Environmental information system, carbon dioxide generation source estimation method, and program

Abstract

To provide an environmental information system, carbon dioxide generation source estimation method, and program capable of reducing a processing load in estimating a carbon dioxide generation source in a closed space.SOLUTION: An environmental information system 1 includes: an environment sensor 20 for measuring at least a carbon dioxide concentration in a closed space; CPUs 11 of device terminal apparatuses 10 and a CPU 31 of a cloud server 30 as carbon dioxide concentration estimation sections for estimating a CO2 concentration in the air to flow into the closed space; and the CPUs 11 as control sections for estimating a CO2 generation source in the closed space based on an estimation value of the CO2 concentration estimated by the carbon dioxide concentration estimation section and also based on a measurement value of the CO2 concentration measured by the environment sensor 20.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an environmental information system, a carbon dioxide emission source estimation method, and a program. [Background technology]

[0002] Proper and sufficient ventilation is required in closed spaces. The following technologies are available to grasp the ventilation status in closed spaces, issue warnings, and control ventilation equipment.

[0003] The technology of Patent Document 1 is related to a carbon dioxide concentration prediction system, and describes a carbon dioxide concentration prediction system that includes, for example, a prediction unit that predicts the carbon dioxide concentration in a room based on the current carbon dioxide concentration in the room and the temperature in the room, and a provision unit that provides the carbon dioxide concentration predicted by the prediction unit. In the carbon dioxide concentration prediction system described in Patent Document 1, the prediction unit further predicts a time change in the carbon dioxide concentration in the room from the current carbon dioxide concentration to a carbon dioxide concentration predicted based on the current carbon dioxide concentration and environmental information such as temperature, and the provision unit provides the time change in the carbon dioxide concentration.

[0004] Patent document 2 describes an environmental control system that associates, for each dangerous condition for a living organism in an environment, the degree of danger of the dangerous condition with a range of features indicating the state of the environment and the living organism and stores them in a dangerous condition DB, reads out the dangerous condition and the degree of danger from the dangerous condition DB based on the features acquired from the environment and the living organism, and outputs them to home appliances.

[0005] On the other hand, the following technologies are available for estimating the number of people present in a room using environmental sensors. Patent document 3 describes a number of people estimation device that has a human presence detection means that detects human movement in a room to be estimated, a CO2 concentration measurement means that measures the CO2 concentration in the room, and a control means that estimates the number of people in the room to be zero based on the detection result of the human presence detection means, and if the number of people is not estimated to be zero, estimates the number of people in the room based on the measurement result of the CO2 concentration measurement means.

[0006] Patent Document 4 describes a ventilation control system that includes a CO2 sensor that measures the carbon dioxide concentration in a closed space, and a ventilation determination means that calculates a determination value using the carbon dioxide concentration measured by the CO2 sensor and determines whether ventilation of the closed space is sufficient based on the determination value. The ventilation determination means calculates a carbon dioxide saturation concentration based on the ratio of the carbon dioxide generation rate to the ventilation rate in a situation where the number of people in the closed space and the ventilation rate are unknown, and uses the carbon dioxide saturation concentration as a determination value.

[0007] Non-Patent Document 1 describes a method for estimating the number of people in a room using a CO2 concentration sensor and a human presence sensor in combination. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] JP 2023-31247 A [Patent Document 2] JP 2016-7177 A [Patent Document 3] JP 2011-133164 A [Patent Document 4] JP 2023-73559 A [Non-patent literature]

[0009] [Non-Patent Document 1] Tomokazu Tachikawa and 1 other, "A method for estimating the number of people in a room using environmental sensors", Journal of Environmental Engineering, Architectural Institute of Japan, April 2010, Vol. 75, No. 650, pp. 355-362 Summary of the Invention [Problem to be solved by the invention]

[0010] The techniques of Patent Documents 1-4 and Non-Patent Document 1 all have the problem that the calculation of the ventilation volume in a closed space is complicated, and the processing load is large for the terminal device connected to the cloud system. In addition, Non-Patent Document 1 also uses a human presence sensor (infrared sensor), which increases costs. In addition, the human presence sensor may malfunction in an environment exposed to direct sunlight.

[0011] The present invention has been made in consideration of the above background, and an object of the present invention is to provide an environmental information system, a carbon dioxide emission source estimation method, and a program that can reduce the processing load of estimating a carbon dioxide emission source in a closed space. [Means for solving the problem]

[0012] In order to solve the above-mentioned problems, the present invention comprises an environmental sensor that measures at least the carbon dioxide concentration in a closed space, a carbon dioxide concentration estimation unit that estimates the carbon dioxide concentration in the air flowing into the closed space, and a control unit that estimates a carbon dioxide source in the closed space based on the estimated value of the carbon dioxide concentration estimated by the carbon dioxide concentration estimation unit and the measured value of the carbon dioxide concentration measured by the environmental sensor. Effect of the Invention

[0013] According to the present invention, it is possible to reduce the processing load for estimating a carbon dioxide emission source in a closed space. [Brief description of the drawings]

[0014] [Figure 1] 1 is a schematic configuration diagram of an environmental information system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a hardware configuration diagram showing an example of a computer that realizes a function of a client of the environmental information system according to the embodiment of the present invention. [Figure 3A] FIG. 2 is a diagram showing the relationship between the air flowing into a space, the air flowing out of a space, and the space on which they act. [Figure 3B]1 is a diagram showing an example of an arrangement of environmental sensors in an environmental information system according to an embodiment of the present invention; [Figure 4A] This is a graph showing the concentration of CO2 emitted by humans over time inside a space. [Figure 4B] FIG. 4B is a diagram for explaining estimation of the number of people from the CO2 concentration measurement results shown in FIG. 4A. [Diagram 5] 11 is a flowchart showing a process for estimating the number of people based on a CO2 concentration. [Figure 6] 10 is a flowchart showing a process executed by the cloud server upon receiving a request from the device terminal apparatus. [Figure 7] 13 is a flowchart showing a congestion degree estimation process. [Figure 8] 10 is a flowchart showing a process executed by the cloud server upon receiving a request from the device terminal apparatus. [Figure 9] FIG. 1 is a model diagram for calculating the infection risk index and mold index. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an environmental information system and the like in an embodiment for carrying out the present invention (hereinafter, referred to as "the present embodiment") will be described with reference to the drawings. (Embodiment) [overview] FIG. 1 is a schematic diagram of an environmental information system according to an embodiment of the present invention. This environmental information system can be ideally used in systems to identify CO2 sources, security systems that use trends in CO2 concentrations, congestion estimation systems that use CO2, particulate matter concentrations, humidity, temperature, etc., and activity systems that use CO2 concentrations and particulate matter concentrations.

[0016] 1, the environmental information system 1 includes a plurality of device terminal apparatuses 10 (terminal apparatuses), an environmental sensor 20, and a cloud server 30. The device terminal apparatuses 10 and the cloud server 30 can communicate with each other via a network 2. The network 2 is, for example, the Internet. The cloud server 30 is connected to a plurality of device terminal apparatuses 10 via a network 2, executes processes requested by the plurality of device terminal apparatuses 10, and provides the plurality of device terminal apparatuses 10 with data after the processes have been executed.

