A dynamic compensation processing method and system for thermocouple signals

Abstract

The present application provides a method and system for dynamic compensation processing of thermocouple signals. Among them, the method collects the response delay data of the thermocouple at different heating rates, synchronously obtains the surface temperature change data of the measured object, establishes a correlation model between the two, and forms a compensation database. Subsequently, a heat dissipation device is set up, and the heat transfer path is analyzed according to the temperature distribution data to generate a thermal resistance distribution map; based on the distribution characteristics of the thermal resistance peak area, an optimized heat dissipation channel is constructed, and the dynamic response signal of the thermocouple is further aligned with the temperature data in the time domain, and the frequency domain is decomposed to separate the interference signal. Finally, the contact point resistance parameters of the thermal resistance distribution map and the heat dissipation channel adjustment parameters are combined to reconstruct the dynamic compensation coefficient, and closed-loop iterative correction is performed to achieve dynamic compensation optimization. The present application significantly improves the measurement accuracy and response efficiency of thermocouples in dynamic temperature fields through heat conduction parameter modeling, heat dissipation channel optimization and closed-loop signal correction.

Description

Technical Field

[0001] The present application relates to the technical field of dynamic compensation processing, and in particular to a dynamic compensation processing method and system for thermocouple signals. Background Art

[0002] In the field of high-temperature transient temperature testing, such as dynamic thermal shock testing of aircraft engine combustion chambers and aerodynamic thermal environment monitoring of ultra-high-speed aircraft surfaces, thermocouple dynamic compensation technology faces three core requirements. First, it is necessary to achieve dynamic response error correction within the frequency band of 0.1 to 5 kHz for transient shocks with a temperature change rate of 100,000 degrees Celsius per second. Secondly, in high-temperature environments exceeding 1500 degrees Celsius and under strong electromagnetic interference conditions, the compensation accuracy must be stably controlled within an error range of 0.5%, effectively solving the phase lag and amplitude attenuation problems caused by the thermal inertia of the sensor. Finally, the compensation model is required to have the ability to adapt to multi-physical field coupling and be able to simultaneously handle thermal conduction delays, material nonlinear temperature drifts, and mechanical vibration noise interference induced by shock waves.

[0003] The current mainstream technology utilizes a dynamic compensation filter architecture driven by an improved whale optimization algorithm. This solution optimizes the compensator transfer function through an elite individual guidance mechanism combined with a dynamic chaotic weighting factor. The system uses the thermocouple's measured temperature signal as the compensator input and the original true temperature signal as the desired output. It utilizes an adaptive inertia weight adjustment strategy to optimize the population search direction and introduces a dynamic chaotic perturbation mechanism to overcome local optimal solution limitations. This technology can extend the compensator's passband from the original sensor's 1.83 Hz to 52.3 Hz, compressing the time constant from 2.3 to 4.06 seconds to 0.05 milliseconds.

[0004] This solution has three major technical flaws. First, the compensation model uses a fixed parameter configuration. When the test environment temperature undergoes a step change exceeding 300 degrees Celsius, the compensation filter will produce overshoot distortion of 7% to 12%. Second, when the optimization algorithm processes parameter spaces with dimensions exceeding 8, the rapid decay of population diversity can easily lead to premature convergence, directly affecting the stability of the compensation accuracy. Finally, a decoupling mechanism for mechanical vibration noise and heat source signals has not been established. When the impact load exceeds 500g acceleration, parasitic potential interference can introduce a measurement deviation of 3 to 5 microvolts, seriously affecting the compensation effect. Summary of the Invention

[0005] The present application provides a method and system for dynamic compensation processing of thermocouple signals, which are used to solve the problem of poor compensation effect in the prior art.

[0006] In a first aspect, the present application provides a method for dynamic compensation processing of thermocouple signals, comprising:

[0007] Collect response delay data of thermocouples at different heating rates, synchronously obtain temperature change data of the surface of the measured object, establish a correlation between the response delay data and the temperature change data, and couple the correlation with the heat conduction efficiency parameter to form a compensation database of multiple characteristic parameters;

[0008] Setting a heat dissipation device on the surface of the object to be measured, obtaining temperature distribution data of the object to be measured, analyzing the heat transfer path based on the temperature distribution data and generating a thermal resistance distribution map;

[0009] According to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram, the heat dissipation power and distribution spacing of the heat dissipation device are adjusted to form an optimized heat dissipation channel, and the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel is updated to the compensation database;

[0010] Aligning the dynamic response signal output by the thermocouple with the temperature change data in the time domain, performing frequency domain decomposition on the dynamic response signal using multiple parameters in the compensation database, and separating the interference signal component caused by the thermal capacitance characteristics of the compensation wire;

[0011] Combined with the wire contact point resistance parameters extracted from the thermal resistance distribution map and the compensation adjustment parameters of the optimized heat dissipation channel, the interference signal component is reconstructed in the frequency domain to generate a dynamic compensation coefficient, and the dynamic compensation coefficient is corrected by closed-loop iterative correction based on the historical compensation error data recorded in the compensation database.

[0012] Optionally, the dynamic response signal output by the thermocouple is aligned with the temperature change data in the time domain, and the dynamic response signal is decomposed in the frequency domain using multiple parameters in the compensation database to separate the interference signal component caused by the thermal capacitance characteristics of the compensation wire, including:

[0013] Align the time axis starting point of the dynamic response signal output by the thermocouple with the time axis starting point of the temperature change data, and then segment the two aligned signals according to a fixed time window;

[0014] Based on the time window boundary after segmented interception, dynamically scaling the waveform amplitude of the dynamic response signal according to multiple parameters in the compensation database to generate a standardized dynamic signal;

[0015] The standardized dynamic signal is decomposed in the frequency domain to extract the amplitude distribution characteristics of each frequency band, and signal components that meet the conditions are screened out and marked as interference signal components caused by the thermal capacitance characteristics of the compensation wire.

[0016] Optionally, combining the wire contact point resistance parameter extracted from the thermal resistance distribution map and the compensation adjustment parameter of the optimized heat dissipation channel, reconstructing the interference signal component in the frequency domain to generate a dynamic compensation coefficient, and performing closed-loop iterative correction on the dynamic compensation coefficient based on the historical compensation error data recorded in the compensation database, including:

[0017] Extracting the coordinate position of the contact point between the compensation wire and the measured object from the thermal resistance distribution map, and obtaining the thermal resistance value corresponding to the coordinate position as the wire contact point resistance parameter;

[0018] Reading the heat dissipation power parameter and the distribution spacing parameter of the optimized heat dissipation channel from the compensation database, calculating the heat dissipation power parameter and the distribution spacing parameter to generate a heat dissipation compensation adjustment parameter;

[0019] Performing weighted superposition on the wire contact point resistance parameter and the heat dissipation compensation adjustment parameter to generate a reconstruction weight factor of the interference signal component;

[0020] According to the reconstruction weight factor, reversely adjusting the frequency domain amplitude of the interference signal component to generate an initial dynamic compensation coefficient;

[0021] Retrieving historical compensation error data from the compensation database, and calculating a deviation between the initial dynamic compensation coefficient and the historical compensation error data;

[0022] The initial dynamic compensation coefficient is corrected in a closed loop iterative manner according to the deviation, and the dynamic compensation coefficient is output when the deviation is lower than a set threshold.

[0023] Optionally, according to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram, adjusting the heat dissipation power and distribution spacing of the heat dissipation device to form an optimized heat dissipation channel, and updating the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel to the compensation database, including:

[0024] Extract the center coordinates of all thermal resistance peak areas from the thermal resistance distribution map, and define the heat dissipation control sub-areas with the center coordinates as the center and a preset radius as the range;

[0025] Adjusting the heat dissipation power of the heat sink according to the total area of ​​the thermal resistance peak area in the heat dissipation control sub-area, and adjusting the distribution spacing of the heat sink according to the distribution density of the thermal resistance peak area in the heat dissipation control sub-area;

[0026] In the heat dissipation control sub-area, heat dissipation devices are arranged according to the adjusted heat dissipation power and distribution spacing, so that the heat dissipation devices are arranged in a straight line along the transmission path to form an optimized heat dissipation channel;

[0027] The parameters of the optimized heat dissipation channel are updated into the compensation database as temperature compensation efficiency parameters.

[0028] Optionally, a heat dissipation device is provided on the surface of the object to be measured, and temperature distribution data of the object to be measured is obtained. Based on the temperature distribution data, a heat transfer path is analyzed and a thermal resistance distribution map is generated, including:

[0029] Evenly arranging multiple temperature collection points on the surface of the object to be measured, collecting temperature values ​​of each point at consecutive time points during the heating process, and generating temperature distribution data;

[0030] Determining the direction of heat transfer from the highest temperature region to the lowest temperature region based on the temperature difference between each point at the same time in the temperature distribution data;

[0031] Dividing the surface of the object under test into a plurality of continuous heat transfer segments along the direction of the heat transfer path, and calculating the ratio of the heat change per unit time to the corresponding temperature change in the heat transfer segment as the thermal resistance value of each segment;

[0032] In a coordinate grid on the surface of the object being measured, mapping the thermal resistance values ​​to corresponding coordinate regions to generate an initial thermal resistance distribution map;

[0033] Performing regional cluster analysis on the initial thermal resistance distribution map, marking the region in the continuous coordinate region where the thermal resistance value exceeds a set threshold as a thermal resistance peak region, so as to generate a thermal resistance distribution map.

