Temperature calibration method and system for temperature measuring device

Abstract

The invention relates to the field of detection of temperature measuring devices, in particular to a temperature calibration method and system for a temperature measuring device, and the method is executed in an automatic system, and comprises the following steps: fixing a temperature measuring device to be calibrated and a reference temperature device on a heat conduction substrate in a coplanar manner; the temperature of the cavity is adjusted to a preset calibration point through a temperature control system, and heat balance dynamic judgment is executed; at least two bias currents with different amplitudes are applied to a to-be-calibrated temperature measuring device, corresponding original voltage signals are collected, a zero-power theoretical resistance value for eliminating the influence of the self-heating effect is calculated based on the original voltage signals, a corresponding reference temperature T is adopted as a data pair, parameter fitting is conducted on the resistance-temperature relation model, and the temperature measuring device is calibrated. A temperature correction calculation formula is established, a synthesis uncertainty evaluation model is established, each uncertainty source is analyzed and synthesized, and the synthesis uncertainty is calculated and output. According to the method, the risk of calibration failure caused by thermal stress or environmental interference is reduced.

Description

Technical Field

[0001] This invention relates to the field of temperature measuring device detection, and more specifically, to a temperature calibration method and system for a temperature measuring device. Background Technology

[0002] Temperature is one of the most fundamental physical quantities in industrial production, scientific research, and daily life. Various temperature measuring devices are widely used for accurate temperature measurement. However, these devices (especially high-sensitivity resistance temperature measuring devices) have significant variations in their factory characteristics, and the physical signal they output (such as resistance value) does not have a simple linear relationship with temperature. Therefore, precise temperature calibration must be performed before use to establish an accurate conversion relationship between their signal and temperature.

[0003] Conventional temperature calibration methods typically include the following steps: placing the device to be calibrated and a high-precision reference thermometer together in a controlled constant temperature environment; then simultaneously recording the readings of the reference thermometer and the output signal of the device to be calibrated at multiple preset temperature points; finally, establishing a mathematical model (such as a polynomial or the classical Steinhart-Hart equation) through data fitting as the calibration curve for the device.

[0004] When a temperature sensing device applies a bias current for measurement, it inevitably generates Joule heating, also known as the "self-heating effect." This causes the device's own temperature to be slightly higher than the actual temperature of the environment. This error is particularly pronounced in high-precision or cryogenic environments, and traditional methods often fail to correct for it. Secondly, traditional methods are rather crude in determining whether the system has reached true thermal equilibrium. They typically rely solely on the stability of the temperature displayed by the temperature control device, ignoring the thermal hysteresis between the measured device and the environment. Data acquisition under unsteady conditions introduces significant errors.

[0005] Traditional calibration point selection typically employs a dense sampling strategy with fixed intervals to cover the entire temperature range. This "one-size-fits-all" approach results in a large number of redundant and time-consuming measurements in temperature ranges where the device response is smooth, while in critical temperature ranges with drastic nonlinear response, the number of sampling points may be insufficient, creating a dilemma between efficiency and accuracy. On the other hand, most of the temperature control systems used are based on traditional PID controllers. For complex nonlinear time-varying systems such as high vacuum and large heat capacity systems, PID controller parameter tuning is difficult, often leading to slow response, large temperature overshoot, and long settling time during the heating and cooling process, which seriously affects the overall calibration efficiency.

[0006] In summary, existing temperature measurement device calibration methods have several technical problems that urgently need to be addressed in terms of accuracy, efficiency, and the reliability and traceability of the results. Summary of the Invention

[0007] Therefore, the purpose of this invention is to provide a temperature calibration method and system for a temperature measuring device, which integrates high precision, high efficiency, and high intelligence, and can provide a complete uncertainty assessment.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A temperature calibration method for a temperature measuring device, the method being executed in an automated system, includes the following steps:

[0010] S1. Environment setup: The temperature measuring device to be calibrated and the reference temperature device are fixed coplanarly on a thermally conductive substrate and placed in a high-vacuum insulated cavity to construct a thermophysical equivalent field.

