An electronic thermometer and a method for measuring body temperature

Abstract

The application discloses an electronic thermometer and a body temperature measuring method, wherein the electronic thermometer comprises a thermistor sensor, an infrared temperature sensor and a microprocessor; the thermistor sensor is used for collecting first body temperature data; the infrared temperature sensor is used for collecting second body temperature data; the microprocessor is used for storing the first body temperature data and the second body temperature data according to a time corresponding relationship and calculating a final temperature value according to the stored first body temperature data and second body temperature data. The application combines the thermistor sensor and the infrared temperature sensor to collect body temperature data and calculate the final temperature value, so that the fast and accurate measuring effect is realized. The application can be widely applied in the field of medical instruments.

Description

Technical Field

[0001] This invention relates to the field of medical devices, and more particularly to an electronic thermometer and a method for measuring body temperature. Background Technology

[0002] Human body temperature measurement is divided into two categories based on the form of the temperature probe: one is direct contact, which uses thermal equilibrium to obtain body temperature, such as thermistor sensors. This method is currently the standard method for body temperature measurement, but its disadvantage is that the measurement time is long because thermal equilibrium needs a certain amount of time to be reached. The other type is non-contact, which uses the body's thermal radiation to obtain body temperature, such as infrared sensors. This method is characterized by short measurement time, but the measurement error is relatively large.

[0003] To achieve both effects, existing solutions combine a thermistor sensor and an infrared sensor into a single electronic thermometer, allowing users to select different measurement methods via a switch. For example, a thermistor sensor is used for more accurate temperature measurement, while an infrared sensor is used for faster measurement. However, this approach still cannot achieve both fast and accurate temperature measurement. Summary of the Invention

[0004] In order to at least partially solve one of the technical problems existing in the prior art, the purpose of this invention is to provide an electronic thermometer and a method for measuring body temperature.

[0005] The technical solution adopted in this invention is:

[0006] An electronic thermometer, comprising:

[0007] A thermistor sensor is used to collect initial body temperature data;

[0008] Infrared temperature sensor, used to collect secondary body temperature data;

[0009] The microprocessor is used to store the first body temperature data and the second body temperature data according to the time correspondence, and to calculate the final temperature value based on the stored first body temperature data and the second body temperature data.

[0010] Furthermore, the microprocessor calculates the final temperature value in the following manner:

[0011] Store the first body temperature data and the second body temperature data according to the timestamp;

[0012] Calculate the difference between the first temperature value measured by the thermistor sensor and the second temperature value measured by the infrared temperature sensor at each time point;

[0013] The first body temperature value with the smallest difference and the second body temperature value corresponding to its time are used as feature points;

[0014] The final temperature value is calculated based on the obtained feature points.

[0015] Furthermore, the microprocessor is also used to detect whether the first temperature value is normal, specifically:

[0016] The heat transfer coefficient is calculated based on the first temperature value. The difference between the heat transfer coefficients at adjacent time points is calculated. If the difference is greater than a preset value, the first temperature value is determined to be abnormal.

[0017] Furthermore, the microprocessor is also used to correct abnormal data, specifically as follows:

[0018] After determining that the first temperature value is abnormal, obtain the second temperature value corresponding to the time of the first temperature value;

[0019] Calculate the heat transfer coefficient based on the second temperature value, calculate a new temperature value based on the heat transfer coefficient, and update the first temperature value with the new temperature value.

[0020] Calculate the difference between the updated first temperature value and the second temperature value corresponding to the time, and determine whether to use the updated first temperature value to calculate the final temperature value based on whether the difference is greater than a preset difference.

[0021] Furthermore, it includes a housing and a circuit board disposed within the housing, the housing comprising a main body portion and a detection portion;

[0022] The detection part includes a temperature measuring cap and a temperature measuring window. The thermistor sensor is disposed inside the temperature measuring cap, and the position of the infrared temperature measuring sensor matches the position of the temperature measuring window.

[0023] Another technical solution adopted in this invention is:

[0024] A method for measuring body temperature includes the following steps:

[0025] The first body temperature data was collected using a thermistor sensor;

[0026] Second body temperature data is collected using an infrared temperature sensor;

[0027] The first body temperature data and the second body temperature data are stored according to the time correspondence, and the final temperature value is calculated based on the stored first body temperature data and the second body temperature data.

