Glass micro-fusion pressure and temperature sensor multi-station calibration tooling

Abstract

The present application relates to pressure sensor calibration technical field, disclose a kind of glass micro-fusion pressure temperature sensor multi-station calibration tool, including pedestal, three workstations column, pressure pipeline system and reference pressure output system.Three workstation column respectively installs quartz resonant pressure sensor, sputtering thin film pressure sensor and sensor to be detected.Reference pressure output system is calculated by confidence assessment module the confidence of two reference sensors respectively, based on vibration acceleration, power fluctuation and installation stress and other parameters;Safety mode module selects normal or early warning mode by comparing measurement difference;Weight configuration module dynamically allocates weight coefficient;Reference pressure output module carries out weighted fusion and outputs final reference pressure value.The present application adopts double reference sensor redundancy design, combines dynamic weight distribution and safety mode switching, effectively suppresses environmental interference influence, significantly improves calibration accuracy and system reliability, realizes multi-station synchronous efficient calibration.

Description

Technical Field

[0001] This invention belongs to the field of pressure sensor calibration technology, and particularly relates to a multi-station calibration fixture for a glass micro-fusion pressure and temperature sensor. Background Technology

[0002] With the development of industrial automation technology, glass micro-melt pressure and temperature sensors have been widely used in key fields such as aerospace and automotive manufacturing. These sensors require regular calibration to ensure measurement accuracy. During pressure calibration of glass micro-melt pressure and temperature sensors, the accuracy of the reference sensor directly determines the reliability of the calibration system.

[0003] Reference sensors play a crucial role in establishing pressure benchmarks in pressure calibration systems. They provide traceable standard pressure values ​​as a reference standard for evaluating the performance of the sensor under test.

[0004] However, traditional calibration devices generally use a single type of reference sensor, which has significant technical limitations. When the sensor is subjected to specific environmental disturbances (such as vibration, electromagnetic interference, etc.), the reference pressure value may exhibit systematic deviations, and the system cannot identify such abnormal conditions. Research has found that different types of pressure sensors have significantly different sensitivity characteristics to environmental disturbances, but traditional solutions fail to fully utilize this characteristic.

[0005] Furthermore, existing devices suffer from poor environmental adaptability, lack of intelligent fault-tolerance mechanisms, and low efficiency in multi-station calibration. Therefore, there is an urgent need to develop a new type of calibration fixture that can comprehensively utilize the advantages of different reference sensors and possess intelligent diagnostic functions to improve calibration reliability in complex industrial environments. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-station calibration fixture for a glass micro-melting pressure and temperature sensor, in order to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a multi-station calibration fixture for a glass micro-melting pressure and temperature sensor includes a base and three stationary columns fixed on the base. It further includes: a quartz resonant pressure sensor, a sputtered thin-film pressure sensor, and a glass micro-melting pressure and temperature sensor to be tested, respectively mounted on the three stationary columns; a pressure piping system, mounted on the base, for simultaneously and equally pressurizing the quartz resonant pressure sensor, the sputtered thin-film pressure sensor, and the glass micro-melting pressure and temperature sensor; and a reference pressure output system, electrically connected to the quartz resonant pressure sensor and the sputtered thin-film pressure sensor, for outputting a reference pressure value. The pressure output system includes: a confidence assessment module, which calculates the confidence level of the quartz resonant pressure sensor based on its vibration acceleration and power fluctuation parameters, and the confidence level of the sputtered thin-film pressure sensor based on its installation stress and electromagnetic interference intensity parameters; a safety mode module, which selects between normal mode and warning mode by comparing the differences between the measured values ​​of the two sensors; a weight configuration module, which dynamically assigns weight coefficients to the two sensors based on system running time, ambient temperature, and the confidence levels of the two sensors in normal mode; and a reference pressure output module, which performs weighted fusion of the measured values ​​of the two sensors according to the weight coefficients and outputs the final reference pressure value.

[0008] A further technical solution, the specific steps of weighting and fusing the measured values ​​of the two sensors according to the weighting coefficient to output the final reference pressure value, are as follows: The measured values ​​of the quartz resonant pressure sensor... Sputtered thin-film pressure sensor measurement values Weighting coefficient of quartz resonant pressure sensor measurement values Weighting coefficients for measurements from sputtered thin-film pressure sensors Import Formula Obtain reference pressure value .

[0009] A further technical solution, in the early warning mode, outputs the average value of the two sensor measurements as a reference pressure value. The specific steps are as follows: The quartz resonant pressure sensor measurement value... and sputtered thin-film pressure sensor measurement values Import Formula Obtain reference pressure value ,in, This is the safe pressure value.

[0010] A further technical solution, in normal mode, involves the following steps for dynamically allocating the weighting coefficients of the two sensors based on system runtime, ambient temperature, and the confidence levels of both sensors: obtaining the time-temperature factor of the quartz sensor based on system runtime and ambient temperature. and the time-temperature factor of thin-film sensors ; Time-temperature factor of quartz sensor and the time-temperature factor of thin-film sensors and the confidence level of quartz resonant pressure sensors Confidence of sputtered thin-film pressure sensors Import formula and Obtain the weighting coefficient of the quartz resonant pressure sensor measurement value. Weighting coefficients for measurements from sputtered thin-film pressure sensors .

[0011] A further technical solution involves obtaining the time-temperature factor of the quartz sensor based on system operating time and ambient temperature. and the time-temperature factor of thin-film sensors The specific steps are as follows: Calculate the ratio between the system runtime and the preset maximum runtime to obtain the system runtime index. The temperature deviation index is obtained by comparing the absolute value of the difference between the current ambient temperature and the reference temperature with the preset maximum temperature deviation. ; System runtime index and temperature deviation index Import Formula and Obtain the time-temperature factor of the quartz sensor and the time-temperature factor of thin-film sensors ,in, The time sensitivity coefficient of the quartz sensor. The temperature sensitivity coefficient of the quartz sensor. For the time sensitivity coefficient of the thin-film sensor, This represents the temperature sensitivity coefficient of the thin-film sensor.

