Temperature measuring method and temperature measuring nut

Abstract

The invention discloses a temperature measurement method and a temperature measurement nut, and belongs to the technical field of equipment temperature measurement. According to the method, the dynamic thermal radiation interference coefficient calculation model is constructed through the historical temperature measurement data and the equipment operation parameters, so that the dynamic acquisition of the dynamic thermal radiation interference coefficient can be realized based on the real-time temperature measurement data, and the real-time temperature measurement data is corrected to obtain the corrected temperature measurement data; performing spectral analysis on the real-time temperature measurement data to determine a thermal radiation change frequency correction coefficient, constructing a temperature response function based on the real-time temperature measurement data, and further determining a thermal response time constant based on the temperature response function; and then thermal inertia lag compensation processing is carried out on the corrected temperature measurement data through a thermal radiation change frequency correction coefficient, a thermal response time constant and equipment operation parameters to obtain actual temperature data of the to-be-measured equipment, so that the problem of inaccurate temperature measurement in a high-temperature environment in the prior art can be solved. And the accuracy of temperature measurement in a high-temperature environment is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of equipment temperature measurement, and in particular relates to a temperature measurement method and a temperature measuring nut. Background Art

[0002] Currently, temperature monitoring of industrial equipment in high-temperature environments is mainly carried out through two methods: wired temperature sensors and external wireless temperature measurement devices. Wired temperature sensors directly contact the surface of the equipment through thermocouples or platinum resistance sensors, and are connected to external data collectors with wires to collect temperature data; external wireless temperature measurement devices rely on batteries or external power supplies to power the wireless temperature sensors, which are installed on the outside of the equipment for temperature measurement.

[0003] Currently, temperature measurement is performed using wired temperature sensors and external wireless temperature measurement devices. Although temperature data can be measured in high-temperature environments, both solutions are limited by the thermal stability of their packaging materials and the stability of their power supply in high-temperature environments. Continuous temperature measurement in high-temperature environments of industrial equipment can not only cause stress distortion within the sensor, but also cause the measured temperature data to be affected by interference factors such as conducted heat and ambient thermal radiation, resulting in a large difference between the measured temperature data and the actual temperature data. High-temperature environments can even cause damage to the sensor due to high temperatures, all of which lead to inaccurate temperature data. Therefore, there is an urgent need for a temperature measurement method and temperature measuring nut to address the shortcomings of the existing technology. Summary of the Invention

[0004] The present invention aims to provide a temperature measurement method and a temperature measuring nut to solve the problem of inaccurate temperature measurement in high-temperature environments in the prior art. By obtaining the dynamic thermal radiation interference coefficient, the thermal radiation change frequency correction coefficient and the thermal response time constant, the temperature measurement data can be corrected, thereby improving the accuracy of temperature measurement in high-temperature environments.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a temperature measurement method, including:

[0006] Obtain historical temperature measurement data, real-time temperature measurement data and equipment operating parameters of the device to be measured;

[0007] Constructing a dynamic thermal radiation interference coefficient calculation model based on the historical temperature measurement data and equipment operating parameters;

[0008] Inputting the real-time temperature measurement data into the dynamic thermal radiation interference coefficient calculation model to determine the dynamic thermal radiation interference coefficient, and correcting the real-time temperature measurement data based on the dynamic thermal radiation interference coefficient to obtain corrected temperature measurement data;

[0009] Performing spectrum analysis on the real-time temperature measurement data to determine a thermal radiation change frequency correction coefficient;

[0010] constructing a temperature response function based on the real-time temperature measurement data, and determining a thermal response time constant based on the temperature response function;

[0011] The corrected temperature measurement data is subjected to thermal inertia lag compensation processing based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operation parameters to determine the actual temperature data of the equipment to be temperature measured.

[0012] It can be understood that, compared with the prior art, the present invention constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement data and equipment operating parameters, thereby realizing dynamic acquisition of the dynamic thermal radiation interference coefficient based on real-time temperature measurement data, and then correcting the real-time temperature measurement data to obtain corrected temperature measurement data; then, the real-time temperature measurement data is subjected to spectral analysis to determine the thermal radiation change frequency correction coefficient, and a temperature response function is constructed based on the real-time temperature measurement data, and then the thermal response time constant is determined based on the temperature response function; then, the corrected temperature measurement data is subjected to thermal inertia lag compensation processing through the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to obtain the actual temperature data of the equipment to be measured, thereby realizing the correction of the temperature measurement data with the dynamic thermal radiation interference coefficient, the thermal radiation change frequency correction coefficient and the thermal response time constant, and improving the accuracy of temperature measurement in high temperature environments. The present invention constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement and equipment operating parameters, and then combines it with real-time temperature measurement data to realize dynamic thermal radiation interference correction of the temperature measurement equipment, which can accurately reduce the influence of thermal radiation interference on temperature data in a high-temperature environment; by performing spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient, the thermal radiation fluctuation characteristics of the temperature measurement equipment can be accurately extracted, thereby further avoiding the influence of thermal radiation fluctuation on the corrected temperature measurement data; by constructing a temperature response function and determining the thermal response time constant, the thermal response time constant can be used to explore the temperature transfer lag characteristics caused by thermal inertia between the temperature measurement equipment and the real-time temperature measurement data, so that the thermal inertia lag compensation processing can comprehensively consider the thermal radiation interference of high-temperature environment temperature measurement, the thermal inertia and the mutual influence between the temperature measurement equipment, thereby improving the accuracy of temperature measurement in a high-temperature environment.

[0013] As a preferred solution, the dynamic thermal radiation interference coefficient calculation model is constructed based on the historical temperature measurement data and equipment operating parameters, specifically including:

[0014] Determine the reference value of the thermal radiation interference coefficient according to a preset thermal radiation simulation method;

[0015] The historical temperature measurement data includes: historical main sensor temperature measurement values, historical reference sensor temperature measurement values ​​and historical environmental sensor temperature measurement values; the equipment operation parameters include: surface emissivity of the equipment to be measured and surface roughness of the equipment to be measured;

[0016] Constructing a dynamic thermal radiation interference coefficient training set based on the historical main sensor temperature measurement values, the historical reference sensor temperature measurement values, the historical environmental sensor temperature measurement values, the surface emissivity of the temperature-measured device, the surface roughness of the temperature-measured device, and the thermal radiation interference coefficient reference value;

[0017] A preset support vector machine model is trained based on the dynamic thermal radiation interference coefficient training set to determine a dynamic thermal radiation interference coefficient calculation model.

[0018] This preferred solution constructs a dynamic thermal radiation interference coefficient training set through historical main sensor temperature measurement values, historical reference sensor temperature measurement values, historical environmental sensor temperature measurement values, surface emissivity of the temperature-measured equipment, surface roughness of the temperature-measured equipment, and thermal radiation interference coefficient reference values, so that the dynamic thermal radiation interference coefficient calculation model obtained by training can consider the actual temperature environment of the temperature-measured equipment in multiple dimensions, thereby dynamically adjusting the thermal radiation interference coefficient, avoiding the low degree of fit with the actual temperature environment caused by fixed coefficients or empirical formulas in the existing technology, and can accurately reduce the impact of thermal radiation interference on temperature data in high temperature environments, thereby improving the accuracy of temperature measurement in high temperature environments.

[0019] As a preferred solution, determining the reference value of the thermal radiation interference coefficient according to a preset thermal radiation simulation method specifically includes:

[0020] Constructing a thermal radiation simulation environment according to the operating environment of the device to be temperature measured;

[0021] Based on the thermal radiation simulation environment, the actual temperature of the thermal radiation simulation environment, the temperature measured by the main sensor of the thermal radiation simulation environment, the temperature measured by the reference sensor of the thermal radiation simulation environment, and the temperature of the thermal radiation simulation environment are obtained;

[0022] Calculating a first thermal radiation simulated environment measurement difference based on the actual temperature of the thermal radiation simulated environment and the temperature measured by the thermal radiation simulated environment main sensor;

[0023] Calculating a second thermal radiation simulation environment measurement difference based on the thermal radiation simulation environment reference sensor measurement temperature and the thermal radiation simulation environment temperature;

[0024] A thermal radiation interference coefficient reference value is determined based on the first thermal radiation simulation environment measurement difference and the second thermal radiation simulation environment measurement difference.

[0025] This preferred solution constructs a thermal radiation simulation environment through the operating environment of the temperature-measured equipment, and thereby determines a thermal radiation interference coefficient reference value based on the relevant temperature measurement values ​​of the thermal radiation simulation environment, so that the thermal radiation interference coefficient reference value can fully characterize the actual operating environment of the temperature-measured equipment, avoiding the error of the thermal radiation interference coefficient reference value, thereby improving the accuracy of the dynamic thermal radiation interference coefficient training set and the dynamic thermal radiation interference coefficient calculation model obtained through training, and further improving the accuracy of temperature measurement in high-temperature environments.

