Parameter measurement method of multi-sensor system based on sensor paper certificate

Abstract

The invention discloses a parameter measurement method of a multi-sensor system based on a sensor paper certificate, and solves the problems that the transcription and storage of a paper calibration certificate of an existing large-scale system are manual operation, the input process is time-consuming and the error rate is high. According to the invention, the original sensor calibration certificate input time can be shortened from 1 hour to less than 10 minutes. The number of workers is reduced from not less than two to one, the labor intensity of post workers is greatly reduced, and the automation level is improved.

Description

Technical Field

[0001] This invention relates to a method for measuring parameters of large industrial systems, specifically a method for measuring parameters of multi-sensor systems based on sensor certificates. Background Technology

[0002] In testing and experimentation of certain large industrial systems, the voltage substitution method is typically used to calibrate the sensors and transmitters. The calibration is based on the paper sensor certificates provided with the system. Therefore, it is necessary to record the high-precision standards, applied physical quantities, and output voltages from the paper sensor certificates into a calibration table. The fundamental reason is that the calibration data provided by the sensor certificates serves as the "starting point" and "golden reference" for this recalibration. Specifically: 1. Establish performance benchmarks and traceability sources; the data on sensor certificates represents the results of calibration performed by the manufacturer in a controlled laboratory environment using equipment traceable to national or international standards. Obtaining this information is equivalent to obtaining the sensor's performance "identity file" at the time of manufacture, ensuring that recalibration work begins with a known, high-confidence benchmark, thereby establishing a complete metrological traceability chain.

[0003] 2. Guidance and Verification of the Recalibration Process: The voltage-physical quantity correspondence on the sensor certificate provides a theoretical basis for planning recalibration points (such as zero point and full-scale point). During recalibration, the data measured on-site can be compared with the original data on the sensor certificate. Any significant deviation can immediately reveal drift in the sensor's own performance or additional errors introduced by the entire measurement system (including cables, data acquisition cards, etc.), thereby achieving accurate fault diagnosis.

[0004] 3. Ensure the completeness and traceability of calibration: Using sensor certificate information as the initial part of the calibration form constitutes indispensable technical evidence. It fully records the evolution of the sensor from its "factory state" to its "current system state." This record is crucial for meeting quality system audit requirements, addressing data challenges, and conducting long-term stability trend analysis, proving that all calibration activities are based on an authoritative initial reference value. In short, obtaining and documenting sensor certificate information is a key prerequisite for elevating an isolated on-site calibration to a traceable, clearly traceable, and reliable scientific metrology process.

[0005] Therefore, acceptance personnel need to manually count the number of paper sensor certificates and certificates of conformity provided with the system, and the paper sensor certificates need to be manually transcribed into the database by the relevant personnel to form digital certificates. Then, the key parameters of the system are measured based on the formed digital certificates. However, the existing technology has the following problems: 1. Depending on the number of test points, staff need to manually input a dozen to a hundred sensor certificates each time and repeatedly perform manual verification. The input process takes about 1.5 to 8 hours, which is time-consuming and has a high error rate.

[0006] 2. Engine certificate data disappears when the system leaves the factory, with no archived data, leading to difficulties in data traceability.

[0007] 3. Paper certificates for sensors may have printing errors or creases, leading to scanning errors. For example, the number "1" at the crease might be identified as "-1", or "-1" might be identified as "1" due to the crease, resulting in incorrect identification results. However, with hundreds of data points on a single paper certificate, manual searching is very difficult and time-consuming. Summary of the Invention

[0008] To address the technical problems of existing large-scale systems where the transcription and storage of paper calibration certificates is done manually, resulting in a time-consuming and error-prone process, difficulties in data traceability due to the lack of data retention and archiving, and inaccurate system parameter measurement results, this invention provides a parameter measurement method for multi-sensor systems based on sensor paper certificates.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: A parameter measurement method for a multi-sensor system based on sensor paper certificates, characterized by the following steps: Step 1: Extract certificate information; The paper certificate of the sensor to be entered is scanned and marked with the current task code. The scan result is used to determine whether a QR code exists. If a QR code exists, it is directly parsed to obtain the certificate information. If no QR code exists, the certificate information is obtained through OCR technology. Step 2: Classification and extraction of certificate information; The certificate information is analyzed and classified according to a preset configuration table. Then, the calibration data is extracted based on the classification results. Step 3: Data verification; The slope K0 of the correlation data is calculated using the least squares method. A slope threshold is set based on the slope K0. If a single calibration data falls within the sensor's range and the fitting slope K1 of the single calibration data falls within the slope threshold range, then the single calibration data is used as the verification data. The correlation data consists of the verification data of N sensors of the same model and batch in the certificate database, where N≥3. Step 4: Create verified certificate information based on all the verification data obtained in Step 3; the verified certificate information includes the regression equation of the corresponding sensor; Step 5: Obtain the output information of the multi-sensor system. Using the regression equation obtained in Step 4, calculate the pressure, flow rate, and / or temperature of each sensor in the output information to complete the parameter measurement of the multi-sensor system.