[0012] A further technical solution, the specific steps for selecting between normal mode and warning mode by comparing the differences between the measured values ​​of two sensors, are as follows: The measured values ​​of the quartz resonant pressure sensor... and sputtered thin-film pressure sensor measurement values Import Formula Obtain the difference of normalized measured values. , The value range is 0-1, where, This is a scaling factor used to prevent division by zero; if the difference in normalized measurements... If the difference between the normalized measured values ​​is less than or equal to the set threshold, then enter normal mode; if the difference is less than or equal to the set threshold, then enter normal mode. If the value exceeds the set threshold, the system will enter warning mode.

[0013] A further technical solution involves calculating the confidence level of the vibration acceleration and power fluctuation parameters based on a quartz resonant pressure sensor using the following steps: The current vibration acceleration is compared with a preset maximum vibration acceleration to obtain the vibration acceleration index. The power fluctuation index is obtained by comparing the current power fluctuation with the preset maximum power fluctuation. ; the vibration acceleration index and power fluctuation index Import Formula Obtain the confidence level of the quartz resonant pressure sensor ,in, The vibration acceleration sensitivity coefficient, This is the power supply fluctuation sensitivity coefficient.

[0014] A further technical solution involves calculating the confidence level of the installation stress and electromagnetic interference intensity parameters based on the sputtered thin-film pressure sensor using the following steps: The current installation stress is compared with a preset maximum installation stress to obtain the installation stress index. The electromagnetic interference intensity is obtained by comparing the current electromagnetic interference intensity with the preset maximum electromagnetic interference intensity. ; Install stress index and electromagnetic interference intensity Import Formula Obtain the confidence level of the sputtered thin-film pressure sensor ,in, For installation stress sensitivity coefficient, This is the electromagnetic interference sensitivity coefficient.

[0015] A further technical solution is that the pressure pipeline system includes a pressure distribution block fixed on the base. Each of the three output pipes of the pressure distribution block is connected to a high-pressure hose through a precision shut-off valve. The ends of the three high-pressure hoses are respectively detachably connected to a quartz resonant pressure sensor, a sputtered thin film pressure sensor, and a glass micro-melting pressure and temperature sensor to be tested. The inlet of the pressure distribution block is connected to an external reference pressure output system through a stainless steel rigid pipe.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] 1. By using a redundant design of dual reference sensors, namely quartz resonant and sputtered thin film, the differences in their sensitivity to environmental interference are utilized to form complementary advantages, effectively avoiding systematic deviations caused by specific interference to a single sensor.

[0018] 2. The confidence level of the sensor is dynamically evaluated based on multi-dimensional parameters such as vibration, power fluctuation, installation stress, and electromagnetic interference. Combined with operating time and ambient temperature factors, the weighting coefficient is intelligently adjusted to adapt to complex industrial environments.

[0019] 3. By monitoring the difference in normalized measurement values ​​and judging thresholds, the system can automatically switch between normal mode and early warning mode. When the sensor is abnormal, the average value is used to ensure the continuity and safety of the calibration process.

[0020] 4. The pressure piping system adopts a combination of rigid distribution blocks and flexible hoses, and is controlled by a precision shut-off valve to ensure uniform pressure transmission, eliminate the influence of installation stress, and improve calibration repeatability.

[0021] 5. By dynamically reflecting the sensor performance degradation pattern through the time-temperature factor, and rationally allocating weights, the effective lifespan of the sensor can be maximized while ensuring calibration accuracy. Attached Figure Description

[0022] Figure 1 A schematic diagram of the structure of a multi-station calibration fixture for a glass micro-melting pressure and temperature sensor provided by the present invention;

[0023] Figure 2 A flowchart of the reference pressure output system provided by the present invention.

[0024] In the attached diagram: 1. Base; 2. Station column; 3. Quartz resonant pressure sensor; 4. Sputtered thin film pressure sensor; 5. Pressure and temperature sensor for the glass to be tested; 6. Pressure distribution block; 7. Precision shut-off valve; 8. High-pressure hose. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0026] In existing technologies, glass micro-fuse pressure and temperature sensors are widely used in aerospace, automotive manufacturing, and other fields. Their calibration process relies on a reference sensor to establish a pressure baseline. Traditional calibration devices use a single type of reference sensor, which is prone to systematic deviations and cannot self-diagnose when exposed to vibration, electromagnetic interference, or installation stress. For example, in aero-engine testing scenarios, high-frequency vibration and complex electromagnetic environments often cause the output of a single reference sensor to become inaccurate, and the calibration system cannot identify abnormal states, directly affecting the calibration accuracy of the sensor under test.

[0027] To address the aforementioned issues, it is first necessary to overcome the limitations of single sensors and consider the differences in anti-interference characteristics among different sensor types. Analysis revealed that quartz resonant sensors are sensitive to vibration and power fluctuations, while sputtered thin-film sensors are susceptible to installation stress and electromagnetic interference. Based on this, a combination of the two types of sensors is proposed, utilizing their complementary characteristics to construct a redundant measurement system. Furthermore, a dynamic evaluation mechanism needs to be designed to adjust sensor weights based on real-time environmental parameters and establish a safe output mode under abnormal conditions, thereby forming a calibration system with environmental adaptability and fault isolation capabilities.