[0034] Optionally, response delay data of thermocouples at different heating rates are collected, and temperature change data of the surface of the measured object are simultaneously obtained. A correlation relationship between the response delay data and the temperature change data is established, and the correlation relationship is coupled with a heat conduction efficiency parameter for modeling to form a compensation database of multiple characteristic parameters, including:

[0035] The heating device is controlled to heat up at a constant rate, and the time difference between the initial temperature and the stable temperature of the thermocouple is recorded as the response delay data in each mode;

[0036] Synchronously collecting the actual temperature value of the surface of the measured object at the same time point in each mode through a non-contact temperature measuring device to generate temperature change data;

[0037] Matching the response delay data under each heating mode with the temperature change amplitude within the same time window in the temperature change data in sections, calculating the ratio of the delay time in the response delay data to the temperature rise amplitude of the corresponding section in the temperature change data, and establishing a correlation between delay and temperature;

[0038] Extracting thermal conductivity efficiency parameters of the contact area between the object under test and the thermocouple, performing multi-dimensional coupling modeling based on the delay time at each heating rate in the association relationship and combining the thermal conductivity efficiency parameters, generating a characteristic parameter combination, and storing the characteristic parameter combination as an index structure classified by heating rate to form the compensation database.

[0039] Optionally, performing frequency domain decomposition on the standardized dynamic signal, extracting amplitude distribution characteristics of each frequency band, screening out signal components that meet conditions, and marking them as interference signal components caused by the thermal capacitance characteristics of the compensation wire, includes:

[0040] Decomposing the normalized dynamic signal into signal components in a plurality of preset frequency intervals, and calculating the ratio of the sum of the amplitudes of the signal components to the sum of the amplitudes of all frequency intervals;

[0041] Comparing the ratio with a preset ratio threshold corresponding to the thermal capacitance characteristic of the compensation wire in the compensation database, and if the ratio of the interval exceeds the preset ratio threshold, determining that the signal component of the frequency interval is an interference signal component caused by the thermal capacitance characteristic of the compensation wire;

[0042] Extracting, according to the frequency interval corresponding to the interference signal component, a segment in the signal waveform of the frequency interval in which the amplitude fluctuation direction is opposite to the temperature change trend in the temperature change data as a reverse interference segment;

[0043] The duration of the reverse interference segment is correlated with the temperature change rate of the corresponding time window in the temperature change data. If the correlation calculation result is lower than the set negative threshold, the reverse interference segment is merged into a complete interference signal component and marked.

[0044] Optionally, reading the heat dissipation power parameter and the distribution spacing parameter of the optimized heat dissipation channel from the compensation database, calculating the heat dissipation power parameter and the distribution spacing parameter to generate the heat dissipation compensation adjustment parameter includes:

[0045] According to the number of the heat dissipation control sub-region marked in the thermal resistance distribution map, the heat dissipation power parameter of the corresponding heat dissipation device is extracted from the compensation database according to the region index, and the distribution spacing parameter within the heat dissipation control sub-region is simultaneously obtained;

[0046] Normalizing the heat dissipation power parameter, and multiplying the normalized heat dissipation power parameter by the distribution spacing parameter to obtain a product parameter of each heat dissipation control sub-region;

[0047] Taking the arithmetic square root of the product parameter and then calculating the reciprocal to generate an initial parameter for heat dissipation compensation adjustment;

[0048] According to the thermal resistance gradient change rate of adjacent sub-regions in the thermal resistance distribution diagram, the initial heat dissipation compensation adjustment parameter is corrected across regions smoothly, and the heat dissipation compensation adjustment parameter is output.

[0049] In a second aspect, the present application provides a dynamic compensation processing system for thermocouple signals, comprising:

[0050] An acquisition module is used to collect response delay data of thermocouples at different heating rates, synchronously obtain temperature change data of the surface of the measured object, establish a correlation between the response delay data and the temperature change data, and couple the correlation with the heat conduction efficiency parameter to form a compensation database of multiple characteristic parameters;

[0051] A generating module, configured to set a heat dissipation device on the surface of the object to be measured, obtain temperature distribution data of the object to be measured, analyze the heat transfer path based on the temperature distribution data, and generate a thermal resistance distribution map;

[0052] an adjustment module, configured to adjust the heat dissipation power and distribution spacing of the heat dissipation device according to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram to form an optimized heat dissipation channel, and update the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel to the compensation database;

[0053] a separation module, configured to perform time domain alignment on the dynamic response signal output by the thermocouple and the temperature change data, perform frequency domain decomposition on the dynamic response signal using multiple parameters in the compensation database, and separate an interference signal component caused by the thermal capacitance characteristics of the compensation wire;

[0054] A compensation module is used to combine the wire contact point resistance parameters extracted from the thermal resistance distribution map and the compensation adjustment parameters of the optimized heat dissipation channel to perform frequency domain reconstruction of the interference signal component to generate a dynamic compensation coefficient, and perform closed-loop iterative correction on the dynamic compensation coefficient based on the historical compensation error data recorded in the compensation database.

[0055] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a dynamic compensation processing method for thermocouple signals as described in the first aspect above.

[0056] In a fourth aspect, the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a dynamic compensation processing method for thermocouple signals as described in the first aspect.

[0057] This application collects the response delay data of thermocouples at different heating rates and simultaneously obtains the surface temperature change data of the object under test, establishes a correlation between the two, and then couples the data with the heat conduction efficiency parameters to form a compensation database. Its technical effect is to quantify the physical correlation between the dynamic response of the thermocouple and the temperature change, eliminate the compensation error caused by the difference in the heat conduction path, and provide a high-precision characteristic parameter library for signal correction under multiple working conditions. By setting a heat dissipation device on the surface of the object under test and generating a thermal resistance distribution map based on the temperature distribution data, its technical effect is to identify the local thermal resistance peak area in the heat transfer path, reveal the location of the heat dissipation bottleneck, and provide a spatial thermodynamic basis for optimizing the heat dissipation channel. By adjusting the power and spacing of the heat dissipation device, an optimal The heat dissipation channel is optimized and the compensation database parameters are updated. The technical effect is to reduce the energy accumulation effect in the thermal resistance peak area, improve the heat dissipation efficiency, and enhance the adaptability of the database to dynamic working conditions. The dynamic response signal of the thermocouple and the temperature change data are aligned in the time domain, and the frequency domain decomposition is performed in combination with the compensation database parameters. The technical effect is to separate the low-frequency interference signal component caused by the thermal capacitance characteristics of the compensation wire, breaking through the limitation of traditional time domain filtering on insufficient retention of dynamic signal characteristics. The dynamic compensation coefficient is generated by combining the thermal resistance parameters and the compensation adjustment parameters and closed-loop iterative correction is based on the historical error data. The technical effect is to realize the frequency domain reconstruction and compensation of the interference signal, and improve the dynamic fidelity of the thermocouple output signal and the system anti-interference ability.

[0058] Furthermore, by aligning the time axis starting point of the thermocouple dynamic response signal with the temperature change data and segmenting it according to a fixed time window, the signal amplitude is dynamically scaled based on multiple parameters of the compensation database to generate a standardized dynamic signal, and then the amplitude characteristics of each frequency band are extracted through frequency domain decomposition and the interference components are screened and marked. The technical effect is that the signal delay and amplitude distortion errors are eliminated through time synchronization and segmented standardization processing, and the thermal capacitance interference component is accurately located in combination with the frequency domain feature screening mechanism, providing a highly reliable signal separation basis for subsequent dynamic compensation, significantly reducing the influence of the parasitic thermal effect of the compensation wire on the measurement accuracy, and ensuring the dynamic stability and anti-interference performance of the temperature measurement system in complex heat conduction scenarios through the trend prediction and interference filtering mechanism driven by historical data.

[0059] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0061] Figure 1 A flow chart of a method for dynamic compensation of thermocouple signals provided by the present application is shown;

[0062] Figure 2 A schematic structural diagram of a dynamic compensation processing system for thermocouple signals provided by the present application is shown;

[0063] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0064] In order to enable people skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0065] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0066] Research has found that existing dynamic compensation technology in the field of high-temperature transient temperature testing faces three key bottlenecks: First, when the test environment temperature undergoes a step change exceeding 300°C, the fixed-parameter compensation model experiences a 7%-12% overshoot distortion in the compensation filter due to the spatiotemporal mismatch between the heat conduction efficiency and the heat dissipation path. Second, when traditional optimization algorithms process parameter spaces with dimensions greater than 8, the nonlinear coupling between the thermal resistance distribution characteristics and the compensation coefficients causes a decrease in population diversity and premature convergence. Third, mechanical vibration noise and heat source signals alias in the frequency domain. When the impact load exceeds 500g acceleration, parasitic potential interference is amplified by the thermal capacitance effect of the compensation wire, resulting in measurement deviations on the order of 3-5μV. These issues stem from the inadequate decoupling of the dynamic characteristics of the heat conduction path and the multi-physics coupling effects of existing methods.

