Dynamic compensation method for temperature and pressure composite sensor

Abstract

The invention provides a dynamic compensation method for a temperature-pressure composite sensor, which comprises the following steps of: acquiring amplitude-frequency and phase-frequency characteristic curves through frequency response test, determining bandwidth limitation and phase lag characteristics, and designing a digital compensation filter by adopting an inverse system method to improve amplitude flatness and phase linearity in a preset frequency band; meanwhile, linear subintervals are divided according to piecewise nonlinear characteristics, a dynamic compensation model is established, and gain and phase correction coefficients are extracted; and according to a measured value, switching subinterval models in real time and adjusting a filtering coefficient to ensure a dynamic tracking characteristic. According to the invention, the optimized frequency response characteristic is finally integrated to an output signal processing flow, and the measurement precision and response speed of the temperature and pressure sensor under complex working conditions are significantly improved.

Description

Technical Field

[0001] This invention relates to a dynamic compensation method for a temperature and pressure composite sensor. Background Technology

[0002] Temperature-pressure composite sensors can simultaneously detect changes in temperature and pressure, enabling accurate measurements under complex operating conditions. However, as application scenarios increasingly demand faster dynamic response and higher measurement accuracy, improvements to temperature-pressure composite sensors often focus on hardware optimization or simple calibration methods. These methods frequently overlook the complex behavior of sensors under different operating conditions, particularly when faced with rapidly changing signals, making it difficult to effectively address signal distortion and response delays. Existing solutions often fail to adapt to varying sensor characteristics under diverse operating conditions when processing sensor output, resulting in significant deviations between measurement results and actual values.

[0003] A deeper analysis of the challenges in this field reveals that the frequency response characteristics of sensors are a core issue. Frequency response characteristics refer to a sensor's ability to react to signals of different frequencies, including changes in signal amplitude and phase. Because sensors have limitations in their response at different frequencies, especially with high-frequency signals where amplitude attenuation and phase lag are common, the measurement results cannot accurately reflect rapidly changing realities. This frequency response limitation leads to another critical problem: sensors exhibit different behavioral characteristics when faced with wide-ranging temperature and pressure variations. These variations make it difficult for a single calibration method to cover all operating conditions. For example, when the temperature rises rapidly from low to high while the pressure fluctuates dramatically, the sensor may react slowly in some ranges while being overly sensitive in others, resulting in output data that fails to accurately reflect the actual operating conditions.

[0004] Therefore, how to achieve accurate and real-time dynamic correction of sensors under rapidly changing operating conditions, addressing the limitations of sensor frequency response and changes in characteristics, has become a key problem that this research urgently needs to solve. Summary of the Invention

[0005] This invention proposes a dynamic compensation method for a temperature and pressure composite sensor, which aims to solve the dynamic distortion problem caused by the piecewise nonlinear characteristics of the sensor across the entire measurement range. It can achieve accurate and real-time dynamic correction under rapidly changing operating conditions.

[0006] The technical solution of this invention is implemented as follows: In one aspect, the present invention provides a dynamic compensation method for a temperature-pressure composite sensor, comprising the following steps: The amplitude-frequency and phase-frequency characteristic curves of the temperature and pressure channels of the temperature and pressure sensors are obtained by frequency response testing. The relationship between the amplitude and phase of the output signal and the excitation frequency is characterized, and the bandwidth limitation and phase hysteresis characteristics of the temperature and pressure sensors are determined. Based on the characteristic curve, a digital compensation filter is designed using the inverse system method. An inverse model corresponding to the sensor transfer function is constructed to compensate for the sensor's dynamic distortion, and the amplitude flatness and phase linearity parameters of the compensated sensor in a predetermined frequency band are obtained. The piecewise nonlinear characteristics of the temperature and pressure sensor across its full measurement range are analyzed. The sensor's operating range is divided into multiple linear sub-intervals, and a corresponding linear dynamic compensation model is established within each linear sub-interval. Compensation parameters for the corresponding sub-interval are extracted from each linear dynamic compensation model. The linear sub-interval to which the current measurement value belongs is determined, and the corresponding compensation parameters are selected. If the current measurement value exceeds the boundary threshold of the current linear sub-interval, the sensor is switched to an adjacent linear sub-interval. The compensation parameters corresponding to the switched linear sub-interval are obtained according to the parameter mapping table, and real-time correction is performed. The filter coefficients of the digital compensation filter are adjusted based on the compensation parameters after real-time correction to determine the dynamic tracking characteristics of the sensor system for rapidly changing input signals after adjustment. By integrating dynamic tracking characteristics into the output signal processing flow of the temperature and pressure sensor, the frequency response characteristics of the sensor under varying operating conditions can be optimized.

