Testing machine matching method and device, equipment and storage medium

Abstract

The invention discloses a testing machine matching method, device and equipment and a storage medium, and belongs to the technical field of material mechanical property detection. The method comprises the following steps: acquiring sample size information and sample element information of a sample; determining the predicted tensile strength of the sample based on the sample element information and a tensile strength prediction model; and determining a target testing machine matched with the sample based on the sample size information and the predicted tensile strength. By utilizing the technical scheme provided by the invention, the matching accuracy and intelligence of the testing machine can be improved, so that the reliability and efficiency of material mechanical property detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of material mechanical property detection, and in particular to a testing machine matching method, device, equipment and storage medium. Background Art

[0002] Tensile testing is a crucial step in determining the mechanical properties of materials. Currently, matching the testing machine and specimen for tensile testing relies primarily on manual judgment. This involves operators relying on experience to match the specimen type and size to the appropriate testing machine. This inefficient matching method is inefficient. Existing mechanical testing systems are unable to accurately assemble specimens onto the appropriate testing machine, as humans do, leading to a high risk of misselection and collision, potentially damaging both the machine and the specimen.

[0003] As the demand for materials testing continues to grow and the requirements for test accuracy continue to increase, traditional manual matching methods are no longer able to meet today's needs. Failure to select the right testing machine will not only prevent accurate measurement of the material's mechanical properties, but may also cause irreversible damage to the equipment and specimens due to equipment precision mismatch, increasing testing costs and time.

[0004] Therefore, a more reliable solution needs to be provided. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a testing machine matching method, device, equipment and storage medium, which can improve the accuracy and intelligence of testing machine matching, and improve the reliability and efficiency of material mechanical property testing.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In one aspect, the present invention provides a testing machine matching method, the method comprising:

[0008] Obtaining sample size information and sample element information of the sample;

[0009] Determining the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model;

[0010] A target testing machine matching the sample is determined based on the sample size information and the predicted tensile strength.

[0011] In some possible implementations, determining the predicted tensile strength of the sample based on the sample element information and a tensile strength prediction model includes:

[0012] Determining sample type information of the sample based on the sample element information;

[0013] Based on the sample type information, determining a tensile strength prediction model corresponding to the sample type information;

[0014] The sample element information is input into the tensile strength prediction model to obtain the predicted tensile strength of the sample.

[0015] In some possible implementations, determining a target testing machine that matches the sample based on the sample size information and the predicted tensile strength includes:

[0016] Determining cross-sectional area information of the sample based on the sample size information;

[0017] determining a target load for the specimen based on the cross-sectional area information and the predicted tensile strength;

[0018] The target testing machine is determined based on the target load and the sample size information.

[0019] In some possible implementations, after determining a target testing machine that matches the sample based on the sample size information and the tensile strength, the method includes:

[0020] Acquiring sample test information of the sample, wherein the sample test information is obtained by controlling a target testing machine to perform a mechanical test on the sample;

[0021] According to a preset model updating algorithm, the tensile strength prediction model is updated in combination with the predicted tensile strength and the sample test information to obtain an updated tensile strength prediction model, wherein the updated tensile strength prediction model is used to determine the predicted tensile strength of the sample.

[0022] In some possible implementations, obtaining sample size information and sample element information of the sample includes:

[0023] Controlling the size measuring device to perform size measurement processing on the sample to obtain sample size information;

[0024] The type identification device is controlled to perform element identification processing on the sample to obtain sample element information.

