Wide-range multi-component sensor calibration equipment and method

Abstract

The invention relates to wide-range multi-component sensor calibration equipment and a wide-range multi-component sensor calibration method. The method comprises the following steps: firstly, checking equipment parts and setting a target temperature range and a compensation model of each temperature interval; then, dynamic temperature control is started, jig deformation is monitored in real time, a transient temperature gradient suppression technology is applied, multi-physical-quantity calibration is executed, and a compensation measurement value is output; then setting multi-environment coupling calibration test conditions to simulate actual working conditions, and monitoring and recording calibration data of each physical quantity; and finally, calculating a calibration error by combining a calibration result and a reference standard value. According to the process, by precisely setting parameters and combining dynamic control, real-time monitoring and multi-physical-quantity calibration, the transient temperature gradient influence is effectively inhibited, and the measurement precision of the sensor in a complex environment is ensured; the temperature control performance is improved through the micro soaking film and the intelligent temperature control strategy, the actual working condition is simulated through multi-environment coupling testing, the accuracy and reliability of the calibration result are remarkably improved through compensation and error calculation, and guarantee is provided for precise application of the sensor.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor calibration, and in particular to a large-range multi-component sensor calibration device and method. Background Art

[0002] In modern industry, scientific research, and high-precision measurement, large-range, multi-component sensors can simultaneously measure physical quantities in multiple directions over a wide range, meeting the needs of complex scenarios. However, with technological development and increasing application requirements, the requirements for their measurement accuracy are becoming increasingly stringent. However, in actual use, these sensors are affected by various factors such as temperature changes, environmental interference, and mechanical stress, resulting in measurement deviations that affect accuracy and reliability. Traditional calibration equipment and methods are unable to meet these complex and high-precision calibration requirements, and they do not fully consider practical factors such as the coupling of multiple physical quantities and temperature changes, resulting in distorted calibration results.

[0003] In the field of calibrating long-range, multi-component sensors, existing calibration equipment and methods face numerous practical challenges. Temperature fluctuations can alter the sensor's internal material properties, causing zero drift and sensitivity variations, resulting in significant measurement errors. Existing calibration methods offer simple compensation schemes, unable to accurately compensate for different temperature ranges and meeting high-precision requirements. Sensors operate under the influence of multiple coupled physical quantities, resulting in complex outputs. Current calibration equipment and methods primarily focus on calibrating a single physical quantity, lacking comprehensive calibration capabilities. This leads to deviations between calibration results and actual applications. Furthermore, the calibration process suffers from deficiencies in dynamic temperature control, fixture deformation monitoring, and real-time compensation, impacting the reliability of calibration results. Summary of the Invention

[0004] The present invention aims to solve the technical problems existing in the prior art and provides a large-scale multi-component sensor calibration device and method to improve the calibration accuracy and reliability.

[0005] The present invention solves the above-mentioned technical problem with the following technical solution: A method for calibrating a large-range multi-component sensor, comprising: S1, check the various components of the calibration equipment for a large range and multi-component sensor, and set the target temperature range and the compensation model for each temperature range in the calibration equipment control system; S2: The calibration equipment starts dynamic temperature control and monitors the fixture deformation in real time. It applies transient temperature gradient suppression technology, performs multi-physical quantity calibration, and outputs compensated measurement values. S3, setting multi-environment coupling calibration test conditions to simulate the actual working environment, real-time monitoring and recording the calibration data of various physical quantities in different environments; S4, combining the calibration results with the reference standard values ​​to calculate the calibration errors of various physical quantities.

[0006] Preferably, the S2 includes: 2A, the calibration equipment starts the dynamic temperature control module, which controls the operation of the thermoelectric cooler according to the set target temperature range and temperature change rate limit; 2B, the calibration equipment activates the temperature-displacement sensor array to monitor the deformation of the fixture at each temperature point in real time and transmits the deformation data to the calibration equipment control system; At 2C, the micro-heat spreader film encapsulated on the sensor surface starts working, and the calibration equipment control system dynamically adjusts the temperature control strategy according to the temperature change rate limit; 2D, real-time collection of sensor output values, current temperature values, and fixture deformation data; 2E, calculates and corrects the temperature drift error and deformation error of each physical quantity based on the set compensation model and deformation data; 2F, the calibration device outputs the compensated measurement values ​​of each physical quantity and records them in the calibration device control system.

[0007] Preferably, the real-time monitoring of the deformation of the fixture at each temperature point includes: The calibration equipment control system sends a start command to the temperature-displacement sensor array, and each sensor in the sensor array begins to work. During the temperature adjustment process, the control system monitors the temperature sensor data in real time. When the temperature fluctuates within a certain range of the target temperature and lasts for n minutes, the temperature is considered stable. After the temperature stabilizes, the control system reads the deformation data of each sensor in the temperature-displacement sensor array in sequence according to the set sampling frequency.

[0008] Preferably, the S1 includes: S11, set the temperature cycle curve and the number of cycles; S12, collecting and analyzing the temperature rise and fall output data of the sensor during the temperature cycle, establishing a segmented compensation model and performing real-time compensation; S13, monitoring and recording the zero reference value of each temperature cycle low temperature constant temperature stage, establishing a zero drift correction model and applying the correction; S14, after checking and setting the calibration equipment parameters, start the temperature cycle calibration and collect data, output and record the calibration results after real-time compensation and correction.