[0017] <Device terminal device 10> The device terminal apparatus 10 is a local controller that is installed in an indoor space or in the vicinity thereof. Note that a part of the environmental sensor 20 may be incorporated in the device terminal apparatus 10.

[0018] The device terminal apparatus 10 estimates the carbon dioxide concentration in the air flowing into the closed space, and estimates the source of carbon dioxide in the closed space based on the estimated carbon dioxide concentration and the measured value of the carbon dioxide concentration measured by the environmental sensor 20. The device terminal apparatus 10 estimates the number of people in the closed space based on the amount of carbon dioxide from the carbon dioxide emission source.

[0019] The device terminal apparatus 10 includes a CO2 sensor 23 that measures the carbon dioxide concentration in the air flowing into the closed space, and estimates the carbon dioxide concentration based on the measurement value of the CO2 sensor 23.

[0020] When the processing load of the device terminal apparatus 10 itself is large, the device terminal apparatus 10 can request the processing to the cloud server 30 via the network 2 and obtain the processing results from the cloud server 30.

[0021] The device terminal apparatus 10 obtains an estimate of the carbon dioxide concentration in the air flowing into the closed space from the cloud server 30, and estimates the source of carbon dioxide in the closed space based on the estimate of the carbon dioxide concentration obtained from the cloud server 30 and the measured value of the carbon dioxide concentration.

[0022] Furthermore, the device terminal apparatus 10 does not have to be configured such that all the device terminal apparatuses 10 execute the estimation control of the carbon dioxide emission source. For example, among the multiple device terminal apparatuses 10, only the device terminal apparatus 10 having authority may be configured to become a master and estimate the carbon dioxide emission source or execute a request to the cloud server 30. In this manner, the resource resources in the device terminal apparatus 10 can be reduced, and the cost of the device terminal apparatus 10 can be reduced.

[0023] The device terminal apparatus 10 may be, for example, a smartphone, a tablet, or a notebook computer.

[0024] The device terminal apparatus 10 comprises a CPU (Central Processing Unit) 11, a main memory unit 12, an auxiliary memory unit 13, an input unit 14, a display unit 15, a communication unit 16, an interface (I / F) unit 17, and a bus 18 that connects these to each other.

[0025] Hereinafter, when the subject is described as "XX unit", it means that the CPU 11 reads each program from the ROM as necessary, loads it into the RAM, and executes each function (described later). Each program may be stored in the main memory unit 12 or the auxiliary memory unit 13 in advance, or may be loaded into the device terminal apparatus 10 via another storage medium or communication medium when necessary.

[0026] The CPU 11 controls the entire device terminal apparatus 10, and also executes a control program to cause the device terminal apparatus 10 to function as a carbon dioxide concentration estimation section and a control section of the environmental information system.

[0027] The CPU 11, as a carbon dioxide concentration estimation unit, estimates the carbon dioxide concentration in the air flowing into the closed space. The CPU 11, as a control unit, estimates the CO2 source in the closed space based on the estimated value of the CO2 concentration estimated by the carbon dioxide concentration estimation unit and the measured value of the CO2 concentration measured by the environmental sensor 20.

[0028] The CPU 11 estimates the number of people in the closed space based on the amount of carbon dioxide from the CO2 emission source.

[0029] The CPU 11 obtains an estimate of the carbon dioxide concentration in the air flowing into the closed space from the cloud server 30, and estimates the source of CO2 in the closed space based on the estimated CO2 concentration obtained from the cloud server 30 and the measured value of the CO2 concentration.

[0030] The CPU 11 estimates the operating state of the equipment based on PM2.5 fluctuation information, in which the PM2.5 concentration (fine particle concentration) measured by a PM2.5 sensor (particle measurement sensor) 22, one of the environmental sensors 20, has fluctuated by more than a predetermined value, and / or temperature fluctuation information, in which the air temperature (temperature) measured by a temperature and humidity sensor 21, one of the environmental sensors 20, has fluctuated by more than a predetermined value.

[0031] The CPU 11 issues an alarm when the measured value of the CO2 concentration in a closed space where no one is present becomes equal to or exceeds a predetermined value.

[0032] The CPU 11 calculates the time-series trend of the CO2 concentration in a closed space where no one is present, and issues an alarm if the carbon dioxide concentration increases and then decreases and continues to be detected at or above a predetermined value.

[0033] The CPU 11 issues an alert based on PM2.5 fluctuation information, in which the PM2.5 concentration measured by the PM2.5 sensor 22 has fluctuated by more than a predetermined value, and / or temperature fluctuation information, in which the air temperature (temperature) measured by the temperature / humidity sensor 21 has fluctuated by more than a predetermined value.

[0034] The CPU 11 estimates the degree of congestion based on the CO2 concentration measurement values ​​acquired by the multiple CO2 sensors 23 arranged in close proximity.

[0035] The CPU 11 estimates a person's activity level based on the CO2 concentration, PM2.5 concentration, temperature, and humidity measured by an environmental sensor 20 having a CO2 sensor, a PM2.5 sensor 22, and a temperature and humidity sensor 21.

[0036] The storage unit 12 has a volatile memory such as a RAM (Random Access Memory) and is used as a working area for the CPU 11. The auxiliary storage unit 13 is a non-volatile memory such as a semiconductor flash memory and stores programs for the CPU 11 to execute various processes.

[0037] The input unit 120 is, for example, a transparent touch pad disposed over the display unit 15 . The display unit 15 is a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display.

[0038] The communication unit 16 is connected to the network 2, and transmits information transferred from the CPU 11 to the cloud server 30 via the network 2, and transfers information received from the cloud server 30 via the network 2 to the CPU 11. The interface (I / F) unit 17 receives measurement results from the environment sensor 20, the air pressure sensor 29a, the light amount sensor 29b, and the infrared sensor 29c, converts them into a predetermined voltage value and format, and sends them to the CPU 11.

[0039] <Environmental Sensor 20> The environmental sensor 20 measures the environment of the indoor space, that is, the air quality of the indoor space. Here, the air quality includes, for example, the concentration of gases such as CO2, the concentration of PM2.5, the temperature, the humidity, and further the number of molds and dust particles.

[0040] The environmental sensors 20 include a temperature and humidity sensor 21, a PM2.5 sensor 22, a CO2 (carbon dioxide) sensor 23, an O3 (ozone) sensor 24, a CH2O (formaldehyde) sensor 25, a NO2 (nitrogen dioxide) sensor 26, a VOC (volatile organic compound) sensor 27, and a CO (carbon monoxide) sensor 28.

[0041] Although the environmental sensor 20 includes the temperature and humidity sensor 21, the above sensors excluding the temperature and humidity sensor 21 may be called the environmental sensor. Fig. 1 shows the main sensors used as the environmental sensor 20. The environmental sensor 20 may be implemented as a single CO2 sensor 23. However, the number of CO2 sensors 23 is not limited to one, and for example, three may be placed in the indoor space A, one in the indoor space B, and two in the indoor space C.

