Temperature detection device, temperature detection method, and program

Abstract

This invention provides a temperature detection device, a temperature detection method, and a program that can easily estimate deep-seated temperatures even when there are temperature changes in deep areas that cannot be directly measured. [Solution] The processing unit of the temperature detection device acquires time-series data of a pair of data: a first temperature (T1) of a first part (P1) and a second temperature (T2) of a second part (P2) located further from the heat source than the first part (P1) of an object having a heat source. The processing unit extracts multiple pairs of data from the time-series data in which the conditions related to the change state of the deep temperature (Tb) are met. The processing unit estimates the first coefficient (a) and the second coefficient (b) in the formula for calculating the deep temperature (Tb) based on a thermal model including a first thermal resistance (Rb), a second thermal resistance (Rs), and heat capacity (Cb), and estimates the deep temperature (Tb) based on the first coefficient (a), the second coefficient (b), the first temperature (T1), and the second temperature (T2).

Description

Technical Field

[0001] This invention relates to a temperature detection device, a temperature detection method, and a program.

Background Art

[0002] There is a temperature detection device that detects the temperature of a site (detection target site) that cannot be directly measured. As such a temperature detection device, based on the measurement results of the temperature and heat flow at a measurable site, a heat model is used to calculate the temperature of the detection target site from the heat flow between the detection target site and the temperature measurable site.

[0003] As a heat model, there is a heat model in which the heat flow rate is specified by the temperature difference and thermal resistance between two points and the capacitance component is not considered. In Patent Document 1, the temperature between two points with known thermal resistance is measured twice on the premise that the temperature of the detection target site has not changed, and the thermal resistance between one of the two points and the detection target site is estimated, and a technique for calculating and estimating the temperature of the detection target site is disclosed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, when a temperature change occurs, since the heat capacity of each part affects and a delay occurs in heat transfer, there is a problem that the temperature of the detection target site cannot be correctly estimated.

[0006] An object of this invention is to provide a temperature detection device, a temperature detection method, and a program that can easily estimate the temperature of a detection target site even when there is a temperature change in the detection target site that cannot be directly measured.

Means for Solving the Problems

[0007] To achieve the above objective, the present invention provides a temperature detection device comprising a processing unit that acquires time-series data of pairs of a first temperature at a certain first part of an object having a heat source and a second temperature at a second part further away from the heat source than the first part; extracts a plurality of sets of data from the time-series data where certain conditions relating to the temperature change state of the heat source are met; estimates plausible values ​​of a first coefficient that shows the relationship between the first thermal resistance and the second thermal resistance and a second coefficient that shows the relationship between the first thermal resistance and the heat capacity, which appear in the calculation formula for the temperature of the heat source based on the first and second temperatures represented by a thermal model expressed by the first thermal resistance between the heat source and the first part, the second thermal resistance between the first part and the second part, and the heat capacity of the first part; and estimates the temperature of the heat source based on the first coefficient, the second coefficient and the detected first and second temperatures. [Effects of the Invention]

[0008] According to the present invention, even when there is a temperature change in the detection target area that cannot be directly measured, the temperature of the detection target area can be easily estimated. [Brief explanation of the drawing]

[0009] [Figure 1] This is a diagram illustrating the temperature detection device of this embodiment. [Figure 2] This diagram illustrates a method for estimating deep-sea temperatures. [Figure 3] This diagram illustrates the relationship between the deep temperature and the detected temperature. [Figure 4] This flowchart shows the control procedure for the deep temperature estimation process performed by the temperature detection device. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. As shown in Figure 1(a), the temperature detection device 1 is a device that estimates and detects the core temperature (core body temperature) of a living organism, in this case a human body, which is an object having a heat source, based on the measurement results of the temperature of a measurable part. The temperature detection device 1 may, for example, be inserted into the ear canal of a living organism and measure the temperature at two locations: inside the ear and near the entrance. Core body temperature generally refers to the temperature of heat-generating parts (heat sources) inside the body, such as internal organs and the brain.