[0009] Preferably, S11, setting the temperature cycle curve and the number of cycles, includes: In the calibration equipment control system, a complete temperature cycle curve is set, including heating, constant temperature, cooling, and low-temperature constant temperature stages. The temperature range is determined according to the sensor tolerance range and calibration requirements, and the specific duration of each stage is also specified.

[0010] Preferably, the sensor temperature increase and temperature decrease output data includes: Multiple micro stress sensors are arranged inside the sensor to collect and transmit stress data to the control system in real time. An early warning threshold is set and an early warning is issued when the threshold is exceeded. A stress-temperature relationship model is established through experiments, and the control system dynamically adjusts the temperature change rate and constant temperature time based on the model and real-time stress data. The contact resistance at the interface between the silicon-based chip and the ceramic substrate is monitored in real time, and a compensation model is established for real-time compensation. When the resistance drift exceeds the range, an electric pulse or thermal pulse repair mechanism is activated. The signal jump is monitored in real time, and when the signal jump amplitude exceeds the set threshold, the parameters of the filtering algorithm are dynamically adjusted.

[0011] Preferably, the dynamic adjustment of the temperature change rate and the constant temperature time includes: By experimentally measuring the stress values ​​at the interface between the silicon-based chip and the ceramic substrate at different temperatures, a stress-temperature relationship model is established; based on the stress-temperature relationship model and the real-time monitored interface stress data, the control system of the calibration equipment dynamically adjusts the temperature cycle parameters.

[0012] Preferably, the dynamic adjustment of the temperature change rate and the constant temperature time includes: The sensor's internal drift data is collected at multiple scales and data features are extracted. The multi-scale drift data is integrated to form a drift dataset, enabling the model to learn the correlation and change patterns between drifts at different time scales. The multi-scale drift data collected in real time is input into the cross-scale drift fusion prediction model to obtain the prediction results of sensor drift in the future time window. Based on the prediction results, a corresponding dynamic compensation strategy is formulated.

[0013] Preferably, the multi-scale acquisition includes: Second-level drift data collection, hour-level drift data collection, and monthly-level drift data collection.

[0014] The present application also provides a large-range multi-component sensor calibration device, the device comprising: The data acquisition and processing module checks the various components of the large-scale multi-component sensor calibration equipment, and sets the target temperature range and compensation model for each temperature interval in the calibration equipment control system; Dynamic temperature control module: The calibration equipment starts dynamic temperature control and monitors fixture deformation in real time. It applies transient temperature gradient suppression technology, performs multi-physical quantity calibration, and outputs compensated measurement values. Resistance measurement module, real-time monitoring of the contact resistance at the interface between silicon-based chip and ceramic substrate; The simulation and recording module sets multi-environment coupling calibration test conditions to simulate the actual working environment, and monitors and records the calibration data of various physical quantities in different environments in real time; The error calculation and analysis module combines the calibration results with the reference standard values ​​to calculate the calibration errors of various physical quantities.

[0015] The beneficial effects of the present invention are: By precisely setting the target temperature range and compensation model, combined with dynamic temperature control, real-time deformation monitoring, and multi-physics calibration, the effects of transient temperature gradients are effectively suppressed, ensuring the sensor's measurement accuracy in complex environments. Micro-heat-dispersing films and intelligent temperature control strategies further enhance the stability and response speed of temperature control. Multi-environment coupling testing simulates actual operating conditions. Ultimately, through compensation and error calculation, the accuracy and reliability of calibration results are significantly improved, providing strong support for the sensor's precise application.

[0016] By setting a complete temperature cycling curve and rationally planning the number of cycles, the sensor's actual usage scenarios can be effectively simulated. A segmented compensation model is established to compensate for thermal hysteresis in real time, significantly reducing the difference in output between rising and falling temperatures. Zero-point reference values ​​are monitored and recorded, and a correction model is established to effectively correct for zero-point drift caused by accumulated residual stress during temperature cycling. This entire solution comprehensively considers the impact of temperature cycling on the sensor, significantly improving sensor calibration accuracy in complex temperature environments, ensuring sensor output stability and accuracy, and providing strong support for the reliable application of the sensor in various temperature-varying scenarios.

[0017] By placing stress sensors at the sensor's heterogeneous interface, real-time stress monitoring and early warning are achieved to ensure equipment safety. A stress-temperature relationship model is established to dynamically adjust temperature parameters, improving calibration stability. Contact resistance is monitored and compensated in real time to ensure signal accuracy, initiating a repair mechanism to maintain performance when the range is exceeded. Filter algorithm parameters are dynamically adjusted to address signal jumps and reduce interference. This overall solution effectively improves the sensor's stability, accuracy, and reliability under complex operating conditions, extending equipment life and reducing maintenance costs.

[0018] By precisely collecting drift data from the sensor at multiple scales and extracting features, the system integrates this data to build a model for cross-scale drift fusion prediction, and formulates a targeted dynamic compensation strategy based on the prediction results. This solution comprehensively and accurately monitors and predicts sensor drift at different time scales, dynamically compensating for each type of drift in a timely manner. This effectively addresses the inability of traditional solutions to handle multi-timescale coupling effects, significantly improving the sensor's performance stability and accuracy under long-term temperature cycling, and ensuring its long-term reliable operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a calibration method for a large-range multi-component sensor according to the present invention; Figure 2 This is a structural block diagram of a large-range multi-component sensor calibration device of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0021] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0022] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0023] Example 1 Figure 1 1 is a flow chart of a method for calibrating a large-range multi-component sensor according to a first embodiment of the present invention, comprising the following steps: S1, check the various components of the large-scale multi-component sensor calibration equipment, and set the target temperature range and the compensation model of each temperature interval in the calibration equipment control system.