[0010] Furthermore, step 4 also includes: Generate an information report with preset rules from all verified certificate information, and enter the information report into the certificate database based on the current task code obtained in step 1. Step 5, which involves obtaining the output information of the multi-sensor system, specifically includes: Obtain the standard signal from the information report of the current task code, send the standard signal to the corresponding sensor through the standard source, and obtain the output information of the multi-sensor system.

[0011] Furthermore, step 3 specifically includes: Step 3.1: For a single calibration data point, automatically generate two hypothetical values: Assuming value A1: Calibration data; Assuming value A2: Multiply the calibration data by (-1); Step 3.2: If any assumed value does not conform to the sensor characteristics, directly exclude that assumed value, determine another assumed value as the verification data, and end the data verification; if both assumed values ​​conform to the sensor characteristics, proceed to step 3.3; the sensor characteristics include: the pressure / thrust calibration value is positive, and the absolute value of the error does not exceed the limit of the national military standard; Step 3.3: Calculate the slope K0 of the numerical change in the associated data using the least squares method; set the slope threshold to (-0.05K0, +0.05K0) based on the slope K0 of the associated data; the associated data consists of calibrated data from N sensors of the same model and batch in the certificate database, where N≥3; if the fitting slope K1 of both hypothetical values ​​falls within the slope threshold range, then call the historical verification data of M sensors of the same model in the certificate database, and use the hypothetical value with the highest matching degree as the verification data, where M≥3; if only one hypothetical value's fitting slope K1 falls within the slope threshold range, then use that hypothetical value as the verification data; end the data verification.

[0012] Furthermore, the multi-sensor system is a measurement and acquisition system for rocket engine testing.

[0013] A parameter measurement method for a multi-sensor system based on sensor paper certificates, characterized by the following steps: Step 1: Extract certificate information; The paper certificate of the sensor to be entered is scanned and marked with the current task code. The scan result is used to determine whether a QR code exists. If a QR code exists, it is directly parsed to obtain the certificate information. If no QR code exists, the certificate information is obtained through OCR technology. Step 2: Classification and extraction of certificate information; The certificate information is analyzed and classified according to a preset configuration table. Then, the calibration data and regression coefficient B are extracted based on the classification results. Step 3: Data verification; The fitting slope k2 of the calibration data is calculated using the least squares method. If the difference between the fitting slope k2 and the regression coefficient B is within the threshold range, then step 5 is executed; if the difference between the fitting slope k2 and the regression coefficient B is outside the threshold range, then step 4 is executed. Step 4: Data correction; The corresponding calibration data corresponding to the fitting slope k2 that exceeds the threshold is marked and displayed. The original data is manually found in the corresponding paper certificate to be entered according to the displayed content. The original data is used to correct the extracted calibration data or regression coefficient B, and then the process returns to step 3. Step 5: Generate verified certificate information based on the data calibrated in Step 3; the verified certificate information includes the regression equation of the corresponding sensor; Step 6: Obtain the output information of the multi-sensor system. Using the regression equation obtained in Step 5, calculate the pressure, flow rate, and / or temperature of each sensor in the output information to complete the parameter measurement of the multi-sensor system.

[0014] Furthermore, step 5 also includes: Generate an information report with preset rules from all verified certificate information, and enter the information report into the certificate database based on the current task code obtained in step 1. Step 6, which involves obtaining the output information of the multi-sensor system, specifically includes: Obtain the standard signal from the information report of the current task code, send the standard signal to the corresponding sensor through the standard source, and obtain the output information of the multi-sensor system.

[0015] Furthermore, the multi-sensor system is a measurement and acquisition system for rocket engine testing.

