Cold and hot alternating compensation method for temperature holding force detection instrument

Abstract

The invention discloses a cold and hot alternating compensation method for a temperature retention detection instrument, and belongs to the technical field of test data processing and automatic control, and the method comprises the steps: obtaining and analyzing real-time environment temperature data and original retention data of a preset detection instrument, and generating an environment temperature disturbance signal; performing feature analysis and preprocessing on the original retention data to generate stage compensation parameters; correcting the stage compensation parameter based on the environment temperature disturbance signal to generate fusion compensation data; and adjusting the original retention force data based on the fusion compensation data, generating a retention force curve after initial compensation, and generating an optimized accurate retention force compensation result in combination with the real-time environment temperature data. The technology that environment temperature disturbance characteristics are analyzed and subjected to dynamic fusion and closed-loop correction with multi-dimensional characteristics of original retention force data is adopted, high-precision and self-adaptive compensation of the retention force data can be achieved, and the stability and accuracy of a detection result are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of automatic control system technology, and in particular to a method for compensating for alternating hot and cold temperatures in a temperature holding capacity testing instrument. Background Technology

[0002] In the fields of precision measurement and industrial control, accurate monitoring of non-electrical physical quantities such as force, pressure, and displacement is a core element in ensuring product quality and system safety. However, these measurement processes are generally subject to severe interference from external environmental factors, with fluctuations in ambient temperature being one of the most significant and difficult-to-overcome sources of interference. Temperature changes directly alter the physical characteristics of sensor elements, leading to zero-point drift and sensitivity variations. Furthermore, thermal expansion and contraction affect the instrument's mechanical structure, introducing complex measurement errors. This results in a lack of comparability and consistency in measurement data obtained under different temperature conditions, severely impacting the accuracy and reliability of the final results.

[0003] To address these issues, traditional technical solutions mainly fall into two categories: physical isolation and algorithmic compensation. Physical isolation methods typically rely on constructing costly constant temperature and humidity laboratories or equipping testing equipment with complex temperature control systems to create a stable physical environment and avoid temperature interference. Traditional algorithmic compensation, on the other hand, often employs simplified linear models. This involves pre-calibrating a fixed temperature compensation coefficient and then performing simple addition and subtraction operations on the original data based on the real-time temperature during measurement. This is an open-loop, static compensation method.

[0004] However, the aforementioned traditional methods have significant drawbacks. While physical isolation solutions are effective, their construction and operation costs are extremely high, energy consumption is enormous, and they severely limit the application scenarios of the instruments, failing to meet the needs of portable or distributed detection. Traditional static algorithm compensation methods are too crude; they completely ignore the dynamic process of temperature change, such as the rate of temperature change and the thermal hysteresis effect of the equipment itself, and cannot cope with the nonlinear effects of temperature changes on signal-noise characteristics. Therefore, when the ambient temperature fluctuates rapidly or changes nonlinearly, this simple linear compensation model will completely fail, and may even introduce larger errors, failing to meet the requirements of modern high-precision measurement. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a thermal alternation compensation method for a holding force testing instrument. This method employs a technical approach that analyzes the characteristics of environmental temperature disturbances and dynamically fuses and corrects them with the multi-dimensional features of the original holding force data. This approach can accurately eliminate nonlinear and time-varying measurement errors introduced by temperature changes, achieving high-precision, adaptive compensation for holding force data and significantly improving the stability and accuracy of the testing results.

[0006] The above objectives can be achieved through the following approach: A method for compensating for alternating hot and cold temperatures in a temperature-holding force testing instrument includes: acquiring real-time ambient temperature data and original holding force data of a preset testing instrument; analyzing the fluctuation characteristics of the real-time ambient temperature data to generate an ambient temperature disturbance signal; performing feature analysis and preprocessing on the original holding force data to generate staged compensation parameters; correcting the staged compensation parameters based on the ambient temperature disturbance signal to generate fused compensation data; adjusting the original holding force data based on the fused compensation data to generate an initial compensated holding force curve; and generating an optimized, precise holding force compensation result based on the initial compensated holding force curve and the real-time ambient temperature data.

[0007] Optionally, generating the ambient temperature disturbance signal includes: calculating the temperature change rate parameter and the equipment thermal hysteresis effect parameter of the real-time ambient temperature data; and fusing the temperature change rate parameter and the equipment thermal hysteresis effect parameter to generate the ambient temperature disturbance signal.

[0008] Optionally, the generation of phased compensation parameters includes: performing baseline interference filtering on the original holding force data to generate preprocessed data; performing noise suppression and abrupt change point enhancement on the preprocessed data to generate high signal-to-noise ratio feature data; and generating phased compensation parameters based on the high signal-to-noise ratio feature data.

[0009] Optionally, generating high signal-to-noise ratio feature data includes: adjusting a preset filter correction coefficient according to the ambient temperature disturbance signal; performing differential calculation on the preprocessed data based on the adjusted filter correction coefficient to generate high signal-to-noise ratio feature data.

