Information processing system equipped with odor measuring device

Abstract

This system enables the estimation of target information from measurement results obtained by an odor measuring device, and allows the target information to be viewed from a location different from where the odor measuring device is installed. [Solution] The information processing system (100) includes an acquisition unit (11) that acquires a target detection signal from an odor measuring device (30), an estimation unit (13) that estimates target information from the target detection signal, and an output control unit (14) that causes the target information to be output to an output device.

Description

Technical Field

[0001] The present invention relates to an information processing system including an odor measurement device.

Background Art

[0002] In recent years, devices for evaluating a target based on the odor of the target have been developed. For example, in Patent Document 1, a coke odor and a tar odor, which are odors collected from odor sources assumed in a steel mill, are used as reference odors, and a specific device for identifying the source and cause of an unknown odor in a steel mill or the like is described.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, since a conventional odor identification device requires hardware having a high computing ability to be installed at the odor source, there is room for improvement from the viewpoint of cost. In addition, since the identification of the cause of the odor can be performed only at the installation location of the odor identification device, there is room for improvement from the viewpoint of convenience.

[0005] One aspect of the present invention aims to realize an information processing system with improved convenience of an odor measurement device.

Means for Solving the Problems

[0006] An information processing system according to one embodiment of the present invention includes an acquisition unit that acquires target detection signals from one or more odor measuring devices that output target detection signals corresponding to the odor of a target at regular intervals; an estimation unit that estimates target information relating to the target from the acquired target detection signals using an estimation model; and an output control unit that causes an output device to output the estimated target information.

[0007] An information processing method according to one aspect of the present invention comprises: an acquisition step of acquiring target detection signals from one or more odor measuring devices that output target detection signals corresponding to the odor of a target at regular intervals; an estimation step of estimating target information relating to the target from the acquired target detection signals using an estimation model; and an output control step of causing an output device to output the estimated target information. [Effects of the Invention]

[0008] According to one aspect of the present invention, an information processing system can be realized that improves the convenience of the odor measuring device. [Brief explanation of the drawing]

[0009] [Figure 1] This is a schematic diagram showing an example of the configuration of an information processing system according to one embodiment of the present invention. [Figure 2] This is a functional block diagram showing an example of an information processing system including an odor measuring device according to one embodiment of the present invention. [Figure 3] This is a top view showing an example of the configuration of an odor sensor element. [Figure 4] This is a functional block diagram showing an example of an information processing system including an estimation device according to one embodiment of the present invention. [Figure 5] This flowchart shows an example of the processing flow for an information processing system according to one embodiment of the present invention to estimate target information. [Figure 6] This is a functional block diagram showing an example of an information processing system equipped with a user terminal according to one embodiment of the present invention. [Figure 7]This flowchart shows an example of the processing flow for an information processing system according to one embodiment of the present invention that outputs target information. [Figure 8] This is a schematic diagram showing an example of an information processing system including an estimation device according to one embodiment of the present invention. [Figure 9] This is a functional block diagram showing an example of the processing flow for an information processing system according to one embodiment of the present invention to estimate target information. [Modes for carrying out the invention]

[0010] One embodiment of the present invention is described below, but the present invention is not limited thereto. Unless otherwise specified in this specification, "A to B" representing a numerical range means "A or greater and B or less".

[0011] [Embodiment 1] (Information processing system 100) First, an overview of the information processing system 100 according to one embodiment of the present invention will be described using Figure 1. Figure 1 is a schematic diagram showing an example of the configuration of the information processing system 100 according to one embodiment of the present invention.

[0012] As shown in Figure 1, the information processing system 100 includes an odor measuring device 30 and an estimation device 10. The information processing system may further include a user terminal 50. The odor measuring device 30 includes an odor sensor element 31. When the odor of a target is measured using the odor measuring device 30, the estimation device 10 estimates target information about the target using the target detection signal output from the odor measuring device 30. The user terminal 50 acquires and outputs the target information output from the estimation device 10. As shown in Figure 1, the odor measuring device 30 and the estimation device 10 may be connected via a wide-area communication network 40.

[0013] With the above configuration, the estimation device 10 can acquire the target detection signal output by the odor measuring device 30 via the wide-area communication network 40. Therefore, the estimation device 10 can perform target information estimation at any location where communication is possible. In other words, the estimation device 10 can acquire the target detection signal even at a distance from the odor measuring device 30. Furthermore, the estimation device 10 can acquire target detection signals output from multiple odor measuring devices 30 located in different locations and perform target information estimation.

