Synchronous correction, preprocessing and acquisition method for test data of reinforcing steel bar testing machine

Abstract

The invention aims to provide a synchronous correction preprocessing acquisition method for test data of a reinforcing steel bar testing machine, which comprises the following steps of: acquiring the test data of the testing machine, and cleaning and converting the data; displaying the collected testing data of the testing machine in real time, and performing curve fitting on the data; analyzing the sample number, and positioning a database file corresponding to the sample number; matching the testing machine database curve with the generated real-time testing machine test data curve by taking the interval residual error as a basis; and verifying an acquired data result, and synchronizing the acquired data into a database.

Description

Technical Field

[0001] The invention relates to data reading, data storage, data synchronization, data uploading, and in particular to accurate reading, processing and uploading of test data of a testing machine. Background Art

[0002] In modern industry and scientific research, the application of testing machines has covered multiple industries, including key links such as material testing, mechanical analysis, and structural verification. Especially in high-tech fields such as aerospace, automobile manufacturing, and biomedicine, the accuracy and timeliness of test data directly affect research results and product quality. However, traditional data recording and uploading methods mainly rely on manual operations and are easily interfered by human factors, resulting in problems such as untimely, incomplete, or inaccurate data recording. In addition, traditional methods often lack systematic management in the data processing process, which is not only inefficient, but may also cause data loss or difficulty in tracing due to compatibility issues between devices.

[0003] With the development of intelligent and digital technologies, the use of testing machines has gradually evolved from a single test function to integration and automation, and test data management has also shown higher requirements. Test data needs to be collected, processed and analyzed in real time, especially in scenarios where large amounts of data need to be processed, multivariate tests or long-term monitoring need to be conducted, and traditional methods are unable to cope with the situation. At present, some testing machines have the ability to automatically record data, but the real-time processing and uploading functions of data are still not perfect. Traditional systems that lack flexibility and compatibility can hardly meet the diverse needs of modern industrial production and scientific research.

[0004] The above-mentioned specific patent comparative documents are:

[0005] 1) "A data synchronization correction method based on material balance", patent number is CN109902264B. This patent proposes a data synchronization correction method based on material balance, which specifically includes: by analyzing significant errors related to measurement and missing material flow information, a support vector regression method is used to establish a model; VC dimension is used to characterize the quantitative relationship between measurement errors and missing material flow information; by minimizing structural risks, a mixed integer linear programming method is used to detect and correct measurement errors and missing material flow information. The difference between the present invention and the above patents is that through efficient curve matching algorithms and data verification mechanisms, the reliability of the test data of the testing machine is significantly improved, and the automation of data synchronization and correction is realized. Compared with existing patents, the present invention has a higher level of intelligence, can fully support the compatibility and scalability of different types of testing machines and data acquisition systems, and has a wider application value.

[0006] 2) "A multifunctional real-time synchronous acquisition Internet of Things gateway", patent number CN104811373B. This patent relates to an innovative Internet of Things gateway for the signal synchronization acquisition needs in the field of mechanical equipment fault diagnosis. This patent aims to solve the impact of phase deviation on the monitoring and diagnosis effect of unit faults. The gateway not only has a wide range of equipment access capabilities and typical management and protocol conversion functions, but also has outstanding functions of predicting the speed according to the start-stop status of the equipment, pre-filtering and data synchronization correction. It can collect physical quantities and electrical signals output by various sensors in real time, such as temperature, pressure, flow, acceleration, speed, eddy current, speed, etc., and support data communication with other systems. After completing data acquisition and synchronous processing, the gateway can also perform detailed data analysis and calculate characteristic values ​​including effective value of vibration velocity, true peak value of vibration acceleration, true peak-to-peak value of vibration displacement, multiple frequency values ​​of vibration signal, GIE value, etc. The synchronous correction and preprocessing method of the present invention is different from the prior art. For the data of the steel bar testing machine, it has higher accuracy, intelligence and efficiency advantages, significantly improves the data processing capability and result accuracy in the steel bar testing process, and provides strong technical support for the intelligent upgrading of steel bar testing equipment. Summary of the invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide a method for synchronous correction preprocessing and acquisition of test data of a steel bar testing machine.

[0008] The purpose of the present invention is achieved through the following technical solutions:

[0009] A synchronous correction preprocessing and acquisition method for test data of steel bar testing machine.

