Detection device, detection method, and program

Abstract

The present invention provides a technology for identifying or quantifying allergens such as pollen, mold, and mites.　This detection device uses a plurality of semiconductor sensors. With respect to the same gas component, the plurality of semiconductor sensors indicate relatively different response characteristics. The detection device: acquires signal patterns detected by the plurality of semiconductor sensors; identifies or quantifies an allergen from the plurality of signal patterns; and outputs a result of identification or quantification.

Description

Detection device, detection method, and program

[0001] The present disclosure relates to a detection device, a detection method, and a program.

[0002] Allergies caused by airborne allergens plague many people. Typical allergens include pollen, mold (fungi), and house dust (mites), but hay fever is the most common allergy in Japan. Because pollen volume correlates with the onset and severity of hay fever, pollen volume has traditionally been measured. Mold (fungi) and house dust (mites) have also traditionally been measured using morphological and immunological methods.

[0003] A technology for detecting minute particles using a laser has been devised (see, for example, Patent Document 1). Patent Document 1 discloses a technology for detecting particles of a desired particle diameter using a simple light-receiving means by detecting light diffracted by particles having a predetermined particle diameter from laser light.

[0004] Japanese Patent Application Publication No. 07-294415

[0005] However, with conventional techniques, there is a possibility that particles other than allergens may also be detected if they are similar in size to the allergens, making it difficult to identify the allergens and therefore difficult to quantify each allergen.

[0006] The present disclosure provides techniques for identifying or quantifying allergens such as pollen, mold, and dust mites.

[0007] A detection device in a first aspect of the present disclosure is a detection device that identifies or quantifies allergens in a target space, in which a control unit acquires signal patterns detected by multiple semiconductor sensors that show relatively different response characteristics to the same gas component, identifies or quantifies allergens from the multiple signal patterns acquired from the multiple semiconductor sensors, and outputs the results of the identification or quantification.

[0008] According to the first aspect of the present disclosure, allergens such as pollen, mold, and dust mites can be identified or quantified.

[0009] A detection device according to a second aspect of the present disclosure is the detection device according to the first aspect, wherein the signal pattern of the plurality of semiconductor sensors changes when a gas component is adsorbed thereon.

[0010] A detection device in a third aspect of the present disclosure is the detection device according to the second aspect, wherein the signal pattern includes a pattern of current, voltage, electrical conductivity, or resistance that changes due to adsorption of a gas component to the semiconductor sensor.

[0011] A detection device in a fourth aspect of the present disclosure is the detection device according to the third aspect, wherein the signal pattern includes a pattern of current, voltage, electrical conductivity, or resistance that changes as a gas component adsorbed on the semiconductor sensor is released.

[0012] A detection device according to a fifth aspect of the present disclosure is the detection device according to any one of the first to fourth aspects, wherein the gas component is introduced into the semiconductor sensor without being pressurized or liquefied.

[0013] A detection device in a sixth aspect of the present disclosure is a detection device according to any one of the first to fourth aspects, wherein the control unit identifies or quantifies allergens in a target space by inputting the multiple signal patterns of the multiple semiconductor sensors into a model generated by machine learning the relationship between the multiple signal patterns and the type or amount of allergens.

[0014] A detection device in a seventh aspect of the present disclosure is the detection device according to any one of the first to sixth aspects, wherein the plurality of semiconductor sensors include two or more types of semiconductor sensors, and the two or more types of semiconductor sensors have different metal oxide compositions.

[0015] A detection device in an eighth aspect of the present disclosure is the detection device according to the fourth aspect, wherein the electrical conductivity repeats a cycle of decrease and recovery, and the control unit extracts a waveform between two maximum values ​​or two minimum values ​​of the electrical conductivity as the signal pattern, and repeatedly uses the signal pattern to identify or quantify allergens.

[0016] A detection device in a ninth aspect of the present disclosure is the detection device described in the eighth aspect, wherein the control unit controls the environmental equipment to reduce or not increase the amount of allergen when the quantified amount of allergen exceeds a threshold value or when the slope of the amount of allergen over time exceeds a threshold value.

[0017] A detection device according to a tenth aspect of the present disclosure is the detection device according to any one of the first to ninth aspects, wherein the control unit displays the change in the amount of allergens over time for each type of allergen.

[0018] A detection device according to an eleventh aspect of the present disclosure is the detection device according to any one of the first to tenth aspects, wherein the allergen is pollen, mold, or dust mites.

[0019] A detection device in a twelfth aspect of the present disclosure is a detection device according to any one of the first to eleventh aspects, wherein the plurality of semiconductor sensors simultaneously output signals relating to a plurality of gas components, and the control unit acquires the signal pattern including the signals simultaneously output by the plurality of semiconductor sensors.

[0020] A thirteenth aspect of the present disclosure provides a detection method in which a detection device identifies or quantifies allergens in a target space, wherein a control unit acquires signal patterns detected by a plurality of semiconductor sensors that exhibit relatively different response characteristics to the same gas component, identifies or quantifies allergens from the plurality of signal patterns acquired from the plurality of semiconductor sensors, and outputs the results of the identification or quantification. According to the thirteenth aspect of the present disclosure, allergens such as pollen, mold, and dust mites can be identified or quantified.

[0021] A program according to a fourteenth aspect of the present disclosure causes a computer to function as the detection device according to any one of the first to twelfth aspects. According to the fourteenth aspect of the present disclosure, allergens such as pollen, mold, and dust mites can be identified or quantified.

[0022] The present disclosure can provide a technique for identifying or quantifying allergens such as pollen, mold, and dust mites.

[0023] 1 is a diagram illustrating an outline of the system configuration of an example of an allergen detection system and an allergen detection method. FIG. 1 is a diagram illustrating a modified example of the system configuration of the allergen detection system. FIG. 2 is a diagram illustrating an example of the system configuration of the allergen detection system. FIG. 3 is a diagram illustrating an example of the hardware configuration of an information processing device. FIG. 4 is an example of a functional block diagram illustrating, by block, the functions of an information processing device and an environmental device (or a sensor unit). FIG. 5 is a diagram illustrating a TIC chromatogram of volatile compounds obtained from 10 mg of cedar, cypress, red pine, and chestnut pollen. FIG. 6 is an image diagram of an example of the detection principle of a semiconductor sensor. FIG. 7 is a diagram illustrating an example of a signal pattern detected by a semiconductor sensor. FIG. 8 is a diagram illustrating an example of a neural network used for training by a model generation unit. FIG. 9 is a diagram illustrating training of a neural network. FIG. 10 is a diagram illustrating training of an allergen identification model. FIG. 11 is a diagram illustrating a signal pattern detected by a semiconductor sensor and an example of a method for sampling resistance values. FIG. 12 is a diagram schematically illustrating training data. FIG. 13 is an example of a time-series signal waveform. FIG. 14 is an example of a circumscribing rectangle of a signal pattern between local maximum values ​​(or local minimum values). FIG. 15 is a diagram illustrating an example of a model configuration when a neural network corresponding to image data is used. Fig. 1 is a diagram showing an example of an allergen detection result screen displayed by a user terminal or a remote control. Fig. 2 is a diagram showing an example of an outdoor pollen detection result screen displayed by a user terminal. Fig. 3 is an example of a sequence diagram illustrating the process in which the allergen detection system displays an allergen detection result screen display and controls environmental equipment.

[0024] Hereinafter, an allergen detection system and an allergen detection method performed by the allergen detection system will be described as an example of an embodiment of the present disclosure.

