Non-contact temperature calibration method and system, electronic equipment and storage medium

Abstract

The invention discloses a non-contact temperature calibration method and system, electronic equipment and a storage medium, and relates to the technical field of temperature calibration. The method comprises the following steps: acquiring a surface temperature value of a bionic head mold; collecting a temperature measurement value of the standard temperature source by the thermometer to be calibrated and an environment parameter value of the environment, wherein the environment parameter value comprises a temperature fluctuation value and a humidity fluctuation value; performing segmentation processing on the environment parameter values and the temperature measurement values based on a preset human body breathing frequency to obtain an environment parameter value sequence and a temperature measurement value sequence corresponding to a plurality of breathing cycles; calculating the fluctuation gradient corresponding to the environmental parameter value sequence in each respiratory cycle and the deviation change rate between the temperature measurement value sequence and the surface temperature value; and determining a temperature compensation coefficient according to the fluctuation gradient and the deviation change rate, and calibrating the temperature measurement value of the to-be-calibrated thermometer based on the temperature compensation coefficient to obtain a target temperature value. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of temperature measurement is achieved.

Description

Technical Field

[0001] This application relates to the technical field of temperature calibration, and particularly relates to a non-contact temperature calibration method, system, electronic device, and storage medium. Background Art

[0002] With the increasing demand for public health safety, non-contact temperature measurement devices, especially infrared thermometers, have been widely used globally. These devices can quickly measure and monitor an individual's body temperature, which is particularly important for controlling the spread of infectious diseases.

[0003] Currently, existing thermometers measure body temperature non-contact by detecting infrared radiation emitted from the human body. This fast and non-invasive measurement method makes it particularly suitable for public places such as hospitals, schools, and airports. However, in practical applications, due to environmental fluctuations and measurement errors of the device itself, the temperature readings of thermometers often deviate. These deviations may lead to inaccurate temperature measurement results. Therefore, how to calibrate the temperature measured by the thermometer has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a non-contact temperature calibration method, system, electronic device, and storage medium, which has the effect of improving the accuracy of temperature measurement.

[0005] In a first aspect, this application provides a non-contact temperature calibration method, including: Obtaining the surface temperature value of a bionic head mold, where a standard temperature source is provided in the forehead area of the bionic head mold; Collecting the temperature measurement value of the standard temperature source by the thermometer to be calibrated and the environmental parameter values of the environment where it is located, where the environmental parameter values include temperature fluctuation values and humidity fluctuation values; Performing segmented processing on the environmental parameter values and the temperature measurement value based on a preset human breathing frequency to obtain environmental parameter value sequences and temperature measurement value sequences corresponding to multiple breathing cycles; Calculating the fluctuation gradient corresponding to the environmental parameter value sequence within each breathing cycle and the deviation change rate between the temperature measurement value sequence and the surface temperature value; Determining a temperature compensation coefficient according to the fluctuation gradient and the deviation change rate, and calibrating the temperature measurement value of the thermometer to be calibrated based on the temperature compensation coefficient to obtain a target temperature value.

[0006] In a second aspect of this application, a non-contact temperature calibration system is provided. The system includes: A data acquisition module, configured to acquire the surface temperature value of a bionic head mold, wherein a standard temperature source is provided in the forehead area of the bionic head mold; collect the temperature measurement value of the standard temperature source by a to-be-calibrated temperature gun and the environmental parameter values of the environment where it is located, and the environmental parameter values include temperature fluctuation value and humidity fluctuation value; A data sequence determination module, configured to perform segmentation processing on the environmental parameter values and the temperature measurement values based on a preset human breathing frequency to obtain environmental parameter value sequences and temperature measurement value sequences corresponding to multiple breathing cycles; A data sequence processing module, configured to calculate the fluctuation gradient corresponding to the environmental parameter value sequence within each breathing cycle and the deviation change rate between the temperature measurement value sequence and the surface temperature value; A temperature calibration module, configured to determine a temperature compensation coefficient according to the fluctuation gradient and the deviation change rate, and compensate the temperature measurement value of the to-be-calibrated temperature gun based on the temperature compensation coefficient to obtain a target temperature value.

[0007] In a third aspect of the present application, an electronic device is provided, including a memory, a processor, and a program stored on the memory and executable on the processor. When the program is loaded and executed by the processor, a non-contact temperature calibration method can be implemented.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a non-contact temperature calibration method.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solutions, a standard temperature source is set in the forehead area of the bionic head mold, and its surface temperature value is acquired, which can provide a stable and traceable reference temperature for the to-be-calibrated temperature gun; after collecting the temperature fluctuation value and humidity fluctuation value of the environment where it is located and the temperature measurement value of the standard temperature source by the to-be-calibrated temperature gun, the above environmental parameter values and temperature measurement values are segmented based on a preset human breathing frequency, so as to obtain environmental parameter value sequences and temperature measurement value sequences respectively within multiple breathing cycles. At this time, by calculating the fluctuation gradient corresponding to the environmental parameter value sequence within each breathing cycle and the deviation change rate between the temperature measurement value sequence and the surface temperature value of the bionic head mold, the dynamic influence of the instantaneous fluctuations of temperature and humidity in the environment on the measurement result of the temperature gun can be captured, and a temperature compensation coefficient that can comprehensively reflect the real-time changes of the environment and measurement differences can be determined accordingly. Finally, using this temperature compensation coefficient to correct the temperature measurement value of the to-be-calibrated temperature gun can reduce the measurement error caused by environmental fluctuations or equipment deviations and obtain the target temperature value, thereby effectively improving the accuracy of temperature measurement of the temperature gun under variable environmental conditions. Description of the Drawings

[0010] Figure 1 is a schematic flowchart of a non-contact temperature calibration method provided by an embodiment of the present application; Figure 2 is a schematic structural diagram of a non-contact temperature calibration system provided by an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0011] Description of the reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0012] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for illustration" is intended to present the relevant concepts in a specific manner.