[0014] In this specification, the term "subject" is not particularly limited as long as it is something whose odor can be measured by an odor measuring device. The subject may be, for example, an object such as food, cosmetics, pharmaceuticals, luxury goods, chemicals, manufactured products, and pollutants; it may be a living organism such as animals, plants, and insects; it may be a space such as a room, corridor, toilet, and garden; or it may be an area such as a road, residential area, housing complex, and other private property. In one embodiment, the subject may be an area around the source of the odor, or an area far from the source of the odor.

[0015] In this specification, "target information" means any information about a target estimated based on the odor detection signal output from the odor measuring device. Target information may include at least one type of information selected from the name of the target, the state of the target, and the classification of the target. The name of the target may be, for example, a specific name if the target is an object, or a scientific name if the target is a living organism. The state of the target may include, for example, whether or not it is contaminated, whether or not it has an odor, whether or not it is decayed, and whether or not it is fermented. The classification of the target may include, for example, the variety of the target, the degree of danger of the target, the intensity of the odor of the target, the intensity of the smell of the target, and the nature of the smell of the target (e.g., unpleasant image).

[0016] According to the above configuration, in the information processing system 100, the estimation device 10 can estimate object information about an object based on the information transmitted from the odor measuring device 30. In other words, the estimation device 10 can estimate object information about an object from the odor of the object.

[0017] The number of the odor measurement devices 30 included in the information processing system 100 according to an embodiment of the present invention is not particularly limited, and may be one or more, three or more, five or more, or ten or more. When the information processing system 100 includes a plurality of odor measurement devices 30, each odor measurement device may be installed at a different location, or may be installed at different places or different positions at the same location. Further, the measurement by the odor measurement device 30 may be performed on a plurality of different types of objects, or may be performed only on a single object. When the information processing system 100 includes a plurality of odor measurement devices 30, each odor measurement device 30 may operate simultaneously. In one embodiment, the stop time and the operation time of the odor measurement device 30 may be controlled by an external trigger (for example, a timer or the like).

[0018] The number of the user terminals 50 included in the information processing system 100 according to an embodiment of the present invention is not particularly limited, and may be one or more, three or more, five or more, or ten or more. When the information processing system 100 includes a plurality of user terminals 50, each of the user terminals 50 may be used by a different user, or the same user may use a plurality of terminals.

[0019] The wide-area communication network 40 is not particularly limited and may be any network capable of long-distance communication, such as the Internet, telephone lines, mobile communication networks, CATV communication networks, and satellite communication networks. The wide-area communication network 40 allows for the estimation of target information from the target detection signal, regardless of the distance between the odor measuring device 30 and the estimation device 10. Furthermore, the wide-area communication network 40 may be connected to a cloud server that stores information transmitted by the user terminal 50 and estimated values ​​output by the estimation device 10. If the wide-area communication network 40 is connected to a cloud server, the estimation device 10 may be implemented as the cloud server. The user terminal 50 and the estimation device 10 may be connected via a local area network connection without an intermediary provider, a telephone line network, or via serial communication, a mobile phone network including a 5G communication network, or LPWA (Low Power Wide Area). The devices may be connected via Wide Area Network (WROOM), Wi-Fi (registered trademark), PAN (Personal Area Network), Zigbee (registered trademark), SigFox (registered trademark), LoRa (registered trademark), Bluetooth (registered trademark), NFC (registered trademark), Wi-SUN (registered trademark), Z-Wave (registered trademark), PLC (Power Line Communication), NB-IoT, Ethernet, serial communication, etc.

[0020] Furthermore, the estimation device 10 may transmit the estimation results to the user terminal 50. The user terminal may be the owner of the estimation device 10 or a user of the information processing system 100. Examples of the user terminal 50 include a personal computer, smartphone, tablet terminal, etc.

[0021] Furthermore, the estimation device 10 may display the estimation results on a web page or the like accessible by the user terminal 50. The estimation device 10 may issue a dedicated address to the user who requested the target information and provide access information to the web page or the like. The estimation device 10 may also transmit the estimation results to the user terminal 50, which has dedicated software installed, via said dedicated software.

[0022] The information processing system 100 may also include measuring devices other than the odor measuring device 30. These measuring devices are not particularly limited, but examples include cameras, sound sensors, motion sensors, temperature sensors, humidity sensors, wind direction sensors, wind speed sensors, barometric pressure sensors, and position sensors. The position sensor may be a GPS receiver that receives signals from multiple GPS (Global Positioning System) satellites.

[0023] (Odor measuring device 30) The following describes the overview and effects of the odor measuring device 30 to which the odor sensor element 31 is applied, using Figure 2. Figure 2 is a functional block diagram showing an example of the configuration of an information processing system 100 equipped with the odor measuring device 30 to which the odor sensor element 31 is applied. The odor measuring device 30 includes an odor sensor element 31 for detecting odor substances, a power supply 32 (power supply), a clock 33 (timer), a control unit 34, and a communication unit 35. As described above, the odor measuring device 30 may be connected to a wide-area communication network 40.

