JP6866838B2 - Data generator, data generation method, data generator and sensor device - Google Patents

Description

The present disclosure relates to a data generation device, a data generation method, a data generation program and a sensor device.

In addition to the sensors (physical sensors) that are actually placed, the sensing data (physical sensing data) generated by one or more physical sensors observing the sensing target is analyzed and processed, and new sensing data is created. A virtual sensor technology (program module) that generates (virtual sensing data) is known. By designing a virtual sensor that generates sensing data that meets the user's requirements, the user can use the desired sensing data even if such a physical sensor does not actually exist.

Patent Document 1 states that "a bangle-shaped sensor node 1 provided with an acceleration sensor is used as a sensor for detecting a user's movement (or state), and the acceleration of the arm is detected as biological information" ([0023]). , "Aggregate the sensing data measured by the acceleration sensor of the user's arm and calculate the aggregated value for each unit time (for example, 1 minute)" (0034), and "Sleeping state from the aggregated data." Detecting a group of regions presumed to be "([0051]).

Japanese Patent No. 5466713

Patent Document 1 has a problem that a group of regions in which a person is presumed to be in a sleeping state cannot be detected unless a wearable device such as the "bracelet type sensor node 1" is attached to the person.

Also, the situation is made up of various elements. Therefore, when determining a certain element based on the physical sensing data, noise or the like caused by another element may have an influence.

An object of the present disclosure is to provide a technique for generating virtual sensing data that describes information on the living conditions of people around a physical sensor.

The data generation device according to the first aspect of the present disclosure is a determination of a first acquisition unit that acquires physical sensing data obtained by observing a sensing target by a physical sensor and a situation around the physical sensor. A second acquisition unit that acquires the first virtual sensing data representing the result, a third acquisition unit that acquires a plurality of determination criteria defined for the situation item to be determined, and the plurality of acquired units. For each of the selection unit that selects one judgment criterion corresponding to the first virtual sensing data from the judgment criteria of the above and the situation item, the physical sensing data acquired by using the selected judgment criteria. It is provided with a determination unit that determines the living conditions of a person around the physical sensor based on the physical sensor and generates a second virtual sensing data representing the determination result of the living conditions of the person.

According to this data generation device, it is possible to use a judgment standard in which the influence of noise and the like due to the situation indicated by the first virtual sensing data is taken into consideration, so that the living condition of the person around the physical sensor can be accurately judged. Is possible.

In the data generation device according to the first aspect, the determination criterion may include a reference value for at least one of the raw data of the physical sensing data and the processed data of the raw data. As a result, the living condition of a person can be determined by comparing the reference value included in the determination standard with the raw data of the physical sensing data corresponding to the reference value or the processed data thereof.

In the data generation device according to the first aspect, the determination criterion determines the living conditions of a person from the learning physical sensing data generated under the situation indicated by the first virtual sensing data associated with the determination criterion. A trained model created by performing determination machine learning may be included.

As a result, the living condition of a person can be determined by giving the raw data of the physical sensing data as the input data or the processed data thereof to the neural network in which the trained model is set.

In the data generation device according to the first aspect, the situation items are the first item dealing with whether or not a person is cooking around the physical sensor that generates the physical sensing data, and the surroundings of the physical sensor. It may include at least one of the second items dealing with whether a person is sleeping or not. According to this data generation device (hereinafter, referred to as a data generation device according to the second aspect of the present disclosure), it is possible to determine the first to second items.

In the data generator according to the second aspect, the status item includes the first item, and the physical sensing data is at least one of illuminance data, sound pressure data, pressure data, temperature data, and gas data. For at least one of the raw data of the illuminance data, the sound pressure data, the pressure data, the temperature data and the gas data, and the processed data of the raw data. The selected determination criteria may be applied to make a determination for the first item. Thereby, for example, the determination regarding the first item can be realized without the need for a wearable device.

In the data generation device according to the second aspect, the situation item includes the second item, the physical sensing data includes at least one of sound pressure data and illuminance data, and the determination unit includes the determination unit. Even if the selected criterion is applied to at least one of the raw data of each of the sound pressure data and the illuminance data and the processed data of the raw data, and the determination is made for the second item. Good. Thereby, for example, the determination regarding the second item can be realized without the need for a wearable device.

In the data generation method according to the third aspect, the computer acquires the physical sensing data obtained by observing the sensing target by the physical sensor, and represents the determination result about the surrounding situation of the physical sensor. Acquiring the virtual sensing data of 1, acquiring a plurality of judgment criteria defined for the situation items to be judged, and converting the acquired plurality of judgment criteria into the first virtual sensing data. Select one corresponding criterion, and for each of the status items, use the selected criterion to determine the living conditions of people around the physical sensor based on the acquired physical sensing data. The present invention includes generating a second virtual sensing data that makes a determination and represents a determination result regarding the living condition of the person.

According to this data generation method, it is possible to use a judgment standard in which the influence of noise or the like due to the situation indicated by the first virtual sensing data is taken into consideration, so that the living condition of the person around the physical sensor can be accurately judged. Is possible.

The data generation program according to the fourth aspect obtains the physical sensing data obtained by observing the sensing target by the physical sensor on the computer, and represents the determination result about the surrounding situation of the physical sensor. Acquiring the virtual sensing data of 1, acquiring a plurality of judgment criteria defined for the situation items to be judged, and converting the acquired plurality of judgment criteria into the first virtual sensing data. The living conditions of people around the physical sensor are determined based on the acquired physical sensing data using the selected criterion for selecting one corresponding criterion and for each of the status items. It is a program for making a determination and generating a second virtual sensing data representing a determination result about the living condition of the person.

According to this data generation program, it is possible to use a judgment standard that takes into consideration the influence of noise and the like due to the situation indicated by the first virtual sensing data, so that the living conditions of people around the physical sensor can be accurately judged. Is possible.

The sensor device according to the fifth side surface includes the data generation device according to the first side surface or the second side surface, and the physical sensor. This makes it possible to provide an intelligent sensor device that generates virtual sensing data indicating a determination result of a person's living condition in addition to the physical sensing data.

According to the present disclosure, it is possible to provide a technique for generating virtual sensing data that describes information on the living conditions of people around a physical sensor.

The block diagram which shows the application example of the data generation apparatus which concerns on embodiment. The block diagram which illustrates the hardware configuration of the data generation apparatus which concerns on embodiment. The block diagram which illustrates the functional structure of the data generation apparatus which concerns on embodiment. The figure which illustrates the data distribution system including the data generation apparatus which concerns on embodiment. The block diagram which illustrates the 1st virtual sensing data generation part of FIG. The figure which illustrates the status item of the virtual sensing data and the physical sensing data used for making a judgment about the status item. The figure which illustrates the data chart used to make the judgment about the situation item "cooking". The figure which exemplifies the judgment criteria used for making a judgment about the situation item "cooking". The figure which shows the comparison result of the data chart of FIG. 7 and the determination criterion of FIG. A graph illustrating physical sensing data used to make a determination about the status item "cooking". The figure which illustrates the data chart used to make the judgment about the situation item "cooking". The figure which exemplifies the judgment criteria used for making a judgment about the situation item "cooking". The figure which shows the comparison result of the data chart of FIG. 11 and the determination criterion of FIG. A graph illustrating physical sensing data used to make a determination about the status item "sleep". The figure which illustrates the data chart used to make the judgment about the situation item "sleep". The figure which exemplifies the judgment criteria used for making a judgment about the situation item "sleep". The figure which shows the comparison result of the data chart of FIG. 15 and the determination criterion of FIG. The block diagram which illustrates the 2nd virtual sensing data generation part of FIG. The figure which illustrates the status item of the 2nd virtual sensing data, the corresponding item in the 1st virtual sensing data, and the physical sensing data used to supplement the corresponding item. The flowchart which illustrates the operation of the 1st virtual sensing data generation part of FIG. The flowchart which illustrates the operation of the 2nd virtual sensing data generation part of FIG. FIG. 3 is a block diagram illustrating a sensor device including the data generation device of FIG. FIG. 3 is a block diagram illustrating a communication device including the data generation device of FIG. FIG. 3 is a block diagram illustrating a server including the data generator of FIG.

Hereinafter, embodiments relating to one aspect of the present disclosure (hereinafter, also referred to as “the present embodiment”) will be described with reference to the drawings.

Hereinafter, elements that are the same as or similar to the elements described will be designated by the same or similar reference numerals, and duplicate explanations will be basically omitted. For example, when a plurality of the same or similar elements exist, a common code may be used to explain each element without distinction, and the common code may be used to explain each element separately. In addition, the branch number may be used.

§1 Application example
First, an application example of this embodiment will be described with reference to FIG. FIG. 1 schematically shows an application example of the data generation device according to the present embodiment. The data generation device 100 determines the living conditions of people around the physical sensor based on the physical sensing data for each of a plurality of predetermined status items as determination targets, and has a value according to the determination result. Virtual sensing data (hereinafter, also referred to as second virtual sensing data for convenience) is generated.