[0010] A. Collect test data from the testing machine, clean and transform the data;

[0011] B. Real-time display of the collected test data of the testing machine and curve fitting of the data;

[0012] C. Parse the sample number and locate the database file corresponding to the sample number;

[0013] D. Using interval residuals as a basis, match the test machine database curve and the generated real-time test machine test data curve;

[0014] E. Verify the collected data results and synchronize the collected data to the database.

[0015] Compared with the prior art, the present invention has the following advantages:

[0016] The real-time testing machine test curve is generated by high-frequency reading and dynamic data processing, which significantly improves the data processing efficiency; it has the characteristics of precision and intelligence, can accurately judge the test status and automatically trigger the corresponding process to avoid human intervention errors; it has high reliability in data management, supports rapid retrieval and verification of local database files, and ensures the location and integrity of data; it adopts an efficient curve matching algorithm to accurately calculate the curve matching rate and provide deviation analysis to ensure data consistency; it has the ability to verify and synchronize results, can verify and correct the accuracy of key data, and automatically synchronize when differences are found, and generate detailed logs at the same time; it provides flexible parameter configuration and log management functions, supports adjustment of parameters such as reading frequency and matching threshold, and realizes real-time tracking and log management of key events; in terms of compatibility and scalability, it can adapt to a variety of steel bar testing machines and data acquisition systems, and the modular design facilitates functional expansion; it improves the overall test efficiency and reliability, realizes the automation of the entire process from data acquisition to result synchronization, and provides solid technical support for the intelligent upgrade of testing machines. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is an operation flow chart of the synchronous correction preprocessing acquisition method for the test data of the steel bar testing machine;

[0018] Figure 2 It is a data schematic diagram of a synchronous correction preprocessing acquisition method for test data of a steel bar testing machine;

[0019] Figure 3 It is a schematic diagram of a data synchronization algorithm for a synchronous correction preprocessing acquisition method of test data of a steel bar testing machine. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with embodiments and drawings.

[0021] like Figure 1 As shown, a method for synchronous correction preprocessing and acquisition of test data of a steel bar testing machine is provided, and the process includes the following steps:

[0022] Step 10: Collect test data from the testing machine, clean and transform the data;

[0023] Step 20 displays the collected test data of the testing machine in real time and performs curve fitting on the data;

[0024] Step 30 parses the sample number and locates the database file corresponding to the sample number;

[0025] Step 40 uses the interval residual as a basis to match the test machine database curve and the generated real-time test machine test data curve;

[0026] Step 50 verifies the collected data results and synchronizes the collected data into the database.

[0027] The above step 10 specifically includes: reading the real-time data file generated by the test machine at a high frequency or requesting serial port device data, and dynamically processing the read data, filtering negative values, large values ​​and other abnormal data through data cleaning and data conversion, eliminating noise, and ensuring data quality; the original data and the cleaned data meet the following relationship:

[0028] D raw ={(t 1 ,F(t 1 )),(t 2 ,F(t 2 )),···,(t n ,F(t n ))}

[0029] D final ={(t i ,round(F(t i ),3))|0≤F(t i )≤F max ,noise-filtered(F(t i ))}

[0030] The original test data collected by the testing machine is D raw ;

[0031] After data cleaning and data conversion, the final test machine test data is D final ;

[0032] The maximum load is F max ;

[0033] round(x,3) means rounding x to three decimal places;

[0034] noise-filtered(F(t i )) represents the load value processed by the noise filtering algorithm.

[0035] The above step 20 specifically includes: fitting the experimental data by using a suitable polynomial fitting mathematical model, and updating the display curve in real time according to the fitting result, automatically eliminating over-large or under-large data, and providing accurate experimental result analysis and correction basis. The collected test machine test data satisfies the following relationship:

[0036] After data cleaning, the test data of the testing machine is a series of discrete data points:

[0037] (t 1 ,F 1),(t 2 ,F 2 ),...,(t n ,F n )

[0038] F i is the time t in the test data of the testing machine i The corresponding load value.

[0039] In order to perform polynomial fitting on the test machine test data, a polynomial fitting model can be used. We assume that an m-degree polynomial is used to fit the test machine test data:

[0040] F(t)≈P m (t) = a m t m +a m-1 t m-1 +···+a 1 t+a 0

[0041] Among them, a m ,a m-1 ,···,a 1 ,a 0 are the coefficients of the polynomial to be fitted, which are solved by methods such as the least squares method. This step is performed by minimizing the following loss function:

[0042]

[0043] Minimizing the loss function L can obtain the optimal coefficients of the fitting polynomial.