[0067] To address the above-mentioned issues, the present invention proposes a thermocouple signal compensation method based on dynamic perception of thermal resistance distribution characteristics. This method achieves a triple breakthrough by establishing a compensation database coupled with heat conduction efficiency parameters and a heat dissipation channel optimization mechanism. First, by synchronously collecting response delay data and temperature distribution characteristics at different heating rates, a compensation database containing the spatiotemporal evolution law of the thermal resistance peak area is constructed. Combined with the optimized heat dissipation channel formed by dynamic power regulation of the heat dissipation device, the compensation parameters can match the temperature field changes, reducing the overshoot distortion of the 300°C step temperature change. Secondly, the resistance parameters of the wire contact point are extracted based on the thermal resistance distribution map, and the dynamic compensation coefficients are reconstructed through frequency domain decomposition. Combined with closed-loop iterative correction of historical error data, the population diversity of the high-dimensional parameter space is effectively maintained, and the convergence stability of parameter optimization with dimensions above 8 is improved. Finally, by optimizing the heat dissipation channel to reduce the thermal capacitance effect of the compensation wire, and combining the frequency domain interference signal separation technology, the parasitic potential interference is suppressed to below 0.8μV under a 500g acceleration shock load, achieving effective decoupling of mechanical vibration noise and heat source signals. This solution fundamentally solves the compensation failure problem caused by the mismatch of thermal conduction dynamic characteristics and physical field coupling effects in traditional methods.

[0068] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0069] Figure 1 A flowchart of a method for dynamic compensation processing of a thermocouple signal is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0070] 101. Collect response delay data of thermocouples at different heating rates, synchronously acquire temperature change data of the surface of the measured object, establish a correlation between the response delay data and the temperature change data, and couple the correlation with a heat conduction efficiency parameter to form a compensation database of multiple characteristic parameters;

[0071] Optionally, step 101 includes:

[0072] 1011. Control the heating device to increase the temperature at a constant rate, and record the time difference between the initial temperature and the stable temperature of the thermocouple as the response delay data in each mode;

[0073] 1012. Synchronously collect actual temperature values ​​of the surface of the measured object at the same time point in each mode through a non-contact temperature measuring device to generate temperature change data;

[0074] 1013. Segmentally match the response delay data in each heating mode with the temperature change amplitude within the same time window in the temperature change data, calculate the ratio of the delay time in the response delay data to the temperature rise amplitude of the corresponding segment in the temperature change data, and establish a correlation between delay and temperature;

[0075] 1014. Extract the thermal conductivity efficiency parameters of the contact area between the object under test and the thermocouple, perform multi-dimensional coupling modeling based on the delay time at each heating rate in the association relationship in combination with the thermal conductivity efficiency parameters, generate a characteristic parameter combination, and classify the characteristic parameter combination by heating rate and store it as an index structure to form the compensation database.

[0076] In the above scheme, response delay data refers to the time difference required for a thermocouple to reach a stable temperature from an initial temperature when the heating device heats the thermocouple at a constant rate. Temperature change data is a time-varying sequence of actual surface temperature values ​​of the measured object, collected synchronously by a non-contact temperature measurement device (such as an infrared thermal imager). The heat transfer efficiency parameter characterizes the heat transfer capacity of the contact area between the measured object and the thermocouple and is typically determined by the material's thermal conductivity and contact area. Segmented matching involves partitioning the response delay data and temperature change data into the same time window for comparative analysis. A time window is a fixed time interval set for synchronous data analysis. The temperature change amplitude is the increase or decrease in temperature within a specific time window. The ratio is the quotient of the response delay time and the temperature change amplitude within the corresponding time window, which is used to quantify the strength of the correlation between delay and temperature change. The compensation database is an indexed database that stores delay-temperature correlations, heat transfer parameters, and characteristic parameter combinations at different heating rates. The characteristic parameter combinations include the coupled calculation results of parameters such as delay time, temperature change rate, and heat transfer efficiency. The index structure is a fast retrieval organization format for storing data categorized by heating rate.

[0077] In the embodiment of the present application, first, in step 1011, the heating device is precisely controlled to increase the temperature at a preset constant rate. Simultaneously, a high-precision timing device (e.g., a microsecond timer) is used to closely monitor and record the total time difference between the thermocouple's response from the initial ambient temperature (e.g., 25 degrees Celsius) and its output signal reaching the steady-state value corresponding to the set target stable temperature, e.g., 100 degrees Celsius. This time difference is explicitly recorded as the response delay data for that specific heating rate. For example, in a heating rate mode of 10 degrees Celsius per minute, this process is measured to take 15 seconds. To ensure the accuracy and reliability of the data, an independent temperature calibration device is also used during this heating and recording process to verify and monitor the actual heating rate of the heating device to ensure that it strictly meets the preset constant rate requirement and eliminate errors caused by heating fluctuations.

[0078] Secondly, through step 1012, a non-contact temperature measurement device (such as an infrared thermal imager) is deployed on a completely synchronized timeline of the operation of the heating device and recording of the thermocouple response delay to continuously collect the actual temperature value of the surface of the object being measured during the entire heating process at a set sampling frequency (for example, once per second). These temperature readings, strictly sorted by timestamps, are integrated to generate a continuous, high-time-resolution temperature change data set; the time base of this data set is ensured to maintain strict millisecond-level or even higher precision alignment with the recording timeline of the aforementioned response delay data through a synchronization signal or a unified clock source, thereby achieving essentially synchronous collection of the two key data.

[0079] Next, for each independently run constant heating rate mode (i.e., each heating experiment), the response delay data (i.e., total delay time) obtained in this mode and the corresponding temperature change data collected synchronously are subjected to refined segmented matching processing according to the same time window pre-defined or dynamically divided according to the temperature change characteristics. The specific operation is to divide the total heating response time into a number of equal or unequal length analysis windows, for example, 15 seconds is divided into three consecutive 5-second windows. Within each window, on the one hand, the thermocouple response delay component corresponding to the window period is extracted. For example, the thermocouple response lags by 3 seconds in the first 5-second window. On the other hand, the actual increase in the surface temperature of the object under test from the start time to the end time of the window period is accurately calculated. For example, the surface temperature in the window rises from 30 degrees Celsius to 50 degrees Celsius, an increase of 20 degrees Celsius. Then, the instantaneous ratio between the delay time component and the temperature increase in the window is calculated. For example, in the previous example, a delay of 3 seconds corresponds to a temperature increase of 20 degrees Celsius, and the ratio is 20°C / 3s ≈ 6.67. °C / s. Through this windowed segmented calculation, a quantitative correlation is systematically established between the response delay time of the thermocouple in a specific time segment and the actual temperature change amplitude of the measured object surface in the corresponding time period.

[0080] Finally, step 1014 introduces a key physical parameter characterizing the heat transfer characteristics between the object under test and the thermocouple's temperature measuring end: the thermal conductivity efficiency parameter. For example, a material thermal conductivity tester is used to accurately measure and extract the material in the contact area between the two. For example, the thermal conductivity coefficient of aluminum alloy is 237 W / (m·K). Based on the correlation between delay time and temperature change amplitude under different heating rates established in the previous step, that is, the ratio data of each window, and combined with this key thermal conductivity efficiency parameter, multivariate analysis techniques (such as multivariate linear regression models or more complex machine learning algorithms) are used to perform in-depth multidimensional coupled modeling. This model aims to reveal and quantify the inherent interactions between characteristic parameters such as response delay, temperature change rate, and thermal conductivity efficiency. After modeling is completed, the calculated or modeled characteristic parameter combinations that can comprehensively reflect the dynamic response characteristics of the system are classified and structured according to their corresponding original heating rate patterns. Ultimately, an index database with efficient query capabilities is constructed, such as a hash index or B-tree index based on the heating rate values, thus forming the final compensation database.

[0081] In a practical application, a heating device in a laboratory is first set to increase the temperature at a constant rate of 5 degrees Celsius per minute. The three-minute delay required for the thermocouple to rise from an initial temperature of 20 degrees Celsius to a stable temperature of 100 degrees Celsius is recorded. Simultaneously, an infrared thermal imager collects temperature data from the surface of the object being measured at a sampling rate of once per second, generating a temperature change dataset with a timestamp. Next, the entire heating process is segmented into 30-second time windows. Within each window, the ratio of the thermocouple delay time to the corresponding temperature rise is calculated. For example, within a window with a 10-second delay and a 15-degree Celsius temperature rise, the ratio is 0.67 seconds per degree Celsius. Subsequently, the thermal conductivity parameter of the contact area between the object being measured and the thermocouple is combined, calculated as the ratio of thermal conductivity to contact area. A multivariate regression model is used to couple the delay data, temperature change, and thermal conductivity parameter at different heating rates. Finally, all characteristic parameters are classified and stored in the compensation database according to heating rates such as 10 degrees Celsius per minute and 15 degrees Celsius per minute, forming a complete index structure.

[0082] In step 101 above, by synchronously collecting data on thermocouple response delay and surface temperature changes, a segmented dynamic correlation model is established between the two. This is combined with the contact area heat conduction efficiency parameters to construct a multidimensional compensation feature. An indexed structure based on classified storage of heating rates forms a rapidly searchable compensation database. This step quantitatively models the dynamic error in temperature measurement, providing data support for dynamic temperature compensation and significantly improving temperature tracking accuracy under complex operating conditions.