[0011] S2. Stable state: The temperature of the cavity is adjusted to the preset calibration point by the temperature control system, and the dynamic judgment of thermal balance is performed until the temperature fluctuation rate in the cavity meets the preset stability condition.

[0012] S3. Measurement and Calibration: Apply at least two bias currents of different amplitudes to the temperature measuring device to be calibrated, which is already in thermal equilibrium, acquire the corresponding raw voltage signals, and calculate the zero-power theoretical resistance value to eliminate the influence of self-heating effect using an extrapolation algorithm. ;

[0013] S4. Modeling and Calibration: The original voltage signal is denoised, and then the theoretical resistance value is used. Using the corresponding reference temperature T as a data pair, the parameters of the preset resistance-temperature relationship model that can describe the nonlinear characteristics of a wide temperature range are fitted, thereby establishing a unique temperature correction calculation formula for each temperature measuring device to be calibrated.

[0014] S5. Uncertainty Assessment: Establish a combined uncertainty assessment model, analyze and combine various uncertainty sources, calculate and output the combined uncertainty of the temperature correction calculation formula within the preset operating temperature range. .

[0015] The present invention is further configured such that the resistance-temperature relationship model in step S4 is defined by the following formula:

[0016]

[0017] The coefficients A, B, and C are a unique set of parameters characterizing the physical characteristics of the specific device to be calibrated. A is the constant offset term of the model, B is the logarithmic coefficient used to characterize the main linear relationship between the logarithm of resistance and the reciprocal of temperature, and C is the higher-order nonlinear correction coefficient used to accurately compensate for the device's response characteristics that deviate from this linear relationship in a wide temperature range.

[0018] The present invention is further configured such that: the noise reduction process in step S4 adopts a wavelet-empirical mode decomposition composite algorithm. This algorithm performs preliminary decomposition of the signal through multi-layer wavelet transform, and then identifies and filters out a number of preset intrinsic mode functions containing noise through empirical mode decomposition.

[0019] The present invention is further configured such that: the temperature control system used in step S2 employs an adaptive temperature control algorithm, which is based on a state-space model, specifically as follows:

[0020] ;

[0021] Where T[k] is the system temperature at the current time k, u[k] is the applied control input, B is the control input matrix, w[k] is the process noise; A(k) is the state transition matrix, which is an adaptive parameter whose value is dynamically adjusted in each control cycle based on the real-time temperature error e[k] and its rate of change Δe[k] through a preset fuzzy logic rule base.

[0022] The present invention is further configured such that: the number and position of the preset calibration points in step S2 are determined by an adaptive strategy. The adaptive strategy first selects a set of initial equally spaced calibration points in the target temperature range, and then performs preliminary modeling in steps S3 and S4 on the set of points. After that, it calculates the fitting residuals of the model in each interval, and automatically inserts new calibration points in the interval with the largest residuals. The process is iterated until the fitting residuals of all intervals are less than a preset accuracy threshold.

[0023] The present invention is further configured such that: the preset stability condition in step S2 is specifically: the absolute value of the rate of change of the temperature reading of the reference temperature device within a continuous 60-second time window is less than... .

[0024] The present invention is further configured such that: the synthesis uncertainty assessment model in step S5 has the following form:

[0025]

[0026] in, For model fitting residuals, Errors introduced by noise reduction processing This represents the deviation compared to non-contact infrared measurement.

[0027] A temperature calibration system for a temperature measuring device, comprising:

[0028] Temperature control and environmental module;

[0029] Programmable power supply module;

[0030] Multi-channel data acquisition module;

[0031] The central processing and control unit is configured to execute steps S2 to S5 through a built-in software algorithm.

[0032] The present invention is further configured such that the thermally conductive substrate is yttrium-doped alumina ceramic.

[0033] The invention is further configured such that: the high-vacuum insulated cavity is a Dewar flask, the inner wall of which is provided with a multi-layer reflective coating, the coating being an alternating deposition of metal / dielectric thin films.

[0034] Compared with the shortcomings of the prior art, the beneficial effects of the present invention are as follows:

[0035] This method combines a precise adaptive temperature control algorithm with zero-power theoretical resistance value calculation to eliminate self-heating effects, thereby minimizing error accumulation during the measurement process and outputting a high-precision, high-reliability temperature correction formula.

