WO2023047939A1

WO2023047939A1 - Measurement device, measurement method, measurement program, and assessment system

Info

Publication number: WO2023047939A1
Application number: PCT/JP2022/033383
Authority: WO
Inventors: 孝浩山崎
Original assignee: 株式会社村田製作所
Priority date: 2021-09-21
Filing date: 2022-09-06
Publication date: 2023-03-30
Also published as: JPWO2023047939A1

Abstract

A measurement device (1) is provided with: a storage device (12) for storing first time-series data of the CO2 concentration in a first environment in which no CO2 emission source other than humans exists as an emission source for emitting CO2 into the atmosphere; and, a data acquisition device (13) for acquiring second time-series data of the CO2 concentration in a second environment in which a CO2 emission source other than humans exists. A control device (11) calculates a reference value for assessing the CO2 concentration in the second environment on the basis of the difference between the first time-series data and the second time-series data.

Description

Measuring device, measuring method, measuring program, and judgment system

The present disclosure relates to a measuring device, measuring method, measuring program, and determination system for measuring carbon dioxide concentration.

In recent years, in order to prevent the spread of virus infection, it has been advocated to avoid crowding in closed environments. Techniques such as determining the carbon dioxide concentration to evaluate the density of people are also known. For example, Japanese Unexamined Patent Application Publication No. 2020-166709 (Patent Document 1) discloses an estimation device that estimates the number of people in a room based on the temporal change in the carbon dioxide concentration in the indoor space and the respiratory rate per person in the room. do.

Japanese Patent Application Laid-Open No. 2020-166709

According to the estimation device disclosed in Patent Document 1, the number of people in the room can be estimated by determining the carbon dioxide concentration in the indoor space. Depending on the environment, carbon dioxide may be emitted into the atmosphere not only by humans but also by non-human carbon dioxide emission sources. Carbon dioxide emitted from Therefore, it is difficult for the estimation device disclosed in Patent Document 1 to accurately determine the concentration of carbon dioxide derived from people, and there is a risk that the density of people cannot be determined accurately.

The present disclosure has been made to solve such problems, and its object is to provide a technology for accurately determining the density of people in an environment where there are carbon dioxide emission sources other than humans. That is.

A measuring device according to an aspect of the present disclosure is a storage device that stores first time-series data of carbon dioxide concentration in a first environment where there are no carbon dioxide emission sources other than humans as emission sources that emit carbon dioxide into the atmosphere. , a data acquisition device for acquiring second time-series data of carbon dioxide concentration in a second environment in which carbon dioxide emission sources other than humans are present; and a control device. The control device calculates a reference value for determining the carbon dioxide concentration in the second environment based on the difference between the first time series data and the second time series data.

A measurement method according to another aspect of the present disclosure includes: (a) storing first time-series data of carbon dioxide concentration in a first environment where there are no carbon dioxide emission sources other than humans as emission sources that emit carbon dioxide into the atmosphere; (b) obtaining second time-series data of carbon dioxide concentration in a second environment in which non-human carbon dioxide emission sources are present; (c) first time-series data and second time-series data and calculating a reference value for determining the carbon dioxide concentration in the second environment based on the difference between.

A measurement program according to another aspect of the present disclosure provides a computer with: (a) a first time series of carbon dioxide concentration in a first environment where there are no carbon dioxide emission sources other than humans as emission sources that emit carbon dioxide into the atmosphere; (b) obtaining a second time series of carbon dioxide concentration in a second environment in which non-human carbon dioxide sources are present; (c) the first time series and the second and calculating a reference value for determining the carbon dioxide concentration in the second environment based on the difference from the time-series data.

According to the present disclosure, first time-series data of carbon dioxide concentration in a first environment where there are no non-human carbon dioxide emission sources and second time series data of carbon dioxide concentration in a second environment where non-human carbon dioxide emission sources are present A reference value for determining the carbon dioxide concentration in the second environment is generated based on the difference between the two time-series data. As a result, it is possible to determine the concentration of carbon dioxide in an environment where non-human carbon dioxide emission sources exist, so that it is possible to accurately determine the density of people in an environment where non-human carbon dioxide emission sources exist. can be done.

FIG. 2 is a diagram for explaining an application example of the determination system according to Embodiment 1; FIG. FIG. 4 is a diagram showing an example of a standard value of human density with respect to carbon dioxide concentration discharged in each of the first environment and the second environment. 1 is a diagram showing the configuration of a determination system according to Embodiment 1; FIG. It is a figure which shows an example of the change of time-series data. FIG. 4 is a diagram for explaining timings of processing of the measuring device and the determination device according to the first embodiment; It is a figure which shows the display mode of a display. 4 is a diagram showing an example of calculation of a difference between first time-series data and second time-series data by the measuring device according to Embodiment 1; FIG. 4 is a flowchart relating to reference value calculation processing executed by the measuring device according to Embodiment 1. FIG. FIG. 4 is a diagram for explaining generation of first feature data based on time-series data in a first environment; FIG. 4 is a diagram for explaining an example of calculation of feature values; FIG. 10 is a diagram for explaining generation of first feature data based on time-series data in a second environment; FIG. 9 is a diagram showing an example of calculation of a difference between first feature data and second feature data by the measuring device according to Embodiment 2; 10 is a flowchart relating to reference value calculation processing executed by the measuring device according to Embodiment 2; 10 is a flowchart related to difference calculation processing executed by the measuring device according to Embodiment 2; FIG. 11 is a diagram showing an example of calculation of a difference between first feature data and second feature data by the measuring device according to Embodiment 3; FIG. 11 is a diagram showing an example of calculation of a difference between first feature data and second feature data by the measuring device according to Embodiment 3; FIG. 11 is a diagram showing an example of calculation of a difference between first feature data and second feature data by the measuring device according to Embodiment 3; FIG. 11 is a flow chart relating to difference calculation processing executed by the measuring device according to Embodiment 3. FIG. FIG. 12 is a diagram showing an example of calculation of a difference between first feature data and second feature data by the measuring device according to Embodiment 4; FIG. 13 is a flow chart relating to difference calculation processing executed by the measuring device according to the fourth embodiment; FIG. FIG. 13 is a diagram showing the configuration of a determination system according to Embodiment 5; FIG. 14 is a flowchart relating to determination processing executed by the measuring device according to Embodiment 6. FIG.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. The same or corresponding parts in the drawings are denoted by the same reference numerals, and the description thereof will not be repeated.

<Embodiment 1>
A measuring device 1 according to Embodiment 1 and a determination system 100 including the measuring device 1 will be described with reference to FIGS. 1 to 8. FIG.

[Application example]
FIG. 1 is a diagram for explaining an application example of the determination system 100 according to the first embodiment.

As shown in FIG. 1, restaurants can become crowded as the number of customers or clerks increases. It has been advocated to avoid crowding in crowded environments.

Therefore, in Embodiment 1, the sensor 25 of the sensor module 2 measures the concentration of carbon dioxide (hereinafter also referred to as "CO2" (carbon dioxide)) in the indoor space, which changes over time. The determination system 100 then analyzes the time-series data of the CO2 concentration acquired from the sensor module 2 to predict changes in the CO2 concentration in the future. Furthermore, the determination system 100 compares the predicted value of CO2 concentration with a reference value for determining the density of people, and if the predicted value of CO2 concentration exceeds the reference value, or if the predicted value of CO2 concentration is likely to exceed the standard value, it is determined that the density of people is increasing, and in order to prevent the spread of virus infection, the ozonizer 26 is driven, the display 31 included in the notification device 3 or the speaker 32 is used to urge the user to ventilate, or to notify a warning to suppress further increase in the number of people.

Here, the environment in which CO2 is emitted includes an environment in which there are no CO2 emission sources other than humans as emission sources for emitting CO2 into the atmosphere, such as a conference room (hereinafter also referred to as a "first environment"); There is an environment (hereinafter also referred to as “second environment”) in which CO2 emission sources other than humans exist, such as restaurants and factories. For example, in a restaurant, CO2 is emitted by the breathing of customers and clerks, the cooking of ingredients such as grilling, the operation of cooking utensils, and the like. In a manufacturing site such as a factory, CO2 is emitted by the breathing of people such as workers and the operation of manufacturing equipment. Thus, in the second environment, the CO2 concentration tends to be higher than in the first environment due to CO2 emissions from non-humans. Therefore, in the second environment, it is necessary to determine the CO2 concentration in consideration of CO2 emitted from CO2 emission sources other than humans.

FIG. 2 is a diagram showing an example of the reference value of the density of people with respect to the concentration of carbon dioxide emitted in each of the first environment and the second environment. As shown in FIG. 2, in the first environment, CO2 emitted by humans is added to CO2 originally contained in the atmosphere.

