Method for predicting abnormal tension of reinforcing steel bar tensile testing machine

Abstract

The invention relates to a method for predicting abnormal tensile force of a reinforcing steel bar tensile testing machine, which relates to the field of equipment testing and comprises the following steps: acquiring first reinforcing steel bar sample parameters including a first component proportion parameter, a first diameter parameter and a first length parameter; configuring a first test tensile force; performing steel bar deformation analysis according to the first steel bar sample parameter to obtain a first steel bar reference tensile state; starting the reinforcing steel bar tensile testing machine to perform tensile testing on the first reinforcing steel bar sample based on the first testing tensile force, and collecting a first reinforcing steel bar monitoring tensile state; comparing the first steel bar reference stretching state with the first steel bar monitoring stretching state to obtain a first state deviation; and when the state deviation is greater than or equal to the state deviation threshold, carrying out tension abnormity identification on the steel bar tensile testing machine. The technical problem that the reliability of steel bar state threshold data is difficult to guarantee due to the fact that setting of the steel bar threshold state generally depends on user experience in the prior art is solved.

Description

Technical Field

[0001] The invention relates to the field of equipment testing, and in particular to a method for predicting abnormal tension of a steel bar tensile testing machine. Background Art

[0002] In the existing field of building material testing, steel bar tensile testing is an important means of evaluating the mechanical properties of steel bars. When conducting tensile tests, the setting of the steel bar threshold state of traditional steel bar tensile testing machines usually relies on the user's experience, lacking precise quantitative analysis and scientific design standards. This experience-based method is not only inefficient, but may also lead to inaccurate test results due to human factors.

[0003] The existing steel bar tensile testing machine lacks accurate analysis of steel bar sample parameters during the tensile test, which affects the reliability of steel bar state threshold data. Moreover, the method that relies on experience is not only inefficient, but also not conducive to mass application. Summary of the invention

[0004] The present invention aims to solve the technical problem that the reliability of steel bar status threshold data is difficult to ensure because the setting of steel bar threshold status usually depends on the user's experience in the prior art, and provides a method for predicting the tensile abnormality of a steel bar tensile testing machine to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for predicting tension anomaly of a steel bar tensile testing machine, comprising: obtaining a first steel bar sample parameter in response to a tension detection request of the tensile testing machine from a user end, wherein the first steel bar sample parameter includes a first group of distribution ratio parameters, a first diameter parameter and a first length parameter; randomly configuring a first test tension according to a tension rated interval; performing a steel bar deformation analysis on the first test tension according to the first group of distribution ratio parameters, the first diameter parameter and the first length parameter to obtain a first steel bar reference tensile state; starting the steel bar tensile testing machine to perform a tensile test on the first steel bar sample based on the first test tension, and collecting a first steel bar monitoring tensile state through a displacement sensor; comparing the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation; and marking the steel bar tensile testing machine for tension anomaly when the first state deviation is greater than or equal to a state deviation threshold.

[0007] In a second aspect, the present invention provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing the method for predicting tensile abnormality of a steel bar tensile testing machine as described in the first aspect.

[0008] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, the method for predicting tensile anomaly of a steel bar tensile testing machine described in the first aspect is implemented.

[0009] The beneficial effects of the present invention are as follows: first, the parameters of the steel bar sample are obtained, including the distribution ratio parameter, the diameter parameter and the length parameter. Then, the steel bar deformation analysis is performed on the test tension according to these parameters to obtain the reference tensile state. During the actual tensile test, the tensile state is collected and monitored by the displacement sensor, and compared with the reference tensile state to obtain the state deviation. When the state deviation exceeds the preset threshold, the system will automatically mark the tensile abnormality. This method not only improves the intelligence level of the test, but also ensures the accuracy and reliability of the test results, and provides a strong technical support for the quality control of building materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of a method for predicting abnormal tension of a steel bar tensile testing machine provided by the present invention;

[0011] Figure 2 A schematic diagram of a tensile force anomaly identification process of a tensile force anomaly prediction method for a steel bar tensile testing machine provided by the present invention;

[0012] Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention;

[0013] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0014] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0015] Electronic device 500 , memory 510 , processor 520 , first computer program 511 , computer-readable storage medium 600 , second computer program 611 . DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0019] Embodiment 1:

[0020] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting abnormal tension of a steel bar tensile testing machine, comprising the steps of:

[0021] S10: In response to a tensile force detection request of a tensile testing machine at a user end, obtaining first steel bar sample parameters, wherein the first steel bar sample parameters include a first group distribution ratio parameter, a first diameter parameter, and a first length parameter;

[0022] In a preferred embodiment, the tensile testing machine tensile detection request refers to a request for a tensile testing machine to perform a tensile test, which is made by a user through an interface; the first steel bar sample parameter refers to the basic data of the sample used to test the tensile force of the tensile testing machine, preferably including at least the component ratio of the steel bar sample, the diameter of the steel bar sample and the length of the steel bar sample.