[0011] The temperature detection device 1 includes an insertion part 10a and a main body part 10b. The insertion part 10a is inserted into the ear canal. The insertion part 10a houses a first measurement unit 11 (first temperature sensor) and a second measurement unit 12 (second temperature sensor), while the main body part 10b houses a CPU 13 (processing unit; Central Processing Unit), a storage unit 14, a communication unit 15, and the like.

[0012] The first measurement unit 11 measures (detects) the temperature of the first part (first temperature) located in the inner part of the ear, i.e., close to the heat-generating area, and outputs the measurement result to the CPU 13. The second measurement unit 12 measures the temperature of the second part (second temperature) located on the opening side of the ear, further away from the heat-generating area than the first part, and outputs the measurement result to the CPU 13. The first measurement unit 11 and the second measurement unit 12 may each be, for example, contact-type temperature sensors using thermistors. Alternatively, one or both may be non-contact temperature sensors such as infrared temperature sensors.

[0013] The CPU 13 is a hardware processor that performs calculations and provides overall control over the operation of the temperature detection device 1. This may include a dedicated microcontroller (microcomputer unit) or IC chip. The CPU 13 does not need to perform relatively high-load processing such as real-time data analysis at high speed. Instead, the CPU 13 may generate less heat.

[0014] The memory unit 14 includes RAM (Random Access Memory) and non-volatile memory. The RAM provides the CPU 13 with a working memory space to store temporary data. The non-volatile memory stores control programs and configuration data executed by the CPU 13. The non-volatile memory is, for example, flash memory.

[0015] As shown in the block diagram of Figure 1(b), the memory unit 14 stores a program 141 and temperature data 142. The program 141 is stored in non-volatile memory and includes the operation control program for the temperature detection device 1. The temperature data 142 includes measurement results from the first measurement unit 11 and the second measurement unit 12, calculation parameters determined based on the measurement results, and the core body temperature (core body temperature) at each timing estimated from these measurement results and calculation parameters. This data may be stored in RAM or in non-volatile memory. For example, if the temperature detection device 1 continuously measures temperature and transmits the measurement results to external devices via the communication unit 15 as appropriate, the temperature data 142 may be stored only in RAM. If the operation of the temperature detection device 1 is switched on or off by a switch operation, and it is necessary to retain data not transmitted to external devices even when it is off, the temperature data 142 is stored in non-volatile memory.

[0016] The communication unit 15 performs wireless communication with external devices. The wireless communication standard is determined so as not to cause adverse effects on the human body, including radio wave intensity. The wireless communication standard may be, for example, UWB (Ultra Wide Band) compliant with BAN (Body Area Network), or the Bluetooth® Low Energy standard. The communication unit 15 may transmit measurement results to external devices in real time under the control of the CPU 13. Based on these measurement results, the external device may display or output audio of measurement data from the first measurement unit 11 and the second measurement unit 12 in real time using an application program or the like.

[0017] The CPU 13, the memory unit 14, and the communication unit 15 need to ensure that the heat generated during their operations does not affect the measured temperatures of the first measurement unit 11 and the second measurement unit 12. For example, the temperature detection device 1 may have a heat insulation layer between the portion where the CPU 13, the memory unit 14, and the communication unit 15 of the main body 10b are located and the portion where the first measurement unit 11 and the second measurement unit 12 are located. This heat insulation layer may have a size, position, and orientation that do not prevent heat dissipation from near the ear of the living body. On the other hand, if the main body 10b is large, it may cause a decrease in the wearing comfort, especially disturbing sleep, so it may have a small (thin) shape.

[0018] In addition to the above, the temperature detection device 1 can incorporate a battery, and each part operates by receiving power supply from the battery. The battery may be a dry battery or the like, and may be appropriately detachable and replaceable. Alternatively, the battery may be a rechargeable secondary battery.

[0019] Also, the temperature detection device 1 may have a switch (not shown). The temperature detection device 1 may perform the temperature measurement operation only while the switch is on, or may continuously perform the temperature measurement operation without having a switch. Alternatively, the temperature detection device 1 may have a contact sensor or the like, and may change the presence or absence of temperature measurement and the measurement interval based on the determination result of whether the device itself is inserted into the ear. Further, the duration from the start to the end of temperature measurement, and the start time and end time when temperature measurement is performed may be set, and the measurement may be automatically terminated (started). In this case, the temperature detection device 1 may count the current date and time, and determine the temperature measurement start timing and / or measurement end timing based on the current date and time.