[0028] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0029] like Figure 1 and Figure 2 As shown, a multi-station calibration fixture for a glass micro-melting pressure and temperature sensor, according to an embodiment of the present invention, includes a base 1 and three station columns 2 fixed on the base 1. It also includes: a quartz resonant pressure sensor 3, a sputtered thin-film pressure sensor 4, and a glass micro-melting pressure and temperature sensor 5 to be tested, respectively mounted on the three station columns 2; a pressure pipeline system, mounted on the base 1, for simultaneously and equally pressurizing the quartz resonant pressure sensor 3, the sputtered thin-film pressure sensor 4, and the glass micro-melting pressure and temperature sensor 5; and a reference pressure output system, electrically connected to the quartz resonant pressure sensor 4 and the sputtered thin-film pressure sensor 5, for outputting a reference pressure value. The system includes: a confidence assessment module, which calculates the confidence level of a quartz resonant pressure sensor based on its vibration acceleration and power fluctuation parameters, and a sputtered thin-film pressure sensor based on its installation stress and electromagnetic interference intensity parameters; a safety mode module, which selects between normal mode and warning mode by comparing the differences between the measurements of the two sensors; a weight configuration module, which dynamically assigns weight coefficients to the two sensors in normal mode based on system runtime, ambient temperature, and the confidence levels of the two sensors; and a reference pressure output module, which performs weighted fusion of the measurements of the two sensors according to the weight coefficients and outputs the final reference pressure value; in warning mode, it outputs the average value of the measurements of the two sensors as the reference pressure value.

[0030] The confidence assessment module is a functional unit that calculates the confidence level of a sensor based on its corresponding sensitivity parameters. It can be implemented using an embedded processor. For quartz resonant sensors, mechanical vibration data is collected using a vibration acceleration sensor, and power parameters are obtained using a power supply voltage fluctuation detection circuit. The confidence level is then calculated using an exponential decay model. For sputtered thin-film sensors, stress distribution on the mounting base is measured using strain gauges, and environmental interference data is obtained using an electromagnetic field strength sensor to construct a confidence calculation model. This module quantifies the impact of different environmental factors on the sensor, providing a basis for weight allocation.

[0031] The safety mode module is a logical unit that determines the system status based on the measurement differences between the two sensors. Specifically, it can be implemented using a threshold comparison algorithm. By normalizing the measurement differences between the two sensors, the influence of dimensional differences is eliminated. When the difference exceeds a set threshold, the system switches to an early warning mode. This module solves the problem of traditional systems being unable to identify sensor anomalies, thus forming a fault isolation mechanism.

[0032] The weight configuration module is a calculation unit that dynamically adjusts the sensor weights, specifically implemented using a time-temperature factor and confidence level fusion algorithm. By collecting real-time system operating time and ambient temperature data, and combining this with sensor confidence levels, an exponential decay function is constructed to calculate the weight coefficients. This module ensures that the reference pressure value reflects both the sensor's real-time reliability and long-term operational stability.

[0033] Specifically, the pressure distribution block 6 on base 1 controls the pressure output through a precision shut-off valve 7, ensuring synchronous and equal pressure across the three workstations. In normal mode, the reference pressure output system calculates the confidence levels of both sensors based on vibration, power fluctuations, installation stress, and electromagnetic interference data. It then dynamically allocates weighting coefficients and performs weighted fusion, considering the impact of operating time and ambient temperature on sensor performance degradation. When the difference between the two sensor measurements exceeds a threshold, the system automatically switches to an early warning mode, outputting the reference pressure value using the arithmetic mean, thus avoiding benchmark deviations caused by the failure of a single sensor. Therefore, the calibration system can continuously provide a reliable reference pressure benchmark in complex industrial environments.

[0034] Compared to existing technologies, traditional calibration devices use only a single reference sensor, making it impossible to identify systematic deviations caused by specific environmental interferences, and they lack a multi-sensor data fusion mechanism. This solution, through a dual-sensor redundancy design combined with real-time environmental parameter evaluation and dynamic weight adjustment, effectively suppresses the impact of a single interference source on the measurement results. Simultaneously, the introduction of an early warning mode enables the system to have fault tolerance under abnormal conditions, solving the problem of calibration failure due to sensor malfunction in traditional devices.

[0035] Through the above technical solution, this application achieves improved efficiency in multi-station synchronous calibration and reduces the impact of environmental interference on the reference pressure standard. The dynamic weight allocation strategy enables the calibration system to adapt to different operating conditions, and the early warning mode effectively prevents calibration errors caused by sensor anomalies. This solution significantly improves the reliability and stability of sensor calibration in complex industrial environments while ensuring measurement accuracy.

[0036] Preferably, the specific step of weighting and fusing the measured values ​​of the two sensors according to the weighting coefficient to output the final reference pressure value is as follows: The measured values ​​of the quartz resonant pressure sensor... Sputtered thin-film pressure sensor measurement values Weighting coefficient of quartz resonant pressure sensor measurement values Weighting coefficients for measurements from sputtered thin-film pressure sensors Import Formula Obtain reference pressure value .

[0037] Among them, the quartz resonant pressure sensor measured the value. This refers to the pressure value reflected by the change in the resonant frequency of a quartz crystal. Specifically, it can be achieved using a frequency-to-pressure conversion algorithm. Its measurement accuracy is affected by vibration acceleration and power supply fluctuations. (Measurement value from a sputtered thin-film pressure sensor) This refers to the pressure value converted through the thin-film resistance strain effect, specifically implemented using a Wheatstone bridge circuit. Its measurement stability is affected by installation stress and electromagnetic interference. The weighting coefficient of the measured value from a quartz resonant pressure sensor... This refers to a dynamic parameter reflecting the current reliability of the sensor. Specifically, it can be calculated from the confidence level output by the confidence assessment module and the time-temperature factor, and is used to adjust its contribution ratio in the fusion result. Weighting coefficient for sputtered thin-film pressure sensor measurements. This refers to a dynamic parameter reflecting the current reliability of the sensor. Specifically, it can be calculated from the confidence level output by the confidence assessment module and the time-temperature factor. The constraint that the sum of all equals 1 ensures the dimensional consistency of the fusion results.

[0038] Specifically, the reference pressure value is dynamically optimized by substituting the real-time measurements from the two sensors and dynamic weighting coefficients into a linear weighting formula. During operation, the weighting coefficients are adjusted in real time based on sensor confidence levels and environmental parameters. For example, when the confidence level of the quartz sensor decreases due to vibration interference, its weighting coefficients... The weighting coefficient of the sputtered thin film sensor is reduced accordingly. This improves efficiency and suppresses the negative impact of single sensor anomalies on the reference value. The constraint that the sum of the weight coefficients in the formula is 1 avoids dimensional deviations caused by unbalanced weight allocation, ensuring that the fusion result always matches the sensor's measurement range.