[0026] As a preferred solution, performing spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient specifically includes:

[0027] Performing spectrum analysis on the real-time temperature measurement data according to a preset fast Fourier transform algorithm to obtain a frequency distribution spectrum of thermal radiation changes;

[0028] Extracting frequency components from the frequency distribution spectrum of the thermal radiation change to determine the frequency of the thermal radiation change;

[0029] The thermal radiation variation frequency is fitted based on a preset fitting algorithm to determine a thermal radiation variation frequency correction coefficient.

[0030] This preferred solution performs spectrum analysis on the real-time temperature measurement data through a preset fast Fourier transform algorithm, which can quickly and accurately identify the periodic change characteristics of thermal radiation in the high-temperature environment of the device to be measured. Then, by extracting the frequency components and fitting the thermal radiation change frequency, it can effectively separate the noise data of the thermal radiation change frequency distribution spectrum and extract more accurate thermal radiation change data, so that the thermal radiation change frequency correction coefficient can fully characterize the periodic change characteristics of thermal radiation in the high-temperature environment of the device to be measured, thereby further avoiding the influence of thermal radiation fluctuations on the corrected temperature measurement data. This improves the accuracy of temperature measurement in high-temperature environments.

[0031] As a preferred solution, the step of constructing a temperature response function based on the real-time temperature measurement data and determining a thermal response time constant based on the temperature response function specifically includes:

[0032] Constructing a temperature change curve based on the real-time temperature measurement data;

[0033] Constructing an initial temperature response function according to a preset least squares method, and constructing a temperature error function according to the real-time temperature measurement data and the initial temperature response function;

[0034] Solving the temperature error function based on a preset matrix operation algorithm to obtain a temperature fitting coefficient solution set;

[0035] Reconstructing the initial temperature response function based on the fitting coefficient solution set to obtain a temperature response function;

[0036] A thermal response time constant is determined based on the temperature response function and the temperature variation curve.

[0037] This preferred solution constructs an initial temperature response function through the least squares method, and then constructs a temperature error function in combination with real-time temperature measurement data, which can characterize the influence of thermal inertia lag on the temperature measurement data of the temperature measurement device to be measured; then, the temperature error function is solved by a matrix operation algorithm to obtain a temperature fitting coefficient solution set, and then the temperature response function is determined. The matrix operation algorithm can significantly reduce the computational complexity of the temperature fitting coefficient solution set, and the temperature fitting coefficient solution set can be used to adaptively adjust the temperature response function, ensuring that the temperature response function can accurately characterize the influence of thermal inertia lag on the temperature measurement data; the temperature change curve is constructed by real-time temperature measurement data, which can quantify the evolution of real-time temperature measurement data over time, and thus a more accurate thermal response time constant can be obtained, so that the temperature transfer lag characteristics caused by thermal inertia between the temperature measurement device to be measured and the real-time temperature measurement data can be explored, thereby improving the accuracy of temperature measurement in high temperature environments.

[0038] As a preferred solution, the temperature error function is solved based on a preset matrix operation algorithm to obtain a temperature fitting coefficient solution set, specifically including:

[0039] Performing partial derivative processing on each temperature fitting coefficient in the temperature error function to obtain a temperature fitting coefficient equation group;

[0040] Constructing a temperature design matrix, a temperature fitting coefficient vector and a temperature measurement vector based on the temperature fitting coefficient equation group;

[0041] Constructing a temperature transposed matrix and a temperature inverse matrix based on the temperature design matrix;

[0042] The temperature fitting coefficient vector is solved based on the temperature transposed matrix, the temperature inverse matrix and the temperature measurement vector to obtain a temperature fitting coefficient solution set.

[0043] This preferred solution constructs a temperature design matrix, a temperature fitting coefficient vector and a temperature measurement vector by calculating the partial derivative of each temperature fitting coefficient in the temperature error function. The matrix operation can significantly reduce the complexity of solving the temperature fitting coefficient solution set, avoid the error of the temperature fitting coefficient solution set caused by overly complex calculations, thereby ensuring the accuracy of the temperature fitting coefficient solution set, and then the accuracy of the subsequent thermal inertia lag compensation processing, thereby improving the accuracy of temperature measurement in high temperature environments.

[0044] As a preferred solution, performing thermal inertia lag compensation processing on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to determine the actual temperature data of the device to be temperature measured specifically includes:

[0045] The equipment operating parameters also include: the heat capacity of the equipment to be measured;

[0046] Determine the temperature change value of the heat capacity of the device to be measured according to the real-time temperature measurement data and the heat capacity of the device to be measured;

[0047] Determining the real-time thermal radiation change frequency of the device to be temperature measured according to the real-time temperature measurement data;

[0048] Determine the thermal radiation temperature change value of the device to be measured according to the real-time temperature measurement data, the thermal radiation change frequency correction coefficient and the real-time thermal radiation change frequency of the device to be measured;

[0049] Determine the thermal response time temperature change value of the device to be temperature measured according to the real-time temperature measurement data and the thermal response time constant;

[0050] Based on the heat capacity temperature change value, the heat radiation temperature change value, the thermal response time temperature change value pair and the corrected temperature measurement data, the actual temperature data of the device to be temperature measured is determined.

[0051] This preferred solution can fully consider the thermal radiation interference, thermal inertia and mutual influence between the temperature measurement equipment in high-temperature environment by calculating the temperature change value of heat capacity, thermal radiation temperature change value and thermal response time temperature change value, so that the actual temperature data after compensation is more accurate, thereby improving the accuracy of temperature measurement in high-temperature environment.

[0052] Accordingly, an embodiment of the present invention provides a temperature measuring nut, comprising: a nut body for implementing a temperature measurement method as described above, and further comprising: a temperature measuring cavity and a non-temperature measuring cavity;

[0053] The non-temperature measuring cavity and the temperature measuring cavity are both arranged on the outer surface of the nut;

[0054] The temperature data output terminal of the temperature measuring cavity is electrically connected to the temperature data input terminal of the non-temperature measuring cavity;

[0055] The temperature measurement cavity is used to obtain real-time temperature measurement data of the device to be measured;

[0056] The non-temperature measurement cavity is used to obtain historical temperature measurement data and equipment operating parameters of the device to be temperature measured, and receive real-time temperature measurement data collected by the temperature measurement cavity;

[0057] The non-temperature measurement cavity is further used to construct a dynamic thermal radiation interference coefficient calculation model based on the historical temperature measurement data and equipment operating parameters, input the real-time temperature measurement data into the dynamic thermal radiation interference coefficient calculation model, determine the dynamic thermal radiation interference coefficient, and correct the real-time temperature measurement data based on the dynamic thermal radiation interference coefficient to obtain corrected temperature measurement data;

[0058] The non-temperature measurement cavity is also used to perform spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient; construct a temperature response function based on the real-time temperature measurement data, and determine the thermal response time constant based on the temperature response function; perform thermal inertia lag compensation on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to determine the actual temperature data of the equipment to be measured.

[0059] It can be understood that, compared with the existing technology, the temperature measuring nut constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement data and equipment operating parameters, so that it can realize the dynamic acquisition of the dynamic thermal radiation interference coefficient based on the real-time temperature measurement data, and then correct the real-time temperature measurement data to obtain corrected temperature measurement data; then the real-time temperature measurement data is subjected to spectral analysis to determine the thermal radiation change frequency correction coefficient, and a temperature response function is constructed based on the real-time temperature measurement data, and then the thermal response time constant is determined based on the temperature response function; then the corrected temperature measurement data is subjected to thermal inertia lag compensation processing through the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to obtain the actual temperature data of the equipment to be measured, and the correction of the temperature measurement data with the dynamic thermal radiation interference coefficient, the thermal radiation change frequency correction coefficient and the thermal response time constant is realized, thereby improving the accuracy of temperature measurement in high temperature environment. This temperature measuring nut constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement and equipment operating parameters, and then combines real-time temperature measurement data to realize dynamic thermal radiation interference correction of the temperature measurement equipment, which can accurately reduce the influence of thermal radiation interference on temperature data in high temperature environment; by performing spectrum analysis on real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient, the thermal radiation fluctuation characteristics of the temperature measurement equipment can be accurately extracted, thereby further avoiding the influence of thermal radiation fluctuation on the corrected temperature measurement data; by constructing a temperature response function and determining the thermal response time constant, the thermal response time constant can be used to explore the temperature transfer lag characteristics caused by thermal inertia between the temperature measurement equipment and real-time temperature measurement data, so that the thermal inertia lag compensation processing can comprehensively consider the thermal radiation interference of high temperature environment temperature measurement, thermal inertia and the mutual influence between the temperature measurement equipment, thereby improving the accuracy of temperature measurement in high temperature environment.

[0060] As a preferred solution, the temperature measurement cavity includes: a temperature measurement module;

[0061] The temperature measurement module includes several layers of concentric cylinders, a main sensor and several reference sensors;

[0062] The main sensor is arranged at the center of the innermost concentric cylinder and is in contact with the inner wall of the temperature measurement cavity;

[0063] The reference sensors are evenly arranged in each layer of concentric cylinders except the innermost layer, and the number of reference sensors in each layer of concentric cylinders except the innermost layer is the same;

[0064] The inner wall of the innermost concentric cylinder is provided with a metal shielding layer; and thermal conductive silicone grease is filled between the concentric cylinders.