[0007] Preferably, the amplitude-frequency response curve is traversed to identify the cutoff frequency and establish the bandwidth limit value. The bandwidth limit value is mapped to the phase-frequency response curve to locate the phase angle value. The phase hysteresis characteristics of the temperature and pressure sensor are quantified based on the phase angle value.

[0008] Preferably, the process of obtaining the amplitude flatness and phase linearity parameters includes: The sensor transfer function is extracted from the amplitude-frequency response curve and the phase-frequency response curve. The coefficient set of the sensor transfer function is obtained by processing the characteristic curve through Fourier transform. An inverse model is constructed, and the parameter set of the inverse model is generated by matrix inversion operation. A digital compensation filter is designed based on the parameter set of the inverse model. The filter coefficients are adjusted to match different sensor types to obtain the compensation filter structure. A compensation filter structure is used to perform dynamic distortion compensation on the sensor output signal, and the compensated signal sequence is obtained. The amplitude flatness parameter and phase linearity parameter are calculated within a predetermined frequency band to determine the dynamic performance index of the compensated sensor system.

[0009] Preferably, a corresponding linear dynamic compensation model is established, including: The parameters are processed by curve fitting to identify nonlinear inflection points, resulting in a piecewise nonlinear characteristic distribution. The sensor's working range is divided into multiple linear sub-intervals. Temperature and pressure fusion calibration data are collected in each linear sub-interval. The fusion calibration data is processed by boundary point calibration operations to determine the boundary values ​​of the linear sub-intervals. A dynamic compensation model corresponding to the linear sub-interval is constructed. The least squares method is used to fit the model coefficients by minimizing the sum of squared residuals to obtain the compensation model coefficient set. The filter coefficients are adjusted to match the temperature and pressure sensor type. The sensor output signal is processed through the compensation filter structure to obtain the compensated signal sequence. The amplitude flatness and phase linearity parameters of the compensated signal sequence within a predetermined frequency band are obtained. If the parameters exceed the preset threshold, a new linear dynamic compensation model is established for each linear sub-interval. The dynamic performance index of the compensated sensor system is judged by iteratively adjusting the model parameters.

[0010] Preferably, determining the dynamic tracking characteristics of the adjusted sensor system for rapidly changing input signals includes: The filter coefficient adjustment value is obtained by real-time corrected compensation parameters to determine the updated configuration of the digital compensation filter; based on the updated configuration, the response data is obtained from the rapidly changing signal using the input signal simulation method to obtain the preliminary dynamic response of the sensor system. For signal stability analysis, the response time index is judged to determine the error minimization level; if the error minimization level exceeds the preset threshold, the tracking accuracy parameter is adjusted through response time evaluation to obtain the optimized system response; from the optimized system response, the dynamic tracking characteristic value is obtained to determine the tracking capability of the adjusted sensor system for rapidly changing input signals.

[0011] Preferably, optimizing the frequency response characteristics of the sensor under varying operating conditions includes:

[0012] The output signal is acquired by real-time data acquisition temperature and pressure sensor. The signal fluctuation amplitude is judged based on the signal peak-valley difference under varying operating conditions. Dynamic tracking characteristic parameters are obtained and integrated into the output signal processing flow. Adaptive filtering is used to process noise signal as input and output filtered signal to determine the initial value of frequency response curve. The response deviation is obtained at the initial value of the frequency response curve. When the deviation exceeds a preset threshold, the feedback loop is adjusted by gain adjustment to obtain the optimized response characteristics and match them with the variable operating conditions. If the matching degree is insufficient, the system parameters are calibrated based on the deviation distribution to obtain the threshold dynamic setting. The performance index is quantified according to the threshold dynamic setting and integrated into the overall process of the sensor system to determine the frequency response characteristic optimization result.

[0013] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the method described thereon.

[0014] In another aspect, the present invention also provides a computer program product, comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method described herein.

