Waveguide signal temperature compensation method for magnetostrictive sensors

Abstract

The present application belongs to the technical field of sensor signal processing and magnetostrictive displacement measurement temperature compensation, and particularly relates to a waveguide signal temperature compensation method for a magnetostrictive sensor, which comprises the following steps: obtaining the current environment temperature of the sensor and the previous environment temperature of the sensor; obtaining the temperature fluctuation difference of the sensor and the data sampling interval of the sensor; obtaining the temperature change rate of the sensor; obtaining the safe temperature difference limit of the sensor; obtaining the thermal lag weight ratio of the sensor; calculating the equivalent internal temperature of the sensor; obtaining the dynamic compensation sound speed of the sensor; obtaining the high-precision displacement value of the sensor; and packaging and outputting the high-precision displacement value of the sensor. The present application solves the technical problem that the absolute value of the external environment temperature is directly input into the compensation model without considering the thermal conduction time lag of the metal protective sleeve and the internal air layer, which leads to excessive compensation of the algorithm and jump error of the output displacement value.

Description

Technical Field

[0001] This invention relates to the field of sensor signal processing and temperature compensation technology for magnetostrictive displacement measurement. More specifically, this invention relates to a method for temperature compensation of waveguide signals in a magnetostrictive sensor. Background Technology

[0002] In the fields of industrial automation and precision position detection, magnetostrictive sensors rely on the propagation time of waveguide signals within waveguide wires to achieve high-precision displacement measurement. However, the propagation of waveguide signals is not an ideal, constant motion, but rather a physical process easily affected by ambient temperature. Fluctuations in ambient temperature directly alter the elastic modulus of the waveguide wire and the propagation speed of the waveguide signal. To ensure the measurement accuracy of the sensor under complex operating conditions, accurate prediction and dynamic compensation control of the sound velocity drift of the waveguide signal during transmission are essential.

[0003] In related technologies, for example, Chinese patent application CN121877064A discloses a magnetostrictive sensor based on adaptive dynamic calibration and its calibration method. The application describes a method that uses a temperature sensor in an environmental parameter monitoring module to monitor the external ambient temperature in real time. The acquired absolute value of the current ambient temperature is directly input into a pre-stored Gaussian process regression model, which then outputs a predicted mean for subsequent dynamic compensation. This control method can achieve basic environmental disturbance compensation and passive nonlinear error correction under quasi-static conditions where the ambient temperature changes slowly and internal and external heat are fully balanced.

[0004] However, analysis of the performance of existing technologies in magnetostrictive sensors under variable temperature conditions such as sudden thermal shocks and high-temperature steam tank cleaning reveals that this control architecture, which directly inputs the absolute value of the external ambient temperature into the compensation model and relies on static probability mapping, has the defect of not considering the underlying control conflicts caused by the physical heat conduction causality law and dynamic thermal inertia time delay.

[0005] First, the core waveguide wire of the sensor is typically tightly mounted inside a thick metal protective sleeve. Heat transfer from the external environment to the internal waveguide wire must overcome the dual physical thermal resistance of the metal wall and the internal air layer, resulting in a significant time lag in heat conduction. Current technology directly feeds the absolute value of the instantaneous rise or fall of the ambient temperature outside the sensor into the sound velocity compensation model without time buffering or timing checks. As the external ambient temperature changes, the compensation algorithm issues large correction commands, but the actual physical temperature of the waveguide wire inside the sleeve has not yet changed synchronously.

[0006] Because the control system lacks time-series calibration of the dynamic rate of change of external temperature and lacks a physical thermal damping buffer mechanism, this direct mapping that ignores the causal laws of heat transfer between different media leads to overcompensation by the sensor algorithm during the transition phase of drastic changes in ambient temperature. This results in transient jump errors in the final output displacement measurement value. This objective physical time difference caused by the material's heat capacity and thermal conductivity cannot be resolved by fine-tuning the hyperparameters of the mathematical probability model or adding static calibration points. Summary of the Invention

[0007] To address the technical problem in existing technologies that directly input the absolute value of the external ambient temperature into the compensation model without considering the thermal conduction lag between the metal protective sleeve and the internal air layer, leading to overcompensation and jump errors in the output displacement value under conditions of sudden thermal shock, high-temperature tank washing, etc., where the external temperature changes abruptly while the internal waveguide wire temperature has not changed synchronously, this invention provides a waveguide signal temperature compensation method for magnetostrictive sensors, comprising: S1: Obtain the current ambient temperature and the previous ambient temperature of the sensor through the hardware communication interface; S2: Based on the thermal field distribution of adjacent sampled data, perform discrete time-domain difference to obtain the sensor temperature fluctuation difference and the sensor data sampling interval; divide the sensor temperature fluctuation difference by the sensor data sampling interval to obtain the sensor temperature change rate; extract the mean and standard deviation of historical temperature change rates, and linearly add them to obtain the sensor's safe temperature difference limit; S3: Perform a dynamic damping weight mapping algorithm on the sensor's safe temperature difference limit and the sensor temperature change rate to obtain the sensor's thermal hysteresis weight ratio; based on the sensor's thermal hysteresis weight ratio, calculate the sensor's equivalent internal temperature according to the first-order thermal inertia dynamic hysteresis principle; perform a linear temperature drift compensation algorithm on the sensor's equivalent internal temperature to obtain the sensor's dynamic compensation sound velocity; S4: Based on the kinematic principle of sound wave time-of-flight ranging, perform time-of-flight ranging calculation on the sensor's dynamic compensation sound velocity to obtain the sensor's high-precision displacement value; encapsulate and output the sensor's high-precision displacement value according to the magnitude of the sensor's temperature change rate.