[0065] This preferred solution can measure the temperature of the temperature-measured device from multiple angles by setting a main sensor and multiple reference sensors. By using reference sensors at different positions to monitor the ambient thermal radiation at different positions and angles, it can capture the changes in thermal radiation more comprehensively, thereby providing a more accurate reference basis for temperature measurement. By setting a metal shielding layer and filling thermal conductive silicone grease, the measurement accuracy of the main sensor and the reference sensor can be further improved, thereby improving the accuracy of the real-time temperature measurement data of the temperature-measured device, thereby providing an accurate data basis for the subsequent correction of the real-time temperature measurement data by the non-temperature measurement cavity, and improving the accuracy of temperature measurement in high-temperature environments.

[0066] As a preferred solution, the surface of the non-temperature measurement cavity is provided with a plurality of heat insulation structures;

[0067] The thermal insulation structure comprises: a nano-silica aerogel thermal insulation coating, a nano-silica thermal insulation felt, a silver-plated copper foil thermal insulation reflective layer, a high-temperature resistant aluminum silicate fiber layer and a silicone rubber thermal insulation sleeve;

[0068] Each insulation structure is bonded together using high-temperature ceramic adhesive.

[0069] This preferred solution avoids the operating stability of the non-temperature measuring cavity in a high-temperature environment by setting up several insulation structures on the surface of the non-temperature measuring cavity and bonding the insulation structures with high-temperature ceramic adhesive, thereby ensuring the accuracy of the correction of the real-time temperature measurement data by the non-temperature measuring cavity, and thus improving the accuracy of temperature measurement in a high-temperature environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flow chart of the steps of a temperature measurement method provided by an embodiment of the present invention;

[0071] Figure 2 A schematic structural diagram of a temperature measuring nut provided by an embodiment of the present invention;

[0072] Figure 3A schematic structural diagram of a temperature measurement module provided by an embodiment of the present invention;

[0073] Figure 4 A schematic structural diagram of another temperature measurement module provided by an embodiment of the present invention;

[0074] Figure 5 A schematic structural diagram of a non-temperature measurement cavity provided by an embodiment of the present invention;

[0075] Figure 6 A schematic structural diagram of another temperature measuring nut provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0077] Example 1

[0078] To solve the problem of inaccurate temperature measurement in high temperature environment in the existing technology, please refer to Figure 1 , is a step flow chart of a temperature measurement method provided by an embodiment of the present invention, including steps S101 to S106.

[0079] Step S101: Acquire historical temperature measurement data, real-time temperature measurement data, and device operating parameters of the device to be temperature measured.

[0080] Step S102: constructing a dynamic thermal radiation interference coefficient calculation model based on the historical temperature measurement data and equipment operating parameters.

[0081] Step S103: inputting the real-time temperature measurement data into the dynamic thermal radiation interference coefficient calculation model to determine the dynamic thermal radiation interference coefficient, and correcting the real-time temperature measurement data based on the dynamic thermal radiation interference coefficient to obtain corrected temperature measurement data.

[0082] Step S104: performing spectrum analysis on the real-time temperature measurement data to determine a thermal radiation variation frequency correction coefficient.

[0083] Step S105: constructing a temperature response function based on the real-time temperature measurement data, and determining a thermal response time constant based on the temperature response function.

[0084] Step S106: performing thermal inertia lag compensation processing on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operation parameters to determine the actual temperature data of the equipment to be temperature measured.

[0085] This embodiment constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement data and equipment operating parameters, so that the dynamic thermal radiation interference coefficient can be dynamically acquired based on real-time temperature measurement data, and then the real-time temperature measurement data is corrected to obtain corrected temperature measurement data; then the real-time temperature measurement data is subjected to spectral analysis to determine the thermal radiation change frequency correction coefficient, and a temperature response function is constructed based on the real-time temperature measurement data, and then the thermal response time constant is determined based on the temperature response function; then the corrected temperature measurement data is subjected to thermal inertia lag compensation processing through the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to obtain the actual temperature data of the equipment to be measured, thereby realizing the correction of the temperature measurement data with the dynamic thermal radiation interference coefficient, the thermal radiation change frequency correction coefficient and the thermal response time constant, and improving the accuracy of temperature measurement in high temperature environments. This embodiment constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement and equipment operating parameters, and then combines it with real-time temperature measurement data to realize dynamic thermal radiation interference correction of the equipment to be measured, which can accurately reduce the impact of thermal radiation interference on temperature data in a high-temperature environment; by performing spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient, the thermal radiation fluctuation characteristics of the equipment to be measured can be accurately extracted, thereby further avoiding the impact of thermal radiation fluctuations on the corrected temperature measurement data; by constructing a temperature response function and determining the thermal response time constant, the thermal response time constant can be used to explore the temperature transfer lag characteristics caused by thermal inertia between the equipment to be measured and the real-time temperature measurement data, so that the thermal inertia lag compensation processing can comprehensively consider the thermal radiation interference of high-temperature environment temperature measurement, thermal inertia and the mutual influence between the equipment to be measured, thereby improving the accuracy of temperature measurement in a high-temperature environment.

[0086] Example 2

[0087] In order to solve the problem of inaccurate temperature measurement in high temperature environment in the existing technology, Figure 1 As shown, the flowchart of a temperature measurement method provided in this embodiment includes steps S101 to S106. The temperature measurement method described in this embodiment can be applied not only to the temperature-measuring nut described in Example 3, but also to other temperature measurement devices capable of temperature measurement, such as existing wired temperature sensors and external wireless temperature measurement devices. The implementation of the temperature measurement method will be described in detail below.

[0088] Step S101 is to obtain historical temperature measurement data, real-time temperature measurement data and equipment operating parameters of the device to be measured.

[0089] In an optional embodiment, temperature measurement is performed by providing a temperature sensor. Specifically, this embodiment uses a PT1000 platinum resistor as the temperature sensor. The resistance value of the PT1000 platinum resistor is highly linearly related to temperature within the temperature range of 0-100°C, with a linearity error of less than ±0.05%. Therefore, the use of a PT1000 platinum resistor can obtain more accurate temperature measurement data. Multiple PT1000 platinum resistors are divided into main sensors and reference sensors. The main sensor is used to collect the actual temperature of the device to be measured, and the reference sensor is used to quantify the thermal radiation interference value of the device to be measured in a high temperature environment. The thermal radiation interference value can be represented by a temperature value. In addition, this embodiment also uses an ambient temperature sensor to measure the ambient temperature of the device to be measured. The ambient temperature sensor can also be a PT1000 platinum resistor. The operating parameters of the device to be measured include: the surface emissivity of the device to be measured and the surface roughness of the device to be measured. The surface emissivity of the device to be measured can be measured by an emissivity meter, and the surface roughness of the device to be measured can be measured by a roughness meter.

[0090] It should be noted that, in a high-temperature environment, the temperature data measured by the sensor will be affected by thermal radiation interference, which will cause deviations in the temperature data. Currently, there are also thermal radiation interference corrections for temperature data, but traditional thermal radiation interference corrections only rely on fixed coefficients or simple empirical formulas, which cannot match the actual temperature environment of the temperature-measured equipment well. Therefore, this embodiment performs dynamic thermal radiation interference correction through steps S102 and S103, thereby improving the accuracy of thermal radiation interference correction.

[0091] Step S102 is to construct a dynamic thermal radiation interference coefficient calculation model based on the historical temperature measurement data and equipment operating parameters.

[0092] In this embodiment, the dynamic thermal radiation interference coefficient calculation model is constructed based on the historical temperature measurement data and the equipment operating parameters, specifically including: determining the thermal radiation interference coefficient reference value according to a preset thermal radiation simulation method; the historical temperature measurement data include: historical main sensor temperature measurement values, historical reference sensor temperature measurement values ​​and historical environmental sensor temperature measurement values; the equipment operating parameters include: surface emissivity of the device to be temperature measured and surface roughness of the device to be temperature measured; constructing a dynamic thermal radiation interference coefficient training set based on the historical main sensor temperature measurement values, historical reference sensor temperature measurement values, historical environmental sensor temperature measurement values, surface emissivity of the device to be temperature measured, surface roughness of the device to be temperature measured and the thermal radiation interference coefficient reference value; training a preset support vector machine model based on the dynamic thermal radiation interference coefficient training set to determine the dynamic thermal radiation interference coefficient calculation model.

[0093] This embodiment constructs a dynamic thermal radiation interference coefficient training set through historical main sensor temperature measurement values, historical reference sensor temperature measurement values, historical environmental sensor temperature measurement values, surface emissivity of the temperature device to be measured, surface roughness of the temperature device to be measured and thermal radiation interference coefficient reference value, so that the dynamic thermal radiation interference coefficient calculation model obtained by training can consider the actual temperature environment of the temperature device to be measured in multiple dimensions, thereby dynamically adjusting the thermal radiation interference coefficient, avoiding the low fit with the actual temperature environment caused by fixed coefficients or empirical formulas in the existing technology, and can accurately reduce the impact of thermal radiation interference on temperature data in high temperature environments, thereby improving the accuracy of temperature measurement in high temperature environments.