[0025] In some possible implementations, the dimension measuring device includes a laser ranging multi-sensor, and controlling the dimension measuring device to perform dimension measurement processing on the sample to obtain the sample dimension information includes:

[0026] Controlling a laser ranging multi-sensor to emit a laser beam to the sample at different target angles, and determining the emission time of each laser ranging sensor to transmit the laser beam, wherein the laser ranging multi-sensor is a preset number of laser ranging sensors arranged at relative positions in the thickness direction of the sample;

[0027] After each laser ranging sensor receives the laser beam reflected from the sample surface, determining a reception time of the laser ranging sensor receiving the laser beam;

[0028] Determining the time difference of each laser ranging sensor according to the emission time and the reception time of each laser ranging sensor;

[0029] Determining thickness information of the sample based on the time difference of each laser ranging sensor and the longitudinal distance between the preset number of laser ranging sensors;

[0030] Controlling the sample to move at a constant speed along a measuring length track under a laser ranging sensor;

[0031] Determine the time from when the sample first contacts the laser beam of the laser ranging sensor to when it leaves the laser beam of the laser ranging sensor as the measurement time;

[0032] determining length information of the sample based on the moving speed of the sample and the measurement time;

[0033] The sample size information is determined based on the thickness information and the length information.

[0034] In some possible implementations, the type identification device includes a spectrum analyzer; and controlling the type identification device to perform element identification processing on the sample to obtain element information of the sample includes:

[0035] The spectrum analyzer is controlled to perform element identification processing on the sample to obtain element information of the sample.

[0036] Another aspect provides a testing machine matching device, the device comprising:

[0037] An information acquisition module is used to obtain sample size information and sample element information of the sample;

[0038] a strength determination module, configured to determine the predicted tensile strength of the sample based on the sample element information and a tensile strength prediction model;

[0039] A testing machine matching module is used to determine a target testing machine that matches the sample based on the sample size information and the predicted tensile strength.

[0040] On the other hand, an electronic device is provided, which includes a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the test machine matching method as described above.

[0041] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction and at least one program are stored in the computer storage medium, and the at least one instruction and the at least one program are loaded and executed by a processor to implement the test machine matching method described above.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] In the present invention, by obtaining the sample size information and sample element information of the sample, and then determining the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model; then determining the target testing machine that matches the sample based on the sample size information and the predicted tensile strength, it is possible to achieve intelligent and precise matching of the testing machine and the sample according to the sample type and size, thereby avoiding damage caused by improper selection of the testing machine, and by determining the testing machine that matches different types of samples through the predicted tensile strength of different types of samples combined with the test size information, it is possible to achieve automation of testing machine matching and improve the efficiency and accuracy of testing machine matching, thereby improving the efficiency, accuracy and reliability of material mechanical properties testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a flow chart of a test machine matching method provided by an embodiment of the present invention;

[0046] Figure 2 It is a structural schematic diagram of a testing machine matching device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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 ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0048] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0049] In the embodiments of the present invention, the term "module" or "unit" refers to a computer program or portion of a computer program that has a predetermined function and works together with other related components to achieve a predetermined goal. The term "module" or "unit" refers to a computer program or portion of a computer program that has a predetermined function and works together with other related components to achieve a predetermined goal. The term "module" or "unit" may be implemented in whole or in part using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a single processor (or multiple processors or memories) may be used to implement one or more modules or units. Furthermore, each module or unit may be part of an overall module or unit that incorporates the functionality of that module or unit.

[0050] Various exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0051] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0052] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0053] In addition, numerous specific details are provided in the following detailed description to better illustrate the present invention. Those skilled in the art will appreciate that the present invention may be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of the present invention.

[0054] Figure 1 It is a flowchart of a test machine matching method provided by an embodiment of the present invention. This specification provides method operation steps such as the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative work. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in sequence or in parallel (for example, in a parallel processor or multi-threaded processing environment) according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 1 As shown, the above method may include:

[0055] S101: Obtaining sample size information and sample element information of the sample;

[0056] In a specific embodiment, the sample can be a sample used for mechanical property testing; the sample size information can characterize the geometric properties of the material sample; optionally, the sample size information can include the length, thickness, etc. of the material sample; the sample element information can characterize the component information contained in the material sample.

[0057] In an optional embodiment, the above-mentioned acquisition of the sample size information and sample element information of the sample may include:

[0058] Controlling the size measuring device to perform size measurement on the sample to obtain sample size information;

[0059] The type identification device is controlled to perform element identification processing on the sample to obtain element information of the sample.