[0010] Optionally, generating the fused compensation data includes: weighting the phased compensation parameters based on the ambient temperature disturbance signal to generate phased weighted compensation parameters; obtaining preset historical phased compensation parameters; and fusing the phased weighted compensation parameters with the historical phased compensation parameters to generate fused compensation data.

[0011] Optionally, the generation of phased weighted compensation parameters includes: extracting features from the phased compensation parameters to obtain high-frequency force fluctuation features and low-frequency baseline drift features; obtaining temperature change rate parameters based on the ambient temperature disturbance signal; generating high-frequency feature compensation weights and low-frequency feature suppression weights based on the temperature change rate parameters; and weighting and fusing the high-frequency force fluctuation features and low-frequency baseline drift features based on the high-frequency feature compensation weights and low-frequency feature suppression weights to generate phased weighted compensation parameters.

[0012] Optionally, generating the optimized precise holding force compensation result includes: evaluating the stability of the initial compensated holding force curve to generate a simulated baseline curve; comparing the simulated baseline curve with preset historical holding force data to generate a compensation effect deviation value; and based on the compensation effect deviation value and combined with the real-time ambient temperature data, adjusting the generation strategy of the fused compensation data to output the optimized precise holding force compensation result.

[0013] Optionally, generating the simulated baseline curve includes: performing continuity analysis on the initial compensation holding force curve to generate a baseline attenuation trajectory curve; superimposing the baseline attenuation trajectory curve based on the ambient temperature disturbance signal; and recombining the superimposed disturbance-induced baseline attenuation trajectory curve to generate the simulated baseline curve.

[0014] Optionally, generating the initial compensation holding force curve includes: generating a force attenuation feature based on the fused compensation data; and superimposing the force attenuation feature onto the original holding force data to generate the initial compensation holding force curve.

[0015] Based on the same inventive concept, this invention also provides a thermal alternation compensation system for a temperature holding force testing instrument, comprising: a real-time data acquisition module for acquiring real-time ambient temperature data and original holding force data of a preset testing instrument; a temperature disturbance analysis module for analyzing the fluctuation characteristics of the real-time ambient temperature data and generating an ambient temperature disturbance signal; a parameter feature extraction module for performing feature analysis and preprocessing on the original holding force data to generate staged compensation parameters; a fusion dynamic calibration module for correcting the staged compensation parameters based on the ambient temperature disturbance signal to generate fused compensation data; a holding force curve generation module for adjusting the original holding force data based on the fused compensation data to generate an initial compensated holding force curve; and a precise compensation optimization module for generating an optimized precise holding force compensation result based on the initial compensated holding force curve and the real-time ambient temperature data.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This method elevates the impact of ambient temperature from simple linear subtraction to nonlinear, time-varying dynamic compensation by dynamically generating and correcting compensation parameters. It not only considers the instantaneous temperature value but also analyzes its fluctuation characteristics and the thermal hysteresis effect of equipment, enabling the compensation model to more realistically reflect the complex heat conduction processes of the physical world, thereby fundamentally improving the accuracy of the compensation results.

[0017] 2. By differentiating high- and low-frequency characteristics of the raw holding force data and dynamically adjusting the compensation weights and filter coefficients based on environmental disturbance signals, this method can intelligently adapt to different noise environments and interference modes. Furthermore, by introducing a closed-loop feedback and optimization strategy for the compensation effect, the system can autonomously learn and iterate its compensation model, ensuring optimal compensation performance during long-term operation or application across different testing instruments, greatly enhancing the system's environmental adaptability and the reliability of the results.

[0018] 3. By combining historical and real-time data, processing high-frequency fluctuations and low-frequency drifts separately, and integrating feedforward control and feedback optimization, this multi-source information fusion mechanism effectively avoids the limitations of a single compensation algorithm. It allows the compensation process to comprehensively consider both short-term fluctuations and long-term trends, resulting in a more accurate holding force output with smaller errors and significantly higher stability and consistency than traditional methods. This provides a solid data foundation for demanding quality control and scientific research.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the thermal alternation compensation method of the temperature holding force testing instrument according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram illustrating the generation of environmental temperature disturbance signals in the thermal alternation compensation method of the temperature holding capacity testing instrument according to an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the high / low frequency characteristic dynamic compensation weight model of the thermal alternation compensation method of the temperature holding force testing instrument according to an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of the thermal alternation compensation system of the temperature holding force testing instrument according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Reference Figure 1 One embodiment of the present invention proposes a thermal alternation compensation method for a holding force testing instrument. By analyzing the characteristics of environmental temperature disturbance and dynamically fusing and closing-loop correcting them with the multi-dimensional characteristics of the original holding force data, the nonlinear and time-varying measurement errors introduced by temperature changes can be accurately eliminated, achieving high-precision and adaptive compensation for holding force data, and significantly improving the stability and accuracy of the test results.