[0025] <Additional information on allergen measurement methods> As many as 47.8% of Japanese people feel that they have hay fever (see Non-Patent Document 1 below), and in addition to pollen, mold (fungi), house dust (mites), etc. are known as typical allergens. Methods for evaluating environmental allergen contamination are broadly divided into morphological methods and immunological methods. The Durham method (see Non-Patent Document 2) is known as the standard method for the morphological method of pollen. In the Durham method, a preparation coated with Vaseline is exposed outdoors for 24 hours, and the particles that adhere to it are measured by a person who examines them under a microscope to determine whether they are pollen or not. The pollen dispersion amount is 1 [cm 2 ] is expressed as the number of deposits per 1000 particles.

[0026] Another well-known pollen collector is the one developed by Burkard in the UK. This pollen collector is based on an idea by Hirst (see Non-Patent Document 3), in which adhesive tape is attached to a rotating drum in a chamber exposed to the atmosphere, and the pollen attached to the tape is counted by a human under a microscope.

[0027] In recent years, simpler automatic analyzers have become popular instead of microscope observation. The pollen observation system provided by the Ministry of the Environment (see Non-Patent Document 4) calculates the pollen concentration (particles / m) from the count of laser scattering by pollen. ３ This pollen observation system is automated and simple, but has the drawback of measuring particles similar in size to pollen as pollen (see Non-Patent Document 5).

[0028] Therefore, several new analytical methods have been investigated. For example, Coulter counting (see Non-Patent Document 6), which is based on the change in current that occurs when particles pass between two electrodes, Fourier transform infrared absorption (see Non-Patent Document 7), or Raman spectroscopy (see Non-Patent Document 8) have been proposed for identifying pollen. Furthermore, attempts have been made to measure the concentration of Cry j1 (Ubisch bodies), an allergen, in cedar pollen extracts using surface plasmon resonance (Non-Patent Document 9). Other attempts have also been made to distinguish pollen types based on particle size estimated from scattered light under ultraviolet irradiation and the blue / red fluorescence intensity ratio (Non-Patent Document 10). With the exception of Cry j1, all of these methods involve physical detection of pollen.

[0029] As an immunological method, ELISA (Enzyme-linked Immunosorbent Assay) is used to quantify a single allergen (major allergen).

[0030] However, the standard morphological method (Durham method, rotating drum method) requires microscopic observation and is not suitable for automation. The automated analyzer that is an improvement on the standard method measures particles of similar size as allergens, and the accumulated allergens must be periodically removed from the analyzer, making continuous measurement impossible and further reducing specificity.

[0031] Quantitative determination of a single allergen using immunological methods such as ELISA is highly specific but does not allow for continuous measurement. Gas analysis methods require a mechanism to prevent new contamination from the gases generated, making them unsuitable for miniaturization. In particular, for dust mites, unlike pollen, morphological methods cannot be used and no automated analysis technology is known.

[0032] <<Non-patented documents>> ・Non-patented documents 1 Nobal Tissue Factory Co., Ltd. "Occupational Status Survey on Pollinosis" (Survey period: March to April 2020).・Non-patented document 2 OCDurham,「The volumetric incidence of atmospheric allergens; a proposed standard method of gravity sampling, counting, and volumetric interpolation of results」,J.Allergy, vol79, p79 ,1946 ・Non-patented document 3 JMHirst,「AN AUTOMATIC VOLUMETRIC SPORE TRAP」,Annals of Applied Biology, vol39, p257, 1952 ・Non-patented document 4 http: / / kafun.taiki.go.jp / ・Non-patented document 5 Yoda Shigetoshi,「The amount of pollen intrusion in indoor places」,Journal of the Japanese Society of Otolaryngology, vol108, p801, 2005 ・Non-patented document 6 Z.Zhang et al.「An electronic pollen detection method using Coulter counting principle」,Atmospheric Environment, vol39, p5446, 2005 ・Non-patented document 7 CSPappas et al., “New Method for Pollen Identification by FT-IR Spectroscopy”, Applied Spectroscopy, vol57, p23, 2003 ・Non-patented document 8 F.Schulte et al., “Chemical Characterization and Classification of Pollen”, Analytical Chemistry, vol80, p9551, 2008 ・Non-patented document 9 Q.Wang et al., "Diurnal and Nocturnal Behavior of Airborne Cryptomeria japonica Pollen Grains and the Allergenic Species in Urban Atmosphere of Saitama, Japan," Asian Journal of Atmospheric Environment, vol. 7, p. 65, 2013. Non-Patent Document 10: Mitsumoto et al., "Development of a novel real-time pollen-sorting counter using species-specific pollen autofluorescence," Aerobiologia, vol. 26, p. 99, 2010. <Identification and Quantification of Allergens Using the Semiconductor Sensor of the Present Disclosure> One of the objects of the present disclosure is to continuously identify or quantify allergens such as pollen, mold, and dust mites floating in the air. To this end, the present disclosure detects allergens using two or more types of semiconductor sensors 11 having different metal oxide compositions. The semiconductor sensor 11 changes its electrical conductivity (the reciprocal of its resistance) in response to low concentrations of VOCs (volatile organic compounds) contained in unpressurized or unliquefied gas. This allows the detection device to be compact enough for home use. Furthermore, the VOC measurement cycle using the semiconductor sensor 11 is only a few minutes, and cleaning is not required, allowing for continuous allergen measurement. Measurements can be performed automatically, without the need for human intervention. Furthermore, in this disclosure, a model generated by learning the signal patterns of the semiconductor sensor 11 identifies and quantifies allergens, enabling the identification and quantification of pollen, mold, and dust mites with high specificity.

[0033] <Terminology> The target space is a space that has air sufficient for humans to survive, such as an indoor living space. The target space may also be outdoors.

[0034] Gas components are nitrogen, oxygen, water vapor, and trace gases that make up air, and trace gases include VOCs. Although pollen and the like are not gases, in this embodiment, allergens may be included as one of the gas components in the description. This is because some VOCs are derived from allergens.

[0035] Allergens are substances floating in the target space that may cause an allergic reaction. The semiconductor sensor detects VOCs, assuming that the VOCs are included in the composition of allergens.

[0036] Mites include mite-derived substances such as mite carcasses, droppings, and exoskeleton. Mold may include yeast and mushrooms. The detected mold is primarily mycelium, but spores may also be detected.

[0037] 1 is a diagram illustrating an outline of an allergen detection method and the system configuration of an example of an allergen detection system 100. An environmental device 10, a sensor unit 8, a user terminal 70, and an information processing device 60 are communicably connected via a network N.

[0038] The sensor unit 8 is installed preferably in each room of a building in which a resident 9 resides. Only one sensor unit 8 may be installed in a building. The sensor unit 8 preferably has an air intake mechanism and has multiple semiconductor sensors 11 built into the air flow path. The semiconductor sensors 11 change in electrical conductivity in response to allergens in the target space 7 in which the resident 9 resides.

[0039] The environmental device 10 is a device that controls an environment related to air quality, such as an air conditioner, a ventilation device, or an air purifier. The environmental device 10 may have multiple functions, such as an air conditioner, a ventilation device, or an air purifier, or there may be an environmental device 10 for each function.

[0040] The user terminal 70 is a terminal device used by the resident 9. A web browser or native application is running on the user terminal 70, and the user terminal 70 receives information to be displayed on the display from the information processing device 60 via the network N. The resident 9 checks the type and amount of allergens currently detected and operates the environmental device 10 to reduce or prevent the amount of allergens from increasing. The user terminal 70 can be carried by the resident 9, and does not need to be installed in the same space as the sensor unit 8.

[0041] In the present disclosure, the information processing device 60 (an example of a detection device) is a server device that performs various information processing. The information processing device 60 generates an allergen quantification model and an allergen identification model, which will be described later, and identifies and quantifies allergens by inputting signal patterns detected by the semiconductor sensor 11 into the allergen quantification model and the allergen identification model.