[0036] It can effectively construct and maintain a highly stable temperature field across a wide temperature range, from cryogenic to high-temperature. After comprehensive uncertainty analysis and evaluation, the final calibration results have extremely low combined uncertainty within their preset operating temperature range, meeting the most stringent metrological requirements and reducing the risk of calibration failure due to thermal stress or environmental interference. Attached Figure Description

[0037] Figure 1 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0038] Reference Figure 1 The embodiments of the present invention will be further described below.

[0039] This invention provides a temperature calibration method for temperature measuring devices and a corresponding temperature calibration system for temperature measuring devices. This system can perform high-precision temperature calibration of various temperature measuring devices (such as resistance temperature detectors, thermocouples, infrared sensors, etc.) over a wide temperature range with low uncertainty in a highly controllable automated environment.

[0040] A temperature calibration system for temperature measuring devices, through an integrated modular design, achieves full automation from environmental setup, temperature control, signal measurement to data processing and calibration output. The system mainly includes the following core modules:

[0041] Temperature and Environment Module: Responsible for creating and maintaining a high-precision, stable, and thermophysically equivalent calibration environment.

[0042] High-vacuum insulated cavity: This cavity employs a Dewar flask structure design, with its inner walls coated with a multi-layered reflective coating composed of alternating deposited metal / dielectric thin films (such as a multi-layered aluminum / silicon oxide structure). This structure significantly reduces heat exchange (through radiation) between the cavity's interior and exterior, achieving highly efficient thermal insulation. A high-precision temperature sensor can be integrated within the cavity (for monitoring the cavity wall temperature), and an interface is provided for fixing the thermally conductive substrate. The designed vacuum level should reach a high vacuum to minimize heat loss or gain due to gas molecule conduction and convection.

[0043] Thermally conductive substrate: Yttrium-doped alumina ceramic was selected as the thermally conductive substrate. Yttrium-doped alumina ceramic exhibits excellent thermal conductivity and good thermal stability over a wide temperature range (including both low and high temperatures). Especially under cryogenic conditions such as -196°C (liquid nitrogen temperature), its critical fracture toughness is not lower than […]. Its properties are far superior to ordinary ceramics, effectively preventing brittle fracture caused by thermal stress. The substrate surface is designed with structural grooves or mounting holes for precise positioning and fixing of the temperature measuring device to be calibrated and the high-precision reference temperature device, ensuring that the two are fixed on the same plane and achieving excellent thermal coupling.

[0044] Precision temperature control system: Composed of heater, cooling interface, temperature sensor (monitoring point inside the cavity or substrate integrated sensor), and corresponding drive and feedback circuits. This system is the foundation for achieving precise temperature setting.

[0045] Programmable Power Supply Module: This module is a high-precision, low-noise, low-drift current or voltage source capable of outputting a stable and precisely programmable bias current. Its primary task is to provide at least two constant bias currents of different amplitudes to the temperature sensing device being calibrated, inducing a predictable self-heating effect during measurement. The current accuracy (e.g., ±0.01%) and stability of this module are crucial for accurately measuring the voltage response of the device under different currents.

[0046] Multi-channel data acquisition module: It includes a high-resolution (e.g., 24-bit or higher), high-sampling-rate (e.g., >100kS / s) analog-to-digital converter (ADC) capable of simultaneously acquiring the raw voltage signal from the temperature sensing device to be calibrated and the high-precision temperature reading from the reference temperature device. Simultaneously, it should possess extremely low input voltage noise and high input impedance to avoid undue load effects on the measured signal. The multi-channel design allows for the simultaneous acquisition of multiple temperature sensing devices and reference temperatures.

[0047] Central Processing and Control Unit: This unit is typically a high-performance industrial PC or a dedicated embedded controller, with built-in foundational software algorithms at its core. These algorithms are configured to execute preset control programs, collaboratively completing the entire calibration method. It coordinates the work of all modules, performing environment setup, temperature control, measurement sequences, data processing (noise reduction, model fitting), uncertainty assessment, and final calibration data output.

