Spatial-state prediction method and spatial-state prediction system

Abstract

This spatial-state prediction method predicts a quantity of state in a space (10) by performing data assimilation processing using a measurement value of a sensor (200) that detects the quantity of state in the space (10). The method includes: a step for acquiring a measurement value of the sensor (200) arranged at a predetermined position in the space (10); and a step for moving the sensor (200) if a variation of the measurement value of the sensor (200) from a first time (t1) to a second time (t2) is less than a predetermined first management value (V1).

Description

Spatial state prediction method and spatial state prediction system

[0001] The present disclosure relates to a space state prediction method and a space state prediction system for predicting a state quantity of a space within a building.

[0002] Conventionally, there has been known an air conditioning control system that predicts state quantities such as temperature or heat load of a building in a space inside a building where equipment such as air conditioners is installed. Patent Document 1 discloses an air conditioning control system that predicts heat load of a building. In this air conditioning control system, temperature, humidity, or CO ２ A fixed sensor for sensing concentration etc. is installed indoors or outdoors.

[0003] Patent No. 5951120

[0004] In the air conditioning control system disclosed in Patent Document 1, when the airflow or temperature in a space fluctuates over time or space due to, for example, the operating conditions of the air conditioning equipment, the opening and closing of windows or doors, the weather, the movement of people, etc., it may not be possible to measure these fluctuations depending on the installation position of the fixed sensor. Therefore, when the state quantity in the space is predicted using this fixed sensor, there is a problem in that the prediction accuracy of the state quantity in the space decreases.

[0005] The present disclosure is intended to solve the above-mentioned problems, and provides a spatial state prediction method and the like that can suppress a decrease in the prediction accuracy of state quantities in a space.

[0006] A spatial state prediction method according to one aspect of the present disclosure is a method for predicting a state quantity in a space by performing a data assimilation process using measurement values ​​of a sensor that detects a state quantity in the space, and includes the steps of acquiring measurement values ​​of the sensor placed at a predetermined position in the space, and moving the sensor when a fluctuation in the measurement value of the sensor from a first time to a second time is equal to or less than a predetermined first control value.

[0007] A spatial state prediction system according to one aspect of the present disclosure is a spatial state prediction system that performs data assimilation processing using measurement values ​​of a sensor that detects state quantities in a space and predicts state quantities in the space, and includes an information acquisition unit that acquires measurement values ​​of the sensor placed at a predetermined position in the space, and a control unit that outputs a movement signal to move the sensor when a fluctuation in the measurement value of the sensor from a first time to a second time is equal to or less than a predetermined first management value.

[0008] In addition, the general or specific aspects of the present disclosure may be realized as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium.

[0009] According to the spatial state prediction method and the like of the present disclosure, it is possible to suppress a decrease in the prediction accuracy of state quantities in space.

[0010] 1 is a block diagram showing the basic configuration of a space state prediction system. FIG. 2 is a diagram showing an example of a space within a building. FIG. 3 is a diagram showing a computer constituting a space state prediction device included in the space state prediction system. FIG. 4 is a block diagram showing the configuration of a space state prediction system of an embodiment. FIG. 5 is a diagram showing an example of a sensor moving body arranged within a space. FIG. 6 is a diagram showing an example of a space within a building to which prediction by the space state prediction system of an embodiment is applied. FIG. 7 is a diagram showing an example of a change in state quantity within a space. FIG. 8 is a diagram showing changes in measurement values ​​of sensors arranged within a space. FIG. 9 is a flowchart showing a space state prediction method of an embodiment. FIG. 10 is a flowchart showing a space state prediction method of a first modified example. FIG. 11 is a flowchart showing a space state prediction method of a second modified example. FIG. 12 is a flowchart showing a space state prediction method of a third modified example.

[0011] Hereinafter, embodiments will be described with reference to the drawings. The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection configurations, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components not described in independent claims will be described as optional components.

[0012] Furthermore, each figure is a schematic diagram and is not necessarily an exact illustration. Furthermore, in each figure, substantially the same configuration is assigned the same reference numeral, and duplicate explanations may be omitted or simplified. Furthermore, even when the same object is illustrated in each figure, the scale may be changed for convenience.

[0013] [Basic Configuration of Space State Prediction System] FIG. 1 is a block diagram showing the basic configuration of a space state prediction system.

[0014] The space state prediction system is a system that predicts the state quantities of a space within a building. The space to be predicted is, for example, a space separated by walls in a building such as a house, office, store, public facility, entertainment facility, art gallery, museum, factory, or warehouse. The state quantities of a space are physical quantities that indicate the state of the space, such as the temperature distribution, humidity distribution, wind speed (including wind direction) distribution, gas concentration distribution, and PM2.5 (fine particulate matter) distribution in the space. The space state prediction system 1 includes a space state prediction device 500, one or more sensors 200 that can communicate via a network 2, and equipment 600.