[0024] The power supply 32 is a power source for supplying power to the odor sensor element 31. The power supply 32 may be a constant voltage power supply, a constant current power supply, or an AC power supply. If the power supply 32 is a constant voltage power supply, it supplies current (for example, a DC current of 1 μA to 10 mA) to the odor sensor element 31 via lead wires. The voltage value supplied by the power supply 32 is, for example, 0.01 V to 10 V, and more specifically, 0.1 V to 5.0 V.

[0025] Clock 33 measures the time. Clock 33 transmits the measured time to the control unit 34. Clock 33 may be a clock whose time is set by the user, or it may be a radio-controlled clock. Clock 33 may correct its own time using the time obtained from the communication unit 35. Furthermore, Clock 33 can measure the time even when the power to the odor measuring device 30 is turned off.

[0026] The control unit 34 controls all parts of the odor measuring device 30. The control unit 34 also outputs the odor detected by the odor sensor element 31 as a detection signal. The control unit 34 may also output the odor detection signal according to the time measured by the clock 33.

[0027] The communication unit 35 transmits the detection signal output by the control unit 34. The communication unit 35 transmits the detection signal to the wide-area communication network 40, and the transmitted detection signal is acquired by the estimation device 10.

[0028] The odor measuring device 30 may also include a housing, although this is not a mandatory component. The housing is a container capable of enclosing the components of the odor measuring device 30. If the odor measuring device 30 includes a housing, the odor sensor element 31 is installed inside the housing. The odor sensor element 31 can measure the odor of an odor substance by being exposed to air containing the odor substance. With the above configuration, the odor sensor element 31, control unit 34, communication unit 35, etc., can be protected from wind, rain, sunlight, dust, etc. The housing may also include a configuration or mechanism for fixing the odor measuring device 30 to the installation location.

[0029] The odor measuring device 30 outputs a detection signal that shows the change in the electrical conductivity of the odor sensor element 31 over time, before and after odor substances are adsorbed onto the odor sensor element 31. This makes it possible to detect and identify various odor substances.

[0030] <Odor sensor element 31> The odor measuring device 30 comprises a plurality of odor sensor elements 31 capable of outputting detection signals, and each of the plurality of odor sensor elements 31 is equipped with an odor substance receiving layer 315 in which interactable odor substances are different from each other. The number of odor sensor elements 31 is not particularly limited, but may be two or more, three or more, five or more, ten or more, or fifteen or more. In one embodiment, the number of odor sensor elements 31 may be 16.

[0031] Figure 3 is a top view showing an example of the configuration of the odor sensor element 31. The odor sensor element 31 comprises an odor substance receiving layer 315 containing the resin composition described above, a first metal wiring 313A, and a second metal wiring 313B. In the following, when the first metal wiring 313A and the second metal wiring 313B are not distinguished, they may be referred to simply as metal wiring 313.

[0032] The first metal wiring 313A and the second metal wiring 313B are metal wirings that function as electrodes for measuring changes in the electrical conductivity of the odor substance receiving layer 315 (i.e., the resin composition). That is, the first metal wiring 313A and the second metal wiring 313B are spaced apart from each other, and the odor substance receiving layer 315 is in contact with at least a portion of the first metal wiring and at least a portion of the second metal wiring. In one example, the first metal wiring 313A and the second metal wiring 313B are metal wirings that are not in direct contact with each other, and may be substantially parallel to each other, as shown in Figure 3.

[0033] As shown in Figure 3, the metal wiring 313, including the first metal wiring 313A and the second metal wiring 313B, may be arranged on a substrate 311. The substrate 311 may be a substrate such as glass epoxy, which is commonly used in electronic circuits. The metal wiring 313 may be made of copper or gold. The thickness of the first metal wiring 313A and the second metal wiring 313B, as viewed from a direction perpendicular to the surface of the substrate, may be, for example, 10 μm to 2 mm.

[0034] The odor substance receiving layer 315 may be in contact with at least a portion of the first metal wiring 313A and at least a portion of the second metal wiring 313B. The odor substance receiving layer 315 may be arranged to fill the region sandwiched between the first metal wiring 313A and the second metal wiring 313B, for example, as shown in Figure 3.

[0035] If the electrical conductivity of the odor substance receiving layer 315 (i.e., the electrical conductivity of the odor sensor element 31) is low, it is desirable that the distance between the first metal wiring 313A and the second metal wiring 313B be less than or equal to a predetermined distance (for example, 500 μm).