For example, when the result of adding CO2 emitted from people to about 400 ppm of CO2 originally contained in the atmosphere is less than 1000 ppm, it can be said that people are not densely populated. When the addition result is 1000 ppm or more and less than 1500 ppm, it can be said that caution is required because people may be crowded. When the addition result is 1500 ppm or more, it can be said that people are dense. These standards are based on figures announced by the Ministry of Health, Labor and Welfare. For details, please refer to the following web address.
https://www.mhlw.go.jp/content/10900000/000616069.pdf

On the other hand, in the second environment, the CO2 emitted by humans is added to the CO2 originally contained in the atmosphere, and the CO2 emitted by non-humans is also added. That is, in the second environment, the CO2 concentration emitted from CO2 emission sources other than humans is added, so the addition result is larger than the CO2 concentration in the first environment. Therefore, in the second environment, the reference value used in the first environment cannot be used as it is, and it is necessary to determine the CO2 concentration in consideration of CO2 emitted from CO2 emission sources other than humans. .

Therefore, the determination system 100 according to Embodiment 1 is configured to determine the CO2 concentration in consideration of CO2 emitted from CO2 emission sources other than humans, as described below.

[Configuration of Judgment System]
FIG. 3 is a diagram showing the configuration of the determination system 100 according to Embodiment 1. As shown in FIG. As shown in FIG. 3 , the determination system 100 includes a measurement device 1 and a determination device 4 .

The measurement device 1 is a device that calculates a reference value for determining the CO2 concentration in the environment in which the sensor module 2 is installed, based on the time-series data of the CO2 concentration acquired from the sensor module 2. The control device 11; A storage device 12 and a data acquisition device 13 are provided.

The control device 11 is an example of a computer such as a processor, and is a computing entity that executes various processes according to various programs. The processor is composed of, for example, a microcontroller, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an MPU (Multi Processing Unit). The processor has the function of executing various processes by executing programs. It may be implemented using a dedicated hardware circuit. A "processor" is not limited to processors in a narrow sense that execute processing in a stored program format, such as CPUs or MPUs, but may include hardwired circuits such as ASICs or FPGAs. As such, processor may also be referred to as processing circuitry having processes pre-defined by computer readable code and/or hardwired circuitry. Note that the control device 11 may be composed of one chip, or may be composed of a plurality of chips. Furthermore, the control device 11 includes volatile memory such as DRAM (Dynamic Random Access Memory) and SRAM (Static Random Access Memory), and non-volatile memory such as ROM (Read Only Memory) and flash memory.

The storage device 12 includes nonvolatile memories such as HDDs (Hard Disk Drives) and SSDs (Solid State Drives). The storage device 12 stores various programs and data such as a measurement program 121 executed by the control device 11 , calculation data 122 referred to by the control device 11 , and time-series data 123 acquired from the sensor module 2 .

The measurement program 121 includes a program that defines the processing procedure of the control device 11 (processing flows shown in FIGS. 8, 13, 14, 18, 20, and 22). The calculation data 122 is data used when executing processing according to the measurement program 121, and includes data for calculating a reference value for determining the CO2 concentration.

Note that the measuring device 1 may store the measuring program 121 and the computing data 122 in advance in the storage device 12, or may acquire the measuring program 121 and the computing data 122 from an external device (not shown) through communication. good. Furthermore, the measurement apparatus 1 may further include a media reader (not shown), and the measurement program 121 and the calculation data 122 may be obtained from a removable disk, which is a storage medium, by the medium reader.

The data acquisition device 13 receives data from the sensor module 2 by performing wired communication or wireless communication with the sensor module 2 via the network 50 . For example, the data acquisition device 13 acquires time-series data of CO2 concentration from the sensor module 2 by communicating with the sensor module 2 .

The sensor module 2 is a device that acquires time-series data of CO2 concentration in the environment in which the sensor module 2 is installed, and includes a control device 21, a communication device 23, and a sensor 25.

The control device 21 is an example of a computer such as a processor, and is a computing entity that executes various processes according to various programs. A processor is configured by, for example, a microcontroller, a CPU, a GPU, or an MPU. Note that the processor has the function of executing various processes by executing programs, but some or all of these functions may be implemented using a dedicated hardware circuit such as ASIC or FPGA. . A "processor" is not limited to processors in a narrow sense that execute processing in a stored program format, such as CPUs or MPUs, but may include hardwired circuits such as ASICs or FPGAs. As such, processor may also be referred to as processing circuitry having processes pre-defined by computer readable code and/or hardwired circuitry. Note that the control device 11 may be composed of one chip, or may be composed of a plurality of chips. Further, the control device 21 includes volatile memory such as DRAM and SRAM, and nonvolatile memory such as ROM and flash memory.

The communication device 23 transmits and receives data to and from the measuring device 1 by performing wired communication or wireless communication with the measuring device 1 via the network 50 . For example, the communication device 23 transmits time-series data of the CO2 concentration to the measuring device 1 by communicating with the measuring device 1 .

The sensor 25 periodically (for example, every minute) measures the CO2 concentration in the environment in which the sensor module 2 is installed. The time-series data of the CO2 concentration obtained by the measurement of the sensor 25 is transmitted to the measurement device 1 by the communication device 23 .

The determination device 4 determines the CO2 concentration in the environment in which the sensor module 2 is installed based on the CO2 concentration reference value calculated by the measurement device 1 from the CO2 concentration time-series data acquired by the sensor module 2. A device comprising a control device 41 , a storage device 42 , a data acquisition device 43 and a data output device 44 .

The control device 41 is an example of a computer such as a processor, and is a computing entity that executes various processes according to various programs. The processor is composed of, for example, a microcontroller, CPU, FPGA, GPU, or MPU. Note that the processor has the function of executing various processes by executing programs, but some or all of these functions may be implemented using a dedicated hardware circuit such as ASIC or FPGA. . A "processor" is not limited to processors in a narrow sense that execute processing in a stored program format, such as CPUs or MPUs, but may include hardwired circuits such as ASICs or FPGAs. As such, processor may also be referred to as processing circuitry having processes pre-defined by computer readable code and/or hardwired circuitry. Note that the control device 41 may be composed of one chip, or may be composed of a plurality of chips. Further, the control device 41 includes volatile memory such as DRAM and SRAM, and nonvolatile memory such as ROM and flash memory.

The storage device 42 includes nonvolatile memories such as HDDs and SSDs. The storage device 42 stores various programs and data such as a determination program 421 executed by the control device 41 and reference value data 422 referred to by the control device 41 .

The determination program 421 includes a program in which the processing procedures of the control device 41 are defined. Specifically, the determination program 421 defines a processing procedure for determining the CO2 concentration in the environment in which the sensor module 2 is installed. The reference value data 422 is data indicating a reference value for determining the CO2 concentration in the environment in which the sensor module 2 is installed, and is acquired from the measuring device 1 .

Note that the determination device 4 may store the determination program 421 in the storage device 42 in advance, or may acquire the determination program 421 from an external device (not shown) through communication. Furthermore, the determination device 4 may further include a media reader (not shown), and the determination program 421 may be acquired from a removable disk, which is a storage medium, by the media reader.

The data acquisition device 43 receives data from the measuring device 1 by performing wired communication or wireless communication with the measuring device 1 via the network 50 . For example, the data acquisition device 43 acquires the reference value data 422 indicating the reference value for judging the CO2 concentration from the measuring device 1 .

The data output device 44 transmits data to each of the notification device 3 and the ozonizer 26 by performing wired or wireless communication with each of the notification device 3 (not shown) and the ozonizer 26 (not shown) via the network 50. . For example, data output device 44 transmits control signals for controlling notification device 3 and ozonizer 26 to each of notification device 3 and ozonizer 26 .

In the determination system 100 configured as described above, the measuring device 1 calculates a reference value of the CO2 concentration for determining the density of people in the environment where the sensor module 2 is installed.

The determination device 4 analyzes the time-series data of the CO2 concentration acquired by the sensor module 2, and uses the reference value calculated by the measurement device 1 to determine the CO2 concentration in the environment in which the sensor module 2 is installed. , determine the density of people in the environment where the sensor module 2 is installed. In addition to determining the human density from the obtained CO2 concentration, the determination device 4 can also determine the human density from the CO2 concentration that will be reached when the current state continues. A prediction model is used to predict changes in time-series data in the future. Using a prediction model makes it possible to predict changes in time-series data, and to determine the CO2 concentration by comparing the predicted value of the CO2 concentration with a reference value.

A prediction model is defined by a function (formula) that expresses the relationship between elapsed time and data that changes over time. For example, the prediction model is defined by a function (formula) represented by the following formula (1).

In Equation (1), _C∞ is represented by Equation (2) below.