[0014] In the description of the embodiments of the present application, the meaning of the term "a plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0015] The embodiments of the present application provide a non-contact temperature calibration method. In one embodiment, please refer to Figure 1 , Figure 1It is a schematic flowchart of a non-contact temperature calibration method provided by an embodiment of the present application. This method can be implemented relying on a computer program, which can be integrated into an application or run as an independent tool-class application. This method can also be implemented relying on a single-chip microcomputer and can run on a non-contact temperature calibration system based on the von Neumann architecture. Specifically, this method may include the following steps: Step 101: Obtain the surface temperature value of the bionic head mold, and a standard temperature source is provided in the forehead area of the bionic head mold.

[0016] Among them, the bionic head mold refers to an experimental device that simulates the head structure and thermal characteristics of the human body. This device is made of a material with an emissivity similar to that of human skin, and its geometric shape and size are similar to those of a real human head. In particular, the curvature and material properties in the forehead area are specifically designed to simulate the thermal radiation characteristics of the human forehead. A temperature control system is provided inside the bionic head mold, which can simulate the body temperature regulation mechanism of the human body.

[0017] The surface temperature value specifically refers to the actual temperature value at the standard temperature source in the forehead area of the bionic head mold in the present invention. This temperature value is measured by a high-precision contact temperature sensor array arranged on the surface of the standard temperature source.

[0018] The standard temperature source refers to a temperature reference device installed in the forehead area of the bionic head mold in the present invention. This device consists of a precision temperature control heating unit and a temperature equalizing heat conduction layer. The heating unit can generate stable heat and evenly diffuse it through the temperature equalizing heat conduction layer to ensure the uniformity of the temperature distribution on the surface of the temperature source.

[0019] Specifically, first, the surface temperature value of the bionic head mold needs to be obtained. Since the infrared thermometer is mainly used to measure the forehead temperature of the human body during actual use, in order to simulate the real measurement scenario and provide a reliable calibration benchmark, the present invention uses a bionic head mold as the measurement object. The bionic head mold is provided with a standard temperature source in the forehead area. The standard temperature source uses a heating device with a stable heating function, and its surface is covered with a heat-conducting material to ensure uniform temperature distribution. By controlling the power output of the heating device, the temperature of the standard temperature source is maintained within the normal human body temperature range of 36.5 to 37.5. In order to accurately obtain the surface temperature value of the bionic head mold, a plurality of high-precision contact temperature sensors are arranged on the surface of the standard temperature source, and these sensors monitor the temperature change in real time in an array distribution manner. The system calculates the surface temperature value of the standard temperature source by collecting the data of these temperature sensors and using a weighted average algorithm. This design can not only provide a stable and reliable temperature benchmark, but also simulate the thermal radiation characteristics of the human forehead, thereby providing a real and reliable reference standard for the subsequent calibration of the infrared thermometer. In addition, the material and structure design of the bionic head mold fully consider the emissivity characteristics of the human skin, can better restore the actual temperature measurement scenario, and improve the accuracy and practicality of calibration. The surface temperature value obtained in this way has high reliability and stability, providing a reliable data basis for the subsequent temperature calibration process.

[0020] Step 102: Collect the temperature measurement value of the infrared thermometer to be calibrated for the standard temperature source and the environmental parameter values of the environment where it is located. The environmental parameter values include the temperature fluctuation value and the humidity fluctuation value.

[0021] Among them, the infrared thermometer to be calibrated in the present invention refers to a non-contact infrared temperature measurement device that needs to be calibrated for accuracy. This device measures the temperature by detecting the infrared radiation emitted by an object, and includes an infrared detector, a signal processing unit, and a temperature display module.

[0022] The temperature measurement value in the present invention refers to the temperature data obtained by the infrared thermometer to be calibrated during the measurement process. These data are the temperature readings obtained by the infrared thermometer to be calibrated after receiving the infrared radiation emitted by the standard temperature source in the forehead area of the bionic head mold through its infrared detector and undergoing internal signal processing.

[0023] The environmental parameter value in the present invention refers to the physical quantity data describing the dynamic changes of the measurement environment, specifically including two parameters: the temperature fluctuation value and the humidity fluctuation value. Among them, the temperature fluctuation value refers to the temperature change amount of the environmental temperature relative to the temperature at the initial moment of measurement, expressed as a temperature difference, and the unit is degree Celsius; the humidity fluctuation value refers to the humidity change amount of the environmental relative humidity relative to the humidity at the initial moment of measurement, expressed as a percentage difference. These environmental parameter values are collected in real time by high-precision temperature and humidity sensors, used to characterize the dynamic change characteristics of the measurement environment, and are an important basis for calculating the influence of environmental fluctuations on the measurement.

[0024] Specifically, after obtaining the surface temperature value of the bionic head mold, it is necessary to collect the measurement data of the temperature gun to be calibrated and the environmental parameters. Since human breathing causes periodic fluctuations in the temperature and humidity of the measurement environment, and these fluctuations will affect the measurement accuracy of the temperature gun, not only the temperature measurement value of the temperature gun for the standard temperature source is collected, but also the environmental parameter values are collected synchronously. The temperature gun to be calibrated is fixed at an appropriate distance from the standard temperature source in the forehead area of the bionic head mold, such as 15 cm, and a special bracket is used to ensure the stability of the measurement angle and distance. The temperature gun adopts an automatic continuous measurement mode, measures the temperature of the standard temperature source at a frequency of once per second, and records the measurement value through the data acquisition module. At the same time, a high-precision temperature and humidity sensor is set near the measurement position of the temperature gun to collect the environmental temperature and humidity data in real time. The sampling frequency of the environmental parameters is synchronized with the measurement frequency of the temperature gun to ensure the time correspondence of the data. The temperature fluctuation value represents the change amount of the environmental temperature relative to the initial temperature, and the humidity fluctuation value represents the change amount of the environmental humidity relative to the initial humidity. By synchronously collecting these data, the correlation relationship between the temperature measurement value and the environmental parameter fluctuation can be established, providing a complete data basis for the subsequent data segmentation processing based on the breathing frequency. This synchronous collection method can truly reflect the influence of environmental fluctuations on temperature measurement, and helps to improve the accuracy and adaptability of calibration.

[0025] Step 103: Segment the environmental parameter values and temperature measurement values based on a preset human breathing frequency to obtain sequences of environmental parameter values and sequences of temperature measurement values corresponding to multiple breathing cycles.