In the following description, the situation around the physical sensor may include the state of the virtual sensor's sensing object, such as a person or other living or inanimate object in the space around the physical sensor. The surroundings of the physical sensor are the operating conditions (for example, accuracy, resolution, dynamic range, etc.) of the physical sensor that generates the physical sensing data that is directly or indirectly used as the base of the input data of the virtual sensor, and the physics. It can be determined based on the characteristics of the sensor's sensing target (for example, light, sound, temperature, etc.) and the surrounding environment (for example, in air, water, vacuum, etc.).

The situation item can be, for example, an item for subdividing the situation. Specifically, the status items include "human presence" that handles information on whether or not there is a person around the physical sensor, air conditioning around the physical sensor, and information on the operating status of each of the microwave oven and TV. It may include "air conditioning", "microwave oven" and "TV" to handle, "cooking" to handle information on whether or not a person is cooking around a physical sensor, and the like. Here, the situation item includes at least an item belonging to a person's living situation (for example, the above-mentioned "cooking"). Specific examples of items belonging to human living conditions will be described later.

The data generation device 100 includes a physical sensing data acquisition unit 101, a virtual sensing data acquisition unit 102, a determination standard acquisition unit 103, a determination standard selection unit 111, and a situation determination unit 112.

The physical sensing data acquisition unit 101 acquires physical sensing data. The physical sensing data may include, for example, illuminance data, sound pressure data, acceleration data, gas data, barometric pressure data, temperature data, humidity data and the like. When the data generation device 100 is built in the sensor device, the physical sensing data acquisition unit 101 may acquire the physical sensing data from the sensor in the sensor device. Further, the physical sensing data acquisition unit 101 may receive the physical sensing data generated by the physical sensor unit built in the external device.

The virtual sensing data acquisition unit 102 acquires the first virtual sensing data representing the primary determination result of the situation. The first virtual sensing data may be generated by an external device such as a host system, or may be generated by the data generation device 100.

The determination standard acquisition unit 103 acquires a determination standard predetermined for the situation item. The criteria are applied to generate a second virtual sensing data that represents the secondary determination result of the situation. Here, the situation indicated by the second virtual sensing data includes at least the living situation of a person. In addition, a plurality of judgment criteria may be set for the situation item.

Here, it was stated that the first virtual sensing data shows a primary judgment result and the second virtual sensing data shows a secondary judgment result, but the modifications are "primary" and "secondary". Only states the order in which the judgments of the situation are made and is not intended to define any relationship, including superiority or inferiority between the two.

When a plurality of judgment criteria are defined for a given situation item, the judgment criterion selection unit 111 selects one judgment criterion for this situation item. Specifically, the determination criterion selection unit 111 can select the determination criterion corresponding to the first virtual sensing data.

As a criterion for judging the situation item "cooking", the judgment criterion 1 used when the situation item "human presence" is true (there is a person around the physical sensor), the situation item "air conditioning" Is true (the air conditioner around the physical sensor is ON), the criterion 2 used, and the situation item "microwave oven" is true (the microwave oven around the physical sensor is ON). The criterion 3 used in the above and the criterion 4 used when the situation item "TV" is true (the TV around the physical sensor is ON) are included. Here, the determination criterion selection unit 111 may select the determination criterion 2 when the first virtual sensing data indicates that the air conditioner is ON around the physical sensor.

The status item included in the first virtual sensing data does not have to match the status item included in the second virtual sensing data. If they do not match, the data generator 100 may include a data conversion unit for converting the value of the status item of the first virtual sensing data into the value of the corresponding item of the second virtual sensing data. .. For example, when the first virtual sensing data includes the value of the situation item "number of people" that handles the information of the number of people existing around the physical sensor, this data conversion unit sets the value of the "number of people" to the above situation item "person". It may be converted to the value of "existing".

The determination criteria may include reference values for at least one of the raw data of the physical sensing data and the processed data thereof that should be referred to for determining the status item. For example, the reference value may be 200 [lux] with respect to the raw data of the illuminance data, 50 [%] with respect to the time ratio in which the sound pressure exceeds 50 [dB] in 30 seconds, and the like. The reference value is, for example, the raw data of the physical sensing data generated under the situation corresponding to the situation item targeted by the judgment standard, or the processed data thereof and the physical sensing data generated under the situation other than that. It can be designed by analyzing raw data or its processed data.

In this case, the situation determination unit 112 prepares the data necessary for applying the determination standard, that is, the raw data of the physical sensing data in which the reference value is determined or the processed data thereof. The processed data of the raw data of the physical sensing data may be included in the physical sensing data, may be calculated by the status determination unit 112 or other components included in the data generation device 100, or may be calculated by an external device. May be calculated. The status determination unit 112 compares the data prepared in this way with the reference value, and sets the value of the status item in the second virtual sensing data according to the comparison result.

Alternatively, the criteria may include a trained model used to determine a situation item. This trained model may be created by performing machine learning that determines the situation from the physical sensing data for learning. For example, in the trained model as the above-mentioned criterion 2, the raw data of each learning physics sensing data generated when the air conditioning is ON around the physics sensor and a person is cooking around the physics sensor. It can be created by performing supervised learning using and / or the processed data as learning data with a correct answer label. In addition, the raw data and / or the processed data of each learning physical sensing data generated when the air conditioning is ON around the physical sensor and the person is not cooking around the physical sensor is incorrectly labeled. It may be used as training data with.

In this case, the situation determination unit 112 processes the raw data of the data necessary for applying the determination criteria, that is, the raw data of the physical sensing data for inputting to the neural network in which the trained model as the determination criteria is set, or its processing. Prepare the completed data. The situation determination unit 112 gives the data prepared in this way to the neural network in which the trained model as the determination criterion is set, and sets the value of the situation item based on the output value. The trained model may be created through machine learning to acquire the ability to judge a plurality of situation items at the same time. In this case, a common criterion will be established among these plurality of situation items.

As described above, the data generation device 100 according to the application example uses the determination criteria used for determining the living condition of a person based on the first virtual sensing data representing the primary determination result of the situation. select. Therefore, according to this data generation device 100, it is possible to use a determination criterion in which the influence of noise or the like due to the situation indicated by the first virtual sensing data is taken into consideration, so that the living conditions of people around the physical sensor can be accurately determined. It becomes possible to judge.

§2 Configuration example
[Hardware configuration]
Next, an example of the hardware configuration of the data generation device 200 according to the present embodiment will be described with reference to FIG. FIG. 2 schematically illustrates an example of the hardware configuration of the data generation device 200 according to the present embodiment.

As illustrated in FIG. 2, the data generation device 200 according to the present embodiment includes a control unit 211, a storage unit 212, a communication interface 213, an input device 214, an output device 215, an external interface 216, and a drive. The computer may be electrically connected to the 217. In FIG. 2, the communication interface and the external interface are described as "communication I / F" and "external I / F", respectively.

The control unit 211 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and the like. The CPU expands the program stored in the storage unit 212 into the RAM. Then, when the CPU interprets and executes this program, the control unit 211 can execute various information processing, for example, processing or control of the components described in the item of functional configuration.

The storage unit 212 is a so-called auxiliary storage device, and is, for example, an internal or external semiconductor memory such as a hard disk drive (HDD: Hard Disk Drive), a solid state drive (SSD: Solid State Drive), or a flash memory. obtain. The storage unit 212 is a program executed by the control unit 211 (for example, a program for causing the control unit 211 to execute data generation processing), data used by the control unit 211 (for example, various physical sensing data, various virtual sensing). Data, judgment criteria), etc. are stored.

The communication interface 213 is, for example, various wireless communication modules for BLE (Bluetooth (registered trademark) Low Energy), mobile communication (3G, 4G, etc.) and WLAN (Wireless Local Area Network), and is via a network. It may be an interface for performing wireless communication. Further, the communication interface 213 may further include a wired communication module such as a wired LAN module in addition to the wireless communication module or in place of the wireless communication module.

The input device 214 may include a device for receiving user input such as a touch screen, a keyboard, and a mouse. The input device 214 may also include a sensor that measures a predetermined physical quantity and generates and inputs physical sensing data. The output device 215 is, for example, a device for outputting a display, a speaker, or the like.

The external interface 216 is a USB (Universal Bus) port, a memory card slot, or the like, and is an interface for connecting to an external device.

The drive 217 is, for example, a CD (Compact Disc) drive, a DVD (Digital Versailles Disc) drive, a BD (Blu-ray (registered trademark) Disc) drive, or the like. The drive 217 reads the program and / or data stored in the storage medium 218 and passes it to the control unit 211. In addition, a part or all of the program and data explained that it can be stored in the above-mentioned storage unit 212 may be read from the storage medium 218 by the drive 217.

Storage medium 218 is a medium that stores programs and / or data in a machine-readable format, including computers, by electrical, magnetic, optical, mechanical, or chemical action. The storage medium 218 is, for example, a removable disk medium such as a CD, DVD, or BD, but is not limited to this, and may be a flash memory or other semiconductor memory.