[0023] Specifically, when a request for a tensile test of a steel bar is received from the user through the interface, the corresponding test process will be started, and the user will be reminded to enter the basic parameters of the sample steel bar. The user enters the group ratio parameters, diameter parameters and length parameters through the interface, which are stored as the first steel bar sample parameters and wait for the next step to be called.

[0024] S20: randomly configuring a first test tension according to the rated tension interval;

[0025] In a preferred embodiment, the rated tension range refers to the range of tension that the steel bar tensile testing machine can apply according to safety and performance standards, which is usually provided by the manufacturer; the first test tension is a value randomly selected from the rated tension range. By randomly configuring the test tension, different load conditions that the steel bar may encounter in actual applications can be simulated, so as to evaluate its mechanical properties under different tensions. The configuration of random test tension also helps to reduce the influence of human factors on the test results, and improves the objectivity and fairness of the test.

[0026] S30: performing a steel bar deformation analysis on the first test tension according to the first group ratio parameter, the first diameter parameter and the first length parameter to obtain a first steel bar reference tensile state;

[0027] In a preferred embodiment, the steel bar deformation analysis refers to predicting and analyzing the deformation behavior of the steel bar when subjected to tension by means of engineering mechanics. The preferred process is:

[0028] Step 1: Taking the first set of distribution ratio parameters, the first diameter parameter, the first length parameter, and the first test tension as constraints, collect trouble-free steel bar tensile testing machines of the same model, and monitor the tensile deformation state of the steel bars when stretching the steel bars with the same first set of distribution ratio parameters, first diameter parameters, and first length parameters with the first test tension, preferably including the stretching length: which refers to the difference between the original length and the length after stretching; the mean stretching speed: the mean speed of change of steel bar length; the diameter shortening length, which is the difference between the diameter after stretching and the diameter before stretching.

[0029] Step 2: Set the historically recorded stretching length, stretching speed average, and diameter shortening length as the first steel bar reference stretching state.

[0030] Through the precise analysis of the steel bar specimen, its performance during the tensile process can be predicted, providing a scientific basis for the experiment and ensuring the accuracy and reliability of the experimental results. This method improves the intelligence level of tensile testing, reduces the interference of human factors, and makes the test results more stable and repeatable.

[0031] S40: starting a steel bar tensile testing machine to perform a tensile test on a first steel bar sample based on the first test tensile force, and collecting the first steel bar to monitor the tensile state through a displacement sensor;

[0032] In a preferred embodiment, the first test tension is applied to the steel bar sample by starting the steel bar tensile testing machine. During the test, a displacement sensor is used to monitor and record the elongation or displacement change of the steel bar during the tensile process in real time, that is, the specific tensile length, tensile speed average, and diameter shortening length are stored as the first steel bar monitoring tensile state.

[0033] S50: Compare the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation;

[0034] Further, the first steel bar reference tensile state is compared with the first steel bar monitoring tensile state to obtain a first state deviation, and step S50 includes the following steps:

[0035] S51: Obtaining a reference stretching length characteristic value, a reference stretching length change speed average characteristic value, and a reference diameter reduction length characteristic value of the first steel bar reference stretching state;

[0036] S52: Obtaining a monitoring stretch length characteristic value, a monitoring stretch length change speed average characteristic value, and a monitoring diameter reduction length characteristic value of the first steel bar monitoring stretch state;

[0037] S53: When the stretching length deviation between the reference stretching length characteristic value and the monitoring stretching length characteristic value is greater than or equal to the stretching length deviation threshold, or / and the stretching length change speed mean deviation between the reference stretching length change speed mean characteristic value and the monitoring stretching length change speed mean characteristic value is greater than or equal to the stretching length change speed mean deviation threshold, or / and the diameter reduction length deviation between the reference diameter reduction length characteristic value and the monitoring diameter reduction length characteristic value is greater than or equal to the diameter reduction length deviation threshold, the first state deviation is equal to 1;

[0038] S54: Otherwise, the first state deviation is equal to 0.

[0039] In a preferred embodiment, the first state deviation represents the state deviation degree between the first steel bar reference tensile state and the first steel bar monitoring tensile state, and the determination process is preferably as follows:

[0040] Step 1: Obtain a baseline stretching length characteristic value, a baseline stretching length change rate mean characteristic value and a baseline diameter reduction length characteristic value of a baseline stretching state of the first steel bar, and obtain a monitoring stretching length characteristic value, a monitoring stretching length change rate mean characteristic value and a monitoring diameter reduction length characteristic value of the first steel bar monitoring stretching state.