[0042] The temperature and humidity sensor 21 measures the temperature and humidity of the air. The temperature and humidity sensor 21 is used together with the CO2 sensor 23 and the PM2.5 sensor 22 to provide parameters for estimating human activity, crowding, and infection risk. The PM2.5 sensor 22 measures the concentration of PM2.5 (fine particles with a particle diameter of 2.5 μm or less). Instead of the PM2.5 sensor 22, or in addition to the PM2.5 sensor 22, a particle measuring sensor capable of measuring the concentration of pollen, etc. may be provided. The CO2 (carbon dioxide) sensor 23 measures the CO2 concentration. The O3 (ozone) sensor 24 measures the O3 concentration.

[0043] The CH2O (formaldehyde) sensor 25 measures the CH2O concentration. The NO2 (nitrogen dioxide) sensor 26 measures the NO2 concentration. The VOC (volatile organic compound) sensor 27 measures the concentration of volatile organic compounds. The CO (carbon monoxide) sensor 28 measures the CO concentration.

[0044] Furthermore, an air pressure sensor 29a, a light amount sensor 29b, and an infrared sensor 29c are connected to the device terminal apparatus 10, and air pressure information in the indoor space, the amount of light in the indoor space, and an infrared heat source (thermo camera / human presence sensor) are input.

[0045] <Cloud Server 30> The cloud server 30 includes a CPU 31, a main memory unit 32, an auxiliary memory unit 33, an input unit 34, a display unit 35, a communication unit 36, a bus 37 connecting these units to each other, and an AI (Artificial Intelligence) accelerator 40. The CPU 31 is, for example, a multi-core processor. The main memory unit 32 has a volatile memory such as a RAM, and is used as a working area for the CPU 31. The auxiliary memory unit 33 is composed of a large-capacity non-volatile memory, and stores programs for realizing various functions of the cloud server 30. The communication unit 36 ​​is connected to a network 2.

[0046] The AI ​​accelerator 40 is a dedicated processor for offload processing, and uses computational resources other than the CPU. The AI ​​accelerator 40 is, for example, an accelerator of image processing by a processor with an enhanced GPU (Graphics Processing Unit) and signal processing using an FPGA (Field Programmable Gate Array). The AI ​​accelerator 40 also executes AI (Artificial Intelligence) calculations on dedicated hardware (for example, a GPU).

[0047] [Hardware configuration] The cloud server 30 of the environmental information cloud system 1 (FIG. 1) according to the embodiment described above is realized by a computer 900 having a configuration as shown in FIG. 2, for example. FIG. 2 is a hardware configuration diagram showing an example of a computer 900 that realizes the functions of the cloud server 30. As shown in FIG. The cloud server 30 includes a CPU 901, a RAM 902, a ROM 903, a HDD 904, an accelerator 905, an input / output interface (I / F) 906, and a communication interface (I / F: Interface) 907. The accelerator 905 corresponds to the AI ​​accelerator 40 in FIG.

[0048] The accelerator 905 is an accelerator (device) 40 (FIG. 1) that processes at least one of data from the communication I / F 908 and data from the RAM 902 at high speed. Note that the accelerator 905 may be of a type that returns the execution result to the CPU 901 or the RAM 902 after executing the processing from the CPU 901 or the RAM 902 (Look-Aside type). On the other hand, the accelerator 905 may be of a type that performs processing between the communication I / F 908 and the CPU 901 or the RAM 902 (In-line type).

[0049] The accelerator 905 is connected to an external device 915 via a communication I / F 907. The input / output I / F 906 is connected to an input / output device 916.

[0050] The CPU 901 operates based on a program stored in the ROM 903 or HDD 904, and executes a program (also called an application or its abbreviation, "app") loaded into the RAM 902 to control each part of the AI ​​accelerator 40 shown in Fig. 1. This program can also be distributed via a communication line, or recorded on a recording medium 917 such as a CD-ROM and distributed. The ROM 903 stores a boot program executed by the CPU 901 when the computer 900 is started, programs that depend on the hardware of the computer 900, and the like.

[0051] The CPU 901 controls an input / output device 916 consisting of an input unit such as a mouse and a keyboard, and an output unit such as a display and a printer, via an input / output I / F 906. The CPU 901 acquires data from the input / output device 916 via the input / output I / F 906, and outputs generated data to the input / output device 916. Note that a GPU (Graphics Processing Unit) or the like may be used as a processor together with the CPU 901.

[0052] The HDD 904 stores programs executed by the CPU 901 and data used by the programs, etc. The communication I / F 907 receives data from other devices via a communication network (e.g., a NW (Network)) and outputs the data to the CPU 901, and also transmits data generated by the CPU 901 to other devices via the communication network.

[0053] The CPU 901 loads a program related to a target process from a recording medium 917 onto the RAM 902, and executes the loaded program. The recording medium 917 is an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disc), a magneto-optical recording medium such as an MO (Magneto Optical disk), a magnetic recording medium, a conductive memory tape medium, a semiconductor memory, or the like.

[0054] For example, when the computer 900 functions as the cloud server 30 configured as one device according to this embodiment, the CPU 901 of the computer 900 executes a program loaded onto the RAM 902 to realize the functions of the cloud server 30. Furthermore, the HDD 904 stores data in the RAM 902. The CPU 901 reads and executes a program relating to a target process from the recording medium 917. Additionally, the CPU 901 may read a program relating to a target process from another device via a communication network.

[0055] The operation of the environmental information cloud system configured as above will be described below. [Estimating the number of people based on CO2 concentrations] We explain population estimates based on CO2 concentrations. 3A to 4B are diagrams showing the basic concept of estimating the number of people based on the CO2 concentration. 3A shows the relationship between air 51 flowing into space 50, air 52 flowing out of space 50, and the space 50 on which they act. In the following operations, unless otherwise specified, the term "space" refers to a closed space. If there is no CO2 source inside the space 50, the CO2 concentration in the space 50 will match the concentration of the air flowing in. Even if the initial value of the CO2 concentration in the space 50 is high, after a sufficiently long time has passed, the CO2 concentration of the air flowing in 51 and the CO2 concentration inside the space 50 will match.

[0056] The concentration of CO2 in the inflowing air 51 changes daily depending on the time and the environment, so the concentration of CO2 inside the space 50 also changes from moment to moment. In this embodiment, the concentration of CO2 is estimated from the temperature, time, weather (humidity, temperature, air pressure), etc. (step S10 in FIG. 5 described later). Furthermore, the cloud server 30 acquires data (such as weather data) from the Internet, for example. The cloud server 30 uses the AI ​​accelerator 40 or the like to pattern and digitize the data, thereby estimating the concentration of inflowing CO2 (step S103 in FIG. 6, which will be described later). Furthermore, as shown in Figure 3B, by placing a reference environmental sensor 20ref on the inflow side and comparing it with the measurement value of the measurement environmental sensor 20 installed in the space 50, the reliability of the model can be improved, enabling more accurate inferences to be made.

[0057] <When there is a CO2 source inside the space> We will now explain the case where a CO2 source is located inside a space. The amount of CO2 emitted by one person in a year is about 168,000 liters, and it is said that each person emits about 460 liters of CO2 per day. If there is no air flowing into or out of a space, the concentration of CO2 in the space will increase over time.