[0083] 102. Install a heat dissipation device on the surface of the object to be measured, obtain temperature distribution data of the object to be measured, analyze the heat transfer path based on the temperature distribution data, and generate a thermal resistance distribution map;

[0084] Optionally, step 102 includes:

[0085] 1021. Evenly arrange multiple temperature collection points on the surface of the object to be measured, collect temperature values ​​of each point at consecutive time points during the heating process, and generate temperature distribution data;

[0086] 1022. Determine the direction of heat transfer from the highest temperature region to the lowest temperature region based on the temperature difference between each point at the same time in the temperature distribution data;

[0087] 1023. Divide the surface of the object under test into a plurality of continuous heat transfer segments along the heat transfer path, and calculate the ratio of the heat change per unit time to the corresponding temperature change in the heat transfer segment as the thermal resistance value of each segment;

[0088] 1024. Map the thermal resistance value to a corresponding coordinate area in a coordinate grid on the surface of the measured object to generate an initial thermal resistance distribution map;

[0089] 1025. Perform regional cluster analysis on the initial thermal resistance distribution map, and mark the region in the continuous coordinate region where the thermal resistance value exceeds a set threshold as a thermal resistance peak region, so as to generate a thermal resistance distribution map.

[0090] In the above scheme, the temperature distribution data is the spatiotemporal distribution information of the temperature at each position on the surface of the measured object during the heating process, which is obtained through multiple evenly arranged temperature collection points; the heat transfer path is the trajectory direction of heat diffusion from the high-temperature area to the low-temperature area determined by the temperature difference; the thermal resistance value is the ratio of the heat change per unit time in the heat transfer section to the corresponding temperature change, reflecting the degree of heat transfer obstruction in the local area; the thermal resistance distribution map is a visual map generated by mapping the thermal resistance values ​​of each area to the coordinate grid on the surface of the measured object; the temperature collection points are the temperature transfer points on the surface of the measured object distributed in a regular manner. The sensor deployment location; the direction of the transfer path is determined by calculating the gradient of the temperature difference at each point at the same time; the heat transfer segment is a continuous analysis interval divided along the direction of the transfer path; the heat change per unit time refers to the difference between the incoming and outgoing heat in the transfer segment; the coordinate grid is the coordinate unit that divides the surface of the measured object into regularly arranged units; the initial thermal resistance distribution map is the original thermal resistance spatial distribution map without optimization; regional cluster analysis uses an algorithm (such as DBSCAN) to group the thermal resistance values ​​of continuous coordinate areas by similarity; the thermal resistance peak area is the key control area where the thermal resistance value exceeds the set threshold after cluster analysis.

[0091] In an embodiment of the present application, first, through step 1021, multiple temperature collection points are evenly deployed on the surface of the object to be measured according to a preset geometric accuracy. Specifically, an array of 64 thermocouple sensors with an 8 by 8 grid structure is formed by arranging them at equal intervals of 10 mm, for example. During the heating process, the temperature readings of all points at continuous timestamps are synchronously recorded at a high sampling rate of 10 times per second, thereby generating a three-dimensional temperature distribution data set that integrates spatial coordinates, time dimensions and temperature values, and completely capturing the dynamic evolution process of the thermal field.

[0092] Secondly, in step 1022, based on the temperature distribution data, the direction of the heat transfer path is determined by analyzing the spatial temperature differences of all the collection points at a specific moment. Specifically, the system calculates the gradient distribution of the temperature field on the surface of the object. For example, at time point t1, the temperature at the coordinate position X3Y5 is detected to be 85 degrees Celsius, while the temperature at the coordinate position X7Y2 is only 50 degrees Celsius. Through vector field analysis, it is determined that the direction of maximum temperature attenuation is southeast, that is, at an angle of 120 degrees with the X-axis, thereby accurately identifying the main transfer path of heat diffusing from the high-temperature core area to the low-temperature edge area.

[0093] Next, step 1023 is used to divide the surface of the object into continuous heat transfer segments with clear physical meanings along the direction of this transfer path. For example, the southeast path is divided into several analysis intervals with a fixed step size of 20 mm. In each transfer segment, the ratio of the heat change ΔQ flowing through the segment per unit time in joules per second to the temperature change ΔT in the corresponding spatial segment in degrees Celsius is calculated using a thermodynamic formula. This ratio is defined as the thermal resistance value R of the microelement area in joules per degree Celsius. For example, if a certain transfer segment monitors a heat transfer of 500 joules and a temperature drop of 10 degrees Celsius within 1 second, the measured thermal resistance value is 50 joules per degree Celsius.

[0094] Then, in step 1024, the calculated thermal resistance values ​​of all transfer sections are mapped to a preset coordinate grid system according to their corresponding surface coordinate positions. The grid system covers the X1Y1 to X8Y8 areas, and a visual heat map is generated through color coding technology. The red color is set to represent the high resistance area, that is, the area greater than 55 joules per degree Celsius, and the blue color is set to represent the low resistance area, that is, the area less than 30 joules per degree Celsius, thereby constructing an initial thermal resistance distribution map.

[0095] Finally, a density-based DBSCAN cluster analysis is performed on this initial distribution map through step 1025 to automatically identify and mark abnormal areas in the continuous coordinate area where the thermal resistance value is significantly higher than the average level. A dynamic judgment threshold is specifically set. For example, the global thermal resistance mean plus twice the standard deviation is used as the threshold. Assuming that the calculated threshold is 55 joules per degree Celsius, when the thermal resistance value of a connected area continues to exceed the threshold, for example, the thermal resistance of 6 consecutive grid cells in the coordinate area X4Y4 to X6Y6 are all in the range of 58 to 65 joules per degree Celsius, they are marked as the thermal resistance peak area. Finally, a complete thermal resistance distribution map integrating the spatial thermal resistance distribution characteristics and the abnormal area markings is output, providing a precise spatial positioning basis for subsequent heat dissipation structure optimization.

[0096] In a practical application, when analyzing the temperature distribution of a metal component, 12 temperature sensors were first evenly distributed on its surface in a 5 cm by 5 cm grid. Temperature values ​​were continuously collected at a frequency of 0.5 Hz at each location during the heating process. By comparing the temperature differences at different coordinates at the same time, for example, the temperature at coordinate A3 was 85 degrees Celsius and at coordinate D6 was 60 degrees Celsius, it was determined that the heat transfer path from the high-temperature area to the low-temperature area was southeast. This path was then divided into sections every 10 cm, and the heat change per unit time was calculated for each section. Specifically, the heat change value was calculated by multiplying the heat flux density and the cross-sectional area. The heat change in the second section was 120 joules, and the temperature difference was 8 degrees Celsius, so the thermal resistance value was calculated to be 15 joules per degree Celsius. Based on this, the thermal resistance values ​​of each section were mapped to a two-dimensional coordinate grid on the component surface, generating a grayscale thermal resistance distribution map. Darker areas represent areas with thermal resistance values ​​exceeding 20 joules per degree Celsius. Finally, a K-means clustering algorithm was used to mark the continuous superthreshold regions, successfully identifying three significant thermal resistance peaks.

[0097] In step 102, heat transfer paths are analyzed based on multi-node surface temperature distribution data. A thermal resistance distribution map is generated through segmented thermal resistance calculation and coordinate mapping, and regional clustering is used to identify peak thermal resistance areas. This step precisely locates heat dissipation bottlenecks and quantifies the spatial heterogeneity of surface heat transfer efficiency. This provides a spatial thermodynamic basis for heat dissipation device optimization and guides the targeted allocation of heat dissipation resources.

[0098] 103. Adjust the heat dissipation power and distribution spacing of the heat dissipation device according to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram to form an optimized heat dissipation channel, and update the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel to the compensation database;

[0099] Optionally, step 103 includes:

[0100] 1031. Extract the center coordinates of all thermal resistance peak areas from the thermal resistance distribution map, and define a heat dissipation control sub-area with the center coordinates as the center and a preset radius as the range;

[0101] 1032. Adjust the heat dissipation power of the heat dissipation device according to the total area of ​​the thermal resistance peak area in the heat dissipation control sub-area, and adjust the distribution spacing of the heat dissipation device according to the distribution density of the thermal resistance peak area in the heat dissipation control sub-area;

[0102] 1033. In the heat dissipation control sub-area, heat dissipation devices are arranged according to the adjusted heat dissipation power and distribution spacing, so that the heat dissipation devices are arranged in a straight line along the transmission path to form an optimized heat dissipation channel;

[0103] 1034. Update the parameters of the optimized heat dissipation channel into a compensation database as temperature compensation efficiency parameters.

[0104] In the above scheme, the thermal resistance peak area is a continuous area of ​​high thermal resistance values ​​that needs to be regulated first, which is marked in the thermal resistance distribution diagram; the heat dissipation regulation sub-area is a circular regulation range with the center coordinate of the thermal resistance peak as the center and the preset radius as the range; the heat dissipation power is the energy output intensity of the heat dissipation device (such as a fan, liquid cooling system) per unit time; the distribution spacing is the deployment distance between adjacent heat dissipation devices; the optimized heat dissipation channel is an enhanced heat dissipation structure arranged in a straight line along the heat transfer path by adjusting the heat dissipation power and spacing; the temperature compensation efficiency parameter is a set of physical quantities that affect the temperature correction effect, including heat dissipation power, distribution spacing, etc.; the center coordinate is the geometric center position of the thermal resistance peak area; the preset radius is the radius of the regulation area set according to the heat influence range; the distribution density is the number of thermal resistance peak areas per unit area; the linear arrangement is the spatial organization form in which the heat dissipation device is linearly deployed in the direction of the transfer path; the parameter update is the operation of writing the optimized heat dissipation parameters into the compensation database according to the index structure.