[0041] Step 2: The stretching length deviation threshold, the stretching length change speed mean deviation threshold and the diameter reduction length deviation threshold refer to the tensile force tolerance deviation that can be allowed by the steel bar tensile testing machine, which can be customized by the user. When the stretching length deviation between the reference stretching length characteristic value and the monitoring stretching length characteristic value is greater than or equal to the stretching length deviation threshold, or / and the stretching length change speed mean deviation between the reference stretching length change speed mean characteristic value and the monitoring stretching length change speed mean characteristic value is greater than or equal to the stretching length change speed mean deviation threshold, or / and the diameter reduction length deviation between the reference diameter reduction length characteristic value and the monitoring diameter reduction length characteristic value is greater than or equal to the diameter reduction length deviation threshold, the first state deviation is equal to 1, otherwise, the first state deviation is equal to 0.

[0042] S60: When the first state deviation is greater than or equal to a state deviation threshold, marking a tensile abnormality on the steel bar tensile testing machine.

[0043] Furthermore, the state deviation threshold is equal to 1.

[0044] In a preferred embodiment, the state deviation threshold is equal to 1. When the first state deviation is equal to 1, it is greater than or equal to the state deviation threshold, indicating that the allowable tensile fault tolerance deviation has been exceeded. At this time, the steel bar tensile testing machine is marked as tensile abnormal. If the state deviation threshold is equal to 0, it is less than the state deviation threshold, indicating that the allowable tensile fault tolerance deviation has not been exceeded. At this time, the steel bar tensile testing machine is marked as tensile qualified. By marking the steel bar tensile testing machine, the user can determine the tensile abnormality of each steel bar tensile testing machine, which is convenient for the subsequent targeted maintenance of the steel bar tensile testing machine, thereby ensuring the accuracy of the tensile test.

[0045] Further, such as Figure 2 As shown, when the first state deviation is greater than or equal to the state deviation threshold, the steel bar tensile testing machine is marked as tensile abnormal, and step S70 is also included. Step S70 includes:

[0046] S71: Repeat the test by replacing the steel bar sample until the Nth steel bar reference tensile state and the Nth steel bar monitoring tensile state are obtained, where N is an integer, and N≥5;

[0047] S72: Compare the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation;

[0048] S73: until the Nth steel bar reference tensile state is compared with the Nth steel bar monitoring tensile state to obtain an Nth state deviation;

[0049] S74: Counting the proportion of the first state deviations until the Nth state deviation that is greater than or equal to the state deviation threshold, and setting it as the tension abnormality;

[0050] S75: When the tension abnormality is greater than or equal to the abnormality threshold, the steel bar tensile testing machine is marked as tensile abnormality, wherein 10% ≥ abnormality threshold ≥ 3%.

[0051] In a preferred embodiment, considering the error probability of a single detection, at least 5 steel bar samples are selected to perform abnormal detection on the steel bar tensile testing machine, and the first state deviation until the Nth state deviation is the result obtained by testing N steel bar samples, and the Nth steel bar reference tensile state and the Nth steel bar monitoring tensile state are the reference tensile state and monitoring tensile state of the Nth steel bar sample. The first steel bar reference tensile state is compared with the first steel bar monitoring tensile state to obtain the first state deviation, until the Nth steel bar reference tensile state is compared with the Nth steel bar monitoring tensile state to obtain the Nth state deviation. The proportion of the first state deviation until the Nth state deviation that is greater than or equal to the state deviation threshold is counted and set as the tension abnormality. When the tension abnormality is greater than or equal to the abnormality threshold preset by the user, the steel bar tensile testing machine is marked for tension abnormality, wherein 10% ≥ abnormality threshold ≥ 3%, preferably, the abnormality threshold defaults to 3%, and the user can adjust it adaptively.

[0052] By selecting at least 5 specimens for tensile testing, accidental factors are eliminated and the accuracy of the test results is improved.

[0053] Further, according to the first group ratio parameter, the first diameter parameter and the first length parameter, the first test tension is subjected to a steel bar deformation analysis to obtain a first steel bar reference tensile state, and step S30 comprises the following steps:

[0054] S31: taking the first group of distribution ratio parameters, the first diameter parameter, the first length parameter, and the model of the tensile testing machine as constraints, collecting steel bar deformation samples for the first test tension to obtain a first-level steel bar deformation sample, wherein the first-level steel bar deformation sample has a second group of distribution ratio parameters, a second diameter parameter, and a second length parameter;

[0055] S32: taking the second group of distribution ratio parameters, the second diameter parameter, the second length parameter, and the model of the tensile testing machine as constraints, collecting steel bar deformation samples for the first test tension to obtain secondary steel bar deformation samples;

[0056] S33: until the Mth group ratio parameter, the Mth diameter parameter, the Mth length parameter, and the model of the tensile testing machine are constrained, steel bar deformation samples are collected for the first test tension to obtain M-level steel bar deformation samples, wherein 5≥M≥2, and M is an integer;

[0057] S34: Aggregate the first-level steel bar deformation sample, the second-level steel bar deformation sample, and the M-level steel bar deformation sample to generate the first steel bar reference tensile state.