[0094] In this embodiment, the determination of the thermal radiation interference coefficient reference value according to the preset thermal radiation simulation method specifically includes: constructing a thermal radiation simulation environment according to the operating environment of the device to be temperature measured; obtaining the actual temperature of the thermal radiation simulation environment, the temperature measured by the main sensor of the thermal radiation simulation environment, the temperature measured by the reference sensor of the thermal radiation simulation environment and the temperature of the thermal radiation simulation environment based on the thermal radiation simulation environment; calculating the first thermal radiation simulation environment measurement difference based on the actual temperature of the thermal radiation simulation environment and the temperature measured by the main sensor of the thermal radiation simulation environment; calculating the second thermal radiation simulation environment measurement difference based on the temperature measured by the reference sensor of the thermal radiation simulation environment and the temperature of the thermal radiation simulation environment; and determining the thermal radiation interference coefficient reference value based on the first thermal radiation simulation environment measurement difference and the second thermal radiation simulation environment measurement difference.

[0095] This embodiment constructs a thermal radiation simulation environment through the operating environment of the temperature-measured device, and thereby determines a thermal radiation interference coefficient reference value based on the relevant temperature measurement values ​​of the thermal radiation simulation environment, so that the thermal radiation interference coefficient reference value can fully characterize the actual operating environment of the temperature-measured device, avoiding the error of the thermal radiation interference coefficient reference value, thereby improving the accuracy of the dynamic thermal radiation interference coefficient training set and the dynamic thermal radiation interference coefficient calculation model obtained through training, and further improving the accuracy of temperature measurement in high-temperature environments.

[0096] Step S103 is to input the real-time temperature measurement data into the dynamic thermal radiation interference coefficient calculation model, determine the dynamic thermal radiation interference coefficient, and correct the real-time temperature measurement data based on the dynamic thermal radiation interference coefficient to obtain corrected temperature measurement data.

[0097] In an optional embodiment, a thermal radiation simulation environment is constructed based on the operating environment of the temperature-measured device. In this embodiment, the thermal radiation simulation environment is constructed in a constant temperature box, and a stable thermal radiation simulation environment temperature T is set and maintained in the constant temperature box. 环境,k and heat source temperature, thereby simulating the operating environment of the device to be measured, and then using a high-precision contact thermometer to measure the actual temperature T of the thermal radiation simulation environment 真实基准,kThen use the main sensor to measure the thermal radiation simulation environment and obtain the main sensor measurement temperature T 主,k ; The reference sensor measures the temperature T of the thermal radiation simulation environment 参考,k ; Then the actual temperature of the thermal radiation simulation environment T 真实基准,k And thermal radiation simulation environment main sensor measures temperature T 主,k Perform a subtraction operation to obtain the first thermal radiation simulation environment measurement difference T 主,k -T 真实基准,k ; Then the thermal radiation simulation environment reference sensor measures the temperature T 参考,k and thermal radiation simulation ambient temperature T 环境,k Perform subtraction operation to obtain the second thermal radiation simulation environment measurement difference T 参考,k -T 环境,k ; Then calculate the thermal radiation interference coefficient reference value y; Among them, the thermal radiation interference coefficient reference value y is specifically In particular, multiple experiments can be performed to obtain multiple thermal radiation interference coefficient reference values ​​y, and then statistical analysis can be performed, such as calculating the average value, to improve the accuracy of the thermal radiation interference coefficient reference value y;

[0098] Then, the temperature measurement value T of the main sensor is measured by the historical 主,history , historical reference sensor temperature measurement value T 参考,history , historical environmental sensor temperature measurement value T 环境,history , the surface emissivity ε of the temperature measurement equipment, the surface roughness R of the temperature measurement equipment a And the thermal radiation interference coefficient reference value y is used to construct the dynamic thermal radiation interference coefficient training set. Each data sample in the dynamic thermal radiation interference coefficient training set can be expressed as (x i ,y),x i =[T 主,history,i ,T 参考,history,i ,T 环境,history,i ,ε i ,R a,i ,...]; then the preset support vector machine model is trained based on the dynamic thermal radiation interference coefficient training set, and the radial basis kernel function K(x i ,x j )=exp(-γ‖x i -x j ‖ 2 ); where γ is the radial basis kernel function parameter, and the optimal value of the radial basis kernel function parameter γ is determined by cross-validation method, and then the dynamic thermal radiation interference coefficient calculation model k=f(x) is determined; where k is the dynamic thermal radiation interference coefficient;

[0099] Then the real-time temperature measurement data is input into the dynamic thermal radiation interference coefficient calculation model, that is, the real-time main sensor temperature measurement value T主,now , Real-time reference sensor temperature measurement value T 参考,now , real-time environmental sensor temperature measurement value T 环境,now , the surface emissivity ε of the temperature measurement equipment, the surface roughness R of the temperature measurement equipment a The data sequence composed of [T 主,now ,T 参考,now ,T 环境,now ,ε,R a ] Input the dynamic thermal radiation interference coefficient calculation model to obtain the dynamic thermal radiation interference coefficient, and then calculate the corrected temperature measurement data T 真实 , the calculation process is T 真实 =T 主,now -k(T 参考,now -T 环境,now ).

[0100] It should be noted that the preset support vector machine model refers to the SVM model, i.e., the Support Vector Machine (SVM) model, which is a supervised learning algorithm primarily used for classification and regression tasks. The dynamic thermal radiation interference coefficient training set constructed in this embodiment is not limited to the aforementioned parameter types. Experimenters can modify the content of the training set accordingly based on actual temperature measurement requirements.

[0101] This optional embodiment simulates the operating environment of the device to be temperature measured and constructs a thermal radiation simulation environment, thereby calculating the reference value of the thermal radiation interference coefficient, so that the reference value of the thermal radiation interference coefficient can better fit the actual thermal radiation interference situation of the device to be temperature measured, and then the dynamic thermal radiation interference coefficient training set constructed subsequently can more accurately characterize the actual temperature changes of the device to be temperature measured under thermal radiation interference, thereby improving the training accuracy of the dynamic thermal radiation interference coefficient calculation model, so that the dynamic thermal radiation interference coefficient can be accurately and dynamically solved based on the dynamic thermal radiation interference coefficient calculation model and real-time temperature measurement data, ensuring that the influence of thermal radiation interference on real-time temperature measurement data can be better eliminated, and the accuracy of temperature measurement in high temperature environment is improved.

[0102] Step S104 is to perform spectrum analysis on the real-time temperature measurement data to determine a thermal radiation variation frequency correction coefficient.

[0103] In this embodiment, the spectral analysis of the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient specifically includes: performing spectral analysis on the real-time temperature measurement data according to a preset fast Fourier transform algorithm to obtain a thermal radiation change frequency distribution spectrum; extracting frequency components of the thermal radiation change frequency distribution spectrum to determine the thermal radiation change frequency; fitting the thermal radiation change frequency based on a preset fitting algorithm to determine the thermal radiation change frequency correction coefficient.

[0104] In an optional embodiment, a spectrum analysis is performed on the real-time reference sensor temperature measurement value within a preset time interval (i.e., the real-time reference sensor temperature measurement value in the real-time temperature measurement data) according to a preset fast Fourier transform algorithm to obtain a frequency distribution spectrum, and then the frequency component with the largest energy proportion is extracted as the thermal radiation change frequency f. Then, a temperature deviation experimental environment is constructed, and the thermal radiation change frequency of the temperature deviation experimental environment is modified multiple times to measure the corresponding temperature deviation; then, the thermal radiation change frequency f is used as the independent variable and the temperature deviation is used as the dependent variable, and the thermal radiation change frequency correction coefficient p is obtained by a polynomial fitting method; wherein, p=b0+b1f+b2f 2 ; Among them, b0, b1, and b2 are fitting coefficients, and their values ​​can be determined by the least squares method.

[0105] It should be noted that the preset fast Fourier transform algorithm refers to the fast Fourier transform algorithm (Fast Fourier Transform, FFT), which is an algorithm for efficiently calculating discrete Fourier transform and its inverse transform.

[0106] This embodiment performs spectrum analysis on the real-time temperature measurement data using a preset fast Fourier transform algorithm, which can quickly and accurately identify the periodic variation characteristics of thermal radiation in a high-temperature environment of the device to be measured. Subsequently, by extracting the frequency components and fitting the thermal radiation variation frequency, it can effectively separate the noise data of the thermal radiation variation frequency distribution spectrum and extract more accurate thermal radiation variation data. This allows the thermal radiation variation frequency correction coefficient to fully characterize the periodic variation characteristics of thermal radiation in a high-temperature environment of the device to be measured, thereby further avoiding the impact of thermal radiation fluctuations on the corrected temperature measurement data. This improves the accuracy of temperature measurement in high-temperature environments.

