Fixed endoscopic illumination type remote monitoring system in high-temperature industrial environment

Abstract

The invention provides a fixed endoscopic illumination type remote monitoring system in a high-temperature industrial environment, which comprises the following steps of: testing a standard sample under a small temperature difference condition by using high-temperature industrial environment monitoring equipment, and fitting to obtain a coefficient for representing a function relationship between the temperature and the thermal radiation rate of the standard sample; the method comprises the following steps: testing a standard sample under a large temperature difference condition by using a fixed endoscopic illumination type remote monitoring system to obtain weight coefficients of a plurality of high-temperature sensors in a heat surface center metering area of the sample; and testing the to-be-tested sample by using the fixed endoscopic illumination type remote monitoring system, and correcting the temperature measurement values of the plurality of high-temperature sensors in the heat surface center metering area of the to-be-tested sample based on the weight coefficient to obtain the corrected heat surface temperature. According to the invention, the temperature measurement precision and reliability under the extremely high temperature condition can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-temperature industrial environment monitoring, and more specifically, to a fixed endoscopic illumination remote monitoring system under a high-temperature industrial environment. Background Art

[0002] In the prior art, monitoring of high-temperature industrial environments usually relies on traditional temperature measurement equipment and methods, which are often limited in accuracy and reliability under extreme high temperature conditions. Due to the complexity of factors such as thermal radiation, heat conduction and heat convection in high-temperature environments, it is difficult for traditional temperature measurement technology to accurately reflect the actual temperature distribution. Especially in fixed endoscopic illumination remote monitoring systems, since the sensors are affected by the high-temperature environment, their measurement results often have large errors, which makes it difficult to meet the high requirements for temperature control accuracy in industrial production.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the temperature measurement of the standard sample in a high temperature environment often needs to rely on a complex calibration process, and the existing monitoring system has insufficient temperature measurement accuracy under large temperature difference conditions, resulting in the inability to accurately evaluate the performance of the material at high temperature. In addition, the prior art lacks an effective correction method to compensate for the measurement deviation of the high temperature sensor in extreme environments, which limits the widespread use of the monitoring system in actual industrial applications. Summary of the invention

[0004] The present invention provides a fixed endoscope illumination remote monitoring system in a high temperature industrial environment, comprising:

[0005] Step S1: using high temperature industrial environment monitoring equipment to test the standard sample under small temperature difference conditions, and fitting to obtain the coefficient that characterizes the functional relationship between the temperature of the standard sample and the thermal emissivity;

[0006] Step S2: using a fixed endoscopic illumination remote monitoring system to test the standard sample under large temperature difference conditions, based on the heat radiation density of the standard sample through the thickness direction of the standard sample at different hot surface center temperatures, the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center metering area, the temperature measurement values ​​of multiple high-temperature sensors in the cold surface center metering area and the coefficient, obtain the weight coefficient of multiple high-temperature sensors in the hot surface center metering area of ​​the sample;

[0007] Step S3: testing the sample to be tested using a fixed endoscopic illumination remote monitoring system, and correcting the temperature measurement values ​​of multiple high-temperature sensors in the central metering area of ​​the hot surface of the sample to be tested based on the weight coefficient to obtain a corrected hot surface temperature.

[0008] Furthermore, in step S1, the functional relationship between the standard sample temperature and the thermal emissivity is:

[0009]

[0010] Where, T represents the standard sample temperature at the i-th test, i = 1, 2, ..., P; P represents the number of tests; ρ(T i ) indicates that at temperature T i Thermal radiation rate at time; a0, a1, a2, ..., a M is the coefficient, M≤P.

[0011] Furthermore, based on the collected P standard sample temperature and thermal emissivity data, the coefficients a0, a1, a2, ..., a are obtained by least squares fitting. M .

[0012] Furthermore, the step S2 comprises:

[0013] Step S21: Based on the heat radiation density of the standard sample through the thickness direction of the standard sample at different hot surface center temperatures, the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center metering area, the temperature measurement values ​​of multiple high-temperature sensors in the cold surface center metering area and the coefficient, the average temperature of the hot surface of the sample at the corresponding hot surface center temperature is obtained:

[0014]

[0015] Where N is the total number of high-temperature sensors in the central measurement area of ​​the hot surface; T j，k Represents the temperature measurement value of the kth high temperature sensor during the jth test.