[0008] This invention addresses the shortcomings of existing magnetostrictive sensor temperature compensation methods, which directly input the absolute value of the external ambient temperature into the compensation model without considering the thermal conduction lag between the metal protective sleeve and the internal air layer. Under conditions of rapid temperature changes such as sudden thermal shocks or high-temperature tank washing, the external temperature abruptly changes while the internal waveguide wire temperature has not yet changed synchronously, leading to overcompensation and jump errors in the output displacement value. This invention calculates the temperature change rate by collecting temperatures from adjacent periods and generates an adaptive safe temperature difference limit based on historical statistical data. Based on the temperature change rate, it calculates the thermal lag weight ratio and estimates the equivalent internal temperature of the waveguide wire by simulating the thermal conduction damping effect using first-order inertial weighting. Then, it corrects the waveguide sound velocity based on the equivalent internal temperature and calculates a high-precision displacement value based on the signal propagation time, outputting the compensation result only when the actual temperature change is confirmed. Starting from the physical thermal conduction causal chain, this invention solves the overcompensation problem during temperature abrupt changes by simulating the thermal inertial lag of the metal sleeve through an algorithm, eliminating displacement jump errors under varying temperature conditions, and improving the measurement accuracy and output stability of magnetostrictive sensors under complex varying temperature conditions.

[0009] Preferably, acquiring the current ambient temperature of the sensor and the preceding ambient temperature of the sensor includes: The system calls the hardware registers of the environmental parameter monitoring module to synchronously extract the external thermistor signal of the current control cycle and convert it into the current ambient temperature of the sensor. At the same time, it retrieves the thermodynamic data cached in the previous control cycle of the system as the previous ambient temperature of the sensor.

[0010] Preferably, obtaining the sensor temperature fluctuation difference and the sensor data sampling interval includes: The absolute physical difference between the current ambient temperature of the sensor and the preceding ambient temperature of the sensor is calculated to obtain the sensor temperature fluctuation difference. At the same time, the underlying hardware clock chip is called to extract the real time span between two data samplings and determine it as the sensor data sampling interval.

[0011] Preferably, obtaining the sensor temperature change rate includes: Divide the sensor temperature fluctuation difference by the sensor data sampling interval to obtain the sensor temperature change rate.

[0012] This invention divides the temperature fluctuation difference by the sampling interval to obtain the temperature change per unit time, i.e., the sensor temperature change rate. This method elevates the static absolute temperature value to a dynamic temperature change trend indicator, which can assess the intensity of external thermal shock, distinguish between slow natural temperature rise and sudden thermal shock, and provide core dynamic characteristic quantities for thermal hysteresis judgment.

[0013] Preferably, obtaining the sensor's safe temperature difference limit includes: Access the data storage unit via the communication bus to extract the mean and standard deviation of the historical temperature change rate recorded during the historical steady-state operation within the target time period; then linearly add the mean and standard deviation of the historical temperature change rate with the same dimensions to obtain the sensor's safe temperature difference limit.

[0014] This invention extracts the mean and standard deviation of the temperature change rate during historical steady-state operation, and linearly adds the two to obtain the sensor's safe temperature difference limit. This method adaptively generates a temperature change tolerance threshold based on the statistical data of the sensor's own operation, replacing the fixed threshold set manually. It can automatically adapt to the normal temperature fluctuation range of different installation environments and different seasons, avoiding misjudging normal process temperature fluctuations as thermal shock.

[0015] Preferably, obtaining the sensor thermal hysteresis weight ratio includes: In the data conversion module, the sensor's safe temperature difference limit is used as the numerator scale, and the sum of the sensor's safe temperature difference limit and the sensor's temperature change rate is used as the denominator scale. A division-based dimension reduction mapping operation is performed to calculate the sensor's thermal hysteresis weight ratio.

[0016] This invention uses a division method with the safe temperature difference limit as the numerator and the sum of the safe temperature difference limit and the rate of temperature change as the denominator to reduce dimensionality and obtain the sensor thermal hysteresis weight ratio. This method converts the rate of external temperature change into a weight coefficient between 0 and 1. The faster the temperature changes, the lower the weight of the current external temperature and the higher the weight of the preceding temperature. From an algorithmic perspective, this simulates the damping and attenuation effect of the metal casing and air layer on heat, realizing the conversion of physical thermal inertia into software control parameters.

[0017] Preferably, calculating the equivalent internal temperature of the sensor includes: Extract the current ambient temperature of the sensor, the previous ambient temperature of the sensor, and the previously calculated sensor thermal hysteresis weight ratio. Use the sensor thermal hysteresis weight ratio to perform a forward numerical multiplication and weighting on the current ambient temperature of the sensor. Use the absolute difference between the constant 1 and the sensor thermal hysteresis weight ratio to perform a reverse numerical multiplication and weighting on the previous ambient temperature of the sensor. Add the two weighted product results algebraically to calculate the equivalent internal temperature of the sensor.

[0018] This invention utilizes a thermal hysteresis weight ratio to perform a bidirectional weighted summation of the current temperature and the preceding temperature to obtain the equivalent internal temperature of the sensor. In other words, it uses a first-order inertial filter to simulate the time delay process of heat conduction from the outer sleeve to the inner waveguide wire, thereby estimating the true temperature of the waveguide wire. This avoids the overcompensation problem caused by directly using the external sudden temperature change, and eliminates the compensation error caused by the heat conduction time difference from the root.