In Equation (1), t represents a certain timing (time). C is a parameter representing the CO2 concentration ([ppm]) generated in a closed environment (indoor space) at timing t. V is a parameter representing the volume ([m ³ ]) of the closed environment (indoor space). Q is a parameter representing the ventilation rate ([m ³ /h]) in a closed environment (indoor space). In Equation (2), G is a parameter representing the CO2 concentration emission amount ([ppm*m ³ /h]) emitted from a closed environment (indoor space). C _out is a parameter representing the CO2 concentration ([ppm]) of outside air (CO2 concentration originally contained in the atmosphere), and is approximately 400 ppm.

[Specific example of forecasting changes in time-series data]
Prediction of changes in time-series data will be described with reference to FIGS. 4 and 5. FIG. FIG. 4 is a diagram showing an example of changes in time-series data.

FIG. 4 shows a graph of time-series data with the CO2 concentration on the vertical axis and the time on the horizontal axis. Line A represents the measured CO2 concentration actually obtained by the sensor 25 after _t0 . Furthermore, with the time point at which changes in future time-series data are predicted as _t0 , changes in time-series data at time _t0 are predicted using the prediction model represented by the above formulas (1) and (2). The result is represented by line G.

As shown in FIG. 4, the predicted value line G approximates and roughly matches the measured value line A. That is, by using the prediction model represented by the above-described formulas (1) and (2), changes in the time-series data can be predicted so as to roughly match the CO2 concentration actually measured by the sensor 25. It becomes possible to

FIG. 5 is a diagram for explaining processing timings of the measuring device 1 and the determination device 4 according to the first embodiment. In FIG. 5, the timing of processing performed by the sensor module 2, the timing of processing performed by the measuring device 1, and the timing of processing performed by the determination device 4 are shown.

As shown in FIG. 5, when the sensor module 2 acquires the time-series data of the CO2 concentration from _t10 to _t11 , the measuring device 1 collects the time-series data (from _t10 to _t11) after t11 _. (time-series data)) to calculate the reference value for judging the CO2 concentration. The determination device 4 uses the reference value calculated by the measurement device 1 to determine the CO2 concentration after _t12 . The period at which the sensor module 2 acquires the time-series data (for example, the data acquisition period in the period of t ₁₀ to t ₁₁ or t ₁₁ to t ₁₂ ) is, for example, 1-minute intervals, 5-minute intervals, or 10-minute intervals. be. A period (for example, a period from t ₁₁ to t ₁₅ ) in which the measuring device 1 calculates the reference value is, for example, one week or ten days. The sensor module 2 acquires time-series data during the period (for example, the period from t ₁₂ to t ₁₃ or from t ₁₃ to t ₁₄ ) in which the determination device 4 determines the CO2 concentration using the reference value calculated by the measurement device 1. For example, 1 minute, 5 minute, or 10 minute intervals. In FIG. 5, the time-series data used for reference value calculation and the time-series data used for CO2 determination are separated, but both may be shared.

In this way, the measuring device 1 periodically calculates the reference value based on the time-series data acquired by the sensor module 2 for each predetermined time interval (for example, one week or ten days).

[Display mode]
FIG. 6 is a diagram showing a display mode of the display 31. As shown in FIG. As shown in FIG. 6, the screen displayed on the display 31 includes an icon 311 indicating the current CO2 concentration in the environment (e.g., restaurant) where the sensor module 2 is installed, and a graph 312 indicating changes in the CO2 concentration. , an icon 313 indicating the remaining time (predicted arrival time) until ventilation is required, and an icon 314 for enabling voice notification of the determination result by the speaker 32 .

Note that the user may enable or disable the voice notification by performing a touch operation on the icon 314, or enable or disable the voice notification by operating the icon 314 using a tool such as a mouse (not shown). You can set it to disabled.

The change in CO2 concentration is predicted using a prediction model, and the remaining time until ventilation is required is calculated by comparing the predicted value with the reference value. The display 31 notifies the user of the calculated remaining time with an icon 313 .

[Calculation of reference value]
Calculation of the reference value by the measuring device 1 will be described with reference to FIGS. 7 and 8. FIG. 7 and 8, an example in which the measuring device 1 calculates the reference value of the CO2 concentration in the second environment such as the restaurant shown in FIG. 1 will be described.

FIG. 7 is a diagram showing an example of calculation of the difference between the first time-series data and the second time-series data by the measuring device 1 according to Embodiment 1. FIG. In preparation for calculating the reference value in the second environment, the measuring device 1 acquires time-series data (hereinafter also referred to as "first time-series data") of the CO2 concentration in the first environment such as a conference room in advance. .

For example, FIG. 7(A) shows a graph of the first time-series data with CO2 concentration on the vertical axis and time on the horizontal axis. As shown in FIG. 7A, the measuring device 1 obtains first time-series data of CO2 concentration measured periodically (for example, every minute) in the first environment. In the graph of FIG. 7A, it is assumed that a meeting is being held in a conference room, for example, in a portion where the amount of change in CO2 concentration is large. In other words, it is assumed that CO2 is generated due to people present in the first environment.

The measuring device 1 stores the first time-series data in the first environment as shown in FIG. Data (hereinafter also referred to as “second time-series data”) is acquired.

For example, FIG. 7(B) shows a graph of the second time-series data with the CO2 concentration on the vertical axis and the time on the horizontal axis. As shown in FIG. 7B, the measuring device 1 obtains second time-series data of the CO2 concentration measured periodically (for example, every minute) in the second environment. In the graph of FIG. 7(B), it is assumed that the portion where the amount of change in the CO2 concentration is large indicates, for example, that the number of customers in a restaurant has increased, or that foodstuffs such as grilling are being cooked. In other words, in addition to CO2 being generated by people present in the second environment, it is assumed that CO2 is also being generated by causes other than people present in the second environment.

The measuring device 1 calculates the difference between the first time-series data and the second time-series data. Specifically, the measuring device 1 calculates a first calculated value based on the first time-series data, calculates a second calculated value based on the second time-series data, and calculates the first calculated value from the second calculated value. Calculate the difference by subtracting.

For example, the measuring device 1 calculates, as the first calculated value, an average value (for example, 636.08) of multiple CO2 concentrations acquired in time series in the first environment included in the first time series data. In addition, the measuring device 1 calculates, as a second calculated value, an average value (for example, 942.48) of a plurality of CO2 concentrations acquired in time series in the second environment included in the second time series data. Then, the measuring device 1 calculates the difference (eg, 306.4) by subtracting the first calculated value (eg, 636.08) from the second calculated value (eg, 942.48).

The difference described above arises from the environmental difference between the first environment and the second environment. That is, the difference described above is a value resulting from CO2 emitted from CO2 emission sources other than humans in the second environment. Therefore, the measuring device 1 calculates a reference value for determining the CO2 concentration in the second environment, taking into consideration the calculated difference, using the reference value of the CO2 concentration used in the first environment as a reference.

Specifically, the measuring device 1 adds the difference to the reference value of the CO2 concentration in the first environment (hereinafter also referred to as the “first reference value”) to obtain the reference value of the CO2 concentration in the second environment ( hereinafter also referred to as a “second reference value”). For example, if the first reference value of the CO2 concentration in the first environment is 1000 ppm, the measuring device 1 adds 306.4 ppm (difference) to 1000 ppm to calculate 1306.4 as the second reference value. That is, the fact that the CO2 concentration exceeds the second reference value of 1306.4 ppm in the second environment means that the density of people is increasing in the second environment, and caution is required.

Alternatively, the determination device 4 may remove in advance the CO2 concentration emitted from CO2 emission sources other than humans from the second time-series data of the CO2 concentration in the second environment by subtracting the difference from the second time-series data. good. That is, the measuring device 1 converts the second time-series data of the CO2 concentration in the second environment to the time-series data of the CO2 concentration in the first environment without changing the reference value of the CO2 concentration for determining the density of people. It can be converted accordingly. In this way, the determination device 4 can compare the converted time-series data with the first reference value in the first environment in the second environment.

[Processing by measuring device]
FIG. 8 is a flowchart regarding processing executed by the measuring device 1 according to the first embodiment. The control device 11 of the measurement device 1 executes the measurement program 121 stored in the storage device 12 to periodically execute the process of the flowchart shown in FIG. In the drawings, "S" is used as an abbreviation for "STEP".

As shown in FIG. 8, the control device 11 determines whether or not second time-series data for a predetermined period of time (for example, one day's worth) has been acquired (S1). If the control device 11 has not acquired the second time-series data for the predetermined time (NO in S1), the process ends.

On the other hand, when the control device 11 acquires the second time-series data for a predetermined time (YES in S1), it calculates the first calculated value based on the first time-series data (S2). For example, as shown in FIG. 7A, the control device 11 calculates the average value of the first time-series data as the first calculated value based on the first time-series data in the first environment. Furthermore, the control device 11 calculates a second calculated value based on the second time-series data (S3). For example, as shown in FIG. 7B, the control device 11 calculates the average value of the second time-series data as the second calculated value based on the second time-series data in the second environment. Then, the control device 11 calculates the difference by subtracting the first calculated value from the second calculated value (S4).