[0027] The method described in this embodiment specifically includes: Acquire real-time ambient temperature data and original holding force data from the preset testing instrument; Analyze the fluctuation characteristics of the real-time ambient temperature data to generate an ambient temperature disturbance signal; The original holding force data is subjected to feature analysis and preprocessing to generate staged compensation parameters; The phased compensation parameters are corrected based on the ambient temperature disturbance signal to generate fused compensation data. Based on the fused compensation data, the original holding force data is adjusted to generate an initial compensation holding force curve; Based on the initial compensation holding force curve and the real-time ambient temperature data, an optimized and accurate holding force compensation result is generated.

[0028] Specifically, a dynamic compensation model based on dual-path information fusion and closed-loop optimization is constructed. Its core is the parallel processing of two information paths. One path analyzes the temporal fluctuation characteristics of ambient temperature data, rather than just its static values, to generate an ambient temperature disturbance signal that quantifies the strength and characteristics of dynamic interference from the heat source. The other path performs deep feature extraction and staged processing on the original holding force data to generate staged compensation parameters that characterize the inherent laws of force value changes. The key innovation of this invention lies in the dynamic coupling of these two independent analysis paths. Specifically, the ambient temperature disturbance signal is used to correct and weight the staged compensation parameters extracted from the force value data in real time, generating fused compensation data that integrates the thermal disturbance state and the inherent characteristics of the force value. Subsequently, the original holding force data is initially adjusted based on this fused data. Finally, this method introduces a feedback optimization loop. By evaluating the curve after initial compensation and combining it with real-time temperature information, the compensation strategy is iteratively adjusted, forming a self-improving closed-loop system that ultimately outputs accurate compensation results. This significantly improves the measurement accuracy and reliability of temperature holding force testing instruments in complex alternating hot and cold environments. By deeply analyzing the dynamic process of temperature change rather than a single temperature point, this method can accurately capture and quantify the measurement errors caused by complex factors such as drastic temperature fluctuations and equipment thermal hysteresis, overcoming the limitations of traditional compensation methods in unsteady thermal environments. Its unique fusion calibration mechanism can intelligently distinguish and process true force attenuation and thermally induced spurious fluctuations, avoiding damage to the effective signal while suppressing interference. The final closed-loop optimization design gives the compensation system adaptive capabilities, enabling it to continuously cope with changing test environments and ensuring that the output holding force curve can eliminate environmental interference to the greatest extent, truly reflecting the intrinsic mechanical properties of the tested sample, thereby greatly enhancing the stability and reliability of the test results.

[0029] Optionally, generating the ambient temperature disturbance signal includes: calculating the temperature change rate parameter and the equipment thermal hysteresis effect parameter of the real-time ambient temperature data; and fusing the temperature change rate parameter and the equipment thermal hysteresis effect parameter to generate the ambient temperature disturbance signal.

[0030] Specifically, the temperature change rate parameter is obtained by performing time-difference operations on real-time ambient temperature data. The calculation method is as follows: , Where R(t) is the rate of temperature change parameter at the current moment, and T(t) is the real-time ambient temperature data at the current moment. This is the real-time ambient temperature data from the previous sampling time. This represents the data sampling time interval. The thermal hysteresis effect refers to the fact that the actual temperature of key internal components of the instrument, especially the force sensor, cannot instantly reach equilibrium with the ambient temperature, resulting in measurement drift. Calculating this parameter first requires estimating the equivalent temperature inside the instrument, which can be simulated by using an exponentially weighted moving average of historical ambient temperatures. Then, the difference between the current real-time ambient temperature and this internal equivalent temperature is obtained to obtain the thermal hysteresis effect parameter. The thermal hysteresis effect parameter quantifies the current temperature imbalance between the instrument's internal and external environment. The temperature change rate parameter is then fused with the thermal hysteresis effect parameter: , in, This is the final generated ambient temperature disturbance signal. and These are weighting coefficients for the rate of temperature change and the thermal hysteresis effect, respectively. These two coefficients need to be pre-set through experimental calibration based on the thermodynamic characteristics of the specific testing instrument. For example... Figure 2 The diagram shows the generation of ambient temperature disturbance signals.

[0031] Through the above method, this invention no longer relies solely on a single real-time ambient temperature reading for compensation, but rather deeply understands the two core physical processes of temperature influence: rapid temperature change and the inherent delay in equipment response. By separately calculating the temperature change rate parameter and the equipment thermal hysteresis effect parameter, the description of temperature disturbances is expanded from a static single point to a dynamic process. The ambient temperature disturbance signal generated by fusing these two parameters can more comprehensively and accurately characterize the real thermodynamic disturbances imposed on the detection instrument by complex ambient temperature fluctuations. This provides high-quality input for subsequent compensation algorithms, enabling compensation to move beyond simple linear correspondence and cope with complex disturbances involving nonlinearity and time delay, thereby fundamentally improving the accuracy and reliability of temperature holding force detection results in alternating hot and cold environments.

