A data testing method for collecting sensors

Abstract

The present invention discloses a data testing method for a sensor, which relates to the technical field of sensor data testing, and comprises the following steps: adjusting the baseline offset, the adjusted sensitivity and the comprehensive performance index under the output time t of the test module. The present invention has the ability to comprehensively consider the influence of environmental variables such as temperature and humidity and the nonlinear characteristics of the sensor itself, realizes the accurate calibration and dynamic adjustment of the sensor output, significantly improves the calibration accuracy and adaptability, and the present invention can comprehensively and systematically evaluate multiple key indicators such as the sensitivity, linearity, and noise level of the sensor, providing a scientific basis for the performance optimization of the sensor, and the test method of the present invention is not limited to the measurement of specific variables, and can be applied to various types of sensors and different application scenarios, significantly enhancing the versatility and flexibility of the sensor, thereby realizing the comprehensive and accurate testing and evaluation of the sensor performance.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor data testing, and in particular to a data testing method for an acquisition sensor. Background Art

[0002] With the rapid development of science and technology, the application fields of data acquisition sensors have also greatly expanded. Data acquisition sensors are devices that collect data, such as temperature and humidity variables, to achieve the purpose of monitoring. The performance of data acquisition sensors needs to be tested before they leave the factory to ensure the quality of the products after they leave the factory.

[0003] However, the current data testing methods for sensor acquisition may only calibrate a single variable, ignoring the interaction between different environmental variables and the impact of the sensor's own nonlinear characteristics on the measurement results. This leads to inaccurate calibration results and makes it difficult to meet the requirements of high-precision measurements. In addition, the existing testing methods may lack a comprehensive and systematic comprehensive performance evaluation system and may not be able to comprehensively evaluate multiple key indicators such as the sensor's sensitivity, linearity, and noise level, thereby failing to fully reflect the sensor's overall performance. Summary of the Invention

[0004] The purpose of the present invention is to provide a data testing method for collecting sensors, which solves the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a data testing method for collecting sensor data, comprising the following testing steps:

[0006] Step I: First, install the acquisition sensor on the test bench, then adjust the temperature and humidity of the test environment through the environmental control equipment, and connect the acquisition sensor to the data acquisition module, which collects the test output value of the sensor;

[0007] Step II: The processing module cleans the sensor's test output value and outputs a basic baseline offset;

[0008] Step III: inputting the basic baseline offset into the test module, and outputting the adjusted baseline offset, adjusted sensitivity and comprehensive performance index at time t;

[0009] Step IV: Based on the analysis module, the adjusted baseline offset, adjusted sensitivity, and comprehensive performance index at time t are determined to judge the factory performance of the acquisition sensor, and a test report is compiled. The test results are then fed back to the production department, and unqualified acquisition sensors are repaired or eliminated.

[0010] Optionally, the test module includes: a calibration submodule, a sensitivity submodule, a performance evaluation submodule and an adjustment submodule.

[0011] Optionally, the calculation formula of the calibration submodule is as follows:

[0012]

[0013] in:

[0014] ADC t represents the adjusted baseline offset at time t;

[0015] BAOF stands for basic baseline offset;

[0016] TU represents the current temperature value, TU r Indicates the reference temperature value;

[0017] HO indicates the current humidity value, HO r Indicates the reference humidity value;

[0018] N represents the total number of times, SP ti Indicates the sensor reading at the i-th measurement, RED ti represents the reference value of the sensor reading at the i-th measurement;

[0019] A1, A2 and A3 represent TU, HO and SP respectively ti -RED ti The influence coefficients of 1, 2 and 3;

[0020] A1×(TU-TU r ) represents the effect of temperature difference on the offset, A2×(HO-HO r ) represents the effect of humidity difference on the offset;

[0021] The processing process of the calibration submodule is as follows: the basic baseline offset BAOF is input to the calibration submodule, and based on the current temperature value TU and the current humidity value HO collected by the environmental control device, the adjusted baseline offset ADC under time t is output t .

[0022] Optionally, the calculation formula of the sensitivity submodule is as follows:

[0023] ADSY=SVIT / 1+B1×ADC t +B2×NOEVE+B3×TECT;

[0024] in:

[0025] ADSY represents adjusted sensitivity;

[0026] SVIT represents the sensitivity before adjustment, NOEVE represents the sensor noise level, and TECT represents the parameter that indicates the effect of temperature on sensor performance.