The control device 11 calculates the second reference value of the CO2 concentration in the second environment based on the difference (S5). For example, the control device 11 calculates the second reference value of the CO2 concentration in the second environment by adding the difference to the predetermined first reference value of the CO2 concentration in the first environment. After that, the control device 11 terminates this process.

As described above, the measuring device 1 according to the first embodiment provides the first time-series data of the CO2 concentration in the first environment in which there are no non-human CO2 emission sources, and the second time-series data in which there are non-human CO2 emission sources. Calculate the difference between the CO2 concentration in the environment and the second time-series data, and calculate a reference value (second reference value in this example) for determining the CO2 concentration in the second environment based on the calculated difference . As a result, the determination device 4 can determine the CO2 concentration in the second environment by using the reference value calculated by the measurement device 1, taking into account CO2 emitted from CO2 emission sources other than humans. Even in the second environment where there are CO2 emission sources other than humans, the CO2 concentration can be accurately determined. That is, using the reference value calculated by the measuring device 1, the determination device 4 can accurately determine the density of people in the second environment where there are CO2 emission sources other than people.

The step (S2) of obtaining the first calculated value from the first time-series data in the flow of FIG. 8 may be a step of reading the first calculated value from the storage device 12. That is, the control device 11 calculates in advance the first time-series data or the first calculated value regarding the first environment and stores it in the storage device 12, and when calculating the second reference value from the second time-series data, can also calculate the difference from the second calculated value by reading out the stored first calculated value.

Also, the second time-series data in the second environment acquired by the sensor module 2 differs according to the second environment in which the sensor module 2 is installed. For example, in a restaurant, the concentration of CO2 emitted from non-human CO2 emission sources depends on whether the ingredients are cooked such as grilling, the cooking time, the number of cooking utensils, and the size of the restaurant. is different. Alternatively, in a manufacturing site such as a factory, the concentration of CO2 emitted from non-human CO2 emission sources varies depending on the number of manufacturing apparatuses, the operating time of the manufacturing apparatuses, the size of the factory, and the like. Therefore, it is preferable that the reference value for determining the CO2 concentration is also set according to the second environment in which the sensor module 2 is installed.

In this regard, the measuring device 1 acquires second time-series data of the CO2 concentration for each second environment in which the sensor module 2 is installed, and calculates the difference between the acquired second time-series data and the first time-series data. By doing so, the reference value can be set according to the second environment in which the sensor module 2 is installed. Thereby, the determination device 4 can appropriately determine the CO2 concentration using the reference value calculated by the measurement device 1 according to the second environment in which the sensor module 2 is installed.

In some cases, it is possible to measure only the CO2 concentration emitted by people other than people, but especially in restaurants, it is necessary to stop the business and cook in order to measure the CO2 concentration, or the person who cooks In some cases, it is difficult to measure CO2 concentrations other than human-derived CO2 concentrations, such as the fact that the influence of CO2 cannot be eliminated. In this regard, the measuring device 1 can set the reference value for measuring the CO2 concentration while the actual business is being carried out, so that the influence on the business and manufacturing can be minimized, and the actual cooking can be performed. It is possible to use the concentration of CO2 emitted by people other than people in the state of operation.

<Embodiment 2>
A measuring device according to a second embodiment will be described with reference to FIGS. 9 to 14. FIG. Only parts of the measuring device according to the second embodiment that are different from the measuring device 1 according to the first embodiment will be described below.

[Calculation of difference]
FIG. 9 is a diagram for explaining generation of first feature data based on time-series data in the first environment. FIG. 9A shows a graph of the first time-series data in which the vertical axis represents CO2 concentration and the horizontal axis represents time. As shown in FIG. 9A, the measuring device 1 acquires first time-series data of CO2 concentration measured periodically (for example, every minute) in the first environment.

Next, the measuring device 1 generates first feature data based on the first time-series data in the first environment, and stores the generated first feature data in the storage device 12 as calculation data 122 .

Specifically, the measuring device 1 generates a histogram from the first time-series data as the first feature data. For example, FIGS. 9B and 9C show histograms with the number on the vertical axis and G/Q on the horizontal axis. As shown in FIGS. 9(B) and 9(C), the measuring device 1 obtains the first feature data representing the feature of the first time-series data by representing the number for each value of G/Q in a histogram. Generate. Note that the measuring device 1 may use Q/V or G/V as the horizontal axis in the histogram corresponding to the first feature data, instead of G/Q.

As explained using formulas (1) and (2), G/Q is a parameter that represents the CO2 concentration per unit ventilation. Q/V is a parameter representing the ventilation volume per unit volume. G/V is a parameter representing the CO2 concentration emission amount per unit volume. In generating the first feature data (histogram), the measuring device 1 may calculate any of G/Q, Q/V, and G/V feature values instead of C (CO2 concentration) itself.

Here, calculation of feature values will be described with reference to FIG. FIG. 10 is a diagram for explaining an example of calculation of feature values. FIG. 10 shows a graph of time-series data of the CO2 concentration, with the vertical axis representing the CO2 concentration and the horizontal axis representing time. The measuring device 1 calculates the characteristic value from the equations (1) and (2) by using simultaneous equations.

Specifically, as shown in FIG. 10, the measuring device 1 extracts a specific number of pieces of data from the first time-series data. For example, the measuring device 1 extracts the CO2 concentration C(t ₀ ) at t ₀ , the CO2 concentration C(t ₁ ) at t ₁ , and the CO2 concentration C(t _{2 ) at t 2} _.

The measuring device 1 calculates the following simultaneous equations (3) from the equation (1) and the extracted C(t ₀ ), C(t ₁ ), and C(t ₂ ).

The measuring device 1 calculates the following formula (4) from the simultaneous equations (3).

The measuring device 1 calculates the following formula (5) from the formula (4).

The measuring device 1 can calculate the following equation (6) from equations (2) and (5).

Also, the measuring device 1 can calculate the following formula (7) from the formulas (3) and (5).

Thus, the measuring device 1 can calculate G/Q (equation (6)) and Q/V (equation (7)) from the first time-series data. Furthermore, the measuring device 1 can calculate G/V from G/Q (equation (6)) and Q/V (equation (7)).

In the example of FIG. 10, the measuring device 1 calculates the feature value using the CO2 concentration (C(t)) at three extraction timings of t ₀ , t ₁ and t ₂ , but these extraction timings can be changed. A plurality of feature values can be calculated by In the example of FIG. 9A, the CO2 concentration is acquired every minute, so if t0 is 15:00, the measuring device 1 can set _t0 to 15:01, _t1 to 15:01, _{t2 to} should be set to 15:02. Furthermore, the measuring device 1 updates t for one minute, and if _t0 is set to 15:01, _t1 is set to 15:02 and _t2 is set to 15:03.

Returning to FIG. 9, the measuring device 1 generates a histogram based on multiple feature values calculated from the first time-series data. Here, the measuring device 1 may calculate the characteristic value (G/Q in this example) using all the data included in the first time-series data shown in FIG. A feature value (G/Q in this example) may be calculated using data selected according to a predetermined rule from all data included in the first time-series data shown in (A).

For example, the measuring device 1 may calculate the feature value using the CO2 concentration during the period in which the amount of change in the CO2 concentration in the first time-series data exceeds the threshold (for example, 200 ppm). Specifically, in the first time-series data, when the CO2 concentration that changes in time series changes from a minimum value to a maximum value, the measuring device 1 detects the difference between the minimum value and the maximum value (that is, the CO2 concentration A period in which the amount of change) exceeds the threshold value (200 ppm) may be specified, and the feature value may be calculated using the CO2 concentration acquired within the period.

As an example, in FIG. 9A, the CO2 concentration acquired at timing _t21 is the minimum value, the CO2 concentration acquired at timing _t22 is the maximum value, and the minimum value and the maximum value exceeds 200 ppm, the measuring device 1 calculates the feature value using the CO2 concentration acquired during the period from timing _t21 to timing _t22 .

When the measuring device 1 calculates a plurality of feature values using all the data included in the first time-series data shown in FIG. 9(A), the histogram corresponding to the calculated feature values is shown in FIG. 9(B). is represented by On the other hand, when the measuring device 1 calculates a plurality of feature values using data selected according to the above-described predetermined rule among all the data included in the first time-series data shown in FIG. A histogram corresponding to the obtained feature values is shown in FIG. 9(C).