[0107] Step S105 is to construct a temperature response function based on the real-time temperature measurement data, and determine a thermal response time constant based on the temperature response function.

[0108] In this embodiment, constructing a temperature response function based on the real-time temperature measurement data and determining a thermal response time constant based on the temperature response function specifically include: constructing a temperature change curve based on the real-time temperature measurement data; constructing an initial temperature response function according to a preset least squares method, and constructing a temperature error function based on the real-time temperature measurement data and the initial temperature response function; solving the temperature error function based on a preset matrix operation algorithm to obtain a temperature fitting coefficient solution set; reconstructing the initial temperature response function based on the fitting coefficient solution set to obtain a temperature response function; and determining a thermal response time constant based on the temperature response function and the temperature change curve.

[0109] This embodiment constructs an initial temperature response function through the least squares method, and then constructs a temperature error function in combination with real-time temperature measurement data, which can characterize the influence of thermal inertia lag on the temperature measurement data of the temperature measurement device to be measured; then, the temperature error function is solved by a matrix operation algorithm to obtain a temperature fitting coefficient solution set, and then the temperature response function is determined. The matrix operation algorithm can significantly reduce the computational complexity of the temperature fitting coefficient solution set, and the temperature fitting coefficient solution set can be used to adaptively adjust the temperature response function, ensuring that the temperature response function can accurately characterize the influence of thermal inertia lag on the temperature measurement data; the temperature change curve is constructed by real-time temperature measurement data, which can quantify the evolution process of real-time temperature measurement data over time, and thus a more accurate thermal response time constant can be obtained, so that the temperature transfer lag characteristics caused by thermal inertia between the temperature measurement device to be measured and the real-time temperature measurement data can be explored, thereby improving the accuracy of temperature measurement in high temperature environments.

[0110] In this embodiment, the temperature error function is solved based on a preset matrix operation algorithm to obtain a temperature fitting coefficient solution set, which specifically includes: taking partial derivatives of each temperature fitting coefficient in the temperature error function to obtain a temperature fitting coefficient equation group; constructing a temperature design matrix, a temperature fitting coefficient vector and a temperature measurement vector based on the temperature fitting coefficient equation group; constructing a temperature transpose matrix and a temperature inverse matrix based on the temperature design matrix; solving the temperature fitting coefficient vector based on the temperature transpose matrix, the temperature inverse matrix and the temperature measurement vector to obtain a temperature fitting coefficient solution set.

[0111] This embodiment constructs a temperature design matrix, a temperature fitting coefficient vector and a temperature measurement vector by calculating the partial derivative of each temperature fitting coefficient in the temperature error function. The matrix operation can significantly reduce the complexity of solving the temperature fitting coefficient solution set, avoid the error of the temperature fitting coefficient solution set caused by overly complex calculations, thereby ensuring the accuracy of the temperature fitting coefficient solution set, and then the accuracy of the subsequent thermal inertia lag compensation processing, thereby improving the accuracy of temperature measurement in high temperature environments.

[0112] In an optional embodiment, a temperature change curve is constructed based on real-time temperature measurement data. The temperature change curve is a segmented temperature change curve, that is, the temperature change curve is divided into a rapid rise segment, a stable segment, etc.; then the output signal T of the PT1000 platinum resistor in each segment of the temperature change curve is recorded. PT (t); then the initial temperature response function is constructed according to the preset least squares method, that is, the initial temperature response function is defined as T fit (t) = a0 + a1t + a2t 2 +...+a m t m ; a0,a1,a2,...,a mare fitting coefficients, t is time, which is an unknown quantity in the initial temperature response function; then the real-time temperature measurement data (i.e. the output signal T of PT1000 platinum resistance) is used to calculate the temperature. PT (t)) and the initial temperature response function T fit (t) constructing a temperature error function E, wherein the temperature error function E is a minimized error function; Where N is the number of temperature measurement data; T fit (t j ) indicates the PT1000 platinum resistance at t j The measured value at time T PT (t j ) indicates the PT1000 platinum resistance at t j The output signal at the moment; then each fitting coefficient a in the temperature error function E i (i=0,1,...,m) and perform partial derivative processing to obtain the temperature fitting coefficient equation group; the temperature fitting coefficient equation group is specifically:

[0113]

[0114] The temperature fitting coefficient equation group is then expanded to obtain the following formula:

[0115]

[0116] Then, the temperature design matrix, temperature fitting coefficient vector and temperature measurement vector are constructed based on the expanded temperature fitting coefficient equation group; wherein, the temperature design matrix X is specifically:

[0117]

[0118] The temperature fitting coefficient vector a is specifically:

[0119]

[0120] Temperature measurement vector y ′ Specifically:

[0121]

[0122] When the temperature design matrix X is full rank, the temperature fitting coefficient vector a can be solved by the matrix inversion method, that is, according to the matrix operation rule a=(X T X) -1 X T y ′ , construct the temperature transposed matrix X based on the temperature design matrix X T , temperature inverse matrix (X T X) -1 ; Where the temperature inverse matrix (X T X)-1 For X T The inverse matrix of X is used to solve the temperature fitting coefficient vector a to obtain the temperature fitting coefficient solution set; wherein the temperature fitting coefficient solution set refers to each fitting coefficient a i (i=0,1,...,m) value solution set; then substitute the temperature fitting coefficient solution set into the initial temperature response function to determine the temperature response function T fit (t);

[0123] Then, the final temperature stability value T of each temperature change is determined based on the temperature change curve. 最终 , plot the temperature response function T fit (t) corresponds to the fitting curve, and find the value of the curve that changes from the initial value T1 to 0.632 (T 最终 -T1)+T1 time point t τ , then the thermal response time constant τ=t τ .

[0124] Step S106 is to perform thermal inertia lag compensation processing on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operation parameters to determine the actual temperature data of the equipment to be temperature measured.

[0125] In this embodiment, the thermal inertia lag compensation processing is performed on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to determine the actual temperature data of the equipment to be measured, specifically including: the equipment operating parameters also include: the thermal capacity of the equipment to be measured; the thermal capacity temperature change value of the equipment to be measured is determined according to the real-time temperature measurement data and the thermal capacity of the equipment to be measured; the real-time thermal radiation change frequency of the equipment to be measured is determined according to the real-time temperature measurement data; the thermal radiation temperature change value of the equipment to be measured is determined according to the real-time temperature measurement data, the thermal radiation change frequency correction coefficient and the real-time thermal radiation change frequency of the equipment to be measured; the thermal response time temperature change value of the equipment to be measured is determined according to the real-time temperature measurement data and the thermal response time constant; the actual temperature data of the equipment to be measured is determined based on the thermal capacity temperature change value, the thermal radiation temperature change value, the thermal response time temperature change value pair and the corrected temperature measurement data.

[0126] In an optional embodiment, the heat capacity of the device to be measured is defined as C, which can be obtained by performing a heating experiment on the device to be measured. The heat Q input during the heating experiment and the temperature change ΔT of the device to be measured are then calculated based on the heat capacity formula. Calculate the heat capacity C of the device to be measured. The heat input Q during the heating experiment can be measured by a calorimeter, and the temperature change ΔT of the device to be measured can be measured by a high-precision temperature sensor. Then, determine the heat capacity temperature change value of the device to be measured based on the real-time temperature measurement data and the heat capacity of the device to be measured. Determine the real-time thermal radiation change frequency f (i.e., the thermal radiation change frequency mentioned above) of the device to be measured according to the real-time temperature measurement data; then determine the thermal radiation temperature change value of the device to be measured according to the real-time temperature measurement data, the thermal radiation change frequency correction coefficient, and the real-time thermal radiation change frequency of the device to be measured Then, the thermal response time temperature change value of the device to be measured is determined based on the real-time temperature measurement data and the thermal response time constant. Then, based on the heat capacity temperature change value, the heat radiation temperature change value, the thermal response time temperature change value pair and the corrected temperature measurement data, the actual temperature data T of the device to be measured is determined. 真实修正 ; The actual temperature data T 真实修正 The calculation formula is:

[0127]

[0128] This embodiment can fully consider the thermal radiation interference, thermal inertia and mutual influence between the temperature measurement equipment in high-temperature environment by calculating the temperature change value of heat capacity, thermal radiation temperature change value and thermal response time temperature change value, so that the actual temperature data after compensation is more accurate, thereby improving the accuracy of temperature measurement in high-temperature environment.