[0027] B1, B2 and B3 represent ADC t , NOEVE and TECT adjustment coefficients, which are used to control the influence of different factors on sensitivity;

[0028] B2×NOEVE represents the influence of noise level on sensitivity, and B3×TECT represents the influence of temperature effect on sensitivity;

[0029] The processing process of the sensitivity submodule is as follows: the adjusted baseline offset ADC at time t t It is input to the sensitivity submodule and outputs the adjusted sensitivity ADSY based on the sensitivity SVIT before adjustment, the sensor noise level NOEVE and the parameter TECT that represents the effect of temperature on sensor performance.

[0030] Optionally, the calculation formula of the performance evaluation submodule is as follows:

[0031]

[0032] in:

[0033] PFDX stands for Comprehensive Performance Index;

[0034] LIER represents the adjusted linearity error, NOFR represents the coefficient for adjusting the influence of noise level on performance index, e represents a constant, TDN represents the deviation between actual temperature and reference temperature, TEM represents the threshold value or standard deviation of temperature deviation, and NOEVE×NOFR represents the contribution of noise level to performance index.

[0035] The processing process of the evaluation submodule is as follows: the adjusted sensitivity ADSY and the adjusted baseline offset ADC at time t are t They are all input to the performance evaluation submodule, and the comprehensive performance index PFDX is output based on the adjusted linearity error LIER and the deviation TDN between the actual temperature and the reference temperature.

[0036] Optionally, the processing process of the adjustment submodule is as follows:

[0037] S1: BAOF new =BAOF+α×|PFDQ-PFDX|;

[0038] S2: Establish iteration termination constraints;

[0039] Constraint 1: |PFDX new-PFDX old |<γ; Constraint 2: The number of iterations is 65;

[0040] When any of the conditions 1 and 2 are met, the iteration is terminated immediately;

[0041] in:

[0042] BAOF new Indicates the basic baseline offset after adjustment;

[0043] α represents the learning rate, γ = 0.02, PFDQ represents the target performance index, PFDX new -PFDX old It represents the difference between the comprehensive performance index PFDX after iteration and the previous comprehensive performance index PFDX;

[0044] Based on the calculation of the comprehensive performance index PFDX and the target performance index PFDQ, the change of the basic baseline offset BAOF is adjusted by the learning rate α, and the adjusted basic baseline offset BAOF is output. new , the adjusted basic baseline offset BAOF new Replace the basic baseline offset BAOF in the calibration submodule to continuously optimize the ADC t , ADSY and PFDX, and the purpose of iterative termination convergence is achieved through constraints one and two.

[0045] Optionally, the acquisition module specifically uses a data collector to connect to the acquisition sensor to receive the test output value output by the acquisition sensor in real time, and requires the acquisition sensor to output a signal in a no-input state, where the no-input state is a state in which the sensor is not affected by any external signal.

[0046] Optionally, the processing module specifically checks the test output values, eliminates abnormal values ​​and noise interference, and then calculates the average value of the test output values ​​of the collected sensors, which is the basic baseline offset.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The present invention outputs the adjusted baseline offset ADC at time t through the calibration submodule t , ADC tThe calculation takes into account dynamic changes in temperature and humidity, and can achieve accurate calibration of sensor readings. This calibration method can significantly improve the measurement accuracy of the sensor under different environmental conditions, and can automatically calculate and adjust the offset in the data test before leaving the factory, without the need for human intervention, thereby improving test efficiency and real-time performance, thereby reducing measurement errors caused by environmental factors, improving data consistency and reliability, and providing more accurate basic data for subsequent data processing and analysis.

[0049] 2. The present invention outputs the adjusted sensitivity through the sensitivity submodule. The calculation of the adjusted sensitivity not only takes into account the sensitivity of the sensor itself but also introduces the influence of external factors such as noise level and temperature effect, thereby achieving a comprehensive evaluation of the sensor performance. Accurate sensitivity evaluation helps to timely discover and correct sensor performance deviations during data testing, thereby improving measurement accuracy. The sensitivity calculation of this submodule enhances the detection capability of the acquisition sensor for weak signals, improves the measurement sensitivity and resolution, and thus provides an important basis for test fault diagnosis and performance optimization of the acquisition sensor.

[0050] 3. The present invention outputs a comprehensive performance index through the performance evaluation submodule. The calculation of the comprehensive performance index PFDX can integrate multiple factors such as offset, linearity error, noise level and sensitivity to form a comprehensive performance evaluation indicator. The performance index obtained by calculation can intuitively quantify the overall performance level of the sensor for easy comparison and evaluation. The level of the comprehensive performance index can guide the optimization direction during the sensor design, manufacturing and testing process. The calculation of the comprehensive performance index provides a more comprehensive and objective evaluation standard for pre-shipment data testing and safety testing, which facilitates the screening of sensor products with excellent performance and improves market competitiveness.