[0042] Assuming that the learning phase for generating the allergen quantification model and allergen identification model has been completed, the flow of identifying and quantifying allergens will be described. As will be described later, the allergen quantification model and allergen identification model output the type and amount of allergens from the signal pattern detected by the semiconductor sensor 11.

[0043] (1) The sensor unit 8 transmits signal data (instantaneous values) detected by the semiconductor sensor 11 to the information processing device 60.

[0044] (2) The information processing device 60 accumulates signal data, and when a signal pattern for one waveform is obtained, it inputs the data into the allergen quantification model and allergen identification model that have been generated, thereby continuously and automatically identifying and quantifying allergens.

[0045] (3) The information processing device 60 transmits the type and amount of allergens obtained for the signal pattern to the user terminal 70. Depending on the amount of allergens, the information processing device 60 transmits a recommendation to operate the environmental device 10 to reduce or prevent the amount of allergens from increasing.

[0046] (4) The user terminal 70 displays the type and amount of allergens and a recommendation to operate the environmental device 10. When the user inputs an operation to operate the environmental device 10, the user terminal 70 transmits a control request for the environmental device 10 to the information processing device 60.

[0047] (5) The information processing device 60 converts the control request for the environmental device 10 into control information for the environmental device 10 and transmits it to the environmental device 10. This allows the environmental device 10 to be controlled so as to reduce the amount of allergens or to prevent them from increasing.

[0048] In this way, the present disclosure uses the semiconductor sensor 11 to continuously and automatically identify and quantify allergens such as pollen, mold, and dust mites without increasing the size of the environmental device 10.

[0049] <<Modifications of System Configuration>> Instead of the sensor unit 8 existing independently, the semiconductor sensor 11 may be built into the indoor unit 10b as shown in Fig. 2. Fig. 2 shows a modification of the system configuration of the allergen detection system 100. The allergen detection system 100 mainly has one outdoor unit 10a as a heat source unit, one or more indoor units 10b as usage units, and a remote control device (hereinafter referred to as "remote control 15") as an input device for inputting commands related to various settings.

[0050] The outdoor unit 10a and the indoor unit 10b are referred to as air conditioners. The outdoor unit 10a and the indoor unit 10b are connected by a refrigerant communication pipe (gas communication pipe GP) to form a refrigerant circuit. The allergen detection system 100 also includes multiple communication networks (network NW1, network NW2) that function as signal transmission paths between the indoor and outdoor units. The network NW2 may be wired or wireless.

[0051] The remote control 15 is a user interface that accepts settings such as temperature and humidity. The functions of the information processing device 60 may be the same as those shown in FIG. 1. (1) The indoor unit 10b has a built-in semiconductor sensor 11 installed in an air intake or the like. The indoor unit 10b transmits signal data to the outdoor unit 10a. (2) The outdoor unit 10a transmits signal data to the information processing device 60. (3) The information processing device 60 accumulates the signal data, and when a signal pattern for one waveform is obtained, inputs the data into the allergen quantification model and allergen identification model that have been generated, thereby continuously and automatically identifying and quantifying the allergens. (4) The information processing device 60 transmits the type and amount of allergens obtained for the signal pattern to the remote control 15 via the outdoor unit 10a and the indoor unit 10b. (5) The remote control 15 displays the type and amount of allergens. Depending on the amount of allergen, the remote control 15 displays a message recommending operation of the environmental device 10 to reduce or prevent the amount of allergens from increasing. When the user inputs an operation to operate the environmental device 10, the remote control 15 transmits the setting information to the indoor unit 10b. (6) The indoor unit 10b controls itself based on the setting information. This allows the environmental device 10 to be controlled to reduce or prevent the amount of allergens from increasing.

[0052] <System Configuration of Allergen Detection System> Next, the system configuration of the allergen detection system 100 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the system configuration of the allergen detection system 100.

[0053] The allergen detection system 100 provides a variety of IoT-based services to administrators and general users by connecting various types of environmental devices 10, such as air conditioners, ventilators, and purifiers, with a cloud-side information processing device 60 via a network N. The edge device 80, environmental devices 10, sensor switches 53, and user terminal 70 are installed on the customer side, while the information processing device 60 is installed in a cloud such as a data center or the Internet. Note that the edge device 80 is a device that centrally manages the environmental devices 10 and sensor switches 53, so the edge device 80 is not necessary.

[0054] As described above, the environmental device 10 is preferably a device that comes into contact with the air in the living space, such as an air conditioner, a ventilation system, or a purifier. The living space may be indoors or outdoors. Generally, the edge device 80 may be connected to security equipment, heat source equipment, fire alarms, air handling units (AHUs), electricity meters, lighting, and the like.

[0055] The environmental device 10 and the sensor switches 53 are controlled by the edge device 80. In other words, the edge device 80 applies required operations to the environmental device 10 and the sensor switches 53 to suit the purposes of the environmental device 10 and the sensor switches 53. The content of the control varies depending on the type of the environmental device 10 and the sensor switches 53, but for example, if the environmental device 10 is an air conditioner, it may include all controls related to the functions of the air conditioner, such as cooling / heating mode, set temperature, air volume, humidity, air direction, etc., which can be generally set on an air conditioner. The sensor switches 53 may include a semiconductor sensor 11.

[0056] The environmental device 10 collects operating data appropriate for the environmental device 10 and transmits it mainly periodically to the edge device 80. Periodically means, for example, once per minute, once per 10 minutes, once per 60 minutes, etc., but this may be set by the user or the information processing device 60. The operating data varies depending on the environmental device 10, but in the case of an air conditioner, for example, the operating data may include the high and low pressures of the refrigerant, the refrigerant temperature, the fan rotation speed, and the CPU temperature of the microcomputer.

[0057] The edge device 80 is a controller that controls the environmental device 10 and the sensor switches 53. If the edge device 80 is not present, the information processing device 60 controls the environmental device 10 and the sensor switches 53.

[0058] The information processing device 60 may be one or more server devices. Although one information processing device 60 is shown in FIG. 3, the information processing device 60 may be installed in several separate units according to function. Furthermore, the functions of the information processing device 60 may be consolidated into one server device. Furthermore, multiple information processing devices 60 with the same function may be provided, and the multiple information processing devices 60 may communicate with each other and perform processing like a server cluster.

[0059] The information processing device 60 inputs the signal patterns into an allergen quantification model and an allergen identification model to identify and quantify allergens, and provides the results to a user terminal 70, etc. The information processing device 60 can identify and quantify allergens not only for facilities such as individual residences and buildings, but also for each region, and these may be shared with public broadcasting, etc.

[0060] 3, an information processing device that generates an allergen quantification model and an allergen identification model may exist separately from the information processing device 60. In this case, the allergen quantification model and the allergen identification model generated by the separate information processing device are introduced into the information processing device 60. In the present disclosure, for convenience of explanation, it is assumed that the information processing device 60 generates the allergen quantification model and the allergen identification model.

[0061] The information processing device 60 may also have the functionality of a web server. In response to requests from client software (web clients) such as a web browser operated by a user, the web server provides the client with screen information written in HTML files, XML, CSS files, JavaScript (registered trademark), etc. An application that uses web mechanisms in this way is called a web application.

[0062] It is preferable that the information processing device 60 is compatible with cloud computing, which is a form of use in which resources on a network are used without being aware of specific hardware resources.

[0063] The user terminal 70 is a client terminal that displays various screens provided by the information processing device 60. The user terminal 70 may be used by an administrator or a general user (a resident 9 in the present disclosure). If the environmental device 10 is in a general home, the administrator may be a family member of the resident, or the resident 9 may also serve as the administrator. If the environmental device 10 is in a building or the like managed by a company, the administrator may be, for example, a facility manager.