[0058] Further, taking the first group ratio parameter, the first diameter parameter, the first length parameter, and the tensile testing machine model as constraints, steel bar deformation samples are collected for the first test tension to obtain a first-level steel bar deformation sample, S31 includes the steps of:

[0059] S311: Obtain a steel bar sample to be analyzed, wherein the steel bar sample to be analyzed includes a recorded model of a tensile testing machine, a recorded test tensile force, a recorded component ratio parameter, a recorded diameter parameter, and a recorded length parameter;

[0060] S312: When the recorded model of the tensile testing machine is the same as the model of the tensile testing machine, and the recorded test tension is the same as the first test tension, a static condition pass mark is generated;

[0061] S313: After the static condition passes the identification, the first group allocation ratio parameter, the first diameter parameter, the first length parameter, and the record group allocation ratio parameter, the record diameter parameter, and the record length parameter are compared to obtain a dynamic condition deviation degree;

[0062] S314: When the dynamic condition deviation is equal to 0, a dynamic condition pass flag is generated;

[0063] S315: After the dynamic condition passes the mark, the steel bar sample to be analyzed is added to the first-level steel bar deformation sample.

[0064] Further, the first group allocation ratio parameter, the first diameter parameter, the first length parameter, and the record group allocation ratio parameter, the record diameter parameter, and the record length parameter are compared to obtain the dynamic condition deviation, and step S313 includes the steps of:

[0065] S3131: Construct group distribution ratio deviation comparison function:

[0066]

[0067] Among them, ξ1 represents the group distribution ratio similarity parameter, X represents the first group distribution ratio parameter, Y represents the record group distribution ratio parameter, X∩Y represents the number of intersection component types of the first group distribution ratio parameter and the record group distribution ratio parameter, X∪Y represents the number of union component types of the first group distribution ratio parameter and the record group distribution ratio parameter, x i Characterizes the proportion of the i-th component type of X, y i represents the proportion of the i-th component type of Y, Q represents the number of component types, a represents a small constant, 0<a≤0.000001;

[0068] S3132: Calculate a diameter deviation parameter between the first diameter parameter and the recorded diameter parameter;

[0069] S3133: Calculate a length deviation parameter between the first length parameter and the record length parameter;

[0070] S3134: When the group allocation ratio similarity parameter is less than or equal to the group allocation ratio similarity parameter threshold, or / and the diameter deviation parameter is greater than or equal to the diameter deviation parameter threshold, or / and the length deviation parameter is greater than or equal to the length deviation parameter threshold, the dynamic condition deviation degree is equal to 1;

[0071] S3135: When the group allocation ratio similarity parameter is greater than the group allocation ratio similarity parameter threshold, and the diameter deviation parameter is less than the diameter deviation parameter threshold, and the length deviation parameter is less than the length deviation parameter threshold, the dynamic condition deviation degree is equal to 0.

[0072] In the preferred embodiment, since there are relatively few sample data that can be analyzed in actual production, directly collecting the first group of samples with the same distribution ratio parameter, first diameter parameter, first length parameter, tensile testing machine model and first test tension cannot meet the data volume requirements of big data analysis. Therefore, a multi-level sampling mechanism is designed to expand the sample volume. The detailed process is as follows:

[0073] Step 1: The first-level steel bar deformation sample refers to a sample set obtained by collecting steel bar deformation samples for the first test tension with the first group of distribution ratio parameters, the first diameter parameter, the first length parameter, and the tensile testing machine model as constraints. By default, the first-level steel bar deformation samples include at least 5, and the sample selection rules for each level are the same. The present application embodiment uses the sorting rules of the first-level steel bar deformation samples as an example:

[0074] Collect basic information of the steel bar samples to be analyzed: The steel bar samples to be analyzed refer to the samples that will be analyzed to see if they meet the screening criteria for the first-level steel bar deformation samples. They can be collected independently from networked data or a database provided by the user. Any steel bar sample to be analyzed includes the tensile testing machine record model, test tension, group ratio parameters, diameter parameters, length parameters, and tensile state data of the steel bar sample to be analyzed.