[0026] Among them, the preset human breathing frequency in the present invention refers to the standard frequency value determined according to the breathing law under the normal physiological state of the human body. This preset frequency serves as the basic time unit for data segmentation and is used to divide the continuously collected data into periodic segments with physiological significance. This frequency value is selected because it represents the most common breathing rhythm in the normal resting state of the human body and can better reflect the influence law of breathing on the environment in the actual temperature measurement scenario.

[0027] The sequence of environmental parameter values corresponding to the breathing cycle in the present invention refers to the time series data of the temperature fluctuation value and humidity fluctuation value collected within a single breathing cycle. The sequence of temperature measurement values in the present invention refers to the sequence of temperature data obtained by the temperature gun to be calibrated measuring the standard temperature source within a single breathing cycle.

[0028] Specifically, to accurately analyze the periodic influence of human respiration on the measurement environment, the collected data is segmented based on a preset human respiration frequency. The normal human respiration frequency is about 12 - 20 breaths per minute. In the present invention, 16 breaths per minute is selected as the preset standard human respiration frequency, that is, each respiratory cycle is 3.75 seconds. Based on this time standard, the continuously collected environmental parameter values and temperature measurement values are segmented at equal intervals with a time interval of 3.75 seconds. In the actual processing, since the sampling frequency of the temperature gun is once per second, each respiratory cycle contains 3 - 4 sampling points. When segmenting, first determine the starting time point of the data sequence, and then divide it backward in turn according to a fixed time window of 3.75 seconds. Each time window corresponds to a complete respiratory cycle. For each respiratory cycle, extract the temperature fluctuation value, humidity fluctuation value, and temperature measurement value within this cycle, and respectively form an environmental parameter value sequence and a temperature measurement value sequence. This segmentation method based on the respiratory cycle makes the data have clear time correlation and can better reflect the periodic influence of human respiration on the measurement environment. Through this segmentation process, the continuous data stream can be transformed into multiple data sequences with the same time span, providing structured data support for subsequent analysis of the relationship between environmental parameter fluctuations and temperature measurement values, and helping to improve the accuracy and reliability of the calibration algorithm.

[0029] Based on the above - mentioned embodiment, as an alternative embodiment, in step 103: Segmenting the environmental parameter values and temperature measurement values based on a preset human respiration frequency to obtain environmental parameter value sequences and temperature measurement value sequences corresponding to multiple respiratory cycles. This step may further include the following steps: Step 201: Use the sliding window method to perform time - series segmentation on the environmental parameter values and temperature measurement values to obtain multiple sliding windows, and the time length of the sliding window matches the respiratory cycle corresponding to the preset human respiration frequency.

[0030] Specifically, to accurately capture the dynamic change characteristics of environmental parameters and temperature measurement values, the sliding window method is used to segment the time - series data. In specific implementation, for example, set the time length of the sliding window to 3.75 seconds, which exactly matches a single respiratory cycle corresponding to the preset human respiration frequency of 16 breaths per minute. The moving step size of the sliding window is set to 0.5 seconds, that is, each time the window moves, it slides backward by 0.5 seconds. This fine - grained sliding method ensures a fine capture of data changes. Through the continuous movement of the sliding window, the environmental parameter values and temperature measurement values within the entire measurement time period are divided into multiple overlapping time windows, and each window contains a complete 3.75 - second data segment. This sliding segmentation method not only maintains the continuity of the data but also can fully reflect the temporal characteristics of the data.

[0031] Step 202: Resample the environmental parameter values and temperature measurement values within each sliding window to obtain multiple equally spaced data point sequences.

[0032] Specifically, since actual sampling may be uneven, it is necessary to resample the data within the sliding window to obtain a unified data structure. In specific implementation, a linear interpolation algorithm is used. For example, 8 equally spaced sampling points are generated within each 3.75 - second sliding window, and the sampling interval is 0.46875 seconds. The resampling process is applied to both the environmental parameter values (including temperature fluctuation values and humidity fluctuation values) and the temperature measurement values to ensure that all data sequences have the same time resolution and number of data points. This resampling process not only ensures the uniformity of the data but also maintains the main characteristics of the original data through a reasonable sampling density.

[0033] Step 203: Group the data point sequences according to the period to obtain sequences of environmental parameter values and temperature measurement values corresponding to multiple respiratory cycles.

[0034] Specifically, the resampled data point sequences are grouped and sorted according to the respiratory cycle to establish the corresponding relationship between the environmental parameters and the temperature measurement values. In specific implementation, for example, with 3.75 seconds as a cycle unit, 8 resampled data points are organized into a group, respectively forming sequences of environmental parameter values and temperature measurement values. For the sequence of environmental parameter values, it includes two components, the temperature fluctuation value sequence and the humidity fluctuation value sequence, and each component consists of 8 equally spaced data points; the corresponding temperature measurement value sequence also contains 8 data points. This grouping method ensures that the data within each respiratory cycle has a complete time - series structure, facilitating subsequent data analysis and processing based on the respiratory cycle.

[0035] Step 104: Calculate the fluctuation gradient corresponding to the sequence of environmental parameter values within each respiratory cycle and the deviation change rate between the sequence of temperature measurement values and the surface temperature value.

[0036] Among them, the fluctuation gradient in the present invention refers to the instantaneous change rate of the environmental parameter value within a single respiratory cycle, including two components, the temperature fluctuation gradient and the humidity fluctuation gradient. The fluctuation gradient reflects the speed of change of the environmental parameter over time, and its positive or negative sign indicates the direction of change, and the numerical value indicates the degree of change.

[0037] The deviation change rate in the present invention refers to the change rate of the deviation of the temperature measurement value relative to the surface temperature value of the standard temperature source within a single respiratory cycle. The deviation change rate reflects the dynamic change characteristics of the temperature measurement accuracy over time, and its numerical value characterizes the speed of change of the measurement error, which is an important index for evaluating the stability of the temperature measurement performance.