[0060] In a specific embodiment, the size measuring device is used to measure the size of the sample, and the structure and installation of the size measuring device can be set in combination with actual application requirements; the type identification device is used to identify the elements of the sample, and the structure and installation of the type identification device can be set in combination with actual application requirements.

[0061] In an optional embodiment, the size measuring device may include a laser ranging multi-sensor, and the control of the size measuring device to perform size measurement processing on the sample to obtain sample size information may include:

[0062] Controlling the laser ranging multi-sensor to emit laser beams to the sample at different target angles, and determining the emission time of each laser ranging sensor to send the laser beam, the laser ranging multi-sensor being a preset number of laser ranging sensors set at relative positions in the thickness direction of the sample;

[0063] After each laser distance measuring sensor receives the laser beam reflected from the surface of the sample, a receiving time of the laser distance measuring sensor receiving the laser beam is determined;

[0064] Determine the time difference of each laser ranging sensor according to the emission time and the reception time of each laser ranging sensor;

[0065] Determine the thickness information of the sample based on the time difference of each laser ranging sensor and the longitudinal distance between a preset number of laser ranging sensors;

[0066] Control the sample to move at a constant speed along the measuring length track under the laser ranging sensor;

[0067] The time from the first contact of the sample with the laser beam of the laser ranging sensor to the time it leaves the laser beam of the laser ranging sensor is determined as the measurement time;

[0068] Determine the length information of the sample based on the moving rate of the sample and the measurement time;

[0069] Based on the thickness information and the length information, the sample size information is determined.

[0070] In a specific embodiment, the dimension measuring device can be equipped with a preset number of high-precision laser ranging sensors. The preset number of high-precision laser ranging sensors can be set at relative positions in the thickness direction of the sample. The preset number of laser ranging sensors can be set based on actual application requirements. Specifically, the preset number of laser ranging sensors can be two groups of laser ranging sensors. The two groups of laser ranging sensors can be set at relative positions in the thickness direction of the sample. Optionally, when the sample is a round rod, the two groups of laser ranging sensors are set at relative positions in the diameter direction of the round rod; when the sample is a plate, the two groups of laser ranging sensors are set at relative positions in the thickness direction of the plate, that is, the laser ranging sensors can be set at relative positions above and below the plate. The longitudinal distance between the preset number of laser ranging sensors can be the distance between the preset number of laser ranging sensors, specifically, the distance between the two groups of laser ranging sensors set at relative positions in the thickness direction of the sample.

[0071] Optionally, determining the sample thickness information based on the time difference between each laser ranging sensor and the longitudinal distance between a preset number of laser ranging sensors may include: determining a target distance between each laser ranging sensor and the sample based on the time difference between each laser ranging sensor; and determining the sample thickness information based on each target distance and the longitudinal distance between the preset number of laser ranging sensors. Specifically, the target distance between each laser ranging sensor and the sample = speed of light * (time difference / 2), and the sample thickness is the difference between the longitudinal distance between the laser ranging sensors and each target distance.

[0072] In one specific embodiment, the time from the time the specimen first contacts the laser beam of the laser ranging sensor to the time it leaves the laser beam of the laser ranging sensor is determined as the measurement time. The laser ranging sensor may be any identical laser ranging sensor among a preset number of laser ranging sensors. Optionally, the length of the specimen = specimen movement rate * measurement time.

[0073] In a specific embodiment, the dimension measurement device may further include an image acquisition device; controlling the dimension measurement device to perform dimension measurement processing on the specimen to obtain specimen dimension information may include: controlling the image acquisition device to acquire images of the specimen from multiple angles to obtain panoramic image data of the specimen; and determining the specimen dimension information of the specimen based on the panoramic image data. The specimen's outline may be identified based on the panoramic image data, and then the pixel size of the specimen in the image may be converted to the specimen's actual dimension through proportional calculation.