Regarding the specific hardware configuration of the data generation device 200, the components can be omitted, replaced, or added as appropriate according to the embodiment. For example, the control unit 211 may include a plurality of processors. The data generation device 200 may be an information processing device designed exclusively for the provided service, or may be a general-purpose information processing device such as a smartphone, a tablet PC (Personal Computer), a laptop PC, or a desktop PC. There may be. Further, the data generation device 200 may be composed of a plurality of information processing devices and the like.

[Functional configuration]
Next, an example of the functional configuration of the data generation device 200 according to the present embodiment will be described with reference to FIG. FIG. 3 schematically shows an example of the functional configuration of the data generation device 200.

As shown in FIG. 3, the data generation device 200 includes a physical sensing data acquisition unit 301, a virtual sensing data acquisition unit 302, a determination standard acquisition unit 303, a first virtual sensing data generation unit 310, and a second. It includes a virtual sensing data generation unit 320 and a data output unit 330.

The data generation device 200 generates virtual sensing data 11 and virtual sensing data 12 (corresponding to the above-mentioned second virtual sensing data) and outputs them.

The data generation device 200 does not have to generate a part of the virtual sensing data 11 and the virtual sensing data 12. When the virtual sensing data 11 is not generated, the first virtual sensing data generation unit 310 can be omitted. When the virtual sensing data 12 is not generated, the second virtual sensing data generation unit 320 can be omitted. The virtual sensing data 11 and the virtual sensing data 12 can be utilized in various business areas such as marketing activities.

The virtual sensing data 11 and the virtual sensing data 12 may be provided directly to the user side from the data generation device 200, or may be provided to the user side through the data distribution system described below. In any case, the data generation device 200 may be incorporated in a (physical) sensor device, a server, an application device, or the like, or may be configured as an information processing device independent of these.

As described above, the data generator 200 may be incorporated into any of the various devices that form the data distribution market. That is, the data generation device 200 may be incorporated in a sensor device that generates physical sensing data, or a communication device (for example, a smartphone, etc.) that relays the physical sensing data to a platform server, a matching server, an application device on the user side, or the like. It may be incorporated in various PCs, etc.), or it may be incorporated in a platform server, a matching server, or an application device. In this case, the data generation device 200 can use the hardware of the device in which the data generation device 200 is incorporated. Alternatively, the data generation device 200 may be configured as an information processing device independent of these devices.

FIG. 4 schematically shows an example of a data distribution system including the data generation device 200. The data distribution system includes sensor devices 400-1, ..., 400-5, communication devices 410-1, ..., 410-3, server 420, and application devices 430-1, ..., 430. -3 and is included. The number of each device illustrated in FIG. 4 is merely an example. Therefore, the branch numbers assigned to the symbols of each device will be described without any particular distinction.

The sensor device 400 includes a sensor that measures a physical quantity, a communication I / F that transmits physical sensing data obtained by digitizing the measured value of the sensor, and a control unit that controls the sensor and the communication I / F. The sensor device 400 is connected to the communication device 410 by using a communication technique such as WBAN (Wireless Body Area Network) or WPAN (Wireless Personal Area Network). The sensor device 400 transmits physical sensing data (and virtual sensing data, if any) to the communication device 410.

The communication device 410 may be, for example, a smartphone or various PCs. The communication device 410 includes a communication I / F that transmits / receives data and a control unit that controls the communication I / F. The communication device 410 receives the physical sensing data from the sensor device 400. Then, the communication device 410 uses communication technology such as WLAN, WMAN (Wireless Molecular Area Network), WWAN (Wireless Wide Area Network), and physically sensing data (and, if any,) to the server 420 via a gateway or a base station. Virtual sensing data) is transmitted. Further, the communication device 410 may transmit the provider side data catalog (DC) for performing the buying and selling matching of the sensing data to the server 420.

The provider data catalog includes, for example, the data catalog number, the sensing data provider, the sensing data name, the measurement date / time / measurement location of the sensing data, the observation target / characteristic, the event data specifications, the sensing data provision period, and the transaction conditions. , Data trading conditions, etc. can be included.

The application device 430 may be, for example, a smartphone, various PCs, or a server. The application device 430 includes a communication I / F that transmits / receives data and a control unit that controls the communication I / F. The application device 430 may transmit a user-side data catalog (DC) for performing trading matching of sensing data to the server 420.

Here, the user side data catalog is, for example, identification information of the data catalog, user of sensing data, name of sensing data, measurement date / time / measurement location of sensing data, observation target / characteristic, event data specification, use of sensing data. Various items such as period, transaction conditions, data trading conditions, etc. can be included.

The application device 430 receives the physical sensing data (and virtual sensing data, if any) purchased through the buying and selling matching from the server 420. Then, the application device 430 processes physical sensing data (and virtual sensing data, if any) according to the purpose of individual utilization.

The server 420 includes a communication I / F for transmitting and receiving data, a storage unit for storing data, and a control unit for controlling the storage unit and the communication I / F and performing trading matching described later. The server 420 receives the physical sensing data from the communication device 410. Then, the server 420 stores this physical sensing data (and virtual sensing data, if any).

Further, the server 420 acquires and stores the provider side data catalog and the user side data catalog, respectively, and compares the two to perform trading matching. The provider data catalog and the user data catalog may be acquired by receiving from the communication device 410, the application device 430, or another communication device, or may be acquired by other means such as direct input. When the server 420 finds a provider data catalog that matches the user data catalog, it provides the user with physical sensing data (and virtual sensing data, if any) corresponding to the provider data catalog. That is, the server 420 transmits the physical sensing data (and the virtual sensing data, if any) to the application device 430.

The form of the data distribution system is not limited to the example of FIG. For example, the sensor device 400 uses communication technology such as WLAN, WMAN, WWAN, etc. to directly send physical sensing data and / or to the server 420 or application device 430 via a gateway or base station without going through the communication device 410. Alternatively, virtual sensing data may be transmitted.

Further, the server 420 may not transmit the physical sensing data and / or the virtual sensing data to the application device 430 immediately after the establishment of the sale / purchase matching, and may once request the provider side or the user side to approve the sale / purchase. Further, the server 420 may perform data flow control without transmitting the physical sensing data and / or the virtual sensing data to the application device 430. For example, the server 420 may instruct the sensor device 400 or the communication device 410 to transmit the physical sensing data and / or the virtual sensing data to the application device 430 that has purchased the physical sensing data and / or the virtual sensing data. .. Alternatively, the server 420 may be divided into a server that performs trading matching and a server that stores physical sensing data and / or virtual sensing data.

Further, the server 420 may entrust the trading matching to a matching server (not shown) without directly performing the trading matching. This matching server may realize a secondary market that does not distinguish between platforms by performing trading matching across multiple platforms, and physical sensing data and / or virtual sensing data (which is provided without going through the platforms). For example, by adding (data collected from a personally installed sensor device 400) to the target of trading matching, a secondary market that does not distinguish the source of the data may be realized.

Hereinafter, individual components of the data generation device 200 illustrated in FIG. 3 will be described.
The physical sensing data acquisition unit 301 acquires the physical sensing data and sends it to the first virtual sensing data generation unit 310 and the second virtual sensing data generation unit 320. The physical sensing data may include, for example, illuminance data, sound pressure data, acceleration data, gas data, barometric pressure data, temperature data, humidity data and the like. The physical sensing data may be raw data, processed data of raw data, or a combination thereof.

When the data generation device 200 is incorporated in the sensor device 400, the physical sensing data acquisition unit 301 may acquire the physical sensing data from the sensor included in the sensor device 400. On the other hand, when the data generation device 200 is not incorporated in the sensor device 400, the physical sensing data acquisition unit 301 receives the physical sensing data from the sensor device 400 as the transmission source, thereby receiving the physical sensing data. Can be obtained. It should be noted that not all of the physical sensing data need to be acquired from the same sensor device 400, and for example, one physical sensing data and another physical sensing data may be acquired from different sensor devices 400.

The virtual sensing data acquisition unit 302 acquires the virtual sensing data 13 (that is, the first virtual sensing data) indicating the primary determination result of the situation and sends it to the second virtual sensing data generation unit 320. The virtual sensing data 13 may be generated by an external device such as a host system, a sensor device 400, a communication device 410, a server 420, or an application device 430, or is generated by a first virtual sensing data generation unit 310. It may be the virtual sensing data 11 that has been created.

Alternatively, the virtual sensing data acquisition unit 302 may acquire the virtual sensing data 12 generated by the second virtual sensing data generation unit 320 as the virtual sensing data 13. For example, when the second virtual sensing data generation unit 320 repeatedly determines a given situation, it is assumed that the generated virtual sensing data 12 is repeatedly used. Specifically, the second virtual sensing data generation unit 320 repeatedly uses the virtual sensing data 12 to determine the situation step by step from a simple or general situation item to a complicated or detailed situation item. May be good.