[0032] Optionally, the generation of phased compensation parameters includes: performing baseline interference filtering on the original holding force data to generate preprocessed data; performing noise suppression and abrupt change point enhancement on the preprocessed data to generate high signal-to-noise ratio feature data; and generating phased compensation parameters based on the high signal-to-noise ratio feature data.

[0033] Optionally, generating high signal-to-noise ratio feature data includes: adjusting a preset filter correction coefficient according to the ambient temperature disturbance signal; performing differential calculation on the preprocessed data based on the adjusted filter correction coefficient to generate high signal-to-noise ratio feature data.

[0034] Specifically, baseline interference filtering is performed on the raw holding power data. Algorithms such as moving average subtraction are used to process the raw holding power data to filter out or remove the low-frequency baseline component, thereby generating preprocessed data that contains no or significantly reduces baseline drift. ,have: , in For preset filter coefficients, This is the preprocessed data from the previous time step. This is the raw holding force data at the current moment. The original holding force data from the previous moment is used. Based on the real-time value of the ambient temperature disturbance signal, a preset filter correction coefficient is dynamically adjusted using a preset mapping function or lookup table. This filter correction coefficient is a key control parameter in the digital filter algorithm. When the ambient temperature disturbance signal value is large, indicating severe thermal interference, the mapping function will output a correction coefficient that makes the filter have a stronger suppression capability; conversely, when the disturbance is small, a correction coefficient that can retain more signal details is selected. Based on the real-time adjusted filter correction coefficient, the preprocessed data is filtered, and differential calculation is performed on the preprocessed data to generate high signal-to-noise ratio feature data. ,have: , in These are the adjusted filter correction coefficients. This is the preprocessed data from the previous time step. Preprocessing data for the current moment: By monitoring high signal-to-noise ratio (SNR) characteristic data, the peak amplitude of the impact is obtained. Then, by integrating the monitored high SNR characteristic data, the integrated energy reflecting the total impact is obtained, and the event duration is recorded simultaneously. The stage compensation parameters are obtained by weighted fusion after multiplying the peak amplitude, integrated energy, and event duration by preset weights respectively.

[0035] Optionally, generating the fused compensation data includes: weighting the phased compensation parameters based on the ambient temperature disturbance signal to generate phased weighted compensation parameters; obtaining preset historical phased compensation parameters; and fusing the phased weighted compensation parameters with the historical phased compensation parameters to generate fused compensation data.

[0036] Optionally, the generation of phased weighted compensation parameters includes: extracting features from the phased compensation parameters to obtain high-frequency force fluctuation features and low-frequency baseline drift features; obtaining temperature change rate parameters based on the ambient temperature disturbance signal; generating high-frequency feature compensation weights and low-frequency feature suppression weights based on the temperature change rate parameters; and weighting and fusing the high-frequency force fluctuation features and low-frequency baseline drift features based on the high-frequency feature compensation weights and low-frequency feature suppression weights to generate phased weighted compensation parameters.

[0037] Specifically, the staged compensation parameters are decomposed into high-frequency force fluctuation characteristics representing instantaneous disturbances and low-frequency baseline drift characteristics reflecting slow changes. Based on the ambient temperature disturbance signal, the temperature change rate parameter is obtained by taking its time derivative. High-frequency feature compensation weights and low-frequency feature suppression weights are generated based on the temperature change rate parameter. For the high-frequency feature compensation weights… ,have: , in For a pre-set rate threshold, For the rate of temperature change, Here, e is the base of the natural logarithm. The low-frequency feature suppression weight is obtained by subtracting the high-frequency feature suppression weight from 1. The stage-weighted compensation parameters are obtained by weighting and fusing the results of multiplying the high-frequency force fluctuation feature and the low-frequency baseline drift feature by the high-frequency feature compensation weight and the low-frequency feature suppression weight, respectively. Preset historical stage-weighted compensation parameters are retrieved from the internal storage unit. These historical parameters originate from a large amount of data from previous experiments or stable phases in current tests, representing the typical holding force decay law of materials or products under ideal conditions, and exhibiting high stability. The stage-weighted compensation parameters, carrying real-time information but weighted by reliability, are fused with the historical stage-weighted compensation parameters representing the stability benchmark to generate the fused compensation data. This fusion process can be implemented using a dynamic weighted average algorithm. , in, To integrate compensation data, These are the phase-based weighted compensation parameters for the current stage. These are historical periodic compensation parameters. The dynamic fusion weights have values ​​between 0 and 1. When the ambient temperature disturbance signal is small, indicating good measurement conditions, The value approaches 1, and vice versa. This formula ensures that the contribution of real-time data and historical data to the final compensation result can be dynamically adjusted under different confidence levels. For example... Figure 3 The diagram shows a dynamic compensation weight model for high / low frequency features.