[0015] Each sensor 200 is a device that detects state quantities and boundary conditions at a predetermined location within a building. The state quantities at a predetermined location are, for example, the temperature, humidity, wind speed, gas concentration, and amount of PM2.5 at the predetermined location. The boundary conditions at a predetermined location are physical quantities that indicate the external environment that affects the state quantities of the space or the state of the boundary area between the space and the outside. Examples of the external environment that affects the state quantities of the space include the outside air temperature, exterior wall temperature, outside humidity, outside wind speed, outside gas concentration, outside PM2.5 amount, and solar radiation. The state of the boundary area between the space and the outside is, for example, the opening area (or opening angle) of a door or window installed in the building. The sensors 200 are, for example, a thermometer, a hygrometer, anemometer, gas concentration meter, PM2.5 meter, pyranometer, door sensor, and window sensor, and are installed within the space, outside the space, or in the boundary area between the space and the outside. The sensors 200 are attached to a mobile device such as a self-propelled robot or drone. The configuration of the sensor 200 and the mobile device will be described later.

[0016] The detection information detected by the sensor 200 is transmitted to the space state prediction device 500 via the network 2. If the sensor 200 is an image sensor, information regarding the position of a person in the space may be transmitted to the space state prediction device 500.

[0017] Each device 600 is a device that forms the spatial environment within a building, such as an air conditioner, a ventilation fan, an air purifier, a circulator, or a gas diffusion device. The device 600 is installed within the building space or in the boundary area between the space and the outside. The device 600 also transmits operation information, including its current operating conditions and past operation history, to the space state prediction device 500. The operating conditions of the device 600 include physical quantities such as the temperature, humidity, air volume, and wind direction of the air sent out from the device 600. If the device 600 is an air conditioner, the operation information may include information on the set air temperature, blown air volume, suction air volume, fan rotation speed, and power supply to the heat exchanger. If the device 600 is a gas diffusion device that emits a diffusible substance such as hypochlorous acid, disinfecting ions, or an air freshener, the operation information may include information on the concentration, amount, and direction of the emitted gas of the diffusible substance.

[0018] The space state prediction device 500 is also communicatively connected to an information terminal 310 and an external information source 320 via a network 2 .

[0019] The information terminal 310 is a terminal device owned and carried by a user, and may be, for example, a smart device such as a smartphone, a tablet terminal, or a wearable terminal, or a portable terminal such as a personal computer. The information terminal 310 may be used to receive information about the prediction system held by the space state prediction device 500 via the network 2 and notify the user of the information.

[0020] The external information source 320 is Internet of Things (IoT) data available on the Internet, such as meteorological data.

[0021] The space state prediction device 500 is provided in a computer 100, which will be described later. The space state prediction device 500 may also be provided in a computer on the cloud. The space state prediction device 500 has an assimilation unit 510 that performs data assimilation processing, and a storage unit 105 that stores various information for performing simulations.

[0022] The assimilation unit 510 integrates the detection information detected by the sensor 200 with the simulation model (prediction model) and determines appropriate initial values, boundary values, and parameters to reproduce the phenomenon. The storage unit 105 stores space layout information in advance. The layout information includes information about the shape and size of the space, as well as information about objects such as desks and partitions placed within the space and their positions. For example, the space shape information is data obtained by 3D modeling the space to be analyzed and then converting it into a point cloud using the finite volume method.

[0023] FIG. 2 is a diagram showing an example of a space 10 inside a building.

[0024] FIG. 2 shows a view of space 10 from above. Sensors 200 for detecting state quantities within space 10 include a thermometer, a hygrometer, and an anemometer. Equipment 600 for forming the environment of space 10 includes an air conditioner, a circulator, a ventilation fan, and an air purifier. Also shown in FIG. 2 are sensors 200 for detecting boundary conditions, including a door sensor for detecting the open / closed state of a door and a window sensor for detecting the open / closed state of a window. Each of the door sensor and window sensor can detect not only whether a window is open or closed, but also the amount of open / close. Note that gas concentration meters and gas diffusion devices are not shown in FIG. Detection information detected by sensors 200 and operation information of equipment 600 are transmitted to space state prediction device 500 via network 2.

[0025] The space state prediction device 500 simulates the space 10 based on various information stored in the memory unit 105, detection information obtained from the sensor 200, and IoT data obtained from an external information source 320, and predicts the state quantities of the space 10.

[0026] FIG. 3 is a diagram showing a computer 100 constituting a space state prediction device 500 included in the space state prediction system 1.

[0027] The computer 100 includes an input unit 101, an arithmetic circuit 102, a memory 103, an output unit 104, a storage unit 105, and a communication unit 106.

[0028] The communication unit 106 communicates wirelessly or wired with the sensor 200, the device 600, the information terminal 310, and the external information source 320 via the network 2. The communication method for wireless communication may be Wi-Fi (registered trademark), Bluetooth (registered trademark), or ZigBee (registered trademark), or may be another method.

[0029] The input unit 101 functions as an HMI (Human Machine Interface) that accepts input operations by a user, and includes, for example, a keyboard, a mouse, a touch sensor, a touchpad, etc. Part of the layout information of the space 10 may be input to the computer 100 via the input unit 101.