[0051] 4. The comprehensive performance index of the present invention iterates the basic baseline offset in the calibration submodule in a loop, which can significantly improve the accuracy of data acquisition, and realizes automation in the iterative process, reducing the need for manual intervention. It not only improves the test efficiency, but also the system can gradually learn the optimal calibration parameters as the number of iterations increases, showing certain intelligent characteristics. The performance of different acquisition sensors in different environments may be different. The iterative calibration method driven by the comprehensive performance index can automatically adapt to these changes to ensure that accurate data can be obtained under different conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the steps of the data testing method for collecting sensors;

[0053] Figure 2 This is a schematic diagram of the overall structure of the data testing method for collecting sensors;

[0054] Figure 3 This is a structural diagram of the test module of the data testing method for collecting sensors. DETAILED DESCRIPTION

[0055] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] The data testing method of this acquisition sensor is different from existing data testing methods for acquisition sensors. Existing data testing methods mostly test and calibrate a single variable, ignoring the interaction between different environmental variables and the impact of the acquisition sensor's own nonlinear characteristics on the measurement results. This results in inaccurate calibration results and makes it difficult to meet the requirements of high-precision measurement. Existing testing methods often lack a comprehensive and systematic comprehensive performance evaluation system and are unable to comprehensively evaluate multiple key indicators such as sensor sensitivity, linearity, and noise level, thereby failing to fully reflect the overall performance of the sensor.

[0057] The data testing method of the acquisition sensor comprehensively considers the influence of environmental variables such as temperature and humidity and the nonlinear characteristics of the sensor itself, realizes the precise calibration and dynamic adjustment of the acquisition sensor output, and significantly improves the calibration accuracy and adaptability. The present invention can conduct a comprehensive and systematic comprehensive evaluation of multiple key indicators such as the sensor's sensitivity, linearity, and noise level, providing a scientific basis for the performance optimization of the sensor. The testing method of the present invention is not limited to the measurement of specific variables, and can be applied to various types of sensors and different application scenarios, significantly enhancing the versatility and flexibility of the sensor.

[0058] Example 1: Please refer to Figures 1 to 3 , this embodiment provides a data collection sensor testing method, including the following testing steps:

[0059] Step I: First, install the acquisition sensor on the test bench, then adjust the temperature and humidity of the test environment through the environmental control equipment, and connect the acquisition sensor to the data acquisition module, which collects the test output value of the sensor;

[0060] Step II: The processing module cleans the sensor's test output value and outputs a basic baseline offset;

[0061] Step III: inputting the basic baseline offset into the test module, and outputting the adjusted baseline offset, adjusted sensitivity and comprehensive performance index at time t;

[0062] Step IV: Based on the analysis module, the adjusted baseline offset, adjusted sensitivity, and comprehensive performance index at time t are determined to judge the factory performance of the acquisition sensor, and a test report is compiled. The test results are then fed back to the production department, and unqualified acquisition sensors are repaired or eliminated;

[0063] The test module includes: a calibration submodule, a sensitivity submodule, a performance evaluation submodule and an adjustment submodule.

[0064] In this embodiment, multiple submodules are combined and applied to a sensor data testing method, especially in pre-shipment data testing and safety testing. This method is not dedicated to the measurement of specific variables, and its advantages and effects are reflected in multiple aspects. The multiple submodules in the test module together constitute a comprehensive and integrated sensor performance evaluation system. Baseline calibration ensures measurement accuracy, sensitivity evaluation focuses on the core performance indicators of the sensor, and comprehensive performance evaluation takes these indicators and factors such as noise level into consideration to form a comprehensive performance portrait. Sensitivity evaluation can ensure that the sensor maintains high accuracy and reliability during the measurement process, which is crucial for application scenarios requiring high-precision measurement, such as medical and industrial control. At the same time, comprehensive performance evaluation further verifies the overall performance level of the sensor and improves the reliability of the test. The test module can realize automated testing processes, reduce manual intervention and testing time. In pre-shipment data testing and safety testing, automated testing can significantly improve test efficiency, reduce test costs, and ensure the consistency and repeatability of test results. Potential problems or deficiencies in sensor performance can be discovered in a timely manner through the calculation of the comprehensive performance index, providing a basis for fault warning and performance optimization, which helps to make necessary preparations before the sensor is put on the market. The improvement and optimization of the sensor performance can improve the overall quality and market competitiveness of the product. Through comprehensive performance evaluation and calibration, the quality of the data collected by the sensor can be significantly improved, including reducing measurement errors, improving data consistency, and reducing noise interference, providing more reliable basic data for subsequent data processing and analysis. The comprehensive performance index can serve as an important reference for the credibility of sensor data. During the data analysis and application process, the sensor data can be weighted or screened according to the evaluation results to improve the accuracy and reliability of the data analysis and application results. This method comprehensively considers the impact of multiple performance indicators and environmental factors on sensor performance, providing more comprehensive and accurate performance evaluation results. Due to its dynamic adjustment capability, this method can better adapt to the testing requirements under different environmental conditions, which makes it more flexible and adaptable in sensor testing that is not dedicated to specific variables. The automated test process reduces manual intervention and test time, improving test efficiency. At the same time, the comprehensive performance evaluation results can quickly feedback the sensor performance status, providing timely information support for subsequent improvement and optimization. Through comprehensive performance evaluation and calibration, this method can significantly improve the reliability and credibility of sensor data, which is particularly important for application scenarios requiring high-precision and reliable measurement.