[0024] Specifically, according to the calibration requirements and the operating environment of the sensor, the target temperature range is set in the calibration equipment control system. When setting the target temperature range, the temperature control capability of the equipment and the tolerance range of the sensor must be considered to ensure that the set temperature range is within the achievable range of the equipment and will not cause damage to the sensor.

[0025] The compensation model setting is divided into low temperature zone compensation model setting, high temperature zone compensation model setting and intermediate temperature zone compensation model setting.

[0026] Low temperature compensation model setting: In low temperature areas, the performance of the sensor may be affected by temperature and produce large errors. The exponential compensation model is used, which is in the form of , where y is the compensated measurement value, T is the temperature, and a, b, and c are compensation coefficients. These coefficients are obtained by fitting experimental data. For example, at different low temperatures (such as -50°C, -30°C, and -10°C), the sensor output and actual values ​​are measured and the values ​​of a, b, and c are determined using fitting methods such as the least squares method.

[0027] High-temperature compensation model setting: In high-temperature regions, the sensor's error characteristics may differ from those in low-temperature regions. Therefore, a logarithmic compensation model is used: y = d*ln(T) + e, where d and e are compensation coefficients. These coefficients are also determined by fitting experimental data. Measurements and fitting are performed at different high-temperature points (e.g., 300°C, 500°C, and 700°C).

[0028] Compensation model setting for the intermediate temperature zone: In the intermediate temperature zone, the sensor error may be more complex, so a polynomial fitting compensation model is used. ,in , ,f ,..., The polynomial coefficients are obtained by measuring multiple temperature points (such as 50°C, 100°C, and 150°C) in the intermediate temperature range and fitting them using the least squares method.

[0029] In S2, the calibration equipment starts dynamic temperature control and monitors the fixture deformation in real time. It applies transient temperature gradient suppression technology, performs multi-physical quantity calibration, and outputs compensated measurement values.

[0030] Specifically, 2A, the calibration device starts the dynamic temperature control module, and controls the operation of the thermoelectric cooler according to the set target temperature range and temperature change rate limit.

[0031] The control system of the calibration equipment reads the set target temperature range ( ) and temperature change rate limits , initialize the thermoelectric cooler (TEC), including checking the connection status of the TEC, initial current and voltage settings, etc. The temperature of the current environment or calibration area is collected in real time through a high-precision temperature sensor The temperature sensor converts the collected temperature signal into an electrical signal and transmits it to the control system, which then Compare with the target temperature range. If the target temperature is not within the range, the temperature adjustment phase will begin. If it is already within the target temperature range, the monitoring status will continue.

[0032] The control system records the temperature change within a time window (the time window in this application is defined as 1 minute) , where t is the current time, Δt is the time interval, calculate the temperature change rate The calculated temperature change rate With the set temperature change rate limit Compare. , then it is necessary to adjust the TEC operating parameters to control the temperature change rate; if , then continue to adjust the temperature according to the current control strategy.

[0033] If the current temperature Below the lower limit of the target temperature range , and the temperature change rate does not exceed the limit, the control system will increase the heating current of TEC. And the temperature change rate requirements, determine the increase in heating current. Above the upper limit of the target temperature range , and the temperature change rate does not exceed the limit, the control system will increase the cooling current of TEC. Also according to the difference between the target temperature and the current temperature Based on the temperature change rate requirements, the PID control algorithm determines the amount of cooling current to increase. If the temperature change rate exceeds the limit, the control system appropriately reduces the TEC's heating or cooling current to slow the temperature change. For example, if the temperature rises too quickly, the heating current is reduced or the cooling current is increased; if the temperature drops too quickly, the cooling current is reduced or the heating current is increased. The control system then sends the calculated TEC current adjustment command to the TEC driver circuit, which adjusts the TEC's operating current accordingly, thereby controlling the TEC's heating or cooling power.

[0034] During TEC operation, the control system continuously collects temperature data in real time and repeats the aforementioned steps of temperature monitoring, target determination, rate of change calculation and determination, and control strategy development and execution. Based on the real-time monitored temperature and temperature rate of change, the control system dynamically adjusts the TEC's operating parameters to ensure that the temperature stably and accurately reaches and remains within the target range while meeting the temperature rate of change constraints.

[0035] 2B, the calibration equipment activates the temperature-displacement sensor array to monitor the deformation of the fixture at each temperature point in real time and transmit the deformation data to the calibration equipment control system.

[0036] The calibration equipment control system sends a start-up command to the temperature-displacement sensor array, and each sensor in the sensor array begins to operate. The sensor array is initialized, including setting parameters such as the sensor's sampling frequency, measurement range, and data output format. Based on the calibration requirements, the control system sets the target temperature point and adjusts the operating state of the thermoelectric cooler (TEC) or other heating / cooling device so that the temperature of the fixture's environment gradually approaches the target temperature. During the temperature adjustment process, the control system monitors the temperature sensor data in real time. When the temperature fluctuates within a certain range (±0.1°C) near the target temperature and persists for a period of 5 minutes, the temperature is considered stable and deformation measurement can be performed.

[0037] After the temperature stabilizes, the control system sequentially reads the deformation data from each sensor in the temperature-displacement sensor array at the set sampling frequency. Each sensor converts the measured deformation value into an electrical signal and transmits it to the control system via a communication interface. The control system verifies the received deformation data for completeness and validity. If any data anomalies are detected, appropriate processing is performed, such as rereading the data or flagging the abnormal data.