[0074] In the above embodiment, by measuring the size of the sample using the size measuring device, errors in manual measurement can be avoided, labor can be reduced, and the accuracy and efficiency of the sample size measurement can be improved, thereby improving the accuracy of the testing machine matching.

[0075] In an optional embodiment, the type identification device may include a spectrum analyzer; and controlling the type identification device to perform element identification processing on the sample to obtain element information of the sample may include:

[0076] Control the spectrum analyzer to perform element identification processing on the sample to obtain the sample element information.

[0077] In a specific embodiment, the spectrum analyzer may analyze the ratio of the main component elements of the sample. Optionally, the sample element information may include the sample elements and the sample element ratios.

[0078] In the above embodiment, the element identification processing of the sample by the type identification device can improve the accuracy and efficiency of the sample element identification, so as to facilitate the subsequent matching of the testing machine and improve the accuracy of the testing machine matching.

[0079] S102: Determine the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model;

[0080] In a specific embodiment, the tensile strength prediction model can be used to predict the tensile strength of a determined specimen.

[0081] In an optional embodiment, the above-mentioned determination of the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model may include:

[0082] Determining sample type information of the sample based on the sample element information;

[0083] Based on the sample type information, determining a tensile strength prediction model corresponding to the sample type information;

[0084] The sample element information is input into the tensile strength prediction model to obtain the predicted tensile strength of the sample.

[0085] In a specific embodiment, the sample type information may be classification information of the sample according to the sample element information. Optionally, the sample type information may include aluminum alloy, titanium alloy, magnesium alloy, etc.

[0086] In a specific embodiment, different types of samples may have corresponding tensile strength prediction models. Optionally, taking 2xxx (e.g., 2024) series aluminum alloy as an example, the main strengthening element is Cu, supplemented by Mg, Mn, etc., the tensile strength prediction model corresponding to the aluminum alloy can be determined by multivariate regression fitting. The tensile strength prediction model can be shown as follows:

[0087] ;

[0088] in, The predicted tensile strength of the aluminum alloy can be represented by a, b, c, d, e, and f. These regression coefficients can be obtained by fitting experimental data (e.g., tensile test results of different specimens). Alternatively, the elemental information of the aluminum alloy specimen can be input into the tensile strength prediction model to obtain the predicted tensile strength of the aluminum alloy.

[0089] Specifically, in Al-Cu-Mg alloy, the tensile strength prediction model of Al-Cu-Mg can be Specifically, the sample element information of the Al-Cu-Mg alloy can be input into the tensile strength prediction model to obtain the predicted tensile strength of the Al-Cu-Mg alloy.

[0090] Optionally, if the sample type information is steel, the tensile strength of steel is closely related to its carbon content and is also affected by alloying elements (such as Mn, Cr, and Ni). The carbon equivalent converts the effect of each element on strength and hardenability into an equivalent carbon content. Based on the sample type information, a tensile strength prediction model corresponding to the steel can be determined. This tensile strength prediction model can determine the carbon equivalent of the steel based on the sample element information, and then determine the predicted tensile strength of the steel based on the carbon equivalent. Optionally, the carbon equivalent can be determined based on the following formula:

[0091] ;

[0092] Optionally, when CE<0.4%, the tensile strength of the steel material can be 400-500 MPa; for every 0.1% increase in CE, the strength of the steel material can be increased by 50-80 MPa.

[0093] Optionally, when the sample type information is titanium alloy, such as Ti-6Al-4V, the tensile strength prediction model of the titanium alloy can be as follows:

[0094] .

[0095] Optionally, when the sample type information is a magnesium alloy, such as AZ31, the tensile strength prediction model of the magnesium alloy may be as follows:

[0096] .

[0097] In the above embodiment, the corresponding tensile strength prediction model is determined based on the sample type information, and then the predicted tensile strength of the sample is determined based on the sample element information. This can improve the accuracy of the predicted tensile strength, and then improve the accuracy of the sample testing machine matching, thereby avoiding unnecessary damage caused by improper testing machine selection.

[0098] S103: Based on the sample size information and the predicted tensile strength, determine a target testing machine that matches the sample.