[0065] See also Figures 1 to 3 , the processing process of the calibration submodule is as follows:

[0066]

[0067] in:

[0068] ADC t The adjusted baseline offset at time t is the adjustment value for the difference between the collected sensor reading and the actual value.

[0069] BAOF stands for basic baseline offset, which is the initial offset of the sensor under reference conditions;

[0070] TU represents the current temperature value, TU r Indicates the reference temperature value;

[0071] HO indicates the current humidity value, HO r Indicates the reference humidity value;

[0072] N represents the total number of times, SP ti Indicates the sensor reading at the i-th measurement, RED ti represents the reference value of the sensor reading at the i-th measurement;

[0073] A1, A2 and A3 represent TU, HO and SP respectively ti -RED ti The influence coefficients of 1, 2 and 3;

[0074] A1×(TU-TU r ) represents the effect of temperature difference on the offset, A2×(HO-HO r ) represents the effect of humidity difference on the offset;

[0075] The processing process of the calibration submodule is as follows: the basic baseline offset BAOF is input to the calibration submodule, and based on the current temperature value TU and the current humidity value HO collected by the environmental control device, the adjusted baseline offset ADC under time t is output t .

[0076] In this embodiment: Why should there be an adjusted baseline offset ADC under time t t The calculation of the sensor is carried out before it leaves the factory for baseline calibration, mainly to ensure that the sensor can output accurate and stable readings under standard or preset environmental conditions, such as specific temperature and humidity. This is because during the manufacturing process, due to the influence of various factors such as materials, processes, and assembly, the performance of each sensor will have certain differences. Through baseline calibration, these differences can be eliminated or reduced, so that sensors of the same batch have similar performance under the same conditions. In addition, the baseline calibration before leaving the factory can provide a relatively accurate starting point for subsequent use, which is convenient for users to perform further calibration and adjustment during use, and ADC tIt provides a relatively accurate performance benchmark for the acquisition sensor, making it easier for users to evaluate and compare performance during use. Secondly, through pre-factory measurement and calibration, problems that may exist in the sensor manufacturing process can be discovered and corrected in a timely manner, improving product quality and customer satisfaction. Finally, pre-factory measurement and calibration is also an important part of product quality control for sensor manufacturers, helping to enhance the company's brand image and market competitiveness.

[0077] ADC t By considering dynamic changes such as temperature and humidity, accurate calibration of sensor readings is achieved. This calibration method can significantly improve the measurement accuracy of the sensor under different environmental conditions. In the data test before delivery, this submodule can automatically calculate and adjust the offset without manual intervention, which improves the test efficiency and real-time performance, reduces the measurement error caused by environmental factors, improves the consistency and reliability of the data, and provides more accurate basic data for subsequent data processing and analysis. t It is the basis for ensuring the accuracy of sensor readings.

[0078] See also Figures 1 to 3 , the processing process of the sensitivity submodule is as follows:

[0079] ADSY=SVIT / 1+B1×ADC t +B2×NOEVE+B3×TECT;

[0080] in:

[0081] ADSY represents adjusted sensitivity;

[0082] SVIT represents the sensitivity before adjustment;

[0083] In order to reasonably evaluate the sensitivity and linearity of the acquisition sensor, we can use the least squares method to fit a straight line and calculate the residual. First, based on the adjusted baseline offset ADC at time t t The slope and intercept are obtained by linear fitting of the values, which are calculated by statistical software, which is the existing technology for processing data. Then the sensitivity SVIT before adjustment is calculated;

[0084] NOEVE represents the sensor noise level, and TECT represents the parameter of the effect of temperature on sensor performance;

[0085] B1, B2 and B3 represent ADC t , NOEVE and TECT adjustment coefficients, which are used to control the influence of different factors on sensitivity;

[0086] B2×NOEVE represents the influence of noise level on sensitivity, and B3×TECT represents the influence of temperature effect on sensitivity;

[0087] The processing process of the sensitivity submodule is as follows: the adjusted baseline offset ADC at time t t It is input to the sensitivity submodule and outputs the adjusted sensitivity ADSY based on the sensitivity SVIT before adjustment, the sensor noise level NOEVE and the parameter TECT that represents the effect of temperature on sensor performance.