[0048] Based on the above system architecture, the temperature calibration method is executed step by step according to steps S1 to S5 in the claims.

[0049] S1. Environment Setup: The temperature sensing device to be calibrated (transistor or thermistor) and a precisely calibrated reference temperature device (a known low-temperature temperature sensor such as a cooled cavity sensor of a GaInSb infrared detector) are coplanarly fixed on the aforementioned yttrium-doped alumina ceramic thermally conductive substrate. The fixing method uses adhesives with low thermal expansion coefficients or precision mechanical clamps to ensure that the sensing regions of the two devices are in the same two-dimensional plane and form a good thermal conduction path with the substrate. Subsequently, the substrate assembly is carefully placed in a vacuum (…). The device is housed within a high-vacuum insulated cavity in a Dewar flask. In this way, a thermophysical equivalent field is constructed that maximizes the isolation from external thermal disturbances and places the device to be calibrated, the reference device, and the substrate in the same uniform temperature field.

[0050] S2. Temperature setting and stabilization: The temperature control system (driven by the central processing and control unit) starts to adjust the cavity temperature to the first preset calibration point. During this process, the core of the temperature control algorithm is the adaptive temperature control algorithm, which is based on a dynamically adjusted state space model.

[0051] The mathematical form of the model is: ;

[0052] Where T[k] represents the system state vector at discrete time step k, which usually includes the system's temperature measurement value (from a reference temperature device) and its first or second derivative (i.e., the rate of temperature change and the rate of change of the rate of change), used to describe the dynamic behavior of the system.

[0053] A(k) is the state transition matrix, which is the core adaptive parameter of the algorithm. It describes how the system state evolves from time k to time k+1. Its value is dynamically adjusted within each control cycle based on the real-time feedback temperature error e[k] and its rate of change Δe[k], using a pre-defined fuzzy logic rule base. When the temperature approaches the setpoint, the adjustment of A(k) will stabilize the system and suppress overshoot; when the temperature deviates significantly, it will enhance the system's response speed.

[0054] B is the control input matrix, which maps external control signals to changes in the system state.

[0055] u[k] is the applied control input, which is usually a voltage or current signal applied to the heater inside the cavity to change the cavity temperature.

[0056] w[k] is the process noise, representing random disturbances not included in the model, unknown system parameter changes, and sensor noise, etc.

[0057] The temperature control system precisely controls the heating / cooling power based on this model, bringing the cavity temperature close to the preset calibration point. Simultaneously, the system performs dynamic thermal balance determination, continuously monitoring the readings of the reference temperature sensor. When the absolute value of the rate of change of the reference temperature sensor's reading within a continuous 60-second time window is less than... At that time, it is assumed that the temperature fluctuation rate of the cavity meets the preset stability condition and the cavity temperature reaches thermal equilibrium.

[0058] S3. Measurement and Calibration: After the cavity temperature reaches thermal equilibrium and the stability condition is met (the rate of temperature change within the stability time window is less than...), For the temperature measuring device to be calibrated at this stable temperature, the programmable power supply module precisely controls the application of at least two different amplitudes (e.g., current amplitudes of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... and ,and The bias current is set to a value, for example, 1mA and 2mA. At each bias current, the original voltage signal corresponding to the temperature measuring device to be calibrated is acquired (e.g., measured by the four-wire method).

[0059] The self-heating effect causes the measured resistance value to be higher than the zero-power resistance value when there is no actual power loss. The power dissipation of the device is as follows when different currents I are applied: The self-heating generated will cause a change in resistance. Assuming that within a small range, this shift is approximately linearly related to power, or approximated by a model ( Then measure the resistance. By measuring the resistance (or voltage) at at least two currents, and using these data points, an extrapolation algorithm can be employed to calculate the theoretical zero-power resistance value, eliminating the effects of self-heating. A common extrapolation method is linear extrapolation, which assumes... (Here, k is a coefficient related to the temperature coefficient of resistance and thermal resistance), fitted using the least squares method. and Find the relationship. =0 The value is... .