[0020] Also, the temperature detection device 1 may have a display unit. The display unit may be capable of displaying the measured temperature, the current date and time, the duration, or the time zone as described above. Alternatively, the display unit may only indicate the presence or absence of the display of a simple label indicating that it is in operation.

[0021] Next, the method for estimating the deep temperature, which is the temperature detection method of this embodiment, will be described. In the temperature detection device 1 of this embodiment, the CPU 13 acquires the first temperature of the first part and the second temperature of the second part from the first measurement unit 11 and the second measurement unit 12, respectively, at appropriate time intervals. From these measured temperature data, data from the same time are matched, or if they are not measured at the same time, one of them is linearly interpolated to obtain data from the same time, and the data for each set is accumulated. Those that satisfy the conditions described later are extracted, and the parameters necessary for estimating the deep temperature are determined according to those conditions. Once these parameters are determined, the deep temperature can be calculated and estimated based on these parameters and the temperatures of the first part and the second part.

[0022] As shown in Figure 2, the temperature detection device 1 considers heat flow perpendicular to the body surface using a thermal model represented by a thermal circuit having thermal resistance and heat capacity. Let Tb(t) be the temperature (deep temperature) of the target part Pb corresponding to the heat source, T1(t) be the first temperature of the first part P1, T2(t) be the second temperature of the second part P2, and Te(t) be the temperature of the external environment. Let the thermal resistance between the target part Pb and the first part P1 be the first thermal resistance Rb, and the thermal resistance between the surface containing the first part P1 and the surface containing the second part P2 be the second thermal resistance Rs. Based on these, the heat flow Ib(t) between the target part Pb and the first part P1 can be calculated using the following formula (1). Ib(t)=(Tb(t)-T1(t)) / Rb … (1) Furthermore, the heat flow Is(t) between the surface containing the first part P1 and the surface containing the second part P2 can be calculated using the following formula (2). Is(t)=(T1(t)-T2(t)) / Rs … (2)

[0023] On the other hand, if we consider the body's heat capacity Cb to be concentrated in the first site P1 for simplicity, the heat flow Ic(t) entering and leaving the body can be calculated using the following equation (3). Ic(t) = Ib(t) - Is(t) … (3) Since the heat capacity Cb corresponds to the value obtained by dividing the amount of heat Q stored by the temperature rise ΔT of that heat capacity (Q = Cb / ΔT), the following equation (4) is obtained based on the derivative of this equation. Ic(t) = Cb·dT1(t) / dt … (4)

[0024] By rearranging the above formulas (1) to (4), we obtain the following formula (5) as the formula for calculating the deep temperature Tb(t). Tb(t)=Ti(t)+(Rb / Rs)(T1(t)-T2(t))+Rb·Cb(dT1(t) / dt) … (5) In other words, the deep temperature Tb is expressed as a linear combination of the difference between the first temperature T1 and the second temperature T2 (T1(t)-T2(t)), the time derivative of the first temperature T1 dT1 / dt, and the first temperature T1. If the coefficient of (T1(t)-T2(t)) is the first coefficient a, then a = Rb / Rs. The first coefficient a represents the relationship between the first thermal resistance Rb and the second thermal resistance Rs, that is, it is the value obtained by dividing the first thermal resistance Rb by the second thermal resistance Rs. If the coefficient of dT1 / dt is the second coefficient b, then b = Rb·Cb. The second coefficient b represents the relationship between the first thermal resistance Rb and the heat capacity Cb, and is the product of the first thermal resistance Rb and the heat capacity Cs. Note that the coefficient for the first temperature T1 is "1". By differentiating equation (5) with respect to time, equation (6) is obtained. dTb(t) / dt=dTi(t) / dt+a·(T1(t)-T2(t))+b·(d 2 T1(t) / dt 2 ) … (6)