[0088] In this embodiment: Sensitivity ADSY not only takes into account the sensitivity of the sensor itself, but also introduces the influence of external factors such as noise level and temperature effect, thereby achieving a comprehensive evaluation of sensor performance. Accurate sensitivity evaluation helps to timely discover and correct sensor performance deviations during data testing, thereby improving measurement accuracy. The calculation of this submodule enhances the detection capability of the acquisition sensor for weak signals, improves the sensitivity and resolution of the measurement, and provides an important basis for test fault diagnosis and performance optimization of the acquisition sensor. Compared with the evaluation method in the prior art that only considers the sensitivity of the sensor itself, this submodule is more comprehensive and accurate, and improves the adaptability and reliability of the sensor in different application scenarios.

[0089] Sensitivity is a key indicator of sensor performance, reflecting its sensitivity to input changes. By evaluating sensitivity, we can ensure that the sensor is sensitive enough to detect weak signals or rapidly changing processes, which is crucial for applications requiring high-precision measurements. Linearity describes the linear relationship between sensor output and input. Good linearity means that the sensor maintains consistent sensitivity throughout its entire measurement range, reducing nonlinear errors in the measurement results. This is crucial for applications requiring accurate measurement of a wide range of input changes. Sensitivity evaluation can identify deficiencies in sensor performance and enable optimization accordingly. For example, by adjusting the sensor structure, improving the manufacturing process, or adopting more advanced signal processing algorithms, we can improve the sensor's sensitivity and linearity.

[0090] See also Figures 1 to 3 ,The processing process of the performance evaluation submodule is as follows:

[0091]

[0092] in:

[0093] PFDX stands for Comprehensive Performance Index. PFDX combines multiple factors such as offset, linearity error, noise level, and sensitivity to comprehensively evaluate sensor performance.

[0094] LIER stands for adjusted linearity error. LIER is the linearity error calculated using the adjusted sensor readings.

[0095] NOFR represents the coefficient that adjusts the effect of noise level on the performance index;

[0096] e represents a constant which is also the base of the exponential function, and its value is approximately equal to 2.718281828459045...,

[0097] This is common technical knowledge in this field and other fields;

[0098] TDN represents the deviation between the actual temperature and the reference temperature;

[0099] TEM represents the threshold or standard deviation of temperature deviation, which is used to control the degree of influence of temperature on performance index;

[0100] NOEVE×NOFR indicates the contribution of noise level to the performance index;

[0101] The processing process of the evaluation submodule is as follows: the adjusted sensitivity ADSY and the adjusted baseline offset ADC at time t are t They are all input to the performance evaluation submodule, and the comprehensive performance index PFDX is output based on the adjusted linearity error LIER and the deviation TDN between the actual temperature and the reference temperature.

[0102] In this embodiment, PFDX combines multiple factors, including offset, linearity error, noise level, and sensitivity, to form a comprehensive performance evaluation index. The calculated performance index can intuitively quantify the overall performance level of the sensor, facilitating comparison and evaluation. The level of PFDX can guide optimization during sensor design, manufacturing, and testing, improving overall product quality. PFDX calculation also provides a more comprehensive and objective evaluation standard for pre-shipment data testing and safety testing, helping to screen out high-performance sensor products and enhance market competitiveness. Compared to single performance evaluation indicators, such as those that only consider offset or sensitivity, this submodule is more comprehensive and objective, improving the accuracy and reliability of data testing and reducing risks caused by inadequate performance evaluation.

[0103] Comprehensive performance evaluation results provide an important basis for product screening and optimization. By comparing the performance evaluation results of different sensors, you can select sensor products with better performance. At the same time, you can also make targeted optimizations and improvements based on deficiencies identified in the evaluation results. By comprehensively evaluating sensor performance and continuously optimizing product performance, you can enhance your product's market competitiveness. In the fiercely competitive market, sensor products with excellent performance and high reliability are more likely to gain user recognition and trust.