[0036] The odor substance receiving layer 315 may contain a resin composition. The resin composition may contain a resin and further contain one or more selected from a surfactant and a filler (e.g., a conductive carbon material). In this specification, "odor substance receiving layer" means a layer that adsorbs odor substances to be identified. The odor substance receiving layer 315 is formed from the above-described resin composition. The odor substance receiving layer 315 may be provided as part of an odor sensor element 31. The electrical resistance of this odor substance receiving layer 315 changes in response to the adsorption of odor substances, etc. That is, the odor sensor element 31 is an odor detection device equipped with such an odor substance receiving layer 315, and the odor measurement method of the odor sensor element 31 may be a chemiresistor type.

[0037] When the odor sensor element 31 is a chemisistor type containing a resin composition, the change in electrical conductivity over time differs depending on whether odor substance A is adsorbed or odor substance B, which is different from odor substance A, is adsorbed. Therefore, it is possible to detect and identify various odor substances. In the odor measuring device 30 described later, multiple odor sensor elements 31 are arranged, each having a substrate 311 equipped with a configuration for detecting odor substances (metal wiring 313 and odor substance receiving layer 315). Each substrate 311 has multiple sets of odor substance receiving layers 315, each containing odor substances that can be measured differently from each other. Each of the multiple odor sensor elements 31 may also be equipped with a constant voltage power supply and a voltmeter. In the odor measuring device 30, one configuration for detecting odor substances (metal wiring 313 and odor substance receiving layer 315) may be arranged on each substrate 311. Alternatively, in the odor measuring device 30, multiple sets of configurations for detecting odor substances (metal wiring 313 and odor substance receiving layer 315) may be arranged on a single substrate 311. In the latter case, a constant voltage power supply and a voltmeter are connected to each of the sets provided on the circuit board 311.

[0038] The resin compositions contained in the odor substance receiving layers 315 of the multiple odor sensor elements 31 in the odor measuring device 30 may be the same or different. If the odor substance receiving layers 315 contained in the multiple odor sensor elements 31 have the same composition, each of the multiple odor substance receiving layers 315 can detect the same odor substance. If the multiple odor sensor elements 31 each contain odor substance receiving layers 315 with different compositions, each of the multiple odor substance receiving layers 315 will respond differently to the odor substance. In this way, by providing multiple sets of configurations for detecting odor substances, the accuracy of odor substance identification in the odor measuring device 30 can be improved.

[0039] The odor measuring device 30 described above can output the change in the electrical conductivity of the odor sensor element 31 over time for each odor substance when various odor substances are adsorbed onto the odor sensor element 31. By applying this odor measuring device 30, it is possible to compare the change in the electrical conductivity of the odor sensor element 31 over time when odor substance A is adsorbed onto the odor sensor element 31 with the change in the electrical conductivity of the odor sensor element 31 over time when odor substance B is adsorbed onto the odor sensor element 31.

[0040] (Estimation device 10) The following describes the overview and effects of the estimation device 10. The estimation device 10 is a device that estimates target information about an object from the target detection signal output from the odor measuring device 30 described above.

[0041] Figure 4 is a functional block diagram showing an example of the configuration of the estimation device 10. The estimation device 10 includes a control unit 1 that controls all parts of the estimation device 10, and a storage unit 2 that stores various data used by the estimation device 10, but is not limited to this configuration. For example, the storage unit 2 may be an external device attached to the estimation device 10. Also, as described above, the estimation device 10 may be connected to the wide-area communication network 40.

[0042] In one embodiment, the storage unit 2 may be a database on the cloud. The storage unit 2 may be, for example, an SQL database, a relational database, a NoSQL database, etc. More specifically, the storage unit 2 may be a relational database management system (RDBMS) such as Oracle® database, MySQL®, Microsoft SQL Server®, MongoDB, Redis, Amazon DynamoDB®, and IBM DB2® database.

[0043] <Control Unit 1> The control unit 1 will now be described. The control unit 1 comprises an acquisition unit 11, an extraction unit 12, an estimation unit 13, and an output control unit 14. Furthermore, some of the blocks included in the control unit 1 may be assigned to other devices that can communicate with the estimation device 10, and those blocks may be omitted from the control unit 1. For example, the function of the output control unit 14 may be assigned to another device. In this case, the estimation device 10 may output the results estimated by the estimation unit 13 through the other device.

[0044] The acquisition unit 11 acquires the target detection signal output from the odor measuring device 30 via the wide-area communication network 40. The acquisition unit 11 acquires the target detection signal output from the odor measuring device 30 at regular intervals. The acquisition unit 11 may acquire the target detection signal at intervals of, for example, every hour, every 30 minutes, every 10 minutes, every 5 minutes, every minute, every 30 seconds, or every second. The acquisition unit 11 may also be configured to acquire detection signals stored on the wide-area communication network 40 at regular intervals. The acquisition unit 11 may store the acquired target detection signals in the storage unit 2. If the storage unit 2 is an SQL database, the acquisition unit 11 may store the acquired target detection signals in the SQL database.