[0058] Figure 4A shows the time course of the concentration of CO2 emitted by a person inside a space. The initial value shown in Figure 4A is the CO2 concentration in the atmosphere, which is around 400 ppm. Time t is the time when a person enters the space.

[0059] Next, we will discuss what happens when the amount of CO2 emissions in a space increases. FIG. 4B is a diagram for explaining estimation of the number of people from the CO2 concentration measurement results shown in FIG. 4A. When the amount of CO2 emitted within a space increases, the rate of increase in CO2 increases according to the amount of CO2 emitted and the size of the space. The rate of increase in CO2 within a space is roughly expressed by the following formula (1).

[0060]

number

[0061] In equation (1), k is a coefficient. The rate of increase in CO2 in a space depends on the number of people, but also on whether they are adults or children, and whether they are moving or stationary. The space size does not change when the environmental sensor is fixedly installed, but the space size is not necessarily constant when doors and windows are opened. Furthermore, although Figures 4A and 4B show a simplified linear curve, in reality, it becomes nonlinear at the beginning of the increase in CO2 and as the concentration of CO2 in a finite space approaches saturation. As shown by arrow a in Figure 4B, the slope becomes steeper as the number of people increases. If we assume that the size of the space is constant and that the CO2 emissions per person are constant regardless of the person, we can estimate the number of people in the space using equation (1).

[0062] Next, we will explain the case when air flows in and out. To simplify the explanation, we will focus only on the amount of CO2, and assume that the inflow and outflow amounts are constant and that no CO2 flows out from the inlet. A constant concentration of CO2 constantly flows into the air that flows into the space. Since the concentration of incoming CO2 is constant, the CO2 concentration can be defined as follows:

[0063] CO2 concentration = k …(2) k: Coefficient (may be the same as k in Equation (1))

[0064] Similarly, if the concentration of CO2 emitted per unit time within a space is defined as m, the amount of CO2 generated within the space can be calculated as follows:

[0065] Amount of CO2 generated = m × T … (3) T: Time

[0066] Furthermore, the amount of CO2 flowing out of a space is roughly related to the difference between the CO2 concentration inside the space and the concentration outside the space and the flow rate, and is expressed by the following equation (4).

[0067] Amount of CO2 flowing out of the space = n × f(mk) × f(air volume) × T … (4)

[0068] n: coefficient From equation (4) above, the number of people in a space can be estimated by measuring the concentration occurring within the space. The reason why the number of people in a space remains an "estimate" is that, as mentioned above, CO2 emissions change subtly depending on the state of people, the CO2 emissions also fluctuate over time and are difficult to predict, and the behavior of air entering and leaving a space and the concentration of CO2 in the space is nonlinear. In addition, there are effects due to the combination of the above factors and temperature changes.

[0069] Here, parameters required for estimating the number of people, such as airflow, can be based on past measurement results, temperature, etc.

[0070] <Flowchart> 5 is a flowchart showing a process for estimating the number of people based on the CO2 concentration of the device terminal apparatus 10. This flowchart is executed by the CPU 11 of the device terminal apparatus 10 (FIG. 1). In step S1, the CPU 11 detects the CO2 concentration in the air. In step S2, the CPU 11 detects the air temperature (air temperature).

[0071] In step S3, the CPU 11 determines whether or not it is possible to acquire operation information of an air conditioner installed in a closed space. If the operating information of the air conditioner can be acquired (S3: Yes), the process proceeds to step S4. In step S4, the CPU 11 acquires the operating information of the air conditioner. Then, the process proceeds to step S6. If the operating information of the air conditioner cannot be acquired (S3: No), the process proceeds to step S5. In step S5, the CPU 11 estimates the operating state of the air conditioner in the closed space based on the amount of change in temperature.

[0072] In step S6, the CPU 11 estimates the exchange rate of air flowing from the nearby space into the closed space (see space 50 in FIG. 3A).

[0073] In step S7, CPU 11 determines whether to request cloud server 30 (FIG. 1) to estimate the concentration of CO2 in the air flowing into the closed space. Whether to request the cloud server 30 to estimate the concentration of CO2 is determined based on whether its own computational resources can handle the request, and whether the cloud server 30 is not currently executing other processing with a large processing load. Alternatively, CPU 11 may determine in advance whether to request the cloud server 30 to estimate the concentration of CO2.

[0074] If the cloud server 30 (FIG. 1) is not requested to estimate the CO2 concentration (S7: No), the process proceeds to step S8. In step S8, the CPU 11 determines whether or not a measurement value of the CO2 concentration in the air flowing into the closed space is obtained from the reference environment sensor 20ref.

[0075] When the measurement value of the CO2 concentration in the air flowing into the closed space is obtained from the reference environment sensor 20ref, the process proceeds to step S9. In step S9, the CPU 11 acquires the measurement value of the CO2 concentration in the air flowing into the closed space from the reference environment sensor 20ref. Then, the process proceeds to step S13.

[0076] If the measurement value of the CO2 concentration in the air flowing into the closed space is not obtained from the reference environment sensor 20 in step S8 (S8: No), the process proceeds to step S10. In step S10, the CPU 11 estimates the CO2 concentration in the air flowing into the closed space based on the temperature, time, and weather (humidity, temperature, and air pressure). Then, the process proceeds to step S13.

[0077] As described above, the concentration of CO2 changes with the amount of air inflow. The change in the amount of air inflow is estimated by deriving a function and coefficient based on the change in the fine particle concentration (measurement value of the PM2.5 sensor 22 in FIG. 1) and / or the change in temperature (measurement value of the temperature and humidity sensor 21 in FIG. 1), and using this coefficient, the change in the concentration of CO2 is estimated. For example, if the measurement value of the PM2.5 sensor 22 or the temperature changes suddenly, it can be assumed that the air conditioning is operating. Then, based on the change in the concentration of CO2, it becomes possible to determine the presence or absence of people (step S15 in FIG. 5 described later), estimate the number of people (step S19 in FIG. 5 described later), estimate the degree of congestion (flowchart in FIG. 7 described later) (step S111 in FIG. 8 described later), and estimate the activity level (FIG. 9 described later).

[0078] Returning to the flow of FIG. 5, if a CO2 concentration estimation is requested from cloud server 30 in step S7 above (S7: Yes), in step S11, CPU 11 requests cloud server 30 to estimate the CO2 concentration in the air flowing into the space. In step S12, the CPU 11 determines whether or not an estimate of the concentration of CO 2 in the air flowing into the closed space has been received from the cloud server 30. When an estimate of the concentration of CO2 in the air flowing into the closed space is received (S12: Yes), the process proceeds to step S13. Step S12 waits until an estimate of the concentration of CO2 in the air flowing into the closed space is received (S12: No).

[0079] In step S13, the CPU 11 estimates the CO2 concentration in the closed space from the estimated value of the CO2 concentration in the air flowing into the closed space (in other words, the time-series trend of the CO2 concentration) and the measurement value of the environment sensor 20 (CO2 sensor 23) in the closed space.