[0038] This method establishes a dynamic adaptive fusion mechanism by introducing historical data as a reference and using environmental temperature disturbance signals as a benchmark for evaluating the reliability of real-time data. It effectively avoids the compensation inaccuracy problem caused by relying entirely on real-time measurement parameters that may be contaminated by instantaneous and severe thermal disturbances. When real-time data is reliable, the system focuses on using the latest measurement information for accurate compensation; when the reliability of real-time data decreases due to harsh environments, the system relies more on stable historical patterns, thus ensuring the continuity and robustness of the compensation output. This fusion strategy enables the final fused compensation data to both quickly respond to real changes in holding force and effectively suppress spurious fluctuations introduced by sudden environmental changes, fundamentally improving the stability of the compensation model and its adaptability to complex operating conditions.

[0039] Optionally, generating the optimized precise holding force compensation result includes: evaluating the stability of the initial compensated holding force curve to generate a simulated baseline curve; comparing the simulated baseline curve with preset historical holding force data to generate a compensation effect deviation value; and based on the compensation effect deviation value and combined with the real-time ambient temperature data, adjusting the generation strategy of the fused compensation data to output the optimized precise holding force compensation result.

[0040] Optionally, generating the simulated baseline curve includes: performing continuity analysis on the initial compensation holding force curve to generate a baseline attenuation trajectory curve; superimposing the baseline attenuation trajectory curve based on the ambient temperature disturbance signal; and recombining the superimposed disturbance-induced baseline attenuation trajectory curve to generate the simulated baseline curve.

[0041] Specifically, a continuity analysis is performed on the holding force curve after initial compensation to generate a baseline attenuation trajectory curve. The continuity analysis uses polynomial fitting to extract the smooth, monotonic macroscopic attenuation trend of the curve, filtering out all high-frequency components and minor fluctuations, thus generating an idealized baseline attenuation trajectory curve. This idealized baseline attenuation trajectory curve is then combined with actual environmental thermal disturbances to construct a benchmark that allows for fair comparison under the same disturbance conditions. An environmental temperature disturbance signal is acquired, and based on this signal, the baseline attenuation trajectory curve is perturbed and superimposed using a preset transfer function. For the preset transfer function, we have: , in, It is the curve after superimposed perturbations. It is the baseline decay trajectory curve generated in the previous step. This is an ambient temperature disturbance signal. This is a pre-calibrated thermo-coupling coefficient. The curve after superimposed perturbation is finalized and constrained, and any physical inconsistencies caused by the perturbation are checked and corrected. Data points are ensured to be formatted correctly with a time series, generating a simulated baseline curve that smoothly reflects the core attenuation characteristics and accurately reproduces the thermal perturbation effects in this test. This simulated baseline curve is then compared with preset historical holding force data. The historical holding force data stores typical holding force curves of similar samples measured under standard, perturbation-free conditions, representing the most realistic physical process. The compensation effect deviation value is obtained by calculating the root mean square error between the simulated baseline curve and the historical holding force data. The compensation effect deviation value intuitively reflects the gap between the current compensation strategy and the ideal state. When a significant compensation effect deviation value is detected, it is immediately correlated with the rate of change of the ambient temperature to diagnose the root cause of the problem. If the deviation occurs in a relatively stable temperature environment, it is diagnosed as an overly strong suppression effect of the current strategy, i.e., unnecessary overcompensation of the effective low-frequency signal, and accordingly, its rate threshold is increased while its sensitivity coefficient is decreased. Conversely, if the deviation is accompanied by drastic temperature changes, it is judged that the strategy's compensation is insufficient, failing to effectively keep up with and filter out baseline drift introduced by the environment. In this case, the opposite adjustment will be made: reducing the rate and increasing the sensitivity coefficient. In both cases, the magnitude of the parameter adjustment is proportional to the normalized deviation value and scaled by a preset gain coefficient.

[0042] The technical advantage of this method lies in constructing a closed-loop optimization system with self-learning and adaptive capabilities. It goes beyond one-time open-loop compensation, achieving dynamic evolution of the compensation algorithm through continuous evaluation and feedback of the compensation results. By comparing the compensated curve with ideal benchmarks and real historical data, the system can quantitatively diagnose the shortcomings of the current compensation strategy. More importantly, it can correlate these shortcomings with the environmental conditions at the time, thereby enabling targeted and intelligent parameter adjustments. This closed-loop optimization mechanism ensures that the compensation method is not only effective in its initial state but also continuously improves its accuracy during long-term operation and under various unknown or changing conditions. Ultimately, regardless of changes in the external environment, the instrument can output highly stable and accurate holding force compensation results that closely approximate the real physical process, greatly enhancing the long-term reliability and environmental adaptability of the testing instrument.