[0016] Step S22: Based on the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center measurement area of ​​the standard sample at different hot surface center point temperatures and the average temperature of the hot surface of the sample, the weight coefficients of multiple high-temperature sensors in the hot surface center measurement area of ​​the sample are obtained:

[0017]

[0018] Among them, w k Represents the weight coefficient of the kth high temperature sensor.

[0019] Furthermore, the average value of the cold surface temperature of the standard sample is obtained by averaging the temperature measurement values ​​of multiple high-temperature sensors in the central metering area of ​​the cold surface.

[0020] Furthermore, when the central metering area is a square, high temperature sensors are arranged at the following positions of the hot surface and the cold surface: the center point of the sample, the four vertices of the central metering area, and the midpoints of the four sides of the central metering area.

[0021] Furthermore, the cross-sectional dimensions of the sample are 300 mm×300 mm, and the central measuring area is an area of ​​100 mm×100 mm in the center of the sample.

[0022] Furthermore, the small temperature difference means that the temperature difference between the hot surface and the cold surface of the standard sample does not exceed 30°C.

[0023] Furthermore, when the standard sample is tested under small temperature difference conditions using high temperature industrial environment monitoring equipment, the temperature of the standard sample does not exceed 600°C.

[0024] Furthermore, the large temperature difference means that the temperature difference between the hot surface and the cold surface of the standard sample exceeds 50°C, and the center point temperature of the cold surface of the standard sample is maintained at 30°C±10°C.

[0025] According to the above-mentioned embodiment of the present invention, at least the following beneficial effects are achieved: the temperature measurement correction method of the fixed endoscope illumination remote monitoring system in the high-temperature industrial environment can significantly improve the accuracy of temperature measurement under extreme high temperature conditions through standardized test procedures and accurate coefficient fitting. By fitting the functional relationship between the temperature and thermal emissivity of the standard sample using the least squares method, a more accurate coefficient can be obtained, and then the measured value of the sensor can be effectively corrected under large temperature difference conditions to ensure the reliability of the measurement results. In addition, the method can further optimize the temperature measurement process by testing the standard sample at different hot surface center temperatures and calculating the weight coefficients of multiple high-temperature sensors in the center measurement area of ​​the sample hot surface according to the test results. This method can not only reduce the errors caused by environmental factors, but also improve the adaptability and flexibility of the monitoring system, so that it can better serve the monitoring needs of the high-temperature industrial environment and provide more accurate and reliable temperature data support for industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0027] Figure 1 A flow chart of a temperature measurement correction method for a fixed endoscopic illumination remote monitoring system in a high-temperature industrial environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0029] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0030] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0031] Reference below Figure 1 , Figure 1 A flow chart of a temperature measurement correction method for a fixed endoscope illumination remote monitoring system in a high-temperature industrial environment provided by an embodiment of the present invention. Figure 1 As shown, the fixed endoscope illumination remote monitoring system 100 in a high temperature industrial environment includes:

[0032] Step S1: using high temperature industrial environment monitoring equipment to test the standard sample under small temperature difference conditions, and fitting to obtain the coefficient that characterizes the functional relationship between the temperature of the standard sample and the thermal emissivity;

[0033] Step S2: using a fixed endoscopic illumination remote monitoring system to test the standard sample under large temperature difference conditions, based on the heat radiation density of the standard sample through the thickness direction of the standard sample at different hot surface center temperatures, the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center metering area, the temperature measurement values ​​of multiple high-temperature sensors in the cold surface center metering area and the coefficient, obtain the weight coefficient of multiple high-temperature sensors in the hot surface center metering area of ​​the sample;

[0034] Step S3: testing the sample to be tested using a fixed endoscopic illumination remote monitoring system, and correcting the temperature measurement values ​​of multiple high-temperature sensors in the central metering area of ​​the hot surface of the sample to be tested based on the weight coefficient to obtain a corrected hot surface temperature.