[0025] If two coefficients a and b are determined for three parameters, namely the first thermal resistance Rb, the second thermal resistance Rs, and the heat capacity Cb, then the deep temperature Tb(t) can be estimated from the first temperature T1(t) and the second temperature T2(t) using equation (5). Although the first thermal resistance Rb, the second thermal resistance Rs, and the heat capacity Cb are all unknown, in this embodiment, the two coefficients in the equation are directly determined without independently specifying these three parameters. For example, in equation (6), multiple sets of measured values ​​may be extracted when the deep temperature Tb(t) has not changed much over time, that is, when the derivative of the deep temperature Tb on the left side (the state of change of the heat source) is almost zero (satisfying a certain condition). For the multiple sets of extracted measured values, the first coefficient a and the second coefficient b may be determined so that the absolute value of the right side is as small as possible. For this purpose, measured data that satisfies the condition of left side ≈ 0 is extracted. The most likely first coefficient a and second coefficient b should be estimated such that the sum of the squares of the values ​​on the right-hand side calculated for multiple extracted pairs of first temperatures T1(t) and second temperatures T2(t), i.e., the sum of the squares of the time derivatives of the deep temperature Tb, is minimized. The coefficients a and b can be determined using the least squares method for multiple variables (or two variables).

[0026] As shown in Figure 3(a), even if the deep temperature Tb does not change, the measured temperatures T1(t) and T2(t) change in response to the change in the external ambient temperature Te. In the normal range where deep temperature Tb > ambient temperature Te, heat from the deep flows out to the outside in response to the ambient temperature Te, and the temperature change is larger closer to the surface. That is, as shown in Figure 3(b), which shows the changes in dT1(t) / dt and dT2(t) / dt over time, (A) comparing the absolute values ​​of the changes, |dT2(t) / dt| > |dT1(t) / dt|. (B) The sign (direction of change) of dT2(t) / dt (second change) during the temperature change is the same as the sign of dT1(t) / dt (first change). The sign is the same as the sign related to the change in ambient temperature Te, but here it is not necessary to separately measure the change in ambient temperature Te. Furthermore, due to the influence of changes in ambient temperature Te, heat escapes earlier closer to the surface, so the time change of (C)dT2(t) / dt precedes the time change of dT1(t) / dt along the time axis. Although the waveforms of the time changes are not identical, the degree of precedence can be determined by calculating the cross-correlation coefficient while varying the time difference Δt within an appropriate time range and finding the time difference Δt at which the value is maximized. The appropriate time range may be variable depending on the trend of change in ambient temperature Te, etc.

[0027] If the deep temperature Tb is changing, the timing, magnitude, and sign of dT1(t) / dt and dT2(t) / dt may differ due to the influence of both the deep temperature Tb and the ambient temperature Te. Therefore, conditions (A), (B), and (C) above may be used to determine whether the condition that the deep temperature Tb has not changed significantly is met. The data satisfying conditions (A), (B), and (C) do not have to be continuous. That is, the data may be obtained by aggregating data from multiple different periods.

[0028] The derivative values ​​such as dT1(t) / dt can be obtained simply by taking the difference between time-adjacent data, or they can be obtained by smoothing data over a certain time range. Smoothing can be performed, for example, by first converting data over a certain time range to the frequency domain, dropping components in the frequency band corresponding to noise, and then converting back to the time domain. Alternatively, smoothing can be performed by representing data over a certain time range using polynomial approximation. Components of a higher order than the order defined by the polynomial are dropped as noise. Furthermore, the change amounts dT1(t), dT2(t), etc., can be determined based on the output of a Savitzky-Golay differential filter using polynomial approximation. Second derivative value d 2 T1 / dt 2 This can also be determined based on the two first-order derivatives before and after the given value.

[0029] The deep body temperature estimation process shown in Figure 4 is started when program 141 is read by the CPU 13 from non-volatile memory or the like. The conditions for starting the deep body temperature estimation process may be, for example, when measurements are taken continuously for a predetermined time or period after the start of operation of the temperature detection device 1, such as until a sleep period has elapsed.