[0015] The beneficial effects of this invention are as follows: Amplitude-frequency and phase-frequency characteristic curves are obtained through frequency response testing to determine bandwidth limitations and phase lag characteristics. A digital compensation filter is designed using the inverse system method to improve amplitude flatness and phase linearity within a predetermined frequency band. Simultaneously, linear sub-intervals are divided to address piecewise nonlinear characteristics, a dynamic compensation model is established, and gain and phase correction coefficients are extracted. The sub-interval model is switched and the filter coefficients are adjusted in real time based on measured values ​​to ensure dynamic tracking characteristics. Finally, this invention integrates the optimized frequency response characteristics into the output signal processing flow, significantly improving the measurement accuracy and response speed of the temperature and pressure sensor under complex operating conditions. Attached Figure Description

[0016] 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] A dynamic compensation method for a temperature-pressure composite sensor includes the following steps: S1. The amplitude-frequency and phase-frequency characteristic curves of the temperature and pressure channels are obtained from the temperature and pressure sensors through frequency response testing. The relationship between the output signal amplitude and phase and the excitation frequency is characterized, and the bandwidth limitation and phase hysteresis characteristics of the temperature and pressure sensors are determined.

[0020] The time-domain response sequence generated by the discrete sweep excitation frequency applied to the temperature and pressure sensor is obtained. The time-domain response sequence is digitally filtered to separate the temperature and pressure output signal. The output signal is transformed and fitted to construct the amplitude-frequency characteristic curve and phase-frequency characteristic curve describing the dynamic behavior of the system. The amplitude-frequency characteristic curve is traversed to identify the cutoff frequency and establish the bandwidth limit value. The bandwidth limit value is mapped to the phase-frequency characteristic curve to locate the phase angle value. The phase hysteresis characteristics of the temperature and pressure sensor are quantified based on the phase angle value.

[0021] S2. Based on the characteristic curve, a digital compensation filter is designed using the inverse system method. An inverse model corresponding to the sensor transfer function is constructed to compensate for the sensor's dynamic distortion, and the amplitude flatness and phase linearity parameters of the compensated sensor in the predetermined frequency band are obtained.

[0022] The sensor transfer function is extracted from the amplitude-frequency response curve and the phase-frequency response curve. The coefficient set of the sensor transfer function is obtained by processing the characteristic curve through Fourier transform. An inverse model is constructed, and the parameter set of the inverse model is generated by matrix inversion. A digital compensation filter is designed using the parameter set of the inverse model. The filter coefficients are adjusted to match different sensor types to obtain the compensation filter structure. The compensation filter structure is used to perform dynamic distortion compensation on the sensor output signal. The compensated signal sequence is obtained, and the amplitude flatness parameter and phase linearity parameter are calculated within a predetermined frequency band to determine the dynamic performance index of the compensated sensor system.

[0023] When processing the amplitude-frequency and phase-frequency response curves of a temperature and pressure sensor, data points from these curves can be collected first. These data points are usually obtained experimentally, such as by applying excitation signals of different frequencies to the sensor in a laboratory environment and then recording the changes in output amplitude and phase. Extracting the sensor's transfer function involves treating these curves as frequency domain responses. First, a Fourier transform is performed on the amplitude-frequency curves to convert the time-domain signal to the frequency domain. By calculating the amplitude and phase at each frequency point, a set of complex transfer function coefficients is formed. For example, assuming the curve shows stable amplitude in the low-frequency range and attenuation in the high-frequency range, the coefficients obtained after the transformation will reflect the distribution of the system's poles and zeros, helping to quantify the sensor's dynamic response.

[0024] When constructing the inverse model, these transfer function coefficient sets are used as the basis. The inverse model is essentially the reciprocal of the original transfer function and is used to compensate for distortion. When performing matrix inversion, the coefficient sets are arranged into a matrix form, and then the inverse matrix is ​​obtained through Gaussian elimination to generate the inverse model parameter set. These parameter sets include polynomial coefficients to describe the compensation behavior. If the original model has a delay term, the inverse model parameters will be adjusted accordingly to offset this delay.

[0025] When designing a digital compensation filter, the order and coefficients of the filter are determined by the inverse model parameter set. The adjustment process involves iterative optimization, such as using the least squares method to match different sensor types, such as high-temperature or high-pressure temperature and pressure sensors. Finally, the compensation filter structure is obtained. This structure is an IIR filter framework that includes a feedback loop to achieve accurate compensation.

[0026] When performing dynamic distortion compensation on the sensor output signal, the real-time signal is input into this filter structure and processed through convolution operation to obtain the compensated signal sequence. The original signal may be distorted due to bandwidth limitations, but the compensated sequence will restore the waveform characteristics of the original excitation, thus improving measurement accuracy.

[0027] When calculating the amplitude flatness parameter, the root mean square value of the amplitude deviation is statistically calculated for the compensated signal sequence within a predetermined frequency band, such as 0-100Hz. The phase linearity parameter is evaluated by fitting the slope of the phase curve. If the flatness is less than 1dB and the linearity deviation is less than 5 degrees, it indicates that the dynamic performance index is good.