[0035] It should be noted that the temperature measurement correction method is mainly to solve the error problem that may occur when the fixed endoscope illumination remote monitoring system performs temperature measurement in a high-temperature industrial environment. The method uses the test data of the standard sample through a series of steps to correct the temperature measurement value of the actual sample to be tested to improve the accuracy of the measurement.

[0036] Specifically, the test of the standard sample under small temperature difference conditions using high temperature industrial environment monitoring equipment mentioned in step S1 refers to the measurement of temperature and thermal emissivity of the standard sample using specific monitoring equipment in a controlled high temperature environment. The small temperature difference condition here means that the temperature difference between the hot surface and the cold surface of the sample does not exceed 30°C. Fitting the coefficients that characterize the functional relationship between the temperature and thermal emissivity of the standard sample involves using a mathematical model to describe the relationship between temperature and thermal emissivity for subsequent data processing and correction.

[0037] Preferably, in step S2, the operation of obtaining the thermal radiation density of the standard sample through the thickness direction of the standard sample at different temperatures of the center point of the hot surface can be further refined. Specifically, multiple high-temperature sensors can be arranged at different positions of the sample, such as the center point of the sample, the four vertices of the central measurement area, and the midpoints of the four edges to obtain more comprehensive thermal radiation data. In addition, the measurement accuracy and sampling frequency of the sensor can be set to ensure the quality and reliability of the data. In the calculation of the weight coefficient, the weighted least square method can also be used to further improve the accuracy of the correction.

[0038] More specifically, the testing of the sample to be tested using the fixed endoscopic illumination remote monitoring system in step S3 can be further optimized. For example, the layout and measurement parameters of the sensor can be adjusted according to the characteristics of the sample to be tested and the specific conditions of the monitoring environment. At the same time, a machine learning algorithm can be introduced to dynamically adjust the weight coefficient to adapt to different test conditions and improve the adaptability of the correction. In addition, the use of different types of sensors or monitoring equipment can also be considered to provide more measurement options and improve the flexibility of the system.

[0039] In some embodiments, in step S1, the functional relationship between the standard sample temperature and thermal emissivity is:

[0040]

[0041] Where, T represents the standard sample temperature at the i-th test, i = 1, 2, ..., P; P represents the number of tests; ρ(T i ) indicates that at temperature T i Thermal radiation rate at time; a0, a1, a2, ..., a M is the coefficient, M≤P.

[0042] It should be noted that the functional relationship is a polynomial model used to fit the mathematical relationship between the temperature and thermal emissivity of the standard sample. This model can capture the complex nonlinear relationship between temperature and thermal emissivity and provide an accurate mathematical basis for temperature measurement correction.

[0043] Specifically, the coefficients a0, a1, a2, ..., a in the functional relationship between the standard sample temperature and the thermal emissivity are M , representing the different orders of the polynomial. In practical applications, these coefficients can be obtained by measuring the thermal emissivity of the standard sample at different temperatures and fitting it using the least squares method. For example, a0 can be regarded as a constant term, while a1 represents the linear term coefficient, a2 represents the quadratic term coefficient, and so on. The specific values ​​of the coefficients depend on the physical properties of the standard sample and the test conditions.

[0044] Preferably, in order to improve the accuracy of the fitting, the highest order of the polynomial may not exceed 5 to avoid overfitting. In actual testing, an appropriate test temperature range and test number P may be selected according to the thermal radiation characteristics of the standard sample to ensure the representativeness and reliability of the data. For example, 10 tests may be performed in the range of 100°C to 500°C to obtain sufficient data points for fitting. In addition, in order to improve the stability of the fitting, a regularization method, such as ridge regression or LASSO, may be used to adjust the coefficient estimate.

[0045] In some embodiments, based on the collected P standard sample temperature and thermal emissivity data, the coefficients a0, a1, a2, ..., a are obtained by least squares fitting. M .

[0046] It should be noted that this step involves using the test data of multiple standard samples collected to determine the coefficient of the functional relationship between temperature and thermal emissivity through the least square method. This requires mathematical processing of the data to ensure that the obtained coefficient can most accurately reflect the relationship between the two.