[0030] When the deep temperature estimation process is started, the CPU 13 performs initial settings such as starting the first measurement unit 11 and the second measurement unit 12 and setting the memory area, and then starts temperature measurement by the first measurement unit 11 and the second measurement unit 12 (step S1). The measurement timing of the first measurement unit 11 and the measurement timing of the second measurement unit 12 may be simultaneous, but there may be a difference. If there is a difference, the measurement value of the other may be obtained by linear interpolation to match the measurement timing of one of them, as described above. Also, even if measurements are taken simultaneously, the timing of sending the measurement data to the CPU 13 may be staggered.

[0031] The CPU 13 acquires the first temperature data detected by the first measurement unit 11 and the second temperature data detected by the second measurement unit 12, respectively, and stores them as time-series data in the storage unit 14 (step S2; acquisition means). The stored data is stored in a way that allows for the identification of the measurement date and time. That is, in the storage unit 14, the measurement date and time and the measured temperature may be associated with each other, or only the storage start date and time and the measurement interval may be set as initial information, and thereafter only the measured temperature may be stored sequentially. In this case, if a measurement error occurs after the start of data acquisition, the measurement data should not be skipped, but rather data indicating the error should be stored. The CPU 13 may also transmit the measurement data stored in the storage unit 14 to an external device via the communication unit 15. As described above, the CPU 13 may, for example, calculate the temperature at the same timing as the first temperature data by linear interpolation of the second temperature data obtained at timings before and after the measurement timing of the first temperature data. This provides a pair of first and second temperature data at the same timing.

[0032] The CPU 13 determines whether the measurement has finished (step S3). The end of the measurement may include the end of the measurement time or measurement time period, as well as the manual turning off of the switch or the temperature detection device 1 being removed from the ear. If it is determined that the measurement has not finished ("NO" in step S3), the CPU 13 returns to step S2.

[0033] If it is determined that the measurement is complete ("YES" in step S3), the CPU 13 removes outlier data from the stored array of measured temperatures (step S4). Outliers may be detected by, for example, using the standard deviation of multiple measured temperatures within a certain period to detect measured temperatures that deviate more than the reference deviation. Alternatively, the central absolute deviation (such as a Hampel identifier) ​​may be used to detect measured temperatures that deviate more than the reference. Error data may also be included as outliers. Detected outlier measurements can be replaced with interpolated values ​​or the median of the surrounding data. The surrounding data for which interpolated values ​​or the median of the surrounding data are to be calculated may consist of multiple data points each.

[0034] The CPU 13 calculates the derivative from the array of measurement data (step S5). The calculated derivative is the first derivative dT1 / dt and the second derivative d of the first temperature T1 of the first part P1 shown in the above formula (5). 2 T1 / dt 2 , and the first derivative d(T1-T2) / dt of the difference between the first temperature T1 and the second temperature T2. In addition, dT2 / dt, which is used for condition determination, is also calculated as a derivative value. The CPU 13 sets data with a specified width before and after dT1 / dt from the obtained data. The CPU 13 also sets data of the same width for dT2 / dt, shifting it further from the same timing by a maximum width of ±Δt. The CPU 13 determines whether the data of the set width satisfies the above conditions (A), (B), and (C), and extracts the above three data related to the formula (5) for the timing that satisfies conditions (A), (B), and (C) (Step S6; Extraction means). The CPU 13 calculates coefficients a and b from the extracted data using the least squares method (Step S7; Coefficient estimation means).

[0035] The CPU 13 uses the obtained coefficients a and b to calculate the deep temperature Tb(t) using equation (5) and obtain an estimated value (step S8; heat source temperature estimation means). Note that equation (5) itself is valid even if dTb / dt=0, so the deep temperature Tb(t) is estimated for all values ​​in the original dataset except for the outliers excluded in step S4. Then the CPU 13 terminates the deep temperature estimation process.

[0036] The CPU 13 may also transmit history data of a set of first temperature T1(t), second temperature T2(t), and deep temperature Tb(t) to an external device via the communication unit 15 before the end of processing. Alternatively, the CPU 13 may transmit history data only when a communication command is received, such as through manual operation.