[0045] In one embodiment, the acquisition unit 11 may further acquire other measurement data besides the target detection signal. Examples of other measurement data include gas chromatography measurement results, odor intensity or odor concentration measured by an odor assessor, camera images, recordings from a recording device, detection results from a motion sensor, weather data measurement results from various sensors (temperature, humidity, wind direction, wind speed, atmospheric pressure, etc.), location information (e.g., signals from GPS satellites), and infrared spectral data.

[0046] The acquisition unit 11 may acquire the target detection signal via a gateway such as a data transfer adapter. The gateway can be appropriately selected depending on the wide-area communication network 40 used.

[0047] The extraction unit 12 extracts feature quantities from the target detection signal acquired by the acquisition unit 11. The feature quantities extracted by the extraction unit 12 may be values ​​relating to at least one of the following: the amount of change in the detection signal, the rate of change in the detection signal, and the waveform (frequency change) of the detection signal. In one embodiment, the extraction unit 12 may further extract feature quantities from other measurement data other than the target detection signal acquired by the acquisition unit 11. Furthermore, if the other measurement data is data obtained from, for example, multiple odor assessors or other people, the extraction unit 12 may perform statistical processing or the like on the obtained data as necessary to extract feature quantities.

[0048] The estimation unit 13 estimates target information about an object from the target detection signal. The method by which the estimation unit 13 estimates target information is not particularly limited. The estimation unit 13 may use the estimation model 21 to estimate target information, or it may use any computer to estimate target information. If the estimation unit 13 does not use the estimation model 21, the estimation unit 13 calculates one or more correlations between two or more features extracted by the extraction unit 12 based on the target detection signal. If training data exists, the estimation unit 13 compares the calculated correlations with the training data to estimate target information. If training data does not exist, the estimation unit 13 identifies correlations from the obtained correlations based on pre-set criteria and estimates target information. These processes of the estimation unit 13 are merely examples, and the content of the processes executed by the estimation unit 13 is not particularly limited as long as it is possible to estimate target information.

[0049] In one embodiment, the estimation unit 13 may input the target detection signal or the feature quantities extracted by the extraction unit 12 to the estimation model 21 to estimate the target information. The estimation unit 13 may also store the estimated target information in the storage unit 2.

[0050] If the acquisition unit 11 is acquiring other measurement data, the estimation unit 13 may also use that other measurement data for estimation. This can improve the estimation accuracy of the target information.

[0051] The output control unit 14 outputs the target information output by the estimation unit 13 to the user terminal 50 via the wide-area communication network 40. The output control unit 14 may further output the target detection signal and / or other measurement data acquired by the acquisition unit 11 to the user terminal 50. The output control unit 14 may transmit the target information directly to the user terminal 50, or it may transmit the estimated value to a web page or web service on the wide-area communication network 40 that is accessible to the user terminal 50. The output control unit 14 may transmit different target information to the user terminal 50 based on the user terminal information 24. The output control unit 14 may issue a different web address for each user terminal 50 based on the user terminal information 24 and transmit different target information to each address, or it may transmit different target information for each user terminal 50 based on the user terminal information 24.

[0052] The output control unit 14 may be another device that has output control functionality, which is capable of acquiring estimated values ​​from the estimation device 10. In this case, the estimation device 10 may transmit the target information estimated by the estimation unit 13 via the other device. In one embodiment, the output control unit 14 may further include a communication unit function.

[0053] <Storage section 2> Next, the memory unit 2 will be described. The memory unit 2 stores the estimation model 21, the target detection signal 22, the target information 23, and the user terminal information 24.

[0054] The estimation model 21 is trained by machine learning using training data. The training data may include, as explanatory variables, a sample detection signal corresponding to the odor of the sample, and as a dependent variable, sample information relating to the sample corresponding to the sample detection signal.

[0055] In this specification, the term "specimen" is not particularly limited to any object that can be selected as an object as described above. The specimen may be the same as or different from the object. In one embodiment, the specimen is preferably similar to the object, and more preferably of the same kind as the object, such as an object, organism, space, or region.

[0056] In this specification, "sample information" means any information relating to a sample. Sample information is not particularly limited as long as it is information about a sample that can be selected as the target information described above. The desired information can be selected from the sample information depending on the target information to be estimated.

[0057] In the machine learning process for the estimated model 21, further data preprocessing and feature extraction may be performed. Furthermore, machine learning algorithms may be used to generate the estimated model 21.