The determination standard acquisition unit 303 acquires a determination standard predetermined for the situation item. The judgment criteria are those applied to generate the virtual sensing data 11 (hereinafter, also referred to as the first judgment criterion) and those applied to generate the virtual sensing data 12 (hereinafter, the second judgment). Also called a standard). A judgment standard acquisition unit may be provided individually for each of the first judgment standard and the second judgment standard. The first criterion and the second criterion may be partially common or completely different. The determination standard acquisition unit 303 sends the first determination standard to the first virtual sensing data generation unit 310, and sends the second determination criterion to the second virtual sensing data generation unit 320.

The judgment standard acquisition unit 303 may acquire the judgment standard by reading the judgment standard stored in the judgment standard storage unit (not shown in FIG. 3) built in the data generation device 200, or may acquire the judgment standard from an external device. The judgment standard may be acquired by receiving the transmitted judgment standard.

The first virtual sensing data generation unit 310 receives physical sensing data from the physical sensing data acquisition unit 301, and receives a determination criterion (first determination criterion) from the determination criterion acquisition unit 303. The first virtual sensing data generation unit 310 determines the situation based on the physical sensing data using the determination standard, and generates the virtual sensing data 11. The virtual sensing data 11 can indicate a determination result regarding a situation, for example, a person's living situation, for each situation item. The first virtual sensing data generation unit 310 sends the virtual sensing data 11 to the data output unit 330.

A specific method for generating the virtual sensing data 11 will be described later. For example, when the criterion defined for a certain situation item includes the reference value for the raw data of the physical sensing data or the processed data thereof, the first The virtual sensing data generation unit 310 of 1 may prepare raw data of physical sensing data corresponding to a reference value or processed data thereof, and compare the two to determine the status item. Alternatively, when the determination criterion is a trained model for determining one or a plurality of situation items, the first virtual sensing data generation unit 310 sets this trained model in the neural network and the neural network. The raw data of the physical sensing data defined as the input data of the above or the processed data thereof may be prepared, and the prepared data may be given to the neural network to make a judgment.

The second virtual sensing data generation unit 320 receives the physical sensing data from the physical sensing data acquisition unit 301, receives the virtual sensing data 13 from the virtual sensing data acquisition unit 302, and receives the determination standard (second) from the determination standard acquisition unit 303. Judgment criteria) is received. When a plurality of determination criteria are defined for a given situation item, the second virtual sensing data generation unit 320 selects one corresponding to the virtual sensing data 13 from the plurality of determination criteria. Then, the second virtual sensing data generation unit 320 determines the living condition of a person based on the physical sensing data using the selected determination criterion, and generates the virtual sensing data 12. The virtual sensing data 12 can indicate a determination result regarding a situation, for example, a person's living situation, for each situation item. The second virtual sensing data generation unit 320 sends the virtual sensing data 12 to the data output unit 330.

A specific method for generating the virtual sensing data 12 will be described later. For example, when the criterion selected for a certain situation item includes the reference value for the raw data of the physical sensing data or the processed data thereof, the first The virtual sensing data generation unit 320 of 2 may prepare the physical sensing data corresponding to the reference value or the processed data thereof, and compare the two to determine the status item. Alternatively, when the determination criterion is a trained model for determining one or a plurality of situation items, the second virtual sensing data generation unit 320 sets this trained model in the neural network and the neural network. The raw data of the physical sensing data defined as the input data of the above or the processed data thereof may be prepared, and the prepared data may be given to the neural network to make a judgment.

The data output unit 330 receives the virtual sensing data 11 from the first virtual sensing data generation unit 310, and receives the virtual sensing data 12 from the second virtual sensing data generation unit 320. The data output unit 330 outputs the received virtual sensing data to the outside of the data generation device 200. Further, the data output unit 330 may form the virtual sensing data or control the output timing of the virtual sensing data.

Hereinafter, the first virtual sensing data generation unit 310 will be further described with reference to FIGS. 5 to 17.
As illustrated in FIG. 5, the first virtual sensing data generation unit 310 includes a situation determination unit 311. The situation determination unit 311 receives the physical sensing data from the physical sensing data acquisition unit 301, and receives the determination standard (first determination standard) from the determination standard acquisition unit 303. The situation determination unit 311 determines the situation based on the physical sensing data using the determination criteria, and generates the virtual sensing data 11. The status determination unit 311 sends the virtual sensing data 11 to the data output unit 330.

The situation items that can be included in the virtual sensing data 11 can belong to the "human living situation" as shown in FIG. 6, for example. The situation item shown in FIG. 6 is only an example, and a situation item different from this may be used. Here, as an example of processed data, in addition to the statistics of the raw data, the frequency spectrum generated by performing the Fourier transform on the raw data, the heat stroke risk and acceleration calculated from the raw data of the temperature data and the humidity data. There may be seismic intensity calculated from the raw data of. FIG. 6 illustrates a situation item belonging to the “human living situation” and physical sensing data used for making a determination about the situation item.

In FIG. 6, the physical sensing data listed in the physical sensing data column is not limited to raw data and may include processed data thereof. Similarly, the physical sensing data listed in the physical sensing data column is merely an example.

For example, it is assumed that the situation determination unit 311 has acquired the determination chart illustrated in FIG. 8 as a determination criterion for the situation item “cooking”. Here, the determination chart is, for example, a list of reference values used for determination. The reference value is, for example, the raw data of the physical sensing data generated under the situation corresponding to the situation item targeted by the judgment standard, or the raw data of the processed data and the raw physical sensing data generated under the situation other than that. It can be designed by analyzing the data or its processed data.

The situation determination unit 311 exemplifies the raw data of the physical sensing data or the processed data thereof whose reference value is set at least in FIG. 8 (that is, used for determining the situation item “cooking”) in FIG. It may be prepared as a data chart. Here, the data chart is, for example, a list of raw data of physical sensing data used for determination and processed data thereof. If the physical sensing data does not include the processed data of the raw data, the situation determination unit 311 may calculate the necessary processed data.

The situation determination unit 311 compares the data chart of FIG. 7 with the determination chart of FIG. 8 and obtains the comparison result exemplified in FIG. In FIG. 9, when the value in the corresponding column of the data chart is equal to or higher than the reference value set in the judgment chart, "○" is set in the judgment chart, and when the value is lower than the reference value, "x" is set in the judgment chart. If there is no reference value, "-" is added.

The situation determination unit 311 converts, for example, "○" and "×" as "1 (true)" or "0 (false)", respectively, or vice versa, and is a logical expression defined as a part of the determination criteria. Alternatively, set the value of the status item by substituting it in the relational expression. The value of the status item may be defined as a binary value, for example, "1 (true)" or "0 (false)", or as a multi-value of 3 or more, for example, a probability value, a percentage, a score, or the like. ..

As described above, the criterion may include a trained model. When the judgment criterion includes a trained model, the situation judgment unit 311 sets the trained model in the neural network, and the raw data of the physical sensing data defined as the input data of the neural network or the processed data thereof. And the prepared data may be given to the neural network to make a judgment.

This trained model may be created by performing machine learning that determines the situation from the physical sensing data for learning. For example, a trained model for making a determination about the situation item "cooking" may take the raw data and / or its processed data of each learning physics sensing data generated while a person is cooking around the physics sensor. , It can be created by performing supervised learning using it as learning data with a correct answer label. Further, the raw data and / or the processed data of each learning physical sensing data generated when a person is not cooking around the physical sensor may be used as the learning data with an incorrect answer label.

Hereinafter, specific examples of determination for various situation items will be described with reference to FIGS. 10 to 17. In all the specific examples described here, the determination using the reference value is performed, but as described above, the determination using the trained model may be appropriately performed.

FIG. 10 shows raw data and processed data of the physical sensing data “illuminance”, “sound pressure” and “atmospheric pressure” used for making a judgment on the situation item “cooking”. The status item "cooking" can handle information about whether or not a person is cooking around a physical sensor.

At the time of cooking, for example, a person turns on the lighting of the kitchen, takes out the food from the refrigerator, and turns on the ventilation fan. Therefore, by paying attention to these behaviors, it is possible to determine whether or not a person is cooking around the physical sensor. In particular, by adding the operating status of the ventilation fan to the determination material, it may be possible to distinguish between human activities such as taking out drinks and storing food and cooking. It should be noted that the behavior of a person during cooking described here is merely an example, and a reference value may be designed in consideration of various other behavior patterns.

If the illumination is in the ON state, the raw data of the physical sensing data "illuminance" may increase due to the illumination light. Therefore, with respect to the raw data of the physical sensing data "illuminance", a value for distinguishing ON / OFF of lighting, for example, "50 [lp]" may be set as a reference value.

Further, if the lighting is switched from the OFF state to the ON state around the physical sensor, the illuminance may suddenly increase. Therefore, the situation determination unit 311 can also use the change in the raw data of the physical sensing data “illuminance” (here, for example, the maximum change for 1 second, referred to as “change 1”) for the determination. For changes in the raw data of the physical sensing data "illuminance", for example, "50 [lp]" may be set as a reference value.