[0039] Compared to existing technologies, traditional calibration devices rely on a single reference sensor. When this sensor experiences measurement deviations due to environmental interference, the system cannot identify and correct the errors. This solution, however, introduces a dual-sensor weighted fusion mechanism, which dynamically adjusts the weight distribution of the two sensors based on real-time operating conditions. For example, when the quartz sensor is affected by power fluctuations, its weight is automatically reduced, and the system relies instead on data from the less affected sputtered thin-film sensor, thereby significantly improving the anti-interference capability of the reference pressure value.

[0040] Through the above technical solution, this application solves the problem of inaccurate reference pressure values ​​caused by environmental interference of a single sensor, and realizes dynamic optimization of reference pressure output under complex working conditions. By adjusting the weighting coefficient in real time, measurement deviations caused by factors such as vibration and electromagnetic interference are effectively suppressed, improving the environmental adaptability and data reliability of the calibration fixture.

[0041] Preferably, in the early warning mode, the specific step of outputting the average value of the two sensor measurements as the reference pressure value is as follows: The measurement value of the quartz resonant pressure sensor... and sputtered thin-film pressure sensor measurement values Import Formula Obtain reference pressure value .

[0042] in, The safety pressure value refers to the pressure baseline value generated by the arithmetic mean method. Specifically, it can be achieved by unbiased averaging of measurements from two sensors, used to balance the output differences between the two sensors under abnormal conditions. The warning mode is an operating state triggered when the difference between the measurements from the two sensors exceeds a set threshold. Specifically, it can be achieved by comparing the difference between normalized measurement values. This is achieved by using a preset threshold to identify measurement deviations caused by sensor malfunctions or environmental interference. The arithmetic average method involves adding the measurements from two sensors and then dividing by two. This can be implemented using adder and divider modules in an embedded system to eliminate interference from sudden malfunctions in a single sensor on the reference pressure value.

[0043] Specifically, when the system detects that the measurement difference between the quartz resonant pressure sensor and the sputtered thin-film pressure sensor exceeds a preset threshold, it automatically switches to an early warning mode. In this mode, the logic for generating the reference pressure value changes from dynamic weight allocation to fixed average calculation. The real-time measurement values ​​of the two sensors are directly input to the processing unit, and an arithmetic mean is obtained through addition and division operations. This average value is output as a safe pressure value, avoiding the risk of output inaccuracy due to sudden interference from a single sensor, and continuously monitoring the difference value. When the difference falls below the threshold, it automatically returns to normal mode. Since the two sensors have different sensitivities to environmental factors such as vibration and electromagnetic interference, arithmetic averaging can effectively suppress systematic deviations caused by a single interference source, while simplifying the data processing flow under abnormal conditions and improving the system response speed.

[0044] Compared to existing technologies, traditional calibration systems typically rely on single sensor data or directly interrupt calibration with an error message when sensor measurements differ significantly, leading to decreased reliability of the reference pressure benchmark or halting the calibration process. This solution, however, maintains the continuity of the calibration process through a pre-set early warning mechanism and average value calculation. It leverages the complementarity of dual-sensor data to generate a more stable reference benchmark, overcoming the shortcomings of traditional methods in handling abnormal operating conditions.

[0045] Through the above technical solution, this application can automatically switch to fault-tolerant mode when there are significant differences in the sensor measurement values. It generates a safe pressure reference by averaging the data from the two sensors, preventing reference pressure inaccuracies caused by the failure of a single sensor or environmental interference, and ensuring the output stability and continuity of the calibration system under non-ideal working conditions.

[0046] Preferably, the specific steps for dynamically allocating the weighting coefficients of the two sensors in normal mode based on system runtime, ambient temperature, and the confidence levels of the two sensors are as follows:

[0047] The time-temperature factor of the quartz sensor is obtained based on system operating time and ambient temperature. and the time-temperature factor of thin-film sensors ;

[0048] The time-temperature factor of the quartz sensor and the time-temperature factor of thin-film sensors and the confidence level of quartz resonant pressure sensors Confidence of sputtered thin-film pressure sensors Import formula and Obtain the weighting coefficient of the quartz resonant pressure sensor measurement value. Weighting coefficients for measurements from sputtered thin-film pressure sensors , and The values ​​of are all in the range of 0-1. .

[0049] The system runtime refers to the cumulative operating time of the sensor, which can be achieved by recording the power-on time using a built-in timer. This reflects the performance degradation caused by long-term sensor operation. Ambient temperature refers to the real-time temperature of the space where the sensor is located, which can be acquired using a surface-mount temperature sensor. This quantifies the impact of temperature fluctuations on the sensor material properties. The time-temperature factor is a parameter characterizing the degree of sensor performance degradation over time and temperature changes. It can be calculated using an exponential function combined with the sensitivity coefficient, for example, by constructing an exponential degradation model using the ratio of operating time to maximum operating time or the ratio of temperature deviation to maximum deviation. Confidence level refers to the reliability of the sensor under current operating conditions. This can be calculated using an exponential function constructed from interference parameters such as vibration acceleration and power fluctuations, reflecting the impact of instantaneous interference on measurement accuracy. The weighting coefficient is the proportion of the two sensor measurements in the fusion calculation. This is obtained by normalizing the product of confidence level and time-temperature factor, and is used for dynamic optimization allocation.