[0030] The output unit 104 has a display that displays images, characters, etc., and the display is, for example, a liquid crystal display, a plasma display, an organic EL (Electro-Luminescence) display, etc. The output unit 104 may have a printer that prints images, characters, etc., and may have a function of storing data output from the arithmetic circuit 102 in the storage unit 105 in a file format.

[0031] The storage unit 105 stores a program (i.e., a computer program) 105a in which instructions to the arithmetic circuit 102 are written. The program 105a is stored in the storage unit 105, for example, on removable media or via the network 2. The removable media is, for example, a CD-ROM (Compact Disc Read Only Memory) or a flash memory. For this reason, the communication unit 106 may be provided with an interface for reading the program 105a from removable media.

[0032] Simulation software for performing numerical analysis is also stored in the storage unit 105. Examples of the simulation software include CFD (Computational Fluid Dynamics) and BIM (Building Information Modeling).

[0033] The storage unit 105 also stores temporary data 105b that is temporarily generated by the processing of the arithmetic circuit 102. The storage unit 105 is a non-volatile recording medium, such as a magnetic storage device such as a hard disk, an optical disk, or a semiconductor memory. In this embodiment, layout information and simulation information of the space 10 are stored in the storage unit 105. The storage unit 105 also stores information such as boundary conditions, state quantities of the space 10, and operating conditions of the equipment during the calculation process.

[0034] The memory 103 temporarily stores a program 105a that is read and expanded by the arithmetic circuit 102. Such memory 103 is, for example, a volatile RAM (Random Access Memory).

[0035] The arithmetic circuit 102 is a circuit that executes the program 105 a loaded in the memory 103, and is, for example, a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). When executing the program 105 a, the arithmetic circuit 102 may use each piece of temporary data 105 b stored in the storage unit 105.

[0036] The arithmetic circuit 102 is a circuit for realizing the function of the space state prediction device 500. The arithmetic circuit 102 performs a simulation of the space 10 using simulation software, and predicts the state quantity of the space 10.

[0037] [Space State Prediction System of the Embodiment] A space state prediction system 1A of the embodiment, which is an example of the space state prediction system 1, will be described.

[0038] The space state prediction system 1A is a system that performs data assimilation processing using measurements of a sensor 200 that detects state quantities in a space 10, and predicts state quantities in the space 10.

[0039] The state of the space 10 within the building changes due to, for example, the operating conditions of air conditioning equipment installed in the space 10, the opening and closing of windows or doors, the surrounding environment such as weather, the movement of people, etc., and these changes also cause fluctuations in the state quantities of the space 10, such as gas concentration, airflow, and temperature. Therefore, the current position of the sensor 200 placed within the space 10 may be an inappropriate measurement position for predicting the state quantities of the space 10. In such cases, the prediction accuracy of the state quantities of the space 10 obtained by the data assimilation process decreases. In this embodiment, in order to prevent a decrease in the prediction accuracy of the state quantities of the space 10, the sensor 200 is configured to be able to be moved to an appropriate position.

[0040] FIG. 4 is a block diagram showing the configuration of a space state prediction system 1A according to the embodiment.

[0041] The space state prediction system 1A of the embodiment includes a space state prediction device 500, a sensor mobile body 250, and equipment 600. The configurations of the space state prediction device 500 and the equipment 600 are similar to those of the embodiment shown in FIG.

[0042] FIG. 5 is a diagram showing an example of a sensor moving body 250 arranged in the space 10. As shown in FIG.

[0043] As shown in the figure, the sensor moving body 250 is composed of a sensor 200 and a moving device 210 to which the sensor 200 is attached.

[0044] The sensor 200 is at least one of a thermometer, a hygrometer, an anemometer, a gas concentration meter, a PM2.5 measuring device, and an actinometer. The sensor 200 measures a state quantity in the space 10, and the measurement data measured by the sensor 200 is output to the mobile device 210.

[0045] The moving device 210 is a device for moving the sensor 200. The moving device 210 is, for example, a self-propelled robot or a drone that moves freely within the space 10. The moving device 210 may also be a mobile robot that moves along rails laid in the space 10. The moving device 210 of this embodiment also determines whether or not the sensor 200 should be moved.

[0046] The moving device 210 includes an information acquisition unit 220 , a control unit 230 , a driving mechanism 240 , and a positioning sensor 201 .

[0047] The drive mechanism 240 is made up of a plurality of parts for moving the sensor moving body 250, and includes parts such as a motor, gears, and wheels.

[0048] The control unit 230 controls the operations of the sensor 200, the positioning sensor 201, the information acquisition unit 220, and the drive mechanism 240. The control unit 230 has a communication function for communicating with the space state prediction device 500.

[0049] The information acquisition unit 220 acquires information relating to the measurement values ​​of the sensor 200 output from the sensor 200. The information relating to the measurement values ​​of the sensor 200 is transmitted to the space state prediction device 500 via the control unit 230.

[0050] The positioning sensor 201 has a GPS (Global Positioning System) function for acquiring its own position information. The positioning sensor 201 may include a gyro sensor and an acceleration sensor. The position information of the sensor 200 obtained by the positioning sensor 201 is transmitted to the space state prediction device 500 via the control unit 230.