Further, if a switch operation sound is generated when the lighting or the ventilation fan is switched from the OFF state to the ON state around the physical sensor, a significant sound pressure may be detected. Therefore, the situation determination unit 311 counts the number of times that the change in the raw data of the physical sensing data "sound pressure" exceeds "+10 dB" and falls below "-10 [dB]" within 1 second. Finished data (hereinafter, also simply referred to as “change number 1”) may be prepared. Regarding the change number 1 of the sound pressure, a value for distinguishing between the case where the lighting or the ventilation fan is switched and the case where the sound pressure is not switched, for example, "1 [times]" may be set as a reference value. The 1 second here is an example of a time domain for capturing the vertical movement of the impulse-like sound pressure accompanying the switch operation sound, and can be changed.

If the refrigerator door is opened and closed around the physical sensor, it may be possible to detect significant sound pressure when the refrigerator door is opened and closed. Therefore, the situation determination unit 311 searches for a peak exceeding "50 [dB]" in the raw data of the physical sensing data "sound pressure" over a predetermined analysis period, for example, the latest 60 seconds, and for the 60 seconds. Processed data (hereinafter, also simply referred to as “raw value number”) may be prepared in which the maximum number of peaks that enter the region for any 10 seconds is calculated. Regarding the number of raw sound pressure values, a value for distinguishing between the case where the refrigerator door is opened and closed and the case where the refrigerator door is not opened and closed, for example, "2 [times]" may be set as a reference value. Here, the length of the region, 10 seconds, is an estimated time required from opening the refrigerator door to closing it, and can be changed as appropriate.

Similarly, changes in the raw data of the physical sensing data "sound pressure" can also be used for determination. Specifically, the situation determination unit 311 determines the number of times that the change in the raw data of the physical sensing data "sound pressure" exceeds "+10 dB" and falls below "-10 [dB]" within 10 seconds. The counted processed data (hereinafter, also simply referred to as “change number 2”) may be prepared. Regarding the number of changes in sound pressure 2, a value for distinguishing between the case where the refrigerator door is opened and closed and the case where it is not, for example, "2 [times]" may be set as a reference value.

If the ventilation fan is in the ON state around the physical sensor, the raw data of the physical sensing data "sound pressure" may increase due to the operating sound. Therefore, the situation determination unit 311 can also use the change in the raw data of the physical sensing data “sound pressure” (here, the difference from the value 5 seconds ago, which is referred to as “change 2”) for the determination. For changes in the raw data of the physical sensing data “sound pressure”, for example, “10 [dB]” may be set as a reference value.

If the ventilation fan is in the ON state around the physical sensor, the raw data of the physical sensing data "atmospheric pressure" may change due to the operation of the ventilation fan. For example, if an air supply type ventilation fan operates, the inflow of air into the room may increase and the raw data of the physical sensing data "atmospheric pressure" may rise. On the other hand, if the exhaust type ventilation fan operates, the outflow of air to the outside may increase and the raw data of the physical sensing data "atmospheric pressure" may decrease. Therefore, the situation determination unit 311 can also use the change 2 of the raw data of the physical sensing data “atmospheric pressure” for the determination. For change 2 of the raw data of the physical sensing data “atmospheric pressure”, for example, “0.02 hPa” may be set as a reference value.

In addition, for example, by capturing the usage status of a heat source or refrigerator based on the physical sensing data "temperature" and the increase in _{volatile organic compound (VOC) or CO 2 concentration due to combustion based on the physical sensing data "gas",} There is a possibility that the situation item "cooking" can be judged more accurately.

It is assumed that the situation determination unit 311 has acquired the determination chart illustrated in FIG. 12 as the determination standard for the situation item “cooking”. The situation determination unit 311 prepares at least the raw data of the physical sensing data whose reference value is determined in FIG. 12 or the processed data thereof as a data chart illustrated in FIG.

The situation determination unit 311 compares the data chart of FIG. 11 with the determination chart of FIG. 12 and obtains the comparison result exemplified in FIG. In FIG. 13, when the value in the corresponding column of the data chart is equal to or higher than the reference value set in the judgment chart, “○” is set in the judgment chart, and when the value is lower than the reference value, “x” is set in the judgment chart. If there is no reference value, "-" is added.

In this example, the raw data of illuminance, the number of changes in sound pressure 1, the number of raw values, the number of changes 2 and 2, and the change 2 in atmospheric pressure are above the reference value, but the change 1 in illuminance is below the reference value. There is. Since the raw illuminance data is above the reference value and the change in illuminance 1 is below the reference value, the lighting is currently in the ON state, but a long time has passed since it was switched from the OFF state to the ON state. It is presumed that the lighting is currently in the OFF state because the ambient light that does not require lighting can be obtained. Therefore, it is possible to make a hypothesis that, for example, a person forgets to turn off the lights in the kitchen and cooks as it is, or cooks in the daytime. Therefore, for example, the situation determination unit 311 may set the value of the situation item “cooking” to, for example, “1 (true)” indicating that a person is cooking around the physical sensor. However, the determination results described here are merely examples, and may differ from the determination results depending on the determination criteria (for example, the above-mentioned logical formula or relational formula) of the situation item “cooking”.

FIG. 14 shows raw data for each of the physical sensing data “illuminance” and “sound pressure” used to make a determination about the situation item “sleep”. The status item "sleep" can handle, for example, information about whether or not a person is sleeping around a physical sensor.

The situation item "sleep" is based on the premise that a person is present around the physical sensor (for example, at home). Therefore, the situation determination unit 311 is limited to the sensor data confirmed to have been obtained in the presence of a person around the physical sensor by the value of the above-mentioned situation item "human presence" or other means. Then, the situation item "sleep" may be determined. This may also apply to other situation items belonging to the "human living situation" illustrated in FIG.

For example, if a person is sleeping around a physical sensor, the lighting may be turned off. Therefore, with respect to the raw data of the physical sensing data "illuminance", a value indicating that the lighting is in the OFF state, for example, "0 [lp]" may be set as a reference value. In all the specific examples described so far, the reference value is the lower limit value imposed on the raw data of the corresponding sensor data or the processed data thereof, but the reference value in this example is not the lower limit value but the upper limit value. Corresponds to the value.

If a person is sleeping around the physical sensor, sounds such as snoring, bruxism, sleep-talking, and body movement may occur, but it is considered to be quieter than when the person is active. Therefore, "35 [dB]" may be set as a reference value for the raw data of the physical sensing data "sound pressure".

It is assumed that the situation determination unit 311 has acquired the determination chart illustrated in FIG. 16 as the determination standard for the situation item “sleep”. The situation determination unit 311 prepares at least the raw data of the physical sensing data for which the reference value is set in FIG. 16 or the processed data thereof as a data chart illustrated in FIG.

The situation determination unit 311 compares the data chart of FIG. 15 with the determination chart of FIG. 16 and obtains the comparison result exemplified in FIG. In FIG. 17, when the value in the corresponding column of the data chart is equal to or less than the reference value set in the judgment chart, "○" is set in the judgment chart, and when the value exceeds the reference value, "x" is set in the judgment chart. If there is no reference value, "-" is added.

In this example, both the raw illuminance data and the raw sound pressure data are below the reference value. Therefore, the situation determination unit 311 may set the value of the situation item "sleep" to, for example, "1 (true)" indicating that a person is sleeping around the physical sensor.

Hereinafter, the second virtual sensing data generation unit 320 will be further described with reference to FIGS. 18 and 19.
As illustrated in FIG. 18, the second virtual sensing data generation unit 320 includes a determination criterion selection unit 321 and a situation determination unit 322.

The determination criterion selection unit 321 receives the virtual sensing data 13 from the virtual sensing data acquisition unit 302, and receives the determination criterion (second determination criterion) from the determination criterion acquisition unit 303. When a plurality of judgment criteria are defined for a given situation item, the judgment criterion selection unit 321 selects one corresponding to the virtual sensing data 13 from the plurality of judgment criteria, and the selected judgment criteria. To the situation determination unit 322.

The situation determination unit 322 receives the physical sensing data from the physical sensing data acquisition unit 301, and receives the determination criteria selected from the determination criterion selection unit 321. The situation determination unit 322 determines the situation based on the physical sensing data using the selected determination criteria, and generates virtual sensing data 12. The status determination unit 322 sends the virtual sensing data 12 to the data output unit 330.

Similar to the virtual sensing data 11, the status item included in the virtual sensing data 12 may belong to the “human living situation”. The situation item shown in FIG. 6 is only an example, and a situation item different from this may be used. Further, the virtual sensing data 11 may include a situation item that does not belong to the "human living situation" illustrated in FIG. 6, and interprets that the situation item listed in FIG. 6 does not belong to the "human living situation". There is also room.

In FIG. 6, the physical sensing data listed in the physical sensing data column is not limited to raw data and may include processed data thereof. Similarly, the physical sensing data listed in the physical sensing data column is merely an example.