[0075] Static condition determination: When the model recorded by the tensile testing machine is the same as the model of the tensile testing machine, and the recorded test tension is the same as the first test tension, a static condition pass mark is generated.

[0076] Dynamic condition determination: After the static condition pass mark is generated, the parameter deviations of the first group allocation ratio parameter, the first diameter parameter, the first length parameter, and the record group allocation ratio parameter, the record diameter parameter, and the record length parameter are compared and stored as the dynamic condition deviation degree. Preferably, 0≤dynamic condition deviation degree≤1, and the closer to 1, the greater the deviation; when the dynamic condition deviation degree is equal to 0, a dynamic condition pass mark is generated.

[0077] After the dynamic condition passes the mark, the steel bar sample to be analyzed is added to the first-level steel bar deformation sample.

[0078] Furthermore, the specific calculation method of the dynamic condition deviation is as follows:

[0079] Construct group distribution ratio deviation comparison function: Among them, ξ1 represents the group distribution ratio similarity parameter, X represents the first group distribution ratio parameter, Y represents the record group distribution ratio parameter, X∩Y represents the number of intersection component types of the first group distribution ratio parameter and the record group distribution ratio parameter, X∪Y represents the number of union component types of the first group distribution ratio parameter and the record group distribution ratio parameter, x i Characterizes the proportion of the i-th component type of X, y i Characterize the i-th component type ratio of Y, Q characterizes the number of component types, a characterizes a small constant, 0<a≤0.000001, and use the group distribution ratio deviation comparison function to calculate the group distribution ratio similarity parameter; calculate the diameter deviation modulus of the first diameter parameter and the recorded diameter parameter, and store it as the diameter deviation parameter; calculate the length deviation modulus of the first length parameter and the recorded length parameter, and store it as the length deviation parameter.

[0080] The group distribution ratio similarity parameter threshold is the evaluation threshold value of the group distribution ratio similarity parameter set by the user, the diameter deviation parameter threshold is the evaluation threshold value of the diameter deviation set by the user, and the length deviation parameter threshold is the evaluation threshold value of the length deviation set by the user.

[0081] Case 1: When the group allocation ratio similarity parameter is less than or equal to the group allocation ratio similarity parameter threshold, or / and the diameter deviation parameter is greater than or equal to the diameter deviation parameter threshold, or / and the length deviation parameter is greater than or equal to the length deviation parameter threshold, the dynamic condition deviation degree is equal to 1, that is, any parameter deviation does not meet the corresponding parameter threshold, then the dynamic condition deviation degree is equal to 1;

[0082] Case 2: When the group allocation ratio similarity parameter is greater than the group allocation ratio similarity parameter threshold, and the diameter deviation parameter is less than the diameter deviation parameter threshold, and the length deviation parameter is less than the length deviation parameter threshold, the dynamic condition deviation degree is equal to 0, that is, all parameter deviations meet the corresponding parameter thresholds, then the dynamic condition deviation degree is equal to 0.

[0083] Step 2: In the collection method of the first-level steel bar deformation sample, the second group of distribution ratio parameters, the second diameter parameter, and the second length parameter of the first-level steel bar deformation sample are sampled as a benchmark to obtain the second-level steel bar deformation sample, until the Mth group of distribution ratio parameters, the Mth diameter parameter, the Mth length parameter, and the model of the tensile testing machine are constrained to collect steel bar deformation samples for the first test tension to obtain M-level steel bar deformation samples, wherein 5≥M≥2, M is an integer, and M is designed to be 2 to 5, which can ensure that the sample size is increased while avoiding the long sampling distance and loss of accuracy.

[0084] Step 3: Aggregate data on the steel bar tensile states of the first-level steel bar deformation samples, the second-level steel bar deformation samples, and the M-level steel bar deformation samples to obtain the first steel bar reference tensile state.

[0085] Preferably, the data aggregation algorithm process of the embodiment of the present application is as follows:

[0086] Further, the first-level steel bar deformation sample, the second-level steel bar deformation sample, and the M-level steel bar deformation sample are aggregated to generate the first steel bar reference tensile state. Step S34 includes the following steps:

[0087] S341: extracting the first i-th level steel bar deformation sample of the i-th level steel bar deformation sample from the first-level steel bar deformation sample, the second-level steel bar deformation sample, and up to the M-level steel bar deformation sample, where the initial value of i is equal to M;

[0088] S342: extracting a first group of i+1 level steel bar deformation samples belonging to the first i level steel bar deformation samples from the first level steel bar deformation samples, the second level steel bar deformation samples, and up to the M level steel bar deformation samples;