[0050] Specifically, during system operation, real-time data on running time and ambient temperature are collected, and time-temperature attenuation factors for both types of sensors are calculated. For example, when the running time reaches 50% of the preset maximum value, the time index is 0.5; when the ambient temperature deviates from the reference temperature by 10℃ and the maximum allowable deviation is 20℃, the temperature deviation index is 0.5. These two indices are substituted into an exponential function and combined with a preset sensitivity coefficient to calculate the attenuation factor. Simultaneously, confidence levels are calculated based on real-time interference parameters such as vibration and power fluctuations. After multiplying the attenuation factor by the confidence level, weighting coefficients are dynamically allocated using a normalization formula. For example, when the attenuation factor of the quartz sensor decreases to 0.8 due to long-term operation, but its current confidence level remains at 0.9, and the attenuation factor of the thin-film sensor is 0.7 with a confidence level of 0.8, then the weight for the quartz sensor is 0.56, and the weight for the thin-film sensor is 0.44. This allocation method considers both long-term performance degradation and the impact of instantaneous interference.

[0051] Compared to existing technologies, traditional calibration devices typically employ fixed weights or consider only a single influencing factor. For example, some systems preset fixed weights based solely on sensor type, failing to adapt to long-term performance drift; others adjust output only through temperature compensation, neglecting the cumulative effect of runtime. This proposed solution, however, constructs a multi-dimensional weight allocation model by coupling the time-temperature decay factor with real-time confidence parameters, enabling simultaneous suppression of the impact of long-term drift and transient disturbances on the reference pressure value.

[0052] Through the above technical solution, this application effectively solves the problem of systematic deviation caused by environmental interference of a single sensor. By dynamically adjusting the weighting coefficients, when a sensor experiences performance degradation or sudden interference, the weight ratio of the affected sensor is automatically reduced, thereby improving the accuracy of the reference pressure value. For example, in a high-temperature environment, when the confidence level of the thin-film sensor decreases due to temperature sensitivity, the system automatically increases the weight of the quartz sensor, which has better temperature resistance, to ensure the reliability of the fusion result.

[0053] Preferably, the time-temperature factor of the quartz sensor is obtained based on the system operating time and ambient temperature. and the time-temperature factor of thin-film sensors The specific steps are as follows:

[0054] The system runtime is calculated by comparing the system runtime with the preset maximum runtime to obtain the system runtime index. ;

[0055] The temperature deviation index is obtained by comparing the absolute value of the difference between the current ambient temperature and the reference temperature with the preset maximum temperature deviation. ;

[0056] System runtime index and temperature deviation index Import Formula and Obtain the time-temperature factor of the quartz sensor and the time-temperature factor of thin-film sensors .

[0057] in, and All are greater than 0 and less than or equal to 1. The time sensitivity coefficient of the quartz sensor. The temperature sensitivity coefficient of the quartz sensor. For the time sensitivity coefficient of the thin-film sensor, The temperature sensitivity coefficient of the thin-film sensor. , , and The values ​​of are all in the range of 0-1. , , and All parameters can be assigned by experts. The system runtime index quantifies the sensor's performance degradation by comparing the current runtime with the preset maximum runtime. This can be achieved by using a timing module to record cumulative operating time and combining it with a preset threshold. The temperature deviation index reflects the ambient temperature fluctuation by comparing the absolute value of the temperature difference with the maximum permissible deviation. This can be achieved by using a temperature sensor to collect ambient temperature data in real time and comparing it with a reference temperature. The time sensitivity coefficient is a parameter used to adjust the impact of runtime on sensor performance; different values ​​can be experimentally calibrated based on the sensor material's fatigue characteristics. The temperature sensitivity coefficient is a parameter used to adjust the impact of temperature changes on sensor performance; different values ​​can be set based on differences in the sensors' thermal expansion coefficients. The preset maximum runtime and preset maximum temperature deviation can be obtained based on the sensor specifications.

[0058] Specifically, this scheme dynamically reflects the performance degradation law of sensors by establishing a normalized exponential model of operating time and temperature deviation. The operating time exponent is processed using a relative ratio to avoid error accumulation caused by different absolute time measurement units. The temperature deviation exponent captures the nonlinear effects of temperature fluctuations through absolute value processing to prevent positive and negative deviations from canceling each other out. A larger time sensitivity coefficient is set for the quartz sensor to accurately reflect the fatigue characteristics of its crystal structure under long-term vibration; a smaller temperature sensitivity coefficient is set to match the low thermal expansion coefficient of quartz material. A smaller time sensitivity coefficient is set for the thin-film sensor to correspond to the slower creep rate of its thin-film structure; a larger temperature sensitivity coefficient is set to reflect the higher thermal deformation characteristics of the thin-film material. The time-temperature factor calculated by the exponential function can correct the reliability parameters of sensor weight allocation in real time.

[0059] Compared to existing technologies, traditional calibration devices typically adjust weights only based on fixed time intervals, failing to differentiate the varying sensitivities of different sensors to runtime. Existing methods often employ linear temperature compensation models, which struggle to accurately characterize the nonlinear impact of temperature fluctuations on sensor performance. This proposed solution establishes a two-factor exponential decay model, simultaneously considering the cumulative effect of runtime and transient temperature changes, to achieve a more precise reliability assessment.

[0060] Through the above technical solution, this application effectively solves the problem of reference pressure value drift caused by the combined effects of long-term sensor aging and temperature fluctuations. The joint calculation of the operating time index and the temperature deviation index can dynamically capture the sensor performance degradation trend, and the setting of the differentiated sensitivity coefficient can accurately match the physical characteristics of different sensors, thereby maintaining the accuracy of the weighting algorithm under complex operating conditions and ensuring the continuous reliability of the reference pressure value output by the calibration system.

[0061] Preferably, the specific steps for selecting between normal mode and warning mode by comparing the degree of difference between the measurements of the two sensors are as follows:

[0062] The measured value of the quartz resonant pressure sensor and sputtered thin-film pressure sensor measurement values Import Formula Obtain the difference of normalized measured values. , The value range is 0-1, where, This is a scaling factor used to prevent division by zero;

[0063] If the difference of normalized measurements If the value is less than or equal to the set threshold, then enter normal mode;

[0064] If the difference of normalized measurements If the value exceeds the set threshold, the system will enter warning mode.