[0060] S4. Modeling and Calibration: The collected raw voltage signal (or the raw resistance value calculated from voltage and current) often contains high-frequency noise and low-frequency drift. Therefore, the collected signal is first denoised. This invention preferably uses a wavelet-empirical mode decomposition (EMD) composite algorithm. This algorithm first uses multi-layer wavelet transform and Daubechies 4 wavelet to perform preliminary decomposition of the raw signal, separating the signal into approximate (low-frequency) and detail (high-frequency) coefficients of different frequency components. Then, empirical mode decomposition is applied to the decomposed low-frequency part or the entire signal. The EMD algorithm can adaptively decompose the signal into a set of intrinsic mode functions and a residual term. By analyzing the spectral characteristics and physical meaning of IMFs, several preset IMFs containing specific noise components (such as equipment noise and environmental fluctuation noise) can be identified and filtered out, thereby obtaining a smooth signal that retains the true temperature response.

[0061] The processed signal (or the calculated signal directly) The resistance-temperature value and the corresponding reference temperature T with known accuracy are used as data pairs. For each temperature measuring device to be calibrated, a preset resistance-temperature relationship model capable of describing the nonlinear characteristics over a wide temperature range is used for parameter fitting. The preferred resistance-temperature relationship model of this invention is the improved Steinhart-Hart empirical model, whose mathematical form is: ;

[0062] Where T represents absolute temperature, and the unit is Kelvin (K). The zero-power theoretical resistance value is calculated through step S3, in ohms (Ω); A is the constant offset term of the model, representing a fundamental offset at extremely low resistance values ​​(or corresponding to extremely high temperatures); B is the logarithmic coefficient, used to characterize... The main linear relationship between the logarithm of temperature and the reciprocal of temperature is the primary parameter describing the response of most thermistors or platinum resistance thermometers in the medium temperature range.

[0063] C is the coefficient of the higher-order nonlinear correction term, used to accurately compensate for the device's response characteristics that deviate from linearity over a wide temperature range (especially at low or high temperatures), thus compensating for the Steinhart-Hart model ( The limitations of the power terms in () allow for more flexible fitting of complex curves.

[0064] By repeating steps S2 and S3 at multiple set calibration points, multiple sets of data were collected. The data pairs are then fitted to the coefficients A, B, and C of the Steinhart-Hart model using least squares or other weighted least squares methods (considering the uncertainty of the data points). The fitting results will yield a unique set of parameters specific to each temperature measuring device to be calibrated. By substituting these fitted parameters back into the model equation, a specific temperature correction calculation formula can be established to accurately convert any measured R_0 value into a temperature value.

[0065] S5. Uncertainty Assessment: In order to quantify the reliability of the calibration results, this invention establishes a combined uncertainty assessment model and conducts a detailed analysis of the sources of various uncertainties in the calibration process, including: the measurement uncertainty of the reference temperature device: which mainly depends on the accuracy class and calibration factor of the reference device itself;

[0066] Uncertainty of cavity temperature stability: Derived from the temperature fluctuation rate in S2, it accumulates into a certain uncertainty during the steady-state time;

[0067] Zero power resistance value The derivation uncertainty stems from the accuracy of the applied current in S3, the noise and resolution of the voltage measurement, and the linearity assumption error of the extrapolation algorithm.

[0068] Model fit uncertainty ( ): The statistic of the residuals (i.e. the deviation between the actual T and the model-predicted T) generated by fitting the model parameters in S4.

[0069] Errors introduced by noise reduction processing ( ): Signal distortion or information loss introduced by the Wavelet-EMD algorithm in S4 when filtering out IMF.

[0070] Deviation compared with non-contact infrared measurement ( If infrared measurements are involved in the calibration process or final verification, the inherent sources of uncertainty, such as emissivity and emissivity, must be considered.

[0071] Estimate the sources of uncertainty mentioned above and convert them into standard uncertainties. Then, sum the squares according to the non-correlation principle and finally take the square root to obtain the combined uncertainty. The evaluation model is as follows:

[0072] The specific model provided in this invention is as follows:

[0073]

[0074] in This represents the uncertainty component caused by the model fit residuals. It is usually quantified by a goodness-of-fit index (such as root mean square error RMSE) or the standard error of the parameters.