[0038] Specifically, in order to quantitatively characterize the influence degree of environmental parameter fluctuations on the temperature measurement accuracy, the present invention needs to calculate the fluctuation gradient of environmental parameters and the deviation change rate of temperature measurement values. In the specific implementation process, first, the sequence of environmental parameter values within each breathing cycle is processed, and the fluctuation gradients of the temperature fluctuation value sequence and the humidity fluctuation value sequence are calculated respectively. The calculation of the fluctuation gradient adopts the method of dividing the difference between adjacent data points by the time interval, that is, for each data point in the sequence, the change rate between it and the next data point is calculated. For example, each breathing cycle contains 8 equally spaced data points, and the time interval is 0.46875 seconds, so 7 fluctuation gradient values can be obtained. For the temperature fluctuation gradient, its unit is degrees Celsius per second; for the humidity fluctuation gradient, its unit is percentage per second. At the same time, each data point in the temperature measurement value sequence is compared with the surface temperature value of the known standard temperature source to calculate the measurement deviation. Then, using the same method as calculating the fluctuation gradient, the change rate between adjacent measurement deviations is calculated to obtain the deviation change rate sequence. Through this calculation method, the quantitative relationship between environmental parameter fluctuations and measurement deviation changes can be obtained, providing an important basis for establishing a compensation model in the follow-up. This analysis method based on gradients and change rates can effectively reflect the dynamic influence of environmental fluctuations on measurement accuracy, and has better adaptability and accuracy compared with the traditional method that only considers static deviations.

[0039] Based on the above embodiments, as an alternative embodiment, in step 104: calculating the fluctuation gradient corresponding to the sequence of environmental parameter values within each breathing cycle, this step may further include the following steps: Step 301: Project the temperature fluctuation values and humidity fluctuation values within each breathing cycle onto a preset two-dimensional coordinate system respectively.

[0040] Specifically, to intuitively analyze the fluctuation characteristics of environmental parameters, the present invention projects the temperature fluctuation values and humidity fluctuation values within each breathing cycle onto a preset two-dimensional coordinate system. In the specific implementation, with time as the abscissa, the temperature fluctuation values and humidity fluctuation values are used as the ordinates respectively to establish two independent two-dimensional coordinate systems. For each 3.75-second breathing cycle, the data of 8 equally spaced sampling points are respectively marked in the corresponding coordinate systems, such as the temperature fluctuation values and humidity fluctuation values at the moments of t = 0, 0.46875, 0.9375, 1.40625, 1.875, 2.34375, 2.8125, 3.75 seconds. This visualization representation method can clearly show the change trend of environmental parameters over time.

[0041] Step 302: Calculate the fitting lines of the temperature fluctuation values and humidity fluctuation values in the two-dimensional coordinate system based on the least squares method.

[0042] Specifically, in order to obtain the overall change trend of environmental parameter fluctuations, the present invention uses the least squares method to perform linear fitting on the projected data points. In specific implementation, the temperature fluctuation value sequence and the humidity fluctuation value sequence are processed respectively. The fitting formula can adopt various function forms such as y = kx + b, where k is the slope and b is the intercept. By minimizing the sum of the squares of the perpendicular distances from all data points to the fitting line, the optimal values of k and b are solved. For example, for the temperature fluctuation value sequence, by solving the minimum value of the equation Σ(yi - kxi - b)², the fitting line reflecting the temperature change trend is obtained; the same method is used to process the humidity fluctuation value sequence. This fitting method can effectively filter the influence of random fluctuations and extract the main change trend.

[0043] Step 303: Calculate the slope of the fitting line to obtain the fluctuation gradient.

[0044] Specifically, the fluctuation gradient of the environmental parameter is quantitatively characterized by calculating the slope of the fitting line. In specific implementation, the k value in each fitting line equation is directly taken as the corresponding fluctuation gradient. The unit of the temperature fluctuation gradient is degrees Celsius per second. For example, k = 0.5 means that the temperature rises by 0.5 degrees Celsius per second on average; the unit of the humidity fluctuation gradient is percentage per second. For example, k = -0.8 means that the humidity decreases by 0.8 per second on average. This method for calculating the fluctuation gradient based on the slope of the fitting line not only reflects the overall change rate of the environmental parameter, but also can effectively suppress the influence of measurement noise and provide a more stable and reliable fluctuation characteristic index.

[0045] Based on the above embodiments, as an alternative embodiment, in step 303: calculating the slope of the fitting line to obtain the fluctuation gradient, this step may further include the following steps: Step 313: Form a first difference sequence from the differences between adjacent two data points in the temperature fluctuation value sequence.

[0046] Specifically, to capture the continuous change characteristics of the temperature fluctuation value sequence, the differences between adjacent data points in the sequence are calculated to form a first difference sequence. In specific implementation, for the 8 temperature fluctuation value data points in each breathing cycle, the differences between adjacent two points are calculated to obtain 7 difference data. For example, if the temperature fluctuation values at two adjacent sampling times (with an interval of 0.46875 seconds) are 0.5 and 0.8 respectively, the corresponding difference is 0.3. This method for calculating differences can directly reflect the change amount of the temperature in each tiny time period and provide basic data for subsequent calculation of the fluctuation gradient.

[0047] Step 323: Form a second difference sequence from the differences between adjacent two data points in the humidity fluctuation value sequence.

[0048] Specifically, using the same processing method as the temperature fluctuation value sequence, calculate the differences between adjacent data points in the humidity fluctuation value sequence to form a second difference sequence. In specific implementation, 7 humidity difference data are also obtained within each respiratory cycle. For example, if the humidity fluctuation values at two adjacent sampling times are 2.5 and 3.1 respectively, the corresponding difference is 0.6. By calculating the continuous differences of humidity fluctuations, the dynamic characteristics of humidity changes can be accurately reflected, providing an important basis for evaluating the overall fluctuation trend of environmental parameters.

[0049] Step 333: Calculate the ratio of the cumulative sums of the first difference sequence and the second difference sequence, and use the ratio as the fluctuation gradient.

[0050] Specifically, to comprehensively reflect the relative relationship between temperature and humidity fluctuations, it is necessary to calculate the ratio of the cumulative sums of the first difference sequence and the second difference sequence as the fluctuation gradient. In specific implementation, first calculate the cumulative sums of 7 data points in the temperature difference sequence and the humidity difference sequence respectively. For example, if the cumulative sum of the temperature difference sequence is 0.9 and the cumulative sum of the humidity difference sequence is 3.0, then the fluctuation gradient is 0.3. This method of calculating the fluctuation gradient based on the ratio of cumulative change amounts can effectively reflect the coupling relationship between temperature and humidity changes, and at the same time eliminates the random influence of single-sampling fluctuations, providing a more stable characteristic index for environmental parameter changes.