In the histogram of FIG. 9B, the feature values are calculated using all the data included in the first time-series data shown in FIG. 9A. When the frequency is low, the features of the histogram regarding the event are difficult to appear. On the other hand, in the histogram of FIG. 9C, among all the data included in the first time-series data shown in FIG. Since the feature values are calculated using the In the first time-series data, if the frequency of occurrence of events is high, that is, if many peaks appear that are selected according to the above-described predetermined rule, the data is used as it is without extracting specific data. do it.

FIG. 11 is a diagram for explaining generation of second feature data based on time-series data in the second environment. The measuring device 1 stores the first feature data of the first time series data in the first environment as shown in FIG. 9B or 9C in the storage device 12, and then stores the first feature data in the second environment. 2. Generate second feature data of time-series data.

For example, FIG. 11(A) shows a graph of the second time-series data with the CO2 concentration on the vertical axis and the time on the horizontal axis. As shown in FIG. 11A, the measuring device 1 acquires second time-series data of the CO2 concentration measured periodically (for example, every minute) in the second environment.

Next, the measuring device 1 generates second feature data based on the second time-series data in the second environment, and stores the generated second feature data in the storage device 12 as calculation data 122 .

Specifically, the measuring device 1 generates a histogram from the second time-series data as the second feature data. For example, FIGS. 11B and 11C show histograms with the number on the vertical axis and G/Q on the horizontal axis. As shown in FIGS. 11(B) and 11(C), the measuring device 1 obtains the second feature data representing the feature of the second time-series data by representing the number for each value of G/Q in a histogram. Generate. Note that the measuring apparatus 1 may use Q/V or G/V as the horizontal axis in the histogram corresponding to the second feature data, instead of G/Q. However, the measuring device 1 uses the same type of feature value (for example, G/Q) for the first feature data and the second feature data. The method of calculating these feature values is the same as the method of calculating the first feature data in the first environment described with reference to FIGS. 9 and 10. FIG.

Here, the measuring device 1 may calculate the characteristic value (G/Q in this example) using all the data included in the time-series data shown in FIG. ), data selected according to a predetermined rule may be used to calculate the characteristic value (G/Q in this example).

For example, the measuring device 1 may calculate the characteristic value using the CO2 concentration during the period in which the amount of change in the CO2 concentration in the second time-series data exceeds the threshold (for example, 200 ppm). Specifically, in the second time-series data, when the CO2 concentration that changes in time series changes from a minimum value to a maximum value, the measuring device 1 detects the difference between the minimum value and the maximum value (that is, the CO2 concentration A period in which the amount of change) exceeds the threshold value (200 ppm) may be specified, and the feature value may be calculated using the CO2 concentration acquired within the period.

As an example, in FIG. 11A, the CO2 concentration acquired at timing _t31 is the minimum value, the CO2 concentration acquired at timing _t32 is the maximum value, and the minimum value and the maximum value exceeds 200 ppm, the measuring device 1 calculates the feature value using the CO2 concentration acquired during the period from timing _t31 to timing _t32 .

When the measuring device 1 calculates a plurality of feature values using all data included in the second time-series data shown in FIG. 11(A), the histogram corresponding to the calculated feature values is shown in FIG. 11(B). is represented by On the other hand, when the measurement device 1 calculates a plurality of feature values using data selected according to the above-described predetermined rule among all the data included in the second time-series data shown in FIG. A histogram corresponding to the obtained feature values is shown in FIG. 11(C).

In the histogram of FIG. 11B, the feature values are calculated using all the data included in the second time-series data shown in FIG. 11A. When the frequency is low, the features of the histogram regarding the event are difficult to appear. On the other hand, in the histogram of FIG. 11(C), among all the data included in the second time-series data shown in FIG. Since the feature values are calculated using the In the second time-series data, if the frequency of occurrence of events is high, that is, if many peaks appear that are selected according to the predetermined rule described above, the data is used as it is without extracting specific data. do it. Whether the specific data is extracted or the data is used as it is, it is preferable to perform the same processing on the data measured in each of the first environment and the second environment.

FIG. 12 is a diagram showing an example of calculation of the difference between the first feature data and the second feature data by the measuring device 1 according to the second embodiment. FIG. 12A shows a histogram of the first feature data in the first environment. That is, the histogram of FIG. 12(A) corresponds to the histogram of the first feature data shown in FIG. 9(C). FIG. 12B shows a histogram of the second feature data in the second environment. That is, the histogram of FIG. 12(B) corresponds to the histogram of the second feature data shown in FIG. 11(C).

As shown in FIG. 12, after generating the second feature data, the measuring device 1 calculates the difference between the first feature data stored in advance in the storage device 12 and the generated second feature data. . Specifically, the measuring device 1 calculates a first calculated value from the first characteristic data, calculates a second calculated value from the second characteristic data, and subtracts the first calculated value from the second calculated value. , to calculate the difference.

For example, as shown in FIG. 12(A), the measuring device 1 calculates the average value of the feature values (G/Q in this example) as the first calculated value based on the histogram in the first environment. Further, as shown in FIG. 12B, the measuring device 1 calculates the average value of the characteristic values (G/Q in this example) as the second calculated value based on the histogram in the second environment. Then, the measuring device 1 calculates the difference by subtracting the first calculated value from the second calculated value.

The difference described above arises from the environmental difference between the first environment and the second environment. That is, the difference described above is a value resulting from CO2 emitted from CO2 emission sources other than humans in the second environment. Therefore, the measuring device 1 calculates data for determining the CO2 concentration in the second environment, taking into consideration the calculated difference, using the reference value of the CO2 concentration used in the first environment as a reference.

Specifically, the determination device 4 calculates the second reference value of the CO2 concentration in the second environment by adding the difference to the first reference value of the CO2 concentration in the first environment. For example, if the first reference value of the CO2 concentration in the first environment is 1000 ppm, the measuring device 1 calculates 1306.4 as the second reference value by adding a difference (for example, 306.4 ppm) to 1000 ppm. do. That is, the fact that the CO2 concentration exceeds the second reference value of 1306.4 ppm in the second environment means that the density of people is increasing in the second environment, and caution is required.

Alternatively, the determination device 4 may add the difference from the second time-series data of the CO2 concentration in the second environment to remove in advance the CO2 concentration emitted from the CO2 emission sources other than humans from the second time-series data. good. That is, the measuring device 1 converts the second time-series data of the CO2 concentration in the second environment to the time-series data of the CO2 concentration in the first environment without changing the reference value of the CO2 concentration for determining the density of people. It can be converted accordingly. In this way, the determination device 4 can compare the converted time-series data with the first reference value in the first environment in the second environment.

In the example of FIG. 12, the measuring device 1 calculates the average value of the characteristic values as the calculated value, but may calculate the variance of the characteristic values as the calculated value. That is, the measuring device 1 may calculate the variance value of the feature value (G/Q in this example) as the first calculated value based on the histogram in the first environment. Further, the measuring device 1 may calculate the variance of the characteristic value (G/Q in this example) as the second calculated value based on the histogram in the second environment. Furthermore, the measuring device 1 may calculate both the average value and the variance value of the feature values as the calculated values. That is, the measuring device 1 may calculate at least one of the average value and the variance value of the feature values as the calculated value.

Note that the measuring device 1 calculates the first calculated value using the histogram of the first feature data shown in FIG. 9(C) as shown in FIG. 12(A). The first calculated value may be calculated using the histogram of the first feature data shown. As shown in FIG. 12(B), the measuring apparatus 1 calculates the second calculated value using the histogram of the second feature data shown in FIG. 11(C). The second calculated value may be calculated using a histogram of two feature data.

[Processing by measuring device]
Processing executed by the measuring device 1 according to the second embodiment will be described with reference to FIGS. 13 and 14. FIG. FIG. 13 is a flowchart relating to reference value calculation processing executed by the measuring device 1 according to the second embodiment. FIG. 14 is a flowchart of difference calculation processing executed by the measuring device according to the second embodiment. The control device 11 of the measurement device 1 executes the measurement program 121 stored in the storage device 12 to periodically execute the processes of the flowcharts shown in FIGS. 13 and 14 . In the drawings, "S" is used as an abbreviation for "STEP".

As shown in FIG. 13, the control device 11 determines whether or not the second time-series data for a predetermined period of time (for example, one day's worth) has been acquired (S11). If the control device 11 has not acquired the second time-series data for the predetermined time (NO in S11), the process ends.

On the other hand, when the control device 11 acquires the second time-series data for a predetermined period of time (YES in S11), the control device 11 controls the second time-series data for the period in which the amount of change in the CO2 concentration exceeds the threshold value (for example, 200 ppm). CO2 concentration is extracted (S12). The control device 11 may skip the process of S12 and proceed to the process of S13 when handling time-series data from which it is not necessary to extract the CO2 concentration. The control device 11 generates a histogram (first feature data) as shown in FIG. 9C from the first time-series data (S13). Further, the control device 11 generates a histogram (second feature data) as shown in FIG. 11C from the extracted second time-series data of the CO2 concentration (S14).

