities across devices). occurrence of the incident).

When detecting patterns with a subset of sensors, expected values for other sensors can be inferred from big data analysis, for example based on correlation models or mappings. It should be noted that in some cases this may result in a range or set of possible values. So, for example, a worst-case estimate can be provided.

Based on the inferred values, the air purifier can be controlled to increase its performance and thus improve the elimination of pollutants. The inferred values of the virtual sensors may be communicated to the consumer via a user interface (either at the purifier or at a separate computing device) and may be reflected, for example, in updated air quality indicating characteristic values or levels.

Big data analytics can be based on one or more of the following:

- Sensor measurements over time (to detect events),

- Sensor values (e.g. PM _2.5 ) and inferred information such as particle size distribution,

- Location information involving nearby air-related public events such as combustion, traffic jams and gas leaks, and

-Consumer input through applications or other forms of UI.

The proposed virtual VOC sensor creation method is based on big data analysis. In the following, an exemplary layout of a corresponding system architecture will be described. To implement a system according to the present disclosure, a set (preferably a large number) of purifiers and/or sensor boxes or wearable sensors can be connected at least temporarily via a network service. Therefore, there is a control center, which is arranged to collect measurement data from the purifier and sensor box, and can also control the purifier to update the built-in control algorithm.

Figure 7 shows the system architecture of a system 118 according to this approach. A control entity/service 120 is provided that is provided with or coupled to the database 122 and has the processing capabilities to execute operating algorithms 124 . Control services 120 may be provided, at least in part, by a network/cloud environment. In alternative embodiments, the control service 120 may be provided at least in part by the control unit of the purifier itself. Furthermore, in alternative embodiments, the control service 120 may be provided at least in part by a computing terminal device (smartphone, tablet, etc.). Distributed control using more than one control entity is conceivable.

A collection of devices 128, 130, 132 is connected to the control entity/service 120. As indicated by the corresponding arrows, the involved devices 128, 130, 132 and the controlling entity/service 120 may exchange information, including uploading to the entity/service 120 and downloading from the controlling entity/service 120. Uploading may involve measurement uploading and downloading may involve correlation algorithm and/or control algorithm downloading, including updates, etc.

These devices may include a first set of devices 128 that are provided with limited sensing capabilities, such as only PM sensors. These devices may include another set of devices 130 provided with expanded sensing capabilities, such as with first type VOC sensors and PM sensors. These devices may include another set of devices 130 provided with expanded sensing capabilities, such as with second type VOC sensors and PM sensors. As mentioned above, there are known various contaminants that fall within the definition of VOC. Therefore, there may also be different types of VOC sensors. As shown by the dashed line between device 130 and device 132 in Figure 7, there may be other sets containing devices of other sensor types.

Sensor measurements from connected purifiers and sensor boxes in defined areas (for example in urban areas) are uploaded to the control center. This measurement can include PM concentration and levels of different types of VOCs (where available). Algorithms at the control entity/service 120 may be arranged to establish relationships/associations between PMs and VOCs in the context of several active events. As a result, the automatic mode control algorithm for the purifier can be updated and sent to the purifier remotely.

Figure 8 shows a schematic diagram of an algorithm 140 that may be executed by the control entity/service 120. There are two basic functions. The first function, represented by 142, involves the detection of events using augmented information such as time/location 146, ambient sound/temperature 148 and air quality 150 as inputs. Another function, represented by 144, involves association establishment, which uses as inputs such as air quality 150, purifier operating status 152, and detected activity events 142. Function 144 builds and adjusts the association model or mapping 152 . Therefore, there can be two types of enhancement information, involving direct attribution to air quality information and indirect attribution to auxiliary enhancement information.

Algorithm 140 may be configured as an online algorithm permanently provided by an online service that may be updated periodically. Furthermore, after the calculation process, an updated version of the association map 152 may be generated. In an exemplary embodiment, association map 152 may include relationships between PM concentrations and different types of VOC levels during different regular daily activity events.

When a new version of the association map 152 is generated, the purifier can be operated according to an automatic mode algorithm, which is based on 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. Therefore, in individual homes, purifiers can be provided with enhanced knowledge to clean VOCs more effectively, even when there is insufficient information about indoor air quality directly obtained through nearby sensing equipment.