[0129] This embodiment constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement data and equipment operating parameters, so that the dynamic thermal radiation interference coefficient can be dynamically acquired based on real-time temperature measurement data, and then the real-time temperature measurement data is corrected to obtain corrected temperature measurement data; then the real-time temperature measurement data is subjected to spectral analysis to determine the thermal radiation change frequency correction coefficient, and a temperature response function is constructed based on the real-time temperature measurement data, and then the thermal response time constant is determined based on the temperature response function; then the corrected temperature measurement data is subjected to thermal inertia lag compensation processing through the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to obtain the actual temperature data of the equipment to be measured, thereby realizing the correction of the temperature measurement data with the dynamic thermal radiation interference coefficient, the thermal radiation change frequency correction coefficient and the thermal response time constant, and improving the accuracy of temperature measurement in high temperature environments. This embodiment constructs a dynamic thermal radiation interference coefficient calculation model through historical temperature measurement and equipment operating parameters, and then combines it with real-time temperature measurement data to realize dynamic thermal radiation interference correction of the equipment to be measured, which can accurately reduce the impact of thermal radiation interference on temperature data in a high-temperature environment; by performing spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient, the thermal radiation fluctuation characteristics of the equipment to be measured can be accurately extracted, thereby further avoiding the impact of thermal radiation fluctuations on the corrected temperature measurement data; by constructing a temperature response function and determining the thermal response time constant, the thermal response time constant can be used to explore the temperature transfer lag characteristics caused by thermal inertia between the equipment to be measured and the real-time temperature measurement data, so that the thermal inertia lag compensation processing can comprehensively consider the thermal radiation interference of high-temperature environment temperature measurement, thermal inertia and the mutual influence between the equipment to be measured, thereby improving the accuracy of temperature measurement in a high-temperature environment.

[0130] Example 3

[0131] Please refer to Figure 2, is a structural schematic diagram of a temperature measuring nut provided by an embodiment of the present invention, the temperature measuring nut comprises: a nut body 1, for implementing a temperature measurement method as described above, the temperature measuring nut further comprises: a temperature measuring cavity 2 and a non-temperature measuring cavity 3; the non-temperature measuring cavity 3 and the temperature measuring cavity 2 are both arranged on the outer surface of the nut; the temperature data output end of the temperature measuring cavity 2 is electrically connected to the temperature data input end of the non-temperature measuring cavity 3; the temperature measuring cavity 2 is used to obtain real-time temperature measurement data of the device to be temperature measured; the non-temperature measuring cavity 3 is used to obtain historical temperature measurement data and device operating parameters of the device to be temperature measured, and receive real-time temperature measurement data collected by the temperature measuring cavity; the non-temperature measuring cavity 3 is also used to obtain real-time temperature measurement data collected by the temperature measuring cavity based on the historical temperature measurement data and device operating parameters The non-temperature measurement cavity 3 is further used to perform spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient; a temperature response function is constructed based on the real-time temperature measurement data, and a thermal response time constant is determined based on the temperature response function; thermal inertia lag compensation is performed on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to determine the actual temperature data of the equipment to be temperature measured.

[0132] In this embodiment, the temperature measuring cavity 2 includes: a temperature measuring module; the temperature measuring module includes several layers of concentric cylinders 21, a main sensor 22 and several reference sensors 23; the main sensor is arranged at the center of the innermost concentric cylinder and is in contact with the inner wall of the temperature measuring cavity; the reference sensors are evenly arranged in each layer of concentric cylinders except the innermost layer, and the number of reference sensors in each layer of concentric cylinders except the innermost layer is the same; the inner wall of the innermost concentric cylinder is provided with a metal shielding layer; and thermal conductive silicone grease is filled between the concentric cylinders.

[0133] In an alternative embodiment, see Figure 3 , is a structural diagram of a temperature measurement module provided by an embodiment of the present invention; Figure 4 , is a structural diagram of another temperature measurement module provided by an embodiment of the present invention; Figure 3 Shown is a concentric cylinder 21 provided with four reference sensors 23; Figure 4 Shown is a concentric cylinder 21 provided with a reference sensor 23; Figure 3 and Figure 4There are 4 concentric cylinders in the apparatus. The number of concentric cylinders in this embodiment can be increased or decreased according to actual needs, but at least 4 concentric cylinders are required. Each concentric cylinder 21 is made of high-purity alumina ceramic, which has good high-temperature resistance and excellent thermal conductivity, and can quickly transfer heat from the surface of the equipment to the main sensor. It also has stable chemical properties and will not react chemically with the main sensor. The main sensor 22 uses a PT1000 platinum resistor and is installed in the center of the innermost concentric cylinder 21 through a high-temperature resistant ceramic bracket. High-temperature welding technology is used between the ceramic bracket and the main sensor 22 to ensure a firm connection and good heat conduction. Filling the main sensor 22 with thermal grease with a thermal conductivity coefficient of >5W / (m·K) can further improve the heat transfer efficiency and reduce thermal resistance. A metal shielding layer (copper or nickel can be used) is plated on the inner wall of the innermost concentric cylinder 21, and the metal shielding layer is grounded, which can effectively shield external electromagnetic interference signals. As Figure 4 As shown, a PT1000 platinum resistor is provided as a reference sensor 23 on each concentric cylinder except the innermost layer. In this embodiment, at least one reference sensor 23 is provided on each concentric cylinder 21 except the innermost layer. The reference sensor 23 is fixed in the concentric cylinder 21 by a ceramic bracket, and thermal grease is filled between the concentric cylinders. The angle formed between the adjacent reference sensors placed on each concentric cylinder 21 and the center of the circle is degrees, where p is the number of reference sensors 23 in the qth concentric cylinder 21. Each reference sensor 23 in the (q+1)th layer of concentric cylinders 21 is placed at the center of the included angle between adjacent reference sensors in the qth layer of concentric cylinders 21. The number of reference sensors 23 in each layer of concentric cylinders 21 is equal.

[0134] This optional embodiment can measure temperature more accurately, reduce measurement errors, and ensure the reliability of measurement results by setting up multiple reference sensors in the temperature measurement module. Since environmental factors are complex and changeable in actual temperature measurement environments, a single reference sensor may not be able to fully reflect changes in ambient thermal radiation. By monitoring ambient thermal radiation from different positions and angles through multiple reference sensors, changes in thermal radiation can be captured more comprehensively, improve adaptability to complex environments, and provide more accurate reference data for temperature measurement. Multiple reference sensors cooperate with each other. Even if a reference sensor fails or is subject to abnormal interference, other reference sensors can still work normally, ensuring the stability and reliability of the entire temperature measurement system and avoiding large deviations in measurement results due to problems with a single sensor.

[0135] In an alternative embodiment, if Figure 3 and Figure 4As shown, when the main sensor 22 fails, the reference sensor 23 in the concentric cylinder 21 after the innermost layer is used as the main sensor. The reference sensors 23 can be numbered and replaced according to the numbers. When the sensors in the two innermost concentric cylinders 21 (main sensor 22 and reference sensor 23) are both faulty, the temperature measuring nut is scrapped. Specifically, the sensor failure can be judged by comparing the redundant sensors. For the reference sensor, let the measurement value of the jth reference sensor 23 in the i-th concentric cylinder 21 be T 参i,j , the number of reference sensors 23 in the i-th layer of concentric cylinders 21 is n, then the measurement average value of the reference sensors 23 in the i-th layer of concentric cylinders 21 is calculated The calculation formula is Then calculate the jth reference sensor 23 measurement value and the measurement average value The difference ΔT 参i,j , the formula is Then set a maximum allowable value of the difference ΔT 参max If T 参i,j >ΔT 参max , ΔT 参max ≥2℃ to judge the reference sensor T 参i,j Fault; for the main sensor, based on the second embodiment, the dynamic thermal radiation interference coefficient k is calculated based on the measurement data of the main sensor, if k exceeds the preset range [k min ,k max ], according to the above description, the reference sensor is used as the main sensor. If the dynamic thermal radiation interference coefficient k corresponding to the new main sensor is restored to [k min ,k max ], it is judged that the original main sensor is faulty.

[0136] This embodiment provides a main sensor and multiple reference sensors to measure the temperature of the device to be measured from multiple angles. By using reference sensors at different positions to monitor the ambient thermal radiation at different positions and angles, it can more comprehensively capture changes in thermal radiation, thereby providing a more accurate reference basis for temperature measurement. By providing a metal shielding layer and filling with thermal conductive silicone grease, the measurement accuracy of the main sensor and the reference sensor can be further improved, thereby improving the accuracy of the real-time temperature measurement data of the device to be measured, thereby providing an accurate data basis for the subsequent correction of the real-time temperature measurement data by the non-temperature measurement cavity, and improving the accuracy of temperature measurement in high-temperature environments.

[0137] In an optional embodiment, the temperature measuring nut also includes an energy collection and storage module; the energy collection and storage module is specifically a thermoelectric power generation sheet 4; wherein the thermoelectric power generation sheet 4 is arranged in the center of the temperature measuring cavity 2; the thermoelectric power generation sheet is made of bismuth telluride (Bi2Te3) material; it generates electrical energy through the temperature difference caused by the higher temperature in the center of the temperature measuring cavity 2 and the lower ambient temperature; the thermoelectric power generation sheet 4 and the temperature measuring cavity 2 are connected with high thermal conductivity and high temperature resistant silicone grease to ensure good heat conduction, so that the thermoelectric power generation sheet 4 can stably obtain the temperature difference energy between the device and the interior.

[0138] In this embodiment, the surface of the non-temperature measurement cavity 3 is provided with several insulation structures; the insulation structures include: nano-silica aerogel insulation coating, nano-silica insulation felt, silver-plated copper foil insulation reflective layer, high-temperature resistant aluminum silicate fiber layer and silicone rubber insulation sleeve; each insulation structure is bonded by high-temperature ceramic adhesive.