[0016] The beneficial effects of this invention are: 1. This invention provides a parameter measurement method for a multi-sensor system based on sensor paper certificates, which can reduce the original sensor calibration certificate entry time from 1 hour to less than 10 minutes. The number of personnel required is reduced from at least 2 to 1, significantly reducing the workload of staff and improving the level of automation.

[0017] 2. This invention provides a parameter measurement method for a multi-sensor system based on sensor-based paper certificates. It transforms the original manual detection, calibration, and result entry into a scientifically automated detection process. Personnel no longer need to worry about whether the certificate itself has a QR code or perform any manual mode switching, greatly improving the level of automation. This ensures the system can handle any new certificate version and old document version, achieving 100% coverage and completely solving the compatibility problems caused by certificate format iterations.

[0018] 3. The present invention provides a parameter measurement method for a multi-sensor system based on sensor paper certificates, which utilizes the absolute accuracy and high speed of QR codes and relies on OCR technology to ensure comprehensive coverage, thereby maximizing overall efficiency. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention provides a parameter measurement method for a multi-sensor system based on sensor paper certificates. This multi-sensor system is a measurement and acquisition system for rocket engine testing.

[0021] The parameter measurement method includes the following steps: Step 1: Extract certificate information; Scan the paper certificates of the sensors to be entered, mark the current task code, and determine whether there is a QR code in them based on the scanning results; If a QR code exists, it will be directly parsed to obtain the certificate information. The QR code pre-encodes the sensor's core information (such as product number, sensor model, calibration data, etc.), and can be directly parsed. After successful parsing, the system will directly obtain all calibration information. The QR code recognition speed is extremely fast (millisecond level) and the accuracy is 100%.

[0022] If a QR code does not exist, certificate information will be obtained using OCR technology, including: (1) Preprocessing before OCR: a. Grayscale conversion: Convert the scanned certificate into a 16-bit grayscale image (grayscale value 0-65535); b. Target area positioning: Position the "Thrust Calibration Value" column (coordinates x=350-900 pixels, y=420-480 pixels, deviation ≤±1 pixel) by template matching; c. Crease detection: Calculate the gradient value using the improved Sobel operator, set T=80, and mark 2 consecutive regions exceeding the threshold (length 6 and 8 pixels). d. Filtering optimization: 3×3 window filtering is used for non-crease areas, and 7×7 and 9×9 window filtering is used for crease areas respectively. After processing, the gray-scale difference of creases is reduced from 120-140 to 25-30. (2) Preliminary OCR recognition: Call the adapted OCR model to recognize the "thrust calibration value column" and output the preliminary result "-150.0kN"; (3) Post-OCR verification: a. Generation of two hypotheses: Hypothesis A = -150.0 kN, Hypothesis B = 150.0 kN; b. Related data extraction: Retrieve the OCR calibration values ​​of three sensors from the same batch: "149.9kN", "150.0kN", and "150.1kN" from the MES system; c. Slope calculation: Using the least squares method, x=1, 2, 3 (sensor number), y=149.9, 150.0, 150.1, the slope of the associated data is obtained as k=0.1; set the threshold [k_min=-0.005, k_max=0.005]; d. Result screening: Assuming that the fitting slope of A is k_A=-300.1 (exceeding the threshold) and the thrust sensor range is "0-300kN", negative values ​​are not compliant; assuming that the fitting slope of B is k_B=0.025 (within the threshold), the final OCR result is determined to be "150.0kN"; (4) Output the results: Associate “150.0kN” with the sensor serial number “SN2025001” and upload it to the quality management system to complete the OCR process.

[0023] This method can: improve OCR recognition accuracy, reducing the OCR negative sign error rate of engine sensor certificates from the traditional 40%-60% to below 0.1%, completely solving OCR data errors caused by crease interference and ensuring safety; enhance OCR scenario adaptability, optimizing OCR preprocessing and verification logic for special paper and standardized data formats of sensor qualification certificates, achieving an OCR recognition accuracy of 99.8% for units such as "MPa" and "kN", and 100% completeness for high-precision data such as "0.001MPa"; and achieve fully automated OCR processing, with no manual intervention required for the entire process of OCR recognition and crease interference elimination, reducing the processing time per certificate from 15 minutes / certificate to 1.2 seconds / certificate, meeting the urgent requirement of "completing the verification of 200 certificates within 2 hours" before launch.