[0043] Optionally, generating the initial compensation holding force curve includes: generating a force attenuation feature based on the fused compensation data; and superimposing the force attenuation feature onto the original holding force data to generate the initial compensation holding force curve.

[0044] Specifically, the original data is corrected point by point using a preset mathematical model, transforming the fused compensation data into a continuous signal that perfectly corresponds to the original holding force data on the time axis. This constructs a time-series curve that can precisely describe the force value to be compensated throughout the entire test period, i.e., the force attenuation characteristic. This force attenuation characteristic is then superimposed onto the original holding force data acquired by the real-time data acquisition module to generate the initial compensated holding force curve. This superposition is a direct arithmetic correction operation, its physical meaning being the subtraction of the precisely modeled and analyzed interference component from the original measured values, which contain both the real signal and various interferences. This operation can be expressed by the following formula. , in, These are the data points on the final generated initial compensation holding force curve. This represents the original holding force data at the corresponding time point. This is the compensation value of the force attenuation characteristic generated by fusing compensation data at the same moment. This formula ensures that a precise compensation based on complex analysis is performed at each data sampling point, and the final set of points constitutes the complete holding force curve after initial compensation.

[0045] The technical advantage of this method lies in its completion of a closed loop, from complex analysis and modeling to the effective correction of the original data. The highly intelligent fusion compensation data obtained through the preceding steps is transformed into a practically operable compensation signal, which is then applied to the original measurement curve in a precise, point-by-point correction manner. This transforms compensation from a coarse adjustment based on a general, averaged offset to a refined and personalized elimination of dynamically changing, nonlinear errors throughout the testing process. The resulting initial compensation holding force curve, compared to the original curve, more clearly and accurately reveals the true mechanical decay behavior of the sample under environmental interference. It not only smooths out noise but, more importantly, corrects baseline drift and morphological distortion caused by complex thermal effects, providing a high-quality, high-fidelity analytical object for subsequent precise optimization.

[0046] Based on the same inventive concept, as shown in the figure, this invention also provides a thermal cycling compensation system for a temperature holding capacity testing instrument, the system comprising: Real-time data acquisition module: used to acquire real-time ambient temperature data and original holding force data of the preset testing instrument; Temperature disturbance analysis module: used to analyze the fluctuation characteristics of the real-time ambient temperature data and generate ambient temperature disturbance signals; Parameter feature extraction module: used to perform feature analysis and preprocessing on the original holding force data to generate staged compensation parameters; Fusion dynamic calibration module: used to correct the phased compensation parameters based on the ambient temperature disturbance signal and generate fusion compensation data; Holding force curve generation module: used to adjust the original holding force data based on the fused compensation data and generate the initial compensation holding force curve; Precise compensation optimization module: used to generate optimized precise holding force compensation results based on the initial compensation holding force curve and the real-time ambient temperature data.

[0047] Example 1: To verify the feasibility of this invention in practice, it was applied to the quality control laboratory of a specialty adhesive tape manufacturer. The core task of this laboratory is to evaluate the temperature retention performance of its adhesive tapes used for bonding automotive electronic modules under simulated extreme automotive environments (e.g., from -20°C in winter to 60°C under intense summer sun). Traditional compensation methods struggle to accurately eliminate measurement errors introduced by drastic, non-linear temperature changes, leading to inaccurate product reliability assessments. This laboratory aims to use this invention to compensate its temperature retention testing instruments to obtain accurate and reliable retention force data under alternating hot and cold conditions.

[0048] In this embodiment, the laboratory used a temperature holding power tester to test a batch of high-strength acrylic tape samples. The experimental group used the method described in this invention, while the control group used a traditional compensation method based on linear correction at a single temperature point. The experiment simulated a complete thermal shock cycle in an environmental chamber: starting from room temperature of 25°C, the temperature dropped to -20°C within 1 hour and held for 1 hour, then rose to 60°C within 1 hour and held for 1 hour, finally dropping back to 25°C. Data processing and compensation were performed throughout the test using the system described in this invention.

[0049] In this embodiment, the system's real-time data acquisition module first acquires the real-time ambient temperature data and the initial holding force data of the testing instrument. During the cooling and heating phases, the initial holding force curve exhibits significant nonlinear drift and noise fluctuations. The initial force value decreases from the initial 10.5 N, but the spurious fluctuations caused by temperature changes reach ±0.8 N, completely masking the true creep decay trend of the tape.

[0050] During the rapid temperature drop from 25℃ to -20℃, the temperature disturbance analysis module analyzed the real-time ambient temperature data. The system calculated the peak temperature change rate parameter R(t) for this stage to be -0.75℃ / minute, and simultaneously estimated the significant equipment thermal hysteresis effect parameter H(t) based on the instrument's thermodynamic model. By performing a preset weighted fusion of R(t) and H(t) (w_r=0.6, w_h=0.4), a high-amplitude ambient temperature disturbance signal S_d(t) was generated. This signal accurately quantifies the comprehensive thermodynamic disturbance of the measurement system caused by this drastic cooling process.