[0139] In an optional embodiment, the non-temperature measuring cavity 3 adopts a five-layer insulation structure; the innermost layer is a nano-silica aerogel insulation coating, which is formed on the outer wall of the non-temperature measuring cavity 3 by a physical vapor deposition process to form a coating with a thickness of 0.1-0.3 mm, which can effectively block the heat input of the equipment; the second layer is a nano-silica insulation felt with a thermal conductivity of less than 0.05W / (m·K), which can reduce heat conduction; the third layer is a silver-plated copper foil insulation reflective layer, which uses the high reflectivity of silver to efficiently reflect radiant heat back and reduce heat penetration; the fourth layer is a high-temperature resistant aluminum silicate fiber layer for thermal insulation; the fifth layer is a silicone rubber insulation sleeve with a thermal conductivity of less than 0.2W / (m·K), which not only serves as a seal but also absorbs heat; each layer of the insulation structure is bonded with a high-temperature ceramic adhesive, which can be an aluminum phosphate-based high-temperature ceramic adhesive or a high-temperature epoxy ceramic adhesive, and its shear strength at 100°C can reach more than 10MPa.

[0140] This embodiment avoids the operating stability of the non-temperature measuring cavity in a high-temperature environment by arranging several insulation structures on the surface of the non-temperature measuring cavity and bonding the insulation structures with high-temperature ceramic adhesive, thereby ensuring the accuracy of the correction of the real-time temperature measurement data by the non-temperature measuring cavity, and further improving the accuracy of temperature measurement in a high-temperature environment.

[0141] In an optional embodiment, the non-temperature measurement cavity 3 is equipped with a wireless transmission module. The wireless transmission module uses one or more of Bluetooth, Wi-Fi, ZigBee, 4G / 5G, RFID, and LoRa. The antenna surface is covered with a layer of high-temperature resistant polyimide film. The surface of the wireless transmission module is coated with a layer of high-temperature resistant silicone resin conformal coating with a thickness of 0.1-0.2mm to ensure that the wireless transmission module is protected from moisture, dust, and corrosive gases in the high-temperature environment of the equipment. When measuring temperature over short distances and with low power consumption, Bluetooth, RFID, or ZigBee are selected; when high-speed data transmission is required and network coverage is available, Wi-Fi or 4G / 5G are selected; when long-distance and low-power consumption are required, LoRa is selected.

[0142] In an alternative embodiment, see Figure 5 , is a structural diagram of a non-temperature measurement cavity provided by an embodiment of the present invention, such as Figure 5As shown, the non-temperature measurement cavity 3 includes: a temperature measurement data acquisition module 31, a dynamic thermal radiation interference coefficient calculation model construction module 32, a thermal radiation interference correction module 33, a thermal radiation change frequency correction coefficient calculation module 34, a thermal response time constant calculation module 35 and a thermal inertia lag compensation processing module 36; wherein, the temperature measurement data acquisition module 31 is used to obtain historical temperature measurement data, real-time temperature measurement data and equipment operating parameters of the temperature-measured device; the dynamic thermal radiation interference coefficient calculation model construction module 32 is used to construct a dynamic thermal radiation interference coefficient calculation model based on the historical temperature measurement data and equipment operating parameters; the thermal radiation interference correction module 33 is used to input the real-time temperature measurement data into the dynamic thermal radiation interference coefficient calculation model, determine the dynamic thermal radiation interference coefficient, and correct the real-time temperature measurement data based on the dynamic thermal radiation interference coefficient to obtain corrected temperature measurement data; the thermal radiation change frequency correction coefficient calculation module 34 performs spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient; the thermal response time constant calculation module 35 is used to construct a temperature response function based on the real-time temperature measurement data, and determine the thermal response time constant based on the temperature response function; The thermal inertia lag compensation processing module 36 is used to perform thermal inertia lag compensation processing on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to determine the actual temperature data of the equipment to be measured; specifically, the dynamic thermal radiation interference coefficient calculation model construction module 32 includes: a dynamic thermal radiation interference coefficient calculation model construction unit; the dynamic thermal radiation interference coefficient calculation model construction unit is used to determine the thermal radiation interference coefficient reference value according to a preset thermal radiation simulation method; the historical temperature measurement data includes: historical main sensor temperature measurement values, historical reference sensor temperature measurement values ​​and historical environmental sensor temperature measurement values; the equipment operating parameters include: surface emissivity of the equipment to be measured and surface roughness of the equipment to be measured; a dynamic thermal radiation interference coefficient training set is constructed based on the historical main sensor temperature measurement values, historical reference sensor temperature measurement values, historical environmental sensor temperature measurement values, surface emissivity of the equipment to be measured, surface roughness of the equipment to be measured and thermal radiation interference coefficient reference value; a preset support vector machine model is trained based on the dynamic thermal radiation interference coefficient training set to determine the dynamic thermal radiation interference coefficient calculation model.The dynamic thermal radiation interference coefficient calculation model construction unit includes: a thermal radiation interference coefficient reference value calculation subunit; the thermal radiation interference coefficient reference value calculation subunit is used to construct a thermal radiation simulation environment according to the operating environment of the temperature-measured equipment; based on the thermal radiation simulation environment, the actual temperature of the thermal radiation simulation environment, the temperature measured by the main sensor of the thermal radiation simulation environment, the temperature measured by the reference sensor of the thermal radiation simulation environment and the temperature of the thermal radiation simulation environment are obtained; based on the actual temperature of the thermal radiation simulation environment and the temperature measured by the main sensor of the thermal radiation simulation environment, a first thermal radiation simulation environment measurement difference is calculated; based on the actual temperature of the thermal radiation simulation environment and the temperature measured by the main sensor of the thermal radiation simulation environment, a second thermal radiation simulation environment measurement difference is calculated based on the temperature measured by the reference sensor of the thermal radiation simulation environment and the temperature of the thermal radiation simulation environment; and a thermal radiation interference coefficient reference value is determined based on the first thermal radiation simulation environment measurement difference and the second thermal radiation simulation environment measurement difference. The thermal radiation change frequency correction coefficient calculation module 34 includes: a thermal radiation change frequency correction coefficient calculation unit; the thermal radiation change frequency correction coefficient calculation unit is used to perform spectrum analysis on the real-time temperature measurement data according to a preset fast Fourier transform algorithm to obtain a thermal radiation change frequency distribution spectrum; extract frequency components from the thermal radiation change frequency distribution spectrum to determine the thermal radiation change frequency; fit the thermal radiation change frequency based on a preset fitting algorithm to determine the thermal radiation change frequency correction coefficient. The thermal response time constant calculation module 35 includes: a thermal response time constant calculation unit; the thermal response time constant calculation unit is used to construct a temperature change curve based on the real-time temperature measurement data; construct an initial temperature response function based on a preset least squares method, and construct a temperature error function based on the real-time temperature measurement data and the initial temperature response function; solve the temperature error function based on a preset matrix operation algorithm to obtain a temperature fitting coefficient solution set; reconstruct the initial temperature response function based on the fitting coefficient solution set to obtain a temperature response function; and determine the thermal response time constant based on the temperature response function and the temperature change curve. The thermal response time constant calculation unit includes: a temperature fitting coefficient solution calculation subunit; the temperature fitting coefficient solution subunit is used to calculate the partial derivative of each temperature fitting coefficient in the temperature error function to obtain a temperature fitting coefficient equation group; based on the temperature fitting coefficient equation group, a temperature design matrix, a temperature fitting coefficient vector and a temperature measurement vector are constructed; based on the temperature design matrix, a temperature transposed matrix and a temperature inverse matrix are constructed; based on the temperature transposed matrix, the temperature inverse matrix and the temperature measurement vector, the temperature fitting coefficient vector is solved to obtain a temperature fitting coefficient solution.The thermal inertia lag compensation processing module 36 includes: a thermal inertia lag compensation processing unit; the thermal inertia lag compensation processing unit is used for the equipment operating parameters and also includes: the thermal capacity of the equipment to be measured; determining the thermal capacity temperature change value of the equipment to be measured according to the real-time temperature measurement data and the thermal capacity of the equipment to be measured; determining the real-time thermal radiation change frequency of the equipment to be measured according to the real-time temperature measurement data; determining the thermal radiation temperature change value of the equipment to be measured according to the real-time temperature measurement data, the thermal radiation change frequency correction coefficient and the real-time thermal radiation change frequency of the equipment to be measured; determining the thermal response time temperature change value of the equipment to be measured according to the real-time temperature measurement data and the thermal response time constant; and determining the actual temperature data of the equipment to be measured based on the thermal capacity temperature change value, the thermal radiation temperature change value, the thermal response time temperature change value pair and the corrected temperature measurement data.