[0064] The user terminal 70 may be, for example, a PC (Personal Computer), a smartphone, a tablet terminal, a PDA (Personal Digital Assistant), or a wearable PC (such as a sunglasses-type or wristwatch-type PC). However, it is sufficient if the device has a communication function and can run a web browser. Furthermore, the user terminal 70 may run a native app dedicated to the allergen detection system 100 instead of a web browser.

[0065] <Hardware configuration of information processing device> The hardware configuration of the information processing device 60 will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the hardware configuration of the information processing device 60. As shown in Fig. 4, the information processing device 60 has a processor 221, a memory 222, an auxiliary storage device 223, an I / F (Interface) device 224, a communication device 225, and a drive device 226. Note that the respective hardware components of the information processing device 60 are connected to each other via a bus 207.

[0066] The processor 221 has various arithmetic devices such as a CPU (Central Processing Unit), etc. The processor 221 reads and executes various programs onto the memory 222. The processor 221 controls the entire information processing device 60.

[0067] The memory 222 includes a main storage device such as a read-only memory (ROM) or a random access memory (RAM). The processor 221 and the memory 222 form a so-called computer, and the processor 221 executes various programs read onto the memory 222.

[0068] The auxiliary storage device 223 stores various programs and various data used when the processor 221 executes the various programs.

[0069] The I / F device 224 is a connection device that connects the information processing device 60 with a display device 230 and an operation device 240, which are examples of external devices. The display device 230 displays the internal state of the information processing device 60. The operation device 240 is used when an administrator of the information processing device 60 inputs various instructions to the information processing device 60.

[0070] The communication device 225 is a communication device for communicating with the edge device 80 and the user terminal 70 via the network N.

[0071] The drive device 226 is a device for loading a recording medium 250. The recording medium 250 here includes media that record information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, or a magneto-optical disk. The recording medium 250 may also include semiconductor memory that records information electrically, such as a ROM or flash memory.

[0072] The various programs to be installed in the auxiliary storage device 223 are installed, for example, by setting the distributed recording medium 250 in the drive device 226 and reading the various programs recorded on the recording medium 250 by the drive device 226. Alternatively, the various programs to be installed in the auxiliary storage device 223 may be installed by being downloaded from the network N via the communication device 225.

[0073] <Functions> Next, the functional configuration of the allergen detection system 100 will be described in detail with reference to Fig. 5. Fig. 5 is an example of a functional block diagram that explains the functions of the information processing device 60 and the environmental device 10 (or the sensor unit 8) by dividing them into blocks. For convenience of explanation, Fig. 5 shows both the functions in the learning phase and the functions in the inference phase.

[0074] The environmental device 10 or the sensor unit 8 includes multiple semiconductor sensors 11, a sensor control unit 12, and a transmission unit 13. The multiple semiconductor sensors 11 are arranged at the air intake of the environmental device 10 or the sensor unit 8. The semiconductor material used for the gas semiconductor sensors 11 is often used in conditions maintained at high temperatures in the air, so thermally and chemically stable metal oxides are used. The semiconductor sensors 11 change their electrical conductivity (i.e., resistance value) in response to the adsorption and desorption of VOCs in the air. Multiple semiconductor sensors 11 are arranged so as to be suitable for detecting different VOCs, and the metal oxide compositions of the multiple semiconductor sensors 11 differ from one another. The multiple semiconductor sensors 11 have different characteristics in terms of how their electrical conductivity and other characteristics change in response to the same gas component.

[0075] The following shows an example of the composition of the metal oxide contained in the semiconductor sensor 11. Examples of metal oxides include, but are not limited to, zinc oxide (ZnO), stannic oxide (SnO), ferric oxide (FeO), tungsten oxide (WO), and indium oxide (InO).

[0076] The sensor control unit 12 controls these semiconductor sensors 11. For example, the sensor control unit 12 generates a driving voltage and supplies it to the semiconductor sensors 11, instructs the semiconductor sensors 11 to detect VOCs, captures signals from the semiconductor sensors 11, removes noise, filters, and removes abnormal values.

[0077] The transmitting unit 13 transmits the signal data detected by the semiconductor sensors 11 to the information processing device 60 via the network N in real time or whenever a certain amount of data is accumulated. The information processing device 60 receives the signal data in chronological order, and therefore can reproduce the signal pattern of each semiconductor sensor 11.

[0078] The information processing device 60 has an acquisition unit 21, a data unit 22, a model generation unit 23, an analysis unit 24, an output unit 25, and an environmental device control unit 26. Each of these units in the information processing device 60 is referred to as a control unit 20, and the control unit 20 is a function or means realized when a processor 221 of the information processing device 60 executes instructions of a program expanded in a memory 222.

[0079] The acquisition unit 21 acquires signal data detected by the semiconductor sensor 11 from the environmental device 10. The acquisition unit 21 may request the signal data from the environmental device 10 and receive the signal data in response, or may receive the signal data without making a request. The acquisition unit 21 may also store the signal data in the data unit 22. If separately prepared training data is available, the model generation unit 23 can reconstruct a model from the signal data stored in the data unit 22 using environmental devices 10 and sensor units 8 operating in the market.

[0080] In the learning phase, training data is stored in the data unit 22. In this embodiment, the training data is, for example, "signal patterns and types of allergens" and "signal patterns and amounts of allergens." Details of the training data will be described later with reference to FIG. 13.

[0081] The model generation unit 23 learns the training data, generates an allergen identification model from the “signal pattern and type of allergen”, and generates an allergen quantification model from the “signal pattern and amount of allergen”. The generated allergen identification model and allergen quantification model are set in the analysis unit 24.

[0082] In the inference phase, the analysis unit 24 accumulates signal data detected in real time by the semiconductor sensor 11 to generate a signal pattern, which is then input to the allergen identification model and the allergen quantification model. The allergen identification model outputs the amount of allergen, and the allergen quantification model outputs the type of allergen. The analysis unit 24 passes the amount and type of allergen to the output unit 25 and the environmental device control unit 26.

[0083] The output unit 25 transmits the type and amount of the detected allergens to the user terminal 70 or the remote control 15. The output unit 25 may generate screen information such as HTML to be displayed by a web application or web page. Since the user terminal 70 or the remote control 15 displays the type and amount of allergens, the user can operate the environmental device 10 to activate, for example, the air purification function.

[0084] When the amount of allergens exceeds a threshold or the rate of increase in the amount of allergens exceeds a threshold, the environmental device control unit 26 transmits control information to the environmental device 10 to activate the air purification function of the environmental device 10. This allows the environmental device 10 to start operating so as to reduce the amount of allergens or to prevent them from increasing.

[0085] In FIG. 5 , the semiconductor sensor 11 and the sensor control unit 12 are located on the environmental device 10 side, and the control unit 20 is located on the information processing device 60 side. However, the semiconductor sensor 11 and the control unit 20 may be located within a single device. For example, in a home air conditioner, air purifier, ventilation system, or the like, a single device may include the semiconductor sensor 11, the sensor control unit 12, and the control unit 20. Furthermore, in a system having an indoor unit and an outdoor unit, such as an air conditioner, the indoor unit may include the semiconductor sensor 11 and the sensor control unit 12, and the outdoor unit may include the control unit 20, or either the indoor unit or the outdoor unit may include the semiconductor sensor 11, the sensor control unit 12, and the control unit 20. Furthermore, in the case of a ventilation-type air conditioner, it is preferable that the semiconductor sensor 11 be capable of detecting allergens both indoors and outdoors. Furthermore, using the semiconductor sensor 11 on both the indoor and outdoor sides can help reduce costs.