Next, the control device 11 executes difference calculation processing for calculating the difference between the first feature data and the second feature data (S15).

As shown in FIG. 14, in the difference calculation process, the control device 11 calculates a first calculated value from the first feature data (S111). For example, as shown in FIG. 12A, the control device 11 uses the histogram (first feature data) in the first environment to calculate the statistic of the feature value (G/Q in this example) as the first calculated value. calculate. Statistics include mean, variance, minimum, median, first quartile, third quartile, skewness, and kurtosis.

The control device 11 calculates a second calculated value from the second feature data (S112). For example, as shown in FIG. 12B, the control device 11 uses the histogram (second feature data) in the second environment to calculate the statistic of the feature value (G/Q in this example) as the second calculated value. calculate. Statistics include mean, variance, minimum, median, first quartile, third quartile, skewness, and kurtosis.

Then, the control device 11 calculates the difference by subtracting the first calculated value calculated from the first feature data from the second calculated value calculated from the second feature data (S113).

Returning to FIG. 13, the control device 11 calculates the second reference value of the CO2 concentration in the second environment based on the difference (S16). For example, the control device 11 calculates the second reference value of the CO2 concentration in the second environment by adding the difference to the predetermined first reference value of the CO2 concentration in the first environment. After that, the control device 11 terminates this process.

As described above, the measuring device 1 according to the second embodiment provides the first feature data generated based on the first time-series data of the CO2 concentration in the first environment in which there are no CO2 emission sources other than humans, Calculating the difference between the second feature data generated based on the second time-series data of the CO2 concentration in the second environment where the CO2 emission source is present, and calculating the CO2 concentration in the second environment based on the calculated difference A reference value (second reference value in this example) for determination is calculated. As a result, the determination device 4 can determine the CO2 concentration in the second environment by using the reference value calculated by the measurement device 1, taking into account CO2 emitted from CO2 emission sources other than humans. Even in the second environment where there are CO2 emission sources other than humans, the CO2 concentration can be accurately determined.

The measuring device 1 subtracts the first calculated value calculated from the first feature data from the second calculated value calculated from the second feature data, thereby obtaining the difference between the first feature data and the second feature data. is calculated, the difference can be calculated relatively easily without performing complicated processing such as generating new feature data for calculating the difference.

In S13 of the flow of FIG. 13, the control device 11 may read the first feature data. That is, the measuring device 1 calculates in advance the first feature data from the first time-series data, stores the first feature data in the storage device 12, and reads out the stored first feature data, thereby shortening the processing time. can be done. Similarly, in S111 of FIG. 14, the measuring device 1 may read the first calculated value. That is, the measuring device 1 calculates the first calculated value from the first feature data in advance and stores it in the storage device 12, so that the first calculated value can be read out from the storage device 12 at the time of determination. .

<Embodiment 3>
A measuring device 1 according to Embodiment 3 will be described with reference to FIGS. 15 to 18. FIG. Only parts of the measuring device 1 according to the third embodiment that differ from the measuring device 1 according to the second embodiment will be described below.

[Calculation of difference]
15 to 17 are diagrams showing an example of calculation of the difference between the first feature data and the second feature data by the measurement device 1 according to Embodiment 3. FIG. FIG. 15A shows a histogram of the first feature data in the first environment. That is, the histogram of FIG. 15(A) corresponds to the histogram of the first feature data shown in FIG. 9(C). FIG. 15B shows a histogram of the second feature data in the second environment. That is, the histogram of FIG. 15(B) corresponds to the histogram of the second feature data shown in FIG. 11(C).

As shown in FIG. 15, after generating the second feature data, the measuring apparatus 1 according to the third embodiment compares the first feature data stored in advance in the storage device 12 with the generated second feature data. Calculate the difference between Specifically, the measuring device 1 generates new feature data (hereinafter also referred to as “third feature data”) by subtracting the first feature data from the second feature data.

For example, the measuring device 1 subtracts the frequency of each class of the histogram of the first feature data shown in FIG. 15(A) from the frequency of each class of the histogram of the second feature data shown in FIG. 15(B). , generates a histogram of the third feature data as shown in FIG. 15(C). Therefore, it is desirable that the histogram of the first environment and the histogram of the second environment are created with the same class width.

Here, if the number of data included in the first feature data is less than the number of data included in the second feature data, the measuring apparatus 1 cannot appropriately generate the third feature data. When the above condition is satisfied, the third feature data is generated.

For example, the measuring device 1 generates the third feature data when the acquisition time of the CO2 concentration in the first time-series data is equal to or longer than the acquisition time of the CO2 concentration in the second time-series data. Alternatively, when the data amount (the number of feature values G/Q) in the first feature data is equal to or greater than the data amount (the number of feature values G/Q) in the second feature data, the measuring apparatus 1 determines the third feature data to generate

Furthermore, when the number of data in the first feature data is smaller than the number of data in the second feature data, the measuring device 1 multiplies the number of data in the first feature data by a predetermined number to obtain the number of data in the first feature data. may be equal to or greater than the number of data in the second feature data, and then the third feature data may be generated. The predetermined number to be multiplied may be, for example, a number (eg, D1/D2) obtained by dividing the number of data in the second feature data (eg, D2) by the number of data in the first feature data (eg, D1).

After generating the third feature data, the measuring device 1 calculates the average value (for example, 394.63) of the feature values (G/Q in this example) as the difference based on the histogram of the third feature data. Note that the measuring device 1 may calculate the statistic of the feature value as the difference based on the histogram of the third feature data. In addition to the mean, the statistics include variance, minimum, median, first quartile, third quartile, skewness, and kurtosis. The measuring device 1 may calculate at least one of the statistics described above. For example, the measuring device 1 may calculate the variance of the feature values as the difference based on the histogram of the third feature data. Furthermore, the measuring device 1 may calculate both the mean value and the variance value of the feature values as the difference based on the histogram of the third feature data. Here, in calculating the difference, the measuring device 1 changes the plurality of feature values included in the histogram of the third feature data so that the lowest value becomes 0.

For example, as shown in FIG. 16A, the measuring apparatus 1 sets all of the plurality of feature values included in the third feature data as change targets, and as shown in FIG. The feature values to be changed are arranged in order from 0 so that the lowest value in is 0.

Alternatively, as shown in FIGS. 17(A) and 17(B), the measuring apparatus 1 determines the maximum value of the feature values included in the first feature data among the plurality of feature values included in the third feature data. As shown in FIG. 17C, the feature values to be changed are arranged in order from 0 so that the minimum value of the feature values to be changed is 0, with the portion including the corresponding feature value being the change target.

As a result, the measuring device 1 can calculate a characteristic value caused by CO2 emitted from CO2 emission sources other than humans in the second environment, and can calculate a difference based on such a characteristic value.

Note that the measuring apparatus 1 generates the third feature data using the histogram of the first feature data shown in FIG. 9(C) as shown in FIG. 15(A). The histogram of the first feature data shown may be used to generate the third feature data. As shown in FIG. 15(B), the measuring apparatus 1 generates the third feature data using the histogram of the second feature data shown in FIG. 11(C). A histogram of two feature data may be used to generate the third feature data.

[Processing by measuring device]
FIG. 18 is a flowchart relating to the difference calculation process (S15 in FIG. 13) executed by the measuring device 1 according to the third embodiment. The control device 11 of the measurement device 1 according to Embodiment 3 executes the measurement program 121 stored in the storage device 12, thereby periodically executing the processing of the flowchart shown in FIG.

As shown in FIG. 13, the control device 11 generates a histogram (first feature data) as shown in FIG. 9C and a histogram (second feature data) as shown in FIG. data) is generated, the difference calculation process shown in FIG. 18 is executed.

As shown in FIG. 18, the control device 11 generates a histogram of the third feature data as shown in FIG. 15(C) by subtracting the first feature data from the second feature data (S121).

As shown in FIGS. 16(B) and 17(C), the control device 11 changes the plurality of feature values so that the lowest value in the histogram of the third feature data is 0, so that the third feature data are rearranged (S122). The control device 11 calculates the difference by calculating the average value of the feature values based on the histogram of the three feature data after rearrangement (S123).

After that, as shown in FIG. 13, the control device 11 calculates the second reference value based on the difference through the process of S16.

As described above, the measuring device 1 according to Embodiment 3 generates the third feature data by subtracting the first feature data from the second feature data, and calculates the difference based on the third feature data. Accordingly, by using the new third feature data to calculate the difference, the difference can be calculated with high accuracy.

<Embodiment 4>
A measuring device 1 according to Embodiment 4 will be described with reference to FIGS. 19 and 20. FIG. Only parts of the measuring device 1 according to the fourth embodiment that are different from the measuring device 1 according to the second embodiment will be described below.