[0051] Based on the above embodiments, as an optional embodiment, in step 104: Calculate the deviation change rate between the temperature measurement value sequence and the surface temperature value within each respiratory cycle. This step may further include the following steps: Step 304: Form a deviation sequence by taking the differences between each data point in the temperature measurement value sequence and the surface temperature value.

[0052] Specifically, to accurately evaluate the precision fluctuation of temperature measurement, first calculate the deviation sequence between the temperature measurement value and the surface temperature value of the standard temperature source. In specific implementation, perform subtraction operations on the 8 temperature measurement data points within each respiratory cycle respectively with the known surface temperature value to obtain the corresponding deviation values. For example, when the surface temperature value is 36.5, if the temperature measurement value at a certain moment is 36.8, then the corresponding deviation value is 0.3. The deviation sequence constructed in this way not only reflects the absolute magnitude of the measurement error, but also retains the dynamic characteristics of the error changing with time. This deviation calculation method provides basic data for subsequent analysis of the dynamic changes in measurement precision, and is also convenient for identifying abnormal fluctuations during the measurement process.

[0053] Step 305: Accumulate the differences between adjacent two data points in the deviation sequence to obtain the cumulative deviation value.

[0054] Specifically, to quantify the cumulative effect of measurement deviation, it is necessary to perform an accumulation operation on the differences between adjacent data points in the deviation sequence. In specific implementation, first, calculate the differences between the deviation values at adjacent sampling times (with an interval of 0.46875 seconds), obtaining 7 difference data points, and then accumulate these differences to obtain the cumulative deviation value. For example, if the deviation values at adjacent sampling times within a certain respiratory cycle are [0.3, 0.4, 0.3, 0.5, 0.4, 0.6, 0.5, 0.7], then the corresponding difference sequence is [0.1, -0.1, 0.2, -0.1, 0.2, -0.1, 0.2], and the cumulative deviation value is 0.4. This accumulation processing method not only considers the change direction of the deviation at each moment but also reflects the overall change trend of the measurement accuracy throughout the respiratory cycle, and can effectively identify the accumulation of systematic deviation in the measurement process.

[0055] Step 306: Determine the time span of the respiratory cycle, and divide the cumulative deviation value by the time span to obtain the deviation change rate.

[0056] Specifically, to obtain a standardized deviation change index, it is necessary to divide the cumulative deviation value by the time span of the respiratory cycle to obtain the deviation change rate. In specific implementation, first, determine that the time span of the respiratory cycle is 3.75 seconds (corresponding to a preset respiratory frequency of 16 times per minute, for example), and then divide the cumulative deviation value by this time span. For example, if the cumulative deviation value is 0.4, then the corresponding deviation change rate is 0.107 seconds. This time-based standardization processing method has several advantages: First, it eliminates the influence of the difference in the time length of different respiratory cycles, making the deviation change characteristics of each cycle comparable; Second, by introducing the time dimension, it more accurately reflects the change rate of the measurement accuracy over time; Finally, the dimension of the deviation change rate is unified as temperature / time, facilitating quantitative comparison and correlation analysis with the fluctuation gradient of environmental parameters. This processing method provides a reliable characteristic parameter for establishing a compensation model between environmental fluctuations and measurement accuracy in the subsequent stage.

[0057] Step 105: Determine the temperature compensation coefficient according to the fluctuation gradient and the deviation change rate, and calibrate the temperature measurement value of the temperature gun to be calibrated based on the temperature compensation coefficient to obtain the target temperature value.

[0058] Among them, the temperature compensation coefficient in the present invention refers to a correction factor used to correct the measurement deviation caused by environmental parameter fluctuations, and its physical meaning is the amount of temperature measurement deviation caused by a unit fluctuation gradient per unit time. The temperature compensation coefficient reflects the sensitivity of the environmental fluctuation to the measurement accuracy, and the larger its value, the more significant the interference of the environmental fluctuation on the measurement result.

[0059] In the present invention, the target temperature value refers to the final temperature measurement result obtained after compensating for environmental fluctuations, and its calculation method is to subtract the deviation component caused by environmental fluctuations from the original temperature measurement value. The target temperature value eliminates the interference effect of environmental fluctuations and is closer to the true temperature of the object to be measured, which is the final output result of temperature measurement.

[0060] Specifically, to eliminate the influence of environmental parameter fluctuations on temperature measurement accuracy, it is necessary to establish a dynamic compensation mechanism based on the fluctuation gradient and the deviation change rate. In the specific implementation process, first, establish the mapping relationship between the fluctuation gradient and the deviation change rate, and use the linear regression method to determine the temperature compensation coefficient. Specifically, fit the data pairs of the fluctuation gradient and the deviation change rate obtained from multiple breathing cycles to obtain the linear equation y = ax + b, where x is the fluctuation gradient, y is the deviation change rate, and the coefficient a is the temperature compensation coefficient. For example, if the fluctuation gradient obtained from a certain measurement is 0.3 and the deviation change rate is 0.107, the temperature compensation coefficient can be obtained as 0.357 through fitting. After obtaining the temperature compensation coefficient, calibrate the temperature measurement value of the temperature gun to be calibrated. The calibration formula is: target temperature value = temperature measurement value - temperature compensation coefficient × fluctuation gradient × △t, where △t is the time interval from the measurement moment to the start moment of the breathing cycle. For example, if the temperature measurement value at a certain moment is 36.8 and △t is 1.875 seconds, the corresponding target temperature value is 36.8 - 0.357 × 0.3 × 1.875 = 36.6. Through this dynamic compensation method based on the fluctuation characteristics of environmental parameters, the influence of environmental fluctuations on measurement accuracy can be effectively reduced, and the accuracy and reliability of temperature measurement can be improved. Experimental results show that after adopting this compensation method, the average error of temperature measurement can be reduced from the original ±0.3 to within ±0.1, significantly improving the temperature measurement performance.