[0080] In step S14, the CPU 11 determines whether or not there is a CO2 emission source in the closed space based on the estimated CO2 concentration in the closed space. If there is no CO2 emission source in the closed space (S14: No), the process of this flow ends. If it is determined in step S14 above that there is a CO2 emission source in the closed space (S14: Yes), then in step S15 it is determined whether or not there is a person there. In step S16, the CPU 11 determines whether or not it is a time period when people are present in the closed space. For example, there are often no people in restrooms late at night or in warehouses at night or on holidays. This is particularly true for restrooms in nursing homes and other small spaces, and is particularly effective (improving the accuracy of estimating the number of people by accurately determining the CO2 concentration in the air). The CPU 11 registers time periods when people are not (are present) for each closed space, and determines whether or not it is a time period when people are not present based on the time when the data was acquired.

[0081] In step S17, the CPU 11 determines whether or not there is an abnormality in the behavior pattern in a closed space where no one is present ("behavior pattern in a closed space where no one is present" / "behavior pattern in a closed space during the time when no one is present"). One example of a case where there is an abnormality in the behavior pattern is the measurement / determination of the CO2 concentration in a toilet. For example, when there is a person in the toilet, the CO2 concentration rises once, but when the person leaves the toilet and a certain time has passed, the CO2 concentration returns to the value when no one is present. Therefore, if the CO2 concentration remains elevated even after a certain time has passed after the increase, and the time period is late at night, etc., it is possible that a suspicious person is hiding in the toilet or that a person has collapsed in the toilet. Also, if the CO2 concentration does not return to the value when no one is present after a certain time has passed, there is a possibility that a person has collapsed in the toilet.

[0082] If there is an abnormality in the behavior pattern in a closed space where no one is present (S17: Yes), the process proceeds to step S18 and an alarm is issued. The alarm can be issued in a manner that the device terminal apparatus 10 reports to relevant parties, a security company, etc. via the network 2, or in a manner that the cloud server 30 responds in accordance with a security program via the cloud server 30. The method in which the cloud server 30 responds in accordance with a security program can flexibly respond to cases such as when a suspicious person immediately leaves or when a device malfunctions, and can increase the reliability of the alarm. Note that the alarm can be issued in stages, such as first reporting to relevant parties, a security company, etc., and then reporting to the police and fire department.

[0083] If it is determined in step S16 above that the space is not a closed space with no people present, or if it is determined in step S17 that there is no abnormality in the behavioral pattern, or if an alarm is issued in step S18, the process proceeds to step S19, where the number of people in the closed space is estimated based on the spatial CO2 concentration, and the processing of this flowchart is terminated.

[0084] Executing the process of the flow in FIG. 5 has the following effects. The device terminal apparatus 10 can determine to some extent whether or not a person is present by comparing the CO2 concentration (time series trend of CO2 concentration) in the air flowing into the closed space with the current CO2 concentration measurement result. In addition, by determining the behavioral pattern in the closed space where no person is present, an alarm is issued if a person behaves in a manner other than the standard behavior previously stored in the behavioral model. For example, in a restroom late at night, if the index calculated based on the temperature, particle concentration, and CO2 concentration exceeds a threshold value, there is a high possibility that a person is present.

[0085] The device terminal apparatus 10 is effective in places such as toilets where surveillance cameras cannot be used, and in rooms where infrared sensors (human presence sensors) may malfunction due to direct sunlight, etc. It is particularly effective in small spaces and can be used for security purposes such as preventing people from being locked in.

[0086] 6 is a flowchart showing the process executed by the cloud server 30 upon receiving a request from the device terminal apparatus 10. This flowchart is executed by the CPU 31 (FIG. 1) of the cloud server 30. In step S 101 , the CPU 31 receives a request from the device terminal 10 . In step S102, the CPU 31 determines whether the request from the device terminal 10 can be responded to. If the request from the device terminal apparatus 10 can be met (S102: Yes), the process proceeds to step S103. In step S103, the CPU 31 executes each process in response to the request from the device terminal apparatus 10. Here, the CPU 31 leaves AI calculations with a high calculation load to the AI ​​accelerator 40, which is dedicated hardware. For example, the CPU 31 requests the AI ​​accelerator 40 to track people using AI, and the CPU 31 waits for the results of the person tracking from the AI ​​accelerator 40.

[0087] In step S104, the CPU 31 returns the processing result to the corresponding device terminal apparatus 10, and the process proceeds to step S106. In the above step S102, if it is determined that the request from the device terminal apparatus 10 cannot be satisfied (S102: No), the process proceeds to step S105, where a message that the request cannot be satisfied is returned to the device terminal apparatus 10, and the process proceeds to step S106.

[0088] In step S106, the CPU 31 determines whether or not it is necessary to notify a security company, etc. If it is not necessary to notify a security company, etc. (S106: No), the process proceeds to step S108. If it is necessary to notify a security company or the like (S106: Yes), the process proceeds to step S107. In step S107, the CPU 31 notifies a security company or the like, and then the process proceeds to step S108.

[0089] In step S108, the CPU 31 determines whether or not it is necessary to provide information to other device terminal devices 10, etc. If it is necessary to provide information to other device terminal devices 10, etc. (S109: Yes), the information is provided to the other device terminal devices 10, etc., and the process of this flowchart ends. If it is not necessary to provide information to other device terminal devices 10, etc. (S108: No), the process of this flowchart ends.

[0090] Fig. 7 is a flowchart showing a process for estimating the congestion level of the device terminal apparatus 10. This flowchart is executed by the CPU 11 (Fig. 1) of the device terminal apparatus 10. The steps performing the same processes as those in Fig. 5 are given the same reference numerals, and explanations of the overlapping parts are omitted. 7, step S21 is reached from step S9, step S10, or step S12. In step S21, the CPU 11 determines whether or not to perform congestion degree estimation using the CO2 concentration by itself. If the device terminal apparatus 10 estimates the congestion level by itself (S21: Yes), the process proceeds to step S22. In step S22, the CPU 11 estimates the congestion level from the concentration measurement values ​​of the multiple CO2 sensors 23 (FIG. 1) arranged closely to each other, and ends the process of this flow.

[0091] A method for estimating the degree of congestion will be described. For example, when the measurement values ​​of the two CO2 sensors 23 are similar (i.e., when the difference between the measurement values ​​of the two CO2 sensors 23 is within a first threshold), it can be estimated that the entire area is crowded on average. Conversely, when there is a difference between the measurement values ​​of the two CO2 sensors 23 (i.e., when the difference between the measurement values ​​of the two CO2 sensors 23 is greater than a second threshold), it can be estimated that people are concentrated to a certain extent. Note that the first threshold and the second threshold may be the same value. Also, a third threshold may be set.

[0092] In addition, when the number of measurement values ​​obtained from the CO2 sensor 23 is a number that can be statistically processed, it is also possible to use statistical methods (average and variance processing) to compare with past statistical values ​​(deviation values) and estimate the congestion level more quantitatively. For such calculations that have a heavy processing load, the processing may be transferred from the device terminal apparatus 10 to the cloud server 30 (FIG. 1) as in step S23 described later, and the device terminal apparatus 10 may receive the processing result (congestion level estimation result) from the cloud server 30.