[0086] <Allergen Detection Method Used for Identification and Quantitation> The disadvantages of conventional GC-MS (gas chromatography-mass spectrometry) are explained with reference to Figure 6. Figure 6 shows a TIC (total ion chromatogram) chromatogram of volatile compounds obtained from 10 mg of cedar, cypress, red pine, and chestnut pollen. As shown in Figure 6, the method of identifying pollen types from VOCs using GC-MS determines the type from the difference in retention time between sample injection and the appearance of a peak. For this reason, the maximum retention time required for the VOC peak to appear is required, making it unsuitable for continuous measurement. Furthermore, the need for a gas heating mechanism and a mechanism for exhausting the heated gas makes it difficult to reduce the cost and size of the device.

[0087] Therefore, in this embodiment, allergens are detected using a semiconductor sensor 11, as shown in Figure 7. Figure 7 is an image diagram of the detection principle of the semiconductor sensor 11. As shown in Figure 7, the semiconductor sensor 11 detects gas components by utilizing the reaction between oxygen adsorbed on the metal oxide surface and reducing gases (CH3, H2, CO, CO2, H2O, etc.). In other words, the reducing gas corresponds to the VOCs contained in the allergens, and the electrical conductivity changes due to the adsorption and desorption of the reducing gas. There are multiple semiconductor sensors 11, and each simultaneously outputs signals related to multiple gas components.

[0088] FIG. 8 shows an example of a signal pattern 110 detected by the semiconductor sensor 11. The vertical axis of FIG. 8 represents resistance, and the horizontal axis represents time. The semiconductor sensor 11 sets a reference resistance value RD when no VOCs are adsorbed (initial state). The resistance value decreases due to consumption of oxygen adsorbed in the sensor electron depletion layer (adsorption of reducing gas), and then recovers due to oxygen supply from the air, repeating this cycle. The length of one cycle varies depending on the semiconductor sensor 11 and the type of VOC, but is, for example, several minutes. The semiconductor sensor 11 can measure a sufficient number of resistance values ​​to reproduce the waveform during one cycle.

[0089] In this way, the resistance value can be measured continuously, making it possible to continuously measure allergen-derived VOCs. Furthermore, the semiconductor sensor 11 has high sensitivity at low concentrations and can detect allergen-derived VOCs without pressurizing or liquefying the air. Furthermore, the semiconductor sensor 11 is characterized by its high cost reduction effect during mass production and its fast response. Therefore, the environmental device 10 can be made smaller. By using the semiconductor sensor 11, it is possible to realize an environmental device 10 and a sensor unit 8 that are less expensive and smaller than GC-MS.

[0090] 8 shows a signal pattern of resistance value, the signal pattern may be a signal pattern of current, voltage, electrical conductivity, or resistance (electrical resistance). The control unit 20 identifies or quantifies allergens from the signal pattern of current, voltage, electrical conductivity, or resistance (electrical resistance).

[0091] <Example of Allergen> As described above, when VOCs derived from allergens are adsorbed onto the semiconductor sensor 11, the resistance value changes depending on the material of the semiconductor sensor 11 and the type of allergen (VOC). That is, each semiconductor sensor 11 detects a different signal pattern depending on the type of allergen. The signal pattern also changes depending on the amount of allergen. Therefore, by machine learning the signal patterns of multiple semiconductor sensors 11, it is possible to identify and quantify allergens.

[0092] Known examples of allergens include pollen, dust mites, and mold. Furthermore, the information processing device 60 can distinguish pollen from cedar, cypress, rice, Japanese silvergrass, ryegrass, Timothy grass, Japanese silvergrass, Japanese orchard grass, Japanese red pine, Japanese black pine, mugwort, tall goldenrod, ragweed, Japanese knotweed, Japanese white birch, and ginkgo. The information processing device 60 of this embodiment can also distinguish between types of pollen, dust mites, and mold.

[0093] <Identification and Quantification of Allergens Using Neural Networks> In this embodiment, an allergen identification model and an allergen quantification model are generated by machine learning. Machine learning is a technology for enabling a computer to acquire human-like learning capabilities, in which the computer autonomously generates algorithms necessary for judgments such as data identification from training data acquired in advance, and applies these algorithms to new data to make predictions. The learning method for machine learning may be any of supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning, or may be a combination of these learning methods. Any learning method for machine learning is acceptable. As an example, an allergen identification model and an allergen quantification model using neural networks will be described.

[0094] An example of a method for creating an allergen quantification model will be described with reference to Fig. 9. Fig. 9 shows an example of a neural network used for learning by the model generation unit 23. The neural network in Fig. 9 is a regression neural network (for predicting continuous values) that outputs one output value for multiple data input to the input layer 41.

[0095] 9 shows a neural network in which L layers (the number of weighted intermediate layers 42 and output layers 43 is assumed to be two) are fully connected from the input layer 41 to the output layer 43. A neural network with a deep hierarchy is called a DNN (Deep Neural Network). The layer between the input layer 41 and the output layer 43 is called an intermediate layer 42 (or hidden layer). The number of intermediate layers 42 and the number of nodes 35a to 35c in each layer have been simplified for the purpose of explanation and are merely an example. The number of nodes 35a in the input layer 41 may be the number of elements in the vector that is the input data, and in the case of predicting continuous values, the number of nodes 35c in the output layer 43 is often one. Therefore, as many allergen quantification models as there are types of allergens are created.

[0096] 9, all nodes 35a in the input layer 41 are connected to one node 35b in the intermediate layer 42, and all nodes 35b in the intermediate layer 42 are connected to one node 35c in the output layer 43 (fully connected). The product of the output z of node 35a in the input layer 41 and the connection weight w is input to node 35b in the intermediate layer 42, and the product of the output z of node 35b in the intermediate layer 42 and the connection weight w is input to node 35c in the output layer 43. Equation (1) shows how to calculate the output signal of node 35b.

[0097] In formula (1), w ji (l,l－1) is the weight between the jth node in the lth layer and the ith node in the l-1th layer, and b j is the bias component in the network. j (l) is the input to the jth node of the lth layer, and z i (l－1) is the output of the i-th node in the l-1th layer. I is the number of nodes in the l-1th layer.

[0098] Also, as shown in equation (2), the input u j(l) is activated by an activation function f, where f represents the node's activation function. Known activation functions include ReLU, tanh, and sigmoid. Note that node 35a in the input layer 41 is not activated; it simply passes the input data to the second layer. Node 35 in the lth layer nonlinearizes the input using an activation function and outputs it to node 35 in the l+1th layer. In a neural network, this process is repeated from the input layer 41 to the output layer 43.

[0099] An activation function for the output layer is used for the node 35c of the output layer 43. The activation function for the output layer 43 of a regression model is generally an identity function (y = x).

[0100] FIG. 10 is a diagram illustrating neural network learning. The neural network processes data input to the input layer 41 and outputs an output value from the output layer 43. For example, node 35c in the output layer 43 outputs "5" in response to input data of "0.1" and "0.3." Assume that training data includes pre-set teacher data of "7," which corresponds to "0.1" and "0.3." The teacher data for the allergen quantification model is the amount of allergens (unit area or volume, and number per unit time) counted by some method, such as the non-patent literature mentioned above.

[0101] In the learning phase, a loss function is used to evaluate the error between the training data and the output value, and the weights w and b are adjusted so that the output value approaches the training data. The loss function for the regression model can be a function that calculates the squared error. The value of the loss function is propagated to the nodes in the input layer 41 using a calculation method called backpropagation. The weights w and b between the nodes are learned during the propagation process.

[0102] Next, a neural network for identifying allergens will be described with reference to Fig. 11. Fig. 11 shows an example of a neural network that the model generation unit 23 uses to train an allergen identification model. The explanation of Fig. 11 will mainly focus on the differences from Fig. 9. The neural network in Fig. 11 is a classification neural network in which, for multiple pieces of data input to the input layer 41, the output layer 43 outputs the probability that the data belongs to each allergen.