[0089] S343: Obtaining a first set of deformation state description characteristic values ​​of the first set of i+1 level steel bar deformation samples, wherein the first set of deformation state description characteristic values ​​includes a stretching length record value set, a stretching length change speed mean value record value set, and a diameter reduction length record value set;

[0090] S344: performing a central tendency analysis on the stretch length record value set to obtain a stretch length characteristic value;

[0091] S345: performing a central tendency analysis on the set of recorded values ​​of the mean value of the stretch length change speed to obtain a characteristic value of the mean value of the stretch length change speed;

[0092] S346: performing a central tendency analysis on the diameter reduction length record value set to obtain a diameter reduction length characteristic value;

[0093] S347: adding the stretch length characteristic value, the stretch length change speed average characteristic value and the diameter reduction length characteristic value to the first i-level steel bar deformation samples, and deleting the first group i+1-level steel bar deformation samples;

[0094] S348: When all i+1 level steel bar deformation samples are aggregated, i is reduced by one, and the analysis is repeated until i=0, and the first steel bar reference tensile state is output.

[0095] In a preferred embodiment, data aggregation adopts a recursive method, which is as follows: first, the M-level steel bar deformation samples are aggregated, then the M-1-level steel bar deformation samples are aggregated, until the first-level steel bar deformation samples are finally aggregated to obtain the first steel bar reference tensile state, which is as follows:

[0096] The i-th level steel bar deformation sample is the upper level sample of the level sample that needs to be aggregated. If the M-level steel bar deformation sample needs to be aggregated, then i=M-1; the first i-level steel bar deformation sample represents any sample of the i-th level steel bar deformation sample. If the M-level steel bar deformation sample needs to be aggregated, then the first i-level steel bar deformation sample represents any sample of the M-1-level steel bar deformation sample; the i+1-level steel bar deformation sample stores samples sorted based on the first i-level steel bar deformation sample, which is the first group of i+1-level steel bar deformation samples belonging to the first i-level steel bar deformation sample.

[0097] Furthermore, a set of stretched length record values, a set of stretched length change speed mean record values, and a set of diameter reduction length record values ​​represent the recorded data of the stretched test state quantity stored in the first group i+1 level steel bar deformation samples; a central tendency analysis is performed on the stretched length record value set to obtain a stretched length characteristic value; a central tendency analysis is performed on the stretched length change speed mean record value set to obtain a stretched length change speed mean characteristic value; a central tendency analysis is performed on the diameter reduction length record value set to obtain a diameter reduction length characteristic value; the stretched length characteristic value, the stretched length change speed mean characteristic value, and the diameter reduction length characteristic value are added to the first i level steel bar deformation samples, and the first group i+1 level steel bar deformation samples are deleted at the same time.

[0098] Continue to aggregate other samples of the i+1 level steel bar deformation samples in the same way. When all the i+1 level steel bar deformation samples are aggregated, i is reduced by one, and the analysis is repeated until i=0, and the first steel bar reference tensile state is output, that is, the characteristic values ​​of each state output after the first level steel bar deformation samples are aggregated are the first steel bar reference tensile state.

[0099] Through recursive aggregation and the use of layer-by-layer central tendency analysis, the probability of accidental errors is reduced, ensuring the objectivity and accuracy of the first steel bar benchmark tensile state.

[0100] The embodiment of the present invention provides a method for predicting abnormal tension of a steel bar tensile testing machine, which has at least the following technical effects:

[0101] First, the parameters of the steel bar sample are obtained, including the distribution ratio parameter, diameter parameter and length parameter. Then, the steel bar deformation analysis is performed on the test tension according to these parameters to obtain the reference tensile state. During the actual tensile test, the tensile state is collected and monitored by the displacement sensor and compared with the reference tensile state to obtain the state deviation. When the state deviation exceeds the preset threshold, the system will automatically mark the tensile abnormality. This method not only improves the intelligence of the test, but also ensures the accuracy and reliability of the test results, providing strong technical support for the quality control of building materials.

[0102] Embodiment 2:

[0103] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented:

[0104] In response to a tensile testing machine tensile force detection request from a user end, obtaining first steel bar sample parameters, wherein the first steel bar sample parameters include a first group distribution ratio parameter, a first diameter parameter, and a first length parameter;

[0105] Randomly configure the first test tension according to the tension rating interval;

[0106] According to the first group of distribution ratio parameters, the first diameter parameter and the first length parameter, performing steel bar deformation analysis on the first test tension to obtain a first steel bar reference tensile state;

[0107] Start the steel bar tensile testing machine to perform a tensile test on the first steel bar sample based on the first test tensile force, and collect the first steel bar to monitor the tensile state through the displacement sensor;

[0108] Comparing the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation;

[0109] When the first state deviation is greater than or equal to the state deviation threshold, the steel bar tensile testing machine is marked as having abnormal tension.