[0037] As described above, the temperature detection device 1 of this embodiment includes a CPU 13 as a processing unit. The CPU 13 acquires time-series data of pairs of a first temperature T1(t) of a certain first part P1 of an object having a heat source and a second temperature T2(t) of a second part P2 that is further from the heat source than the first part P1. From the time-series data, the CPU 13 extracts multiple sets of data in which certain conditions related to the change state of the deep temperature Tb(t), which is the temperature of the heat source, are met. A formula for calculating the deep temperature Tb(t) based on the first temperature T1(t) and the second temperature T2(t) is obtained, based on a thermal model represented by the first thermal resistance Rb between the heat source and the first part P1, the second thermal resistance Rs between the first part P1 and the second part P2, and the heat capacity Cb of the first part P1. The CPU 13 estimates the most likely values ​​of the first coefficient a, which represents the relationship between the first thermal resistance Rb and the second thermal resistance Rs that appear in the calculation formula, and the second coefficient b, which represents the relationship between the first thermal resistance Rb and the heat capacity Cb. Based on the first coefficient a, the second coefficient b, and the detected first temperature T1(t) and second temperature T2(t), the CPU 13 estimates the deep temperature Tb(t). In this way, even with a calculation formula of three parameters including heat capacity Cb, the most likely parameters can be calculated by limiting the calculation to multiple sets of data where certain conditions are met, thereby narrowing it down to just two coefficients. Therefore, with this temperature detection device 1, even when there is a change in the deep temperature Tb, the deep temperature Tb, which cannot be directly measured, can be easily measured.

[0038] Furthermore, the deep temperature Tb(t) may be estimated by a linear combination of the difference between the first temperature T1(t) and the second temperature T2(t), the derivative of the first temperature T1(t), and the first temperature T1(t). The first coefficient a is a coefficient for the difference. The second coefficient is a coefficient for the first derivative of the first temperature T1(t), dT1(t) / dt. The coefficient for the first temperature T1(t) may be 1. In this way, by estimating the above-mentioned integrated plausible coefficients according to the conditions of the deep temperature Tb(t), it becomes easy to estimate the deep temperature Tb(t) even if there is a change in the deep temperature Tb(t).

[0039] Furthermore, the CPU 13 may determine that the above condition is met if the following three requirements are satisfied: (C) The second change dT2(t) / dt of the second temperature T2(t) occurs before the first change dT1(t) / dt of the first temperature T1(t). (B) The directions of the first change dT1(t) / dt and the second change dT2(t) / dt are the same. (A) The absolute value of the second change dT2(t) / dt is greater than the absolute value of the first change dT1(t) / dt. These are necessary conditions when the temperature of the heat source is not changing. Therefore, it cannot be said that there is actually no change in the deep temperature Tb(t) in all sets of extracted data, but it becomes possible to extract data within the range of easily obtainable data and within the range of the required accuracy. Thus, this temperature detection device 1 makes it possible to estimate the deep temperature Tb(t) while reducing the increase in calculation processing.

[0040] Furthermore, one of the conditions may be that the deep temperature Tb(t) does not change over time. In practice, dTb(t) / dt is not limited to "0" and may be any fixed value, but for the sake of detection accuracy and ease of calculation, the case where dTb(t) / dt=0 is the most appropriate for extracting data sets. In addition, the requirements corresponding to this condition may be arbitrarily defined in addition to the three requirements above, but the three requirements are inherently reliable when the deep temperature dTb(t) / dt=0, and the first coefficient a and the second coefficient b can be determined with high accuracy.

[0041] Furthermore, the CPU 13 may estimate the first coefficient a and the second coefficient b that minimize the sum of the squares of the time derivatives dTb(t) / dt of the deep temperature Tb(t) calculated based on the extracted sets of data. By using the time derivatives, it becomes easy to estimate the first coefficient a and the second coefficient b by setting the constant to zero.

[0042] Furthermore, the first coefficient a is the value obtained by dividing the first thermal resistance Rb by the second thermal resistance Rs, and the second coefficient b may be the product of the first thermal resistance Rb and the heat capacity Cb. In this way, the three parameters are reduced to two coefficients in the calculation formula, making the calculation of the coefficients easier.