[0104] See also Figures 1 to 3 , the processing process of the regulation submodule is as follows:

[0105] S1: BAOF new =BAOF+α×|PFDQ-PFDX|;

[0106] S2: Establish iteration termination constraints;

[0107] Constraint 1: |PFDX new -PFDX old |<γ; Constraint 2: The number of iterations is 65;

[0108] When any of the conditions 1 and 2 are met, the iteration is terminated immediately;

[0109] in:

[0110] BAOF new Indicates the basic baseline offset after adjustment;

[0111] α represents the learning rate, γ = 0.02, PFDQ represents the target performance index, PFDX new -PFDX old It represents the difference between the comprehensive performance index PFDX after iteration and the previous comprehensive performance index PFDX;

[0112] Based on the calculation of the comprehensive performance index PFDX and the target performance index PFDQ, the change of the basic baseline offset BAOF is adjusted by the learning rate α, and the adjusted basic baseline offset BAOF is output. new , the adjusted basic baseline offset BAOF new Replace the basic baseline offset BAOF in the calibration submodule to continuously optimize the ADC t , ADSY and PFDX, and the purpose of iterative termination convergence is achieved through constraints one and two.

[0113] In this embodiment, iterative adjustment of the BAOF through PFDX can achieve fine calibration of the sensor output data. The comprehensive performance index PFDX, as a performance evaluation indicator, can directly reflect the quality of the current calibration status, thereby guiding the adjustment direction of the BAOF. This iterative method based on performance feedback can significantly improve the accuracy of data acquisition compared to fixed or manual adjustment of the BAOF. It also achieves automation in the iterative process, reducing the need for manual intervention. This not only improves test efficiency but also reduces the possibility of human error. As the number of iterations increases, the system can gradually learn the optimal calibration parameters, demonstrating certain intelligent characteristics. Different acquisition sensors may have different performance in different environments. Through the PFDX-driven iterative calibration method, the system can automatically adapt to these changes, ensuring accurate data under different conditions. This adaptability is difficult to match with existing fixed calibration methods. The reliability and consistency of the data after PFDX iterative calibration are significantly improved, which is particularly important for application scenarios requiring high-precision data support.

[0114] Existing technologies often use methods that fix or manually adjust calibration parameters. This method is not only inefficient but also difficult to adapt to changes in sensor performance and the influence of the external environment. In addition, manual adjustment is prone to introduce human errors, affecting the accuracy of the data. This method, by introducing PFDX as a performance evaluation indicator, realizes automatic iterative adjustment of calibration parameters. This method not only improves the accuracy and efficiency of calibration, but also enhances the adaptability and intelligence level of the system. Compared with existing technologies, this method has shown significant advantages in many aspects.

[0115] The basic baseline offset (BAOF) is one of the key factors affecting data accuracy in many sensor calibration scenarios. By adjusting the basic baseline offset (BAOF), the sensor's output zero point can be directly changed, thereby correcting the data offset. In comparison, adjusting other parameters may involve more complex mathematical models and more calculations, and may not necessarily bring the same effect as adjusting the basic baseline offset (BAOF). There is a direct correlation between the adjustment of the basic baseline offset (BAOF) and the output comprehensive performance index (PFDX). As a performance evaluation indicator, the comprehensive performance index (PFDX) can reflect the performance of the sensor in the current calibration state in real time. When the PFDX value deviates from the target range, adjusting the BAOF can quickly improve the performance and bring the PFDX value closer to the target range. This correlation makes iterative adjustment of BAOF through PFDX an efficient and effective calibration method.

[0116] In the specific implementation process, the data test system is composed of multiple submodules in this method. The basic baseline offset BAOF is input to the calibration submodule, and based on the current temperature value TU and the current humidity value HO collected by the environmental control device, the adjusted baseline offset ADC under time t is output. t , ADC t The calculation takes into account the dynamic changes of temperature and humidity, thereby achieving accurate calibration of sensor readings. This calibration method can significantly improve the measurement accuracy of the sensor under different environmental conditions. In the data test before delivery, this submodule can automatically calculate and adjust the offset without manual intervention, which improves the test efficiency and real-time performance, reduces the measurement error caused by environmental factors, and thus improves the consistency and reliability of the data, providing more accurate basic data for subsequent data processing and analysis. t It is the basis for ensuring the accuracy of sensor readings;

[0117] The adjusted baseline offset ADC at time t t Input to the sensitivity submodule, and based on the unadjusted sensitivity SVIT, the sensor noise level NOEVE and the parameter TECT of the temperature effect on the sensor performance, output the adjusted sensitivity ADSY. The calculation of the adjusted sensitivity ADSY not only considers the sensitivity of the sensor itself but also introduces the influence of external factors such as the noise level and temperature effect, thereby achieving a comprehensive evaluation of the sensor performance. Accurate sensitivity evaluation helps to timely discover and correct sensor performance deviations during data testing, thereby improving measurement accuracy. The calculation of this submodule enhances the detection capability of the acquisition sensor for weak signals, improves the measurement sensitivity and resolution, and thus provides an important basis for test fault diagnosis and performance optimization of the acquisition sensor. Compared with the evaluation method in the existing technology that only considers the sensitivity of the sensor itself, this submodule more comprehensively and accurately improves the adaptability and reliability of the sensor in different application scenarios.