[0051] FIG. 6 is a diagram illustrating an example of a space 10 in a building to which prediction by the space state prediction system 1A of the embodiment is applied.

[0052] The figure shows an example in which the space 10 is an office space. The figure also shows an example in which the equipment 600 placed in the space 10 is a gas diffusion equipment that emits a predetermined diffusible substance, and the sensors 200a, 200b, and 200c mounted corresponding to the sensor mobiles 250, 250b, and 250c are gas concentration meters that detect the gas concentration of the predetermined diffusible substance. Hereinafter, all or any one of the sensor mobiles 250a, 250b, and 250c may be referred to as the sensor mobile 250, and all or any one of the sensors 200a, 200b, and 200c may be referred to as the sensor 200.

[0053] FIG. 7 is a diagram showing an example of changes in state quantities in the space 10.

[0054] The figure shows the gas concentration distribution of a predetermined diffusing material when viewed from above the space 10, with the darker the color, the lower the gas concentration and the whiter the color. In this example, the diffusing material is emitted from the right side of the device 600, resulting in a higher gas concentration in the area to the right of the device 600. Also, (a), (b), and (c) of FIG. 7 show the gas concentration distributions at a first time t1, a second time t2, and a third time t3, respectively. The second time t2 is 30 minutes after the first time t1, and the third time t3 is even later than the second time t2. State quantities such as gas concentration fluctuate as the situation in the space 10 changes.

[0055] In this embodiment, a sensor with small fluctuations in measurement values ​​is moved to a position different from its current position in order to measure the state quantity of the space 10 at an appropriate location in response to changes in the situation within the space 10. In the example shown in Figure 7, the positions of the three sensors 200a to 200c are fixed during the period from the first time t1 to the second time t2, but at the third time t3, the sensor 200b moves in the direction of the arrow.

[0056] FIG. 8 is a diagram showing changes in the measurements of the sensors 200 a , 200 b , and 200 c arranged in the space 10 .

[0057] Each of the sensors 200a to 200c is initially placed at a predetermined position within the space 10 and measures state quantities within the space 10 at a predetermined sampling period. The predetermined position is a fixed position identified within the space 10. Note that a fixed position does not mean that the position is fixed to a wall or floor, but rather that the position is fixed as a position coordinate. The sampling period is, for example, a period of 1 / 10 or less of the period between the first time t1 and the second time t2. In this example, between the first time t1 and the second time t2, the fluctuations in the measurement values ​​of the sensors 200a and 200c are relatively large, but the fluctuations in the measurement value of the sensor 200b are relatively small.

[0058] The sensor moving body 250 of this embodiment autonomously moves within the space 10 when it is necessary to change the position of the sensor 200, and places the sensor 200 mounted on it at an appropriate position.

[0059] To determine whether the position of the sensor 200 needs to be changed, the information acquisition unit 220 of the sensor moving body 250 acquires the measurement values ​​of the sensor 200 from the first time t1 to the second time t2. The control unit 230 determines whether the fluctuation in the measurement values ​​of the sensor 200 from the first time t1 to the second time t2 is equal to or less than a predetermined first management value V1. The management value is a value that serves as a criterion for determining whether a certain value applies to a specific event. If the fluctuation in the measurement values ​​of the sensor 200 is equal to or less than the first management value V1, the control unit 230 outputs a movement signal to the drive mechanism 240 to move the sensor 200. The drive mechanism 240 is driven based on the movement signal output from the control unit 230 to move the sensor 200.

[0060] For example, the sensor moving body 250 moves the sensor 200 when the difference between the maximum and minimum measurement values ​​of the sensor 200 is equal to or less than the first management value V1. For the sensors 200a and 200c shown in Fig. 8, the difference Δd between the maximum and minimum measurement values ​​is not equal to or less than the first management value V1, so the positions of the sensors 200a and 200b remain fixed even after the second time t2. On the other hand, for the sensor 200b, the difference Δd between the maximum and minimum measurement values ​​is equal to or less than the first management value V1, so the sensor 200b is moved to another position after the second time t2.

[0061] For example, the sensor mobile 250 may move autonomously with random movements like a cleaning robot. The sensor mobile 250 may also determine its movement range or movement route based on the results of machine learning based on its past movement history. A movement-allowed area within the space 10 may be set in advance for the sensor mobile 250 so that multiple sensor mobiles 250 do not exist in the same area at the same time.

[0062] In the above example, the sensor 200 is moved when the difference between the maximum and minimum values ​​measured by the sensor 200 is equal to or less than the first control value V1, but the present invention is not limited to this.

[0063] For example, the sensor moving body 250 may move the sensor 200 when the difference between the measurement value at the first time t1 and the measurement value at the second time t2 is equal to or smaller than a predetermined difference. In other words, the sensor moving body 250 may move the sensor 200 when the change gradient of the measurement value from the first time t1 to the second time t2 is equal to or smaller than a first control value V1 (predetermined control value). Note that the numerical value of the first control value V1 in this case is different from the numerical value of the first control value V1 used to determine the magnitude of the difference between the maximum and minimum measurement values, and is a numerical value related to the change gradient.