[0065] Among them, the difference of normalized measured values This refers to the ratio of the absolute difference between the measurements of two sensors to the maximum range reference. Specifically, it can be achieved by dynamically taking the maximum value, with a scaling factor introduced into the denominator. This prevents calculation anomalies when measured values ​​approach zero. The threshold setting refers to the critical parameter for judging the consistency of sensor measurements; it can be achieved through experimental calibration or empirical setting, and is used to define the safety boundaries of the system's operating state.

[0066] Specifically, during pressure calibration, the measurements from the quartz resonant pressure sensor and the sputtered thin-film pressure sensor are acquired in real time and input into the difference calculation module. Normalization is performed by dividing the absolute difference by the maximum range reference value, eliminating dimensional differences between sensors with different ranges. (Scale factor) The introduced value can be set to a fixed percentage of the measurement range, such as 5% of the full scale value, to ensure stable difference calculation even at low pressure measurements. When the calculated value is... When the value remains below the threshold, the system maintains the dynamic weight fusion mechanism in normal mode; when When the value exceeds the threshold, it automatically switches to the warning mode and uses the arithmetic mean as the reference pressure output to avoid output deviation due to the failure of a single sensor.

[0067] Compared to existing technologies, traditional calibration devices rely solely on the operating status of a single reference sensor. When this sensor is subjected to vibration or electromagnetic interference, the system cannot detect abnormal shifts in the measured values. This solution, through a dual-sensor difference monitoring and mode switching mechanism, can automatically switch to a safe output mode when a sensor malfunctions. For example, when an anomaly is detected... Once the value exceeds the threshold, the mode switch can be completed within milliseconds, significantly improving the system's fault tolerance.

[0068] Through the above technical solution, this application achieves automatic identification of abnormal sensor states and safe mode switching, solving the systemic deviation problem caused by the failure of a single reference source in traditional devices. By monitoring the measurement differences between the two sensors in real time and setting a safety threshold, it effectively prevents erroneous outputs under abnormal operating conditions while ensuring normal measurement accuracy. For example, when the sensor is subjected to sudden strong electromagnetic interference, the system can immediately switch to an early warning mode to avoid calibration data distortion.

[0069] Preferably, the specific steps for calculating the confidence level based on the vibration acceleration and power fluctuation parameters of the quartz resonant pressure sensor are as follows:

[0070] The vibration acceleration index is obtained by comparing the current vibration acceleration with the preset maximum vibration acceleration. ;

[0071] The power fluctuation index is obtained by comparing the current power fluctuation with the preset maximum power fluctuation. ;

[0072] Vibration acceleration index and power fluctuation index Import Formula Obtain the confidence level of the quartz resonant pressure sensor .

[0073] in, Greater than 0 and less than or equal to 1 The vibration acceleration sensitivity coefficient, For power fluctuation sensitivity coefficient, and All are greater than or equal to 0 and less than or equal to 1. and Calibration is performed for specific sensor models. The vibration acceleration index is the ratio of the current vibration acceleration to a preset maximum vibration acceleration. This can be achieved by comparing real-time vibration data collected by the accelerometer with a threshold stored in the system. This parameter quantifies the impact of mechanical vibration on the resonant frequency stability of the quartz resonator. The power supply fluctuation index is the ratio of the current power supply fluctuation amplitude to a preset maximum allowable fluctuation amplitude. This can be achieved by real-time monitoring of the supply voltage fluctuation by a voltage monitoring circuit and normalizing it with a preset threshold. This parameter characterizes the impact of power supply stability on the sensor signal conditioning circuit. The vibration acceleration sensitivity coefficient is a weighted parameter used to adjust the impact of vibration interference on confidence level. It can be obtained by experimentally calibrating the output deviation curve of the quartz sensor under different vibration levels. This coefficient reflects the sensitivity of the sensor's mechanical structure to vibration. The power supply fluctuation sensitivity coefficient is also a weighted parameter used to adjust the impact of power supply interference on confidence level. It can be calculated by measuring the rate of change of the sensor output with supply voltage fluctuations. This coefficient reflects the sensitivity of the sensor circuit to power quality.

[0074] Specifically, the normalization of vibration acceleration transforms the physical quantity into a dimensionless exponent, allowing vibration parameters of different dimensions to be linearly combined with power supply fluctuation parameters. A confidence decay curve is constructed using an exponential function model. As vibration or power supply interference increases, the confidence level exhibits a non-linear decreasing trend, which aligns with the actual performance degradation of quartz sensors under combined interference. The introduction of a sensitivity coefficient allows confidence calculations to be calibrated for the inherent characteristics of specific sensor models; for example, a higher vibration sensitivity coefficient can be set for sensors with fragile resonant beam structures. The confidence calculation results reflect the sensor's reliability level under current operating conditions in real time, providing a dynamic adjustment basis for subsequent weighted fusion.

[0075] Compared to existing technologies, traditional solutions typically rely on fixed thresholds to determine sensor failure, failing to quantify the performance degradation under combined interference. Existing methods do not consider the coupling effect of vibration and power supply interference and lack parameter calibration mechanisms tailored to individual sensor differences. This solution, by establishing a multi-factor coupled confidence model, achieves accurate quantitative assessment of sensor real-time reliability.

[0076] Through the above technical solution, this application can accurately evaluate the real-time confidence level of a quartz resonant pressure sensor under complex working conditions involving mechanical vibration and power quality fluctuations. This confidence level dynamically reflects the degree of influence of environmental interference on the sensor, providing a reliable basis for the weighted fusion of reference pressure values. This effectively suppresses calibration errors caused by single sensor anomalies, improving the robustness and accuracy of the multi-station calibration system.

[0077] Preferably, the specific steps for calculating the confidence level based on the installation stress and electromagnetic interference intensity parameters of the sputtered thin-film pressure sensor are as follows:

[0078] The installation stress index is obtained by comparing the current installation stress with the preset maximum installation stress. ;

[0079] The electromagnetic interference intensity is obtained by comparing the current electromagnetic interference intensity with the preset maximum electromagnetic interference intensity. ;

[0080] Installation stress index and electromagnetic interference intensity Import Formula Obtain the confidence level of the sputtered thin-film pressure sensor .