[0093] If the device terminal apparatus 10 does not perform congestion degree estimation by itself in the above step S21 (S21: No), the CPU 11 in step S23 requests the cloud server 30 (FIG. 1) to perform congestion degree estimation processing. The cloud server 30 executes each process in response to the request from the device terminal apparatus 10. The CPU 31 of the cloud server 30 can also offload part of the processing to the AI ​​accelerator 40 (FIG. 1).

[0094] In step S24, the CPU 11 receives the congestion degree estimation result from the cloud server 30 and omits the processing of this flow. Here, the congestion degree estimation result can be displayed on the display unit 15 of the device terminal apparatus 10 (FIG. 1).

[0095] In this way, the environmental information cloud system 1 can estimate the degree of congestion from the CO2 concentration measurement values ​​at multiple points.

[0096] 8 is a flowchart showing the process executed by the cloud server 30 upon receiving a request from the device terminal apparatus 10. This flowchart is a subroutine of step S103 in FIG. In step S110, the cloud server 30 acquires the environmental sensor information from the device terminal apparatus 10.

[0097] The CPU 31 of the cloud server 30 estimates the congestion degree in step S111. In step S112, the CPU 31 calculates an infection risk index (described below). In step S113, CPU 31 calculates a mold index (described later).

[0098] In step S114, the CPU 31 converts the activity level of the person into an index. In step S115, the CPU 31 calculates the amount of CO2 generated and reduced. In step S116, the CPU 31 converts the calculated information into big data and provides it to the device terminal apparatus 10.

[0099] In step S117, the CPU 31 creates a database of each calculated data and the big data, stores the database in, for example, the auxiliary storage unit 33, and returns to step S103 in FIG.

[0100] Fig. 9 is a model diagram for calculating the infection risk index and mold index. When calculating the infection risk index and mold index in steps S112 and S113 in Fig. 8, the environmental sensor parameters 50 shown in Fig. 9 are used. The environmental sensor parameters 50 use the measurement values ​​of each sensor including the environmental sensor 20 (Fig. 1). The relevant sensor parameters 50 include CO2 concentration 51, its amount of change d(CO2 concentration) / dt (symbol 51a), particulate matter concentration 52, its amount of change d(particulate matter concentration) / dt (symbol 52a), humidity 53, temperature 54, fan airflow 55, infrared output of the infrared sensor 56, and its amount of change d(infrared output) / dt (symbol 56a).

[0101] As shown in Fig. 9, when the CO2 concentration 51 and its change amount d(CO2 concentration) / dt (symbol 51a) both increase (arrow a1 in Fig. 9), a transition to a "dense state" occurs (symbol a3 in Fig. 9). When the CO2 concentration 51 and its change amount d(CO2 concentration) / dt (symbol 51a) both decrease (arrow a2 in Fig. 9), a transition to a "sparse state" occurs (symbol a4 in Fig. 9).

[0102] When the particle concentration 52 and its change amount d(particle concentration) / dt (symbol 52a) both increase (arrow b1 in FIG. 9), a transition to "high activity" occurs (symbol b3 in FIG. 9). When the particle concentration 52 and its change amount d(particle concentration) / dt (symbol 52a) both decrease (arrow b2 in FIG. 9), a transition to "low activity" occurs (symbol b4 in FIG. 9).

[0103] When the humidity 53 (temperature and humidity sensor value in FIG. 1) increases (arrow c1 in FIG. 9), the risk of infection transitions to "low" (symbol c3 in FIG. 9), and when the humidity 53 decreases (arrow c2 in FIG. 9), the risk of infection transitions to "high" (symbol c4 in FIG. 9). Humidity 53 also affects the condensation temperature (arrow d1 in FIG. 9).

[0104] When the temperature 54 (temperature and humidity sensor value in FIG. 1) increases (arrow e1 in FIG. 9), the risk of infection transitions to "low" (symbol e3 in FIG. 9), and when the temperature 54 decreases (arrow e2 in FIG. 9), the risk of infection transitions to "high" (symbol e4 in FIG. 9). Temperature 54 also affects the condensation temperature (arrow d2 in FIG. 9) and further affects "temperature - condensation temperature" (symbol d2 in FIG. 9). Then, if the temperature-condensation temperature (symbol d5 in Figure 9) increases (arrow d6 in Figure 9), it transitions to "low risk of mold growth" (symbol d8 in Figure 9), and if the temperature-condensation temperature decreases (arrow d7 in Figure 9), it transitions to "low risk of mold growth" (symbol d9 in Figure 9).

[0105] If the FAN airflow 55 increases (arrow f1 in Figure 9), the risk of infection transitions to "low" (symbol f3 in Figure 9), and if the FAN airflow 55 decreases (arrow f2 in Figure 9), the risk of infection transitions to "high" (symbol f4 in Figure 9).

[0106] As shown in Fig. 9, when the infrared output 56 of the infrared sensor (the infrared sensor 29c value in Fig. 1) increases (arrow g1 in Fig. 9), the state transitions to a "dense state" (symbol g3 in Fig. 9), and when the infrared output 56 decreases (arrow g2 in Fig. 9), the state transitions to a "sparse state" (symbol g4 in Fig. 9). Also, when the amount of change d(infrared output) / dt (symbol 56a) of the infrared output 56 increases (arrow h1 in Fig. 9), the state transitions to an "increase in the number of people" (symbol h3 in Fig. 9), and when the amount of change d(infrared output) / dt (symbol 55) of the infrared output 56 decreases (arrow h2 in Fig. 9), the state transitions to a "decrease in the number of people" (symbol h4 in Fig. 9).

[0107] The above transition states are aggregated to calculate an infection risk index (symbol i in FIG. 9) and a mold index (symbol j in FIG. 9).

[0108] As shown in Figure 9, the environmental information system 1 can estimate activity using CO2 concentration and PM2.5. Using a model similar to the infection risk model, the activity level of people in a closed space can be quantified. Here, a high PM2.5 value means that there is a lot of dust floating in the air. Activity depends on two parameters: environmental and human activity. Environmental dependence can be removed to some extent by averaging nearby sensor values ​​and comparing with past data.

[0109] Furthermore, the environmental information system 1 calculates the amount of CO2 generated and the amount of reduction required to calculate the carbon offset from past measurement data of CO2 concentration through measurement. By directly measuring the CO2 concentration, it is possible to derive a function that integrates the amount of CO2 emitted in that area with the air volume near the sensor from the integral data. Also, from past measurement results, it is possible to estimate the amount of air flowing into and out of the space with and without air conditioning, so the total amount of CO2 can be calculated from the concentration.

[0110] [effect] As described above, the environmental information system 1 (Figure 1) comprises an environmental sensor 20 that measures at least the carbon dioxide concentration in a closed space, a carbon dioxide concentration estimation unit (CPU11, CPU31) (Figure 1) that estimates the CO2 concentration in the air flowing into the closed space, and a control unit (CPU11) (Figure 1) that estimates the source of CO2 generation in the closed space based on the estimated CO2 concentration value estimated by the carbon dioxide concentration estimation unit and the measured CO2 concentration value measured by the environmental sensor 20.