[0024] OCR text recognition requires three steps: front and back OCR certificate recognition, text recognition, and string cleaning and conversion. 1) OCR Certificate Front and Back Recognition: The system uses a dedicated Python preprocessing script to automatically determine and classify image orientation. This script extracts visual features from the image (such as layout structure, key text labels, and specific icon positions), combines them with a pre-trained classification model to automatically detect orientation and recognize the front and back of the input image, and associates the recognition results with the original image before returning them to the main software system. Based on the returned front and back recognition results, the main software system dynamically calls the corresponding region positioning and recognition configurations.

[0025] (2) Text Recognition: The system inputs each segmented image sub-object into a recognition engine optimized based on a specially trained data set for text recognition, thereby obtaining an initial set of discrete strings. To reconstruct the original layout logic, the OCR engine performs post-processing on the recognition results: it automatically aggregates continuous text information that is close in position in the image to form independent text bounding boxes and intelligently sorts them according to the geometric coordinates of the text blocks, outputting a complete string that conforms to the original document layout logic.

[0026] (3) String cleaning and conversion: After obtaining the original string through the OCR engine, in order to transform it into high-quality numerical data that can be analyzed, the system will perform a series of strict data cleaning, conversion and verification operations to clean the original string and remove noise characters. This mainly includes meaningless empty characters and newline characters generated by layout analysis to ensure the purity of the data and prepare for subsequent type conversion.

[0027] Based on the OCR recognition results, data-specific verification logic can also be constructed to eliminate misjudgments of negative signs caused by creases: (1) Dual Hypothesis Generation: For a single OCR recognition data item, two result hypotheses are automatically generated: A. Assumption A: Retain the negative sign recognized by OCR (e.g., OCR result "-25MPa" → Assumption A "-25MPa"); B. Assumption B: Remove the negative sign recognized by OCR (as shown in the above result → Assumption B "25MPa"); (2) Construction of data slope verification rules: A. Related data extraction: Retrieve the OCR related data of sensors in the same batch from the MES system (such as the OCR calibration values ​​of 3 pressure sensors of the same engine model: "24.9MPa", "25.0MPa", and "25.1MPa"). Utilize the data continuity of the batch production of sensors to provide a benchmark for calibration. B. Slope Calculation: The least squares method is used to calculate the slope k of the numerical change of the associated data (formula: k=Σ(x_i y_i)-nΣx_i Σy_i / Σx_i² -n (Σx_i)², where x is the sensor number, y is the OCR associated data, and n≥3). C. Threshold setting: Based on the stability requirements of engine sensor data, set a strict threshold range [k_min, k_max] - pressure / thrust sensor k_min=-0.005, k_max=0.005; temperature sensor k_min=-0.01, k_max=0.01 (1 / 10 of the threshold for normal scenarios). (3) Optimal result selection: A. Prioritize verification of physical meaning: If assumption A does not conform to the sensor characteristics (e.g., the pressure calibration value is negative, or the absolute value of the error exceeds the GJB limit), it is directly excluded; B. Slope compliance judgment: If the fitted slope k_B of assumption B is within the range of [k_min, k_max] and conforms to the sensor range (such as "0-300kN" for thrust sensor), then it is determined to be the final OCR result; Special scenario handling: If both are compliant (e.g., "-0.005kN" is a negative deviation for compliance), call the certificate database to compare with historical OCR data of the same model sensor. The assumption that the matching degree is ≥95% is the final result.

[0028] Step 2: Classification and extraction of certificate information; Analyze the certificate information above and classify it according to the preset configuration table, and then extract the calibration data based on the classification results; Step 3: Data verification; The slope K0 of the correlated data is calculated using the least squares method. A slope threshold is set based on K0. If a single calibration data point falls within the sensor's range and its fitting slope K1 falls within the slope threshold range, then that single calibration data point is used as verification data. The aforementioned correlated data consists of verification data from N sensors of the same model and batch in the certificate database, where N ≥ 3. Specifically, it includes: Step 3.1: For a single calibration data point, automatically generate two hypothetical values: Assuming value A1: Calibration data; Assuming value A2: Multiply the calibration data by (-1); Step 3.2: If any assumed value does not conform to the sensor characteristics, directly exclude that assumed value, determine another assumed value as the verification data, and end the data verification; if both assumed values ​​conform to the sensor characteristics, proceed to step 3.3; the aforementioned sensor characteristics include: the pressure / thrust calibration value is positive, and the absolute value of the error does not exceed the limit of the national military standard. Step 3.3: Calculate the slope K0 of the numerical change in the correlated data using the least squares method; set the slope threshold to (-0.05K0, +0.05K0) based on the slope K0 of the correlated data; the correlated data mentioned above are the calibrated data of N sensors of the same model and batch in the certificate database, N≥3; if the fitting slope K1 of both hypothetical values ​​falls within the slope threshold range, then call the historical verification data of M sensors of the same model in the certificate database, and use the hypothetical value with the highest matching degree as the verification data, M≥3; if only one hypothetical value's fitting slope K1 falls within the slope threshold range, then use that hypothetical value as the verification data; end the data verification.