[0028] Further, the step of storing the first body temperature data and the second body temperature data according to a time correspondence, and calculating the final temperature value based on the stored first body temperature data and the second body temperature data, includes:

[0029] Store the first body temperature data and the second body temperature data according to the timestamp;

[0030] Calculate the difference between the first temperature value measured by the thermistor sensor and the second temperature value measured by the infrared temperature sensor at each time point;

[0031] The first body temperature value with the smallest difference and the second body temperature value corresponding to its time are used as feature points;

[0032] The final temperature value is calculated based on the obtained feature points.

[0033] Further, the step of calculating the final temperature value based on the obtained feature points includes:

[0034] The temperature value corresponding to each feature point is calculated using the following formula:

[0035] T i =D+B*Tb _ntci +C*Tb i

[0036] In the formula, Tb _ntci Tb represents the temperature value measured by a thermistor sensor. i This represents the temperature value measured by the infrared temperature sensor; D, B, and C are all preset coefficients.

[0037] The average of the temperature values ​​corresponding to several feature points is taken as the final temperature value.

[0038] Further, the step of calculating the final temperature value based on the stored first body temperature data and second body temperature data includes:

[0039] The heat transfer coefficient is calculated based on the first temperature value. The difference between the heat transfer coefficients at adjacent time points is calculated. If the difference is greater than a preset value, the first temperature value is determined to be abnormal.

[0040] Furthermore, it also includes steps for correcting outlier data:

[0041] After determining that the first temperature value is abnormal, obtain the second temperature value corresponding to the time of the first temperature value;

[0042] Calculate the heat transfer coefficient based on the second temperature value, calculate a new temperature value based on the heat transfer coefficient, and update the first temperature value with the new temperature value.

[0043] Calculate the difference between the updated first temperature value and the second temperature value corresponding to the time, and determine whether to use the updated first temperature value to calculate the final temperature value based on whether the difference is greater than a preset difference.

[0044] The beneficial effects of this invention are: this invention combines body temperature data collected by a thermistor sensor and an infrared temperature sensor to calculate the final temperature value, achieving a fast and accurate measurement effect. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of the overall structure of an electronic thermometer according to an embodiment of the present invention;

[0047] Figure 2 This is a partial structural schematic diagram of the detection part in an embodiment of the present invention;

[0048] Figure 3 This is a flowchart of the steps of a body temperature measurement method in an embodiment of the present invention;

[0049] Figure 4 This is a fitting curve of rapid temperature measurement using a thermistor sensor in an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of a specific process for a body temperature measurement method in an embodiment of the present invention.

[0051] Reference numerals: 1-Temperature measuring steel cap; 2-Temperature measuring window; 3-Control panel; 4-Display screen; 5-Switch button; 6-Thermistor sensor; 7-Sponge; 8-Infrared temperature sensor; 9-Infrared PCB board; 10-Outer shell. Detailed Implementation

[0052] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0053] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0054] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0055] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0056] Existing electronic axillary thermometers mostly use thermistors (NTCs) as temperature sensors, which are contact-type temperature measurements with a measurement time of 6–10 minutes. Considering that the rise in human body temperature follows a thermal trend, a specific relationship can be established between temperature data measured up to 30 seconds prior and the actual measured value, thus enabling rapid temperature measurement up to 30 seconds prior. However, the accuracy of the rapidly predicted temperature is lower than that of the measured value.

[0057] Current prediction algorithms mostly employ function model fitting methods for prediction. The algorithm design process is as follows: using body temperature data measured at a certain prediction time and actual body temperature data, based on a known function model, the changing trends of the predicted and measured body temperature data are determined. The parameters of this function model are calculated based on the body temperature data measured at a certain prediction time, and the predicted value is the output value of the function model at a specific point in time. However, in actual use, the accuracy of prediction is affected by several factors. For example, the first is the presence of axillary hair in adults. While standard human heat transfer models are based on fixed heat transfer parameters, these parameters can vary between individuals. The second is the vibration that can occur when the temperature is placed under the armpit during measurement, especially in infants (children under 36 months), causing the predicted body temperature data to deviate from the measured value's heat transfer model. These two factors significantly affect the clinical measurement accuracy of prediction algorithms.