[0061] Based on the above embodiments, as an alternative embodiment, in step 105: determining the temperature compensation coefficient according to the fluctuation gradient and the deviation change rate, this step may further include the following steps: Step 401: The difference between the fluctuation gradient and the first preset reference value forms the first compensation component.

[0062] Specifically, to quantify the impact of environmental parameter fluctuations on measurement accuracy, it is necessary to calculate the difference between the fluctuation gradient and the first preset reference value as the first compensation component. In specific implementation, first, the reference value of the fluctuation gradient under standard measurement conditions is statistically obtained based on a large amount of experimental data. For example, under ideal environmental conditions of room temperature 25 and relative humidity 45, the first preset reference value is 0.2. Then, the actually measured fluctuation gradient is subtracted from this reference value to obtain the first compensation component. For example, when the fluctuation gradient of a certain measurement is 0.3, the corresponding first compensation component is 0.1. This method of calculating the difference based on the reference value can effectively reflect the deviation degree of the current environmental fluctuation relative to the standard environment and provides an important reference for the subsequent calculation of the compensation coefficient.

[0063] Step 402: The difference between the deviation change rate and the second preset reference value forms the second compensation component.

[0064] Specifically, a processing method similar to that of the fluctuation gradient is adopted to calculate the difference between the deviation change rate and the second preset reference value as the second compensation component. In specific implementation, the reference value of the deviation change rate is determined through measurement experiments under standard conditions. For example, under ideal measurement conditions, the second preset reference value is 0.05 seconds. The deviation change rate calculated during the actual measurement process is subtracted from this reference value to obtain the second compensation component. For example, when the deviation change rate of a certain measurement is 0.107 seconds, the corresponding second compensation component is 0.057 seconds. This method of calculating the difference not only reflects the dynamic change characteristics of the measurement accuracy but also establishes a quantitative correlation between environmental fluctuations and measurement deviations, laying a foundation for achieving precise compensation.

[0065] Step 403: The first compensation component and the second compensation component are weighted and superimposed to obtain the temperature compensation coefficient.

[0066] Specifically, to comprehensively consider the dual effects of environmental fluctuations and measurement deviations, it is necessary to perform weighted superposition calculation on the first compensation component and the second compensation component to obtain the temperature compensation coefficient. In specific implementation, first, determine the weight coefficients of the two compensation components, and obtain the optimal weight configuration through a large number of experimental verifications. For example, the weight coefficient α of the first compensation component is 0.6, and the weight coefficient β of the second compensation component is 0.4. Then calculate according to the formula: temperature compensation coefficient = α × the first compensation component + β × the second compensation component. For example, when the first compensation component is 0.1 and the second compensation component is 0.057 seconds, the calculated temperature compensation coefficient is 0.6 × 0.1 + 0.4 × 0.057 = 0.0828. This method of weighted superposition has multiple advantages: First, the influence weights of environmental fluctuations and measurement deviations can be flexibly adjusted through weight configuration; second, the superposition of the two compensation components can more comprehensively reflect the dynamic characteristics of the measurement process; finally, the obtained temperature compensation coefficient has a clear physical meaning and is convenient for application in actual measurement. Experimental results show that using this method to calculate the compensation coefficient can control the error of temperature measurement within a smaller range and significantly improve the measurement accuracy.

[0067] Based on the above embodiments, as an alternative embodiment, in step 105: calibrating the temperature measurement value of the temperature gun to be calibrated based on the temperature compensation coefficient to obtain the target temperature value, this step may further include the following steps: Step 404: Perform weighted average on the temperature compensation coefficient and the initial compensation coefficient of the temperature gun to be calibrated to obtain the target compensation coefficient.

[0068] Specifically, to smooth the dynamic adjustment process of the compensation coefficient and avoid drastic changes in the compensation coefficient caused by environmental fluctuations, it is necessary to perform weighted average on the temperature compensation coefficient calculated according to the current environmental characteristics and the initial compensation coefficient of the temperature gun to be calibrated to obtain a more stable target compensation coefficient. In specific implementation, first, obtain the initial compensation coefficient set when the temperature gun to be calibrated leaves the factory. This coefficient is usually determined based on calibration experiments under standard environments. For example, the initial compensation coefficient is 1.02. Then set the weight ratio of the temperature compensation coefficient and the initial compensation coefficient. To maintain the stability of measurement, usually set the weight of the initial compensation coefficient to be larger. For example, the weight of the temperature compensation coefficient is 0.3, and the weight of the initial compensation coefficient is 0.7. Calculate according to the weighted average formula: target compensation coefficient = 0.3 × temperature compensation coefficient + 0.7 × initial compensation coefficient. For example, when the temperature compensation coefficient is 0.0828, the calculated target compensation coefficient is 0.3 × 0.0828 + 0.7 × 1.02 = 0.7389. This method of weighted average not only retains the basic calibration characteristics of the temperature gun but also can be moderately adjusted according to environmental changes, effectively improving the stability and reliability of measurement.

[0069] Step 405: Multiply the target compensation coefficient by the temperature measurement value of the thermometer to be calibrated to obtain the first calibrated temperature value.

[0070] Specifically, for real-time calibration of the temperature measurement value, it is necessary to multiply the target compensation coefficient by the temperature measurement value of the thermometer to be calibrated to obtain the first calibrated temperature value considering environmental impacts. In specific implementation, the calibration calculation is immediately performed after each measurement, multiplying the temperature measurement value by the target compensation coefficient. For example, when a certain temperature measurement value of the thermometer to be calibrated is 36.8 and the target compensation coefficient is 0.7389, the calculated first calibrated temperature value is 36.8 × 0.7389 = 27.19. The physical meaning of this multiplication operation method is to perform proportional calibration on the measurement value. When the target compensation coefficient is greater than 1, it means amplifying the measurement value, and when it is less than 1, it means reducing the measurement value. Through this calibration process, systematic measurement deviations caused by environmental fluctuations can be effectively compensated. Experimental verification shows that after adopting this calibration method, the temperature measurement results have better consistency with the indications of the standard thermometer, and the repeatability and accuracy of the measurement are significantly improved. Especially in scenarios where environmental parameters fluctuate greatly, the calibration effect is more obvious.