[0028] S3. Analyze the piecewise nonlinear characteristics of the temperature and pressure sensor across its full range, divide the sensor's operating range into multiple linear sub-intervals, and establish a corresponding linear dynamic compensation model within each linear sub-interval. Extract the compensation parameters for the corresponding sub-interval from each linear dynamic compensation model, determine the linear sub-interval to which the current measurement value belongs based on the current measurement value, and select the corresponding compensation parameters. If the current measurement value exceeds the boundary threshold of the current linear sub-interval, switch to the adjacent linear sub-interval, obtain the compensation parameters corresponding to the switched linear sub-interval based on the parameter mapping table, and perform real-time correction.

[0029] The parameters are processed using curve fitting to identify nonlinear inflection points, resulting in a piecewise nonlinear characteristic distribution. The sensor's operating range is divided into multiple linear sub-intervals. Temperature and pressure fusion calibration data are collected within each linear sub-interval. Boundary point calibration operations are used to process the fusion calibration data and determine the boundary values ​​of the linear sub-intervals. A dynamic compensation model for the corresponding linear sub-interval is constructed. The least squares method is used to fit the model coefficients by minimizing the sum of squared residuals, obtaining the compensation model coefficient set. The filter coefficients are adjusted to match the temperature and pressure sensor type. The sensor output signal is processed through a compensation filter structure to obtain the compensated signal sequence. The amplitude flatness and phase linearity parameters of the compensated signal sequence within a predetermined frequency band are obtained. If the parameters exceed a preset threshold, a new linear dynamic compensation model is established for each linear sub-interval. The dynamic performance indicators of the compensated sensor system are judged by iteratively adjusting the model parameters.

[0030] When obtaining amplitude flatness and phase linearity parameters from a compensated sensor system, frequency domain analysis of the system output can be performed first. After preliminary compensation, the sensor's response data within the frequency band is collected. The flatness parameter is obtained by calculating the statistical value of the amplitude deviation, and the linearity parameter is obtained by evaluating the slope of the phase change with frequency using a linear regression method. These parameters are then used to analyze the piecewise nonlinear characteristics of the temperature and pressure sensor across its entire range.

[0031] The analysis of these parameters involves plotting the amplitude and phase data points as curves, then applying a polynomial curve fitting method, using a cubic polynomial to approximate the desired linear response, and identifying inflection points in the curve that deviate from a straight line. For example, under high pressure, the sensor may exhibit significant nonlinear bending in the 200 to 800 kPa range. These inflection points are located by calculating the maximum value of the fitting residuals, ultimately yielding a piecewise nonlinear characteristic distribution. This helps in understanding the sensor's distortion modes across different pressure ranges.

[0032] When dividing the sensor's operating range into multiple linear sub-intervals based on the piecewise nonlinear characteristic distribution, the full range of 0 to 1000 kPa can be divided into three sub-intervals, such as low pressure 0 to 300 kPa, medium pressure 300 to 700 kPa, and high pressure 700 to 1000 kPa. Temperature and pressure fusion calibration data are collected within each sub-interval. For example, by simultaneously applying temperature changes from -20 to 80 degrees Celsius and pressure excitation using laboratory equipment, the fused output voltage value is recorded. This data is then processed through boundary point calibration operations, including inserting calibration points at the interval boundaries and calculating the average deviation value, thereby determining the boundary values ​​of the linear sub-intervals. For instance, the upper boundary of the medium pressure interval might be fine-tuned from 700 kPa to 680 kPa to minimize error.

[0033] When constructing the corresponding linear dynamic compensation model using the boundary values ​​of linear sub-intervals, a linear transfer function is first defined for each interval. The model includes gain and time delay terms. Then, the least squares method is used to fit the model parameters. The least squares method constructs the observation data matrix and parameter vector, solves for the coefficients that minimize the sum of squared residuals, and obtains the set of compensation model coefficients. These coefficients reflect the cross-influence of temperature on pressure measurement.

[0034] When adjusting the filter coefficients to match the type of temperature and pressure sensor for the compensation model coefficient set, the coefficients such as the gain factor are fine-tuned from 1.2 to 1.5 depending on whether the sensor is ceramic or silicon based. A compensation filter structure is generated from the filter coefficients, and the sensor output signal is processed by time-domain convolution to obtain the compensated signal sequence.