[0038] The verified and processed deformation data is transmitted via a communication interface to the data processing module of the calibration equipment control system. Data encryption and other technologies can be used during transmission to ensure data security and reliability. The control system stores the received deformation data in a specific format on a local storage device. It can also record relevant information such as the time and temperature of data acquisition.

[0039] The above steps of temperature control and stabilization, deformation data collection, and data transmission and storage are repeated, measuring at different target temperature points until deformation data collection is complete for all preset temperature points. Throughout the measurement process, the control system monitors changes in temperature and deformation data in real time and records the data. If any abnormal fluctuations in temperature or deformation data are detected, an alarm is issued immediately.

[0040] At 2C, the micro-heat spreader film encapsulated on the sensor surface starts working, and the calibration equipment control system dynamically adjusts the temperature control strategy according to the temperature change rate limit.

[0041] The calibration equipment control system sends a start command to the micro-heat spreader packaged on the sensor surface. After the heat spreader is started, the control system monitors the temperature of the sensor surface in real time. and the temperature change rate Temperature monitoring can be achieved by a high-precision temperature sensor in close contact with the sensor surface, which converts the temperature signal into an electrical signal and transmits it to the control system.

[0042] The control system uses the collected temperature data to calculate the temperature change rate at a certain time interval Δt. For example, the temperature is collected at time t and t−Δt respectively. and , then the temperature change rate By approximate formula ≈ The calculated temperature change rate is compared with the set temperature change rate limit. Compare. , indicating that the temperature changes too quickly and the temperature control strategy needs to be adjusted; if , then continue to maintain the current temperature control strategy.

[0043] During heating strategy adjustment, when the temperature change rate exceeds the upper limit ( ): If the system is in the heating state at this time, it means that the heating power is too large, causing the temperature to rise too quickly. The control system will reduce the heating power provided to the heat spreader. The specific adjustment amount can be based on the difference between the temperature change rate and the threshold value. To determine, use the proportional control method to adjust the amount and is proportional to ,in is the heating power adjustment coefficient. When the temperature change rate is lower than the lower limit ( ): If the system is in the heating state, it means that the heating power is insufficient, causing the temperature to drop too quickly. The control system will increase the heating power provided to the heat spreader, and adjust the amount .

[0044] During the cooling strategy adjustment, when the temperature change rate exceeds the upper limit ( ): If the system is in a cooling state, it means that the cooling power is insufficient, causing the temperature to rise too quickly. The control system will increase the cooling power of the heat spreader, such as increasing the speed of the cooling fan (if the heat spreader is equipped with a cooling fan) or enhancing other cooling mechanisms. Adjustment amount The difference between the temperature change rate and the threshold is proportional to ,in is the heat dissipation power adjustment coefficient. When the temperature change rate is lower than the lower limit ( ): If the system is in the heat dissipation state, it means that the heat dissipation power is too large, causing the temperature to drop too quickly. The control system will reduce the heat dissipation power of the heat diffusion film and adjust the amount .

[0045] After adjusting the temperature control strategy, the control system continues to monitor the temperature and temperature change rate of the sensor surface in real time to observe the effect of the adjustment. If the temperature change rate still does not meet the requirements, the temperature control strategy is further adjusted and the above judgment and adjustment process is repeated. Based on the effect of multiple adjustments, the control system can adjust the heating power coefficient. and heat dissipation power adjustment factor Optimize to improve the accuracy and stability of temperature control.

[0046] When the temperature change rate and last for a period of time ( ), the temperature of the sensor surface is considered to have reached a stable state. In the stable state, the control system continues to maintain the current temperature control strategy, regularly monitoring the temperature and temperature change rate to ensure that the temperature always fluctuates within the allowable range.

[0047] 2D, real-time collection of sensor output values, current temperature values, and fixture deformation data.

[0048] 2E, calculates and corrects the temperature drift error and deformation error of each physical quantity based on the set compensation model and deformation data.

[0049] Obtain deformation data collected at different temperatures from the calibration equipment. Using the compensation model, predict the theoretical value of the physical quantity at that temperature based on the current temperature. Compare the actual measured deformation with the theoretical value predicted by the model to calculate the temperature drift error.

[0050] 2F, the calibration device outputs the compensated measurement values ​​of each physical quantity and records them in the calibration device control system.

[0051] S3, set the multi-environment coupling calibration test conditions to simulate the actual working environment, and monitor and record the calibration data of various physical quantities in different environments in real time.

[0052] S4, combining the calibration results with the reference standard values ​​to calculate the calibration errors of various physical quantities.

[0053] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By precisely setting the target temperature range and compensation model, combined with dynamic temperature control, real-time deformation monitoring, and multi-physics calibration, the effects of transient temperature gradients are effectively suppressed, ensuring the sensor's measurement accuracy in complex environments. Micro-heat-dispersing films and intelligent temperature control strategies further enhance the stability and response speed of temperature control. Multi-environment coupling testing simulates actual operating conditions. Ultimately, through compensation and error calculation, the accuracy and reliability of calibration results are significantly improved, providing strong support for the sensor's precise application.