[0058] (Feature extraction) The training data for generating the estimation model 21 by machine learning may be the measured values ​​themselves or features extracted from the measured values. Features may be, for example, statistics, differential and integral values, peak detection values, or autocorrelation values. Examples of statistics include the mean, variance, maximum value, minimum value, the difference between the maximum and minimum values, and the standard deviation. Examples of differential and integral values ​​include the derivative (the slope of a graph showing the change in measured values ​​over time) and the integral (the area of ​​the region defined by the curve showing the change in measured values ​​in a graph showing the change in measured values ​​over time and the horizontal axis (e.g., the time axis)). Examples of peak detection values ​​include the number and height of peaks in the change in measured values ​​(e.g., change over time). Examples of autocorrelation values ​​include the difference in the change in measured values ​​(e.g., change over time). Extraction of these features from measured values ​​can be carried out based on known methods.

[0059] (Pre-treatment method) The training data for generating the estimation model 21 by machine learning may be used for machine learning without preprocessing, or it may be used after predetermined preprocessing as necessary. Furthermore, if preprocessing is performed, it may be performed before feature extraction, after feature extraction, or both before and after feature extraction. Preprocessing may be performed by known methods. Known methods include correction, denoising, standardization, data transformation, smoothing, and data augmentation. Examples of correction include integration, addition, subtraction, and division based on the output ratio, independent component analysis (ICA), or statistics, based on the measurement results of a standard gas by multiple sensor elements or commercially available sensors (e.g., temperature sensors or humidity sensors). Examples of denoising include removal of outliers or white noise. Examples of standardization include normalization and regularization of features. Examples of data transformation include trend removal, frequency transformation, and logarithmic transformation. Examples of smoothing include obtaining the moving average of the data and obtaining the difference. Data augmentation methods include, for example, adding the same sample data (e.g., adding data assuming a normal distribution) or adding new sample data (e.g., adding data related to the mixing ratio of vectors).

[0060] (Machine learning algorithms) Machine learning algorithms that can be used to create the estimated model 21 include regression analysis, classification, trees, time series analysis, neural networks, and clustering. Examples of regression analysis include logistic regression, Lasso regression, elastic network regression, support vector regression (SVR), linear regression, Ridge regression, and ensemble regression. Examples of classification include k-nearest neighbor method, support vector classification (SVC), Naibe Bayes classifier, stochastic gradient descent (SGD), and kernel approximation. Examples of trees include decision trees, regression trees, random forests, boosting (lightGBM, XGboost), and stacking. Examples of time series include AR, MA, ARIMA, and state space. Examples of neural networks include multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), residual neural networks (ResNets), transformers, and graph neural networks (GNNs). Examples of clustering methods include Gaussian mixture models (GMMs), k-means algorithms, mini k-means algorithms, variational Gaussian mixture models (VBGMMs), and kernel approximations.

[0061] The target detection signal 22 includes a detection signal output from the odor measuring device 30. In one embodiment, the target detection signal 22 may be associated with the other measurement data described above.

[0062] The target information 23 is information about the target output from the estimation unit 13. The target information 23 may contain only one type of information about the target, or it may contain multiple types of information.

[0063] The user terminal information 24 is data used to link the user terminal 50 with the target information 23. The user terminal information 24 allows appropriate target information to be transmitted to each user terminal 50.

[0064] (Processing performed by the information processing system 100) A control method for an information processing system 100 according to one embodiment of the present invention will be described with reference to Figure 5. Figure 5 is a flowchart showing an overview of the control method for the information processing system 100.

[0065] In step S1, the acquisition unit 11 acquires the target detection signal output from the odor measuring device 30 when the target odor is measured using the odor measuring device 30 (acquisition step). In step S1, the acquisition unit 11 may further acquire other measurement data besides the target detection signal.

[0066] In step S2, the extraction unit 12 extracts feature quantities from the target detection signal acquired in step S1. The feature quantities extracted by the extraction unit 12 may be values ​​relating to at least one of the following: the amount of change in the detection signal, the rate of change of the detection signal, and the waveform (frequency change) of the detection signal.

[0067] In step S3, the estimation unit 13 estimates target information about the target using the target detection signal or features extracted from the target detection signal (estimation step). If the acquisition unit 11 has acquired other measurement data in step S11, the target information may be further estimated using this measurement data from the viewpoint of improving estimation accuracy.

[0068] In step S4, the output control unit 14 transmits (outputs) the target information output by the estimation unit 13 to the user terminal 50 via the wide-area communication network 40.

[0069] According to the information processing system 100, the measurement results from the odor measuring device 30 and the estimated target information are transmitted via the network. Therefore, the measurement results and target information can be viewed from a location different from the odor measuring device 30, improving the convenience of the odor measuring device 30.

[0070] (User terminal 50) The following describes the overview and effects of the user terminal 50. The user terminal 50 is a terminal used by users of the information processing system 100 to confirm the target information output from the estimation device 10.