[0019] Preferably, obtaining the sensor's dynamically compensated sound velocity includes: The waveguide wire dielectric temperature coefficient and the sensor calibration reference sound velocity are synchronously extracted from the sensor configuration library; the equivalent internal temperature of the sensor calculated in the previous step is extracted; the waveguide wire dielectric temperature coefficient and the sensor equivalent internal temperature are mapped and corrected; the sensor calibration reference sound velocity is numerically fused with the correction result to obtain the sensor dynamic compensation sound velocity.

[0020] Preferably, obtaining high-precision displacement values ​​from the sensor includes: The original propagation time of the current waveguide signal is read by the waveguide signal detection circuit; the sensor dynamic compensation sound velocity and the original propagation time of the waveguide signal are mapped to a single value in the arithmetic logic unit to obtain the sensor's high-precision displacement value; and a temperature change anti-shake judgment parameter is defined in memory as the noise floor threshold.

[0021] Preferably, the high-precision displacement value of the sensor is packaged and output, including: Extract the sensor temperature change rate obtained in the previous step and the temperature change anti-shake judgment parameter set in the previous step; when the sensor temperature change rate is determined to be greater than the temperature change anti-shake judgment parameter, the system confirms that the sensor is in the actual external environment temperature change state, and then extracts the sensor high-precision displacement value calculated in the previous step, encapsulates it and generates a sensor temperature compensation command to be sent to the host computer control system to complete the temperature collaborative compensation output under complex temperature change conditions.

[0022] The beneficial effects of this invention are as follows: Based on the existing temperature detection circuit and processing unit of a magnetostrictive sensor, it eliminates the need for an additional temperature probe inside the waveguide wire, thus reducing sensor hardware costs. The algorithm can be directly embedded into the signal processing chip of existing sensors, making it suitable for magnetostrictive displacement sensors of various ranges and waveguide wire materials. The temperature tolerance threshold, adaptively generated based on historical operating data, eliminates the need for manual adjustment of compensation parameters for different installation environments, allowing the sensor to adapt to different operating conditions immediately after leaving the factory. The thermal hysteresis weighting simulation algorithm effectively solves the measurement jump problem under extreme conditions such as high-temperature steam washing and sudden thermal shocks, effectively reducing displacement measurement errors during rapid temperature changes and preventing malfunctions in industrial equipment caused by sensor output jumps. The anti-shake threshold design ensures the stability of the output value during steady-state operation, preventing frequent numerical fluctuations due to electrical noise. This improves the environmental adaptability and measurement reliability of the magnetostrictive sensor in complex industrial environments such as metallurgy, petrochemicals, and engineering machinery, providing a low-cost and easily implemented temperature compensation technology solution for high-precision industrial displacement detection. Attached Figure Description

[0023] Figure 1 A flowchart illustrating a waveguide signal temperature compensation method for a magnetostrictive sensor according to the present invention is shown schematically. Figure 2This diagram illustrates a comparison between the rate of temperature change and the anti-shake determination parameters in this invention. Figure 3 This diagram illustrates the historical temperature fluctuations and safe temperature difference limits in this invention. Detailed Implementation

[0024] This invention discloses a method for temperature compensation of waveguide signals in a magnetostrictive sensor, referring to... Figure 1 This includes steps S1-S4: S1: Obtain the current ambient temperature and the preceding ambient temperature of the sensor.

[0025] It should be noted that in practical applications of high-precision position detection in industrial automation, the core physical challenge the system faces in order to accurately predict and dynamically compensate for the propagation speed of the waveguide signal of the magnetostrictive sensor lies in how to efficiently capture the transient thermal field state of the external environment. This invention bypasses redundant intermediate processing steps by directly connecting to the underlying hardware communication interface, synchronously extracting the external thermistor signal of the current control cycle and converting it into the current ambient temperature of the sensor with low computational overhead. Simultaneously, it retrieves the thermodynamic data cached in the previous control cycle as the sensor's preceding ambient temperature. As the foundational source of the entire data flow, this invention receives the original physical signals from the underlying hardware, laying a data foundation for the smooth execution of all subsequent timing deductions, differential calculations, and dynamic compensation algorithms, effectively eliminating transmission obstacles caused by missing underlying data acquisition or poor channel performance.

[0026] Specifically, by interfacing with the hardware communication interface, the current ambient temperature of the sensor and the preceding ambient temperature of the sensor, which characterize the external thermal field state of the sensor, are obtained, including: The system calls the hardware registers of the environmental parameter monitoring module to synchronously extract the external thermistor signal of the current control cycle and convert it into the current ambient temperature of the sensor. At the same time, it retrieves the thermodynamic data cached in the previous control cycle of the system as the previous ambient temperature of the sensor.

[0027] It should be noted that the current ambient temperature and the previous ambient temperature of the sensor refer to the absolute values ​​of the external thermal field acquired within two consecutive hardware sampling cycles. These values ​​characterize the state of the most direct heat source outside the sensor's metal sheath. Acquiring the current and previous ambient temperatures of the sensor aims to provide a fundamental thermodynamic tracking anchor point for the system. This invention does not add physical probes; it extracts extremely simplified temperature values ​​by directly connecting to the underlying data bus, ensuring the purity of the input data stream. The temperature electrical signal acquisition and calibration conversion method includes signal conditioning and analog-to-digital conversion of the electrical signal output by the temperature-sensitive element, and conversion based on a pre-stored temperature-electrical signal calibration relationship. Simultaneously, the ambient temperature data cached in the previous control cycle within the system is retrieved as the sensor's previous ambient temperature.