[0103] The configurations of the input layer 41 and the intermediate layer 42 may be similar to those shown in Figures 9 and 10. In the case of classification, a softmax function is often used as the activation function for classification in the nodes 35c of the output layer 43. The softmax function converts the input to each node 35c of the output layer 43 into a probability between 0 and 1.

[0104] For example, for input data of "0.1" and "0.3", each node 35 of the output layer 43 outputs "0.15", "0.80", and "0.05". Assume that the training data has pre-set teacher data of "0 1 0" corresponding to "0.1" and "0.3". For classification, the teacher data can be a one-hot vector. "0 1 0" corresponds to "pollen, dust mites, mold" in order. If the input data detects pollen, the teacher data is "1 0 0", if it detects dust mites, the teacher data is "0 1 0", and if it detects mold, the teacher data is "0 0 1". Therefore, "0 1 0" in Figure 11 indicates that the teacher data is dust mites.

[0105] In the learning phase, a loss function is used to evaluate the error between the training data and the output value, and the weights w and b are adjusted so that the output value approaches the training data. The loss function of the classification model can be a function that calculates cross-entropy. The value of the loss function is propagated to the nodes in the input layer 41 using a calculation method called backpropagation. The weights w and b between the nodes are learned during the propagation process.

[0106] <Preprocessing of Waveform Data> In this embodiment, the input data input to the input layer 41 is waveform data detected by the semiconductor sensor 11. FIG. 12 shows an example of a signal pattern 110 detected by a certain semiconductor sensor 11. As described above, the signal pattern 110 repeats cycles of decrease and recovery. It is considered that one cycle of waveform contains characteristics of how the semiconductor sensor 11 responds to an allergen. Therefore, the analysis unit 24 uses this one cycle (one signal pattern 110) as one piece of input data for identifying and quantifying the allergen. However, a half cycle of the signal pattern 110 may also be used. This enables allergen identification and quantification in a shorter time.

[0107] As shown in FIG. 8 , the analysis unit 24 extracts a signal pattern 110 between local maximum values ​​from the waveform of a continuously acquired signal. Alternatively, a signal pattern between local minimum values ​​of the waveform may be extracted. Then, as shown in FIG. 12 , the analysis unit 24 samples P values ​​from the signal pattern 110 using a sampling window 120. The sampling window 120 is the range (length of time) over which signal data is sampled from the signal pattern 110. One sampling window 120 acquires, for example, P resistance values. To acquire the waveform of the signal pattern 110 without omission, the analysis unit 24 moves the sampling windows 120 over time so that they overlap. If the number of sampling windows 120 covering one signal pattern 110 is Q, the number of data N input for one signal pattern 110 is given by: N = P × Q. The training data is a combination of this input data and training data. As mentioned above, the training data is a one-hot vector indicating the amount of allergen in regression and the type of allergen in classification.

[0108] FIG. 13 shows a schematic diagram of training data. Each row (each record) in FIG. 13 represents one piece of training data. The training data consists of input data and the amount of allergen, or input data and the type of allergen. It is preferable that the input data be normalized to a range between 0 and 1 and standardized. The person in charge prepares a large number of input data, allergen amounts, and allergen types in advance.

[0109] The model generation unit 23 acquires training data of a batch size from the data unit 22, and generates an allergen identification model and an allergen quantification model by repeating the process for a predetermined number of epochs for each batch.

[0110] If the number of data input for one signal pattern 110 is N and the number of semiconductor sensors 11 is M, the number of nodes 35a in the input layer 41 is N×M.

[0111] <<Method of Using Waveform Data as Image Data>> Although it has been described in FIGS. 12 and 13 that the numerical values ​​constituting the signal pattern 110 are used as input data, the input data may also be an image.

[0112] 14A and 14B are diagrams illustrating the conversion of the signal pattern 110 into image data. As a method of converting the signal pattern 110 into image data, the information processing device 60, during preprocessing, plots a time-series signal on a graph or the like, as shown in FIG. 14A. The information processing device 60 preferably maintains the distance between data points. Furthermore, the information processing device 60 cuts out a circumscribing rectangle of the signal pattern 110 between local maximum values ​​(or local minimum values) as shown in FIG. 14B. The analysis unit 24 preferably converts the image data into square screen data with a fixed number of pixels in both the vertical and horizontal directions. Furthermore, in this embodiment, the color of the image data does not contain information about the amount or type of allergen, so the analysis unit 24 binarizes the image data.

[0113] Neural networks that use image data as input can perform convolution and pooling. Convolutional layers have the same function as filters and can extract various features and patterns in images, such as the shape of lines. Pooling can also reduce the amount of data without losing much information and achieve a certain degree of invariance to positional shifts.

[0114] Fig. 15 shows an example of a model configuration using a neural network compatible with image data. The neural network 30 in Fig. 15 is a neural network called a CNN (Convolutional Neural Network). Screen data 31 of the signal pattern 110 for each semiconductor sensor 11 is input to the neural network 30, and the neural network 30 outputs an allergen type 32 or amount 33. For the sake of explanation, Fig. 15 shows one neural network 30 outputting the allergen type 32 or amount 33, but separate neural networks 30 may be constructed to output the allergen type 32 or amount 33. Furthermore, with regard to the allergen amount 33, separate neural networks 30 may be constructed for each allergen type.

[0115] The neural network 30 has convolutional layers 1 and 2, pooling layers 1 and 2, activation functions 1 to 3, and fully connected layers 1 and 2. The convolutional layers 1 and 2 convert lattice-like numerical data called a kernel (or filter) and the numerical data of a partial image (called a window) of the same size as the kernel into a single numerical value by calculating the sum of products for each element. The convolutional layers 1 and 2 convert this conversion process into small lattice-like numerical data (i.e., tensors) by shifting the window slightly.

[0116] Pooling layers 1 and 2 are used to create a single numerical value from the numerical data of a window. Examples include maximum pooling, which selects the maximum value in the window, and average pooling, which selects the average value in the window. Convolutional layers 1 and 2 extract features from the captured image, and pooling layers 1 and 2 blur the precise position of the object. Activation functions 1 to 3 are functions that nonlinearly convert (activate) the input (e.g., ReLU, tanh, sigmoid, etc.). Fully connected layer 1 corresponds to input layer 41, which aggregates the output of activation function 2, and fully connected layer 2 corresponds to output layer 43. The numerical value aggregated in fully connected layer 1 is transmitted to fully connected layer 2 via activation function 3. Fully connected layer 2 has one (quantification) or multiple (identification) output nodes, which output the type or amount of allergen.

[0117] Note that the numbers of convolutional layers 1 and 2, pooling layers 1 and 2, activation functions 1 to 3, and fully connected layers 1 and 2 shown in the figure are merely examples, and the order of processing is also merely an example.

[0118] The training data for the neural network 30 in Fig. 15 is obtained by replacing the N x M pieces of input data in Fig. 13 with M pieces of image data. If the resolution of the image data converted from one signal pattern 110 is, for example, 100 pixels x 100 pixels, and the number of semiconductor sensors 11 is M, then the number of nodes 35 in the input layer 41 is 100 x 100 x M. In the case of the neural network 30 in Fig. 15, the filter contents and the coefficients between the nodes in the fully connected layers 1 and 2 are adjusted through learning.

[0119] <<Other Machine Learning Methods>> Machine learning methods include, in addition to neural networks (perceptrons and deep learning), support vector machines, logistic regression, decision trees, random forests, and the like, and are not limited to the methods described in this embodiment. For example, support vector machines are mainly used for classification models. Support vector machines are a method that maximizes the marginal inter-plane between vectors of positive and negative examples contained in training data. Nonlinear data can also be classified using a kernel method that maps data onto a high-dimensional feature space.