[0099] In an optional embodiment, the determination of a target testing machine that matches the sample based on the sample size information and the predicted tensile strength may include:

[0100] Based on the sample size information, determine the cross-sectional area information of the sample;

[0101] Determine the target load for the specimen based on the cross-sectional area information and the predicted tensile strength;

[0102] Based on the target load and specimen size information, determine the target testing machine.

[0103] In a specific embodiment, the above-mentioned determination of the target testing machine based on the target load and the sample size information may include: determining the initial testing machine based on the target load; determining the target testing machine based on the sample size information and the initial testing machine. Optionally, the target load of the sample can be determined based on the product of the cross-sectional area information of the sample and the predicted tensile strength. Optionally, among the testing machines of different ranges, the minimum range testing machine with a preset safety factor greater than the target load is matched as the initial testing machine of the sample. Optionally, the preset safety factor can be set in combination with actual application requirements. For example, the preset safety factor can be 120%, so as to ensure safety during the mechanical properties test and efficient use of equipment.

[0104] In a specific embodiment, the above-mentioned determination of the target testing machine based on the sample size information and the initial testing machine may include: comparing the sample size information with a preset size threshold of the initial testing machine to obtain a size comparison result; when the size comparison result indicates that the sample size information is less than or equal to the preset size, determining the initial testing machine as the target testing machine that matches the sample. Optionally, the preset size threshold may be a sample size threshold that can be tested within the testing machine's own moving range. After determining the initial testing machine based on the target load, further determining whether the initial testing machine is feasible based on the sample size information can ensure that the testing machine performs mechanical property testing on the sample when the sample size meets the test size of the testing machine, thereby improving the accuracy and authenticity of the material mechanical property testing.

[0105] In an optional embodiment, after determining a target testing machine that matches the sample based on the sample size information and tensile strength, the method may include:

[0106] Acquiring specimen test information of the specimen, the specimen test information is obtained by controlling a target testing machine to perform a mechanical test on the specimen;

[0107] According to the preset model updating algorithm, the tensile strength prediction model is updated in combination with the predicted tensile strength and the sample test information to obtain an updated tensile strength prediction model, wherein the updated tensile strength prediction model is used to determine the predicted tensile strength of the sample.

[0108] In a specific embodiment, the sample test information can represent the results of the mechanical property test of the sample under the target testing machine. Optionally, the sample test information may include strength information, plasticity information, hardness information, toughness information, etc. Optionally, the preset model update algorithm can be set in combination with actual application requirements. Optionally, the preset model update algorithm can be a least squares method, which determines the model loss based on the predicted tensile strength and the actual tensile strength in the sample test information, and iteratively updates the model coefficients based on the model loss; optionally, the model loss can be determined based on a preset loss function, which can be set in combination with actual application requirements. Specifically, the preset loss function can be a mean square error. Specifically, the coefficients of the model can be updated by minimizing the mean square error.

[0109] In the above embodiment, as the sample test information continues to increase, the tensile strength prediction model is continuously iteratively optimized, and the prediction accuracy is gradually improved, which can improve the intelligence and accuracy of the testing machine matching, thereby improving the efficiency and accuracy of the material mechanical properties testing.

[0110] It can be seen from the technical solutions provided in the above embodiments of this specification that this specification obtains the sample size information and sample element information of the sample, and then determines the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model; then determines the target testing machine that matches the sample based on the sample size information and the predicted tensile strength, which can achieve intelligent and precise matching of the testing machine and the sample according to the sample type and size, thereby avoiding damage caused by improper selection of the testing machine, and through the predicted tensile strength of different types of samples, combining the test size information to determine the testing machine that matches different types of samples, it can realize the automation of testing machine matching and improve the efficiency and accuracy of testing machine matching, and can ensure that the accuracy and stroke of the testing machine meet the requirements during the mechanical properties testing process, thereby improving the efficiency, accuracy and reliability of material mechanical properties testing.