[Calculation of difference]
FIG. 19 is a diagram showing an example of calculation of the difference between the first feature data and the second feature data by the measuring device 1 according to the fourth embodiment. FIG. 19A shows a histogram of the second feature data in the second environment. That is, the histogram of FIG. 19(A) corresponds to the histogram of the second feature data shown in FIG. 11(C).

As shown in FIG. 19, after generating the second feature data, the measuring apparatus 1 according to the fourth embodiment compares the first feature data stored in advance in the storage device 12 with the generated second feature data. Calculate the difference between Specifically, the measuring device 1 generates new feature data (hereinafter referred to as "fourth feature data ”) is generated.

For example, the measuring device 1 divides the histogram of the second feature data shown in FIG. 19(A) into the histogram shown in FIG. 19(B) and the histogram shown in FIG. 19(C). The average value of the feature values (G/Q in this example) included in the histogram of FIG. 19B is the feature value (G/Q in this example) included in the histogram in the first environment shown in ). That is, the average value of the feature values included in the histogram of FIG. 19B is the same or substantially the same as the first calculated value of the first feature data.

The measuring device 1 subtracts data having a feature value corresponding to the average value of the first feature data from the second feature data shown in FIG. A histogram including the feature values obtained is generated as the fourth feature data.

Note that the measuring device 1 may calculate a statistic other than the average value of the feature values included in the histogram in the first environment as the first calculated value. Statistics other than mean include variance, minimum, median, first quartile, third quartile, skewness, and kurtosis. Then, the measuring device 1 may generate fourth feature data by subtracting data having a feature value corresponding to the variance value of the first feature data from the second feature data.

After generating the fourth feature data, the measuring device 1 calculates the average value (for example, 464.7) of the feature values (G/Q in this example) based on the histogram of the fourth feature data. Note that the measuring device 1 may calculate the statistic of the feature value as the difference based on the histogram of the fourth feature data. Statistics include mean, variance, minimum, median, first quartile, third quartile, skewness, and kurtosis. The measuring device 1 may calculate at least one of the statistics described above. For example, the measuring device 1 may calculate the variance of the feature values as the difference based on the histogram of the fourth feature data. Furthermore, the measuring device 1 may calculate both the mean value and variance value of the feature values as the difference based on the histogram of the fourth feature data. Here, in calculating the difference, the measuring device 1 changes the plurality of feature values included in the histogram of the fourth feature data so that the lowest value becomes zero.

For example, the measuring device 1 arranges the feature values to be changed in order from 0 so that all of the plurality of feature values included in the fourth feature data are to be changed, and the lowest value among the feature values to be changed is 0. do.

Note that, as shown in FIG. 19(A), the measuring apparatus 1 generates the fourth feature data using the histogram of the second feature data shown in FIG. 11(C). The histogram of the second feature data shown may be used to generate the fourth feature data.

[Processing by measuring device]
FIG. 20 is a flowchart regarding processing executed by the measuring device 1 according to the fourth embodiment. The control device 11 of the measurement device 1 according to Embodiment 4 executes the measurement program 121 stored in the storage device 12, thereby periodically executing the processing of the flowchart shown in FIG.

As shown in FIG. 13, the control device 11 generates a histogram (first feature data) as shown in FIG. 9C and a histogram (second feature data) as shown in FIG. data) is generated, the difference calculation process shown in FIG. 20 is executed.

As shown in FIG. 20, the control device 11 calculates the first calculated value from the first feature data (S131). For example, the control device 11 calculates the average value of the feature values (G/Q in this example) as the first calculated value based on the histogram (first feature data) in the first environment.

Control device 11 subtracts data having a feature value corresponding to the first calculated value of the first feature data from the second feature data, thereby creating a histogram of fourth feature data as shown in FIG. Generate (S132).

The control device 11 rearranges the histogram of the fourth feature data by changing the plurality of feature values so that the lowest value in the histogram of the fourth feature data is 0 (S133). The control device 11 calculates the difference by calculating the average value of the feature values based on the histogram of the rearranged four feature data (S134).

After that, as shown in FIG. 13, the control device 11 calculates the second reference value based on the difference through the process of S16. The control device 11 may calculate the first calculated value in advance from the first characteristic data, store it in the storage device 12, and read the stored first calculated value. That is, the control device 11 may read the first calculated value in S131.

As described above, the measuring device 1 according to Embodiment 4 generates the fourth feature data by subtracting the data having the feature value corresponding to the first calculated value of the first feature data from the second feature data. and the difference is calculated based on the fourth feature data. Thus, by using the new fourth feature data to calculate the difference, it is possible to calculate the difference with high accuracy. Furthermore, the measuring device 1 does not subtract the first feature data from the second feature data as it is, but subtracts data having a feature value corresponding to the first calculated value of the first feature data from the second feature data. As a result, the fourth feature data is generated, so the difference can be calculated with higher accuracy.

<Embodiment 5>
A determination system 100 according to Embodiment 5 will be described with reference to FIG. Only parts of the determination system 100 according to the fifth embodiment that differ from the determination system 100 according to the first embodiment will be described below.

As shown in FIG. 21 , the determination system 100 further includes a notification device 3 in addition to the measurement device 1 and the sensor module 2 . Moreover, the sensor module 2 further includes an ozonizer 26 .

The determination device 4 predicts changes in the CO2 concentration in the future by analyzing the second time-series data of the CO2 concentration acquired from the sensor module 2. Furthermore, the determination device 4 compares the predicted value of the CO2 concentration with the second reference value acquired from the measuring device 1, and if the predicted value of the CO2 concentration exceeds the second reference value, or if the predicted value of the CO2 concentration is likely to exceed the second reference value, the ozonizer 26 is driven, or the display 31 or speaker 32 included in the notification device 3 is used to prompt the user to ventilate in order to prevent the spread of virus infection. do.

The data output device 44 of the determination device 4 outputs a control signal for controlling the ozonizer 26 to the sensor module 2 or outputs a control signal for controlling the notification device 3 to the notification device 3 in accordance with a command from the control device 41 . output.

The ozonizer 26 of the sensor module 2 generates ozone and discharges the generated ozone to the second environment where the sensor module 2 is installed. The control device 21 causes the ozonizer 26 to generate ozone by controlling the ozonizer 26 according to the control signal from the determination device 4 .

The notification device 3 includes a display 31 and a speaker 32. The display 31 displays various images such as an image based on the determination result of the CO2 concentration calculated by the control device 41 according to the control signal from the determination device 4 . For example, the display 31 displays an image for prompting the user to ventilate in accordance with the control signal from the determination device 4 . The speaker 32 outputs various sounds such as sounds based on the determination result of the CO2 concentration calculated by the control device 41 according to the control signal from the determination device 4 . For example, the speaker 32 outputs a sound for prompting the user to ventilate in accordance with the control signal from the determination device 4 .

Note that the notification device 3 is not limited to a configuration different from that of the determination device 4 . The determination device 4 may have at least one of the display 31 and the speaker 32 . Furthermore, the notification device 3 may be a mobile terminal or a PC (Personal Computer) owned by the user of the measurement device 1 or the determination device 4 . For example, the determination device 4 outputs a control signal to a mobile terminal or PC owned by the user through short-range wireless communication or the like, and the mobile terminal or PC (informing device 3) outputs a control signal based on the control signal from the determination device 4 to display An image may be displayed on 31 or sound may be output from speaker 32 .

In the determination system 100 configured in this way, the determination device 4 analyzes the time-series data of the CO2 concentration acquired by the sensor module 2 to predict changes in the CO2 concentration in the future. Then, the determination device 4 compares the predicted value of the CO2 concentration with the second reference value acquired from the measuring device 1, and if the predicted value of the CO2 concentration exceeds the second reference value, or if the predicted value of the CO2 concentration is likely to exceed the second reference value, a control signal is output to the sensor module 2 to drive the ozonizer 26, or a control signal is output to the notification device 3 to control the display 31 or the speaker 32. do.

<Embodiment 6>
A measuring device 1 according to Embodiment 6 will be described with reference to FIG. 22 . Only parts of the measuring device 1 according to the sixth embodiment that are different from the measuring devices 1 according to the first to fourth embodiments will be described below.

The measuring device 1 according to Embodiments 1 to 4 is configured to calculate the second reference value for determining the CO2 concentration in the second environment, but the measuring device 1 according to Embodiment 6 Furthermore, the CO2 concentration in the second environment may be determined using the calculated second reference value. That is, the measuring device 1 may have the function of the determination device 4 or may be a device integrated with the determination device 4 . Specifically, the measuring device 1 according to Embodiment 6 predicts the target value of the CO2 concentration in the second environment acquired from the sensor module 2 by the data acquisition device 13 using a prediction model, and the predicted target value A CO2 concentration in the second environment may be determined based on the value and the second reference value.