[0120] Boosted decision trees are a method of training multiple weak classifiers, such as decision trees, independently, integrating the prediction results of the multiple weak classifiers using a majority vote or other method, and outputting the overall (strong classifier) ​​prediction result. Although boosted decision trees are designed for regression, they can also be applied to regression by using a similar method, boosted regression trees.

[0121] In addition, there are various methods suitable for regression and classification, and any method may be used to generate the allergen identification model and allergen quantification model of this embodiment.

[0122] <Display Example of Allergen Identification and Quantitation> Figure 16 shows an allergen detection result screen 300 displayed by the user terminal 70 or the remote control 15. The allergen detection result screen 300 has the current time on the horizontal axis and the amount of allergen on the vertical axis, and line graphs 301 to 303 represent the temporal change in the amount of allergen. While pollen, mold, and dust mites are shown as allergens in Figure 16 as an example, the allergen detection result screen 300 may display the number of other types of allergens. The resident 9 can operate the user terminal 70 or the remote control 15 to specify which allergen to display. If the user specifies pollen as the allergen, the allergen detection result screen 300 can display the number of each type of pollen; if the user specifies dust mites, the number of each type of dust mite; or if the user specifies mold, the number of each type of mold. The resident 9 may also specify a time range for displaying the line graphs 301 to 303. The allergen detection result screen 300 may display the maximum and minimum values ​​of each allergen over a certain period of time in the past.

[0123] In the allergen detection result screen 300 of Fig. 16, the amount of pollen has increased rapidly since 12:45. In such a case, it is preferable for the output unit 25 to warn the resident 9. In Fig. 16, a message 304 saying "Pollen is increasing. Do you want to turn on the air purification function?", a Yes button 305, and a No button 306 are displayed. When the resident 9 presses the Yes button 305, the information processing device 60 transmits control information to the environmental device 10, for example, to activate the air purification function. Note that the environmental device control unit 26 may perform appropriate control on the environmental device 10 in accordance with the allergen detection result, without the output unit 25 querying the user.

[0124] The output unit 25 may also emphasize (by flashing, thickening, etc.) line graphs that show an increasing trend. Furthermore, if the amount of allergens is reduced by controlling the environmental device 10, the output unit 25 may also display this on the allergen detection result screen 300. The resident 9 can confirm that the amount of allergens has been reduced by controlling the environmental device 10.

[0125] The information processing device 60 of this embodiment can thus continuously detect allergens, alerting the resident 9 in real time and controlling the environmental device 10 without delay. Any reduction in allergens due to control of the environmental device 10 can also be detected in real time, and the resident 9 can be informed of the effectiveness of the air purification function.

[0126] Furthermore, if the environmental device 10 is installed outdoors or can take in outdoor air, the information processing device 60 may provide a map showing the distribution of pollen based on the detection results of allergens (mainly pollen) and the location information of the environmental device 10. The location information of the environmental device 10 may be determined by, for example, an IP address, or the environmental device 10 may transmit a postal code or address to the information processing device 60.

[0127] FIG. 17 shows an outdoor pollen detection result screen 320 displayed on the user terminal 70. The information processing device 60 can receive the pollen amount for each location from the environmental device 10. The information processing device 60 divides the map into meshes and calculates the average pollen amount within each mesh. The information processing device 60 can visually display the pollen amount for each location by, for example, color-coding the mesh according to the amount of pollen. The resident 9 can check the amount of pollen dispersed when, for example, going out to a distant place. The information processing device 60 may also provide the pollen amount for each location to a weather information service.

[0128] <Overall Processing Flow> Next, the overall processing flow of the allergen detection system 100 will be described with reference to Fig. 18. Fig. 18 is a sequence diagram illustrating the processing by which the allergen detection system 100 displays an allergen detection result screen display and controls the environmental device 10.

[0129] S101: The acquisition unit 21 of the information processing device 60 repeatedly acquires signal data measured by the semiconductor sensor 11 from the environmental device 10 or the sensor unit 8, for example, at regular intervals or at regular intervals.

[0130] S102: The analysis unit 24 of the information processing device 60 extracts a signal pattern 110 for one waveform from the time-series signal data, and inputs the signal pattern 110 into an allergen identification model and an allergen quantification model to identify and quantify the allergen.

[0131] S103: The output unit 25 compares the measured amount for each type of allergen with a threshold value and determines whether to recommend operation of the environmental device 10. The output unit 25 also calculates the slope of the amount of allergen with time from the quantitative results of allergen measurements taken in the past, compares the slope with a threshold value, and determines whether to recommend operation of the environmental device 10.

[0132] S104 and S105: When the resident 9 inputs an operation to display the allergen detection result screen 300 on the user terminal 70, the user terminal 70 transmits a request for the allergen detection result screen 300 to the information processing device 60.

[0133] S106: In response to a request from the user terminal 70, the output unit 25 of the information processing device 60 creates the allergen detection result screen 300. The output unit 25 transmits screen information of the allergen detection result screen 300 to the user terminal 70. Note that the remote control 15 may display the allergen detection result screen 300 instead of the user terminal 70. In this case, the allergen detection result screen 300 may be displayed on the remote control 15 even if the resident 9 does not perform an operation to display the allergen detection result screen 300.

[0134] S107: The user terminal 70 receives the screen information of the allergen detection result screen 300 and displays the allergen detection result screen 300.

[0135] S108, S109: When the resident 9 inputs an operation to select the Yes button 305 on the allergen detection result screen 300, the user terminal 70 transmits a control request for the environmental device 10 to the information processing device 60. If the remote control 15, rather than the user terminal 70, displays the allergen detection result screen 300, the setting information may be transmitted to the environmental device 10.

[0136] S110: In response to a control request for the environmental device 10 from the user terminal 70, the environmental device control unit 26 of the information processing device 60 creates control information. The environmental device control unit 26 creates control information, such as activating the air purification function, or starting the environmental device 10 if it is stopped. The environmental device control unit 26 starts the environmental device 10, and may also activate the air purification function.

[0137] S111: The environmental device control unit 26 transmits control information to the environmental device 10. The environmental device 10 converts the control information into setting information and controls itself, so that the amount of allergens in the space where the resident 9 lives can be controlled to be reduced or not to be increased.

[0138] In the processing of FIG. 18, the environmental equipment 10 is controlled when the resident 9 presses the Yes button 305, but depending on the judgment result of step S103, the environmental equipment control unit 26 may control the environmental equipment 10 without inquiring of the resident 9.

[0139] <Major Effects> As described above, the present disclosure detects VOCs contained in allergens using two or more types of semiconductor sensors 11 with different metal oxide compositions. The semiconductor sensors 11 change their electrical conductivity in response to low concentrations of VOCs contained in non-pressurized or non-liquefied gas. This makes the detection device compact enough for home use. Furthermore, the VOC measurement cycle using the semiconductor sensors 11 is only a few minutes, allowing for continuous allergen measurement. Measurement can be performed automatically without the need for human intervention. Furthermore, the present disclosure uses a model generated by machine learning of the signal patterns of the semiconductor sensors 11 to identify allergens, allowing for the identification and quantification of pollen, mold, and dust mites with high specificity.

[0140] <Other Application Examples> Although the best mode for carrying out the present disclosure has been described above using examples, the present disclosure is not limited to these examples in any way, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present disclosure.

[0141] For example, in this embodiment, allergens are detected mainly using the semiconductor sensor 11, but other gas sensors may also be used to detect allergens, such as optical (NDIR) gas sensors that utilize the infrared absorption characteristics of gas molecules, fuel cell gas sensors that utilize the oxidation reaction that occurs at the sensing electrode, or catalytic combustion gas sensors that utilize the heat of combustion on the surface of the sensing element.