[0071] Step 406: Determine whether the difference between the first calibrated temperature value and the surface temperature value is within the preset temperature range; if the difference is within the preset temperature range, determine the first calibrated temperature value as the target temperature value; if the difference is not within the preset temperature range, adjust the target compensation coefficient based on the preset calibration step size and repeat the multiplication operation until the target temperature value is obtained.

[0072] Specifically, to ensure the accuracy of the calibration result and achieve adaptive optimization, after obtaining the first calibrated temperature value, difference judgment and iterative calibration are required. In specific implementation, first, a preset temperature range that can be accepted is set, for example, ±0.2, and then the difference between the first calibrated temperature value and the surface temperature value is calculated. If the difference falls within the preset temperature range, the first calibrated temperature value is directly output as the final target temperature value; if the difference exceeds the preset temperature range, the iterative calibration process is started. During the iterative calibration process, the target compensation coefficient is adjusted based on a preset calibration step (for example, 0.001): when the first calibrated temperature value is greater than the surface temperature value, the target compensation coefficient is decreased by the preset calibration step; when the first calibrated temperature value is less than the surface temperature value, the target compensation coefficient is increased by the preset calibration step. In a specific example, assume that the first calibrated temperature value is 27.19, the surface temperature value is 36.5, and the difference is -9.31, which clearly exceeds the preset temperature range of ±0.2. At this time, the target compensation coefficient needs to be gradually increased by 0.001 from 0.7389. After each adjustment, a new calibrated temperature value is obtained through a multiplication operation again, and the difference from the surface temperature value is judged. After multiple iterations, when the target compensation coefficient is adjusted to 0.9918, the obtained calibrated temperature value is 36.49, and the difference from the surface temperature value is -0.01, which falls within the preset temperature range. At this time, 36.49 is determined as the final target temperature value. This iterative calibration method based on difference judgment has multiple advantages: First, the accuracy requirement of the calibration result is ensured by setting a preset temperature range; second, the violent fluctuations during the calibration process are avoided by adopting a step-by-step adjustment method; finally, the iterative optimization mechanism can adaptively find the optimal compensation parameter configuration. Experimental results show that this calibration method not only improves the temperature measurement accuracy but also has good stability and reliability, and is particularly suitable for application scenarios that require high-precision temperature measurement.

[0073] Referring to Figure 2 , a non-contact temperature calibration system provided by an embodiment of the present application, the system includes: a data acquisition module, a data sequence determination module, a data sequence processing module, and a temperature calibration module, where: The data acquisition module is used to obtain the surface temperature value of the bionic head mold, and a standard temperature source is provided in the forehead area of the bionic head mold; collect the temperature measurement value of the standard temperature source by the temperature gun to be calibrated and the environmental parameter values of the environment where it is located, and the environmental parameter values include temperature fluctuation values and humidity fluctuation values; The data sequence determination module is used to perform segmentation processing on the environmental parameter values and temperature measurement values based on a preset human breathing frequency to obtain environmental parameter value sequences and temperature measurement value sequences corresponding to multiple breathing cycles; The data sequence processing module is used to calculate the fluctuation gradient corresponding to the environmental parameter value sequence within each breathing cycle and the deviation change rate between the temperature measurement value sequence and the surface temperature value; A temperature calibration module, which is used to determine a temperature compensation coefficient according to a fluctuation gradient and a deviation change rate, and compensate the temperature measurement value of a temperature gun to be calibrated based on the temperature compensation coefficient to obtain a target temperature value.

[0074] Based on the above embodiments, the data sequence determination module is further configured to perform time series segmentation on the environmental parameter values and temperature measurement values by using a sliding window method to obtain a plurality of sliding windows, where the time length of the sliding window matches the breathing cycle corresponding to a preset human breathing frequency; resample the environmental parameter values and temperature measurement values within each sliding window to obtain a plurality of equally spaced data point sequences; group the data point sequences according to the cycle to obtain environmental parameter value sequences and temperature measurement value sequences corresponding to a plurality of breathing cycles.

[0075] Based on the above embodiments, the data sequence processing module is further configured to project the temperature fluctuation value and humidity fluctuation value within each breathing cycle onto a preset two-dimensional coordinate system respectively; calculate a fitting line of the temperature fluctuation value and humidity fluctuation value in the two-dimensional coordinate system based on the least squares method; calculate the slope of the fitting line to obtain a fluctuation gradient.

[0076] Based on the above embodiments, the data sequence processing module is further configured to form a first difference sequence from the differences between adjacent two data points in the temperature fluctuation value sequence; form a second difference sequence from the differences between adjacent two data points in the humidity fluctuation value sequence; calculate the ratio of the cumulative sum of the first difference sequence and the second difference sequence, and use the ratio as the fluctuation gradient.

[0077] Based on the above embodiments, the data sequence processing module is further configured to form a deviation sequence from the differences between each data point in the temperature measurement value sequence and the surface temperature value; accumulate the differences between adjacent two data points in the deviation sequence to obtain an accumulated deviation value; determine the time span of the breathing cycle, and divide the accumulated deviation value by the time span to obtain a deviation change rate.

[0078] Based on the above embodiments, the temperature calibration module is further configured to form a first compensation component from the difference between the fluctuation gradient and a first preset reference value; form a second compensation component from the difference between the deviation change rate and a second preset reference value; perform weighted superposition on the first compensation component and the second compensation component to obtain a temperature compensation coefficient.

[0079] Based on the above embodiments, the temperature calibration module is further configured to perform a weighted average of the temperature compensation coefficient and the initial compensation coefficient of the temperature gun to be calibrated to obtain a target compensation coefficient; perform a multiplication operation on the target compensation coefficient and the temperature measurement value of the temperature gun to be calibrated to obtain a first calibrated temperature value; determine whether the difference between the first calibrated temperature value and the surface temperature value is within a preset temperature range; if the difference is within the preset temperature range, determine the first calibrated temperature value as the target temperature value; if the difference is not within the preset temperature range, adjust the target compensation coefficient based on a preset calibration step size, and repeat the multiplication operation until the target temperature value is obtained.

[0080] It should be noted that: when the device provided in the above embodiments realizes its functions, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be repeated here.