[0028] At this point, the current ambient temperature of the sensor and the ambient temperature preceding the sensor have been obtained.

[0029] S2: Obtain the sensor's safe temperature difference limit.

[0030] It should be noted that in real-world scenarios where magnetostrictive sensors face extreme temperature fluctuations such as sudden thermal shocks or high-temperature steam tank cleaning, the core physical challenge lies in accurately determining the severity of the external ambient temperature deviating from its steady-state state. Traditional temperature measurement and control schemes often directly input the current absolute temperature into the model, failing to measure the instantaneous temperature variation within adjacent clock cycles, leading to sluggish or misjudged responses to sudden thermal shocks. This invention extracts the thermal field distribution characteristics of adjacent samples by executing a discrete-time differential mapping algorithm. Using digital domain difference calculations, it calculates the absolute physical difference between the current ambient temperature and the preceding ambient temperature to obtain the sensor temperature fluctuation difference. Simultaneously, it calls the underlying hardware clock chip to extract the actual time span between two data samples, determining it as the sensor data sampling interval to eliminate data gaps in subsequent steps.

[0031] Specifically, based on the thermal field distribution of adjacent sampled data, discrete time-domain difference is performed to obtain the sensor temperature fluctuation difference and the sensor data sampling interval, including: The absolute physical difference between the current ambient temperature of the sensor and the preceding ambient temperature of the sensor is calculated to obtain the sensor temperature fluctuation difference. At the same time, the underlying hardware clock chip is called to extract the real time span between two data samplings and determine it as the sensor data sampling interval.

[0032] Thus, the sensor temperature fluctuation difference and the sensor data sampling interval were obtained.

[0033] It should be noted that in complex operating scenarios where sensors perform temperature compensation, the core physical challenge faced by the system lies in the fact that static temperature values ​​cannot effectively characterize the instantaneous intensity of external thermal shocks. Directly using these values ​​for control and regulation can easily lead to algorithm overshoot. Existing technologies, lacking characterization of the temporal evolution of external temperature, prevent control systems from recognizing the essential difference between quasi-static temperature rise and sudden thermal shocks. This invention transforms static temperature readings into dynamic trend warning indicators by executing a rate-of-change normalization feature extraction algorithm. Specifically, this invention extracts the previously calculated sensor temperature fluctuation difference and the sensor data sampling interval, divides the sensor temperature fluctuation difference by the sensor data sampling interval, and calculates the sensor temperature change rate.

[0034] Preferably, the rate of change of sensor temperature is obtained by dividing the sensor temperature fluctuation difference by the sensor data sampling interval, including: Divide the sensor temperature fluctuation difference by the sensor data sampling interval to obtain the sensor temperature change rate.

[0035] It should be noted that the sensor temperature change rate refers to the evolution derivative of the ambient temperature within a unit time window. It characterizes the instantaneous burst intensity of thermal shock to the sensor. The purpose of constructing this sensor temperature change rate is to upgrade the static temperature readings and transform them into dynamic trend early warning indicators.

[0036] Thus, the sensor temperature change rate was obtained as a result of the instantaneous burst intensity of the thermal shock to the sensor's exterior.

[0037] It should be noted that, under the variable environmental temperature interference in industrial settings, the core logical challenge faced by the system lies in how to scientifically define the tolerance boundary to avoid misjudging normal process fluctuations as sudden thermal shocks. This invention, based on the laws of natural statistical fluctuations, executes a statistical confidence upper limit construction algorithm, abandoning artificially set fixed constants. Specifically, this invention accesses the data storage unit via the communication bus, extracts the historical mean and standard deviation of temperature change rates recorded during historical steady-state operation, and linearly adds the historical mean and standard deviation of temperature change rates with the same dimensions to calculate the sensor's safe temperature difference limit.

[0038] Preferably, the mean and standard deviation of historical temperature change rates are extracted, and the two are linearly added to obtain the sensor's safe temperature difference limit, including: Access the data storage unit via the communication bus to extract the mean and standard deviation of the historical temperature change rate recorded during the historical steady-state operation within the target time period; then linearly add the mean and standard deviation of the historical temperature change rate with the same dimensions to obtain the sensor's safe temperature difference limit.

[0039] It should be noted that the sensor safety temperature difference limit refers to the upper limit of the maximum non-destructive temperature change rate allowed by the system. It represents the critical state of safety fluctuations under natural operating conditions without the need for thermal hysteresis intervention. The purpose of constructing this sensor safety temperature difference limit is to abandon the fixed constant set by humans and generate a safety error prevention limit that conforms to the natural tolerance distribution.

[0040] Thus, the sensor safety temperature difference limit, which characterizes the critical state of safety fluctuations under natural operating conditions without the need for thermal hysteresis intervention, was obtained.

[0041] S3: Obtain the sensor's dynamically compensated sound velocity.

[0042] It should be noted that in scenarios where external temperature changes drastically due to variable temperature conditions, the core physical challenge faced by the system lies in how to effectively convert transient abrupt changes into suppression coefficients at the software control level to overcome the heat transfer lag caused by the physical thermal damping of the metal sheath. This invention establishes a proportional relationship between the abrupt change limit and the dynamic rate of change by executing a dynamic damping weight mapping algorithm. Specifically, this invention extracts the sensor's safe temperature difference limit obtained previously and the sensor's temperature change rate calculated previously. In the data conversion module, the sensor's safe temperature difference limit is used as the numerator scale, and the sum of the sensor's safe temperature difference limit and the sensor's temperature change rate is used as the denominator scale. A division-based dimension reduction mapping operation is performed to calculate the sensor's thermal hysteresis weight ratio.