[0029] The advantage of slope-based data verification is that it breaks through the limitations of traditional OCR systems that only focus on "text recognition" and introduces intelligent data verification functions based on physical meaning. Specifically, it is manifested in the self-verification of sensor slope data recognition and calculation.

[0030] Step 4: Create verified certificate information based on all the verification data obtained in Step 3; the verified certificate information includes the regression equation of the corresponding sensor; then form an information report with preset rules from all the verified certificate information, and enter the information report into the certificate database based on the current task code obtained in Step 1. Step 5: Obtain the standard signal from the information report of the current task code, send the standard signal to the corresponding sensor through the standard source, and obtain the output information of the multi-sensor system.

[0031] Using the regression equation obtained in step 4, the pressure, flow rate, and / or temperature of each sensor in the output information are calculated to complete the parameter measurement of the multi-sensor system.

[0032] Another parameter measurement method for a multi-sensor system based on sensor paper certificates provided in this invention includes the following steps: Step 1: Extract certificate information; The paper certificate of the sensor to be entered is scanned and marked with the current task code. The scan result is used to determine whether a QR code exists. If a QR code exists, it is directly parsed to obtain the certificate information. If no QR code exists, the certificate information is obtained through OCR technology. Step 2: Classification and extraction of certificate information; Analyze the certificate information above and classify it according to the preset configuration table. Then, extract the calibration data and regression coefficient B based on the classification results. Step 3: Data verification; The fitting slope k2 of the calibration data is calculated using the least squares method. If the difference between the fitting slope k2 and the regression coefficient B is within the threshold range, then step 5 is executed; if the difference between the fitting slope k2 and the regression coefficient B is outside the threshold range, then step 4 is executed. Step 4: Data correction; The corresponding calibration data corresponding to the fitting slope k2 that exceeds the threshold is marked and displayed. The original data is manually found in the corresponding paper certificate to be entered according to the displayed content. The original data is used to correct the extracted calibration data or regression coefficient B, and then the process returns to step 3. Step 5: Create verified certificate information based on the data calibrated in Step 3; the verified certificate information includes the regression equation of the corresponding sensor; then, form an information report with preset rules from all the verified certificate information, and enter the information report into the certificate database based on the current task code obtained in Step 1. Step 6: Obtain the standard signal from the information report of the current task code, send the standard signal to the corresponding sensor through the standard source, obtain the output information of the multi-sensor system, and use the regression equation obtained in Step 5 to calculate the pressure, flow rate and / or temperature of each sensor in the output information to complete the parameter measurement of the multi-sensor system.

[0033] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A parameter measurement method for a multi-sensor system based on sensor paper certificates, characterized in that, Includes the following steps: Step 1: Extract certificate information; Scan the paper certificates of the sensors to be entered, mark the current task code, and determine whether there is a QR code in them based on the scanning results; If a QR code exists, it will be directly parsed to obtain the certificate information; if no QR code exists, the certificate information will be obtained through OCR technology. Step 2: Classification and extraction of certificate information; The certificate information is analyzed and classified according to a preset configuration table. Then, the calibration data is extracted based on the classification results. Step 3: Data verification; The slope K0 of the correlation data is calculated using the least squares method. A slope threshold is set based on the slope K0. If a single calibration data falls within the sensor's range and the fitting slope K1 of the single calibration data falls within the slope threshold range, then the single calibration data is used as the verification data. The correlation data consists of the verification data of N sensors of the same model and batch in the certificate database, where N≥3. Step 4: Create verified certificate information based on all the verification data obtained in Step 3; the verified certificate information includes the regression equation of the corresponding sensor; Step 5: Obtain the output information of the multi-sensor system. Using the regression equation obtained in Step 4, calculate the pressure, flow rate, and / or temperature of each sensor in the output information to complete the parameter measurement of the multi-sensor system.