[0058] To address the differences in the aforementioned influencing factors and improve the accuracy of predictive methods in clinical measurements, the temperature measurement component of this invention incorporates an infrared temperature sensor and a thermistor sensor. Based on the body temperature data collected by these two components, the final temperature value is calculated comprehensively. Furthermore, as a further preferred embodiment, a specific algorithm is designed to identify abnormally fluctuating thermistor sensor readings. Secondly, based on the fact that the infrared temperature sensor is unaffected by fluctuations, a comprehensive feature algorithm is designed, using both the infrared and thermistor sensor readings as inputs, to output a body temperature value that most closely approximates the human body temperature. The original feature quantity, the NTC predicted value, is replaced by the infrared temperature measurement value. These two features mutually constrain and correct each other, and a specific internal judgment algorithm is used to output the predicted value.

[0059] like Figure 1 and Figure 2 As shown, this embodiment provides an electronic thermometer, including a housing 10 and a circuit board disposed within the housing 10. The housing 10 includes a main body and a detection part, and the circuit board includes a thermistor sensor 6, an infrared temperature sensor 8, and a microprocessor.

[0060] See Figure 2 The detection part includes a temperature measuring cap and a temperature measuring window 2. The thermistor sensor 6 is set inside the temperature measuring cap, and the position of the infrared temperature sensor 8 matches the position of the temperature measuring window 2.

[0061] Among them, the thermistor sensor 6 is used to collect the first body temperature data;

[0062] Infrared temperature sensor 8 is used to collect secondary body temperature data;

[0063] The microprocessor is used to store the first body temperature data and the second body temperature data according to the time correspondence, and to calculate the final temperature value based on the stored first body temperature data and the second body temperature data.

[0064] As an optional implementation, the circuit board also includes an analog amplification module and an A / D conversion module. The body temperature data collected by the sensor is transmitted to the microprocessor in sequence through the analog amplification module and the A / D conversion module.

[0065] See Figure 2 As a further optional implementation, the temperature measuring cap is a steel temperature measuring cap 1, filled with sponge 7. A thermistor sensor 6 is positioned in the middle of the steel temperature measuring cap 1 and is held in place by the sponge 7 to ensure full contact between the thermistor sensor 6 and the steel temperature measuring cap 1. A temperature measuring window 2 is located below the steel temperature measuring cap 1 and is in close contact with the infrared temperature sensor 8. An infrared PCB board 9 is also provided within the detection section, and the infrared PCB board 9 is connected to the infrared temperature sensor 8.

[0066] See Figure 2 As an optional implementation, a control panel 3 is provided on the housing 10, and the control panel 3 is provided with a display screen 4 for displaying temperature values ​​and a switch button 5.

[0067] Based on the above-mentioned electronic thermometer, such as Figure 3 As shown, this embodiment also provides a body temperature measurement method, including the following steps:

[0068] S1. Collect the first body temperature data through a thermistor sensor;

[0069] S2. Collect second body temperature data through an infrared temperature sensor;

[0070] S3. Store the first body temperature data and the second body temperature data according to the time correspondence, and calculate the final temperature value based on the stored first body temperature data and the second body temperature data.

[0071] Specifically, step S3 includes steps S31-S34:

[0072] S31. Store the first body temperature data and the second body temperature data according to the timestamp;

[0073] S32. Calculate the difference between the first temperature value measured by the thermistor sensor and the second temperature value measured by the infrared temperature sensor at each time point;

[0074] S33. Use at least one set of first body temperature values ​​with the smallest difference and the second body temperature value corresponding to their time as feature points;

[0075] S34. Calculate the final temperature value based on the obtained feature points.

[0076] As an optional implementation, the first temperature value and the second temperature value at the feature point can be averaged to obtain the final temperature value.