[0043] Specifically, a dynamic damping weight mapping algorithm is executed on the sensor's safe temperature difference limit and the sensor's temperature change rate to obtain the sensor's thermal hysteresis weight ratio, including: In the data conversion module, the sensor's safe temperature difference limit is used as the numerator scale, and the sum of the sensor's safe temperature difference limit and the sensor's temperature change rate is used as the denominator scale. A division-based dimension reduction mapping operation is performed to calculate the sensor's thermal hysteresis weight ratio.

[0044] It should be noted that the sensor thermal hysteresis weight ratio is a dimensionless proportionality coefficient used to guide the system in allocating the weight of the current temperature. It characterizes the attenuation and filtering effect of the physical thermal damping of the metal sheath on external thermal shocks. The purpose of constructing this sensor thermal hysteresis weight ratio is to map dynamic abrupt changes into suppression coefficients, realizing the conversion of underlying physical thermal inertia into software control factors. The sensor calibration reference sound velocity characterizes the signal transmission speed at standard temperature and is extracted beforehand to ensure the final compensation calculation.

[0045] Thus, the sensor thermal hysteresis weight ratio, which characterizes the attenuation filtering effect of the physical thermal damping of the metal sleeve on external thermal shock, was obtained.

[0046] It should be noted that when the sensor encounters extreme scenarios such as high-temperature steam washing or sudden cooling spray, the actual temperature change of the core waveguide wire lags behind the external ambient temperature due to the physical thermal resistance isolation of the thick metal sleeve and air layer. Existing methods that directly feed forward the external temperature ignore the heat transfer time of the medium, inevitably leading to overcompensation in the algorithm. This invention is based on the principle of first-order thermal inertia dynamic hysteresis and executes a first-order thermal inertia time delay reconstruction algorithm. That is, this invention extracts the sensor's current ambient temperature, the sensor's previous ambient temperature, and the sensor's thermal hysteresis weight ratio calculated in the previous step. The sensor's current ambient temperature is weighted by forward multiplication using the sensor's thermal hysteresis weight ratio, and the sensor's previous ambient temperature is weighted by backward multiplication using the remainder obtained by subtracting the sensor's thermal hysteresis weight ratio from a constant 1. The two weighted products are algebraically added to calculate the equivalent internal temperature of the sensor. At the same time, the original propagation time of the current waveguide signal is read through the waveguide signal detection circuit.

[0047] Preferably, based on the sensor thermal hysteresis weight ratio and according to the first-order thermal inertia dynamic hysteresis principle, the equivalent internal temperature of the sensor is calculated, including: Extract the current ambient temperature of the sensor, the previous ambient temperature of the sensor, and the previously calculated sensor thermal hysteresis weight ratio. Use the sensor thermal hysteresis weight ratio to perform a forward numerical multiplication and weighting on the current ambient temperature of the sensor. Use the absolute difference between the constant 1 and the sensor thermal hysteresis weight ratio to perform a reverse numerical multiplication and weighting on the previous ambient temperature of the sensor. Add the two weighted product results algebraically to calculate the equivalent internal temperature of the sensor.

[0048] It should be noted that the sensor's equivalent internal temperature refers to the actual temperature of the waveguide wire estimated after software thermal damping buffering. It characterizes the hysteretic thermodynamic state after considering the heat transfer delay of the metal tube wall. The purpose of constructing this sensor's equivalent internal temperature is to smooth out the objectively existing physical heat transfer time difference through pure logical deduction, replacing the original system's blind and direct feedforward external instantaneous temperature.

[0049] Thus, the sensor equivalent internal temperature, which characterizes the true physical state of the waveguide wire inside the sleeve, was obtained.

[0050] It should be noted that after reconstructing the internal thermal hysteresis temperature of the bushing, the next core physical challenge facing the system lies in how to accurately convert the hysteresis-stabilized temperature data into the corresponding sound wave propagation velocity correction. This invention, based on the temperature dependence of material sound velocity, executes a linear temperature drift compensation algorithm. Specifically, this invention extracts the previously calculated equivalent internal temperature of the sensor, the previously acquired sensor calibration reference sound velocity, and the waveguide wire medium temperature coefficient. It then performs a mapping correction between the waveguide wire medium temperature coefficient and the sensor's equivalent internal temperature, and numerically fuses the sensor calibration reference sound velocity with this correction result to calculate the sensor's dynamic compensation sound velocity.

[0051] Preferably, a linear temperature drift compensation algorithm is applied to the equivalent internal temperature of the sensor to obtain the sensor's dynamically compensated sound velocity, including: The waveguide wire dielectric temperature coefficient and the sensor calibration reference sound velocity are synchronously extracted from the sensor configuration library. The equivalent internal temperature of the sensor calculated in the previous step is extracted. The waveguide wire dielectric temperature coefficient and the sensor equivalent internal temperature are mapped and corrected. The sensor calibration reference sound velocity is numerically fused with the correction result to obtain the sensor dynamic compensation sound velocity.