[0071] Figure 6 is a functional block diagram showing an example of the configuration of the user terminal 50. The user terminal 50 includes a control unit 4 that controls all parts of the user terminal 50, an input unit 51, and an output unit 54, but is not limited to this configuration. As described above, the user terminal 50 may be connected to the wide-area communication network 40.

[0072] The input unit 51 is for receiving various input operations from the user. The input unit 51 may be, for example, a keyboard, mouse, touch panel, etc. The input unit 51 can be used by the user to have the acquisition unit 52 acquire information. The input unit 51 may be used to operate the user terminal 50, or it may be used to select the type of target information to be acquired by the acquisition unit 52.

[0073] The acquisition unit 52 acquires the target information output by the estimation device 10. The acquisition unit 52 may also acquire information other than the target information output from the estimation device 10. The acquisition unit 52 may acquire the target information based on the input to the input unit 51.

[0074] The output control unit 53 causes the target information acquired by the acquisition unit 52 to be output to the output unit 54. The output control unit 53 may also cause information other than the target information acquired by the acquisition unit 52 to be output to the output unit 54.

[0075] The output unit 54 outputs the target information acquired by the acquisition unit 52. The output mode of the output unit 54 is not particularly limited. The output unit 54 may be, for example, a display device such as a display, a printing device such as a printer, or an audio output device such as a speaker.

[0076] (Processing performed by user terminal 50) A control method for a user terminal 50 according to one embodiment of the present invention will be described with reference to Figure 7. Figure 7 is a flowchart showing an overview of the control method for the user terminal 50.

[0077] In step S11, the input unit 51 receives input from the user. The input unit 51 may also transmit the user's input to the acquisition unit 52.

[0078] In step S12, the acquisition unit 52 acquires the target information output by the estimation device 10. The acquisition unit 52 may also acquire information other than the target information as needed.

[0079] In step S13, the output control unit 53 causes the target information acquired by the acquisition unit 52 to be output to the output unit 54.

[0080] According to the above configuration, it becomes possible to check the target information output by the estimation device 10 in any location where communication is possible. Therefore, the convenience of the odor measuring device 30 is improved.

[0081] [Embodiment 2] The following outline of an information processing system 100a according to another embodiment of the present invention will be described with reference to Figure 8. Figure 8 is a schematic diagram showing an example of the configuration of an information processing system 100a different from that of Embodiment 1. Matters that have already been explained will be omitted.

[0082] The information processing system 100a includes an estimation device 10a, an odor measuring device 30, and a wide-area communication network 40. In the information processing system 100a, the estimation device 10a and the odor measuring device 30 are connected via the wide-area communication network 40. Alternatively, in the information processing system 100a, the odor measuring device 30 and the estimation device 10a may be connected via a local area network connection that does not involve an internet service provider, a telephone network, or via serial communication, a mobile phone network including a 5G communication network, LPWA (Low Power Wide Area-network), Wi-Fi (registered trademark), PAN (Personal Area Network), etc.

[0083] The information processing system 100a does not have a user terminal 50, and the estimation device 10a also serves the role of the user terminal 50 in Embodiment 1.

[0084] (Estimation device 10a) The following outline of an estimation device 10a according to another embodiment of the present invention will be described with reference to Figure 9. The estimation device 10a includes a control unit 1 that controls all parts of the estimation device 10a, a storage unit 2 that stores various data used by the estimation device 10a, an input unit 15, and an output unit 16, but is not limited to this configuration.

[0085] The input unit 15 is for receiving various input operations from the user and may be, for example, a keyboard, mouse, touch panel, etc. The input unit 15 may be used by a user of the information processing system 100a to operate the estimation device 10a. The input unit 15 may be used to input measurement data other than the target detection signal described above, or it may be used to input the type of target information that the estimation device 10a estimates from the target detection signal.

[0086] The output unit 16 outputs the target information output by the estimation unit 13. The output mode of the output unit 16 is not particularly limited. The output unit 16 may be, for example, a display device such as a display, a printing device such as a printer, or an audio output device such as a speaker.

[0087] With the above configuration, even if the information processing system 100a does not have any measuring devices other than the odor measuring device 30, it is possible to estimate target information using additional measurement data. Furthermore, it becomes easier to view the desired target information. In addition, the time lag until the target information is obtained can be reduced.

[0088] [Examples of implementation using software] The functions of the estimation devices 10 and 10a (hereinafter referred to as "devices") are programs that cause a computer to function as the device, and these can be realized by programs that cause a computer to function as each control block of the device (especially each part included in the control unit 1).

[0089] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., memory) as hardware for executing the program. By executing the program using this control device and storage device, the functions described in each of the embodiments are realized.