[0044] The above step 30 specifically includes: quickly retrieving the local database file generated by the test machine through the sample number, and verifying its integrity through the file verification mechanism, combining the storage path and timestamp information related to the sample number, ensuring the accuracy and availability of the database file location, and improving the security and reliability of the test data of the test machine; the sample data and the database data satisfy the following relationship:

[0045]

[0046] The sample number is S, and the value range is S∈{1,2,3,...,N s}

[0047] The database file number is D, and the value range is D∈{1,2,3,...,N d}

[0048] There is a corresponding relationship f(S,D) between the sample number and the database file number, where:

[0049] If f(S,D)=True, it means that the sample number S corresponds to the database file number D;

[0050] If f(S,D) = False, it means that the two do not correspond;

[0051] Finally, the set of database files corresponding to the sample numbers F S The formula is:

[0052] F S ={F D |f(S,D)=1,D∈{1,2,3,...,N d}}

[0053] The above step 40 specifically includes: using the minimum interval residual as a basis, matching the test machine database curve and the real-time test machine test data curve generated by the program, and determining the optimal matching interval by calculating the residual between the data point and the fitting curve; the calculation process satisfies the following relationship:

[0054] Suppose the database file set F corresponding to the sample number S There is a set of data points (t 1 ,F db,1 ),(t 2 ,F db,2 ),···(t n ,F db,n ), then the residual r i The calculation is the difference between the fitted value and the database value:

[0055] r i =F(t i )-F db,i

[0056] Therefore, the overall residual between the fitted function and the database points is:

[0057]

[0058] In order to match the fitting results with the database data, we can find an interval [t start ,t end ], let the residual R in this interval min Minimum, that is:

[0059]

[0060] The minimum residual is smaller than the configured standard and is considered a correct pairing.

[0061] The above step 50 specifically includes: verifying the difference between the maximum force value and yield force value recorded by the test machine and the real-time data, determining the accuracy of the data by error calculation, and taking the test machine database data as the standard when there is a difference, and synchronously updating it to the acquisition program; if there is no difference, directly recording the real-time data, and recording the detailed information of the verification and synchronization process at the same time and generating a difference report; wherein, the following relationship is satisfied between the verification test machine record and the real-time data:

[0062]

[0063] σ sample =g(F(t))

[0064] σ sample is the yield strength value of the acquisition program, in the database, σ sample With F sample Satisfy the functional relationship of g(x);

[0065] F sample is the maximum force value of the acquisition procedure;

[0066] MAX[F(t)] is the real-time data F(t), in the interval [t start ,t end ] when the maximum load value;

[0067] MAX(F S ) is the maximum load in the test machine database data;

[0068] ε is taken as 1% as the allowable error.

[0069] like Figure 2 As shown, in the synchronous correction preprocessing acquisition method of the test data of the steel bar testing machine, the data acquisition process includes the following steps: the testing machine writes the test data into an INI text file every 10 milliseconds. This program reads the data in the file at the configured frequency. The specific process is: when waiting for the test to start, the program will start a background task to read the text file or request serial port device data at a high frequency, and judge whether the test has started by the read value; when the start condition is met, the test is judged to start, and the read data is displayed as a real-time test machine test data curve chart; during the test, when the end condition is met, the test is judged to end, and returns to the state of waiting for the test to start.

[0070] like Figure 3As shown in the figure, after a single test is completed, the corresponding local database file of the testing machine is found by the sample number. After finding the database file, the minimum interval residual is calculated based on the curve data of this program and the database data. If the residual is less than the configuration requirement, it is considered as this test. Then, the maximum force value and the yield force value are compared. If there is a difference, the accurate value is synchronized to this program.

[0071] Although the embodiments disclosed in the present invention are as above, the above contents are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for synchronous correction preprocessing and acquisition of test data of a steel bar testing machine, characterized in that: The process comprises: A. Collect test data from the testing machine, clean and transform the data; B. Real-time display of the collected test data of the testing machine and curve fitting of the data; C. Parse the sample number and locate the database file corresponding to the sample number; D. Using interval residuals as a basis, match the test machine database curve and the generated real-time test machine test data curve; E. Verify the collected data results and synchronize the collected data to the database.