[0043] Furthermore, the temperature detection method of this embodiment includes the following steps: (1) Acquire time-series data of a pair of first temperatures T1(t) and second temperatures T2(t). (2) Extract multiple pairs of data from the time-series data in which a certain condition related to the change state of the deep temperature Tb(t) is met. (3) Estimate the most likely values ​​of the first coefficient a, which shows the relationship between the first thermal resistance Rb and the second thermal resistance Rs, and the second coefficient b, which shows the relationship between the first thermal resistance Rb and the heat capacity Cb, that appear in the formula for calculating the deep temperature Tb(t) based on the first temperature T1(t) and second temperature T2(t) using a thermal model represented by the first thermal resistance Rb between the heat source and the first part P1, the second thermal resistance Rs between the first part P1 and the second part P2, and the heat capacity Cb of the first part P1. (4) Estimate the deep temperature Tb(t) based on the first coefficient a, the second coefficient b, and the detected first temperature T1(t) and second temperature T2(t). According to this temperature detection method, even with a calculation formula involving three parameters including heat capacity Cb, the parameters can be easily determined by narrowing it down to two coefficients by calculating the most plausible parameters for multiple sets of data where certain conditions are met. Therefore, this temperature detection method makes it possible to easily estimate the deep temperature Tb, which cannot be directly measured, even when there is a change in the deep temperature Tb.

[0044] Furthermore, by installing and running the program 141 related to the above temperature detection method on a computer, the deep temperature Tb(t) can be easily estimated from the temperature measurement data at two locations near the surface.

[0045] It should be noted that the present invention is not limited to the above embodiments, and various modifications are possible. For example, in the above, all data sets that satisfy the three conditions corresponding to the condition that the deep temperature Tb(t) does not change were used to calculate the first coefficient a and the second coefficient b, but this is not limited to this. Once the first coefficient a and the second coefficient b have been determined, data sets that clearly have an error, i.e., a large time change in the deep temperature Tb(t), may be excluded as they do not actually satisfy the condition that the deep temperature Tb(t) does not change. Then, the first coefficient a and the second coefficient b may be calculated again using the remaining data sets, and the deep temperature Tb(t) may be estimated.

[0046] Furthermore, if the three parameters can be combined into two coefficients, the coefficients are not limited to the first coefficient a and the second coefficient b described above. Also, while the case where the change in deep temperature Tb(t) dTb(t) / dt=0 was considered as a data extraction condition for combining into two coefficients, it is not limited to this. Any other condition that allows for the calculation of two coefficients may be used. Alternatively, some or all of the above three requirements may be replaced, or other requirements may be added, as conditions for dTb(t) / dt=0 to hold.

[0047] Furthermore, although the above description assumes that the measurement of the first temperature T1(t) and the second temperature T2(t), and the estimation of the deep temperature Tb(t) are all performed within the temperature detection device 1, this is not limited to this. The deep temperature Tb(t) may be estimated by an external device based on the first temperature T1(t) and the second temperature T2(t) transmitted to that external device.

[0048] Furthermore, while the above explanation used the example of measuring core body temperature by attaching the device to the ear, it is not limited to this. It may also be attached to other parts of the body, for example, the device may be attached to the arm, and core body temperature may be measured at two locations: the surface of the arm and a location away from the arm surface. In this case, some kind of heat-conducting material should be placed between the first location P1 and the second location P2. Also, the object of core body temperature measurement does not have to be a living organism (human body). Temperature measurements may be taken at two locations in the depth direction of an object.

[0049] Furthermore, in the above description, a storage unit 14 consisting of non-volatile memory such as flash memory was used as an example of a computer-readable medium for storing the program 141 related to deep temperature measurement control of the present invention, but the invention is not limited to these. Other computer-readable mediums that can be used include other non-volatile memories such as MRAM, and portable recording media such as CD-ROMs and DVD discs. In addition, a carrier wave can also be used as a medium for providing the program data of the present invention via a communication line. In the above embodiment, the first measurement unit 11 and the second measurement unit 12 were provided by the temperature detection device 1, but instead, the first measurement unit 11 and the second measurement unit 12 may be sensor devices separated from the temperature detection device 1 while maintaining their relative positions. In this case, the sensor devices have a communication unit that transmits the measurement results to the temperature detection device 1, and the temperature detection device 1 has a communication unit that receives the measurement results.