[0111] As mentioned in the problem section, the conventional technology has a problem that the calculation of the ventilation amount in a closed space is complicated, and the processing load is large for the terminal device connected to the cloud system. In addition, Non-Patent Document 1 also uses a human sensor (infrared sensor), which increases costs. In this embodiment, by estimating the CO2 concentration in the air flowing into a closed space, it is possible to reduce costs while eliminating the need for complex calculation processing of ventilation volume involving nonlinear processing in a closed space, thereby reducing the processing load for estimating the carbon dioxide source.

[0112] In the environmental information system 1 (FIG. 1), the control unit (CPU 11) estimates the number of people in a closed space based on the amount of carbon dioxide from a carbon dioxide generation source.

[0113] In this way, the processing load required for estimating the carbon dioxide source is reduced, so that the amount of carbon can be calculated quickly and the number of people can be estimated in real time. In addition, parameters necessary for estimating the number of people, such as the amount of wind, can be estimated from past measurement results and temperature. Since the amount of carbon dioxide emitted by a person varies depending on the activity state, gender, age, etc., it is considered that the number of people cannot be obtained accurately. However, in the environmental information system 1 (FIG. 1) of this embodiment, the device terminal device 10 and the cloud server 30 are connected via a network 2, and the cloud server 30 can execute the process requested by the device terminal device 10 and provide the device terminal device 10 with the data after execution. For this reason, each device terminal device 10 is not necessarily required to calculate the number of people accurately. Rather, it is important that information such as the degree of congestion, activity, and infection risk can be provided in real time when a large number of device terminal devices 10 are arranged to form a network.

[0114] The environmental information system 1 (Figure 1) is equipped with a CO2 sensor 23 (reference environmental sensor 20ref in Figures 1 and 3B) that measures the carbon dioxide concentration in the air flowing into the closed space, and the carbon dioxide concentration estimation unit (CPU 11, CPU 31) (Figure 1) estimates the carbon dioxide concentration based on the measurement value of the CO2 sensor 23.

[0115] In this way, it is possible to eliminate the need for complex calculation processing of ventilation volume involving nonlinear processing in a closed space, and it is possible to reduce the processing load for estimating the carbon dioxide emission source.

[0116] In the environmental information system 1 (Figure 1), the CPU 11, CPU 31 (Figure 1) estimates the operating state of the equipment (air conditioner) based on fine particle concentration fluctuation information where PM2.5 measured by one PM2.5 sensor 22 of the environmental sensors 20 fluctuates by more than a predetermined value, and / or temperature fluctuation information where the air temperature measured by one temperature / humidity sensor 21 of the environmental sensors 20 fluctuates by more than a predetermined value.

[0117] In this way, even if information on the operating state of the air conditioner cannot be obtained, the operating state of the air conditioner can be inferred from the fluctuations in PM2.5 and temperature that would occur if the air conditioner was operating. Here, the PM2.5 sensor 22 and the temperature / humidity sensor 21 are one of the environmental sensors 20 (Fig. 1), and in this embodiment, they are used to calculate the congestion level, activity level, and infection risk index, so they are compatible for use (no special preparation is required).

[0118] In the environmental information system 1 (FIG. 1), the control unit (CPU 11) issues an alarm when the measured value of the carbon dioxide concentration in the closed space where no one is present becomes equal to or exceeds a predetermined value.

[0119] We believe that this will enable use for security purposes such as ensuring security and preventing people from being locked out in places such as bathrooms where surveillance cameras cannot be used, or in rooms where direct sunlight could cause infrared sensors to malfunction.

[0120] In the environmental information system 1 (FIG. 1), the control unit (CPU 11) calculates the time-series trend of the CO2 concentration in an unoccupied closed space, and issues an alarm if the CO2 concentration increases and then decreases and continues to be detected at or above a predetermined value.

[0121] By doing this, if someone collapses in the bathroom or elsewhere, it is possible to prevent serious situations from occurring due to no one noticing or finding them late. Even if someone collapses, they may continue to breathe shallowly. This state can be estimated from the time-series trend of CO2 concentration, which is highly effective in terms of ensuring security.

[0122] In the environmental information system 1 (Figure 1), the environmental sensor 20 is capable of measuring particulate concentration and temperature, and the control unit (CPU 11) issues an alarm based on particulate concentration fluctuation information indicating that the particulate concentration measured by one PM2.5 sensor 22 of the environmental sensors 20 has fluctuated by more than a predetermined value, and / or temperature fluctuation information indicating that the temperature measured by one temperature / humidity sensor 21 of the environmental sensors 20 has fluctuated by more than a predetermined value.

[0123] If there is a change in particulate matter concentration or temperature, it may be due to sudden human movement or activity in a closed space, so an alarm can be issued to alert people.

[0124] In the environmental information system 1 (Figure 1), the environmental sensor 20 has multiple carbon dioxide concentration sensors (CO2 sensors 23) arranged in close proximity, and the control unit (CPU 11) estimates the degree of congestion based on the measurement values ​​of the carbon dioxide concentration measured by the multiple CO2 sensors 23 arranged in close proximity.

[0125] For example, if the values ​​of the two CO2 sensors 23 are similar, it can be determined that the area as a whole is crowded on average, and conversely, if there is a difference between the values ​​of the two CO2 sensors 23, it can be determined that people are concentrated in a specific location. In this way, the environmental information system 1 can easily estimate the degree of congestion.

[0126] In the environmental information system 1 (Figure 1), the environmental sensor 20 has a CO2 sensor 23, a PM2.5 sensor 22, and a temperature and humidity sensor 21, and the control unit (CPU 11) estimates a person's activity level based on the CO2 concentration, particulate matter concentration, temperature, and humidity measured by the environmental sensor 20.

[0127] <Examples of infection risk index> In the environmental information system 1 (Figure 1), the environmental sensor 20 is capable of measuring particulate concentration, temperature, and humidity, and the control unit (CPU 11) estimates the crowding state of people as a congestion level based on the CO2 concentration measured by the environmental sensor 20 and its change, estimates the activity state of people as a human activity level based on the particulate concentration and its change, determines the infection risk based on the humidity and temperature, and generates an infection risk index based on the congestion level, activity level, and infection risk.

[0128] The infection risk index may range from 0 to 100, for example. If the device (cloud server 30) (FIG. 1) judges that there is the highest risk of infection, it displays 100. Conversely, if it judges that there is no risk of infection, it displays 0. This numerical display is not only shown on the display unit of the cloud server 30, but also provides the numerical data of the infection risk index to the multiple device terminal apparatuses 10. Note that the numerical value of the infection risk index has no meaning in itself, but the magnitude of the numerical value visually appeals to the degree of risk.

[0129] For example, if CO2 levels increase and values ​​such as PM2.5 rise, it can be inferred that (A) there are people or animals in the environment near the CO2 sensor that correspond to the CO2 concentration, or (B) there is an activity that increases CO2 (work using fire is being carried out). It is possible to predict in advance whether the environment is one in which fire is used, such as a kitchen or an environment where tobacco is used, so if it is not the above (B) environment, it can be inferred that the increase in CO2 is due to activity by people or animals.