[0105] In an embodiment of the present application, first, based on the spatial distribution characteristics of the thermal resistance peak area marked in the thermal resistance distribution diagram, the geometric center coordinates of all peak areas are extracted. For example, the center of the main peak area is identified to be located at coordinates X5Y5. With these coordinates as the center of the circle, a circular heat dissipation control sub-area is delineated with a preset influence radius of 20 mm. This area must completely cover the peak area and its heat-affected surrounding area.

[0106] Secondly, through step 1032, for each heat dissipation control sub-area, the power output of the heat dissipation device is dynamically adjusted according to the total area ratio of the internal thermal resistance peak area. For example, when it is detected that the peak area ratio in a certain sub-area is as high as 70%, the flow rate of the liquid cooling system is increased from the basic value of 2 liters per minute to 4 liters per minute; at the same time, the physical arrangement spacing of the heat dissipation device is synchronously adjusted according to the distribution density of the peak area in the sub-area. For example, when it is monitored that the peak density reaches 0.8 high resistance points per square millimeter, the unit spacing of the heat dissipation fin array is compressed from the initial 12 mm to 6 mm.

[0107] Next, after completing the adjustment of the power and spacing parameters through step 1033, the heat dissipation devices are deployed in a straight line with the optimized spacing value along the direction of the heat transfer path, i.e., the previously determined southeast 120-degree direction, in each heat dissipation control sub-area. For example, 8 liquid cooling nozzles are arranged in a straight line in the southeast direction at equal intervals of 6 mm to ensure that the direction of the heat dissipation force field is strictly consistent with the natural heat transfer path, thereby constructing an optimized channel for directional enhanced heat dissipation.

[0108] Finally, step 1034 integrates the key operating parameters for optimizing the heat dissipation channel, including the heat dissipation power setting value, such as 4000 revolutions per minute, the device distribution spacing, such as 6 mm, and the measured channel thermal resistance reduction efficiency, such as 40%, into a temperature compensation efficiency parameter combination. This is then updated to the compensation database in a structured manner according to the regional coordinate index number, forming a heat dissipation optimization knowledge base that can be called by subsequent temperature measurement systems.

[0109] In practical applications, for the peak area marked in the thermal resistance distribution diagram of an electronic component, a circular heat dissipation control sub-area with a radius of 3 cm is first delineated with the peak center coordinates X12 and Y7 as the center of the circle. According to the total area of ​​8 square centimeters of the thermal resistance peak in this sub-area, the power of the heat sink is gradually increased from 50 watts to 65 watts to enhance the heat dissipation efficiency. At the same time, due to the high distribution density of the thermal resistance peak in this area, the installation spacing of the heat sink is adjusted from 8 cm to 4 cm to optimize the heat dissipation coverage. Furthermore, the adjusted heat sink is arranged in a straight line along the heat transfer path, that is, from northwest to southeast, to form a continuous heat dissipation channel with a width of 2 cm. Finally, the heat dissipation power parameter of 65 watts, the spacing parameter of 4 cm and the channel direction parameter are integrated into the temperature compensation efficiency parameter, and updated to the compensation database according to the sub-area number Zone-5 to achieve dynamic adaptation of the heat dissipation strategy.

[0110] In step 103 above, the power and spacing of the heat sinks are dynamically adjusted based on the distribution characteristics of the peak thermal resistance area, a directional heat dissipation channel is constructed, and the optimized parameters are fed back to the compensation database. This step dynamically matches heat dissipation performance with the thermal resistance distribution, reducing local temperature gradients through enhanced heat transfer paths. It also converts heat dissipation control parameters into iteratively optimized compensation efficiency factors, supporting thermal management decisions.

[0111] 104. Align the dynamic response signal output by the thermocouple with the temperature change data in the time domain, perform frequency domain decomposition on the dynamic response signal using multiple parameters in the compensation database, and separate the interference signal component caused by the thermal capacitance characteristic of the compensation wire;

[0112] Optionally, step 104 includes:

[0113] 1041. Align the time axis starting point of the dynamic response signal output by the thermocouple with the time axis starting point of the temperature change data, and segmentally intercept the two aligned signals according to a fixed time window;

[0114] 1042. Based on the segmented intercepted time window boundary, dynamically scale the waveform amplitude of the dynamic response signal according to multiple parameters in the compensation database to generate a standardized dynamic signal;

[0115] 1043. Perform frequency domain decomposition on the standardized dynamic signal, extract the amplitude distribution characteristics of each frequency band, screen out signal components that meet the conditions, and mark them as interference signal components caused by the thermal capacitance characteristics of the compensation wire.

[0116] Among them, step 1043 specifically includes: decomposing the standardized dynamic signal into signal components of multiple preset frequency intervals, and calculating the ratio of the sum of the amplitudes of the signal components to the sum of the amplitudes of all frequency intervals; comparing the ratio with a preset ratio threshold corresponding to the thermal capacitance characteristics of the compensation wire in the compensation database; if the ratio of the interval exceeds the preset ratio threshold, then determining that the signal component of the frequency interval is an interference signal component caused by the thermal capacitance characteristics of the compensation wire; according to the frequency interval corresponding to the interference signal component, extracting a segment in the signal waveform of the frequency interval whose amplitude fluctuation direction is opposite to the temperature change trend in the temperature change data as a reverse interference segment; performing a correlation calculation on the duration of the reverse interference segment and the temperature change rate of the corresponding time window in the temperature change data; if the correlation calculation result is lower than the set negative threshold, then merging the reverse interference segment into a complete interference signal component and marking it.

[0117] In the above scheme, the dynamic response signal is the time-varying voltage or current signal output by the thermocouple; time domain alignment is the operation of synchronously aligning the thermocouple signal with the starting point of the time axis of the temperature change data; frequency domain decomposition is an analysis method that converts the signal from the time domain to the frequency domain through Fourier transform; the interference signal component is a noise signal caused by the thermal capacitance characteristics of the compensation conductor and has a trend opposite to the actual temperature change; the time window boundaries are the fixed time interval start and end points set when the signal is segmented; the heat conduction correction coefficient is a scaling factor stored in the compensation database for normalizing the signal amplitude; the standardized dynamic signal is a unified dimension signal that has undergone time alignment and amplitude scaling; the frequency band amplitude distribution feature is the amplitude statistical characteristic of the signal in a specific frequency range; the preset frequency range is a predefined analysis frequency band; the preset proportional threshold is the critical amplitude ratio for determining the interference component; the reverse interference segment is the time segment in the signal waveform where the amplitude fluctuation direction is opposite to the temperature change trend; the correlation calculation quantifies the degree of negative correlation between the reverse interference segment and the temperature change rate using methods such as the Pearson coefficient; and the multiple parameters include various characteristic parameters in the compensation database and the temperature compensation efficiency parameter.

[0118] In an embodiment of the present application, first, through step 1041, the dynamic response signal output by the thermocouple is time-matched with the synchronously acquired temperature change data through a precise time domain alignment operation. Specifically, a cross-correlation algorithm is used to strictly calibrate the time axis starting points of the two. A typical implementation method is to capture the step change point of the thermocouple voltage signal and the temperature mutation point in the infrared temperature measurement data to achieve millisecond-level synchronization. Subsequently, the aligned dual-channel signals are segmented and intercepted according to a preset fixed time window. The standard window width is set to 50 milliseconds to ensure that the two signals in each analysis segment have a strict time correspondence.

[0119] Secondly, through step 1042, for each intercepted time window, the multi-dimensional characteristic parameters and temperature compensation efficiency parameters stored in the compensation database are called, including key data models such as contact thermal resistance correction coefficient, thermocouple sensitivity coefficient, and heat dissipation channel optimization parameters, and the waveform amplitude of the dynamic response signal in the window is intelligently and dynamically scaled. This processing process specifically includes four refined operation levels: the first level is the parameter matching engine, which calculates the similarity between the signal characteristics in the current time window, including the rising edge slope and the peak fluctuation range, and the contact thermal resistance and attenuation model in the database. When the matching degree reaches 90% of the preset trigger threshold, the compensation process is automatically activated; the second level calls the corresponding contact thermal resistance correction coefficient and thermocouple sensitivity coefficient. The transfer function matrix is ​​constructed based on the sensitivity coefficient, where the correction coefficient is used to offset the signal attenuation caused by the thermal conduction lag at the material interface, and the sensitivity coefficient is used to calibrate the nonlinear deviation of the Seebeck coefficient of the thermocouple material. The third level dynamically adjusts the scaling weight factor according to the thermal resistance reduction rate in the heat dissipation channel optimization parameters. For example, when the thermal resistance reduction rate is 40%, the weight factor is set to 1.4 times the baseline value to enhance the signal compensation strength in the heat dissipation area. The fourth level fuses the above parameters into a dynamic scaling operator through convolution operation, and performs pixel-level calibration on the original signal amplitude. For example, when it is detected that the signal amplitude has attenuated by 5% due to contact thermal resistance, the scaling operator is applied to restore it to the theoretical amplitude curve, ultimately generating a standardized dynamic signal that eliminates system-level errors.

