Measurement method, measurement system, and information processing device

Abstract

A drone 30 equipped with a sensor obtains a measurement value y1(t) while moving at a speed v1, and the drone 30 obtains a measurement value y2(t) while moving at a speed v2 different from the speed v1. An information processing device 10 uses the measurement values y1(t), y2(t) and the speeds v1, v2, and derives a time constant τ of the sensor for correcting measurement values of the sensor with respect to a transfer function of the sensor.

Description

[Technical Field]

[0001] The present invention relates to a measurement method, a measurement system, and an information processing device. [Background technology]

[0002] To predict extreme weather, there is a growing demand for technology that can measure specific regions with high precision and frequency. For example, it has been elucidated that linear precipitation bands occur when a sustained inflow of warm, moist air into the low atmosphere, approximately below 1 km in altitude, causes the air to lift due to fronts and topography, leading to the formation of clouds. Cumulonimbus clouds develop in this unstable atmospheric environment, and strong winds aloft cause the cumulonimbus clouds to move downwind and line up in a row. High-precision measurements of the vertical distribution of atmospheric temperature and humidity up to 1 km in altitude are considered important for predicting linear precipitation bands.

[0003] Until now, highly accurate meteorological measurements of the vertical distribution of the atmosphere have been carried out using radiosondes. However, due to the nature of radiosonde measurements, which involve releasing ascending balloons, the following challenges exist for improving the accuracy of extreme weather forecasts. First, the need to inject gas into the radiosonde balloons and the rapid rate at which the gas is consumed make it difficult to increase the number of unmanned repeatable measurements. Second, because radiosondes are blown up by the wind after release and their position cannot be controlled, it is difficult to perform accurate measurements at the desired location.

[0004] In recent years, as drones have become more sophisticated and less expensive, there has been an increase in cases where drones are equipped with measuring instruments and their positions are controlled to perform highly accurate meteorological measurements at precise locations (Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Daiki Nishihara and two others, "Observation of vertical meteorological information of fog using drone technology," Journal of Environmental Information Science, Vol. 34, 2020, pp. 228-233 Summary of the Invention [Problem to be solved by the invention]

[0006] Meteorological measurements using drones require long-term use and operation, so the meteorological sensors mounted on drones must be highly durable and tend to have slow response times.

[0007] There is a demand for obtaining atmospheric vertical distributions with high accuracy and high simultaneity. In order to increase simultaneity, it is necessary to increase the drone's movement speed. Increasing the drone's movement speed increases measurement error due to the effect of the sensor's response speed. This has made it difficult to achieve both measurement accuracy and simultaneity. Figure 1(a) shows the temperature measured by a drone ascending from the ground into the sky and the actual temperature, and Figure 1(b) shows the relationship between the drone's ascent speed and the measurement error at the maximum altitude. As shown in Figure 1(a), the error increases at higher altitudes, and as shown in Figure 1(b), the error increases at faster speeds.

[0008] If the sensor's response speed is accurately known, the actual distribution can be calculated from the measured value, but the sensor's time constant may be unknown. Even if the time constant is listed on the spec sheet, there may be variations between sensors and individual differences. There are many situations where the response time during mobile measurement is unknown accurately. As a result, it is not possible to calculate the actual distribution from the measured value, which has led to the issue of not being able to perform highly accurate measurements.

[0009] The present invention has been made in view of the above, and has an object to determine unknown characteristics of a sensor. [Means for solving the problem]

[0010] A measurement method according to one embodiment of the present invention is a measurement method using a moving body equipped with a sensor, comprising the steps of obtaining a first measurement value while the moving body is moving at a first speed, obtaining a second measurement value while the moving body is moving at a second speed different from the first speed, and deriving a time constant of the sensor for correcting the measurement value of the sensor using the first measurement value, the second measurement value, the first speed, and the second speed for the transfer function of the sensor.

[0011] A measurement system according to one embodiment of the present invention comprises a moving body equipped with a sensor and an information processing device that derives the time constant of the sensor, wherein the moving body obtains a first measurement value while moving at a first speed and obtains a second measurement value while moving at a second speed different from the first speed, and the information processing device comprises an input unit that inputs the first measurement value, the second measurement value, the first speed, and the second speed, and a calculation unit that uses the first measurement value, the second measurement value, the first speed, and the second speed for the transfer function of the sensor to derive the time constant of the sensor to correct the measurement value of the sensor. [Effects of the Invention]