[0035] When obtaining the amplitude flatness and phase linearity parameters of the compensated signal sequence within a predetermined frequency band, if the parameters exceed the preset threshold, a new linear dynamic compensation model is established for each linear sub-interval. The model parameters are iteratively adjusted, such as gradually reducing the time delay coefficient, to determine the dynamic performance index of the compensated sensor system. This iterative process ensures the stability of the system response within a wide frequency band.

[0036] The process of extracting compensation parameters from linear dynamic compensation models first requires understanding how these models are constructed. These models are essentially mathematical representations of the nonlinear characteristics of sensors. Temperature and pressure sensors often exhibit different response behaviors in different operating ranges. When extracting gain correction coefficients, they can be obtained by analyzing the slope part of the model. This coefficient reflects the adjustment amount of signal amplification, while the phase correction coefficient originates from the time delay term of the model and is used to correct the timing deviation of the signal.

[0037] When determining the linear sub-interval, the current measured value is compared one by one with preset boundaries. These boundaries are based on the previously defined sub-intervals. For example, the full range is divided into low-pressure, medium-pressure, and high-pressure intervals. When the measured value falls between 300 and 700 kPa, it can be identified as a medium-pressure sub-interval. This comparison can be implemented using simple threshold logic. First, the boundary values ​​are sorted, and then the matching is scanned from low to high. If the current value is 450 kPa, the system will compare whether it is greater than 300 kPa and less than 700 kPa, thus quickly obtaining the identification. This helps to seamlessly transition to the parameter selection stage.

[0038] After selecting the corresponding compensation parameters, their effectiveness is verified using fused calibration data. This fused calibration data typically includes a joint dataset of temperature and pressure variables, such as pressure response curves collected in a laboratory at temperatures ranging from -10 to 60 degrees Celsius. The verification process involves calculating the residuals after parameter application; for example, applying the gain coefficient to the test signal and checking whether the deviation is less than a preset threshold, such as 0.5%, to determine the set of compensation parameters after verification. If the verification shows that the phase coefficient causes an increase in deviation, it is fine-tuned to optimize the set and ensure its robustness.

[0039] When adjusting the sensor output signal using the verified compensation parameter set, the amplitude and phase components are processed separately. For amplitude adjustment, the gain coefficient is multiplied by the original signal value, while phase adjustment is achieved by adding a correction offset, ultimately resulting in a compensated signal sequence.

[0040] If the interval identifier corresponds to an adjacent interval, it is necessary to switch to the adjacent linear sub-interval model. If switching from the low-voltage model to the medium-voltage model, the system will load the corresponding linear equation set. These equation sets describe the approximate linear relationship of the signal within the interval, such as the slope of the output and input being approximately constant. The compensation parameters are then extracted from the parameter mapping table. This mapping table is a pre-built database that stores the gain coefficient and phase coefficient of each interval. The gain coefficient represents the signal amplification adjustment factor, while the phase coefficient is used to correct the time offset. The gain coefficient, such as 1.05, and the phase coefficient, such as 0.2 milliseconds, are extracted, thus determining the specific compensation value.

[0041] For the compensation parameters, the stability of the parameters is verified by fusing historical calibration data sequences. Here, historical calibration data sequences refer to multiple sets of data accumulated in past laboratory tests. The sequences include different pressure and temperature values. The fusing verification process involves calculating stability indices by applying these data with the current parameters. For example, the variance of the data before and after the parameter application is compared. If the variance is less than a threshold such as 0.3%, it is considered stable, thus obtaining the verified parameter set. This parameter set is a reliable set that has been screened, ensuring the consistency of the parameters in actual operation.

[0042] When performing real-time correction using the validated parameter set, the signal amplitude and phase are adjusted. For the signal sequence output by the sensor, the gain coefficient is first multiplied by each amplitude value, such as multiplying the original amplitude of 10 units by 1.05 to get 10.5 units. Then, a phase coefficient is added to correct the timestamp offset, and finally, a compensated signal sequence is generated. This sequence shows more stable waveform characteristics. This adjustment process forms a complete chain from data acquisition to signal optimization, which can improve the response accuracy of the monitoring system.

[0043] S4. Adjust the filter coefficient of the digital compensation filter according to the compensation parameters after real-time correction, and determine the dynamic tracking characteristics of the sensor system for rapidly changing input signals after adjustment.