[0120] Finally, in step 1043, a fast Fourier transform is performed on the standardized dynamic signal to perform frequency domain decomposition, separating it into multiple signal components with equal frequency widths ranging from 0 to 200 Hz. The ratio of the sum of the amplitudes of each frequency band to the sum of the amplitudes of all frequency bands is calculated, and this ratio is compared and analyzed with a predefined threshold for the thermal capacitance characteristic of the compensation conductor in the compensation database. In a typical scenario, when the amplitude of the 120 to 150 Hz frequency band accounts for as much as 45%, significantly exceeding the preset threshold of 35%, this frequency band is determined to be an interference signal component caused by the thermal capacitance characteristic of the compensation conductor. Segments in the signal waveform of this interference frequency band where the amplitude fluctuation direction shows an inverse relationship with the temperature trend of the temperature change data are further extracted as reverse interference segments. Specifically, these are abnormal segments where the signal amplitude abnormally decreases during a period of continuous temperature increase. Finally, the Pearson correlation coefficient between the duration of the reverse interference segment and the temperature change rate in the corresponding time window is calculated. If this coefficient is lower than the set negative threshold of -0.8, all reverse interference segments are automatically merged to form a complete interference signal component and marked, thereby achieving accurate separation and identification of the interference signal component.

[0121] In a practical application, in a high-precision temperature testing scenario, the dynamic response signal output by the thermocouple is first time-aligned with the temperature data collected by the infrared thermal imager to ensure that their starting points are completely synchronized. The signal is then segmented into fixed 10-second time windows. Based on the thermal conductivity correction factor of 0.85 for Zone 5 in the compensation database, the waveform amplitude of the dynamic response signal is dynamically scaled to generate a normalized signal curve. Next, a fast Fourier transform is used to decompose the normalized signal into three frequency bands: 0 to 10 Hz, 10 to 50 Hz, and 50 to 100 Hz. Analysis reveals that the amplitude of the 10 to 50 Hz band accounts for 62%, significantly exceeding the preset 50% threshold. To accurately identify interference components, segments within this frequency band where the amplitude fluctuations are opposite to the temperature change trend are further extracted. For example, when the temperature increases at 2 degrees Celsius per second, the signal amplitude continuously decreases from 1.2 volts to 0.8 volts for 4 seconds. These reverse fluctuations are ultimately identified as interference signal components caused by the thermal capacitance characteristics of the compensation conductor.

[0122] In step 104, time-domain alignment and frequency-domain decomposition are used to isolate the interference component caused by the thermal capacitance of the compensation conductor. Standardized signal processing and threshold comparison are then combined to extract the reverse interference signature. This step effectively removes interference from ambient thermal noise and inherent device characteristics, restoring the dynamic response characteristics of the true temperature signal and providing a high signal-to-noise ratio signal substrate for dynamic compensation.

[0123] 105. Combine the wire contact point resistance parameters extracted from the thermal resistance distribution diagram and the compensation adjustment parameters of the optimized heat dissipation channel, perform frequency domain reconstruction on the interference signal component to generate a dynamic compensation coefficient, and perform closed-loop iterative correction on the dynamic compensation coefficient based on the historical compensation error data recorded in the compensation database.

[0124] Optionally, step 105 includes:

[0125] 1051. Extract the coordinate position of the contact point between the compensation wire and the measured object from the thermal resistance distribution map, and obtain the thermal resistance value corresponding to the coordinate position as the wire contact point resistance parameter;

[0126] 1052. Read the heat dissipation power parameter and the distribution spacing parameter of the optimized heat dissipation channel from the compensation database, calculate the heat dissipation power parameter and the distribution spacing parameter, and generate a heat dissipation compensation adjustment parameter;

[0127] Among them, step 1052 specifically includes: according to the number of the heat dissipation control sub-area marked in the thermal resistance distribution map, extracting the heat dissipation power parameters of the corresponding heat dissipation device from the compensation database according to the area index, and synchronously obtaining the distribution spacing parameters in the heat dissipation control sub-area; normalizing the heat dissipation power parameters, and multiplying the normalized heat dissipation power parameters with the distribution spacing parameters to obtain the product parameters of each heat dissipation control sub-area; taking the arithmetic square root of the product parameters and calculating the reciprocal to generate the heat dissipation compensation adjustment initial parameters; according to the thermal resistance gradient change rate of adjacent sub-areas in the thermal resistance distribution map, performing cross-region smoothing correction on the heat dissipation compensation adjustment initial parameters, and outputting the heat dissipation compensation adjustment parameters.

[0128] 1053. Perform weighted superposition on the wire contact point resistance parameter and the heat dissipation compensation adjustment parameter to generate a reconstruction weight factor of the interference signal component;

[0129] 1054. Reversely adjust the frequency domain amplitude of the interference signal component according to the reconstruction weight factor to generate an initial dynamic compensation coefficient;

[0130] 1055. Retrieve historical compensation error data from the compensation database, and calculate a deviation between the initial dynamic compensation coefficient and the historical compensation error data;

[0131] 1056. Perform closed-loop iterative correction on the initial dynamic compensation coefficient according to the deviation, and output the dynamic compensation coefficient when the deviation is lower than a set threshold.

[0132] In the above scheme, the resistance parameter of the wire contact point is the thermal resistance value of the contact position between the compensation wire and the object under test; the compensation adjustment parameter is the control quantity such as the heat dissipation power and distribution spacing of the optimized heat dissipation channel; the dynamic compensation coefficient is the correction parameter generated by reconstructing the interference signal; the closed-loop iterative correction is a feedback control process that continuously optimizes the compensation coefficient based on historical error data; the coordinate position is the coordinate of the contact point between the compensation wire and the object under test in the thermal resistance distribution diagram; the normalization process is the operation of converting parameters of different dimensions into dimensionless scalars; the product parameter is the multiplication result of the normalized heat dissipation power and distribution spacing; the arithmetic square The root reciprocal is a mathematical operation that calculates the reciprocal after taking the square root of the product parameter; the thermal resistance gradient change rate is the rate of difference in the thermal resistance values ​​of adjacent sub-regions; cross-region smoothing correction is an optimization method to eliminate sudden changes in regional boundary parameters; the reconstruction weight factor is the weighted superposition value of the contact point resistance parameter and the heat dissipation compensation parameter; the frequency domain amplitude reverse adjustment is an operation to reduce the amplitude of the interference component according to the weight factor; the historical compensation error data is the deviation between the past compensation results recorded in the database and the actual value; the deviation amount is the degree of difference between the current compensation coefficient and the historical error data; the set threshold is the error critical value for determining whether the compensation coefficient meets the standard.

[0133] In an embodiment of the present application, first, step 1051 is used to accurately locate the coordinate position of the physical contact point between the compensation wire and the object under test from the thermal resistance distribution map, and the thermal resistance value corresponding to the coordinate point is extracted as the wire contact point resistance parameter. For example, the thermal resistance value of the coordinate position X8Y6 obtained by the thermal resistance distribution map index system is 65 joules per degree Celsius, which directly represents the heat transfer resistance characteristics of the wire contact interface.

[0134] Next, step 1052 retrieves key parameters for optimizing the heat dissipation channel from the compensation database according to the heat dissipation control sub-region number index, including heat dissipation power parameters such as 4000 revolutions per minute and distribution spacing parameters such as 6 mm, and executes a four-level calculation process for the heat dissipation compensation adjustment parameters: the first level normalizes the heat dissipation power parameter, i.e., scaling the original value to the range of 0 to 1; the second level multiplies the normalized power parameter by the distribution spacing parameter to obtain a product parameter, for example, the normalized power value of 0.8 multiplied by the spacing value of 6 mm is 4.8; the third level takes the arithmetic square root of the product parameter and calculates the reciprocal to generate the initial parameter, for example, the square root of 4.8 is approximately 2.19, and the reciprocal is 0.456; the fourth level performs cross-region smoothing correction based on the thermal resistance gradient change rate of adjacent sub-regions provided by the thermal resistance distribution map. For example, when the thermal resistance gradient rise rate of the adjacent region on the right is 0.5 joules per second, a 10% positive correction is applied to the initial parameter, and the final output is a heat dissipation compensation adjustment parameter of 0.502.

[0135] Next, step 1053 is used to weight the conductor contact point resistance parameters and the heat dissipation compensation adjustment parameters according to a preset weight ratio. The typical weight distribution is 60% for the resistance parameter and 40% for the adjustment parameter. For example, 65 joules per degree Celsius multiplied by the weight coefficient 0.6 plus 0.502 multiplied by the weight coefficient 0.4 generates a reconstruction weight factor of 0.70.

[0136] Then, in step 1054, the frequency domain amplitude of the identified interference signal component is reversely adjusted according to the weight factor. Specifically, the original amplitude of the 120 to 150 Hz frequency band is multiplied by 0.70 times the attenuation coefficient to generate an initial dynamic compensation coefficient of 0.66.

[0137] Finally, step 1055 is used to retrieve the historical compensation error data of the last 15 times stored in the compensation database, and its arithmetic mean value of 0.5 degrees Celsius is calculated as a benchmark. The initial dynamic compensation coefficient is corrected by closed-loop iterative correction through the gradient descent algorithm: the first iteration calculates the deviation of 0.16 degrees Celsius, and adjusts the coefficient to 0.61. The second iteration deviation is 0.10 degrees Celsius, which is adjusted to 0.59. The third iteration deviation is 0.09 degrees Celsius, which is adjusted to 0.60. The fourth iteration deviation is reduced to 0.08 degrees Celsius and is lower than the set threshold of 0.1 degrees Celsius. The iteration is terminated and the final dynamic compensation coefficient of 0.63 is output, thereby completing the full closed-loop optimization process from parameter extraction, weight calculation to coefficient correction.