[0118] Set the adjusted sensitivity ADSY and the adjusted baseline offset ADC at time t tAll of these are input into the performance evaluation submodule, and based on the adjusted linearity error LIER and the deviation TDN between the actual temperature and the reference temperature, a comprehensive performance index PFDX is output. The calculation of the comprehensive performance index PFDX combines multiple factors such as offset, linearity error, noise level, and sensitivity to form a comprehensive performance evaluation index. The calculated performance index can intuitively quantify the overall performance level of the sensor, facilitating comparison and evaluation. The level of PFDX can guide the optimization direction during sensor design, manufacturing, and testing, and improve the overall quality of the product. PFDX calculation provides a more comprehensive and objective evaluation standard for pre-shipment data testing and safety testing, helping to screen high-performance sensor products and improve their market competitiveness. Compared with a single performance evaluation index, this submodule more comprehensively and objectively improves the accuracy and reliability of data testing, significantly reducing the risks caused by insufficient performance evaluation.

[0119] Based on the comprehensive performance index PFDX, the basic baseline offset BAOF is iterated cyclically. Through the iterative adjustment of BAOF by PFDX, fine calibration of the sensor output data can be achieved. The comprehensive performance index PFDX, as a performance evaluation indicator, can directly reflect the pros and cons of the current calibration status, thereby guiding the adjustment direction of BAOF. Compared with fixed or manual adjustment of BAOF, this iterative method based on performance feedback can significantly improve the accuracy of data acquisition, and the automation is achieved in the iterative process, reducing the need for human intervention. This not only improves test efficiency, but also as the number of iterations increases, the system can gradually learn the optimal calibration parameters and show certain intelligent characteristics. The performance of different acquisition sensors in different environments may be different. Through the iterative calibration method driven by PFDX, the system can automatically adapt to these changes to ensure that accurate data can be obtained under different conditions.

[0120] This allows the various sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0121] Example 2: Please refer to Figure 1 、 Figure 2 and Figure 3 The acquisition module specifically uses a data collector to connect with the acquisition sensor to receive the test output value output by the acquisition sensor in real time, and requires the acquisition sensor to output a signal in a no-input state. The no-input state is a state in which the sensor is not affected by any external signal. The processing module specifically checks the test output value, eliminates abnormal values ​​and noise interference, and then calculates the average value of the test output value of the acquisition sensor. The average value is the basic baseline offset.

[0122] In this embodiment, the acquisition sensor can be determined according to the specific situation. This method is not dedicated to the measurement of specific variables. Its advantages and effects are reflected in multiple aspects. When the sensor is tested for factory data, it needs to output a signal under no input or zero input conditions. The sensor type depends on the specific application, such as temperature sensor and pressure sensor. The data collector is used to connect with the acquisition sensor and is responsible for real-time acquisition of the raw data output by the sensor. The data collector should have characteristics such as high precision, high stability and low noise. The environmental control equipment is used to adjust the test environment to a standard state to ensure the accuracy and repeatability of data acquisition. At the same time, during data acquisition, the corresponding temperature and humidity need to be collected. Temperature and humidity are important environmental factors that affect sensor output or system performance. A temperature and humidity sensor can be used to receive and record the current temperature and humidity values ​​in real time. The processing module is based on the data processing software installed in the computer, and is used to receive the raw data transmitted by the data collector and perform processing operations such as cleaning and averaging. The processed test output value is stored in the data storage device. The data storage device includes a hard disk and a solid-state drive for storing processed data and intermediate results. The data collector collects the voltage and current values ​​of the acquisition sensor. These data need to maintain integrity and accuracy for subsequent analysis.