[0044] The filter coefficient adjustment value is obtained by real-time correction of the compensation parameters to determine the updated configuration of the digital compensation filter; according to the updated configuration, the response data is obtained from the rapidly changing signal using the input signal simulation method to obtain the preliminary dynamic response of the sensor system; the response time index is judged based on signal stability analysis to determine the error minimization level; if the error minimization level exceeds the preset threshold, the tracking accuracy parameter is adjusted by response time evaluation to obtain the optimized system response; the dynamic tracking characteristic value is obtained from the optimized system response to determine the tracking capability of the adjusted sensor system for rapidly changing input signals.

[0045] In a sensor system, the process of obtaining the adjusted filter coefficient value through real-time calibrated compensation parameters can be understood as a dynamic optimization mechanism. The compensation parameters typically originate from deviation corrections during actual sensor operation, such as temperature drift or noise interference. These parameters are adjusted using real-time algorithms like Kalman filtering to calculate the incremental value of the filter coefficient. For example, if the compensation parameters show a 2.5% deviation due to a high-temperature environment, the adjusted filter coefficient value might be derived as 1.1 times the original coefficient, thus determining the updated configuration of the digital compensation filter. This includes updating the filter's order and cutoff frequency to ensure the system's accurate response to the input signal.

[0046] Based on the updated configuration, an input signal simulation method is used to obtain response data from rapidly changing signals, thereby obtaining the preliminary dynamic response of the sensor system. After the configuration update, a step input signal, such as a velocity jump from 0 to 100 units, can be simulated. After processing by a digital filter, output response data, including rise time and overshoot, is acquired. These data reflect the preliminary dynamic characteristics of the system and help evaluate its initial performance.

[0047] After obtaining the preliminary dynamic response, the process of signal stability analysis, determining the response time index, and identifying the error minimization level involves evaluating the signal's fluctuation in the time domain, typically quantified using standard deviation or Fourier transform. For example, if the preliminary response shows a response time of 0.5 seconds, stability analysis calculates the signal's variance. If the variance is less than 0.1, the response time index is considered acceptable. Further, the error minimization level is determined; for instance, a root mean square error of 0.05 indicates the system is close to optimization.

[0048] If the error minimization level exceeds a preset threshold, the tracking accuracy parameters are adjusted through response time evaluation to obtain an optimized system response. For example, the preset threshold might be set to 0.1. If the actual error is 0.15, exceeding the threshold, the response time evaluation involves analyzing latency and phase lag. Iterative algorithms, such as PID control, are used to adjust the tracking accuracy parameters, increasing the gain from 1.0 to 1.2, thereby reducing the error and obtaining a more stable optimized response. This improves the system's rapid adaptability to sudden changes in altitude. From the optimized system response, dynamic tracking characteristic values ​​are obtained, and the tracking capability of the adjusted sensor system for rapidly changing input signals is determined. This can be achieved by calculating the tracking error and bandwidth indicators.

[0049] S5 integrates dynamic tracking characteristics into the output signal processing flow of the temperature and pressure sensor, thereby optimizing the frequency response characteristics of the sensor under varying operating conditions.

[0050] The output signal is acquired by a temperature and pressure sensor in real time. The signal fluctuation amplitude is judged based on the peak-to-valley difference under varying operating conditions to obtain dynamic tracking characteristic parameters. These parameters are integrated into the output signal processing flow. Adaptive filtering is used to process noise signals as input and output the filtered signal to determine the initial value of the frequency response curve. The response deviation under the initial value of the frequency response curve is obtained. If the deviation exceeds a preset threshold, the feedback loop is adjusted by gain adjustment to obtain the optimized response characteristics and match them with the varying operating conditions. If the matching degree is insufficient, the system parameters are calibrated based on the deviation distribution to obtain the threshold dynamic setting service. The performance index of the threshold dynamic setting service is quantified according to the threshold dynamic setting service and integrated into the overall sensor system process to determine the frequency response characteristic optimization result.

[0051] The process of acquiring output signals from temperature and pressure sensors through real-time data acquisition can be understood as continuously monitoring the voltage or current signals output by the sensor using an embedded data acquisition module. This acquisition typically employs a high-sampling-rate analog-to-digital converter, such as acquiring 1000 sample points per second, to capture signal changes under varying operating conditions, such as a rapid temperature increase from 20 degrees Celsius to 80 degrees Celsius. When judging the fluctuation amplitude based on the peak-to-valley difference, the difference between the peak and valley values ​​is calculated. For example, if the peak value is 5 volts and the valley value is 2 volts, the difference is 3 volts, thereby assessing the signal fluctuation amplitude and deriving dynamic tracking characteristic parameters, such as a response delay time of 0.5 seconds. This helps quantify the sensor's ability to track rapid changes.