[0075] This represents the error introduced by the noise reduction process, which can be estimated by applying the Wavelet-EMD algorithm to a known noise-free reference signal to assess the perturbation it causes to the true value.

[0076] This represents the deviation compared to non-contact infrared measurements. If the calibrated device will be used in an infrared system, this item quantifies the uncertainty caused by the differences between infrared measurement principles (such as emissivity, target emissivity, atmospheric attenuation) and contact (or indirect contact) calibration.

[0077] The yttrium-doped alumina ceramic substrate serves as a thermal connector and mechanical support, using its high thermal conductivity to uniformly transfer the temperature within the cavity to the temperature measuring and reference devices fixed on it. Its mechanical strength ensures that the structure supporting the devices will not deform or break due to thermal stress under extreme conditions such as low temperatures (e.g., -196°C), maintaining the precise relative position of the devices.

[0078] The Dewar flask base or sidewall features precision-machined mounting interfaces (e.g., threaded interfaces, locating keyways). The substrate is stably and securely fixed to a specific location within the cavity, typically the central region, through these interfaces to achieve optimal temperature uniformity. Fixtures (such as stainless steel bolts, mounting brackets) must be compatible with vacuum environments and cryogenic operation.

[0079] For devices with regular shapes, U-shaped or L-shaped clamps made of low thermal expansion ceramics or special alloys (such as Invar) can be designed and used with fine-tuning screws to press the supporting end (non-temperature-sensing end) of the device into the positioning groove of the substrate. The contact points of the clamps should avoid the temperature-sensing area as much as possible, and the applied pressure should ensure good thermal contact, but not too much, so as not to damage the device or the substrate.

[0080] In certain situations, particularly when the device has an irregular shape or requires excellent thermal contact, high-vacuum compatible, wide-temperature-range (-200°C to +300°C) thermally conductive silver paste, epoxy resin, or ceramic adhesive can be used. The adhesive is applied to the contact surface between the device and the substrate, and after curing, forms a unified whole, ensuring no significant thermal resistance between them. Typically, the adhesive thickness is controlled on the micrometer scale and applied only to the device's support structure or package to avoid affecting the direct temperature response of the sensing element. "Coplanar fixation" is achieved through precision machining of the substrate surface and precise control of the device mounting fixtures / adhesive.

[0081] The Dewar flask cavity provides a high-vacuum, insulated space with a multi-layered reflective coating to reduce radiative heat transfer. Heating / cooling units within the cavity (such as heating films and cooler interfaces) should be uniformly arranged, or the substrate itself needs optimized design to aid temperature homogenization. Where all internal cables (e.g., cables connecting heaters and temperature sensors) must pass through the vacuum wall, high-vacuum seals (such as O-rings, metal gaskets, and glass-metal seals) should be used to ensure vacuum levels. The substrate and the mounted device assembly require a robust internal frame or support structure to prevent displacement during high-vacuum evacuation, device operation, or cavity movement.

[0082] Heaters are typically glued directly to or fixed in specific locations inside the cavity using thermally conductive adhesive to provide heat. Coolers (such as Peltier cooling plates or cryogenic fluid circulation interfaces) are also installed to provide cooling functionality.

[0083] Temperature sensors used to monitor cavity wall temperature or reference sensor readings need to be precisely installed at points representative of ambient temperature and connected to the data acquisition module. All connecting cables (power lines, signal lines) must be sealed when passing through the vacuum wall. All cables must be led out from external devices (programmable power supply, data acquisition module), introduced into the cavity through a vacuum seal, and connected to the corresponding heater, temperature sensing device (usually via its own leads), and reference device. The routing and securing of the cables should also avoid interfering with the thermal environment within the cavity; for example, ceramic insulators should be used for securing and channeling.

[0084] To verify the effectiveness of each technical point, we designed an experimental comparison.

[0085] Steinhart-Hart model parameter fitting and noise reduction algorithm:

[0086] Preferred range: Number of calibration points: 5-15. Temperature interval between each calibration point: dynamically adjusted according to the curvature of the target temperature zone (e.g., 10°C interval in the -196°C to 0°C zone, 20°C interval in the 0°C to 100°C zone, and 50°C interval in the 100°C to 300°C zone).