[0047] Specifically, the least squares method is a commonly used mathematical fitting technique used to determine the linear relationship between multiple variables. In this process, it is necessary to collect the thermal emissivity data of standard samples at different temperatures, and use these data as input to calculate the optimal coefficients a0, a1, a2, ..., a M These coefficients will be used to describe the functional relationship between temperature and thermal emissivity, where a0 is the intercept and a1, a2, ..., a M is the slope, which represents the changing trend of thermal emissivity at different temperatures.

[0048] Preferably, in order to improve the accuracy of the fitting, software tools can be used for data processing, such as the NumPy library in MATLAB or Python. When setting parameters, the frequency of data acquisition and the number of tests can be determined according to the actual test conditions and equipment accuracy. For example, it can be set to collect data every 5°C, and a total of 20 tests can be performed to cover the entire temperature range from room temperature to high temperature. In addition, statistical methods such as cross-validation can also be considered to evaluate the robustness of the fitting model to ensure that accurate prediction results can be obtained under different temperature conditions.

[0049] In some embodiments, step S2 includes:

[0050] Step S21: Based on the heat radiation density of the standard sample through the thickness direction of the standard sample at different hot surface center temperatures, the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center metering area, the temperature measurement values ​​of multiple high-temperature sensors in the cold surface center metering area and the coefficient, the average temperature of the hot surface of the sample at the corresponding hot surface center temperature is obtained:

[0051]

[0052] Where N is the total number of high-temperature sensors in the central measurement area of ​​the hot surface; T j，k Represents the temperature measurement value of the kth high temperature sensor during the jth test.

[0053] Step S22: Based on the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center measurement area of ​​the standard sample at different hot surface center point temperatures and the average temperature of the hot surface of the sample, the weight coefficients of multiple high-temperature sensors in the hot surface center measurement area of ​​the sample are obtained:

[0054]

[0055] Among them, w k Represents the weight coefficient of the kth high temperature sensor.

[0056] It should be noted that this step is about how to use a fixed endoscope illumination remote monitoring system to test standard samples under different temperature conditions and calculate the temperature average and weight coefficient based on these test results. This involves the collection, processing and analysis of sensor data to ensure the accuracy and reliability of the measurement results.

[0057] Specifically, step S2 includes two sub-steps S21 and S22. In step S21, it is necessary to collect the thermal radiation density data of the standard sample at different hot surface center temperatures, as well as the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center metering area and the temperature measurement values ​​of multiple high-temperature sensors in the cold surface center metering area. These data will be used to calculate the average temperature of the hot surface of the sample at the hot surface center temperature, and the formula is:

[0058]

[0059] Where N is the total number of high temperature sensors in the central metering area of ​​the hot surface, T j，k Represents the temperature measurement value of the kth high temperature sensor during the jth test.

[0060] In step S22, based on the temperature measurement value and the temperature average value obtained in step S21, the weight coefficients of multiple high-temperature sensors in the central measurement area of ​​the hot surface of the sample are calculated, and the formula is:

[0061]

[0062] Among them, w k Represents the weight coefficient of the kth high temperature sensor.

[0063] Preferably, in order to improve the accuracy and repeatability of the measurement, high-precision sensors and data acquisition systems can be used. For example, high-temperature sensors with high resolution and fast response time, as well as acquisition systems that can process large amounts of data in real time, can be selected. In addition, when laying out the sensors, it should be ensured that the sensors are evenly distributed in the central metering area of ​​the hot and cold surfaces to obtain representative temperature measurements. When calculating the weight coefficient, it is possible to consider introducing error analysis to evaluate the impact of different sensors on the overall measurement accuracy, and adjust the sensor layout or parameter settings as needed.

[0064] In some embodiments, the average value of the cold surface temperature of the standard sample is obtained by averaging the temperature measurement values ​​of multiple high-temperature sensors in the central metering area of ​​the cold surface.

[0065] It should be noted that the average value of the cold surface temperature of the standard sample refers to the temperature distribution of the standard sample in the cold surface area during the temperature measurement correction method, and a representative average temperature value is calculated based on this. This average temperature value is crucial for understanding and correcting the temperature measurement value of the hot surface center measurement area.