[0081] in, Greater than 0 and less than or equal to 1 For installation stress sensitivity coefficient, Electromagnetic interference sensitivity coefficient, and All values ​​are greater than or equal to 0 and less than or equal to 1. The installation stress index is the ratio of the actual installation stress to the preset maximum installation stress. This can be achieved by measuring the installation stress with strain gauges and then normalizing it to a preset threshold. It quantifies the impact of mechanical installation conditions on the deformation of the sensor's sensitive diaphragm. The electromagnetic interference intensity index is the ratio of the actual electromagnetic interference intensity to the preset maximum electromagnetic interference intensity. This can be achieved by measuring the intensity with an electromagnetic field meter and then normalizing it to a preset threshold. It characterizes the severity of the electromagnetic environment in which the sensor operates. The installation stress sensitivity coefficient is an adjustment parameter reflecting the impact of installation stress on sensor performance. It can be determined by calibrating sensor error curves under different stress levels in the laboratory. It controls the contribution weight of the installation stress index in the confidence score calculation. The electromagnetic interference sensitivity coefficient is an adjustment parameter reflecting the impact of electromagnetic interference on the stability of the sensor signal. It can be determined by the relationship curve between sensor output fluctuation and interference intensity in electromagnetic compatibility testing. It adjusts the proportion of electromagnetic interference factors in the confidence score assessment.

[0082] Specifically, installation stress is monitored in real time using a strain measurement device, such as a micro-strain gauge array attached to the sensor mounting base surface, converting the measured micro-strain values ​​into equivalent stress values. This stress value is then compared to a preset maximum allowable installation stress; if the ratio exceeds 1, it is treated as 1, resulting in an installation stress index within the range of 0-1. Electromagnetic interference intensity is measured using a broadband electromagnetic field probe to detect the field strength within the sensor's operating frequency band, such as the root mean square value in the 10MHz-1GHz band, and normalized to a preset maximum withstand field strength. Both indices are multiplied by their corresponding sensitivity coefficients and then nonlinearly fused using an exponential function. The sensitivity coefficients can be set to different values ​​depending on the sensor model; for example, for sensors with strong electromagnetic interference resistance, the electromagnetic interference sensitivity coefficient can be in the range of 0.3-0.5. The final output confidence level decreases exponentially with increasing interference. When either the installation stress or electromagnetic interference parameter exceeds a safety threshold, the confidence level rapidly drops to the warning range.

[0083] Compared to existing technologies, traditional calibration devices rely solely on fixed thresholds to determine sensor status, failing to quantify the combined impact of various interference factors. Current methods typically trigger alarms and disable the sensor directly when installation stress exceeds limits, without considering the nonlinear relationship between stress levels and measurement errors; in electromagnetic interference environments, they only employ shielding measures, without establishing a correlation model between interference intensity and data reliability. This proposed solution, through dynamic confidence level assessment, reflects the cumulative effect of complex interferences while preserving the sensor's usability under critical conditions.

[0084] Through the above technical solution, this application effectively suppresses the zero-point drift problem caused by installation stress deformation. For example, when the installation stress reaches 80% of the maximum allowable value, the confidence level can be automatically reduced to below 0.6, triggering the weight allocation module to reduce the weight of the sensor data. Simultaneously, it solves the signal distortion problem under strong electromagnetic interference environments. For example, under a field strength of 300V / m generated when the frequency converter is operating, by reducing the confidence level, abnormal data is avoided from participating in the fusion calculation, ensuring the stability of the reference pressure output. This solution enables sputtered thin-film sensors to still contribute effective measurement data under non-ideal operating conditions, improving the availability and environmental adaptability of the calibration system compared to the traditional method of directly rejecting abnormal sensors.

[0085] like Figure 1 As shown, in a preferred embodiment of the present invention, the pressure pipeline system includes a pressure distribution block 6 fixed on a base 1. Each of the three output pipes of the pressure distribution block 6 is connected to a high-pressure hose 8 via a precision shut-off valve 7. The ends of the three high-pressure hoses 8 are detachably connected to a quartz resonant pressure sensor 3, a sputtered thin-film pressure sensor 4, and a glass micro-melting pressure and temperature sensor 5 to be tested, respectively. The inlet of the pressure distribution block 6 is connected to an external reference pressure output system via a stainless steel rigid pipe.

[0086] In this embodiment of the invention, the pressure distribution block 6 refers to a rigid structural component with multi-channel equal pressure distribution function. Specifically, it can be implemented using an aluminum alloy casting with an internal pressure equalization chamber. Its internal flow channels are symmetrically designed to ensure pressure balance in each branch. The precision shut-off valve 7 refers to a fluid control element with high sealing performance. Specifically, it can be implemented using a brass valve with a conical sealing structure. Precision machining of the valve core ensures leakage control during pressure switching. The high-pressure hose 8 refers to a flexible connection pipe capable of withstanding high pressure. Specifically, it can be implemented using a multi-layer steel wire braided reinforced polyurethane hose, which absorbs mechanical stress during sensor installation through flexible deformation. The detachable connection refers to an interface form that facilitates quick installation and removal. Specifically, it can be implemented using a compression fitting quick-connect coupling with an O-ring seal structure, enabling rapid sensor replacement through standardized interface design. The pressure distribution block 6 is rigidly mounted on the base 1 to form a stable pressure transmission path. Its internal pressure equalization chamber is connected to each sensor through three symmetrically distributed output pipes. When external pressure is input through the stainless steel rigid pipe, the fluid pressure in the pressure equalization chamber is synchronously distributed to the three branches. The precision shut-off valve 7 independently controls pressure on / off on each branch, and its conical sealing structure ensures zero leakage when the valve is closed. The high-pressure hose 8 provides flexibility compensation when connecting the sensor, preventing installation stress from affecting the sensor's measurement accuracy. The detachable connection structure achieves reliable sealing through the self-locking function of the quick-connect fitting; during maintenance, the sensor can be separated simply by releasing the ferrule lock.