[0064] In addition, the sensor moving body 250 may calculate the fluctuation range of the previous and next sections while taking a moving average from the first time t1 to the second time t2, and move the sensor 200 if this fluctuation range is less than or equal to the first control value V1.

[0065] The space state prediction device 500 performs data assimilation processing using the measurement values ​​of the sensors 200 a and 200 c, but not using the measurement values ​​of the moving sensor 200 b. Note that the space state prediction device 500 may also perform data assimilation processing using the measurement values ​​of the moving sensor 200 b and the measurement values ​​of the positionally fixed sensors 200 a and 200 c.

[0066] The sensor moving body 250 fixes the position of the sensor 200 when the fluctuation of the measurement value during a predetermined period after the second time t2 is equal to or greater than a predetermined second control value. For example, the second control value is greater than the first control value V1. The predetermined period is preferably the same as the period between the first time t1 and the second time t2, but is not limited thereto and may be a period shorter than the period between the first time t1 and the second time t2.

[0067] 8, the sensor 200b continues to move after the second time t2 and measures the state quantity of the space 10 at a predetermined sampling period. The sensor moving body 250b stops moving when the fluctuation in the measurement value of the sensor 200b is equal to or greater than the second control value. This fixes the position of the sensor 200b.

[0068] The sensor mobile body 250b transmits the measurement values ​​of the sensor 200b, whose position has been fixed, to the space state prediction device 500. The space state prediction device 500 performs data assimilation processing using the measurement values ​​of the sensor 200b, whose position has been newly fixed, and the measurement values ​​of the sensors 200a and 200c, whose positions have already been fixed.

[0069] As described above, the space state prediction system 1A includes an information acquisition unit 220 that acquires measurements from a sensor 200 placed at a predetermined position within the space 10, and a control unit 230 that outputs a movement signal to move the sensor 200 when the fluctuation in the measurement value of the sensor 200 from the first time t1 to the second time t2 is equal to or less than a first control value V1. With this configuration, it becomes possible to move the sensor 200 that has not been able to fully capture a change in the state of the space 10 to a position where it can capture the change in the state of the space 10. This makes it possible to prevent a decrease in the prediction accuracy of the state of the space 10.

[0070] Although the above example shows the case where one of the multiple sensors 200 is moved, the number of sensors 200 to be moved is not limited to 1. For example, if there are two or more sensors 200 with large fluctuations in measurement values, the sensor moving bodies 250 carrying the respective sensors 200 may move simultaneously.

[0071] Although the above example shows the case where the sensor moving body 250 determines whether or not to move the sensor 200, the present invention is not limited to this. For example, the sensor moving body 250 may transmit the measurement value of the sensor 200 to the space state prediction device 500, and the space state prediction device 500 may determine whether or not to move the sensor moving body 250. In other words, the space state prediction device 500 may have the functions of the information acquisition unit 220 and the control unit 230 described above.

[0072] [Spatial State Prediction Method of the Embodiment] A spatial state prediction method of the embodiment will be described with reference to FIG.

[0073] FIG. 9 is a flow chart illustrating an example spatial state prediction method.

[0074] The space state prediction system 1A acquires information about the current position of the sensor 200 (step S001). At this point, the position of the sensor 200 is fixed.

[0075] Next, the space state prediction system 1A starts measurement using the sensor 200 (step S002). The measurement using the sensor 200 is a measurement of the state quantity of the space 10. The time when the measurement starts in this step is the first time t1. The sensor 200 continues measurement at a predetermined sampling period.

[0076] The measurement data obtained by the sensor 200 is transmitted to the assimilation unit 510 of the space state prediction device 500 via the network 2. The assimilation unit 510 performs data assimilation processing based on the measurement data (step S003).

[0077] Next, the space state prediction system 1A temporarily stops the measurement using the sensor 200 (step S004). The time when the measurement ends in this step is the second time t2. The period from the first time t1 to the second time t2 is a period appropriately selected from the range of, for example, 10 minutes to 50 minutes.

[0078] The space state prediction system 1A determines whether or not it is necessary to move the sensor 200 placed in the space 10. The space state prediction system 1A of this embodiment determines whether or not it is necessary to move the sensor 200 based on whether or not the fluctuation in the measurement value of the sensor 200 from the first time t1 to the second time t2 is equal to or less than a predetermined first control value V1 (step S005).

[0079] If the fluctuation in the measurement value is not equal to or less than the first control value V1 (No in S005), the space state prediction system 1A maintains the current position of the sensor 200 and returns to step S002.

[0080] If the fluctuation of the measurement value is equal to or less than the first control value V1 (Yes in S005), the space state prediction system 1A moves the sensor 200 (step S006). Specifically, the space state prediction system 1A moves the sensor 200 by driving the moving device 210 of the sensor moving body 250.

[0081] The space state prediction system 1A measures the state quantity of the space 10 using the sensor 200 even while the sensor 200 is moving (step S007). Note that step S007 may be executed simultaneously with step S006, or may be executed after the movement in step S006 is completed.

[0082] After moving the sensor 200 in step S006, the space state prediction system 1A fixes the sensor 200 in an appropriate position.