For example, FIG. 22 is a flowchart relating to determination processing executed by the measuring device 1 according to the sixth embodiment. The control device 11 of the measurement device 1 executes the measurement program 121 stored in the storage device 12 to periodically execute the processing of the flowchart shown in FIG. 22 . In the drawings, "S" is used as an abbreviation for "STEP".

As shown in FIG. 22, the control device 11 determines whether or not the second time-series data for a predetermined period of time has been acquired (S51). Note that, in S51, the control device 11 may determine whether or not the second time-series data having the amount of data capable of predicting future changes in the CO2 concentration by the prediction model has been acquired. If the control device 11 has not acquired the second time-series data for the predetermined time (NO in S51), the process ends.

On the other hand, when the control device 11 acquires the second time-series data for a predetermined period of time (YES in S51), the control device 11 predicts changes in the second time-series data using the prediction model, thereby calculating the CO2 concentration in the second environment. is calculated (S52). The control device 11 compares the predicted value with the second reference value to determine the CO2 concentration in the second environment (S53), and outputs the determination result (S54). For example, when the predicted value exceeds the second reference value, or when the predicted value is likely to exceed the second reference value, the control device 11 sends a control signal for controlling the ozonizer 26 to the sensor module 2. and outputs a control signal for controlling the notification device 3 to the notification device 3 . After that, the control device 11 terminates this process.

As described above, the measuring device 1 according to Embodiment 6 provides the first time-series data of the CO2 concentration in the first environment in which there are no non-human CO2 emission sources, and the second time-series data in which there are non-human CO2 emission sources. A difference between the CO2 concentration in the environment and the second time-series data is calculated, and the CO2 concentration in the second environment is determined based on the calculated difference and the second time-series data. As a result, the measuring device 1 can determine the CO2 concentration in the second environment in consideration of CO2 emitted from non-human CO2 emission sources. Even if there is, the CO2 concentration can be determined with high accuracy.

<Modification>
In the process of S52 in FIG. 22, the control device 11 calculates a subtraction value by subtracting the difference from the CO2 concentration in the second environment acquired from the sensor module 2 by the data acquisition device 13, and calculates the subtraction value and the first A CO2 concentration in the second environment may be determined based on the first reference value of CO2 concentration in the environment.

Further, the control device 11 predicts the target value of the CO2 concentration in the second environment acquired from the sensor module 2 by the data acquisition device 13 using a prediction model, and subtracts the difference from the estimated target value. You may calculate the subtraction value mentioned above.

Although the measuring device 1 is separate from the sensor module 2 in the above-described embodiment, the measuring device 1 may include each component included in the sensor module 2 . That is, the measuring device 1 according to the modification may include the sensor 25 and the ozonizer 26 , and the control device 11 may control the sensor 25 and the ozonizer 26 . Further, the measurement device 1 is not a single device integrally including a communication unit (data acquisition device 13), a control unit (control device 11), and a storage unit (storage device 12), but for example, the data acquisition device 13 It is divided into a device specializing in a communication section having a corresponding function, a device having a control section having a function corresponding to the control device 11, and a device having a storage section having a function corresponding to the storage device 12. It may be composed of a plurality of devices, such as

Furthermore, the measuring device 1 may include each component included in the notification device 3. That is, the measuring device 1 according to the modification may include a display 31 and a speaker 32 , and the control device 11 may control the display 31 and the speaker 32 .

A plurality of embodiments and modifications have been described above, but the features of each of these embodiments and modifications can be appropriately combined within a range that does not cause contradiction.

The embodiments disclosed this time should be considered illustrative in all respects and not restrictive. The scope of the present disclosure is indicated by the scope of the claims rather than the description of the above-described embodiments, and is intended to include all modifications within the meaning and scope equivalent to the scope of the claims.

1 measuring device, 2 sensor module, 3 reporting device, 4 judging device, 11, 21, 41 control device, 12, 42 storage device, 13, 43 data acquisition device, 23 communication device, 25 sensor, 26 ozonizer, 31 display, 32 speaker, 44 data output device, 50 network, 100 judgment system, 121 measurement program, 122 calculation data, 123 time-series data, 311, 313, 314 icons, 421 judgment program, 422 reference value data.

Claims

A measuring device for measuring carbon dioxide concentration,
a storage device that stores first time-series data of carbon dioxide concentration in a first environment where there are no carbon dioxide emission sources other than humans as emission sources that emit carbon dioxide into the atmosphere;
a data acquisition device that acquires second time-series data of carbon dioxide concentration in a second environment in which the carbon dioxide emission source other than the person exists;
a control device;
The measuring device, wherein the control device calculates a reference value for determining the carbon dioxide concentration in the second environment based on a difference between the first time series data and the second time series data.
The control device is
Calculate a first calculated value based on the first time-series data,
Calculate a second calculated value based on the second time-series data,
The measuring device according to claim 1, wherein said difference is calculated by subtracting said first calculated value from said second calculated value.
The first calculated value includes an average value of the first time-series data,
The measuring device according to claim 2, wherein said second calculated value includes an average value of said second time-series data.
The control device is
generating first feature data based on the first time-series data;
generating second feature data based on the second time-series data;
calculating a first calculated value based on the first feature data;
calculating a second calculated value based on the second feature data;
The measuring device according to claim 1, wherein said difference is calculated by subtracting said first calculated value from said second calculated value.
The control device is
generating first feature data based on the first time-series data;
generating second feature data based on the second time-series data;
generating third feature data by subtracting the first feature data from the second feature data;
The measuring device according to claim 1, wherein said difference is calculated by calculating at least one of an average value and a variance value of said third feature data.
The control device is
generating first feature data based on the first time-series data;
generating second feature data based on the second time-series data;
calculating a first calculated value based on the first feature data;
generating fourth feature data by subtracting data corresponding to the first calculated value from the second feature data;
The measuring device according to claim 1, wherein said difference is calculated by calculating at least one of an average value and a variance value of said fourth feature data.
The measuring device according to claim 6, wherein the first calculated value includes at least one of an average value and a variance value of the first feature data.
The control device is
generating the first feature data based on the carbon dioxide concentration during a period in which the amount of change in the carbon dioxide concentration in the first time-series data exceeds a threshold;
The measurement according to any one of claims 4 to 7, wherein the first feature data is generated based on the carbon dioxide concentration in a period in which the amount of change in carbon dioxide concentration in the second time-series data exceeds the threshold. Device.
The measuring device according to claim 8, wherein the amount of change includes a difference between the minimum value and the maximum value when the carbon dioxide concentration that changes in time series changes from the minimum value to the maximum value.
The control device calculates the reference value for determining the carbon dioxide concentration in the second environment by adding the difference to a first reference value for determining the carbon dioxide concentration in the first environment. , The measuring device according to any one of claims 1 to 9.
The measuring device according to any one of claims 1 to 10;
and a determination device,
The determination system, wherein the determination device determines the carbon dioxide concentration in the second environment based on the reference value calculated by the measurement device.
The determination device is
Predicting the reached value of the carbon dioxide concentration in the second environment,
12. The determination system according to claim 11, wherein the carbon dioxide concentration in said second environment is determined based on said predicted attainment value and said reference value.
The determination device is
calculating a subtraction value by subtracting the difference calculated by the measuring device from the carbon dioxide concentration in the second environment;
13. The determination system according to claim 11 or 12, wherein the carbon dioxide concentration in the second environment is determined based on the subtraction value and a first reference value for determining the carbon dioxide concentration in the first environment.
The determination device is
Predicting the reached value of the carbon dioxide concentration in the second environment,
14. The determination system according to claim 13, wherein said subtraction value is calculated by subtracting said difference calculated by said measuring device from said predicted arrival value.
15. The determination device according to any one of claims 11 to 14, wherein the determination device transmits a signal for controlling the ozonizer placed in the second environment to the ozonizer based on the determination result of the carbon dioxide concentration in the second environment. or the determination system according to item 1.
A measuring method for measuring carbon dioxide concentration by computer,
storing first time-series data of carbon dioxide concentration in a first environment in which there are no non-human carbon dioxide emission sources as emission sources of carbon dioxide into the atmosphere;
obtaining a second time-series data of carbon dioxide concentration in a second environment in which the carbon dioxide emission source other than the person is present;
and calculating a reference value for determining the carbon dioxide concentration in the second environment based on the difference between the first time-series data and the second time-series data.
A measurement program for measuring carbon dioxide concentration,
to the computer,
storing first time-series data of carbon dioxide concentration in a first environment in which there are no non-human carbon dioxide emission sources as emission sources of carbon dioxide into the atmosphere;
obtaining a second time-series data of carbon dioxide concentration in a second environment in which the carbon dioxide emission source other than the person is present;
calculating a reference value for determining the carbon dioxide concentration in the second environment based on the difference between the first time series data and the second time series data.