[0066] Specifically, the step of calculating the average cold surface temperature involves arranging multiple high temperature sensors in the central metering area of ​​the cold surface of the standard specimen. These sensors are evenly distributed in the cold surface area to ensure that the temperature information of the entire area can be accurately captured. The number of sensors can be determined according to the size of the specimen and the required measurement accuracy. For example, five sensors can be arranged in the four corners and the center of the central metering area of ​​the cold surface. Then, the temperature readings of these sensors under the same test conditions are collected, and their arithmetic average is calculated as the average value of the cold surface temperature.

[0067] Preferably, in order to improve the accuracy and reliability of the measurement, a high temperature sensor with high resolution and fast response time can be selected. In addition, the sensor can be calibrated before testing to eliminate possible system errors. In the actual measurement process, a data acquisition system can be used to record the temperature readings of the sensor in real time, and professional data analysis software can be used to calculate the average value and evaluate the consistency of the data. If it is found that the readings of a certain sensor are significantly different from those of other sensors, the sensor can be further checked or replaced to ensure the accuracy of the final calculated average temperature value.

[0068] In some embodiments, when the central metering area is a square, high temperature sensors are arranged at the following positions on the hot surface and the cold surface: the center point of the sample, the four vertices of the central metering area, and the midpoints of the four sides of the central metering area.

[0069] It should be noted that the central metering area refers to a specific area in the sample used for temperature measurement. Usually, the size and shape of this area will be determined according to the actual monitoring needs. In the present claims, the central metering area is defined as a square with a side length of 100mm×100mm in the center of the sample. High-temperature sensors need to be arranged at specific positions on the hot and cold surfaces, including the center point of the sample, the four vertices of the central metering area, and the midpoints of the four sides. Such an arrangement ensures that temperature changes from the center to the edge can be monitored.

[0070] Preferably, in order to further improve the accuracy and reliability of monitoring, it is possible to consider arranging sensors on the diagonal of the central metering area to monitor the temperature gradient. In addition, the arrangement density of the sensors can be adjusted according to the specific size of the sample and the accuracy requirements of the monitoring. For example, if the size of the sample is larger, more sensors may need to be arranged in a larger central metering area. At the same time, it is also possible to consider using different types of sensors, such as fiber optic sensors or thermocouples, to adapt to different measurement environments and accuracy requirements. In practical applications, the arrangement of sensors can also be optimized according to the thermal conductivity characteristics of the sample and the expected heat distribution pattern.

[0071] In some embodiments, the cross-sectional dimensions of the sample are 300 mm×300 mm, and the central metering area is an area of ​​100 mm×100 mm in the center of the sample.

[0072] It should be noted that the purpose of arranging high-temperature sensors is to obtain uniform and accurate temperature readings in the central measurement area of ​​the specimen. Placing sensors at the center points of the hot and cold surfaces, the four vertices, and the midpoints of the four edges ensures that the temperature data obtained from different locations can reflect the temperature distribution of the entire central measurement area. The selection of these locations helps to reduce the impact of local temperature anomalies on the overall measurement results.

[0073] Preferably, in order to further optimize the layout of the sensors, sensors with higher precision and stability can be selected to improve the accuracy of temperature measurement. For example, thermocouples or thermistors with fast response time and high resolution can be selected as sensors. In addition, the layout of the sensors can also be considered, such as whether a symmetrical layout is adopted to reduce installation errors, or whether insulation materials need to be added between sensors to prevent cross-effects of heat. It is also possible to consider using a sensor array and comprehensively analyzing the collected data through software algorithms to obtain a more accurate temperature field distribution map.

[0074] In some embodiments, the small temperature difference means that the temperature difference between the hot surface and the cold surface of the standard sample does not exceed 30°C.

[0075] It should be noted that small temperature difference means that when conducting standard sample testing, the temperature difference between the hot and cold surfaces of the sample is relatively small, which helps to evaluate and calibrate the monitoring system under relatively mild conditions.

[0076] Specifically, the concept of small temperature difference here refers to a temperature range, that is, a temperature difference of no more than 30°C. This setting allows the system to be initially calibrated and tested without extreme temperature changes, thereby ensuring that the system can provide more accurate measurement results in subsequent high-temperature tests. In terms of parameter settings, the maximum temperature of the hot surface can be set to no more than 600°C to ensure that neither the specimen nor the monitoring equipment will be damaged when testing under small temperature difference conditions.