FIG. 9 is a schematic diagram of the layout and mode of operation of a purifier in the context of the present disclosure with reference to block 160 . Because of the presence, type and accuracy of the sensors, different information can be provided to purifiers with different settings (different locations, different types, etc.). Therefore, a robust auto-mode algorithm architecture is proposed that is preferably compatible with different levels of information. In Figure 9, activity event detection is represented by box 162. Additionally, box 164 represents the relevant model or graph used.

Even when, for example, only time information (block 166) and PM concentration information (block 168) are present, the purifier may benefit from an automatic mode algorithm as discussed herein to more efficiently remove VOCs. The output of the automatic mode algorithm is the target operating state of the purifier, which determines, for example, the fan speed, block 170 .

In exemplary embodiments, pre-downloaded fixed data or algorithms may be used, which do not necessarily require immediately programmable purifiers and real-time updates.

Figure 10 illustrates aspects of an exemplary embodiment of data matching operations in accordance with the present disclosure. The database is represented by 180. Database 180 includes sample records. Database 180 contains information related to certain events e (e1, e2, e3...). Each event can be assigned additional auxiliary attributes a (a1, a2, a3...). Attributes can involve auxiliary information, such as time, location, human presence, room ventilation, humidity, purifier status, outdoor PM value, sound, decoration time, etc. The corresponding value v can be assigned to the event's attribute a.

Reference numeral 182 denotes a processed data set, reference is also made to 184, 186 which highlight the corresponding data portion. Data set 182 represents the best match between the available data (PM values plus auxiliary events). Therefore, the TVOC values provided by the database 180 for the data set 182 can be used to control air purifiers that are not provided with corresponding sensing equipment.

Generally, it is challenging to establish an accurate and robust relationship between PM and TVOC when there is no information obtained through direct measurements. However, the prediction of TVOC can be simplified if a sufficient level of relevant (auxiliary) enhancement information is collected.

As a result, "event" data can be recorded in both broad and fine-grained ways. The more data sets provided, the more reliable the matching operation will be. For example, "events" and other information can be represented by vectors. Vectors may include any factor that may indicate (directly or indirectly) indoor air quality, including but not limited to: time of day, sensor location, human presence, room ventilation, indoor humidity, outdoor PM, purifier status, ambient sound, and Renovation time.

Once the database is established and populated, real-time or near-real-time TVOC assessment can be performed. The data matching process can be based on the minimum distance between live data samples and database samples to find the best matching record. Equation (1) shows the principle of minimum distance estimation, where R is the vector of real-time data (such as PM data), D is the set of vectors of the database, d is the sampling of the database, and N is the dimension of the "event" vector.

Similarly, FIG. 11 also illustrates aspects of another exemplary embodiment of a data matching operation based on event classification and probability considerations in accordance with the present disclosure.

In FIG. 11, reference numeral 190 denotes a database. Reference numeral 192 represents a processed data set, with reference also to reference numerals 200, 202, the corresponding data portions are highlighted. Box 194 represents the clustering algorithm. Event clusters 196 are shown in Figure 11 as a vertical series. For each cluster, a relationship between PM and TVOC is established as shown at reference numeral 198.

Subsequently, for real-time or near-real-time TVOC assessment without direct TVOC sensing equipment, the probability of each event cluster (p1, p2, ..., pn) is calculated. For example, maximum likelihood probability considerations can be applied to the current event(s) and ultimately estimate or evaluate the TVOC value.

In this way, a set of possible TVOC values with probabilities can be calculated, rather than just absolute predicted values. This data may advantageously be used to finalize on-demand automatic mode operation of the purifier.

For illustrative purposes, assume that a set of predicted values for three TVOC values is processed based on the available data. There are a total of three values enhanced by the corresponding probabilities {T ₁ , p ₁ }, {T ₂ , p ₂ } and {T ₃ , p ₃ }. Furthermore, assume that the "imaginary" costs of using these values are c ₁ , c ₂ , and c ₃ , and the benefits of using these values are b ₁ , b _{2 ,} and b ₃ . For example, the interest may represent the amount of a pollutant. Cost can represent, for example, power consumption or operating noise.