[0111] The embodiment of the present invention further provides a testing machine matching device, correspondingly, Figure 2 Schematic diagram of a test machine matching device provided by an embodiment of the present invention; Figure 2 As shown, the above device includes:

[0112] The information acquisition module 210 is used to obtain the sample size information and sample element information of the sample;

[0113] a strength determination module 220 for determining the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model;

[0114] The testing machine matching module 230 is configured to determine a target testing machine that matches the sample based on the sample size information and the predicted tensile strength.

[0115] In an optional embodiment, the strength determination module 220 includes:

[0116] a type determining unit, configured to determine sample type information of the sample based on the sample element information;

[0117] a model determining unit, configured to determine, based on the sample type information, a tensile strength prediction model corresponding to the sample type information;

[0118] The strength determination unit is used to input the sample element information into the tensile strength prediction model to obtain the predicted tensile strength of the sample.

[0119] In an optional embodiment, the test machine matching module 230 is specifically configured to:

[0120] Determining cross-sectional area information of the sample based on the sample size information;

[0121] determining a target load for the specimen based on the cross-sectional area information and the predicted tensile strength;

[0122] The target testing machine is determined based on the target load and the sample size information.

[0123] In an optional embodiment, the device includes: a model updating module for

[0124] Acquiring sample test information of the sample, wherein the sample test information is obtained by controlling a target testing machine to perform a mechanical test on the sample;

[0125] According to a preset model updating algorithm, the tensile strength prediction model is updated in combination with the predicted tensile strength and the sample test information to obtain an updated tensile strength prediction model, wherein the updated tensile strength prediction model is used to determine the predicted tensile strength of the sample.

[0126] In an optional embodiment, the information acquisition module 210 includes:

[0127] A size acquisition module is used to control a size measuring device to perform size measurement processing on the sample to obtain sample size information;

[0128] An element acquisition module is configured to control the category identification device to perform element identification processing on the sample to obtain sample element information.

[0129] In an optional embodiment, the size measurement device comprises a laser ranging multi-sensor, and the size acquisition module is specifically configured to

[0130] control the laser ranging multi-sensor to emit laser beams to the sample at different target angles, determine the emission time of each laser ranging sensor sending the laser beam, and the laser ranging multi-sensor is a preset number of laser ranging sensors arranged at relative positions in the thickness direction of the sample;

[0131] determine the receiving time of the laser ranging sensor receiving the laser beam reflected from the sample surface when the laser beam reflected from the sample surface is received by each laser ranging sensor;

[0132] determine the time difference of each laser ranging sensor according to the emission time and the receiving time of each laser ranging sensor;

[0133] determine the thickness information of the sample based on the time difference of each laser ranging sensor and the longitudinal distance between the preset number of laser ranging sensors;

[0134] control the sample to move at a constant speed along the measurement length track under the laser ranging sensor;

[0135] determine the measurement time as the time from when the sample first contacts the laser beam of the laser ranging sensor to when the laser beam leaves the laser ranging sensor;

[0136] determine the length information of the sample based on the moving speed of the sample and the measurement time;

[0137] determine the size information of the sample based on the thickness information and the length information.

[0138] In an optional embodiment, the category identification device comprises a spectrum analyzer, and the element acquisition module is specifically configured to

[0139] control the spectrum analyzer to perform element identification processing on the sample to obtain sample element information.

[0140] As to the device in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the method, and will not be described in detail here.

[0141] An embodiment of the present invention also provides an electronic device, comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the test machine matching method as described in any one of the method embodiments.

[0142] An embodiment of the present invention also provides a computer storage medium, which can be set in a server to store at least one instruction, at least one program, code set or instruction set for implementing the method embodiment. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by the processor to implement the test machine matching method as described in any one of the method embodiments.

[0143] Optionally, in an embodiment of the present invention, the storage medium may be located in at least one of a plurality of network servers in a computer network. Optionally, in an embodiment of the present invention, the storage medium may include, but is not limited to, a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk, among other media capable of storing program code.