[0083] For example, the space state prediction system 1A determines whether the fluctuation in the measurement value of the sensor 200 over a predetermined period is equal to or greater than a second control value (step S008). For example, the second control value is a value greater than the first control value V1. The predetermined period is preferably the same period as the period between the first time t1 and the second time t2, but is not limited thereto and may be a period shorter than the period between the first time t1 and the second time t2.

[0084] If the fluctuation in the measurement value is not equal to or greater than the second control value (No in S008), the space state prediction system 1A returns to step S006 and continues or resumes the movement of the sensor 200. On the other hand, if the fluctuation in the measurement value is equal to or greater than the second control value (Yes in S008), the space state prediction system 1A stops the movement of the sensor 200 and fixes the position of the sensor 200 (step S009).

[0085] The space state prediction system 1A performs data assimilation processing using measurement data from the fixed-position sensor 200 (step S010). These steps S001 to S010 are repeated to execute the space state prediction method of the embodiment.

[0086] As described above, the space state prediction method includes the steps of acquiring measurements from sensor 200 placed at a predetermined position within space 10, and moving sensor 200 when the fluctuation in the measurement values ​​of sensor 200 from first time t1 to second time t2 is equal to or less than first control value V1. According to this method, it is possible to move sensor 200 that has not been able to fully capture changes in the state of space 10 to a position where it can capture the changes in the state of space 10. This makes it possible to prevent a decrease in the prediction accuracy of the state of space 10.

[0087] [Modification 1] A spatial state prediction method according to Modification 1 will be described with reference to Fig. 10. In Modification 1, an example will be described in which the movement of sensor 200 is stopped when the fluctuation range of the measurement value of sensor 200 while sensor 200 is being moved becomes larger than a predetermined fluctuation range.

[0088] 10 is a flowchart showing the spatial state prediction method of Modification 1. Steps S001 to S007 are the same as those in the embodiment.

[0089] In Modification 1, in step S007, the state quantity of space 10 is measured while sensor 200 is moved. The space state prediction system 1A of Modification 1 determines whether the fluctuation range of the measurement value of sensor 200 during a predetermined period while sensor 200 is being moved is equal to or greater than a predetermined fluctuation range (step S008A). The predetermined period may start immediately after second time t2 or may start shortly after second time t2. In other words, the predetermined period may be any period starting from a sampling period after second time t2. Alternatively, the space state prediction system 1A may calculate a moving average over the predetermined period to determine the fluctuation range between the preceding and following sections, and determine whether this fluctuation range is equal to or greater than the predetermined fluctuation range.

[0090] If the fluctuation range of the measurement value is not equal to or greater than the predetermined fluctuation range (No in S008A), the space state prediction system 1A returns to step S006 and continues moving the sensor 200. On the other hand, if the fluctuation range of the measurement value is equal to or greater than the predetermined fluctuation range (Yes in S008A), the space state prediction system 1A stops the movement of the sensor 200 and fixes the position of the sensor 200 (step S009). Step S010 and subsequent steps are the same as in the embodiment.

[0091] [Modification 2] A spatial state prediction method according to Modification 2 will be described with reference to Fig. 11. In Modification 2, an example will be described in which the movement of sensor 200 is stopped when the change gradient of the measurement value of sensor 200 while sensor 200 is being moved becomes larger than a predetermined change gradient.

[0092] 11 is a flowchart showing the spatial state prediction method of Modification 2. Steps S001 to S007 are the same as those in the embodiment.

[0093] In Modification 2, in step S007, the state quantity of space 10 is measured while moving sensor 200. The space state prediction system 1A of Modification 2 determines whether the change gradient of the measurement value of sensor 200 during a predetermined period while moving sensor 200 is equal to or greater than a predetermined change gradient (step S008B). The predetermined period may start immediately after second time t2 or may start shortly after second time t2. In other words, the predetermined period may be any period starting from a sampling period after second time t2.

[0094] If the change gradient of the measurement value is not equal to or greater than the predetermined change gradient (No in S008B), the space state prediction system 1A returns to step S006 and continues moving the sensor 200. On the other hand, if the change gradient of the measurement value is equal to or greater than the predetermined change gradient (Yes in S008B), the space state prediction system 1A stops the movement of the sensor 200 and fixes the position of the sensor 200 (step S009). Steps S010 and onward are the same as in the embodiment.

[0095] [Modification 3] A spatial state prediction method according to Modification 3 will be described with reference to Fig. 12. In Modification 3, an example will be described in which the sensor 200 is moved by a preset time or distance.

[0096] 12 is a flowchart showing the spatial state prediction method of Modification 3. Steps S001 to S005 are the same as those in the embodiment.

[0097] In the third modification, in step S006A, the sensor 200 is moved by a preset time or distance. The preset time is, for example, 1 / 10 of the sampling period. The preset distance is, for example, 1 / 10 of the vertical or horizontal length of the space 10. The preset distance may be the distance of one square of a mesh formed in the space 10 for performing a simulation analysis.

[0098] After moving the sensor 200 in step S006A, the space state prediction system 1A fixes the position of the sensor 200 (step S009). Steps S010 and onward are the same as in the embodiment.