[0051] Subsequently, the parameter feature extraction module processes the disturbed raw holding force data. First, baseline interference is filtered out using a high-pass filter. Then, based on the high-amplitude ambient temperature disturbance signal S_d(t) generated in the previous step, the correction coefficients of the internal filter are dynamically adjusted. This effectively suppresses thermally induced noise while successfully enhancing and identifying a sudden change point with a true amplitude of only 0.05 N, caused by a tiny stick-slip in the adhesive layer, from the background noise. Based on this high signal-to-noise ratio feature data, the system calculates the force attenuation rate for this stage to be -0.02 N / min and records the sudden change point information, which together constitute the stage-specific compensation parameters.

[0052] Next, the dynamic calibration module corrects the generated interim compensation parameters. Since the current S_d(t) value is large, indicating severe interference and low reliability of the real-time measurement data, the system calculates the dynamic fusion weight α(t) to be 0.3 using an inverse proportional function. Simultaneously, the system retrieves historical interim compensation parameters (with a typical decay rate of -0.015 N / min) measured under standard 25℃ constant temperature conditions from its internal storage. Using a dynamic weighted average formula, the less reliable real-time parameters are fused with the more reliable historical parameters to generate fused compensation data that reflects the current testing trend while ensuring stability.

[0053] Based on this fused compensation data, the holding force curve generation module generated continuous force attenuation characteristics and subtracted them point by point from the original holding force data to generate the initial compensated holding force curve. This curve shows that the large fluctuation of ±0.8 N caused by drastic temperature changes was effectively corrected, and the curve as a whole exhibits a smooth attenuation pattern. The residual fluctuation range was reduced to within ±0.1 N, clearly revealing the intrinsic mechanical behavior of the tape.

[0054] Finally, the precise compensation optimization module initiates closed-loop feedback optimization. The module performs polynomial fitting on the initial compensation holding force curve, generating a baseline decay trajectory curve representing its core decay law. Subsequently, the ambient temperature disturbance signal S_d(t) from this test is cleverly superimposed onto this ideal trajectory to generate a simulated baseline curve. This simulated baseline curve is compared with preset, real historical holding force data measured under standard conditions, and the compensation effect deviation is calculated to be 0.08 N. System analysis reveals that the deviation mainly stems from insufficient compensation for the thermal hysteresis effect during the heating stage. Accordingly, the optimization algorithm automatically fine-tunes the thermal hysteresis effect weight w_h in the fusion dynamic calibration module from 0.4 to 0.45. After this closed-loop optimization, the final output precise holding force compensation result shows significantly improved agreement with the historical standard curve, with the deviation value reduced to within 0.02 N.

[0055] **Table 1 Comparison of Original and Compensated Data on Temperature Holding Capacity** **Table 2: Data Table for Generation and Fusion of Compensation Parameters in Key Stages** **Table 3 Comparison of Closed-Loop Optimization Data for Compensation Effects** As can be seen from the data in Tables 1 to 3 above, the method of the present invention has significant advantages in processing temperature retention force detection data under alternating hot and cold environments.

[0056] Table 1 clearly shows that at the 30th and 180th minutes of drastic temperature changes, both the original data and the data compensated by the traditional method exhibit severe distortion. However, the force value optimized by this invention maintains a smooth and reasonable attenuation trend, more closely resembling the true performance of the material. For example, at the 60th minute, the -20℃ low temperature causes a huge positive drift (11.25N) in the sensor. The traditional method only partially corrects this (10.70N), while this invention accurately compensates for 10.38N, highly matching the actual attenuation curve.

[0057] Table 2 illustrates the internal working state of the core algorithm of this invention. At moments of rapid temperature change (such as at the 45th minute and the 135th minute), the system generates high-amplitude ambient temperature disturbance signals and accordingly adopts lower dynamic fusion weights (0.20 and 0.22), relying more on stable historical data, thereby avoiding being misled by severely contaminated real-time data and ensuring the robustness of the compensation results.

[0058] The comparison results in Table 3 ultimately demonstrate the technical effectiveness of this invention. Compared to traditional methods, this invention significantly reduces the maximum deviation from 0.55 N to 0.08 N through initial compensation. More importantly, through a closed-loop optimization mechanism, the system can learn and improve itself, further reducing the deviation to 0.02 N, achieving extremely high compensation accuracy. These data fully demonstrate that this invention, through dynamic disturbance analysis, data fusion, and closed-loop optimization, can fundamentally solve the accuracy problem of temperature holding force detection in complex temperature environments, providing a reliable technical guarantee for product quality control.