[0110] Embodiment three:

[0111] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, the following steps are implemented:

[0112] In response to a tensile testing machine tensile force detection request from a user end, obtaining first steel bar sample parameters, wherein the first steel bar sample parameters include a first group distribution ratio parameter, a first diameter parameter, and a first length parameter;

[0113] Randomly configure the first test tension according to the tension rating interval;

[0114] According to the first group of distribution ratio parameters, the first diameter parameter and the first length parameter, performing steel bar deformation analysis on the first test tension to obtain a first steel bar reference tensile state;

[0115] Start the steel bar tensile testing machine to perform a tensile test on the first steel bar sample based on the first test tensile force, and collect the first steel bar to monitor the tensile state through the displacement sensor;

[0116] Comparing the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation;

[0117] When the first state deviation is greater than or equal to the state deviation threshold, the steel bar tensile testing machine is marked as having abnormal tension.

[0118] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0119] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0124] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for predicting abnormal tensile force of a steel bar tensile testing machine, characterized in that: include: In response to a tensile testing machine tensile force detection request from a user end, obtaining first steel bar sample parameters, wherein the first steel bar sample parameters include a first group distribution ratio parameter, a first diameter parameter, and a first length parameter; Randomly configure the first test tension according to the tension rating interval; According to the first group of distribution ratio parameters, the first diameter parameter and the first length parameter, performing steel bar deformation analysis on the first test tension to obtain a first steel bar reference tensile state; Start the steel bar tensile testing machine to perform a tensile test on the first steel bar sample based on the first test tensile force, and collect the first steel bar to monitor the tensile state through the displacement sensor; Comparing the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation; When the first state deviation is greater than or equal to the state deviation threshold, the steel bar tensile testing machine is marked as having abnormal tension.

2. The method according to claim 1, characterized in that When the first state deviation is greater than or equal to the state deviation threshold, marking the steel bar tensile testing machine for abnormal tension includes: Replace the steel bar sample and repeat the test until the Nth steel bar reference tensile state and the Nth steel bar monitoring tensile state are obtained, where N is an integer and N≥5; Comparing the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation; Until the Nth steel bar reference tensile state is compared with the Nth steel bar monitoring tensile state to obtain an Nth state deviation; Counting the proportion of the first state deviations until the Nth state deviation that is greater than or equal to the state deviation threshold, and setting it as the tension abnormality; When the tension abnormality is greater than or equal to the abnormality threshold, the steel bar tensile testing machine is marked as tensile abnormality, wherein 10% ≥ abnormality threshold ≥ 3%.

3. The method according to claim 1, characterized in that According to the first group ratio parameter, the first diameter parameter and the first length parameter, performing a steel bar deformation analysis on the first test tension to obtain a first steel bar reference tensile state includes: Taking the first group of distribution ratio parameters, the first diameter parameter, the first length parameter, and the model of the tensile testing machine as constraints, collecting steel bar deformation samples for the first test tension to obtain a first-level steel bar deformation sample, wherein the first-level steel bar deformation sample has a second group of distribution ratio parameters, a second diameter parameter, and a second length parameter; Taking the second group distribution ratio parameter, the second diameter parameter, the second length parameter, and the tensile testing machine model as constraints, collecting steel bar deformation samples for the first test tension to obtain secondary steel bar deformation samples; Until the Mth group distribution ratio parameter, the Mth diameter parameter, the Mth length parameter, and the model of the tensile testing machine are constrained, the steel bar deformation sample is collected for the first test tension to obtain the M-level steel bar deformation sample, wherein 5≥M≥2, and M is an integer; The first-level steel bar deformation sample, the second-level steel bar deformation sample, and even the M-level steel bar deformation sample are aggregated to generate the first steel bar reference tensile state.

4. The method according to claim 3, characterized in that Taking the first group ratio parameter, the first diameter parameter, the first length parameter, and the tensile testing machine model as constraints, collecting steel bar deformation samples for the first test tension to obtain a first-level steel bar deformation sample, including: Obtaining a steel bar sample to be analyzed, wherein the steel bar sample to be analyzed includes a recorded model of a tensile testing machine, a recorded test tensile force, a recorded component ratio parameter, a recorded diameter parameter, and a recorded length parameter; When the recorded model of the tensile testing machine is the same as the model of the tensile testing machine, and the recorded test tension is the same as the first test tension, a static condition pass mark is generated; After the static condition is generated, the first group allocation ratio parameter, the first diameter parameter, the first length parameter, and the record group allocation ratio parameter, the record diameter parameter, and the record length parameter are compared to obtain a dynamic condition deviation degree; When the dynamic condition deviation is equal to 0, a dynamic condition pass mark is generated; After the dynamic condition passes the mark, the steel bar sample to be analyzed is added to the first-level steel bar deformation sample.