For example, the judgment standard selection unit 321 has a judgment standard 1 used when the situation item "person presence" is true and a judgment standard 2 used when the situation item "air conditioning" is true for the situation item "cooking". , Judgment standard 3 used when the situation item "microwave oven" is true, and judgment standard 4 used when the situation item "TV" is true are acquired as judgment charts, respectively. Here, the determination chart is, for example, a list of reference values used for determination. The reference value included in the judgment standard is, for example, (1) a situation in which the judgment standard matches the situation (indicated by the virtual sensing data 13) to which the judgment standard is associated and corresponds to the situation item targeted by the judgment standard. Under the situation where the raw data of the physical sensing data generated below or the processed data thereof and (2) the situation where the judgment standard is associated are matched, but the situation item targeted by the judgment standard is not applicable. It can be designed by analyzing the raw data of the physical sensing data generated in the above or the processed data thereof. The determination criterion selection unit 321 may select the determination criterion 2 when the first virtual sensing data indicates that the air conditioner is ON around the physical sensor.

The situation determination unit 322 may prepare raw data of physical sensing data for which at least a reference value is determined in the determination chart selected by the determination criterion selection unit 321 or processed data thereof as a data chart. Here, the data chart is, for example, a list of raw data of physical sensing data used for determination and processed data thereof. If the physical sensing data does not include the processed data of the raw data, the situation determination unit 322 may calculate the necessary processed data.

The situation determination unit 322 compares the data chart with the determination chart and obtains a comparison result. The situation judgment unit 322 converts the comparison result for each reference value as "1 (true)" or "0 (false)", or vice versa, and uses a logical formula or a formula defined as a part of the judgment criteria. Set the value of the status item by substituting it in the relational expression. The value of the status item may be defined as a binary value, for example, "1 (true)" or "0 (false)", or as a multi-value of 3 or more, for example, a probability value, a percentage, a score, or the like. ..

As described above, the criterion may include a trained model. When the judgment criterion includes a trained model, the situation judgment unit 322 sets the trained model in the neural network, and the raw data of the physical sensing data defined as the input data of the neural network or the processed data thereof. And the prepared data may be given to the neural network to make a judgment.

This trained model may be created by performing machine learning that determines the situation from the physical sensing data for learning. For example, when the value of the status item "TV" in the virtual sensing data 13 is true (the TV around the physical sensor is ON), the trained model for determining the status item "cooking" is physical. Learning with a correct answer label of the raw data and / or its processed data of each learning physical sensing data generated when the TV is ON around the sensor and a person is cooking around the physical sensor. It can be created by using it as data and performing supervised learning. In addition, the raw data and / or the processed data of each learning physical sensing data generated when the TV is ON around the physical sensor and the person is not cooking around the physical sensor is incorrectly labeled. It may be used as training data with.

The situation determination unit 322 does not have to make a determination using the determination criteria for a part or all of the status items included in the virtual sensing data 12. Specifically, the situation determination unit 322 may determine a part or all of the determination based on the virtual sensing data 13 acquired from the virtual sensing data acquisition unit 302.

For example, the situation determination unit 322 may use the value of the virtual sensing data 13 as it is or convert it and use it as the value of a specific situation item included in the virtual sensing data 12. Further, the situation determination unit 322 may make a determination about the situation item included in the virtual sensing data 12 by supplementing the corresponding item in the virtual sensing data 13 based on the physical sensing data.

FIG. 19 illustrates a situation item belonging to the “human living situation”, an item of virtual sensing data 13 corresponding to the situation item, and physical sensing data used to supplement the item.

<Others>
Each function of the data generation device 200 will be described in detail in an operation example described later. In this embodiment, an example in which each function of the data generation device 200 is realized by a general-purpose CPU is described. However, some or all of the above functions may be realized by one or more dedicated processors. Further, regarding the functional configuration of the data generation device 200, the functions may be omitted, replaced, or added as appropriate according to the embodiment.

§3 Operation example
Next, an operation example of the data generation device 200 will be described with reference to FIGS. 20 and 21. The processing procedure described below is only an example, and each processing may be changed as much as possible. Further, with respect to the processing procedure described below, steps can be omitted, replaced, and added as appropriate according to the embodiment.

FIG. 20 is a flowchart showing an example of the operation of the first virtual sensing data generation unit 310.
First, the physical sensing data acquisition unit 301 acquires the physical sensing data, and the determination standard acquisition unit 303 acquires the determination standard (first determination standard) (step S501). The status determination unit 311 receives these physical sensing data and determination criteria, and the process proceeds to step S502.

In step S502, the status determination unit 311 selects an unselected item among the status items (for example, the item shown in FIG. 6) included in the virtual sensing data 11. Depending on the judgment criteria, it is possible to judge a plurality of situation items at the same time. For example, the criteria may include a trained model created by machine learning that makes decisions on multiple situation items at the same time. In such a case, a plurality of items may be selected in step S502.

The status determination unit 311 prepares raw data of physical sensing data and processed data thereof necessary for applying the determination criteria determined for the status item (hereinafter simply referred to as a selection item) selected in step S502. (Step S503). Here, the physical sensing data required for applying the determination criteria may be, for example, raw data of the physical sensing data in which the reference value included in the determination criteria is defined or processed data thereof, or the determination criteria. It may be the raw data of the physical sensing data defined as the input data of the neural network in which the trained model is set included in the above, or the processed data thereof.

The situation determination unit 311 determines whether or not the situation corresponds to the selection item by applying the determination criteria determined for the selection item to the data prepared in step S503 (step S504). Applying the criterion to the data may be to compare the reference value included in the criterion with the corresponding data, or to apply the data to the neural network in which the trained model included in the criterion is set. It may be to give.

The situation determination unit 311 sets the value of the selection item in the virtual sensing data 11 according to the determination result in step S504 (step S505). If the processing for all the status items is completed at the end of step S505, the operation of FIG. 20 ends, otherwise the processing returns to step S502 (step S506).

FIG. 21 is a flowchart showing an example of the operation of the second virtual sensing data generation unit 320.
First, the physical sensing data acquisition unit 301 acquires the physical sensing data, the virtual sensing data acquisition unit 302 acquires the virtual sensing data 13, and the determination criterion acquisition unit 303 acquires the determination criterion (second determination criterion) (the second determination criterion). Step S511). The determination criterion selection unit 321 receives the virtual sensing data 13 and the determination criterion, the situation determination unit 322 receives the physical sensing data, and the process proceeds to step S512.

In step S512, the determination criterion selection unit 321 selects an unselected item among the status items (for example, the item shown in FIG. 6) included in the virtual sensing data 12. Depending on the judgment criteria, it is possible to judge a plurality of situation items at the same time. For example, the criteria may include a trained model created by machine learning that makes decisions on multiple situation items at the same time. In such a case, a plurality of items may be selected in step S512.

When a plurality of determination criteria are set for the status item (hereinafter simply referred to as a selection item) selected in step S512, the determination criterion selection unit 321 sets the virtual sensing data 13 acquired in step S511 to the virtual sensing data 13. Select the corresponding one (step S513). If only one determination criterion is set for the selected item, step S513 may be skipped.

The situation determination unit 322 prepares the raw data of the physical sensing data necessary for applying the determination criteria selected in step S513 and the processed data thereof (step S514). Here, the physical sensing data required for applying the determination criteria may be, for example, raw data of the physical sensing data in which the reference value included in the determination criteria is defined or processed data thereof, or the determination criteria. It may be the raw data of the physical sensing data defined as the input data of the neural network in which the trained model is set included in the above, or the processed data thereof.

The situation determination unit 322 determines whether or not the situation corresponds to the selection item by applying the determination criterion selected in step S513 to the data prepared in step S514 (step S515). Applying the criterion to the data may be to compare the reference value included in the criterion with the corresponding data, or to apply the data to the neural network in which the trained model included in the criterion is set. It may be to give.

The status determination unit 322 sets the value of the selection item in the virtual sensing data 12 according to the determination result in step S515 (step S516). If the processing for all the status items is completed at the end of step S516, the operation of FIG. 21 ends, otherwise the processing returns to step S512 (step S517).

[Action / Effect]
As described above, in the present embodiment, the data generation device determines the living condition of a person based on the physical sensing data using the determination criteria. Therefore, according to this data generation device, it is possible to generate virtual sensing data indicating a determination result of a person's living condition.

Further, the data generation device is used to determine the living condition of a person based on the first virtual sensing data representing the primary determination result of the situation, which is generated by itself or generated by an external device. The criterion may be selected. As a result, it is possible to use a determination criterion in which the influence of noise or the like due to the situation indicated by the first virtual sensing data is taken into consideration, so that it is possible to accurately determine the living conditions of people around the physical sensor.

According to such virtual sensing data, it is possible to flexibly provide data that meets the requirements of the user side. Therefore, according to this virtual sensing data, there is a possibility that the utilization of the sensing data on the user side will be promoted.

§4 Modification example
Although the embodiments of the present disclosure have been described in detail above, the above description is merely an example of the present disclosure in all respects. Needless to say, various improvements and modifications can be made without departing from the scope of the present disclosure. For example, the following changes can be made. In the following, the same reference numerals will be used for the same components as those in the above embodiment, and the same points as in the above embodiment will be omitted as appropriate. The following modifications can be combined as appropriate.