[0087] Please refer to Table 1 below for details:

[0088]

[0089] Comparative Example 1 above (single / inadequate noise reduction method): Using median filtering as the noise reduction method, its temperature accuracy (±0.075K) and noise reduction introduced error (±0.020K) are significantly higher than those of the example. Although median filtering can handle impulse noise, its smoothing effect on continuous noise or signals is not as good as Wavelet-EMD.

[0090] Comparative Example 2 (without noise reduction): Directly using the original data for fitting resulted in the worst temperature accuracy (±0.120K) and the largest model fitting residual (±0.080K). This fully demonstrates that noise reduction is essential for weak signals measured at low currents.

[0091] Adaptive temperature control algorithm and stability conditions:

[0092] Preferred parameters: Fuzzification / defuzzification methods in the fuzzy logic rule base (e.g., using membership function types such as Gaussian, triangular, and central average methods for defuzzification), heating / cooling control gain, and time constant (adjusted by fuzzy logic dynamic gain).

[0093] Please refer to Table 2 below for details:

[0094]

[0095] Examples 4-6 (Adaptive State-Space Model + FuzzyLogic):

[0096] Temperature stabilization time: At 100°C, all samples reached stability within 15-20 minutes, with significantly higher efficiency than the control group.

[0097] Target temperature fluctuation rate: controlled within 0.5 * 10⁻⁶ -3 K / s ~ 0.9 * 10 -3 Between K / s, it meets the stringent requirements of high-precision calibration for temperature stability. The complex rule base shows faster stabilization speed and lower volatility, indicating that the refined design of the algorithm can optimize the control effect.

[0098] Comparative Example 3 (Traditional PID Control): It requires a longer settling time (25 min) and the temperature fluctuation rate is also larger, indicating that the traditional fixed parameter PID control is not flexible enough in dealing with the complex dynamic changes in the cavity thermal environment.

[0099] Comparative Example 4 (with fixed model parameters): It had the longest settling time (30+ min) and the highest temperature fluctuation rate, reaching 1.5 * 10⁻⁶. -3 K / s directly illustrates the importance of adaptive parameter dynamic adjustment in state-space models, as models with fixed parameters cannot handle changes in the environment and reference temperature.

[0100] Calibration point adaptive strategy:

[0101] Preferred parameters: Temperature interval between initial calibration points (e.g., every 50°C). Residual accuracy threshold: Fitting residual less than 0.02 K.

[0102] Number of strategy iterations: Usually no more than 5 iterations, to balance accuracy and calibration time, see Table 3 for details:

[0103]

[0104] Examples 7-9 (Adaptive Calibration Point Strategy): The calibration accuracy (2σ @ 0°C) all reached a high level of ±0.015 K to ±0.020 K;

[0105] The calibration time is completed in 4.0-4.8 hours (average 11-12 calibration points), achieving a good balance between accuracy and efficiency.

[0106] Example 9 (Residual Threshold 0.015 K) By using a smaller fitting residual threshold, the nonlinear characteristics of the model can be captured more precisely, thereby achieving the highest calibration accuracy of ±0.015 K.

[0107] Although Comparative Example 5 used more calibration points (16 points, more than the examples), its calibration accuracy (±0.025K) was actually lower than that of Examples 10-12, and the calibration time was longer (6.0 hours). This shows that fixed, dense sampling is not the most efficient strategy, and it failed to allocate resources to regions with large model fitting errors as efficiently as an adaptive strategy.