[0052] It should be noted that the above sound velocity temperature compensation adopts linear mapping logic: using the sensor calibration reference sound velocity measured at 5℃ during factory calibration as the calculation basis, the difference between the sensor's equivalent internal temperature and the 25℃ calibration reference temperature is first calculated. This temperature difference is multiplied by the absolute value of the waveguide wire's dielectric temperature coefficient to obtain the sound velocity correction amplitude at the current temperature. The specific superposition rule is as follows: when the equivalent internal temperature is higher than the 25℃ reference temperature, the sound velocity correction amplitude is subtracted from the sensor calibration reference sound velocity to obtain the actual waveguide sound velocity after the temperature increase; when the equivalent internal temperature is lower than the 25℃ reference temperature, the sound velocity correction amplitude is added to the sensor calibration reference sound velocity to obtain the actual waveguide sound velocity after the temperature decreases. The final calculation result is the sensor dynamic compensation sound velocity that matches the actual temperature of the waveguide wire.

[0053] It should be noted that the sensor's dynamic compensation sound velocity refers to the real-time waveguide signal propagation speed after eliminating the influence of temperature drift. It characterizes the actual sound wave motion characteristics of the sensor under the current complex thermodynamic conditions. Constructing this sensor's dynamic compensation sound velocity aims to provide accurate ranging reference parameters. For waveguide wires of different materials or specifications, the temperature coefficient of the waveguide wire medium can be adjusted according to the factory calibration results. In this embodiment, 20℃ is used as the calibration reference temperature, and the sensor calibration reference sound velocity is taken as... The sensor calibration reference sound velocity is obtained by dividing the known calibration distance by the corresponding waveguide signal propagation time. Using the factory-measured reference sound velocity can reduce the impact of differences in waveguide wire material batches, diameters, pretension, and installation structures on the compensation results.

[0054] Thus, the sensor dynamic compensation sound velocity, which characterizes the true sound wave motion characteristics of the sensor under the current complex thermodynamic state, was obtained.

[0055] S4: Encapsulate and output the high-precision displacement values ​​from the sensor.

[0056] It should be noted that after completing the dynamic compensation sound velocity calculation, the core task of the system is to perform a final kinematic mapping between the corrected sound velocity and the propagation time to obtain a high-precision physical displacement that eliminates temperature drift errors. This invention is based on the kinematic principle of sound wave time-of-flight ranging and executes a time-of-flight ranging algorithm. Specifically, this invention extracts the previously calculated sensor dynamic compensation sound velocity and the previously acquired original propagation time of the waveguide signal. In the arithmetic logic unit, a single-value mapping is performed between the sensor dynamic compensation sound velocity and the original propagation time of the waveguide signal to obtain a high-precision sensor displacement value. Simultaneously, a temperature change anti-shake judgment parameter is defined in memory as a noise floor threshold.

[0057] Preferably, based on the kinematic principle of acoustic time-of-flight ranging, time-of-flight ranging calculation is performed on the dynamically compensated sound velocity of the sensor to obtain high-precision displacement values ​​of the sensor, including: The original propagation time of the current waveguide signal is read by the waveguide signal detection circuit; the sensor dynamic compensation sound velocity and the original propagation time of the waveguide signal are mapped to a single value in the arithmetic logic unit to obtain the sensor's high-precision displacement value; and a temperature change anti-shake judgment parameter is defined in memory as the noise floor threshold.

[0058] It should be noted that the high-precision displacement value of the sensor refers to the final output position coordinates of the sensor after thermal damping compensation correction. It represents the absolute physical displacement after eliminating transient thermal shock interference. The purpose of constructing this high-precision displacement value of the sensor is to complete the entire business process of high-precision position detection. The temperature change anti-shake judgment parameter is a judgment parameter used to filter high-frequency electrical noise in the hardware. It aims to establish the physical authenticity of temperature changes. The specific value range of the temperature change anti-shake judgment parameter is 0.1 to 0.5, preferably 0.2. This range can achieve a balance between anti-electrical disturbance and error prevention response. For example, by substituting specific data for calculation, if the sensor dynamic compensation sound velocity is 2827.49 m / s and the original waveguide signal propagation time is 0.01 s, multiplying them yields a high-precision displacement value of 28.2749, which improves the measurement accuracy under extreme temperature conditions.

[0059] Thus, a high-precision displacement value of the sensor was obtained, representing the absolute physical displacement after eliminating transient thermal shock interference.

[0060] It should be noted that in the final output stage of the entire waveguide signal temperature compensation method, the core logical challenge faced by the system lies in how to effectively prevent unnecessary computational idling and output command oscillation caused by minute electrical noise under completely constant temperature steady-state conditions. This invention uses a comparison module to extract the previously acquired sensor temperature change rate and the previously set temperature change anti-shake judgment parameters. When the sensor temperature change rate is determined to be greater than the temperature change anti-shake judgment parameters, the system confirms that the sensor is in a state of actual external environmental temperature change. It then extracts the previously calculated high-precision sensor displacement value, encapsulates it, and generates a sensor temperature compensation command, which is sent to the host computer control system to complete the collaborative compensation output under complex temperature variations.

[0061] Preferably, the high-precision displacement value of the sensor is packaged and output according to the magnitude of the sensor's temperature change rate, including: Extract the sensor temperature change rate obtained in the previous step and the temperature change anti-shake judgment parameter set in the previous step; when the sensor temperature change rate is determined to be greater than the temperature change anti-shake judgment parameter, the system confirms that the sensor is in the actual external environment temperature change state, and then extracts the sensor high-precision displacement value calculated in the previous step, encapsulates it and generates a sensor temperature compensation command to be sent to the host computer control system to complete the temperature collaborative compensation output under complex temperature change conditions.