[0050] Furthermore, the specific configurations, processing operations, and procedures shown in the above embodiments can be modified as appropriate without departing from the spirit of the present invention. The scope of the present invention includes the scope of the invention described in the claims and its equivalents. [Explanation of Symbols]

[0051] 1 Temperature detection device, 13 CPU, a First coefficient, b Second coefficient, Cb Heat capacity, P1 First part, P2 Second part, Rb First thermal resistance, Rs Second thermal resistance, T1 First temperature, T2 Second temperature, Tb Deep temperature

Claims

1. Time-series data is obtained of a pair of values: a first temperature of a certain first part of an object having a heat source, and a second temperature of a second part that is further away from the heat source than the first part. From the aforementioned time-series data, extract multiple sets of data where certain conditions related to the temperature change state of the heat source are met. The most likely values ​​of the first coefficient, which shows the relationship between the first thermal resistance and the second thermal resistance, and the second coefficient, which shows the relationship between the first thermal resistance and the heat capacity, appear in the formula for calculating the temperature of the heat source based on the first temperature and the second temperature, which is represented by a thermal model expressed by the first thermal resistance between the heat source and the first part, the second thermal resistance between the first part and the second part, and the heat capacity of the first part, are estimated. Based on the first coefficient, the second coefficient, and the detected first and second temperatures, the temperature of the heat source is estimated. A temperature detection device equipped with a processing unit.

2. The temperature of the heat source is estimated by a linear combination of the difference between the first temperature and the second temperature, the derivative of the first temperature, and the first temperature. The first coefficient is a coefficient for the difference value, The second coefficient is a coefficient for the first derivative of the first temperature, The coefficient of the first temperature is 1. The temperature detection device according to claim 1.

3. The temperature detection device according to claim 1, wherein the processing unit determines that a certain condition is met when the second change in the second temperature occurs before the first change in the first temperature, the directions of the first change and the second change are the same, and the absolute value of the second change is greater than the absolute value of the first change.

4. The temperature detection device according to claim 1, wherein the aforementioned condition is that the temperature of the heat source is not changing over time.

5. The temperature detection device according to claim 3, wherein the processing unit estimates the first and second coefficients that minimize the sum of squares of the time derivatives of the temperature of the heat source, which are calculated based on the extracted sets of data.

6. The first coefficient is the value obtained by dividing the first thermal resistance by the second thermal resistance. The second coefficient is the product of the first thermal resistance and the heat capacity. The temperature detection device according to claim 1.

7. Time-series data is obtained of a pair of a first temperature detected at a certain first part of an object having a heat source and a second temperature detected at a second part that is further away from the heat source than the first part. From the aforementioned time-series data, extract multiple sets of data where certain conditions related to the temperature change state of the heat source are met. The most likely values ​​of the first coefficient, which shows the relationship between the first thermal resistance and the second thermal resistance, and the second coefficient, which shows the relationship between the first thermal resistance and the heat capacity, appear in the formula for calculating the temperature of the heat source based on the first temperature and the second temperature, which is represented by a thermal model expressed by the first thermal resistance between the heat source and the first part, the second thermal resistance between the first part and the second part, and the heat capacity of the first part, are estimated. Based on the first coefficient, the second coefficient, and the detected first and second temperatures, the temperature of the heat source is estimated. Temperature detection method.

8. Computers, Acquisition means for acquiring time-series data of a pair of a first temperature detected at a first part of an object having a heat source, and a second temperature detected at a second part further away from the heat source than the first part. Extraction means for extracting multiple sets of data from the aforementioned time-series data in which a certain condition related to the temperature change state of the heat source is met, A coefficient estimation means for estimating a plausible value of a first coefficient that shows the relationship between the first thermal resistance and the second thermal resistance, and a second coefficient that shows the relationship between the first thermal resistance and the heat capacity, which appear in the formula for calculating the temperature of the heat source based on the first temperature and the second temperature, which are represented by a thermal model expressed by the first thermal resistance between the heat source and the first part, the second thermal resistance between the first part and the second part, and the heat capacity of the first part. A heat source temperature estimation means for estimating the temperature of the heat source based on the first coefficient, the second coefficient, and the detected first and second temperatures. A program that makes it function as such.

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