[0099] (Summary) A spatial state prediction method and the like according to one aspect of the present disclosure will be illustrated.

[0100] The spatial state prediction method of Example 1 is a method for predicting a state quantity in space 10 by performing a data assimilation process using measurement values ​​of sensor 200 that detects a state quantity in space 10, and includes the steps of acquiring measurement values ​​of sensor 200 placed at a predetermined position in space 10, and moving sensor 200 when the fluctuation in the measurement value of sensor 200 from a first time t1 to a second time t2 is equal to or less than a predetermined first control value V1.

[0101] In this way, by moving the sensor 200 when the fluctuation in the measurement value of the sensor 200 is equal to or less than the predetermined first control value V1, it becomes possible to move the sensor 200, which has not been able to fully capture the change in the state of the space 10, to a position where it can capture the change in the state of the space 10. This makes it possible to prevent a decrease in the prediction accuracy of the state of the space 10.

[0102] The spatial state prediction method of Example 2 is the spatial state prediction method described in Example 1, and in the step of moving the sensor 200, the sensor 200 may be moved if the difference between the maximum and minimum measurement values ​​is equal to or less than a first control value V1.

[0103] In this way, by moving the sensor 200 when the difference between the maximum and minimum measured values ​​is equal to or less than the first control value V1, it becomes possible to move the sensor 200, which has not been able to fully detect a change in the state of the space 10, to a position where it can detect the change in the state of the space 10. This makes it possible to prevent a decrease in the accuracy of predicting the state of the space 10.

[0104] The spatial state prediction method of Example 3 is the spatial state prediction method described in Example 1, and in the step of moving the sensor 200, the sensor 200 may be moved if the change gradient of the measurement value from the first time t1 to the second time t2 is equal to or less than the first control value V1.

[0105] In this way, by moving the sensor 200 when the change gradient of the measurement value is equal to or less than the first control value V1, it becomes possible to move the sensor 200, which has not been able to fully capture the change in the state of the space 10, to a position where it can capture the change in the state of the space 10. This makes it possible to prevent a decrease in the prediction accuracy of the state of the space 10.

[0106] The spatial state prediction method of Example 4 may be the spatial state prediction method of any one of Examples 1 to 3, further including the steps of fixing the position of the sensor 200 after moving the sensor 200, and performing a data assimilation process using measurements from the sensor 200 with the position fixed.

[0107] This allows the data assimilation process to be performed using the measurement values ​​of the sensor 200 after the sensor 200 has been moved, thereby preventing a decrease in the accuracy of prediction of the state of the space 10.

[0108] The spatial state prediction method of Example 5 is the spatial state prediction method described in Example 4, and in the step of fixing the position of the sensor 200, the position of the sensor 200 may be fixed if the fluctuation in the measurement value of the sensor 200 over a predetermined period of time is equal to or greater than a predetermined second control value.

[0109] In this way, by fixing the position of the sensor 200 when the fluctuation in the measurement value is equal to or greater than the predetermined second control value, it becomes possible to appropriately capture the change in the state of the space 10. This makes it possible to prevent a decrease in the accuracy of predicting the state of the space 10.

[0110] A spatial state prediction method of Example 6 is the spatial state prediction method described in Example 5, wherein the second control value may be greater than the first control value V1.

[0111] According to this configuration, it is possible to fix the sensor 200 at a position where it can fully detect changes in the state of the space 10. This makes it possible to prevent a decrease in the accuracy of predicting the state of the space 10.

[0112] The spatial state prediction method of Example 7 is the spatial state prediction method described in Example 4, and in the step of fixing the position of the sensor 200, the position of the sensor 200 may be fixed if the fluctuation range of the measurement value over a predetermined period while the sensor 200 is being moved in the step of moving the sensor 200 becomes larger than a predetermined fluctuation range.

[0113] In this way, by fixing the position of the sensor 200 when the fluctuation range of the measurement value becomes larger than a predetermined fluctuation range, it becomes possible to appropriately capture changes in the state of the space 10. This makes it possible to prevent a decrease in the accuracy of prediction of the state of the space 10.

[0114] The spatial state prediction method of Example 8 is the spatial state prediction method described in Example 4, and in the step of fixing the position of the sensor 200, the position of the sensor 200 may be fixed if the change gradient of the measurement value over a predetermined period while the sensor 200 is being moved in the step of moving the sensor 200 becomes larger than a predetermined change gradient.

[0115] In this way, by fixing the position of the sensor 200 when the change gradient of the measured value becomes larger than a predetermined change gradient, it becomes possible to appropriately capture the change in the state of the space 10. This makes it possible to prevent a decrease in the prediction accuracy of the state of the space 10.

[0116] A spatial state prediction method of Example 9 is the spatial state prediction method described in Example 4, wherein the step of moving the sensor 200 may move the sensor 200 by a predetermined time or distance.

[0117] This allows the placement position of the sensor 200 to be determined easily.

[0118] The spatial state prediction method of Example 10 is the spatial state prediction method described in any one of Examples 1 to 9, and the period from the first time t1 to the second time t2 may be a period of 10 minutes or more and 50 minutes or less.