[0052] After integrating the dynamic tracking characteristic parameters into the output signal processing flow, an adaptive filtering method is used to handle noise. Adaptive filtering can be based on the minimum mean square error algorithm. The input noise signal can be, for example, random fluctuations caused by environmental interference. By iteratively adjusting the filter coefficients, the filtered signal is output, thereby determining the initial value of the frequency response curve. For example, in the frequency range from 0 Hz to 100 Hz, the response amplitude decreases from 1 to 0.8. This integration ensures the continuity of signal processing and provides a foundation for subsequent deviation analysis.

[0053] When obtaining the response deviation at the initial value of the frequency response curve, if the deviation exceeds a preset threshold such as 0.1, the feedback loop is adjusted by gain regulation. Specifically, gain regulation involves amplifying or attenuating the feedback signal, for example, adjusting the gain from 1.0 to 1.2 to minimize the deviation and obtain optimized response characteristics. In sensor systems operating under varying conditions such as high-voltage environments, this can significantly improve stability and achieve more accurate dynamic response.

[0054] When optimizing the response characteristics and matching them with varying operating conditions, if the matching degree is insufficient, for example, the response curve deviates from the actual operating conditions by 15%, the system parameters are calibrated based on the deviation distribution, such as a normal distribution model. The calibration process includes analyzing the statistical distribution of the deviation and adjusting parameters, such as the filter cutoff frequency from 50 Hz to 60 Hz, to obtain a dynamic threshold setting service. This service allows the threshold to change in real time according to the operating conditions, thus adapting to complex environments. After quantifying the performance indicators based on the dynamic threshold setting service, it is integrated into the overall sensor system process, ultimately determining the optimized frequency response characteristics. Quantitative indicators, such as signal-to-noise ratio, are verified through the overall process to ensure improved tracking accuracy of the system under varying operating conditions. The goal of this technology is to enhance the robustness of the sensor and provide more reliable real-time data support.

[0055] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0056] In another aspect, the present invention also proposes a computer program product, comprising a computer program, characterized in that the computer program implements the above-described method when executed by a processor.

[0057] In particular, according to some embodiments of this disclosure, the processes described above can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0058] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a task data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated task data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0059] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can interconnect with digital task data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0060] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: determine the network connection status of the switch production line management application in response to detecting a query operation on a production collaboration document in the switch production line management application; replace the webpage entry information corresponding to the production collaboration document with target entry file information and load target webpage resource information in response to determining that the network connection status of the switch production line management application indicates an offline state, so as to display the webpage of the production collaboration document offline in the switch production line management application, wherein the target entry file information is the file information of the entry file corresponding to the webpage of the production collaboration document downloaded in advance, and the target webpage resource information is the resource information corresponding to the webpage stored locally; in response to determining that the network connection status of the switch production line management application indicates an online state and that the webpage resource information corresponding to the production collaboration document is not stored locally, download the webpage resource information of the webpage from the production line document server, wherein the webpage resource information includes an entry file and resource information; display the webpage of the production collaboration document in the switch production line management application according to the webpage resource information, and store the webpage resource information in a local database.

[0061] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including product-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic compensation method for a temperature-pressure composite sensor, characterized in that, Includes the following steps: The amplitude-frequency and phase-frequency characteristic curves of the temperature and pressure channels of the temperature and pressure sensors are obtained by frequency response testing. The relationship between the amplitude and phase of the output signal and the excitation frequency is characterized, and the bandwidth limitation and phase hysteresis characteristics of the temperature and pressure sensors are determined. Based on the characteristic curve, a digital compensation filter is designed using the inverse system method. An inverse model corresponding to the sensor transfer function is constructed to compensate for the sensor's dynamic distortion, and the amplitude flatness and phase linearity parameters of the compensated sensor in a predetermined frequency band are obtained. The piecewise nonlinear characteristics of the temperature and pressure sensor across its full measurement range are analyzed. The sensor's operating range is divided into multiple linear sub-intervals, and a corresponding linear dynamic compensation model is established within each linear sub-interval. Compensation parameters for the corresponding sub-interval are extracted from each linear dynamic compensation model. The linear sub-interval to which the current measurement value belongs is determined, and the corresponding compensation parameters are selected. If the current measurement value exceeds the boundary threshold of the current linear sub-interval, the sensor is switched to an adjacent linear sub-interval. The compensation parameters corresponding to the switched linear sub-interval are obtained according to the parameter mapping table, and real-time correction is performed. The filter coefficients of the digital compensation filter are adjusted based on the compensation parameters after real-time correction to determine the dynamic tracking characteristics of the sensor system for rapidly changing input signals after adjustment. By integrating dynamic tracking characteristics into the output signal processing flow of the temperature and pressure sensor, the frequency response characteristics of the sensor under varying operating conditions can be optimized.