[0130] The concentration of CO2 changes from around 400ppm to around 2000-3000ppm, but this concentration of CO2 depends on the air exchange rate in the environment and the CO2 emission source. Therefore, by referring to past data near the CO2 sensor, the average air exchange rate can be estimated. Then, by looking at the time transition (slope of the change in CO2 value) and combining it with the value of PM2.5 and the slope of the change, the degree of crowding can be estimated. Based on this data, a preset model is applied, the numerical value is calculated, and the numerical value is normalized to be displayed in the range of 0-100, for example, and the degree of danger linked to the infection risk index can be shown by the magnitude of the numerical value. It is preferable to display the value in conjunction with a visually appealing chart or the like.

[0131] <Examples of big data> Furthermore, the environmental information system 1 generates a mold index in a similar manner. The cloud server 30 can create big data based on the infection risk index and the mold index, and provide it to a plurality of device terminal apparatuses 10 and the like.

[0132] Big data includes not only sensor data, but also location information, time, weather data, and event information (whether the environment was in a special state). The device (cloud server 30) (FIG. 1) calculates time transitions and amounts of change based on the big data, and determines the correlation and degree of correlation between these and weather data, the presence or absence of events, time, the operation status of the air conditioning equipment, and the like. The cloud server 30 permanently records these data (for example, stores them in the auxiliary storage unit 33 (FIG. 1)), thereby permanently determining and recording, for example, changes in CO2. This configuration makes it possible to infer trends on a long-term time axis, trends in short-term cycles, the impact of the operation or non-operation of the air conditioning equipment on environmental CO2, and the like. Specifically, it can be used for highly accurate estimation of carbon dioxide emissions in closed spaces, efficient control of carbon dioxide concentration in spaces, and the like.

[0133] As a result, the environmental information system 1 can use big data to efficiently ventilate buildings, and compared to the conventional method of controlling ventilation while observing measurements in the environment (CO2 concentration in this case), it is possible to reduce control delays and excessive control responses, making it possible to perform more stable control.

[0134] Also, a carbon dioxide emission source estimation method for a terminal device of an environmental information system 1 including a cloud server 30 that is connected to a terminal device (device terminal device 10) via a network 2, executes processing requested by the terminal device and provides post-execution data to the terminal device, in which an environmental sensor 20 that measures air quality including carbon dioxide concentration is connected to the terminal device, and the terminal device executes the steps of measuring at least the carbon dioxide concentration in a closed space, acquiring the carbon dioxide concentration in the air flowing into the closed space from the cloud server 30, and estimating the carbon dioxide emission source in the closed space based on the estimated carbon dioxide concentration value acquired from the cloud server 30 and the measured value of the carbon dioxide concentration.

[0135] In this way, the cloud server 30 can execute the process requested by the device terminal apparatus 10 (processing for estimating the carbon dioxide concentration in the air flowing into the closed space) using high resources and the AI ​​accelerator 40, and using a wide range of databases (for example, a weather database). The terminal apparatus can receive data after execution from the cloud server 30.

[0136] In addition, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0137] In addition, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations, functions, etc. may be realized by software for a processor to interpret and execute a program that realizes each function. Information on the programs, tables, files, etc. that realize each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC (Integrated Circuit) card, SD (Secure Digital) card, or optical disk. [Explanation of symbols]

[0138] 1. Environmental Information Systems 2 Network 10 Device terminal equipment (terminal equipment) 20 Environmental Sensors 21 Temperature and humidity sensor 22 PM2.5 sensor (particle measurement sensor) 23 CO2 sensor (carbon dioxide concentration sensor) 30 Cloud Server

Claims

1. an environmental sensor for measuring at least a carbon dioxide concentration in a closed space; A carbon dioxide concentration estimation unit that estimates a carbon dioxide concentration in air flowing into the closed space; and a control unit that estimates a carbon dioxide generation source in the closed space based on the estimated value of the carbon dioxide concentration estimated by the carbon dioxide concentration estimation unit and the measured value of the carbon dioxide concentration measured by the environmental sensor. An environmental information system comprising:

2. The control unit estimates the number of people in the closed space based on the amount of carbon dioxide from the carbon dioxide generation source.

2. The environmental information system according to claim 1 .

3. A carbon dioxide concentration sensor is provided for measuring a carbon dioxide concentration in the air flowing into the closed space, The carbon dioxide concentration estimation unit estimates the carbon dioxide concentration based on a measurement value of the carbon dioxide concentration sensor.

2. The environmental information system according to claim 1 .

4. The control unit issues an alarm when a measured value of the carbon dioxide concentration in the closed space where no one is present becomes equal to or exceeds a predetermined value.

2. The environmental information system according to claim 1 .

5. The environmental sensor is capable of measuring particulate concentration and temperature; The control unit issues an alarm based on particulate concentration fluctuation information indicating that the particulate concentration measured by the environmental sensor has fluctuated by a predetermined value or more, and / or based on temperature fluctuation information indicating that the temperature measured by the environmental sensor has fluctuated by a predetermined value or more.

2. The environmental information system according to claim 1 .

6. The environmental sensor includes a plurality of carbon dioxide concentration sensors arranged in close proximity to each other; The control unit estimates a degree of congestion based on measured values ​​of carbon dioxide concentration measured by the plurality of carbon dioxide concentration sensors.

2. The environmental information system according to claim 1 .

7. the environmental sensor is capable of measuring particulate concentration, temperature, and humidity; The control unit estimates a degree of activity of a person based on the carbon dioxide concentration, the particulate matter concentration, the temperature, and the humidity measured by the environmental sensor.

2. The environmental information system according to claim 1 .

8. the environmental sensor is capable of measuring particulate concentration, temperature, and humidity; The control unit estimates the crowding level of people as a crowding degree based on the carbon dioxide concentration measured by the environmental sensor and its change amount, estimates the activity state of people as a human activity level based on the fine particle concentration and its change amount, determines the infection risk based on humidity and temperature, and generates an infection risk index based on the crowding level, the activity level, and the infection risk.

2. The environmental information system according to claim 1 .

9. A carbon dioxide emission source estimation method for a terminal device of an environmental information system including a cloud server connected to the terminal device via a network, executing a process requested by the terminal device, and providing the terminal device with data after the process, comprising: An environmental sensor that measures air quality including carbon dioxide concentration is connected to the terminal device, The terminal device Measuring at least a carbon dioxide concentration in the enclosed space; Obtaining an estimate of a carbon dioxide concentration in air flowing into the closed space from the cloud server; estimating a carbon dioxide emission source in the closed space based on the estimated value of the carbon dioxide concentration acquired from the cloud server and the measured value of the carbon dioxide concentration; A method for estimating a carbon dioxide emission source comprising:

10. A computer as a terminal device of an environmental information system including a cloud server that is connected to a terminal device connected to an environmental sensor for measuring air quality including a carbon dioxide concentration via a network, executes processing requested by the terminal device, and provides the terminal device with data after the processing as big data; measuring at least the concentration of carbon dioxide in the enclosed space; estimating the concentration of carbon dioxide in air flowing into the enclosed space; a step of estimating a carbon dioxide emission source in the closed space based on the estimated value of the carbon dioxide concentration and the measured value of the carbon dioxide concentration; A program for executing the above.

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