5. The method according to claim 4, characterized in that Comparing the first group allocation ratio parameter, the first diameter parameter, the first length parameter, and the recorded group allocation ratio parameter, the recorded diameter parameter, and the recorded length parameter to obtain a dynamic condition deviation, comprising: Construct group distribution ratio deviation comparison function: Among them, ξ1 represents the group distribution ratio similarity parameter, X represents the first group distribution ratio parameter, Y represents the record group distribution ratio parameter, X∩Y represents the number of intersection component types of the first group distribution ratio parameter and the record group distribution ratio parameter, X∪Y represents the number of union component types of the first group distribution ratio parameter and the record group distribution ratio parameter, x i Characterizes the proportion of the i-th component type of X, y i represents the proportion of the i-th component type of Y, Q represents the number of component types, a represents a small constant, 0<a≤0.000001; Calculating a diameter deviation parameter between the first diameter parameter and the recorded diameter parameter; Calculating a length deviation parameter between the first length parameter and the recorded length parameter; When the group allocation ratio similarity parameter is less than or equal to the group allocation ratio similarity parameter threshold, or / and the diameter deviation parameter is greater than or equal to the diameter deviation parameter threshold, or / and the length deviation parameter is greater than or equal to the length deviation parameter threshold, the dynamic condition deviation degree is equal to 1; When the group allocation ratio similarity parameter is greater than the group allocation ratio similarity parameter threshold, and the diameter deviation parameter is less than the diameter deviation parameter threshold, and the length deviation parameter is less than the length deviation parameter threshold, the dynamic condition deviation degree is equal to 0.

6. The method according to claim 3, characterized in that Aggregating the first-level steel bar deformation sample, the second-level steel bar deformation sample, and the M-level steel bar deformation sample to generate the first steel bar reference tensile state, including: Extracting the first i-th level steel bar deformation sample of the i-th level steel bar deformation sample from the first level steel bar deformation sample, the second level steel bar deformation sample, and the M-th level steel bar deformation sample, wherein the initial value of i is equal to M; Extracting a first group of i+1 level steel bar deformation samples belonging to the first i level steel bar deformation samples from the first level steel bar deformation samples, the second level steel bar deformation samples, and the M level steel bar deformation samples; Obtaining a first set of deformation state description characteristic values ​​of the first set of i+1 level steel bar deformation samples, wherein the first set of deformation state description characteristic values ​​includes a set of stretched length record values, a set of stretched length change speed mean value record values, and a set of diameter reduction length record values; Performing a central tendency analysis on the stretch length record value set to obtain a stretch length characteristic value; Performing a central tendency analysis on the set of recorded values ​​of the mean value of the stretch length change speed to obtain a characteristic value of the mean value of the stretch length change speed; Performing a central tendency analysis on the diameter reduction length record value set to obtain a diameter reduction length characteristic value; Add the stretch length characteristic value, the stretch length change speed average characteristic value and the diameter reduction length characteristic value to the first i-level steel bar deformation samples, and delete the first group i+1-level steel bar deformation samples; When all i+1 level steel bar deformation samples are aggregated, i is reduced by one and the analysis is repeated until i=0, and the first steel bar reference tensile state is output.

7. The method according to claim 6, characterized in that Comparing the first steel bar reference tensile state with the first steel bar monitoring tensile state to obtain a first state deviation includes: Obtaining a reference stretching length characteristic value, a reference stretching length change speed average characteristic value, and a reference diameter reduction length characteristic value of the first steel bar reference stretching state; Obtaining a monitoring stretch length characteristic value, a monitoring stretch length change speed average characteristic value, and a monitoring diameter reduction length characteristic value of the first steel bar monitoring stretch state; When the stretching length deviation between the reference stretching length characteristic value and the monitoring stretching length characteristic value is greater than or equal to the stretching length deviation threshold, or / and the stretching length change speed mean deviation between the reference stretching length change speed mean characteristic value and the monitoring stretching length change speed mean characteristic value is greater than or equal to the stretching length change speed mean deviation threshold, or / and the diameter reduction length deviation between the reference diameter reduction length characteristic value and the monitoring diameter reduction length characteristic value is greater than or equal to the diameter reduction length deviation threshold, the first state deviation is equal to 1; Otherwise, the first state deviation is equal to 0.

8. The method according to claim 7, characterized in that include: The state deviation threshold is equal to 1.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the method for predicting the abnormal tension of a steel bar tensile testing machine as described in any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the method for predicting tensile force anomaly of a steel bar tensile testing machine according to any one of claims 1 to 8 is implemented.

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