[0054] Example 2 In Example 1, although the calibration scheme for the sensor in a temperature environment can guarantee the basic performance of the sensor to a certain extent, it has obvious deficiencies in actual complex temperature change scenarios. During the temperature cycle, the sensor material has thermal hysteresis characteristics, which makes the heating curve and the cooling curve unequal, and Example 1 does not fully consider the impact of this characteristic on the sensor output; at the same time, the residual stress accumulated by the temperature cycle will cause the sensor zero drift, and Example 1 also lacks effective countermeasures. Faced with the complex changes in sensor output under different temperature cycle conditions, if the original scheme is still used, it will inevitably be difficult to accurately compensate and correct the sensor output, affecting the calibration accuracy. In order to more accurately compensate for the output difference caused by thermal hysteresis, correct the zero drift, and thus obtain more accurate calibration results, it is necessary to further optimize and improve the original scheme.

[0055] In some embodiments, step S1 includes: S11, set the temperature cycle curve and the number of cycles.

[0056] Specifically, a complete temperature cycle curve, including heating, constant temperature, cooling, and low-temperature constant temperature phases, is set in the calibration equipment control system. The temperature range should be determined based on the sensor's tolerance and calibration requirements, and the specific duration of each phase should be specified. For example, the heating phase from low temperature to high temperature takes 30 minutes, the constant temperature phase lasts 60 minutes, the cooling phase from high temperature to low temperature takes 30 minutes, and the low-temperature constant temperature phase lasts 60 minutes.

[0057] S12, collecting and analyzing the temperature rise and fall output data of the sensor during the temperature cycle, establishing a segmented compensation model and performing real-time compensation.

[0058] Specifically, during the temperature cycle, the output values ​​of the sensor in the heating and cooling stages, as well as the corresponding temperature values ​​and fixture deformation data, are collected in real time.

[0059] Analyze the collected data and compare the output differences between the heating curve and the cooling curve at the same temperature point to clarify the impact of thermal hysteresis on sensor output. For example, at a certain temperature point, the sensor output is A when the temperature rises and B when the temperature falls. Record the difference between the two.

[0060] According to the analysis results, the temperature cycle curve is divided into multiple temperature intervals, and a temperature rise compensation model and a temperature drop compensation model are established for each interval.

[0061] The temperature rise and temperature drop compensation models can be constructed using linear interpolation. Within a temperature range, select several key temperature points and record the sensor output values ​​at these points during both rising and falling temperatures. For other temperature points within the range, linear interpolation is performed based on the data from the key temperature points to obtain the corresponding compensation values.

[0062] During temperature cycle calibration, the calibration equipment control system selects the appropriate compensation model to compensate the sensor output in real time based on the current temperature and the temperature trend (increasing or decreasing). For example, when the temperature is rising, the rising compensation model is used to correct the sensor output; when the temperature is decreasing, the cooling compensation model is used.

[0063] S13, monitoring and recording the zero reference value of each temperature cycle low temperature constant temperature stage, establishing a zero drift correction model and applying the correction.

[0064] Specifically, during the cryostatic stage of each temperature cycle, the output value of the sensor is collected as the zero reference value of the cycle.

[0065] Record the zero reference value for each cycle and calculate the change in zero reference value between cycles to assess the impact of temperature cycling accumulated residual stress on the sensor zero position. For example, if the zero reference value for the first cycle is C and the zero reference value for the second cycle is D, calculate the difference between the two to determine whether zero position drift has occurred.

[0066] Based on the change of the zero reference value, a zero drift correction model is established. A simple cumulative correction method can be used, that is, the zero reference value of the first cycle is used as the basis, and the measurement value of each subsequent cycle is subtracted from the difference between the zero reference value of the cycle and the first cycle.

[0067] When calibrating the output of the compensated physical quantity measurement values, the zero drift correction model is applied to further correct the measurement values ​​to ensure the zero accuracy of the sensor output.

[0068] S14, after checking and setting the calibration equipment parameters, start the temperature cycle calibration and collect data, output and record the calibration results after real-time compensation and correction.

[0069] Specifically, ensure the normal operation of each component of the large-scale multi-component sensor calibration equipment, set the target temperature range, temperature change rate limit, temperature cycle curve, number of cycles and corresponding compensation model and correction model parameters in the calibration equipment control system.

[0070] The dynamic temperature control module of the calibration equipment is activated to control the temperature according to the set temperature cycle curve. During the temperature cycle, the output values ​​of various physical quantities of the sensor, the current temperature value, and the deformation data of the fixture are collected in real time.

[0071] According to the set temperature rise compensation model, temperature drop compensation model and zero drift correction model, the collected sensor output value is compensated and corrected in real time.

[0072] The calibration equipment outputs the compensated and corrected measurement values ​​of various physical quantities and records them in the calibration equipment control system. It also records relevant data such as temperature and deformation during the calibration process.

[0073] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By setting a complete temperature cycling curve and rationally planning the number of cycles, the sensor's actual usage scenarios can be effectively simulated. A segmented compensation model is established to compensate for thermal hysteresis in real time, significantly reducing the difference in output between rising and falling temperatures. Zero-point reference values ​​are monitored and recorded, and a correction model is established to effectively correct for zero-point drift caused by accumulated residual stress during temperature cycling. This entire solution comprehensively considers the impact of temperature cycling on the sensor, significantly improving sensor calibration accuracy in complex temperature environments, ensuring sensor output stability and accuracy, and providing strong support for the reliable application of the sensor in various temperature-varying scenarios.