2. The parameter measurement method for a multi-sensor system based on sensor paper certificates according to claim 1, characterized in that, Step 4 also includes: Generate an information report with preset rules from all verified certificate information, and enter the information report into the certificate database based on the current task code obtained in step 1. Step 5, which involves obtaining the output information of the multi-sensor system, specifically includes: Obtain the standard signal from the information report of the current task code, send the standard signal to the corresponding sensor through the standard source, and obtain the output information of the multi-sensor system.

3. The parameter measurement method for a multi-sensor system based on sensor paper certificates according to claim 2, characterized in that, Step 3 specifically includes: Step 3.1: For a single calibration data point, automatically generate two hypothetical values: Assuming value A1: Calibration data; Assuming value A2: Multiply the calibration data by (-1); Step 3.2: If any assumed value does not conform to the sensor characteristics, directly exclude that assumed value, determine another assumed value as the verification data, and end the data verification; if both assumed values ​​conform to the sensor characteristics, proceed to step 3.3; the sensor characteristics include: the pressure / thrust calibration value is positive, and the absolute value of the error does not exceed the limit of the national military standard; Step 3.3: Calculate the slope K0 of the numerical change in the associated data using the least squares method; set the slope threshold to (-0.05K0, +0.05K0) based on the slope K0 of the associated data; the associated data consists of calibrated data from N sensors of the same model and batch in the certificate database, where N≥3; if the fitting slope K1 of both hypothetical values ​​falls within the slope threshold range, then call the historical verification data of M sensors of the same model in the certificate database, and use the hypothetical value with the highest matching degree as the verification data, where M≥3; if only one hypothetical value's fitting slope K1 falls within the slope threshold range, then use that hypothetical value as the verification data; end the data verification.

4. The parameter measurement method for a multi-sensor system based on sensor paper certificates according to claim 1 or 2, characterized in that: The multi-sensor system is a measurement and acquisition system used for rocket engine testing.

5. A parameter measurement method for a multi-sensor system based on sensor paper certificates, characterized in that, Includes the following steps: Step 1: Extract certificate information; Scan the paper certificates of the sensors to be entered, mark the current task code, and determine whether there is a QR code in them based on the scanning results; If a QR code exists, it will be directly parsed to obtain the certificate information; if no QR code exists, the certificate information will be obtained through OCR technology. Step 2: Classification and extraction of certificate information; The certificate information is analyzed and classified according to a preset configuration table. Then, the calibration data and regression coefficient B are extracted based on the classification results. Step 3: Data verification; The fitting slope k2 of the calibration data is calculated using the least squares method. If the difference between the fitting slope k2 and the regression coefficient B is within the threshold range, then step 5 is executed. If the difference between the fitted slope k2 and the regression coefficient B is outside the threshold range, then proceed to step 4; Step 4: Data correction; The corresponding calibration data corresponding to the fitting slope k2 that exceeds the threshold is marked and displayed. The original data is manually found in the corresponding paper certificate to be entered according to the displayed content. The original data is used to correct the extracted calibration data or regression coefficient B, and then the process returns to step 3. Step 5: Generate verified certificate information based on the data calibrated in Step 3; the verified certificate information includes the regression equation of the corresponding sensor; Step 6: Obtain the output information of the multi-sensor system. Using the regression equation obtained in Step 5, calculate the pressure, flow rate, and / or temperature of each sensor in the output information to complete the parameter measurement of the multi-sensor system.

6. The parameter measurement method for a multi-sensor system based on sensor paper certificates according to claim 5, characterized in that, Step 5 further includes: Generate an information report with preset rules from all verified certificate information, and enter the information report into the certificate database based on the current task code obtained in step 1. Step 6, which involves obtaining the output information of the multi-sensor system, specifically includes: Obtain the standard signal from the information report of the current task code, send the standard signal to the corresponding sensor through the standard source, and obtain the output information of the multi-sensor system.

7. The parameter measurement method for a multi-sensor system based on sensor paper certificates according to claim 5 or 6, characterized in that: The multi-sensor system is a measurement and acquisition system used for rocket engine testing.