<4.1>
For example, the data generator 200 can be incorporated into a sensor device. FIG. 22 schematically shows an example of the functional configuration of the sensor device incorporating the data generation device 200. The hardware configuration of this sensor device may be the same as or similar to the configuration example shown in FIG.

The sensor device of FIG. 22 includes a data generation device 200, a physical sensor control unit 601, a physical sensor unit 610, a transmission unit 621, a determination reference storage unit 622, and a reception unit 623.

The physical sensor control unit 601 controls the operation of the physical sensor unit 610.
The physical sensor unit 610 is controlled by the physical sensor control unit 601 to measure one or a plurality of types of physical quantities and generate physical sensing data representing the physical quantities. The physical sensor unit 610 sends the physical sensing data to the transmission unit 621 and the data generation device 200.

The physical sensor unit 610 uses, for example, an illuminance sensor 611 for measuring illuminance, a sound pressure sensor 612 for measuring sound pressure, an acceleration sensor 613 for measuring acceleration, a gas sensor 614 for measuring gas concentration such as _{VOC or CO 2, and pressure.} It may include a pressure sensor 615 or the like to be measured. However, the various sensors listed here are merely examples, and the physical sensor unit 610 may include a sensor different from these sensors, or may not include a part or all of these sensors.

The transmission unit 621 receives the physical sensing data from the physical sensor unit 610 and receives the virtual sensing data from the data generation device 200. The transmission unit 621 transmits the physical sensing data and the virtual sensing data to a higher-level communication device, server, or application device. The transmission unit 621 may combine the physical sensing data and the virtual sensing data before transmitting the data, or may transmit the data as separate data. Further, the transmission unit 621 may have different destinations and / or routes for the physical sensing data and the virtual sensing data.

The determination standard storage unit 622 stores the determination criteria used by the data generation device 200. The judgment standard stored in the judgment standard storage unit 622 is read out as necessary by the data generation device 200 (the judgment standard acquisition unit 303 included in the data generation device 200). The determination criteria may be preset in the determination criteria storage unit 622, may be created inside the sensor device of FIG. 22, created by an external device (for example, a server), and received by the receiving unit 623. May be good.

The receiving unit 623 sends, for example, a determination standard created by an external device (for example, a server) to the determination standard storage unit 622. This determination standard is stored in the determination standard storage unit 622. In addition, the receiving unit 623 may receive virtual sensing data from an external device (for example, a higher-level communication device or server) and send the virtual sensing data to the data generation device 200. This virtual sensing data can also be used as, for example, virtual sensing data 13.

The data generation device 200 acquires the physical sensing data from the physical sensor unit 610 and acquires the determination standard from the determination standard storage unit 622. Further, the data generation device 200 can acquire the virtual sensing data generated by the external device from the receiving unit 623. By operating as described above, the data generation device 200 generates a part or all of the virtual sensing data 11 and the virtual sensing data 12 and sends them to the transmission unit 621.

As described above, in the modified example <4.1>, the data generation device 200 according to the embodiment is incorporated in the sensor device. Therefore, according to this modification, it is possible to provide an intelligent sensor device that generates virtual sensing data in addition to physical sensing data. Further, according to this modification, the data generation device 200 can be realized by utilizing hardware resources such as a processor and a memory of the sensor device.

<4.2>
For example, the data generator 200 can be incorporated into a communication device. FIG. 23 schematically shows an example of the functional configuration of the communication device incorporating the data generation device 200. The hardware configuration of this communication device may be the same as or similar to the configuration example shown in FIG.

The communication device of FIG. 23 may be, for example, a smartphone or various PCs. This communication device includes a data generation device 200, a reception unit 701, a determination reference storage unit 702, and a transmission unit 703.

The receiving unit 701 receives the physical sensing data from an external device (for example, a sensor device) and sends it to the data generating device 200 and the transmitting unit 703. Further, the receiving unit 701 may receive virtual sensing data from an external device (for example, a higher-level communication device or server) and send the virtual sensing data to the data generation device 200. This virtual sensing data can also be used as, for example, virtual sensing data 13. Similarly, the receiving unit 701 can receive the determination standard from an external device (for example, a server) and send it to the determination standard storage unit 702. This determination standard is stored in the determination standard storage unit 702.

The determination standard storage unit 702 stores the determination criteria used by the data generation device 200. The judgment standard stored in the judgment standard storage unit 702 is read out as necessary by the data generation device 200 (the judgment standard acquisition unit 303 included in the data generation device 200). The determination criteria may be preset in the determination criteria storage unit 702, may be created inside the communication device of FIG. 23, or may be created by an external device (for example, a server) and received by the receiving unit 701. May be good.

The transmission unit 703 receives the physical sensing data from the reception unit 701, and receives the virtual sensing data from the data generation device 200. The transmission unit 703 transmits the physical sensing data and the virtual sensing data to a higher-level communication device, server, or application device. The transmission unit 703 may transmit the physical sensing data and the virtual sensing data after combining them, or may transmit the separate data as they are. Further, the transmission unit 703 may have different destinations and / or routes for the physical sensing data and the virtual sensing data.

The data generation device 200 acquires the physical sensing data from the receiving unit 701 and acquires the determination standard from the determination standard storage unit 702. Further, the data generation device 200 can acquire the virtual sensing data generated by the external device from the reception unit 701. By operating as described above, the data generation device 200 generates a part or all of the virtual sensing data 11 and the virtual sensing data 12 and sends them to the transmission unit 703.

As described above, in the modified example <4.2>, the data generation device 200 according to the embodiment is incorporated in the communication device. Therefore, according to this modification, even when the sensor device cannot generate at least a part of the virtual sensing data 11 and the virtual sensing data 12 described above, the necessary virtual sensing data is replenished and distributed on the SDTM. Can be done. Further, according to this modification, the data generation device 200 can be realized by utilizing hardware resources such as a processor and a memory of the communication device.

<4.3>
For example, the data generator 200 can be built into the server. FIG. 24 schematically shows an example of the functional configuration of the server incorporating the data generation device 200. The hardware configuration of this server may be the same as or similar to the configuration example shown in FIG.

The server of FIG. 24 is used by the data generation device 200, the reception unit 801, the determination reference storage unit 802, the virtual sensing data storage unit 803, the physical sensing data storage unit 804, the provider side data catalog storage unit 805, and the like. It includes a side data catalog storage unit 806, a matching unit 807, a data management unit 808, and a transmission unit 809.

The receiving unit 801 receives the physical sensing data from an external device (for example, a sensor device) and sends it to the data generation device 200 and the physical sensing data storage unit 804. Further, the receiving unit 801 can receive the virtual sensing data from the external device and send it to the data generating device 200. This virtual sensing data can also be used as, for example, virtual sensing data 13. Similarly, the receiving unit 801 can receive the determination standard from the external device and send it to the determination standard storage unit 802. This determination standard is stored in the determination standard storage unit 802.

The receiving unit 801 can receive the providing side data catalog used for matching from an external device (for example, a communication device) and send it to the providing side data catalog storage unit 805. The provider data catalog is stored in the provider data catalog storage unit 805. Similarly, the receiving unit 801 can receive the user-side data catalog used for matching from an external device (for example, an application device) and send it to the user-side data catalog storage unit 806. This user-side data catalog is stored in the user-side data catalog storage unit 806.

The determination standard storage unit 802 stores the determination criteria used by the data generation device 200. The judgment standard stored in the judgment standard storage unit 802 is read out as necessary by the data generation device 200 (the judgment standard acquisition unit 303 included in the data generation device 200). The determination standard may be preset in the determination standard storage unit 802, may be created inside the server of FIG. 24, or may be created by an external device and received by the receiving unit 801.

The virtual sensing data storage unit 803 stores the virtual sensing data generated by the data generation device 200. The virtual sensing data stored in the virtual sensing data storage unit 803 is read out by the data management unit 808 as needed.

The physical sensing data storage unit 804 stores the physical sensing data received by the receiving unit 801. The physical sensing data stored in the physical sensing data storage unit 804 is read out by the data management unit 808 as needed.

The provider data catalog storage unit 805 stores, for example, the provider data catalog received or directly input by the reception unit 801. The provider data catalog stored in the provider data catalog storage unit 805 is read out by the matching unit 807 as needed.

The user-side data catalog storage unit 806 stores, for example, the user-side data catalog received or directly input by the receiving unit 801. The user-side data catalog stored in the user-side data catalog storage unit 806 is read out by the matching unit 807 as needed.

The matching unit 807 reads the provider data catalog from the provider data catalog storage unit 805, and reads the user data catalog from the user data catalog storage unit 806. The matching unit 807 performs buying and selling matching between the providing side data catalog and the user side data catalog. For example, the matching unit 807 compares at least a part of the items included in the user side data catalog with the corresponding items included in the provider side data catalog, and extracts the provider side data catalog that matches the user side request. When the trading matching is established, the matching unit 807 notifies the data management unit 808 to that effect. When a provider data catalog that matches the user's request is found, the matching unit 807 requests the user and / or the provider to approve the data sale, and then manages the data management of the establishment of the sale matching. The unit 808 may be notified.