[0077] As an alternative implementation, weights can be assigned to the first and second temperature values ​​at the feature points to obtain the final temperature value. For example, the temperature value corresponding to each feature point can be calculated using the following formula:

[0078] T i =D+B*Tb _ntci +C*Tb i

[0079] Then, the average of the temperature values ​​corresponding to multiple feature points is taken as the final temperature value.

[0080] As an optional implementation, step S3 further includes a step of detecting abnormal data. Therefore, during the measurement process, fluctuations may occur, leading to abnormal data. If this abnormal data is directly applied to the temperature calculation, it will severely affect the accuracy of the measurement. Based on this, this embodiment proposes a data anomaly detection method, specifically:

[0081] The heat transfer coefficient is calculated based on the first temperature value. The difference between the heat transfer coefficients at adjacent time points is calculated. If the difference is greater than the preset value, the first temperature value is determined to be abnormal.

[0082] When contact abnormalities such as shaking occur, the heat transfer coefficient is directly affected. If the heat transfer coefficient changes significantly at a certain moment, it is considered an abnormality.

[0083] In response to the above-described anomalies, this embodiment also proposes steps for correcting the abnormal data:

[0084] A1. After determining that the first temperature value is abnormal, obtain the second temperature value corresponding to the time of the first temperature value;

[0085] A2. Calculate the heat transfer coefficient based on the second temperature value, calculate the new temperature value based on the heat transfer coefficient, and update the first temperature value with the new temperature value.

[0086] A3. Calculate the difference between the updated first temperature value and the second temperature value corresponding to the time, and determine whether to use the updated first temperature value to calculate the final temperature value based on whether the difference is greater than the preset difference.

[0087] The above solution will be explained in detail below with reference to the accompanying drawings and specific embodiments.

[0088] 1) NTC rapid (predictive) temperature measurement realization

[0089] The temperature sensor uses the following formula (1) to measure human body temperature:

[0090]

[0091] From the above formula, we obtain:

[0092] T = T1 - (T1 - T2)e -At

[0093] In the formula, T is the current temperature measured by the temperature sensor, T1 is the human body temperature, T2 is the initial temperature of the sensor, A is the heat transfer coefficient, and t is time. The least squares method (also known as the least squares method) is used to find the best function match for the data by minimizing the sum of squared errors. The fitted curve is then used to calculate the human body temperature T1 using the above formula. Specifically, the program implementation can employ an iterative approximation method. Figure 4 It is known that the larger the contact area between the human body and the sensor, the larger the heat transfer coefficient A, and vice versa. Therefore, when the human body shakes, it affects the contact area, which directly affects the heat transfer coefficient A.

[0094] 2) Infrared temperature measurement is realized

[0095] Infrared thermometry has a fast response time; it does not require contact with the human body. It can measure temperature simply by receiving infrared radiation from the human body, with a response time typically in the milliseconds or even microseconds. Furthermore, it has high sensitivity. Since the radiant energy of an object is proportional to the fourth power of its temperature, even a small change in the object's temperature will cause a large change in radiant energy, which can be quickly detected by the infrared sensor.

[0096] Based on the principle of infrared thermometry (formula (2)), it can be transformed into practical application formula (3) to measure the temperature under the armpit of the human body.

[0097] Formula (2) is:

[0098]

[0099] In the formula, E is the radiative exitance (W / m²). 2 ); σ is the Stefan Boltzmann constant, 5.67 × 10⁻⁶. -8 W / (m 2 K 4 ); ε is the emissivity of the object; T is the temperature of the object (in K); T0 is the ambient temperature around the object (in K).

[0100] Formula (3) is:

[0101]

[0102] In the formula, E is the energy emitted by human skin (W / m²).2 T0 is the ambient temperature (in K); K S The coefficient K is a specific constant for a specific structure. S Determined through standard blackbody calibration. T represents the measured temperature of human axillary skin, in Kelvin; where T... g The temperature value was determined based on extensive clinical data, establishing the relationship between surface temperature and body temperature, according to T... g Calculate the body temperature value using T0: T b .