[0142] Furthermore, the configuration examples in Figure 5 and the like are divided according to main functions to facilitate understanding of the processing by the information processing device 60. The present disclosure is not limited by the manner in which the processing units are divided or the names of the processing units. The processing by the information processing device 60 can also be divided into more processing units depending on the processing content. Furthermore, it can also be divided so that one processing unit includes more processes.

[0143] Furthermore, the devices described in the examples are merely illustrative of one of several computing environments for implementing the embodiments disclosed herein. In one embodiment, information processing apparatus 60 includes multiple computing devices, such as a server cluster. The multiple computing devices are configured to communicate with each other via any type of communication link, including a network, shared memory, etc., and perform the processes disclosed herein.

[0144] The functions of the present disclosure described above can be realized not only by software processing through the execution of a program, but also by one or more processing circuits. Here, the term "processing circuit" in this specification includes a processor programmed to perform each function by software, such as a processor implemented by an electronic circuit, as well as devices such as an ASIC (Application Specific Integrated Circuit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), and a conventional circuit module designed to perform each of the functions described above.

[0145] <Reasons for the effects> The first aspect of the present disclosure "acquires signal patterns detected by multiple semiconductor sensors that exhibit relatively different response characteristics to the same gas component, and identifies or quantifies allergens from the multiple signal patterns acquired from the multiple semiconductor sensors," so different signal patterns can be obtained for the same gas component, and the different signal patterns contain a lot of information about the allergen. The detection device analyzes these, making it possible to identify or quantify the allergen. Since the semiconductor sensors do not require cleaning, they can be identified or quantified continuously. Identification or quantification can be performed automatically without the need for human intervention in analyzing the signal patterns.

[0146] - In the second aspect of the present disclosure, "the signal pattern of the multiple semiconductor sensors changes when gas components are adsorbed," so that a signal pattern reflecting the adsorption of VOCs derived from allergens contained in the gas components is obtained.

[0147] In the third aspect of the present disclosure, "the signal pattern includes a pattern of current, voltage, electrical conductivity, or resistance that changes due to adsorption of gas components to the semiconductor sensor," so that a signal pattern is obtained that shows a change in current, voltage, electrical conductivity, or resistance that reflects the adsorption of allergen-derived VOCs.

[0148] The fourth aspect of the present disclosure "includes a pattern of current, voltage, electrical conductivity or resistance that changes as gas components adsorbed to the semiconductor sensor are released," and therefore a signal pattern is obtained that indicates a change in current, voltage, electrical conductivity or resistance that reflects the release of allergen-derived VOCs.

[0149] In the fifth aspect of the present disclosure, "the gas component is introduced into the semiconductor sensor without being pressurized or liquefied," so that a small detection device can be provided that does not require equipment for pressurization or liquefaction and can be used at home, etc.

[0150] - A sixth aspect of the present disclosure is to "identify or quantify allergens in a target space by inputting the multiple signal patterns of the multiple semiconductor sensors into a model generated by machine learning the relationship between the multiple signal patterns and the type or amount of allergens," thereby enabling the identification and quantification of pollen, mold, and dust mites with high specificity.

[0151] In a seventh aspect of the present disclosure, "the plurality of semiconductor sensors includes two or more types of semiconductor sensors, and the two or more types of semiconductor sensors have different metal oxide compositions," so that the plurality of semiconductor sensors can exhibit relatively different response characteristics to the same gas component.

[0152] In the eighth aspect of the present disclosure, "the electrical conductivity repeats a cycle of decrease and recovery, and the control unit extracts a waveform between the maximum values ​​or between the minimum values ​​of the electrical conductivity as the signal pattern, and repeatedly uses the signal pattern to identify or quantify allergens," so that allergens in the target space can be identified or quantified if there is enough time for one cycle of the signal pattern. Furthermore, because the signal pattern repeats in a cycle, allergens can be measured continuously.

[0153] - The ninth aspect of the present disclosure is that "when the quantified amount of allergen exceeds a threshold value, or when the slope of the amount of allergen over time exceeds a threshold value, the environmental equipment is controlled to reduce or prevent the amount of allergen from increasing," so that the environmental equipment can be automatically controlled according to the amount of allergen and how it increases.

[0154] - The tenth aspect of the present disclosure "displays the change in the amount of allergens over time for each type of allergen," allowing the user to visualize the amount of allergens and how they are increasing, and encouraging the user to activate environmental equipment.

[0155] In the eleventh aspect of the present disclosure, the allergen to be identified or quantified is pollen, mold, or dust mite, so that pollen, mold, or dust mite can be identified.

[0156] In a twelfth aspect of the present disclosure, "multiple semiconductor sensors simultaneously output signals relating to multiple gas components" to be identified or quantified, so that allergens can be identified or quantified without waiting for separation as in chromatography.

[0157] This application claims priority based on Japanese Patent Application No. 2023-172258, filed with the Japan Patent Office on October 3, 2023, the entire contents of which are incorporated herein by reference.

[0158] 10 Environmental equipment 11 Semiconductor sensor 60 Information processing device 70 User terminal 100 Allergen detection system

Claims

1. A detection device that identifies or quantifies allergens in a target space, wherein a control unit acquires signal patterns detected by multiple semiconductor sensors that exhibit relatively different response characteristics to the same gas component, identifies or quantifies allergens from the multiple signal patterns acquired from the multiple semiconductor sensors, and outputs the identification or quantification results.

2. The detection device according to claim 1, wherein the signal pattern of the plurality of semiconductor sensors changes when a gas component is adsorbed thereon.

3. The detection device according to claim 2, wherein the signal pattern includes a pattern of current, voltage, electrical conductivity or resistance that changes as a result of the gas component being adsorbed to the semiconductor sensor.

4. A detection device according to claim 3, wherein the signal pattern includes a pattern of current, voltage, electrical conductivity or resistance that changes as the gas component adsorbed on the semiconductor sensor is released.

5. A detection device according to any one of claims 1 to 4, wherein the gas component is introduced into the semiconductor sensor without being pressurized or liquefied.

6. A detection device described in any one of claims 1 to 4, wherein the control unit identifies or quantifies allergens in a target space by inputting the multiple signal patterns into a model generated by machine learning the relationship between the multiple signal patterns of the multiple semiconductor sensors and the type or amount of allergens.

7. The detection device according to claim 1, wherein said plurality of semiconductor sensors includes two or more types of semiconductor sensors, said two or more types of semiconductor sensors having different metal oxide compositions.

8. The detection device described in claim 4, wherein the electrical conductivity repeats a cycle of decrease and recovery, and the control unit extracts a waveform between the maximum values ​​or between the minimum values ​​of the electrical conductivity as the signal pattern, and repeatedly uses the signal pattern to identify or quantify allergens.

9. The detection device described in claim 8, wherein the control unit controls the environmental equipment to reduce or not increase the amount of allergen when the quantified amount of allergen exceeds a threshold value or when the slope of the amount of allergen over time exceeds a threshold value.

10. The detection device according to claim 1, wherein the control unit displays the change in the amount of allergen over time for each type of allergen.

11. The detection device according to claim 1, wherein the allergen is pollen, mold or dust mite.

12. The detection device according to claim 1, wherein the plurality of semiconductor sensors simultaneously output signals relating to a plurality of gas components, and the control unit acquires the signal pattern including the signals simultaneously output by the plurality of semiconductor sensors.

13. A detection method in which a detection device identifies or quantifies allergens in a target space, wherein a control unit acquires signal patterns detected by multiple semiconductor sensors that exhibit relatively different response characteristics to the same gas component, identifies or quantifies allergens from the multiple signal patterns acquired from the multiple semiconductor sensors, and outputs the identification or quantification results.

14. A program for causing a computer to function as the detection device according to claim 1.

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