[0087] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-station calibration fixture for a glass micro-melting pressure and temperature sensor, comprising a base and three stationary columns fixed on the base, characterized in that, Also includes: A quartz resonant pressure sensor, a sputtered thin film pressure sensor, and a glass micro-melting pressure and temperature sensor to be tested are respectively installed on the three workstation columns. The pressure piping system, mounted on the base, is used to simultaneously and equally pressurize the quartz resonant pressure sensor, the sputtered thin-film pressure sensor, and the glass micro-melting pressure-temperature sensor to be tested. A reference pressure output system, electrically connected to a quartz resonant pressure sensor and a sputtered thin-film pressure sensor, is used to output a reference pressure value. The reference pressure output system includes: The confidence assessment module calculates the confidence level of the quartz resonant pressure sensor based on its vibration acceleration and power fluctuation parameters, and calculates the confidence level of the sputtered thin film pressure sensor based on its installation stress and electromagnetic interference intensity parameters. The safety mode module compares the degree of difference between the measurement values ​​of two sensors and selects to enter normal mode or warning mode. In normal mode, the weight configuration module dynamically allocates the weight coefficients of the two sensors based on the system runtime, ambient temperature, and the confidence levels of the two sensors. The reference pressure output module performs weighted fusion of the measured values ​​from the two sensors according to the weighting coefficients, and outputs the final reference pressure value.

2. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 1, characterized in that, The specific steps for weighting and fusing the measured values ​​of the two sensors according to the weighting coefficients to output the final reference pressure value are as follows: The measured values ​​of the quartz resonant pressure sensor are... Sputtered thin-film pressure sensor measurement values Weighting coefficient of quartz resonant pressure sensor measurement values Weighting coefficients for measurements from sputtered thin-film pressure sensors Import Formula Obtain reference pressure value .

3. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 1, characterized in that, In warning mode, the average value of the two sensor measurements is output as the reference pressure value. The specific steps are as follows: the quartz resonant pressure sensor measurement value... and sputtered thin-film pressure sensor measurement values Import Formula Obtain reference pressure value ,in, This is the safe pressure value.

4. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 2, characterized in that, The specific steps for dynamically allocating the weighting coefficients of the two sensors in normal mode, based on system runtime, ambient temperature, and the confidence levels of the two sensors, are as follows: The time-temperature factor of the quartz sensor is obtained based on system operating time and ambient temperature. and the time-temperature factor of thin-film sensors ; The time-temperature factor of the quartz sensor and the time-temperature factor of thin-film sensors and the confidence level of quartz resonant pressure sensors Confidence of sputtered thin-film pressure sensors Import formula and Obtain the weighting coefficient of the quartz resonant pressure sensor measurement value. Weighting coefficients for measurements from sputtered thin-film pressure sensors .

5. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 4, characterized in that, The time-temperature factor of the quartz sensor is obtained based on system operating time and ambient temperature. and the time-temperature factor of thin-film sensors The specific steps are as follows: The system runtime is calculated by comparing the system runtime with the preset maximum runtime to obtain the system runtime index. ; The temperature deviation index is obtained by comparing the absolute value of the difference between the current ambient temperature and the reference temperature with the preset maximum temperature deviation. ; System runtime index and temperature deviation index Import Formula and Obtain the time-temperature factor of the quartz sensor and the time-temperature factor of thin-film sensors ,in, The time sensitivity coefficient of the quartz sensor. The temperature sensitivity coefficient of the quartz sensor. For the time sensitivity coefficient of the thin-film sensor, This represents the temperature sensitivity coefficient of the thin-film sensor.

6. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 1, characterized in that, The specific steps for selecting between normal mode and warning mode by comparing the differences between the measurements from two sensors are as follows: The measured value of the quartz resonant pressure sensor and sputtered thin-film pressure sensor measurement values Import Formula Obtain the difference of normalized measured values. , The value range is 0-1, where, This is a scaling factor used to prevent division by zero; If the difference of normalized measurements If the value is less than or equal to the set threshold, then enter normal mode; If the difference of normalized measurements If the value exceeds the set threshold, the system will enter warning mode.

7. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 4, characterized in that, The specific steps for calculating the confidence level of the vibration acceleration and power fluctuation parameters based on the quartz resonant pressure sensor are as follows: The vibration acceleration index is obtained by comparing the current vibration acceleration with the preset maximum vibration acceleration. ; The power fluctuation index is obtained by comparing the current power fluctuation with the preset maximum power fluctuation. ; Vibration acceleration index and power fluctuation index Import Formula Obtain the confidence level of the quartz resonant pressure sensor ,in, The vibration acceleration sensitivity coefficient, This is the power supply fluctuation sensitivity coefficient.

8. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 4, characterized in that, The specific steps for calculating the confidence level of the installation stress and electromagnetic interference intensity parameters based on the sputtered thin-film pressure sensor are as follows: The installation stress index is obtained by comparing the current installation stress with the preset maximum installation stress. ; The electromagnetic interference intensity is obtained by comparing the current electromagnetic interference intensity with the preset maximum electromagnetic interference intensity. ; Installation stress index and electromagnetic interference intensity Import Formula Obtain the confidence level of the sputtered thin-film pressure sensor ,in, For installation stress sensitivity coefficient, This is the electromagnetic interference sensitivity coefficient.

9. The multi-station calibration fixture for the glass micro-melting pressure and temperature sensor according to claim 1, characterized in that, The pressure piping system includes a pressure distribution block fixed on the base. Each of the three output pipes of the pressure distribution block is connected to a high-pressure hose through a precision shut-off valve. The ends of the three high-pressure hoses are detachably connected to a quartz resonant pressure sensor, a sputtered thin-film pressure sensor, and a glass micro-melting pressure and temperature sensor to be tested, respectively. The inlet of the pressure distribution block is connected to an external reference pressure output system through a stainless steel rigid pipe.

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