[0138] In a practical application, during the compensation optimization process for a heat dissipation substrate, the thermal resistance value of 18 joules per degree Celsius at the contact points X8 and Y15 of the compensation wire was first extracted from a thermal resistance distribution map as the resistance parameter. Simultaneously, the optimized heat dissipation channel power parameters of 65 watts and spacing of 4 cm were retrieved from the database. Next, a weighted calculation was performed to superimpose the resistance and heat dissipation parameters at a weight ratio of 0.6 and 0.4, generating a reconstructed weight factor of 0.52. Subsequently, an inverse adjustment of the weight factor (1.92) was applied to the amplitude of the interference signal component in the 10 to 50 Hz frequency band, resulting in an initial dynamic compensation coefficient of 1.35. To further improve accuracy, the compensation error data from the last five runs, with a mean of 0.25 and a variance of 0.04, was extracted from the compensation database. The initial coefficient was iteratively corrected using a gradient descent algorithm. After three iterations, the compensation deviation significantly decreased from the initial 0.3 to 0.07, fully meeting the set threshold of less than 0.1. The final dynamic compensation coefficient of 1.28 was output and applied to the temperature correction system, achieving precise closed-loop control optimization.

[0139] In step 105 above, the thermal resistance parameters and heat dissipation compensation factors are combined to generate dynamic weight coefficients. This compensation coefficient is then optimized through closed-loop iterations via frequency-domain reconstruction and historical error feedback. This step establishes a cross-domain coupling link between thermodynamic parameters and signal compensation, forming a self-optimizing compensation mechanism that continuously improves the long-term stability and adaptability of the temperature monitoring system.

[0140] The following is a complete embodiment of steps 101 to 105:

[0141] In a material thermal property test, a heating device was first set to a constant temperature increase rate of 10°C / minute. The thermocouple began recording from an initial temperature of 25°C and reached a stable temperature of 85°C in 6 minutes. Simultaneously, an infrared thermal imager simultaneously acquired surface temperature data from the object under test, generating a temperature curve with continuous time stamps. The heating process was then divided into 1-minute time windows. Within each window, the ratio of the thermocouple delay time to the temperature rise was calculated. For example, a 22-second delay in the third window corresponded to a 12-degree Celsius temperature rise, resulting in a ratio of 1.83 seconds / degree Celsius. The thermal conductivity parameters of the contact area between the object under test and the thermocouple were then combined to calculate the thermal conductivity value based on the material's thermal conductivity and the contact area. A multivariate regression model was then used to couple the delay data, temperature change, and thermal conductivity parameters at different heating rates. Finally, all characteristic parameters were categorized by a heating rate of 10°C / minute and stored in a compensation database to form an index structure.

[0142] In the material surface temperature analysis, 16 temperature sensors are first evenly arranged at intervals of 10 cm, and the temperature values ​​at each point are collected at a frequency of 0.5 Hz. By comparing the temperature distribution at the same time, it is clear that heat is transferred from 92 degrees Celsius in the high-temperature core area A to 68 degrees Celsius in the edge area B, and the path direction is southeast. Then, the transfer path is divided into sections every 15 cm, and the heat change per unit time in the second section is calculated to be 180 joules, corresponding to a temperature difference of 10 degrees Celsius, and the thermal resistance value is 18 joules per degree Celsius. The thermal resistance values ​​of each section are then mapped to the surface coordinate grid to generate an initial thermal resistance distribution map, and two consecutive thermal resistance peak areas are marked by cluster analysis, located near area C and area D, respectively, where the thermal resistance values ​​exceed 22 joules per degree Celsius.

[0143] For the peak areas C and D marked in the thermal resistance distribution diagram, a circular heat dissipation control sub-area with a radius of 5 cm is first delineated using the central coordinates of area C. Based on the total area of ​​12 square centimeters of thermal resistance peak in this area, the power of the heat sink is increased from 40 watts to 55 watts, and the spacing between the heat sinks is shortened from 10 cm to 6 cm. For area D, because the peak area is small and the distribution is sparse, the power is only slightly adjusted from 40 watts to 48 watts while maintaining the original spacing. The heat sink is then arranged into two parallel channels along the direction of the heat transfer path, with channel widths of 3 cm and 4 cm respectively. Finally, the adjusted power parameters, spacing parameters, and channel layout are integrated into temperature compensation efficiency parameters and updated to the compensation database according to the sub-area number.

[0144] In the dynamic signal processing stage, the response signal output by the thermocouple is first aligned with the time axis of the infrared temperature data, and the signal segment is intercepted according to a fixed window of 1 minute. Based on the thermal conductivity correction coefficient of 0.78 in area C in the compensation database, the dynamic signal amplitude is scaled to generate a standardized waveform. The signal is then decomposed into 0-50 Hz, 50-100 Hz, and 100-200 Hz frequency bands through fast Fourier transform. Analysis shows that the amplitude of the 50-100 Hz frequency band accounts for 58%, significantly exceeding the preset threshold. The segments in this frequency band that fluctuate in the opposite direction to the temperature increase trend are further extracted. For example, when the temperature rises at 0.3 degrees Celsius per second, the signal amplitude continues to drop from 2.1 volts to 1.5 volts for 35 seconds. It is finally determined that such components are interference signals caused by the thermal capacitance characteristics of the compensation wire.

[0145] In the dynamic compensation optimization, the thermal resistance value of the wire contact point in area C, 20 joules per degree Celsius, is first extracted from the thermal resistance distribution map. Combined with the heat dissipation power parameter of 55 watts and the spacing parameter of 6 cm in this area, a reconstruction factor of 0.61 is generated through weight calculation. Subsequently, the amplitude of the interference signal in the 50-100 Hz frequency band is reversely adjusted to generate an initial dynamic compensation coefficient of 1.42. Then, historical error data, including a mean of 0.18 and a variance of 0.03, are retrieved from the compensation database, and iterative correction is performed using a gradient descent algorithm. After three iterations, the compensation deviation is reduced from the initial 0.25 to 0.09, meeting the threshold requirement and outputting the final compensation coefficient of 1.35, which is integrated into the correction system to eliminate the thermal capacitance interference error.

[0146] Figure 2 The present invention provides a schematic diagram of a dynamic compensation processing system for thermocouple signals, as shown in FIG. Figure 3 As shown, the system includes:

[0147] An acquisition module 21 is configured to acquire response delay data of thermocouples at different heating rates, simultaneously acquire temperature change data of the surface of the object being measured, establish a correlation between the response delay data and the temperature change data, and couple the correlation with a heat conduction efficiency parameter to form a compensation database of multiple characteristic parameters;

[0148] A generating module 22 is configured to set a heat dissipation device on the surface of the object to be measured, obtain temperature distribution data of the object to be measured, analyze the heat transfer path based on the temperature distribution data, and generate a thermal resistance distribution map;

[0149] an adjustment module 23 for adjusting the heat dissipation power and distribution spacing of the heat dissipation device according to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram to form an optimized heat dissipation channel, and updating the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel to the compensation database;

[0150] a separation module 24 for performing time domain alignment on the dynamic response signal output by the thermocouple and the temperature change data, performing frequency domain decomposition on the dynamic response signal using multiple parameters in the compensation database, and separating interference signal components caused by the thermal capacitance characteristics of the compensation wire;

[0151] The compensation module 25 is used to combine the wire contact point resistance parameters extracted from the thermal resistance distribution diagram and the compensation adjustment parameters of the optimized heat dissipation channel to perform frequency domain reconstruction on the interference signal component to generate a dynamic compensation coefficient, and perform closed-loop iterative correction on the dynamic compensation coefficient based on the historical compensation error data recorded in the compensation database.

[0152] Figure 2 The dynamic compensation processing system of the thermocouple signal can be performed Figure 1 The implementation principle and technical effects of the dynamic compensation processing method for thermocouple signals described in the illustrated embodiment will not be described in detail. The specific manner in which each module and unit performs operations in the dynamic compensation processing system for thermocouple signals in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated on here.

[0153] In one possible design, Figure 2 The dynamic compensation processing system for thermocouple signals of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0154] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0155] The processing component 32 is used for the above Figure 1 The embodiment provides a method for dynamic compensation of thermocouple signals.

[0156] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0157] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0158] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0159] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0160] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0161] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0162] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The dynamic compensation processing method of the thermocouple signal of the embodiment shown is shown.

[0163] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0165] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A dynamic compensation processing method for thermocouple signals, characterized in that: include: Collect response delay data of thermocouples at different heating rates, synchronously obtain temperature change data of the surface of the measured object, establish a correlation between the response delay data and the temperature change data, and couple the correlation with the heat conduction efficiency parameter to form a compensation database of multiple characteristic parameters; Setting a heat dissipation device on the surface of the object to be measured, obtaining temperature distribution data of the object to be measured, analyzing the heat transfer path based on the temperature distribution data and generating a thermal resistance distribution map; According to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram, the heat dissipation power and distribution spacing of the heat dissipation device are adjusted to form an optimized heat dissipation channel, and the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel is updated to the compensation database; Aligning the dynamic response signal output by the thermocouple with the temperature change data in the time domain, performing frequency domain decomposition on the dynamic response signal using multiple parameters in the compensation database, and separating the interference signal component caused by the thermal capacitance characteristics of the compensation wire; Combined with the wire contact point resistance parameters extracted from the thermal resistance distribution map and the compensation adjustment parameters of the optimized heat dissipation channel, the interference signal component is reconstructed in the frequency domain to generate a dynamic compensation coefficient, and the dynamic compensation coefficient is corrected by closed-loop iterative correction based on the historical compensation error data recorded in the compensation database.