[0062] It should be noted that generating the sensor temperature compensation command is the final logical output of the entire waveguide signal temperature compensation method. By introducing temperature change anti-shake judgment parameters for the final step of serial interception, the entire anti-shake control process under key operating conditions is ensured.

[0063] It should be noted that, Figure 2 This graph compares the temperature change rate with the anti-jitter judgment parameters. The horizontal axis represents the time period, covering multiple continuous sampling control cycles; the vertical axis represents the change rate. Light-colored bars represent the temperature change rate for each cycle, and dark-colored bars represent the system's preset anti-jitter judgment parameters. When the temperature change rate exceeds the anti-jitter judgment parameters, the system determines it as a real external environmental temperature change and outputs a high-precision displacement value after temperature compensation. When the temperature change rate is less than or equal to the anti-jitter judgment parameters, it is determined as electrical background noise or normal minor fluctuations, maintaining the current output value without triggering compensation. This graph visually presents the anti-jitter output judgment logic of this invention, which can filter out frequent numerical jumps caused by electrical noise and ensure the stability of the output value during steady-state operation. Figure 3This diagram illustrates historical temperature fluctuations and the safe temperature difference limit. The horizontal axis represents the sample number, covering all temperature fluctuation samples from historical steady-state operation; the vertical axis represents temperature fluctuation; the scatter plots represent historical fluctuation data; and the horizontal dashed line represents the safe temperature difference limit. The system extracts the mean and standard deviation of the temperature change rate from historical steady-state operation, and linearly adds them to obtain the safe temperature difference limit. All temperature fluctuation samples under normal operating conditions are distributed below the safe temperature difference limit; fluctuations exceeding the limit are identified as abnormal thermal shocks. This diagram visually demonstrates the logic of this invention, which adaptively generates the safe temperature difference limit based on historical operating statistics. It can automatically adapt to the normal temperature fluctuation range of different installation environments, avoiding misjudging normal process temperature fluctuations as thermal shocks.

[0064] It should be noted that this embodiment uses the temperature compensation process of a magnetostrictive level sensor applied to a 1000m³ refined oil storage tank under high-temperature steam tank cleaning conditions as an example to illustrate the complete calculation process of this method. The sensor uses FeNi42 iron-nickel alloy waveguide wire, and the reference sound velocity of the waveguide wire at the factory calibration reference temperature of 25℃ is 2826m / s, and the temperature coefficient of the waveguide wire medium is... The system temperature sampling period is fixed at 1 second. Statistical analysis of the historical temperature change rate obtained from the steady-state operation data at room temperature for the first 30 minutes of tank washing yielded an average rate of 0.24℃ / s and a standard deviation of 0.16℃ / s. The system's preset temperature change anti-shake judgment parameter is 0.3℃ / s. At the 127th second of the tank washing operation, the outer wall of the sensor's metal protective tube comes into contact with 120℃ high-temperature steam, entering a rapid heating phase. At this time, the current ambient temperature collected by the PT100 thermistor via the hardware communication interface is 64.3℃, and the ambient temperature from the previous sampling period in the system buffer is 58.6℃. The original propagation time of the torsional wave is read as 639.25μs via the waveguide signal detection circuit. First, the temperature fluctuation difference between adjacent sampling periods is calculated as the absolute difference between the current ambient temperature and the previous ambient temperature, which is 5.7℃. The actual time interval between two temperature samples is read as 1s using the hardware RTC clock. Dividing the temperature fluctuation difference by the sampling interval yields a temperature change rate of 5.7℃ / s. The mean and standard deviation of historical temperature change rates are linearly added in the same dimension to obtain a safe temperature difference limit of 0.4℃ / s. Then, using the safe temperature difference limit as the numerator and the sum of the safe temperature difference limit and the temperature change rate as the denominator, a division operation is performed to obtain a thermal hysteresis weighting ratio of approximately 0.0656. Based on the principle of first-order thermal inertia dynamic hysteresis, the thermal hysteresis weighting ratio is used to positively weight the current ambient temperature, and the difference between the constant 1 and the thermal hysteresis weighting ratio is used to negatively weight the previous ambient temperature. The sum of the two weighted results yields an equivalent internal temperature of approximately 58.97℃ for the waveguide wire. Based on the principle of sound wave time-of-flight ranging, the original propagation time unit is converted to seconds and multiplied by the dynamically compensated sound speed to obtain a high-precision displacement value of approximately 1.7889m. Then, the temperature change rate is compared with the anti-shake judgment parameter. The current temperature change rate of 5.7℃ / s is greater than the anti-shake threshold of 0.3℃ / s, so it is determined that the current temperature change is a real external temperature change rather than measurement noise. Finally, the high-precision displacement value is encapsulated into a standard Modbus-RTU data frame and sent to the host computer control system to complete the temperature compensation output for this sampling period. Using existing technology, directly inputting the current external ambient temperature of 64.3℃ into the static compensation model yields a sound velocity of approximately 2794.17 m / s and a calculated displacement of approximately 1.7862 m. Compared to the output of this method, there is a transient jump error of 2.76 mm. This method effectively eliminates the overcompensation error during rapid temperature changes by simulating the thermal conduction time lag between the metal sleeve and the air layer. Under steady-state conditions, when the temperature change rate is less than 0.1℃ / s, the thermal hysteresis weight ratio is automatically adjusted to above 0.8, and the difference between the equivalent internal temperature and the external ambient temperature is less than 0.02℃. The compensation accuracy is consistent with the static temperature compensation method, with no steady-state accuracy loss.