[0074] Example 3 In Example 2, for the performance monitoring and regulation of the sensor in a complex environment, a relatively fixed parameter setting and a single monitoring and regulation strategy are mainly adopted. However, in actual operation, the stress, contact resistance, and signal jump at the heterogeneous interface between the internal silicon-based chip and the ceramic substrate of the sensor will change dynamically with various factors such as temperature and usage time, and there are also performance differences between different individual sensors. Faced with these complex and changing situations, the fixed strategy in Example 2 is difficult to adapt accurately, resulting in insufficient monitoring accuracy and poor regulation effect, and it is impossible to fully guarantee the long-term stable and accurate operation of the sensor.

[0075] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0076] In some embodiments, in step S12, the sensor outputs data of temperature increase and temperature decrease, further comprising: S121, multiple micro stress sensors are arranged inside the sensor to collect and transmit stress data to the control system in real time, and an early warning threshold is set. When the threshold is exceeded, an early warning is issued.

[0077] Specifically, multiple micro-stress sensors are evenly and rationally arranged at the heterogeneous interface between the silicon-based chip and the ceramic substrate inside the sensor. The micro-stress sensors are connected to a data acquisition and processing module to collect interface stress data in real time and transmit the data to the calibration equipment's control system via wired or wireless means. Based on experimental data and theoretical analysis, a warning threshold for interface stress is set. When the monitored interface stress exceeds the warning threshold (defined as 180 MPa in this application), the calibration equipment's control system immediately issues a warning signal.

[0078] S122, a stress-temperature relationship model is established through experiments, and the control system dynamically adjusts the temperature change rate and constant temperature time based on the model and real-time stress data.

[0079] Specifically, the team experimentally measured stress at the interface between the silicon chip and the ceramic substrate at different temperatures to establish a stress-temperature relationship model. For example, within the temperature range of -196°C to 200°C, stress measurements were taken every 20°C, and the interface stress values ​​at different temperatures were recorded to plot a stress-temperature curve.

[0080] Based on the stress-temperature relationship model and real-time interface stress data, the calibration equipment's control system dynamically adjusts the temperature cycle parameters. When the interface stress approaches the warning threshold, the temperature change rate is appropriately reduced or the constant temperature time is extended to minimize the stress variation at the interface. For example, if the originally set temperature change rate is 5°C / min, when the interface stress reaches 160MPa, the temperature change rate is reduced to 3°C / min. If the interface stress continues to decrease and moves away from the lower limit of the safety range, the temperature change rate can be gradually restored to the original set value.

[0081] S123 monitors the contact resistance at the interface between the silicon-based chip and the ceramic substrate in real time, establishes a compensation model for real-time compensation, and activates an electric pulse or thermal pulse repair mechanism when the resistance drift exceeds the range.

[0082] Specifically, a high-precision resistance measurement module is integrated into the sensor circuit to monitor the contact resistance at the interface between the silicon chip and the ceramic substrate in real time. The resistance measurement module should have high precision and low drift, achieving a measurement accuracy of ±0.1mΩ.

[0083] A contact resistance compensation model is established using linear interpolation based on real-time monitored contact resistance and temperature values. For example, within a certain temperature range, several key temperature points are selected, the corresponding contact resistance values ​​are measured, and then a compensation model is established based on this data. During sensor operation, contact resistance is compensated in real time based on the current temperature and the compensation model to ensure accurate sensor output.

[0084] Contact resistance repair mechanism: When the contact resistance drift exceeds a certain range (defined in this application as exceeding 5mΩ), the control system of the calibration equipment activates the contact resistance repair mechanism. By applying a small electrical pulse or thermal pulse to the interface, the microstructure at the interface changes, improving contact performance and reducing contact resistance. For example, an electrical pulse with a frequency of 1kHz and an amplitude of 5V is applied for 10s. After the repair is completed, the contact resistance needs to be measured again. If it still does not meet the requirements, the repair process can be repeated, but a maximum number of repetitions must be set to avoid damage to the interface caused by excessive repair.

[0085] S124, monitoring the signal jump in real time, and dynamically adjusting the parameters of the filtering algorithm when the signal jump amplitude exceeds a set threshold.

[0086] Dynamically adjusting the filtering algorithm parameters involves increasing the filter window size to improve filtering effectiveness when signal transitions are frequent, and reducing it to reduce signal delay and distortion when signal transitions are less frequent. Furthermore, after adjusting the filtering algorithm parameters, the quality of the filtered signal should be evaluated. If the signal quality still does not meet the requirements, further integration of other filtering algorithms or adjustment of other related parameters can be considered.

[0087] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By placing stress sensors at the sensor's heterogeneous interface, real-time stress monitoring and early warning are achieved to ensure equipment safety. A stress-temperature relationship model is established to dynamically adjust temperature parameters, improving calibration stability. Contact resistance is monitored and compensated in real time to ensure signal accuracy, initiating a repair mechanism to maintain performance when the range is exceeded. Filter algorithm parameters are dynamically adjusted to address signal jumps and reduce interference. This overall solution effectively improves the sensor's stability, accuracy, and reliability under complex operating conditions, extending equipment life and reducing maintenance costs.

[0088] Example 4 In Example 3, although certain monitoring and control measures were taken to address the drift problem of the sensor under temperature cycling, the traditional solution only focused on drift processing at a single or partial time scale, and failed to fully consider the coupling effect between drifts at multiple time scales such as seconds, hours, and months. Drifts at different time scales affect each other, making the performance changes of the sensor more complex. It is difficult to ensure the long-term stability of the sensor by only processing drift at a single scale. Moreover, during the long-term operation of different sensors, the combination and impact of multi-time scale drifts vary, and it is difficult to accurately respond to various complex situations using a unified control strategy.