For example, the optimal choice is calculated based on equation (2). If the expectation of total benefit is positive, the maximum probability prediction is used. Otherwise, TVOC estimation is terminated.

The purifier's automatic mode can still use PM measurement. This ensures that statistically negative results do not occur.

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

In step 220, an air treatment device is provided, the device air quality sensor being arranged to detect a first air quality indicative characteristic and to signal a characteristic first air quality value.

In step 222, a first air quality indicative characteristic is sensed. The resulting characteristic first air quality value is signaled to the control unit.

In a further step 224, enhanced information is obtained indicating a second air quality indicating characteristic. Preferably, there are no nearby sensors for the second air quality indicating characteristic. Instead, the proposed virtual sensing procedure replaces the on-site sensors.

In step 226, the enhancement information may be deployed from the communications network or cloud environment. This may include online services provided via the Internet or similar networks. Additionally, facility, building and/or home automation networks may be used.

Additionally, a network or cloud environment may be provided and/or operatively coupled with processing capabilities 230, remote sensor units 232, and database 228 for recording relevant air quality information. The remote sensor unit may be provided with a physical sensor capable of measuring the second air quality indicative characteristic. Processing power can be provided by virtual or discrete servers.

The database may be supplied with sensor data from multiple air handling devices and, if available, different independent sensor units may be provided. Additionally, public measurement and environmental measurement data can be used. Preferably, the database contains a plurality of data sets relating to values for the first air quality indicative characteristic and the second air quality indicative characteristic. Optionally, additional enhanced information called event information or auxiliary information may be provided.

The goal may be to establish a correlation, for example, a correlation model or association between a first air quality value and a second air quality value resulting from a second air quality indicative characteristic.

In step 240, a second air quality value is inferred from the first air quality value and the enhancement information. For this purpose, a (local) database 242 can be used. In the database, temporary data and/or device-specific association mappings can be stored. It goes without saying that also in step 240, data provided from the communication network or cloud environment can be used and processed.

In a further step 244, the air treatment device is operated based on the first air quality value provided by the air quality sensor and the second air quality value inferred from the associated model and enhanced information.

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

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

In improved embodiments, the extrapolated value 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 improved embodiment, the database is stored in a cloud environment and updated based on analysis of sensor data from augmented sensors combined with remote devices and is therefore able to provide a complete data set.

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

Air treatment equipment according to the present disclosure can be operated based on air properties without the presence of physical sensors. Instead, virtual sensing is enabled in accordance with the present disclosure.

Embodiments of devices, systems, and methods according to the present disclosure may be used in the context of connected air purifiers to improve performance in cleaning different types of contaminants, including VOCs. Additionally, aspects and features of the present disclosure may also be used in air quality sensor boxes or wearable sensors to provide more information about air quality in the absence of a corresponding physical sensor 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, and the invention is not limited to the disclosed embodiments. example. Other variations to the disclosed embodiments can be understood and implemented by those skilled in the art in practicing the claimed invention, by studying 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.

The computer program may be stored/distributed on suitable media, such as optical storage media or solid-state media provided with or as part of other hardware, but may also be distributed in other forms, such as over the Internet or other wired or wireless telecommunications system. Any reference signs in the claims shall not be construed as limiting the scope.

Claims (15)

1. An air treatment device (10), comprising:

an inlet (18) for inlet air,

an outlet (20) for outlet air,

an air treatment module (22) arranged between the inlet (18) and the outlet (20), the air treatment module (22) comprising a ventilation 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 cleaning 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), the air quality sensor (14) being arranged to detect a first air quality indication characteristic and to signal a characteristic first air quality value based on the first air quality indication characteristic to the control unit (52), the first air quality indication characteristic being related to a first air contaminant,

Characterized in that the control unit (52) is arranged to operate the air treatment module (22) in dependence of the first air quality value and a second air quality value, wherein the second air quality value is derived based on the first air quality value and enhancement information, wherein the enhancement information indicates a second air quality indication characteristic, and

wherein the air quality sensor (14) is a physical entity arranged at the air treatment device (10) and the control unit (52) is arranged to calculate the second air quality value without a physical sensor for detecting the second air quality indication characteristic.