When the data management unit 808 is notified by the matching unit 807 that the trading matching has been established, the physical sensing data and / or the virtual sensing data on the provider side is stored in the physical sensing data storage unit 804 and / or the virtual sensing data storage unit 803. Is read from and sent to the transmission unit 809.

The transmission unit 809 receives the physical sensing data and / or the virtual sensing data from the data management unit 808, and transmits the physical sensing data and / or the virtual sensing data to the application device. The transmission unit 809 may combine the physical sensing data and the virtual sensing data before transmitting the data, or may transmit the data as separate data. Further, the transmission unit 809 may have different destinations and / or routes for the physical sensing data and the virtual sensing data.

The data generation device 200 acquires the physical sensing data from the receiving unit 801 and acquires the determination standard from the determination standard storage unit 802. Further, the data generation device 200 can acquire the virtual sensing data generated by the external device from the receiving unit 801. By operating as described above, the data generation device 200 generates a part or all of the virtual sensing data 11 and the virtual sensing data 12 and sends them to the virtual sensing data storage unit 803. This virtual sensing data is stored in the virtual sensing data storage unit 803.

As described above, in the modified example <4.3>, the data generation device 200 according to the embodiment is incorporated in the server. Therefore, according to this modification, necessary metadata can be replenished even when a lower-level device such as a sensor device cannot generate at least a part of the virtual sensing data 11 and the virtual sensing data 12 described above. .. Further, according to this modification, the data generation device 200 can be realized by utilizing hardware resources such as a server processor and memory.

The server according to the modified example <4.3> may entrust the trading matching to a matching server (not shown) without directly performing the trading matching. Alternatively, trading matching does not have to be performed. In this case, the components related to trading matching, for example, the provider side data catalog storage unit 805, the user side data catalog storage unit 806, and the matching unit 807 can be omitted.

<4.4>
For example, the data generator 200 can be incorporated into an application device. The functional configuration of this application device may correspond to, for example, replacing the transmission unit 703 in the communication device shown in FIG. 23 with a component for utilizing physical sensing data and / or virtual sensing data.

According to the application device according to this modification <4.4>, necessary virtual sensing data is replenished even when data that does not include at least a part of the above-mentioned virtual sensing data 11 and virtual sensing data 12 is provided. Can be utilized. Further, according to this modification, the data generation device 200 can be realized by utilizing hardware resources such as a processor and a memory of the application device.

<4.5>
The virtual sensing data 11 and / or the virtual sensing data 12 can also be treated as metadata indicating the measurement environment of the physical sensing data and / or the virtual sensing data. By using such metadata, preprocessing for utilization of physical sensing data and / or virtual sensing data can be facilitated. Further, by using the metadata, it becomes easy to organize the physical sensing data and / or the virtual sensing data, for example, to generate a table. Furthermore, it is possible to detect events by using metadata.

<4.6>
In the description of the embodiment, an example of realizing the judgment of the situation and / or the calculation of the reliability by using the neural network in which the trained model is set is introduced. In such an approach using AI (Artificial Intelligence), a causal relationship model, a decision tree, a support vector machine (SVM), and the like can also be used.

However, all of the embodiments described so far are merely examples of the present disclosure in all respects. Needless to say, various improvements and modifications can be made without departing from the scope of the present disclosure. That is, in carrying out the present disclosure, a specific configuration according to the embodiment may be appropriately adopted. The data appearing in each embodiment are described in natural language, but more specifically, the data is specified in a pseudo language, a command, a parameter, a machine language, or the like that can be recognized by a computer.

In addition to the scope of claims, some or all of the above embodiments can be described as shown below, but the present invention is not limited to this.
The first acquisition unit (101) that acquires the physical sensing data obtained by observing the sensing target by the physical sensor, and
A second acquisition unit (102) for acquiring the first virtual sensing data representing the determination result of the surrounding condition of the physical sensor, and the second acquisition unit (102).
A third acquisition unit (103) that acquires a plurality of judgment criteria defined for the situation item to be judged, and
A selection unit (111) that selects one determination criterion corresponding to the first virtual sensing data from the plurality of acquired determination criteria, and
For each of the situation items, the living conditions of a person around the physical sensor are determined based on the acquired physical sensing data using the selected determination criteria, and the determination result of the living conditions of the person is determined. With the determination unit (112) that generates the second virtual sensing data representing
A data generator.

11, 12, 13 ... Virtual sensing data 100, 200 ... Data generator 101, 301 ... Physical sensing data acquisition unit 102, 302 ... Virtual sensing data acquisition unit 103, 303 ... Judgment criteria Acquisition unit 111, 321 ... Judgment standard selection unit 112, 311, 322 ... Situation judgment unit 211 ... Control unit 212 ... Storage unit 213 ... Communication interface 214 ... Input device 215 ...・ Output device 216 ・ ・ ・ External interface 217 ・ ・ ・ Drive 218 ・ ・ ・ Storage medium 310 ・ ・ ・ First virtual sensing data generation unit 320 ・ ・ ・ Second virtual sensing data generation unit 330 ・ ・ ・ Data output Unit 400 ・ ・ ・ Sensor device 410 ・ ・ ・ Communication device 420 ・ ・ ・ Server 430 ・ ・ ・ Application device 601 ・ ・ ・ Physical sensor control unit 610 ・ ・ ・ Physical sensor unit 611 ・ ・ ・ Illumination sensor 612 ・ ・ ・ Sound Pressure sensor 613 ... Acceleration sensor 614 ... Gas sensor 615 ... Pressure sensor 621,703,809 ... Transmission unit 622,702,802 ... Judgment standard storage unit 623,701,801 ... Reception Unit 803: Virtual sensing data storage unit 804: Physical sensing data storage unit 805: Provider side DC storage unit 806: User side DC storage unit 807: Matching unit 808: Data management unit

Claims (9)

The first acquisition unit that acquires the physical sensing data obtained by observing the sensing target by the physical sensor, and
A second acquisition unit that acquires first virtual sensing data representing a determination result of the surrounding situation of the physical sensor, and a second acquisition unit.
A third acquisition unit that acquires a plurality of judgment criteria defined for the situation item to be judged, and
A selection unit that selects one determination criterion corresponding to the first virtual sensing data from the plurality of acquired determination criteria, and a selection unit.
For each of the situation items, the living conditions of a person around the physical sensor are determined based on the acquired physical sensing data using the selected determination criteria, and the determination result of the living conditions of the person is determined. A data generation device including a determination unit that generates a second virtual sensing data representing. The data generation device according to claim 1, wherein the determination standard includes a reference value for at least one of the raw data of the physical sensing data and the processed data of the raw data. The determination criterion was created by performing machine learning to determine a person's living condition from the learning physical sensing data generated under the situation indicated by the first virtual sensing data associated with the determination criterion. The data generator according to claim 1, which includes a trained model. The situation item is a first item that deals with whether or not a person is cooking around the physical sensor that generates the physical sensing data, and whether or not a person is sleeping around the physical sensor. The data generator according to any one of claims 1 to 3, which comprises at least one of the second items to be dealt with. The status item includes the first item.
The physical sensing data includes at least one of illuminance data, sound pressure data, barometric pressure data, temperature data and gas data.
The determination unit was selected for at least one of the raw data of each of the illuminance data, the sound pressure data, the barometric pressure data, the temperature data, and the gas data, and the processed data of the raw data. Apply the judgment criteria and make a judgment for the first item.
The data generator according to claim 4. The status item includes the second item.
The physical sensing data includes at least one of sound pressure data and illuminance data.
The determination unit applies the selected determination criterion to at least one of the raw data of each of the sound pressure data and the illuminance data and the processed data of the raw data, and with respect to the second item. Make a judgment,
The data generator according to claim 4. The computer
Acquiring the physical sensing data obtained by observing the sensing target by the physical sensor,
Acquiring the first virtual sensing data representing the determination result about the surrounding situation of the physical sensor, and
Acquiring multiple judgment criteria defined for the situation item to be judged,
Selecting one judgment criterion corresponding to the first virtual sensing data from the plurality of acquired judgment criteria, and
For each of the situation items, the living conditions of a person around the physical sensor are determined based on the acquired physical sensing data using the selected determination criteria, and the determination result of the living conditions of the person is determined. A data generation method comprising generating a second virtual sensing data representing. On the computer
Acquiring the physical sensing data obtained by observing the sensing target by the physical sensor,
Acquiring the first virtual sensing data representing the determination result about the surrounding situation of the physical sensor, and
Acquiring multiple judgment criteria defined for the situation item to be judged,
Selecting one judgment criterion corresponding to the first virtual sensing data from the plurality of acquired judgment criteria, and
For each of the situation items, the living conditions of a person around the physical sensor are determined based on the acquired physical sensing data using the selected determination criteria, and the determination result of the living conditions of the person is determined. A data generation program for generating and executing a second virtual sensing data representing. The data generator according to any one of claims 1 to 6.
A sensor device including the physical sensor.

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