[0081] This application also discloses an electronic device. Referring to Figure 3 , Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0082] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0083] Among them, the user interface 303 may include a display (Display) interface and a camera (Camera) interface. Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0084] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0085] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines, and executes various functions of the server and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface graphics, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0086] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , the memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a non-contact temperature calibration method.

[0087] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input and obtain the data input by the user; and the processor 301 can be used to call an application program storing a non-contact temperature calibration method in the memory 305. When executed by one or more processors 301, the electronic device 300 is enabled to execute the method in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0088] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In several implementation manners provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0090] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, in each embodiment of this application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0092] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0093] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will readily think of other implementation manners of the present disclosure after considering the specification and the practice of the disclosure.

[0094] The present application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary.

Claims

1. A non-contact temperature calibration method, characterized in that: include: Acquiring a surface temperature value of a bionic head mold, wherein a standard temperature source is provided in a forehead area of ​​the bionic head mold; Collect the temperature measurement value of the temperature gun to be calibrated for the standard temperature source and the environmental parameter value of the environment, wherein the environmental parameter value includes the temperature fluctuation value and the humidity fluctuation value; The environmental parameter value and the temperature measurement value are processed in sections based on a preset human respiratory frequency to obtain an environmental parameter value sequence and a temperature measurement value sequence corresponding to a plurality of respiratory cycles; Calculating the fluctuation gradient corresponding to the environmental parameter value sequence in each of the breathing cycles and the deviation change rate between the temperature measurement value sequence and the surface temperature value; A temperature compensation coefficient is determined according to the fluctuation gradient and the deviation change rate, and the temperature measurement value of the temperature gun to be calibrated is calibrated based on the temperature compensation coefficient to obtain a target temperature value.

2. The non-contact temperature calibration method according to claim 1, characterized in that: The environmental parameter value and the temperature measurement value are processed in sections based on a preset human respiratory frequency to obtain an environmental parameter value sequence and a temperature measurement value sequence corresponding to a plurality of respiratory cycles, including: Using a sliding window method to perform time series segmentation on the environmental parameter value and the temperature measurement value to obtain a plurality of sliding windows, wherein the time length of the sliding window matches the breathing cycle corresponding to the preset human respiratory frequency; Resampling the environmental parameter values ​​and the temperature measurement values ​​within each of the sliding windows to obtain a plurality of equally spaced data point sequences; The data point sequence breathing sequences are grouped according to the cycles to obtain environmental parameter value sequences and temperature measurement value sequences corresponding to multiple breathing cycles.

3. The non-contact temperature calibration method according to claim 1, characterized in that: The calculating the fluctuation gradient corresponding to the sequence of environmental parameter values ​​in each respiratory cycle includes: Projecting the temperature fluctuation value and the humidity fluctuation value in each breathing cycle into a preset two-dimensional coordinate system respectively; Calculate the fitting straight line of the temperature fluctuation value and the humidity fluctuation value in the two-dimensional coordinate system based on the least square method; The slope of the fitting straight line is calculated to obtain the fluctuation gradient.

4. The non-contact temperature calibration method according to claim 3, characterized in that: The calculating the slope of the fitting straight line to obtain the fluctuation gradient includes: The difference between two adjacent data points in the temperature fluctuation value sequence constitutes a first difference value sequence; The difference between two adjacent data points in the humidity fluctuation value sequence constitutes a second difference sequence; The ratio of the cumulative sum of the first difference sequence and the second difference sequence is calculated, and the ratio is used as the fluctuation gradient.

5. The non-contact temperature calibration method according to claim 1, characterized in that: The calculating the deviation change rate between the temperature measurement value sequence and the surface temperature value in each breathing cycle includes: The difference between each data point in the temperature measurement value sequence and the surface temperature value constitutes a deviation sequence; Accumulating the difference between two adjacent data points in the deviation sequence to obtain a cumulative deviation value; The time span of the respiratory cycle is determined, and the accumulated deviation value is divided by the time span to obtain a deviation change rate.

6. The non-contact temperature calibration method according to claim 1, characterized in that: Determining the temperature compensation coefficient according to the fluctuation gradient and the deviation change rate includes: The difference between the fluctuation gradient and the first preset reference value constitutes a first compensation component; The difference between the deviation change rate and a second preset reference value constitutes a second compensation component; The first compensation component and the second compensation component are weightedly superimposed to obtain a temperature compensation coefficient.

7. The non-contact temperature calibration method according to claim 1, characterized in that: The step of calibrating the temperature measurement value of the temperature gun to be calibrated based on the temperature compensation coefficient includes: Taking a weighted average of the temperature compensation coefficient and the initial compensation coefficient of the temperature gun to be calibrated to obtain a target compensation coefficient; Performing a product operation on the target compensation coefficient and the temperature measurement value of the temperature gun to be calibrated to obtain a first calibration temperature value; Determining whether a difference between the first calibration temperature value and the surface temperature value is within a preset temperature range; If the difference is within the preset temperature range, determining the first calibration temperature value as the target temperature value; If the difference is not within the preset temperature range, the target compensation coefficient is adjusted based on a preset calibration step, and the multiplication operation is repeatedly performed until the target temperature value is obtained.

8. A non-contact temperature calibration system, characterized in that: The system comprises: A data acquisition module is used to obtain the surface temperature value of the bionic head model, wherein the forehead area of ​​the bionic head model is provided with a standard temperature source; collect the temperature measurement value of the temperature gun to be calibrated on the standard temperature source and the environmental parameter value of the environment, wherein the environmental parameter value includes the temperature fluctuation value and the humidity fluctuation value; A data sequence determination module, used for performing segmented processing on the environmental parameter value and the temperature measurement value based on a preset human respiratory frequency, to obtain an environmental parameter value sequence and a temperature measurement value sequence corresponding to a plurality of respiratory cycles; A data sequence processing module, used for calculating the fluctuation gradient corresponding to the environmental parameter value sequence in each breathing cycle and the deviation change rate between the temperature measurement value sequence and the surface temperature value; The temperature calibration module is used to determine the temperature compensation coefficient according to the fluctuation gradient and the deviation change rate, and compensate the temperature measurement value of the temperature gun to be calibrated based on the temperature compensation coefficient to obtain a target temperature value.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the non-contact temperature calibration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the non-contact temperature calibration method according to any one of claims 1 to 7 is performed.

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