[0089] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0090] In some embodiments, in step S122, dynamically adjusting the temperature change rate and the constant temperature time further includes: S1221 , collecting drift data inside the sensor at multiple scales and extracting data features.

[0091] Among them, multi-scale collection includes: second-level drift data collection, hour-level drift data collection and monthly-level drift data collection.

[0092] Specifically, the acquisition of second-level drift data involves placing high-precision micro-strain gauges at the heterogeneous interface between the silicon chip and the ceramic substrate inside the sensor, collecting strain data at a frequency of once per second, and converting the strain data into stress data through the data acquisition and processing module and recording it. The collected second-level stress data is statistically analyzed to calculate characteristic parameters such as the stress change rate and stress fluctuation range. For example, the mean and standard deviation of the stress change per second are calculated, and the stress change trend and fluctuation over time are analyzed to identify the second-level drift characteristics caused by material creep.

[0093] Hourly drift data acquisition involves integrating a high-precision resistance measurement module into the sensor circuit and measuring the contact resistance at the interface between the silicon chip and the ceramic substrate every hour. Molecular diffusion causes the contact resistance to slowly increase over time, and regular contact resistance measurements provide hourly drift information. This hourly contact resistance data is processed to calculate characteristics such as the growth rate and growth trend of the contact resistance. For example, by calculating the difference between two consecutive measurements, the temporal variation of the contact resistance can be analyzed to determine the extent of the impact of molecular diffusion on the contact resistance.

[0094] Monthly drift data collection involves installing stress sensors on the sensor substrate and measuring the substrate stress regularly each month. Grain boundary migration causes cumulative changes in substrate stress over time. Regularly measuring substrate stress can help identify monthly drift trends. Based on the monthly substrate stress data, we analyze the cumulative stress trends and calculate characteristics such as monthly stress growth and stress growth rate. By plotting stress versus time, we can visually observe substrate stress changes caused by grain boundary migration.

[0095] S1222, integrating multi-scale drift data to form a drift dataset, so that the model can learn the correlation and change rules between drifts at different time scales.

[0096] S1223: Input the multi-scale drift data collected in real time into the cross-scale drift fusion prediction model to obtain the prediction result of the sensor drift in the future time window.

[0097] S1224: Develop a corresponding dynamic compensation strategy based on the prediction results.

[0098] Specifically, for drift in the second range, real-time adjustment of the sensor's internal stress balance device (such as a micro piezoelectric ceramic) compensates for stress changes caused by material creep. For drift in the hour range, when the contact resistance drift exceeds a certain range, a contact resistance repair mechanism is activated, such as applying a small electrical pulse or heat pulse to improve contact performance. For drift in the month range, the sensor is calibrated or the installation method is adjusted in advance based on the predicted increase in substrate stress to minimize the impact of grain boundary migration on sensor performance. At the same time, after implementing the compensation strategy, the sensor performance needs to be re-evaluated. If the expected effect is not achieved, the compensation strategy needs to be adjusted and optimized.

[0099] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: By precisely collecting drift data from the sensor at multiple scales and extracting features, the system integrates this data to build a model for cross-scale drift fusion prediction, and formulates a targeted dynamic compensation strategy based on the prediction results. This solution comprehensively and accurately monitors and predicts sensor drift at different time scales, dynamically compensating for each type of drift in a timely manner. This effectively addresses the inability of traditional solutions to handle multi-timescale coupling effects, significantly improving the sensor's performance stability and accuracy under long-term temperature cycling, and ensuring its long-term reliable operation.

[0100] Furthermore, an embodiment of the present invention also provides a large-range multi-component sensor calibration device.

[0101] Figure 2 The present invention is a schematic structural diagram of a large-range multi-component sensor calibration device according to an embodiment of the present invention.

[0102] like Figure 2 As shown, a large-range multi-component sensor calibration device includes: a data acquisition and processing module, a dynamic temperature control module, a resistance measurement module, a simulation and recording module, and an error calculation and analysis module.

[0103] The data acquisition and processing module checks the various components of the large-scale multi-component sensor calibration equipment, and sets the target temperature range and compensation model for each temperature interval in the calibration equipment control system; Dynamic temperature control module: The calibration equipment starts dynamic temperature control and monitors fixture deformation in real time. It applies transient temperature gradient suppression technology, performs multi-physical quantity calibration, and outputs compensated measurement values. Resistance measurement module, real-time monitoring of the contact resistance at the interface between silicon-based chip and ceramic substrate; The simulation and recording module sets multi-environment coupling calibration test conditions to simulate the actual working environment, and monitors and records the calibration data of various physical quantities in different environments in real time; The error calculation and analysis module combines the calibration results with the reference standard values ​​to calculate the calibration errors of various physical quantities.

[0104] It should be noted that other specific implementation contents of the large-range multi-component sensor calibration device according to the embodiment of the present invention may refer to the above-mentioned large-range multi-component sensor calibration method.

[0105] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0106] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0110] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0111] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for calibrating a large-scale multi-component sensor, characterized in that: The method comprises: S1, check the various components of the calibration equipment for a large range and multi-component sensor, and set the target temperature range and the compensation model for each temperature range in the calibration equipment control system; S2: The calibration equipment starts dynamic temperature control and monitors the fixture deformation in real time. It applies transient temperature gradient suppression technology, performs multi-physical quantity calibration, and outputs compensated measurement values. S3, setting multi-environment coupling calibration test conditions to simulate the actual working environment, real-time monitoring and recording the calibration data of various physical quantities in different environments; S4, combining the calibration results with the reference standard values ​​to calculate the calibration errors of various physical quantities.