2. The method for synchronous correction preprocessing and acquisition of test data for a steel bar testing machine according to claim 1, characterized in that: In step A, the real-time data file generated by the test machine is read at a high frequency or the serial device data is requested, and the read data is dynamically processed. By cleaning and converting the test data of the test machine, negative values, large value abnormal data are filtered, and noise and illegal data are eliminated; the original data and the cleaned data satisfy the following relationship: D raw ={(t1,F(t1)),(t2,F(t2)),···,(t n ,F(t n ))} D final ={(t i ,round(F(t i ),3))|0≤F(t i )≤F max ,noise-filtered(F(t i ))} Where D raw D is the original test data collected by the testing machine raw ; D final It is the final test machine test data after data cleaning and data conversion; F max is the maximum load value; round(x,3) means rounding x to three decimal places; noise-filtered(F(t i )) represents the load value processed by the noise filtering algorithm.

3. The method for synchronous correction preprocessing and acquisition of test data for a steel bar testing machine according to claim 1, characterized in that: In step B, the test data of the test machine is fitted by using a polynomial fitting mathematical model, and the display curve is updated in real time according to the fitting result to provide accurate experimental result analysis and correction basis; the collected data satisfies the following relationship: After data cleaning, the test data is a series of discrete data points: (t1,F1),(t2,F2),...,(t n ,F n ) F i is the time t in the test data of the testing machine i The corresponding load value. In order to perform polynomial fitting on the test machine test data, a polynomial fitting model is used; assuming that an m-order polynomial is used to fit the test machine test data: F(t)≈P m (t)=a m t m +a m-1 t m-1 +···+a1t+a0 Among them, a m ,a m-1 ,···,a1,a0 are the coefficients of the polynomial to be fitted, which are solved by the least squares method; this is done by minimizing the following loss function: Minimizing the loss function L can obtain the optimal coefficients of the fitting polynomial.

4. The method for synchronous correction preprocessing and acquisition of test data for a steel bar testing machine according to claim 1, characterized in that: In step C, the local database file generated by the testing machine is quickly retrieved by the sample number, and its integrity is verified by the file verification mechanism; wherein the sample data and the database data satisfy the relationship: S is the sample number, and its value range is S∈{1,2,3,...,N s }, D is the database file number, and its value range is D∈{1,2,3,...,N d }, There is a corresponding relationship f(S,D) between the sample number and the database file number, where: If f(S,D)=True, it means that the sample number S corresponds to the database file number D; If f(S,D) = False, it means that the two do not correspond; Finally, the set of database files corresponding to all sample numbers F S The formula is: F S ={F D |f(S,D)=1,D∈{1,2,3,...,N d }}。 5. The method for synchronous correction preprocessing and acquisition of test data for a steel bar testing machine according to claim 1, characterized in that: In step D, the minimum interval residual is used as a basis to match the test machine database curve and the generated real-time test machine test data curve, and the optimal matching interval is determined by calculating the residual between the data point and the fitting curve; the calculation process satisfies the relationship: Suppose the database file set F corresponding to the sample number S There is a set of data points (t1,F db,1 ),(t2,F db,2 ),···(t n ,F db,n ), then the residual r i The calculation is the difference between the fitted value and the database value: r i =F(t i )-F db,i Therefore, the overall residual between the fitted function and the database points is: In order to match the fitting results with the database data, we need to find an interval [t start ,t end ], let the residual R in this interval min Minimum, that is: The minimum residual is smaller than the configured standard and is considered a correct pairing.

6. The method for synchronous correction preprocessing and acquisition of test data for a steel bar testing machine according to claim 1, characterized in that: In the step E, the difference between the maximum force and yield force values ​​recorded by the test machine and the real-time data is verified, the accuracy of the data is determined by error calculation, and when there is a difference, the test machine database data is used as the standard, and the acquisition program is updated synchronously. If there is no difference, the real-time data is directly recorded, and the detailed information of the verification and synchronization process is recorded at the same time and a difference report is generated; wherein, the following relationship is satisfied between the verification test machine record and the real-time data: s sample =g(F(t)) σ sample is the yield strength value of the acquisition program, in the database, σ sample With F sample Satisfy the functional relationship of g(x); F sample is the maximum force value of the acquisition procedure; MAX[F(t)] is the real-time data F(t), in the interval [t start ,t end ] when the maximum load value; MAX(F S ) is the maximum load in the test machine database data; ε is taken as 1% as the allowable error.

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