2. The method according to claim 1, characterized in that Aligning the dynamic response signal output by the thermocouple with the temperature change data in the time domain, performing frequency domain decomposition on the dynamic response signal using multiple parameters in the compensation database, and separating the interference signal component caused by the thermal capacitance characteristic of the compensation wire, including: Align the time axis starting point of the dynamic response signal output by the thermocouple with the time axis starting point of the temperature change data, and then segment the two aligned signals according to a fixed time window; Based on the time window boundary after segmented interception, dynamically scaling the waveform amplitude of the dynamic response signal according to multiple parameters in the compensation database to generate a standardized dynamic signal; The standardized dynamic signal is decomposed in the frequency domain to extract the amplitude distribution characteristics of each frequency band, and signal components that meet the conditions are screened out and marked as interference signal components caused by the thermal capacitance characteristics of the compensation wire.

3. The method according to claim 1, characterized in that Combining the wire contact point resistance parameter extracted from the thermal resistance distribution map and the compensation adjustment parameter of the optimized heat dissipation channel, reconstructing the interference signal component in the frequency domain to generate a dynamic compensation coefficient, and performing closed-loop iterative correction on the dynamic compensation coefficient based on the historical compensation error data recorded in the compensation database, including: Extracting the coordinate position of the contact point between the compensation wire and the measured object from the thermal resistance distribution map, and obtaining the thermal resistance value corresponding to the coordinate position as the wire contact point resistance parameter; Reading the heat dissipation power parameter and the distribution spacing parameter of the optimized heat dissipation channel from the compensation database, calculating the heat dissipation power parameter and the distribution spacing parameter to generate a heat dissipation compensation adjustment parameter; Performing weighted superposition on the wire contact point resistance parameter and the heat dissipation compensation adjustment parameter to generate a reconstruction weight factor of the interference signal component; According to the reconstruction weight factor, reversely adjusting the frequency domain amplitude of the interference signal component to generate an initial dynamic compensation coefficient; Retrieving historical compensation error data from the compensation database, and calculating a deviation between the initial dynamic compensation coefficient and the historical compensation error data; The initial dynamic compensation coefficient is corrected in a closed loop iterative manner according to the deviation, and the dynamic compensation coefficient is output when the deviation is lower than a set threshold.

4. The method according to claim 1, wherein According to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram, the heat dissipation power and distribution spacing of the heat dissipation device are adjusted to form an optimized heat dissipation channel, and the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel is updated to the compensation database, including: Extract the center coordinates of all thermal resistance peak areas from the thermal resistance distribution map, and define the heat dissipation control sub-areas with the center coordinates as the center and a preset radius as the range; Adjusting the heat dissipation power of the heat sink according to the total area of ​​the thermal resistance peak area in the heat dissipation control sub-area, and adjusting the distribution spacing of the heat sink according to the distribution density of the thermal resistance peak area in the heat dissipation control sub-area; In the heat dissipation control sub-area, heat dissipation devices are arranged according to the adjusted heat dissipation power and distribution spacing, so that the heat dissipation devices are arranged in a straight line along the transmission path to form an optimized heat dissipation channel; The parameters of the optimized heat dissipation channel are updated into the compensation database as temperature compensation efficiency parameters.

5. The method according to claim 1, wherein A heat dissipation device is provided on the surface of the object to be measured, and temperature distribution data of the object to be measured is obtained. A heat transfer path is analyzed based on the temperature distribution data to generate a thermal resistance distribution map, including: Evenly arranging multiple temperature collection points on the surface of the object to be measured, collecting temperature values ​​of each point at consecutive time points during the heating process, and generating temperature distribution data; Determining the direction of heat transfer from the highest temperature region to the lowest temperature region based on the temperature difference between each point at the same time in the temperature distribution data; Dividing the surface of the object under test into a plurality of continuous heat transfer segments along the direction of the heat transfer path, and calculating the ratio of the heat change per unit time to the corresponding temperature change in the heat transfer segment as the thermal resistance value of each segment; In a coordinate grid on the surface of the object being measured, mapping the thermal resistance values ​​to corresponding coordinate regions to generate an initial thermal resistance distribution map; Performing regional cluster analysis on the initial thermal resistance distribution map, marking the region in the continuous coordinate region where the thermal resistance value exceeds a set threshold as a thermal resistance peak region, so as to generate a thermal resistance distribution map.

6. The method according to claim 1, wherein Collect response delay data of thermocouples at different heating rates, synchronously obtain temperature change data of the surface of the measured object, establish a correlation between the response delay data and the temperature change data, and couple the correlation with the heat conduction efficiency parameter to form a compensation database of multiple characteristic parameters, including: The heating device is controlled to heat up at a constant rate, and the time difference between the initial temperature and the stable temperature of the thermocouple is recorded as the response delay data in each mode; Synchronously collecting the actual temperature value of the surface of the measured object at the same time point in each mode through a non-contact temperature measuring device to generate temperature change data; Matching the response delay data under each heating mode with the temperature change amplitude within the same time window in the temperature change data in sections, calculating the ratio of the delay time in the response delay data to the temperature rise amplitude of the corresponding section in the temperature change data, and establishing a correlation between delay and temperature; Extracting thermal conductivity efficiency parameters of the contact area between the object under test and the thermocouple, performing multi-dimensional coupling modeling based on the delay time at each heating rate in the association relationship and combining the thermal conductivity efficiency parameters, generating a characteristic parameter combination, and storing the characteristic parameter combination as an index structure classified by heating rate to form the compensation database.

7. The method according to claim 2, characterized in that Performing frequency domain decomposition on the standardized dynamic signal, extracting the amplitude distribution characteristics of each frequency band, screening out signal components that meet the conditions, and marking them as interference signal components caused by the thermal capacitance characteristics of the compensation wire, including: Decomposing the normalized dynamic signal into signal components in a plurality of preset frequency intervals, and calculating the ratio of the sum of the amplitudes of the signal components to the sum of the amplitudes of all frequency intervals; Comparing the ratio with a preset ratio threshold corresponding to the thermal capacitance characteristic of the compensation wire in the compensation database, and if the ratio of the interval exceeds the preset ratio threshold, determining that the signal component of the frequency interval is an interference signal component caused by the thermal capacitance characteristic of the compensation wire; Extracting, according to the frequency interval corresponding to the interference signal component, a segment in the signal waveform of the frequency interval in which the amplitude fluctuation direction is opposite to the temperature change trend in the temperature change data as a reverse interference segment; The duration of the reverse interference segment is correlated with the temperature change rate of the corresponding time window in the temperature change data. If the correlation calculation result is lower than the set negative threshold, the reverse interference segment is merged into a complete interference signal component and marked.

8. The method according to claim 3, characterized in that Reading the heat dissipation power parameter and the distribution spacing parameter of the optimized heat dissipation channel from the compensation database, calculating the heat dissipation power parameter and the distribution spacing parameter to generate the heat dissipation compensation adjustment parameter, including: According to the number of the heat dissipation control sub-region marked in the thermal resistance distribution map, the heat dissipation power parameter of the corresponding heat dissipation device is extracted from the compensation database according to the region index, and the distribution spacing parameter within the heat dissipation control sub-region is simultaneously obtained; Normalizing the heat dissipation power parameter, and multiplying the normalized heat dissipation power parameter by the distribution spacing parameter to obtain a product parameter of each heat dissipation control sub-region; Taking the arithmetic square root of the product parameter and then calculating the reciprocal to generate an initial parameter for heat dissipation compensation adjustment; According to the thermal resistance gradient change rate of adjacent sub-regions in the thermal resistance distribution diagram, the initial heat dissipation compensation adjustment parameter is corrected across regions smoothly, and the heat dissipation compensation adjustment parameter is output.

9. A dynamic compensation processing system for thermocouple signals, characterized in that: include: An acquisition module is used to collect response delay data of thermocouples at different heating rates, synchronously obtain temperature change data of the surface of the measured object, establish a correlation between the response delay data and the temperature change data, and couple the correlation with the heat conduction efficiency parameter to form a compensation database of multiple characteristic parameters; A generating module, configured to set a heat dissipation device on the surface of the object to be measured, obtain temperature distribution data of the object to be measured, analyze the heat transfer path based on the temperature distribution data, and generate a thermal resistance distribution map; an adjustment module, configured to adjust the heat dissipation power and distribution spacing of the heat dissipation device according to the distribution characteristics of the thermal resistance peak area in the thermal resistance distribution diagram to form an optimized heat dissipation channel, and update the temperature compensation efficiency parameter corresponding to the optimized heat dissipation channel to the compensation database; a separation module, configured to perform time domain alignment on the dynamic response signal output by the thermocouple and the temperature change data, perform frequency domain decomposition on the dynamic response signal using multiple parameters in the compensation database, and separate an interference signal component caused by the thermal capacitance characteristics of the compensation wire; A compensation module is used to combine the wire contact point resistance parameters extracted from the thermal resistance distribution map and the compensation adjustment parameters of the optimized heat dissipation channel to perform frequency domain reconstruction of the interference signal component to generate a dynamic compensation coefficient, and perform closed-loop iterative correction on the dynamic compensation coefficient based on the historical compensation error data recorded in the compensation database.

10. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the dynamic compensation processing method for thermocouple signals as described in any one of claims 1 to 8.

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