[0012] According to the present invention, unknown characteristics of a sensor can be determined. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram showing an example of a measurement error. [Figure 2] Figure 2 shows how a drone measures temperature while moving through a target area. [Figure 3] FIG. 3 is a functional block diagram showing an example of the configuration of an information processing device included in the measurement system. [Figure 4] FIG. 4 is a flowchart showing an example of a measurement method using the measurement system. [Figure 5] FIG. 5 is a diagram illustrating an example of a hardware configuration of an information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0015] As shown in FIG. 2, the measurement system of this embodiment is a measurement system that measures the temperature of a measurement target area in the vertical direction using a sensor mounted on a drone 30. This measurement system includes the drone 30 and an information processing device 10 shown in FIG. 3. The information processing device 10 calculates the time constant of the sensor mounted on the drone 30 from two measurement results by the drone 30, and corrects the measurement value using the time constant of the sensor. Note that the measurement target area and temperature are examples, and the measurement target area is not limited to the vertical direction, and the physical quantity to be measured is not limited to temperature. Furthermore, the system can be used not only with the drone 30, but also with any mobile object that can be equipped with a sensor, regardless of whether it is manned or unmanned.

[0016] The information processing device 10 shown in FIG. 3 includes an input unit 11, a calculation unit 12, and a correction unit 13.

[0017] The input unit 11 inputs the measurement results of the measurement target area. More specifically, the input unit 11 inputs two measurement results obtained by flying the measurement target area twice with the drone 30 at different speeds, and the speeds for each of the two flights. The measurement results are time-series measurements of the atmospheric distribution in the measurement target area measured by a sensor. The input unit 11 may sequentially receive the measurement values ​​wirelessly while the drone 30 is flying, or the drone 30 may store the measurement values ​​and input the measurement values ​​after the drone 30 has taken two measurements.

[0018] The calculation unit 12 calculates the time constant of the sensor from the ratio of the two measurement results and the speeds during the two flights. Details of how to calculate the time constant will be described later.

[0019] The correction unit 13 corrects the measurement value using the time constant calculated by the calculation unit 12. After the time constant is calculated, the movement speed of the drone 30 is increased to measure the atmospheric distribution in the measurement target area, and the correction unit 13 corrects the measurement value using the time constant.

[0020] The information processing device 10 may be mounted on the drone 30 or may be configured as a device separate from the drone 30.

[0021] Here, the derivation of the time constant of the sensor mounted on the drone 30 will be described.

[0022] In the first measurement, drone 30 measures the temperature while moving from the start point to the end point within the measurement area at a constant speed v1. The true value of the atmospheric distribution at this time is denoted by x1(t), and the time-series measurement value of the atmospheric distribution obtained by the sensor is denoted by y1(t). t is the time elapsed since drone 30 entered the measurement area and started measurement. It is assumed that drone 30 is located at the start point within the measurement area at t=0.

[0023] In the second measurement, drone 30 measures the temperature while ascending along the same route as in the first measurement at a constant speed v2 different from the speed v1 in the first measurement. The true value of the atmospheric distribution at this time is denoted by x2(t), and the time-series measurement value of the atmospheric distribution obtained by the sensor is denoted by y2(t).

[0024] The first and second measurements are carried out at short intervals, and the true value of the atmospheric distribution is assumed to remain unchanged as θ(l) in both cases. l is the distance from the starting point within the measurement area. If the distance from the starting point to the end point is L, then 0 <l<Lとなる。θ(v1t)=x1(t),θ(v2t)=x2(t)であり、x2(t)=x1(at),a=v2 / v1となる。

[0025] In the following, the Laplace transform of the first and second true values ​​x1(t) and x2(t) and the measured values ​​y1(t) and y2(t) is expressed as follows:

[0026]

number

[0027] The transfer function H(s) of the sensor response, which has a time constant τ, can be expressed as equation (1).

[0028]

number

[0029] Equations (2) and (3) hold for the transfer function H(s) and Laplace transforms X1(s), X2(s), Y1(s), and Y2(s).

[0030]

number

[0031] Here, for X1(s) and X2(s), x2(t) = x1(at), so the relationship in equation (4) holds.

[0032]

number

[0033] Substituting equation (4) into equation (3) gives equation (5), which in turn gives equation (6).

[0034]

number

[0035] When there is no error, the time constant τ can be derived from equations (1), (2), and (6) using the Laplace transforms Y1(s) and Y2(s) of the two measured values ​​y1(t) and y2(t) and the ratio a of the velocities v1 and v2 of the two measurements, as shown in equation (7).

[0036]

number

[0037] Next, an example of a measurement method of the measurement system of this embodiment will be described with reference to the flowchart of FIG.

[0038] In step S11, the drone 30 performs a first measurement. For example, the drone 30 moves within the measurement target area at a constant speed v1 = 20 m / s to obtain a measurement value y1[n] of the atmospheric distribution. The measurement value y1[n] is a discrete time series signal.