[0090] The above program may be recorded on one or more computer-readable recording media, not temporary ones. These recording media may or may not be provided by the above device. In the latter case, the program may be supplied to the above device via any wired or wireless transmission medium.

[0091] Furthermore, some or all of the functions of each of the above control blocks can also be realized by logic circuits. For example, an integrated circuit in which logic circuits functioning as each of the above control blocks are formed is also included in the scope of the present invention. In addition, it is also possible to realize the functions of each of the above control blocks by, for example, a quantum computer.

[0092] Furthermore, each process described in the above embodiments may be performed by AI (Artificial Intelligence). In this case, the AI ​​may operate on the control device described above, or it may operate on other devices (for example, an edge computer or a cloud server).

[0093] The present invention is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0094] 〔summary〕 An information processing system according to embodiment 1 of the present invention includes: an acquisition unit that acquires target detection signals from one or more odor measuring devices that output target detection signals corresponding to the odor of a target at regular intervals; an estimation unit that estimates target information relating to the target from the acquired target detection signals; and an output control unit that causes an output device to output the estimated target information. It is equipped with.

[0095] In the information processing system according to aspect 2 of the present invention, the estimation unit performs estimation using an estimation model, and the estimation model may be generated by machine learning using training data that includes a sample detection signal corresponding to the odor of the sample as an explanatory variable and sample information relating to the sample corresponding to the sample detection signal as an objective variable.

[0096] In the information processing system according to aspect 3 of the present invention, in aspects 1 and 2, the target information may include at least one type of information selected from the name of the target, the state of the target, and the classification of the target.

[0097] The information processing system according to aspect 4 of the present invention, in any of aspects 1 to 3, comprises a plurality of odor sensor elements capable of outputting a detection signal, and each of the plurality of odor sensor elements may have an odor substance receiving layer in which interactable odor substances are different from each other.

[0098] In the information processing system according to aspect 5 of the present invention, in any of aspects 1 to 4, the acquisition unit may acquire the target detection signal via a telephone network.

[0099] In the information processing system according to aspect 6 of the present invention, in any of aspects 1 to 5, the acquisition unit may store the target detection signal in a relational database or a NoSQL database.

[0100] An information processing method according to aspect 7 of the present invention comprises: an acquisition step of acquiring target detection signals from one or more odor measuring devices that output target detection signals corresponding to the odor of a target at regular intervals; an estimation step of estimating target information relating to the target from the acquired target detection signals using an estimation model; and an output control step of causing an output device to output the estimated target information.

[0101] A control program according to aspect 8 of the present invention is a control program for causing a computer to function as an information processing system according to any of aspects 1 to 6, wherein the computer functions as the acquisition unit, the estimation unit, and the output control unit.

[0102] The recording medium according to aspect 9 of the present invention is a computer-readable recording medium on which the control program of aspect 8 is recorded. [Explanation of Symbols]

[0103] 10, 10a Estimation device 11 Acquisition Department 13 Estimation part 30 Odor measuring device 40 Wide-area telecommunications network 50 User terminals 100, 100a Information Processing System

Claims

1. An acquisition unit that acquires the target detection signal from one or more odor measuring devices that output a target detection signal corresponding to the target odor at regular intervals, An estimation unit that estimates target information relating to the target from the acquired target detection signal, An output control unit that causes the estimated target information to be output to an output device, An information processing system equipped with the following features.

2. The estimation unit performs estimation using an estimation model, which is generated by machine learning using training data that includes a sample detection signal corresponding to the odor of the sample as an explanatory variable and sample information about the sample corresponding to the sample detection signal as an objective variable. The information processing system according to claim 1.

3. The information processing system according to claim 1, wherein the aforementioned target information includes at least one type of information selected from the name of the target, the state of the target, and the classification of the target.

4. The odor measuring device is Equipped with multiple odor sensor elements capable of outputting detection signals, Each of the multiple odor sensor elements is Interactable odor molecules have different odor molecule receiving layers. The information processing system according to claim 1.

5. The acquisition unit acquires the target detection signal via the telephone network. The information processing system according to claim 1.

6. The acquisition unit stores the target detection signal in a relational database or a NoSQL database. The information processing system according to claim 1.

7. An acquisition step in which the target detection signal is acquired from one or more odor measuring devices that output a target detection signal corresponding to the target odor at regular intervals, An estimation step in which target information about the target is estimated from the acquired target detection signal using an estimation model, An output control step that causes the estimated target information to be output to an output device, An information processing method comprising the following:

8. A control program for causing a computer to function as an information processing system according to claim 1, wherein the computer functions as the acquisition unit, the estimation unit, and the output control unit.

9. A computer-readable recording medium that stores the control program described in claim 8.

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