[0123] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A data testing method for collecting sensors, characterized in that: The test steps include: Step I: first install the acquisition sensor on the test bench, then adjust the temperature and humidity of the test environment through the environmental control equipment, and connect the acquisition sensor to the data acquisition module, which collects the test output value of the sensor; Step II: Clean the test output value of the sensor through the processing module and output the basic baseline offset; Step III, inputting the basic baseline offset into the test module, and outputting the adjusted baseline offset, adjusted sensitivity and comprehensive performance index at time t; Step IV: Based on the analysis module, the adjusted baseline offset, adjusted sensitivity and comprehensive performance index at time t are determined to judge the factory performance of the acquisition sensor, and the test report is summarized. Then, the test results are fed back to the production department, and unqualified acquisition sensors are repaired and eliminated; The test module includes: a calibration submodule, a sensitivity submodule, a performance evaluation submodule and an adjustment submodule; The calculation formula of the calibration submodule is as follows: ； in: ADC t represents the adjusted baseline offset at time t; BAOF stands for basic baseline offset; TU represents the current temperature value, TU r Indicates the reference temperature value; HO indicates the current humidity value, HO r Indicates the reference humidity value; N represents the total number of times, SP ti Indicates the sensor reading at the i-th measurement, RED ti represents the reference value of the sensor reading at the i-th measurement; A1, A2 and A3 represent TU, HO and SP respectively ti -RED ti The influence coefficient 1, influence coefficient 2 and influence coefficient 3; A1×(TU-TU r ) represents the effect of temperature difference on the offset, A2×(HO-HO r ) represents the effect of humidity difference on the offset; The processing process of the calibration submodule is as follows: the basic baseline offset BAOF is input to the calibration submodule, and based on the current temperature value TU and the current humidity value HO collected by the environmental control device, the adjusted baseline offset ADC at time t is output t ; The calculation formula of the sensitivity submodule is as follows: ADSY=SVIT / 1+B1×ADC t +B2×NOEVE+B3×TECT; in: ADSY means adjusted sensitivity; SVIT represents the sensitivity before adjustment, NOEVE represents the sensor noise level, and TECT represents the parameter that temperature affects sensor performance; B1, B2 and B3 represent ADC t , NOEVE and TECT adjustment coefficients are used to control the influence of different factors on sensitivity; B2×NOEVE represents the influence of noise level on sensitivity, and B3×TECT represents the influence of temperature effect on sensitivity; The processing process of the sensitivity submodule is as follows: The adjusted baseline offset ADC at time t t Input to the sensitivity submodule, and output the adjusted sensitivity ADSY based on the sensitivity SVIT before adjustment, the sensor noise level NOEVE and the parameter TECT of the effect of temperature on sensor performance; The calculation formula of the performance evaluation submodule is as follows: ； in: PFDX stands for Comprehensive Performance Index; LIER represents the adjusted linearity error, NOFR represents the coefficient of the effect of the adjusted noise level on the performance index, e represents a constant, TDN represents the deviation between the actual temperature and the reference temperature, TEM represents the threshold or standard deviation of the temperature deviation, and NOEVE×NOFR represents the contribution of the noise level to the performance index; The processing process of the evaluation submodule is as follows: Set the adjusted sensitivity ADSY and the adjusted baseline offset ADC at time t t All are input to the performance evaluation submodule, and based on the adjusted linearity error LIER and the deviation TDN between the actual temperature and the reference temperature, a comprehensive performance index PFDX is output; The processing process of the adjustment submodule is as follows: S1:BAOF new =BAOF+α×|PFDQ-PFDX|; S2: Establish iteration termination constraints; Constraint 1: |PFDX new -PFDX old |<γ; Constraint 2: The number of iterations is 65; When any of the conditions 1 and 2 are met, the iteration is terminated immediately; in: BAOF new Indicates the basic baseline offset after adjustment; α represents the learning rate, γ = 0.02, PFDQ represents the target performance index, PFDX new -PFDX old It represents the difference between the comprehensive performance index PFDX after iteration and the previous comprehensive performance index PFDX; Based on the calculation of the comprehensive performance index PFDX and the target performance index PFDQ, the change of the basic baseline offset BAOF is adjusted by the learning rate α, and the adjusted basic baseline offset BAOF is output new , the adjusted basic baseline offset BAOF new Replace the basic baseline offset BAOF in the calibration submodule to continuously optimize the ADC t , ADSY and PFDX, and through constraints one and two, the purpose of iteration termination convergence is achieved.

2. The data testing method of the acquisition sensor according to claim 1, characterized in that: The acquisition module specifically uses a data collector to connect with the acquisition sensor to receive the test output value output by the acquisition sensor in real time, and requires the acquisition sensor to output a signal in a no-input state, which is a state in which the sensor is not affected by any external signal.

3. The data testing method of the acquisition sensor according to claim 1, characterized in that: The processing module specifically checks the test output value, eliminates abnormal values ​​and noise interference, and then calculates the average value of the test output value of the acquisition sensor, and the average value is the basic baseline offset.

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