2. A method for calibrating a large-range multi-component sensor according to claim 1, characterized in that: Said S2 comprises: 2A, the calibration equipment starts the dynamic temperature control module, which controls the operation of the thermoelectric cooler according to the set target temperature range and temperature change rate limit; 2B, the calibration equipment activates the temperature-displacement sensor array to monitor the deformation of the fixture at each temperature point in real time and transmits the deformation data to the calibration equipment control system; At 2C, the micro-heat spreader film encapsulated on the sensor surface starts working, and the calibration equipment control system dynamically adjusts the temperature control strategy according to the temperature change rate limit; 2D, real-time collection of sensor output values, current temperature values, and fixture deformation data; 2E, calculates and corrects the temperature drift error and deformation error of each physical quantity based on the set compensation model and deformation data; 2F, the calibration device outputs the compensated measurement values ​​of each physical quantity and records them in the calibration device control system.

3. A method for calibrating a large-range multi-component sensor according to claim 2, characterized in that: The real-time monitoring of the deformation of the fixture at each temperature point includes: The calibration equipment control system sends a start command to the temperature-displacement sensor array, and each sensor in the sensor array begins to work. During the temperature adjustment process, the control system monitors the temperature sensor data in real time. When the temperature fluctuates within a certain range of the target temperature and lasts for n minutes, the temperature is considered stable. After the temperature stabilizes, the control system reads the deformation data of each sensor in the temperature-displacement sensor array in sequence according to the set sampling frequency.

4. A method for calibrating a large-range multi-component sensor according to claim 1, characterized in that: Said S1 comprises: S11, set the temperature cycle curve and the number of cycles; S12, collecting and analyzing the temperature rise and fall output data of the sensor during the temperature cycle, establishing a segmented compensation model and performing real-time compensation; S13, monitoring and recording the zero reference value of each temperature cycle low temperature constant temperature stage, establishing a zero drift correction model and applying the correction; S14, after checking and setting the calibration equipment parameters, start the temperature cycle calibration and collect data, output and record the calibration results after real-time compensation and correction.

5. A method for calibrating a large-range multi-component sensor according to claim 4, characterized in that: S11, set the temperature cycle curve and cycle times, including: In the calibration equipment control system, a complete temperature cycle curve is set, including heating, constant temperature, cooling, and low-temperature constant temperature stages. The temperature range is determined according to the sensor tolerance range and calibration requirements, and the specific duration of each stage is also specified.

6. A method for calibrating a large-range multi-component sensor according to claim 4, characterized in that: The sensor temperature rise and temperature drop output data includes: Multiple micro stress sensors are arranged inside the sensor to collect and transmit stress data to the control system in real time. An early warning threshold is set and an early warning is issued when the threshold is exceeded. A stress-temperature relationship model is established through experiments, and the control system dynamically adjusts the temperature change rate and constant temperature time based on the model and real-time stress data. The contact resistance at the interface between the silicon-based chip and the ceramic substrate is monitored in real time, and a compensation model is established for real-time compensation. When the resistance drift exceeds the range, an electric pulse or thermal pulse repair mechanism is activated. The signal jump is monitored in real time, and when the signal jump amplitude exceeds the set threshold, the parameters of the filtering algorithm are dynamically adjusted.

7. A method for calibrating a large-range multi-component sensor according to claim 6, characterized in that: The dynamic adjustment of the temperature change rate and the constant temperature time includes: By experimentally measuring the stress values ​​at the interface between the silicon-based chip and the ceramic substrate at different temperatures, a stress-temperature relationship model is established; based on the stress-temperature relationship model and the real-time monitored interface stress data, the control system of the calibration equipment dynamically adjusts the temperature cycle parameters.

8. A method for calibrating a large-range multi-component sensor according to claim 6, characterized in that: The dynamic adjustment of the temperature change rate and the constant temperature time includes: The sensor's internal drift data is collected at multiple scales and data features are extracted. The multi-scale drift data is integrated to form a drift dataset, enabling the model to learn the correlation and change patterns between drifts at different time scales. The multi-scale drift data collected in real time is input into the cross-scale drift fusion prediction model to obtain the prediction results of sensor drift in the future time window. Based on the prediction results, a corresponding dynamic compensation strategy is formulated.

9. A method for calibrating a large-range multi-component sensor according to claim 8, characterized in that: The multi-scale acquisition includes: Second-level drift data collection, hour-level drift data collection, and monthly-level drift data collection.

10. A large-range multi-component sensor calibration device, applied to a large-range multi-component sensor calibration method according to any one of claims 1 to 9, characterized in that: The fixtures include The data acquisition and processing module checks the various components of the large-scale multi-component sensor calibration equipment, and sets the target temperature range and compensation model for each temperature interval in the calibration equipment control system; Dynamic temperature control module: The calibration equipment starts dynamic temperature control and monitors fixture deformation in real time. It applies transient temperature gradient suppression technology, performs multi-physical quantity calibration, and outputs compensated measurement values. Resistance measurement module, real-time monitoring of the contact resistance at the interface between silicon-based chip and ceramic substrate; The simulation and recording module sets multi-environment coupling calibration test conditions to simulate the actual working environment, and monitors and records the calibration data of various physical quantities in different environments in real time; The error calculation and analysis module combines the calibration results with the reference standard values ​​to calculate the calibration errors of various physical quantities.

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