[0108] Comparative Example 6 uses only 6 sparse calibration points, resulting in a significant drop in calibration accuracy to ±0.040 K. It also has the shortest calibration time (3.5 hours), but the accuracy loss is too great and does not meet the high accuracy requirements.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any ordinary changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calibrating the temperature of a temperature measuring device, characterized in that, This method is executed in an automated system and includes the following steps: S1. Environment setup: The temperature measuring device to be calibrated and the reference temperature device are fixed coplanarly on a thermally conductive substrate and placed in a high-vacuum insulated cavity to construct a thermophysical equivalent field. S2. Stable state: The temperature of the cavity is adjusted to the preset calibration point by the temperature control system, and the dynamic judgment of thermal balance is performed until the temperature fluctuation rate in the cavity meets the preset stability condition. S3. Measurement and Calibration: Apply at least two bias currents of different amplitudes to the temperature measuring device to be calibrated, which is already in thermal equilibrium, acquire the corresponding raw voltage signals, and calculate the zero-power theoretical resistance value to eliminate the influence of self-heating effect using an extrapolation algorithm. ; S4. Modeling and Calibration: The original voltage signal is denoised, and then the theoretical resistance value is used. Using the corresponding reference temperature T as a data pair, the parameters of the preset resistance-temperature relationship model that can describe the nonlinear characteristics of a wide temperature range are fitted, thereby establishing a unique temperature correction calculation formula for each temperature measuring device to be calibrated. S5. Uncertainty Assessment: Establish a combined uncertainty assessment model, analyze and combine various uncertainty sources, calculate and output the combined uncertainty of the temperature correction calculation formula within the preset operating temperature range. .

2. The temperature calibration method for a temperature measuring device according to claim 1, characterized in that, The resistance-temperature relationship model in step S4 is defined by the following equation: ； The coefficients A, B, and C are a unique set of parameters characterizing the physical characteristics of the specific device to be calibrated. A is the constant offset term of the model, B is the logarithmic coefficient used to characterize the main linear relationship between the logarithm of resistance and the reciprocal of temperature, and C is the higher-order nonlinear correction coefficient used to accurately compensate for the device's response characteristics that deviate from this linear relationship in a wide temperature range.

3. The temperature calibration method for a temperature measuring device according to claim 1, characterized in that, The noise reduction process in step S4 uses a wavelet-empirical mode decomposition composite algorithm. This algorithm performs preliminary decomposition of the signal through multi-layer wavelet transform, and then identifies and filters out several preset intrinsic mode functions containing noise through empirical mode decomposition.

4. The temperature calibration method for a temperature measuring device according to claim 1, characterized in that, The temperature control system used in step S2 employs an adaptive temperature control algorithm, which is based on a state-space model, specifically as follows: ； Where T[k] is the system temperature at the current time k, u[k] is the applied control input, B is the control input matrix, w[k] is the process noise; A(k) is the state transition matrix, which is an adaptive parameter whose value is dynamically adjusted in each control cycle based on the real-time temperature error e[k] and its rate of change Δe[k] through a preset fuzzy logic rule base.

5. The temperature calibration method for a temperature measuring device according to claim 1, characterized in that, The number and location of the preset calibration points in step S2 are determined by an adaptive strategy. This adaptive strategy first selects a set of initial equally spaced calibration points in the target temperature range, then performs preliminary modeling steps S3 and S4 on these points, calculates the fitting residuals of the model in each interval, and automatically inserts new calibration points in the interval with the largest residuals. This process is iterated until the fitting residuals of all intervals are less than a preset accuracy threshold.

6. The temperature calibration method for a temperature measuring device according to claim 1, characterized in that, The preset stability condition in step S2 is specifically: the absolute value of the rate of change of the temperature reading of the reference temperature device within a continuous 60-second time window is less than... .

7. The temperature calibration method for a temperature measuring device according to claim 1, characterized in that, The combined uncertainty assessment model in step S5 has the following form: ； in, For model fitting residuals, Errors introduced by noise reduction processing This represents the deviation compared to non-contact infrared measurement.

8. A temperature calibration system for a temperature measuring device used to implement the method of any one of claims 1-7, characterized in that, include: Temperature control and environmental module; Programmable power supply module; Multi-channel data acquisition module; The central processing and control unit is configured to execute steps S2 to S5 through a built-in software algorithm.

9. A temperature calibration system for a temperature measuring device according to claim 8, characterized in that, The thermally conductive substrate is yttrium-doped alumina ceramic.

10. A temperature calibration system for a temperature measuring device according to claim 8, characterized in that, The high-vacuum insulated cavity is a Dewar flask with a multi-layer reflective coating on its inner wall, which is an alternately deposited metal / dielectric thin film.

Images