[0119] According to this configuration, it is possible to determine whether or not a change in the state of the space 10 has been captured within an appropriate measurement time, thereby preventing a decrease in the accuracy of prediction of the state of the space 10.

[0120] The space state prediction method of Example 11 is the space state prediction method of any one of Examples 1 to 9, in which the sensor 200 may measure the temperature, flow velocity, or gas concentration in the space 10.

[0121] This makes it possible to prevent a decrease in the accuracy of prediction of the state quantities of the space 10, such as temperature, flow velocity, or gas concentration.

[0122] The spatial state prediction system 1A of Example 12 is a spatial state prediction system 1A that performs data assimilation processing using measurement values ​​of a sensor 200 that detects state quantities within a space 10 and predicts state quantities within the space 10, and is equipped with an information acquisition unit 220 that acquires measurement values ​​of the sensor 200 placed at a predetermined position within the space 10, and a control unit 230 that outputs a movement signal to move the sensor 200 when the fluctuation in the measurement value of the sensor 200 from a first time t1 to a second time t2 is less than or equal to a predetermined first management value V1.

[0123] According to this configuration, it is possible to move the sensor 200, which has not been able to fully detect changes in the state of the space 10, to a position where it can detect changes in the state of the space 10. This makes it possible to prevent a decrease in the accuracy of predicting the state of the space 10.

[0124] (Other Embodiments) The spatial state prediction method and the like in the present disclosure have been described above based on the embodiments, but the present disclosure is not limited to these embodiments, etc. As long as they do not deviate from the gist of the present disclosure, various modifications that a person skilled in the art can conceive of to the embodiments and other forms constructed by combining some of the components in the embodiments are also included within the scope of the present disclosure.

[0125] The space state prediction method and the like of the present disclosure can be applied to predicting space state quantities such as the temperature of a space within a building.

[0126] 1, 1A Space state prediction system 2 Network 10 Space 100 Computer 101 Input unit 102 Arithmetic circuit 103 Memory 104 Output unit 105 Storage unit 105a Program 105b Temporary data 106 Communication unit 200 Sensor 201 Positioning sensor 210 Mobile device 220 Information acquisition unit 230 Control unit 240 Driving mechanism 250 Sensor mobile body 310 Information terminal 320 External information source 500 Space state prediction device 510 Assimilation unit 600 Equipment t1 First time t2 Second time t3 Third time V1 First management value

Claims

1. A method for predicting a state quantity in a space by performing data assimilation processing using measurement values ​​of a sensor that detects a state quantity in the space, comprising: a step of acquiring the measurement values ​​of the sensor disposed at a predetermined position in the space; and a step of moving the sensor when the fluctuation in the measurement values ​​of the sensor from a first time to a second time is equal to or less than a predetermined first control value.

2. The method for predicting a spatial state according to claim 1, wherein in the step of moving the sensor, the sensor is moved if the difference between the maximum and minimum values ​​of the measurement values ​​is equal to or less than the first control value.

3. The spatial state prediction method according to claim 1, wherein in the step of moving the sensor, the sensor is moved if a change gradient of the measurement value from the first time to the second time is equal to or less than the first control value.

4. A spatial state prediction method according to any one of claims 1 to 3, further comprising the steps of: fixing the position of the sensor after moving the sensor; and performing data assimilation processing using the measurement values ​​of the sensor whose position is fixed.

5. The spatial state prediction method according to claim 4, wherein in the step of fixing the position of the sensor, the position of the sensor is fixed if the fluctuation in the measurement value of the sensor over a specified period of time is equal to or greater than a predetermined second control value.

6. The spatial state prediction method according to claim 5, wherein the second control value is a value greater than the first control value.

7. The spatial state prediction method according to claim 4, wherein in the step of fixing the position of the sensor, the position of the sensor is fixed if a fluctuation range of the measurement value during a predetermined period while the sensor is being moved in the step of moving the sensor becomes larger than a predetermined fluctuation range.

8. The spatial state prediction method according to claim 4, wherein in the step of fixing the position of the sensor, the position of the sensor is fixed if a change gradient of the measurement value during a predetermined period while the sensor is being moved in the step of moving the sensor becomes larger than a predetermined change gradient.

9. The spatial state prediction method according to any one of claims 1 to 3, wherein in the step of moving the sensor, the sensor is moved by a predetermined time or distance.

10. A spatial state prediction method according to any one of claims 1 to 3, wherein the period from the first time to the second time is a period of 10 minutes or more and 50 minutes or less.

11. A method for predicting a spatial state according to any one of claims 1 to 3, wherein the sensor measures a temperature, a flow velocity or a gas concentration within the space.

12. A spatial state prediction system that performs data assimilation processing using measurement values ​​of a sensor that detects state quantities in a space, and predicts state quantities in the space, comprising: an information acquisition unit that acquires the measurement values ​​of the sensor placed at a predetermined position in the space; and a control unit that outputs a movement signal to move the sensor when the fluctuation in the measurement value of the sensor from a first time to a second time is equal to or less than a predetermined first management value.

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