[0077] Preferably, in order to ensure the accuracy of the test and the stability of the system, the test steps under small temperature difference conditions can be further refined. For example, a specific test process can be set, including stages such as preheating, constant temperature and data acquisition of the sample. In the preheating stage, the sample can be heated to a temperature close to but not exceeding 600°C and maintained for a period of time to ensure uniform temperature. In the constant temperature stage, the sample is maintained within a set temperature range for continuous data acquisition. In addition, multiple sensors can be used to monitor the temperature at different locations to ensure the representativeness and reliability of the test results. As an alternative, computer simulation can be considered to predict the measurement results under different temperature difference conditions to assist the actual testing process.

[0078] In some embodiments, when a standard sample is tested under small temperature difference conditions using high temperature industrial environment monitoring equipment, the temperature of the standard sample does not exceed 600°C.

[0079] It should be noted that the test under the condition of large temperature difference means that the temperature difference between the hot and cold surfaces of the standard sample exceeds 50°C, and the center point temperature of the cold surface of the standard sample is required to be maintained within a relatively stable range. This condition is set to simulate the extreme temperature conditions that may be encountered in actual industrial environments to ensure that the monitoring system can still provide accurate temperature measurement under these conditions.

[0080] Specifically, large temperature difference testing can be achieved through a specific temperature control system that can accurately control the temperature of the hot and cold surfaces of the standard specimen to ensure that the temperature difference exceeds 50°C. For example, a high-temperature furnace can be used to heat the hot surface of the specimen, while a cooling system is used to maintain the temperature of the cold surface. In this process, specific temperature parameters need to be set, such as the hot surface temperature is set to 650°C, while the center point temperature of the cold surface is controlled at 30°C ± 10°C. These parameters can be adjusted according to specific industrial applications and material properties.

[0081] Preferably, in order to ensure the accuracy and repeatability of the test, an automated temperature control system can be used, which can monitor and adjust the temperature of the hot and cold surfaces of the sample in real time. In addition, a high-precision temperature sensor can be used to obtain temperature data, which can be recorded and analyzed by a data acquisition system. During the implementation process, it is also possible to consider using different types of standard specimens to evaluate the applicability of the monitoring system under different material properties, or using different temperature difference ranges to test the adaptability and stability of the monitoring system.

[0082] In some embodiments, the large temperature difference means that the temperature difference between the hot surface and the cold surface of the standard sample exceeds 50°C, and the center point temperature of the cold surface of the standard sample is maintained at 30°C±10°C.

[0083] It should be noted that the large temperature difference condition refers to the temperature difference between the hot and cold surfaces of the standard sample exceeding 50°C. Testing under this condition can simulate extreme industrial environments and ensure that the monitoring system can adapt to large temperature changes in actual applications. At the same time, the center point temperature of the cold surface of the standard sample is maintained at 30°C ± 10°C, which provides a temperature benchmark for the test and helps reduce the impact of ambient temperature fluctuations on the measurement results.

[0084] Preferably, in order to ensure the accuracy and repeatability of the test, a constant temperature control device can be used to maintain the center point temperature of the cold surface of the standard sample. For example, a precise temperature controller and a thermocouple sensor can be used to monitor and adjust the temperature to ensure that it is within the specified range. In addition, in order to adapt to different test requirements, an adjustable test platform can be designed to allow testing under different temperature difference conditions while maintaining the stability of the center point temperature of the cold surface. In actual operation, it is also possible to consider using data logging software to track and analyze temperature changes during the test process in order to further optimize the test conditions.

[0085] The above-mentioned embodiments of the present invention have the following beneficial effects: the temperature measurement correction method of the fixed endoscope illumination remote monitoring system under the high-temperature industrial environment can significantly improve the temperature measurement accuracy under extreme high temperature conditions through a series of precise test steps and coefficient fitting. This method can ensure that reliable temperature data can be obtained even under large temperature difference conditions through the application of standardized test procedures and weight coefficients, thereby enhancing the stability and accuracy of the monitoring system. Furthermore, the method can optimize the measurement value of the sensor and reduce the error caused by the high temperature environment by fitting the relationship between temperature and thermal emissivity through the least squares method. In addition, by testing the standard sample under different temperature difference conditions, the weight coefficient can be determined more accurately, and then the temperature measurement value of the sample to be tested can be corrected. The application of this method can improve the accuracy of temperature control in the industrial production process, thereby ensuring product quality and production safety.