[0143] Please refer to Figure 6 , is a structural diagram of another temperature measuring nut provided by an embodiment of the present invention, such as Figure 6 As shown, it includes a nut 41, a first thermal grease layer 42, a wireless transmission module 43, a thermoelectric power generation module 44 and a second thermal grease layer 45; the thermal grease layer 42 is coated on the nut 41, and the wireless transmission module 43 and the thermoelectric power generation module 44 are both arranged on the upper surface of the first thermal grease layer 42; the second thermal grease layer 45 is coated on the upper surface of the wireless transmission module 43 and the thermoelectric power generation module 44.

[0144] Example 4

[0145] A fourth embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a temperature measurement method of an embodiment of the present invention is implemented.

[0146] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0147] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0148] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0149] Based on the above method embodiments, an embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a temperature measurement method described in any one of the above method embodiments of the present invention.

[0150] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0151] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A temperature measurement method, characterized in that: include: Obtain historical temperature measurement data, real-time temperature measurement data and equipment operating parameters of the device to be measured; Constructing a dynamic thermal radiation interference coefficient calculation model based on the historical temperature measurement data and equipment operating parameters; Inputting the real-time temperature measurement data into the dynamic thermal radiation interference coefficient calculation model to determine the dynamic thermal radiation interference coefficient, and correcting the real-time temperature measurement data based on the dynamic thermal radiation interference coefficient to obtain corrected temperature measurement data; Performing spectrum analysis on the real-time temperature measurement data to determine a thermal radiation change frequency correction coefficient; constructing a temperature response function based on the real-time temperature measurement data, and determining a thermal response time constant based on the temperature response function; The corrected temperature measurement data is subjected to thermal inertia lag compensation processing based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operation parameters to determine the actual temperature data of the equipment to be temperature measured.

2. A temperature measurement method according to claim 1, characterized in that: The dynamic thermal radiation interference coefficient calculation model is constructed based on the historical temperature measurement data and equipment operating parameters, specifically including: Determine the reference value of the thermal radiation interference coefficient according to a preset thermal radiation simulation method; The historical temperature measurement data includes: historical main sensor temperature measurement values, historical reference sensor temperature measurement values ​​and historical environmental sensor temperature measurement values; the equipment operation parameters include: surface emissivity of the equipment to be measured and surface roughness of the equipment to be measured; Constructing a dynamic thermal radiation interference coefficient training set based on the historical main sensor temperature measurement values, the historical reference sensor temperature measurement values, the historical environmental sensor temperature measurement values, the surface emissivity of the device to be temperature measured, the surface roughness of the device to be temperature measured, and the thermal radiation interference coefficient reference value; A preset support vector machine model is trained based on the dynamic thermal radiation interference coefficient training set to determine a dynamic thermal radiation interference coefficient calculation model.

3. A temperature measurement method according to claim 2, characterized in that: Determining the thermal radiation interference coefficient reference value according to the preset thermal radiation simulation method specifically includes: Constructing a thermal radiation simulation environment according to the operating environment of the device to be temperature measured; Acquire the actual temperature of the thermal radiation simulation environment, the temperature measured by the main sensor of the thermal radiation simulation environment, the temperature measured by the reference sensor of the thermal radiation simulation environment, and the temperature of the thermal radiation simulation environment based on the thermal radiation simulation environment; Calculating a first thermal radiation simulated environment measurement difference based on the actual temperature of the thermal radiation simulated environment and the temperature measured by the thermal radiation simulated environment main sensor; Calculating a second thermal radiation simulation environment measurement difference based on the thermal radiation simulation environment reference sensor measurement temperature and the thermal radiation simulation environment temperature; A thermal radiation interference coefficient reference value is determined based on the first thermal radiation simulation environment measurement difference and the second thermal radiation simulation environment measurement difference.

4. A temperature measurement method according to claim 2 or 3, characterized in that: The performing spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient specifically includes: Performing spectrum analysis on the real-time temperature measurement data according to a preset fast Fourier transform algorithm to obtain a frequency distribution spectrum of thermal radiation changes; Extracting frequency components from the frequency distribution spectrum of the thermal radiation change to determine the frequency of the thermal radiation change; The thermal radiation variation frequency is fitted based on a preset fitting algorithm to determine a thermal radiation variation frequency correction coefficient.

5. A temperature measurement method according to claim 1, characterized in that: The step of constructing a temperature response function based on the real-time temperature measurement data and determining a thermal response time constant based on the temperature response function specifically includes: Constructing a temperature change curve based on the real-time temperature measurement data; Constructing an initial temperature response function according to a preset least squares method, and constructing a temperature error function according to the real-time temperature measurement data and the initial temperature response function; Solving the temperature error function based on a preset matrix operation algorithm to obtain a temperature fitting coefficient solution set; Reconstructing the initial temperature response function based on the fitting coefficient solution set to obtain a temperature response function; A thermal response time constant is determined based on the temperature response function and the temperature variation curve.

6. A temperature measurement method according to claim 5, characterized in that: Solving the temperature error function based on a preset matrix operation algorithm to obtain a temperature fitting coefficient solution set specifically includes: Performing partial derivative processing on each temperature fitting coefficient in the temperature error function to obtain a temperature fitting coefficient equation group; Constructing a temperature design matrix, a temperature fitting coefficient vector and a temperature measurement vector based on the temperature fitting coefficient equation group; Constructing a temperature transposed matrix and a temperature inverse matrix based on the temperature design matrix; The temperature fitting coefficient vector is solved based on the temperature transposed matrix, the temperature inverse matrix and the temperature measurement vector to obtain a temperature fitting coefficient solution set.

7. A temperature measurement method according to claim 1, characterized in that: The step of performing thermal inertia lag compensation processing on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant, and the device operating parameters to determine the actual temperature data of the device to be temperature measured specifically includes: The equipment operating parameters also include: the heat capacity of the equipment to be measured; Determine the temperature change value of the heat capacity of the device to be measured according to the real-time temperature measurement data and the heat capacity of the device to be measured; Determining the real-time thermal radiation change frequency of the device to be temperature measured according to the real-time temperature measurement data; Determine the thermal radiation temperature change value of the device to be measured according to the real-time temperature measurement data, the thermal radiation change frequency correction coefficient and the real-time thermal radiation change frequency of the device to be measured; Determine the thermal response time temperature change value of the device to be temperature measured according to the real-time temperature measurement data and the thermal response time constant; Based on the heat capacity temperature change value, the heat radiation temperature change value, the thermal response time temperature change value pair and the corrected temperature measurement data, the actual temperature data of the device to be temperature measured is determined.

8. A temperature measuring nut, comprising: The nut body is characterized in that it is used to implement a temperature measurement method as described in any one of claims 1 to 7 above, and the temperature measuring nut further includes: a temperature measuring cavity and a non-temperature measuring cavity; The non-temperature measuring cavity and the temperature measuring cavity are both arranged on the outer surface of the nut; The temperature data output terminal of the temperature measuring cavity is electrically connected to the temperature data input terminal of the non-temperature measuring cavity; The temperature measurement cavity is used to obtain real-time temperature measurement data of the device to be measured; The non-temperature measurement cavity is used to obtain historical temperature measurement data and equipment operating parameters of the device to be temperature measured, and receive real-time temperature measurement data collected by the temperature measurement cavity; The non-temperature measurement cavity is further used to construct a dynamic thermal radiation interference coefficient calculation model based on the historical temperature measurement data and equipment operating parameters, input the real-time temperature measurement data into the dynamic thermal radiation interference coefficient calculation model, determine the dynamic thermal radiation interference coefficient, and correct the real-time temperature measurement data based on the dynamic thermal radiation interference coefficient to obtain corrected temperature measurement data; The non-temperature measurement cavity is also used to perform spectrum analysis on the real-time temperature measurement data to determine the thermal radiation change frequency correction coefficient; construct a temperature response function based on the real-time temperature measurement data, and determine the thermal response time constant based on the temperature response function; perform thermal inertia lag compensation processing on the corrected temperature measurement data based on the thermal radiation change frequency correction coefficient, the thermal response time constant and the equipment operating parameters to determine the actual temperature data of the equipment to be measured.

9. A temperature measuring nut according to claim 8, characterized in that: The temperature measurement cavity includes: a temperature measurement module; the temperature measurement module includes several layers of concentric cylinders, a main sensor and several reference sensors; The main sensor is arranged at the center of the innermost concentric cylinder and is in contact with the inner wall of the temperature measurement cavity; The reference sensors are evenly arranged in each layer of concentric cylinders except the innermost layer, and the number of reference sensors in each layer of concentric cylinders except the innermost layer is the same; The inner wall of the innermost concentric cylinder is provided with a metal shielding layer; and the concentric cylinders are filled with thermal conductive silicone grease.

10. The temperature measuring nut according to claim 8, characterized in that: The surface of the non-temperature measurement cavity is provided with a plurality of heat insulation structures; The thermal insulation structure comprises: a nano-silica aerogel thermal insulation coating, a nano-silica thermal insulation felt, a silver-plated copper foil thermal insulation reflective layer, a high-temperature resistant aluminum silicate fiber layer and a silicone rubber thermal insulation sleeve; Each insulation structure is bonded together using high-temperature ceramic adhesive.

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