[0039] In step S12, the drone 30 performs a second measurement at a different speed than the first measurement. For example, the drone 30 moves within the measurement area at a constant speed v2 = 5 m / s to obtain a measured value y2[n] of the atmospheric distribution. The measured value y2[n] is a discrete time series signal.

[0040] In step S13, the information processing device 10 receives the first and second measurement values ​​y1[n], y2[n] and the first and second velocities v1, v2 from the drone 30, and calculates the time constant τ of the sensor using equation (7). Note that since the measurement values ​​y1[n], y2[n] are discrete time series signals, Y1[z], Y2[z] obtained by z-transforming the measurement values ​​y1[n], y2[n] using the following equation are used.

[0041]

number

[0042] Here, N is the number of samples of the measured values ​​y1[n] and y2[n].

[0043] The time constant τ can be derived by using the least squares method to find τ that minimizes the following J:

[0044]

number

[0045] After deriving the time constant τ of the sensor, in step S14, the drone 30 increases the movement speed and measures the measurement target area, and in step S15, the information processing device 10 corrects the measurement value using the derived time constant τ.

[0046] In this embodiment, the time constant is obtained by measuring twice in the measurement target area before the actual measurement, but the time constant may be obtained in advance by measuring twice in another location.

[0047] As described above, according to this embodiment, a drone 30 equipped with a sensor acquires a measurement value y1(t) while moving at a speed v1, and acquires a measurement value y2(t) while moving at a speed v2 different from speed v1. The information processing device 10 uses the measurements y1(t) and y2(t) and the speeds v1 and v2 to derive the sensor's time constant τ for correcting the sensor's measurement value for the sensor's transfer function. This allows the sensor's time constant to be accurately determined, and even when measurements are taken at an increased speed of the drone 30, the determined time constant can be used to correct the measurement value, thereby achieving highly accurate measurements. In other words, by using the measurement system of this embodiment, highly accurate and simultaneous measurements can be achieved.

[0048] The information processing device 10 described above can be, for example, a general-purpose computer system including a central processing unit (CPU) 901, a memory 902, a storage 903, a communication device 904, an input device 905, and an output device 906, as shown in Fig. 5. In this computer system, the information processing device 10 is realized by the CPU 901 executing a predetermined program loaded onto the memory 902. This program can be recorded on a computer-readable recording medium such as a magnetic disk, an optical disk, or a semiconductor memory, or can be distributed via a network. [Explanation of symbols]

[0049] 10. Information processing equipment 11 Input section 12 Arithmetic section 13 Correction unit 30 Drone

Claims

1. A measurement method using a moving object equipped with a sensor, obtaining a first measurement while the moving object is moving at a first velocity; obtaining a second measurement value while the moving object is moving at a second speed different from the first speed; and deriving a time constant of the sensor for correcting the measurement value of the sensor using the first measurement value, the second measurement value, the first velocity, and the second velocity for the transfer function of the sensor. Measurement method.

2. 2. The measurement method according to claim 1, [Equation 9] (where Y 1 (s) is the Laplace transform of the first measurement, Y 2 (s) is the Laplace transform of the second measurement, and a is the ratio of the second velocity to the first velocity. Derive the time constant of the sensor using Measurement method.

3. 2. The measurement method according to claim 1, the first measurement value and the second measurement value are discrete time series signals; By the least squares method, [Equation 10] (where Y 1 [z] is the z-transform of the first measurement, Y 2 [z] is the z-transform of the second measurement, and a is the ratio of the second velocity to the first velocity. Derive the time constant of the sensor that minimizes Measurement method.

4. A measurement system including a moving body equipped with a sensor and an information processing device that derives a time constant of the sensor, The moving object obtains a first measurement value while moving at a first speed, and obtains a second measurement value while moving at a second speed different from the first speed; The information processing device includes: an input unit for inputting the first measurement value, the second measurement value, the first speed, and the second speed; a calculation unit that uses the first measurement value, the second measurement value, the first velocity, and the second velocity for the transfer function of the sensor to derive a time constant of the sensor for correcting the measurement value of the sensor; Measurement system.

5. An information processing device that derives a time constant of a sensor mounted on a moving object, an input unit for inputting a first measurement value obtained while the moving object is moving at a first speed, a second measurement value obtained while the moving object is moving at a second speed different from the first speed, and the first speed and the second speed; a calculation unit that uses the first measurement value, the second measurement value, the first velocity, and the second velocity for the transfer function of the sensor to derive a time constant of the sensor for correcting the measurement value of the sensor; Information processing device.

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