[0144] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple flow charts and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple flow charts and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple flow charts and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the function or action of the specification, or can be implemented by a combination of dedicated hardware and computer instructions.

[0149] Finally, it should be noted that the embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A testing machine matching method, characterized in that: The method comprises: Obtaining sample size information and sample element information of the sample; Determining the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model; A target testing machine matching the sample is determined based on the sample size information and the predicted tensile strength.

2. The testing machine matching method according to claim 1, characterized in that: The step of determining the predicted tensile strength of the sample based on the sample element information and the tensile strength prediction model includes: Determining sample type information of the sample based on the sample element information; Based on the sample type information, determining a tensile strength prediction model corresponding to the sample type information; The sample element information is input into the tensile strength prediction model to obtain the predicted tensile strength of the sample.

3. The testing machine matching method according to claim 1, characterized in that: The step of determining a target testing machine that matches the sample based on the sample size information and the predicted tensile strength includes: Determining cross-sectional area information of the sample based on the sample size information; determining a target load for the specimen based on the cross-sectional area information and the predicted tensile strength; The target testing machine is determined based on the target load and the sample size information.

4. The testing machine matching method according to claim 1, characterized in that: After determining a target testing machine that matches the sample based on the sample size information and the tensile strength, the method includes: Acquiring sample test information of the sample, wherein the sample test information is obtained by controlling a target testing machine to perform a mechanical test on the sample; According to a preset model updating algorithm, the tensile strength prediction model is updated in combination with the predicted tensile strength and the sample test information to obtain an updated tensile strength prediction model, wherein the updated tensile strength prediction model is used to determine the predicted tensile strength of the sample.

5. The testing machine matching method according to claim 1, characterized in that: The step of obtaining the sample size information and sample element information includes: Controlling the size measuring device to perform size measurement processing on the sample to obtain sample size information; The type identification device is controlled to perform element identification processing on the sample to obtain sample element information.

6. The testing machine matching method according to claim 5, characterized in that: The dimension measuring device includes a laser ranging multi-sensor, and the control dimension measuring device performs dimension measurement processing on the sample to obtain the sample dimension information, including: Controlling a laser ranging multi-sensor to emit a laser beam to the sample at different target angles, and determining the emission time of each laser ranging sensor to transmit the laser beam, wherein the laser ranging multi-sensor is a preset number of laser ranging sensors arranged at relative positions in the thickness direction of the sample; After each laser ranging sensor receives the laser beam reflected from the sample surface, determining a reception time of the laser ranging sensor receiving the laser beam; Determining the time difference of each laser ranging sensor according to the emission time and the reception time of each laser ranging sensor; Determining thickness information of the sample based on the time difference of each laser ranging sensor and the longitudinal distance between the preset number of laser ranging sensors; Controlling the sample to move at a constant speed along a measuring length track under a laser ranging sensor; Determine the time from when the sample first contacts the laser beam of the laser ranging sensor to when it leaves the laser beam of the laser ranging sensor as the measurement time; determining length information of the sample based on the moving speed of the sample and the measurement time; The sample size information is determined based on the thickness information and the length information.

7. The testing machine matching method according to claim 5, characterized in that: The type identification device includes a spectrum analyzer; the control type identification device performs element identification processing on the sample to obtain sample element information, including: The spectrum analyzer is controlled to perform element identification processing on the sample to obtain element information of the sample.

8. A testing machine matching device, characterized in that: The device comprises: An information acquisition module is used to obtain sample size information and sample element information of the sample; a strength determination module, configured to determine the predicted tensile strength of the sample based on the sample element information and a tensile strength prediction model; A testing machine matching module is used to determine a target testing machine that matches the sample based on the sample size information and the predicted tensile strength.

9. An electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the test machine matching method according to any one of claims 1 to 7.

10. A computer storage medium, wherein at least one instruction and at least one program are stored in the computer storage medium, wherein the at least one instruction and the at least one program are loaded and executed by a processor to implement the trial matching method according to any one of claims 1 to 7.

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