2. The dynamic compensation method for a temperature and pressure composite sensor as described in claim 1, characterized in that, The amplitude-frequency response curve is traversed to identify the cutoff frequency and establish the bandwidth limit value. The bandwidth limit value is mapped to the phase-frequency response curve to locate the phase angle value. The phase hysteresis characteristics of the temperature and pressure sensor are quantified based on the phase angle value.

3. The dynamic compensation method for a temperature and pressure composite sensor as described in claim 1, characterized in that, The process of obtaining amplitude flatness and phase linearity parameters includes: The sensor transfer function is extracted from the amplitude-frequency response curve and the phase-frequency response curve. The coefficient set of the sensor transfer function is obtained by processing the characteristic curve through Fourier transform. An inverse model is constructed, and the parameter set of the inverse model is generated by matrix inversion operation. A digital compensation filter is designed based on the parameter set of the inverse model. The filter coefficients are adjusted to match different sensor types to obtain the compensation filter structure. A compensation filter structure is used to perform dynamic distortion compensation on the sensor output signal, and the compensated signal sequence is obtained. The amplitude flatness parameter and phase linearity parameter are calculated within a predetermined frequency band to determine the dynamic performance index of the compensated sensor system.

4. The dynamic compensation method for a temperature and pressure composite sensor as described in claim 1, characterized in that, Establish the corresponding linear dynamic compensation model, including: The parameters are processed by curve fitting to identify nonlinear inflection points, resulting in a piecewise nonlinear characteristic distribution. The sensor's working range is divided into multiple linear sub-intervals. Temperature and pressure fusion calibration data are collected in each linear sub-interval. The fusion calibration data is processed by boundary point calibration operations to determine the boundary values ​​of the linear sub-intervals. A dynamic compensation model corresponding to the linear sub-interval is constructed. The least squares method is used to fit the model coefficients by minimizing the sum of squared residuals to obtain the compensation model coefficient set. The filter coefficients are adjusted to match the temperature and pressure sensor type. The sensor output signal is processed through the compensation filter structure to obtain the compensated signal sequence. The amplitude flatness and phase linearity parameters of the compensated signal sequence within a predetermined frequency band are obtained. If the parameters exceed the preset threshold, a new linear dynamic compensation model is established for each linear sub-interval. The dynamic performance index of the compensated sensor system is judged by iteratively adjusting the model parameters.

5. The dynamic compensation method for a temperature and pressure composite sensor as described in claim 1, characterized in that, Determine the dynamic tracking characteristics of the adjusted sensor system for rapidly changing input signals, including: The filter coefficient adjustment value is obtained by real-time corrected compensation parameters to determine the updated configuration of the digital compensation filter; based on the updated configuration, the response data is obtained from the rapidly changing signal using the input signal simulation method to obtain the preliminary dynamic response of the sensor system. For signal stability analysis, the response time index is judged to determine the error minimization level; if the error minimization level exceeds the preset threshold, the tracking accuracy parameter is adjusted through response time evaluation to obtain the optimized system response; from the optimized system response, the dynamic tracking characteristic value is obtained to determine the tracking capability of the adjusted sensor system for rapidly changing input signals.

6. The dynamic compensation method for a temperature and pressure composite sensor as described in claim 1, characterized in that, To optimize the frequency response characteristics of the sensor under varying operating conditions, include:. The output signal is acquired by real-time data acquisition temperature and pressure sensor. The signal fluctuation amplitude is judged based on the signal peak-valley difference under varying operating conditions. Dynamic tracking characteristic parameters are obtained and integrated into the output signal processing flow. Adaptive filtering is used to process noise signal as input and output filtered signal to determine the initial value of frequency response curve. The response deviation is obtained at the initial value of the frequency response curve. When the deviation exceeds a preset threshold, the feedback loop is adjusted by gain adjustment to obtain the optimized response characteristics and match them with the variable operating conditions. If the matching degree is insufficient, the system parameters are calibrated based on the deviation distribution to obtain the threshold dynamic setting service. The performance index of the threshold dynamic setting service is quantified and integrated into the overall process of the sensor system to determine the frequency response characteristic optimization result.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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