2. An air treatment device (10) according to claim 1, wherein the control unit (52) is arranged to implement a virtual air quality sensor instead of a physical sensor for the second air quality characteristic, wherein the air quality sensor (14) is arranged as a particulate matter sensor arranged to detect a particulate matter indicating characteristic and to signal a characteristic particulate matter value to the control unit (52), the second air quality value being calculated based on the characteristic particulate matter value, and wherein the control unit (52) is arranged to operate the air treatment module (22) in dependence of the particulate matter value provided by the air quality sensor (14) and in dependence of the second air quality value.

3. The air treatment device (10) according to claim 1, wherein the control unit (52) is arranged to derive a characteristic VOC value based on the first air quality value and based on enhancement information indicative of VOC indicative characteristics; and wherein the control unit (52) is arranged to operate the air treatment module (22) in dependence of the first air quality value provided by the air quality sensor (14) and in dependence of the VOC value.

4. An air treatment device (10) according to claim 1, wherein the control unit (52) is arranged to process auxiliary enhancement information for deriving the second air quality value, the auxiliary enhancement information being selected from the group consisting of: timing information, weather information, season information, wind information, time of day information, temperature information, humidity information, sound information, location information, and combinations thereof.

5. The air treatment device (10) according to claim 1, wherein the enhancement information is obtained from at least one remote sensor unit (70) comprising a remote air quality sensor (74), the remote air quality sensor (74) being arranged to detect a second air quality indication characteristic and to provide a characteristic second air quality value.

6. An air treatment device (10) according to claim 1, wherein the control unit (52) is arranged to communicate with a network providing server processing capability and to obtain enhancement information from a server via said network, said server being supplied with characteristic second air quality values from at least one remote enhancement air quality sensor (74).

7. An air treatment device (10) according to 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 uses the enhancement information.

8. The air treatment device (10) according to claim 1, wherein the enhancement information is configured to adjust a correlation model between the first air quality value and the second air quality value, the correlation model being a multidimensional correlation and taking into account the first air quality value and the enhancement information.

9. A distributed air quality sensing apparatus (60), comprising:

an air treatment device (10) according to claim 1,

-at least one enhanced air quality sensor (74) remote from the air treatment device (10), wherein the at least one enhanced air quality sensor (74) is arranged to detect a second air quality indication characteristic and signal a characteristic second air quality value, and

-a communication service (80) by which the air treatment device (10) is supplied with enhancement information indicative of the second air quality value.

10. The sensing apparatus (60) of claim 9, wherein an association between the first air quality value and the second air quality value is modeled, wherein a change in the second air quality value detected by the at least one enhanced air quality sensor (74) is reflected in the enhanced information, the air handling device (10) being operated based on the enhanced information.

11. The sensing apparatus (60) according to claim 9, wherein the air treatment device (10) is a first type of air treatment device, wherein the at least one enhanced air quality sensor (74) is provided on an enhanced air treatment device (66), the enhanced air treatment device (66) being arranged as a second type of air treatment device (66), wherein the at least one enhanced air quality sensor (14) of the second type of air treatment device is used to supply the enhanced information for the first type of air treatment device (10).

12. A method of operating an air treatment device (10), the method comprising the steps of:

Providing an air quality sensor (14), the air quality sensor (14) being arranged to detect a first air quality indication characteristic and to signal a characteristic first air quality value based on the first air quality indication characteristic to a control unit (52), the first air quality indication characteristic being related to a first air contaminant,

-detecting said first air quality indication characteristic by means of said air quality sensor (14) and signaling said characteristic first air quality value to a control unit (52), and

-operating the air treatment device (10) in dependence of the first air quality value and a second air quality value, wherein the second air quality value is derived based on the first air quality value and enhancement information without a physical sensor for detecting a second air contaminant, wherein the enhancement information is indicative of a second air quality indication characteristic.

13. The method of claim 12, 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 based on a modeled association between the first air quality value and the second air quality value, and wherein the modeled association uses the enhancement information.

14. The method of claim 12, wherein the enhancement information is configured to adjust a correlation model between the first air quality value and the second air quality value, the correlation model being a multidimensional correlation and accounting for the first air quality value and the enhancement 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 12 when said computer program is carried out on said computing device.