[0086] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0087] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. Fixed endoscopic illumination remote monitoring system in high temperature industrial environment, characterized by: The method comprises the following steps: Step S1: using high temperature industrial environment monitoring equipment to test the standard sample under small temperature difference conditions, and fitting to obtain the coefficient that characterizes the functional relationship between the temperature of the standard sample and the thermal emissivity; Step S2: using a fixed endoscopic illumination remote monitoring system to test the standard sample under large temperature difference conditions, based on the heat radiation density of the standard sample through the thickness direction of the standard sample at different hot surface center temperatures, the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center metering area, the temperature measurement values ​​of multiple high-temperature sensors in the cold surface center metering area and the coefficient, obtain the weight coefficient of multiple high-temperature sensors in the hot surface center metering area of ​​the sample; Step S3: testing the sample to be tested using a fixed endoscopic illumination remote monitoring system, and correcting the temperature measurement values ​​of multiple high-temperature sensors in the central metering area of ​​the hot surface of the sample to be tested based on the weight coefficient to obtain a corrected hot surface temperature.

2. The method according to claim 1, characterized in that In step S1, the functional relationship between the standard sample temperature and thermal emissivity is: Where, T represents the standard sample temperature at the i-th test, i = 1, 2, ..., P; P represents the number of tests; ρ(T i ) indicates that at temperature T i Thermal radiation rate at time; a0, a1, a2, ..., a M is the coefficient, M≤P.

3. The method according to claim 2, characterized in that Based on the collected P standard sample temperature and thermal emissivity data, the coefficients a0, a1, a2, ..., a are obtained by least squares fitting. M .

4. The method according to claim 3, characterized in that The step S2 includes Step S21: Based on the heat radiation density of the standard sample through the thickness direction of the standard sample at different hot surface center temperatures, the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center metering area, the temperature measurement values ​​of multiple high-temperature sensors in the cold surface center metering area and the coefficient, the average temperature of the hot surface of the sample at the corresponding hot surface center temperature is obtained: Where N is the total number of high-temperature sensors in the central measurement area of ​​the hot surface; T j，k Represents the temperature measurement value of the kth high temperature sensor during the jth test. Step S22: Based on the temperature measurement values ​​of multiple high-temperature sensors in the hot surface center measurement area of ​​the standard sample at different hot surface center point temperatures and the average temperature of the hot surface of the sample, the weight coefficients of multiple high-temperature sensors in the hot surface center measurement area of ​​the sample are obtained: Among them, w k Represents the weight coefficient of the kth high temperature sensor.

5. The method according to claim 4, characterized in that The average value of the cold surface temperature of the standard sample is obtained by averaging the temperature measurement values ​​of multiple high-temperature sensors in the central metering area of ​​the cold surface.

6. The method according to any one of claims 1 to 5, characterized in that When the central metering area is a square, high temperature sensors are arranged at the following positions on the hot surface and the cold surface: the center point of the sample, the four vertices of the central metering area, and the midpoints of the four sides of the central metering area.

7. The method according to any one of claims 1 to 6, characterized in that The cross-sectional dimensions of the sample are 300 mm×300 mm, and the central measuring area is an area of ​​100 mm×100 mm in the center of the sample.

8. The method according to any one of claims 1 to 7, characterized in that The small temperature difference means that the temperature difference between the hot surface and the cold surface of the standard sample does not exceed 30°C.

9. The method according to any one of claims 1 to 8, characterized in that When the standard sample is tested under small temperature difference conditions using high temperature industrial environment monitoring equipment, the temperature of the standard sample shall not exceed 600°C.

10. The method according to any one of claims 1 to 9, characterized in that The large temperature difference means that the temperature difference between the hot surface and the cold surface of the standard sample exceeds 50°C, and the center point temperature of the cold surface of the standard sample is maintained at 30°C±10°C.

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