[0065] Thus, the waveguide signal temperature compensation method for magnetostrictive sensors was completed.

[0066] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A method for temperature compensation of waveguide signals in a magnetostrictive sensor, characterized in that, include: S1: Obtain the current ambient temperature of the sensor and the ambient temperature of the preceding sensor by connecting to the hardware communication interface; S2: Based on the thermal field distribution of adjacent sampled data, perform discrete time-domain difference to obtain the sensor temperature fluctuation difference and the sensor data sampling interval; divide the sensor temperature fluctuation difference by the sensor data sampling interval to obtain the sensor temperature change rate; extract the mean of historical temperature change rate and the standard deviation of historical temperature change rate, and add the two linearly to obtain the sensor's safe temperature difference limit. S3: Perform a dynamic damping weight mapping algorithm on the sensor's safe temperature difference limit and the sensor's temperature change rate to obtain the sensor's thermal hysteresis weight ratio; based on the sensor's thermal hysteresis weight ratio, calculate the sensor's equivalent internal temperature according to the first-order thermal inertia dynamic hysteresis principle; perform a linear temperature drift compensation algorithm on the sensor's equivalent internal temperature to obtain the sensor's dynamic compensation sound velocity. S4: Based on the kinematic principle of acoustic time-of-flight ranging, the time-of-flight ranging calculation is performed on the dynamic compensation sound speed of the sensor to obtain the high-precision displacement value of the sensor; according to the magnitude of the sensor temperature change rate, the high-precision displacement value of the sensor is packaged and output.

2. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The acquisition of the current ambient temperature of the sensor and the preceding ambient temperature of the sensor includes: The system calls the hardware registers of the environmental parameter monitoring module to synchronously extract the external thermistor signal of the current control cycle and convert it into the current ambient temperature of the sensor. At the same time, it retrieves the thermodynamic data cached in the previous control cycle of the system as the previous ambient temperature of the sensor.

3. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The process of obtaining the sensor temperature fluctuation difference and the sensor data sampling interval includes: The absolute physical difference between the current ambient temperature of the sensor and the preceding ambient temperature of the sensor is calculated to obtain the sensor temperature fluctuation difference. At the same time, the underlying hardware clock chip is called to extract the real time span between two data samplings and determine it as the sensor data sampling interval.

4. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The method of obtaining the sensor temperature change rate includes: Divide the sensor temperature fluctuation difference by the sensor data sampling interval to obtain the sensor temperature change rate.

5. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The process of obtaining the sensor's safe temperature difference limit includes: Access the data storage unit via the communication bus to extract the mean and standard deviation of historical temperature change rates recorded during historical steady-state operation within the target time period; then linearly add the mean and standard deviation of historical temperature change rates with the same dimensions to obtain the sensor's safe temperature difference limit.

6. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The process of obtaining the sensor thermal hysteresis weight ratio includes: In the data conversion module, the sensor's safe temperature difference limit is used as the numerator scale, and the sum of the sensor's safe temperature difference limit and the sensor's temperature change rate is used as the denominator scale. A division-based dimension reduction mapping operation is performed to calculate the sensor's thermal hysteresis weight ratio.

7. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The calculation of the sensor's equivalent internal temperature includes: Extract the current ambient temperature of the sensor, the previous ambient temperature of the sensor, and the previously calculated sensor thermal hysteresis weight ratio. Use the sensor thermal hysteresis weight ratio to perform a forward numerical multiplication and weighting on the current ambient temperature of the sensor. Use the absolute difference between the constant 1 and the sensor thermal hysteresis weight ratio to perform a reverse numerical multiplication and weighting on the previous ambient temperature of the sensor. Add the two weighted product results algebraically to calculate the equivalent internal temperature of the sensor.

8. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The process of obtaining the sensor's dynamically compensated sound velocity includes: The waveguide wire dielectric temperature coefficient and the sensor calibration reference sound velocity are synchronously extracted from the sensor configuration library. The equivalent internal temperature of the sensor calculated in the previous step is extracted. The waveguide wire dielectric temperature coefficient and the sensor equivalent internal temperature are mapped and corrected. The sensor calibration reference sound velocity is numerically fused with the correction result to obtain the sensor dynamic compensation sound velocity.

9. The waveguide signal temperature compensation method for a magnetostrictive sensor according to claim 1, characterized in that, The process of obtaining high-precision displacement values ​​from the sensor includes: The original propagation time of the current waveguide signal is read by the waveguide signal detection circuit; in the arithmetic logic unit, the sensor dynamic compensation sound velocity is mapped to the original propagation time of the waveguide signal to obtain the high-precision displacement value of the sensor; and a temperature change anti-shake judgment parameter is defined in memory as the noise floor threshold.

10. A method for waveguide signal temperature compensation of a magnetostrictive sensor according to claim 1, characterized in that, The process of encapsulating and outputting high-precision displacement values ​​from the sensor includes: Extract the sensor temperature change rate obtained in the previous step and the temperature change anti-shake judgment parameter set in the previous step; when the sensor temperature change rate is determined to be greater than the temperature change anti-shake judgment parameter, the system confirms that the sensor is in the actual external environment temperature change state, and then extracts the sensor high-precision displacement value calculated in the previous step, encapsulates it and generates a sensor temperature compensation command to be sent to the host computer control system to complete the temperature collaborative compensation output under complex temperature change conditions.

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