[0103] 3) Implementation method of comprehensive feature optimization of infrared temperature sensor and thermistor sensor

[0104] See Figure 5 An anomaly detection algorithm is designed to identify whether the temperature data collected by the thermistor sensor is abnormal. Specifically, the temperature measurement data of the thermistor sensor for the first N seconds is selected. For N temperature values, the heat transfer coefficients A1, A2, ... A1 corresponding to the N temperature values ​​are calculated according to formula (1). N Then calculate the difference between two adjacent heat transfer coefficients A to obtain ΔA1, ΔA2, ... ΔA N-1 Based on standard NTC temperature measurement data and abnormally fluctuating NTC temperature measurement data, a threshold value △A_q is designed for △A. Temperature values ​​with a difference greater than △A_q are identified as abnormally fluctuating data. Specifically, when calculating the heat transfer coefficient, a human body temperature value can be set, such as 37℃.

[0105] The infrared thermometer reads temperature data at a high speed of 0.25 seconds per reading, thus the data is unaffected by jitter. When the NTC measurement data is abnormal, such as when jitter occurs, the heat transfer coefficient A of the NTC is corrected by using the high-speed infrared temperature reading to obtain an accurate NTC temperature value. When the NTC measurement data is jitter-free (i.e., without abnormalities), a combined optimization algorithm using the NTC and infrared temperature values ​​is used to obtain an accurate predicted body temperature value.

[0106] If fluctuations occur, the heat transfer coefficient A should be corrected as follows: Read the real-time data measured by the NTC and calculate t0, t1...t using formula (1) respectively. n Heat transfer coefficients A0, A1...A n The temperature values ​​calculated using infrared thermometry formula (3) at the same time are Tb1, Tb2...Tb n Based on the infrared thermometry value and formula (1), the new heat transfer coefficient A is determined. new_1 A new_2 ...A new_n The measured value Tb corresponding to the new NTC is calculated based on the new heat transfer coefficient. _ntc1,Tb _ntc2 ...Tb _ntcn This allows for the correction of abnormal data.

[0107] Difference Judgment Algorithm: Based on the correction steps, the real-time temperature values ​​of NTC and infrared thermometers can be obtained. The difference between the real-time temperature values ​​of NTC and infrared thermometers at each time point is calculated as: diff1, diff2... diff n And for diff1, diff2... diff n Summing is performed to obtain sum_diff. Based on data from the clinical standard library, a threshold is designed: sum_diff_R. If sum_diff is greater than sum_diff_R, the comprehensive feature optimization algorithm is entered; otherwise, NTC and infrared temperature measurement values ​​are continuously output, and sum_diff is calculated. When it is greater than sum_diff_R, the next step is performed.

[0108] Comprehensive Feature Optimization Algorithm: Based on the difference between the NTC and infrared temperature values ​​at each time point: diff1, diff2... diff n The feature points are selected from the ones with the smallest differences (two or more).

[0109] The temperature value of the technical feature point is based on the following formula:

[0110]

[0111] Ti represents the body temperature value at the i-th feature point, Tb_ntci represents the body temperature value obtained by the i-th NTC sensor, and Tbi represents the body temperature value obtained by the i-th infrared thermometer. The coefficients D, B, and C are data from a standard database and can be obtained by fitting using the least squares method.

[0112] In this embodiment, five feature points are used, namely: T1 = f(Tb) _ntc1 Tb1), T2 = f(Tb) _ntc2 ,Tb2),T3=f(Tb _ntc3 ,Tb3),T4=f(Tb _ntc4 ,Tb4),T5=f(Tb _ntc5 ,Tb5).

[0113] Then, based on the temperature values ​​of these 5 feature points, calculate the average to obtain the final temperature value:

[0114]

[0115] T final This is the final output body temperature value.

[0116] The aforementioned electronic thermometer, in experimental testing, can measure human body temperature in as little as 4 seconds when used under the armpit, in the mouth, or in the rectum, far shorter than the measurement time of existing thermometers (6-10 minutes). In summary, this invention utilizes the rapid characteristics of infrared thermometry and the characteristics of NTC thermometry. When predicting temperature under the armpit, it uses a series of algorithms based on two temperature measurement data to derive a relatively accurate predicted value, achieving both fast and accurate body temperature measurement. The fastest overall measurement time can reach 4 seconds, making it extremely convenient for measuring body temperature under the armpit, in the mouth, or in the rectum. Its clinical accuracy is superior to other electronic thermometers.