[0059] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0060] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for compensating for thermal alternation in a temperature holding capacity testing instrument, characterized in that, include: Acquire real-time ambient temperature data and original holding force data from the preset testing instrument; Analyze the fluctuation characteristics of the real-time ambient temperature data to generate an ambient temperature disturbance signal; The original holding force data is subjected to feature analysis and preprocessing to generate staged compensation parameters; The phased compensation parameters are corrected based on the ambient temperature disturbance signal to generate fused compensation data. Based on the fused compensation data, the original holding force data is adjusted to generate an initial compensation holding force curve; Based on the initial compensation holding force curve and the real-time ambient temperature data, an optimized and accurate holding force compensation result is generated.

2. The thermal cycling compensation method for the temperature holding force testing instrument according to claim 1, characterized in that, The generated ambient temperature disturbance signal includes: Calculate the temperature change rate parameter and the equipment thermal hysteresis effect parameter of the real-time ambient temperature data; By integrating the temperature change rate parameter and the equipment thermal hysteresis effect parameter, an ambient temperature disturbance signal is generated.

3. The thermal cycling compensation method for the temperature holding capacity testing instrument according to claim 1, characterized in that, The generation of phased compensation parameters includes: Baseline interference filtering is performed on the raw holding force data to generate preprocessed data; For the preprocessed data, noise suppression and abrupt change enhancement are performed to generate feature data with high signal-to-noise ratio; Based on high signal-to-noise ratio feature data, phased compensation parameters are generated.

4. The thermal compensating method for temperature holding capacity testing instrument according to claim 3, characterized in that, The feature data for generating high signal-to-noise ratio includes: Adjust the preset filter correction coefficient according to the ambient temperature disturbance signal; Differential calculations are performed on the preprocessed data based on the adjusted filter correction coefficients to generate high signal-to-noise ratio feature data.

5. The thermal cycling compensation method for the temperature holding capacity testing instrument according to claim 1, characterized in that, The generated fusion compensation data includes: Based on the ambient temperature disturbance signal, the phased compensation parameters are weighted to generate phased weighted compensation parameters. Obtain preset historical stage compensation parameters; The phased weighted compensation parameters are merged with historical phased compensation parameters to generate merged compensation data.

6. The thermal cycling compensation method for the temperature holding force testing instrument according to claim 5, characterized in that, The generation of phased weighted compensation parameters includes: Feature extraction is performed on the staged compensation parameters to obtain high-frequency force fluctuation features and low-frequency baseline drift features; Based on the ambient temperature disturbance signal, the temperature change rate parameter is obtained; Based on the temperature change rate parameter, high-frequency feature compensation weights and low-frequency feature suppression weights are generated. Based on the high-frequency feature compensation weight and the low-frequency feature suppression weight, the high-frequency force value fluctuation feature and the low-frequency baseline drift feature are weighted and fused to generate staged weighted compensation parameters.

7. The thermal cycling compensation method for the temperature holding capacity testing instrument according to claim 1, characterized in that, The generated optimized and accurate holding force compensation results include: The stability of the holding force curve after initial compensation is evaluated to generate a simulated baseline curve; By comparing the simulated baseline curve with the preset historical holding force data, a compensation effect deviation value is generated; Based on the compensation effect deviation value and combined with the real-time ambient temperature data, the generation strategy of the fused compensation data is adjusted to output the optimized and accurate holding force compensation result.

8. The thermal cycling compensation method for the temperature holding force testing instrument according to claim 7, characterized in that, The generated simulation baseline curve includes: Perform a continuity analysis on the holding force curve after the initial compensation to generate a baseline attenuation trajectory curve; Based on the ambient temperature disturbance signal, the reference attenuation trajectory curve is superimposed; The reference attenuation trajectory curve after superimposed disturbance is reconstructed to generate the simulated reference curve.

9. The thermal cycling compensation method for the temperature holding capacity testing instrument according to claim 1, characterized in that, The generation of the holding force curve after initial compensation includes: Based on the fused compensation data, force attenuation characteristics are generated; The force attenuation characteristics are superimposed onto the original holding force data to generate the holding force curve after initial compensation.

10. A thermal cycling compensation system for a temperature holding capacity testing instrument, applied to the thermal cycling compensation method for a temperature holding capacity testing instrument as described in any one of claims 1-9, characterized in that, The system includes: Real-time data acquisition module: used to acquire real-time ambient temperature data and original holding force data of the preset testing instrument; Temperature disturbance analysis module: used to analyze the fluctuation characteristics of the real-time ambient temperature data and generate ambient temperature disturbance signals; Parameter feature extraction module: used to perform feature analysis and preprocessing on the original holding force data to generate staged compensation parameters; Fusion dynamic calibration module: used to correct the phased compensation parameters based on the ambient temperature disturbance signal and generate fusion compensation data; Holding force curve generation module: used to adjust the original holding force data based on the fused compensation data and generate the initial compensation holding force curve; Precise compensation optimization module: used to generate optimized precise holding force compensation results based on the initial compensation holding force curve and the real-time ambient temperature data.

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