[0117] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0118] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0119] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An electronic thermometer, characterized in that, include: A thermistor sensor is used to collect initial body temperature data; Infrared temperature sensor, used to collect secondary body temperature data; The microprocessor is used to store the first body temperature data and the second body temperature data according to the time correspondence, and to calculate the final temperature value based on the stored first body temperature data and the second body temperature data; The microprocessor calculates the final temperature value in the following manner: Store the first body temperature data and the second body temperature data according to the timestamp; Calculate the difference between the first temperature value measured by the thermistor sensor and the second temperature value measured by the infrared temperature sensor at each time point; The first body temperature value with the smallest difference and the second body temperature value corresponding to its time are used as feature points; The final temperature value is calculated based on the obtained feature points; The microprocessor is also used to detect whether the first temperature value is normal, specifically: The heat transfer coefficient is calculated based on the first temperature value. The difference between the heat transfer coefficients at adjacent time points is calculated. If the difference is greater than a preset value, the first temperature value is determined to be abnormal. The microprocessor is also used to correct abnormal data, specifically: After determining that the first temperature value is abnormal, obtain the second temperature value corresponding to the time of the first temperature value; Calculate the heat transfer coefficient based on the second temperature value, calculate a new temperature value based on the heat transfer coefficient, and update the first temperature value with the new temperature value. Calculate the difference between the updated first temperature value and the second temperature value corresponding to the time, and determine whether to use the updated first temperature value to calculate the final temperature value based on whether the difference is greater than a preset difference.

2. The electronic thermometer according to claim 1, characterized in that, It includes a housing and a circuit board disposed within the housing, the housing comprising a main body portion and a detection portion; The detection part includes a temperature measuring cap and a temperature measuring window. The thermistor sensor is disposed inside the temperature measuring cap, and the position of the infrared temperature measuring sensor matches the position of the temperature measuring window.

3. A method for measuring body temperature, characterized in that, Includes the following steps: The first body temperature data was collected using a thermistor sensor; Second body temperature data is collected using an infrared temperature sensor; The first body temperature data and the second body temperature data are stored according to the time correspondence, and the final temperature value is calculated based on the stored first body temperature data and the second body temperature data. The step of storing the first body temperature data and the second body temperature data according to a time correspondence, and calculating the final temperature value based on the stored first body temperature data and the second body temperature data, includes: Store the first body temperature data and the second body temperature data according to the timestamp; Calculate the difference between the first temperature value measured by the thermistor sensor and the second temperature value measured by the infrared temperature sensor at each time point; The first body temperature value with the smallest difference and the second body temperature value corresponding to its time are used as feature points; The final temperature value is calculated based on the obtained feature points; The step of calculating the final temperature value based on the stored first body temperature data and second body temperature data includes: The heat transfer coefficient is calculated based on the first temperature value. The difference between the heat transfer coefficients at adjacent time points is calculated. If the difference is greater than a preset value, the first temperature value is determined to be abnormal. It also includes steps for correcting anomalous data: After determining that the first temperature value is abnormal, obtain the second temperature value corresponding to the time of the first temperature value; calculate the heat transfer coefficient based on the second temperature value, calculate a new temperature value based on the heat transfer coefficient, and update the first temperature value with the new temperature value; Calculate the difference between the updated first temperature value and the second temperature value corresponding to the time, and determine whether to use the updated first temperature value to calculate the final temperature value based on whether the difference is greater than a preset difference.

4. The body temperature measurement method according to claim 3, characterized in that, The step of calculating the final temperature value based on the obtained feature points includes: The temperature value corresponding to each feature point is calculated using the following formula: T i =D+B*Tb _ntci +C*Tb i In the formula, Tb _ntci Tb represents the temperature value measured by a thermistor sensor. i This represents the temperature value measured by the infrared temperature sensor; D, B, and C are all preset coefficients. The average of the temperature values ​​corresponding to several feature points is taken as the final temperature value.

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