A fully automatic concrete rapid freeze-thaw test working method and system

Abstract

The present invention relates to the technical field of freeze-thaw test, and specifically to a fully automatic concrete rapid freeze-thaw test working method and system, the method comprising: pre-treating the test piece, and numbering the test piece that has completed the pre-treatment; obtaining the initial value of the relevant parameters of each test piece before the test; determining and limiting the process conversion information of the test based on the MPC model; monitoring the mechanical information of the test process, and giving early warning of the problem information; performing external flaw detection on each test piece that has completed a set number of freeze-thaw cycles, and evaluating the antifreeze performance of the test piece. Through the present invention, the problem that the original method is not flexible and accurate enough in parameter control, and the automated monitoring of the entire process is insufficient, and the intelligentization of parameter control and the automated monitoring of the entire test process are improved.

Description

Technical Field

[0001] The invention relates to the technical field of freeze-thaw tests, and in particular to a full-automatic concrete rapid freeze-thaw test working method and system. Background Art

[0002] The concrete freeze-thaw test is a test used to evaluate the performance and durability of concrete materials under freezing and thawing cycles. The test simulates the freeze-thaw effects suffered by concrete structures under cold climate conditions to study and analyze indicators such as concrete's anti-freeze performance and strength changes.

[0003] The advantages of the rapid freeze-thaw test method are that the test cycle is short, the relative dynamic elastic modulus is used to evaluate the frost resistance of concrete accurately and without damage, the test can be repeated, and the test efficiency is high. However, the existing test methods are still not intelligent and flexible enough in controlling relevant parameters, and the monitoring of relevant parameter information is not in place and timely, which affects the final results of the test. Therefore, it is particularly important to further optimize and improve the rapid freeze-thaw test of concrete.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0005] The present invention provides a fully automatic concrete rapid freeze-thaw test working method and system, thereby effectively solving the problems pointed out in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A fully automatic concrete rapid freeze-thaw test working method, the method comprising:

[0008] Pre-treating the test pieces and numbering the test pieces that have completed the pre-treatment;

[0009] Obtaining initial values ​​of relevant parameters of each test piece before the test;

[0010] Based on the MPC model, determining and limiting process transition information of the test;

[0011] Monitor the mechanical information of the test process and issue early warning for problem information;

[0012] External flaw detection is performed on the test piece after each set number of freeze-thaw cycles, and the antifreeze performance of the test piece is evaluated.

[0013] Further, pre-processing the specimens and numbering the pre-processed specimens includes:

[0014] The test piece is made into a prism test piece of 100mm*100mm*400mm;

[0015] Specify the total number of specimens to be tested in a single test;

[0016] The test pieces are numbered and grouped, with three test pieces in each group.

[0017] Three of the test specimens were from the same concrete sample.

[0018] Furthermore, obtaining the initial values ​​of the relevant parameters of each of the test pieces before the test includes:

[0019] Measuring the initial mass of the test piece after the pretreatment;

[0020] The initial value of the transverse fundamental frequency of the specimen is obtained.

[0021] Further, based on the MPC model, the process transition information of the test is determined and restricted, including:

[0022] Based on the MPC model, establish the control model of the entire test process;

[0023] Determine the restriction requirements of the conversion information and control the parameter input within the specified range;

[0024] In each control cycle, calculating the parameter control input within the specified range;

[0025] The parameter control input is adjusted in real time according to the test process status and the value of the objective function.

[0026] Furthermore, based on the MPC model, a test process control model is established, including:

[0027] Conduct system modeling for the entire test process and establish mathematical models for each parameter that needs to be controlled;

[0028] Setting the objective function of each of the parameters that need to be controlled;

[0029] Using the MPC framework, the multi-parameter control problem is transformed into a multi-variable optimization problem.

[0030] Furthermore, the mechanical information of the test process is monitored, and early warning of problem information is issued, including:

[0031] Obtaining mechanical parameter information of the test process;

[0032] Processing and analyzing the mechanical parameter information;

[0033] Using a machine learning algorithm, the mechanical parameter information is compared with preset values ​​to monitor abnormal data;

[0034] When the abnormal data is monitored, an early warning signal is output and the test process is terminated.

[0035] Furthermore, external flaw detection is performed on the test piece after each set number of cycles, and the antifreeze performance of the test piece is evaluated, including:

[0036] Performing external flaw detection on the test piece using a laser sensor;

[0037] Calculating relevant evaluation parameters of the test piece that has passed the external flaw detection;

[0038] Based on the calculation, the antifreeze performance of the test piece is evaluated.

[0039] Further, the relevant parameters of the test piece that passes the external flaw detection are measured and calculated, including:

[0040] measuring the mass of the test piece and calculating the mass loss rate;

[0041] The transverse fundamental frequency value of the specimen is measured and the relative dynamic elastic modulus is calculated using the formula:

[0042]

[0043] Among them, P i is the relative dynamic elastic modulus, f ni is the transverse fundamental frequency value of the specimen after the nth cycle, f 0i is the initial value of the transverse fundamental frequency of the specimen;

[0044] The average values ​​of the mass loss rate and the relative dynamic elastic modulus of each group of test pieces are calculated.

[0045] A fully automatic concrete rapid freeze-thaw test working system, the system comprising:

[0046] A preprocessing module, which preprocesses the specimens and numbers the specimens that have completed the preprocessing;

[0047] An initial parameter acquisition module is used to obtain the initial values ​​of the relevant parameters of each test piece before the test;

[0048] A conversion information determination module, based on the MPC model, determines and limits the process conversion information of the test;

[0049] A mechanical information monitoring module monitors the mechanical information of the test process and issues early warnings for problem information;

[0050] The antifreeze performance evaluation module performs external flaw detection on the test piece after each set number of cycles, and evaluates the antifreeze performance of the test piece.

[0051] Furthermore, the mechanical information monitoring module includes:

[0052] A mechanical information acquisition unit, for acquiring mechanical parameter information of the test process;

[0053] An information processing and analysis unit processes and analyzes the mechanical parameter information;

[0054] An abnormal data monitoring unit uses a machine learning algorithm to compare the mechanical parameter information with a preset value to monitor abnormal data;

[0055] The warning signal output unit outputs a warning signal and terminates the test process when the abnormal data is detected.

[0056] The technical solution of the present invention can achieve the following technical effects:

[0057] It effectively solves the problems of the original method's inflexible and inaccurate parameter control and insufficient automated monitoring of the entire process, and improves the intelligence of parameter control and automation of monitoring in the entire test process.

[0058] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 It is a flow chart of the working method of the fully automatic concrete rapid freeze-thaw test;

[0061] Figure 2 A schematic diagram of the process for pre-treating and numbering the test pieces;

[0062] Figure 3 A flow chart for determining and limiting process transition information for the test;

[0063] Figure 4 A flow chart for establishing the test process control model;

[0064] Figure 5 A schematic diagram of the process for monitoring and early warning of mechanical information during the test process;

[0065] Figure 6 A schematic diagram of the process for conducting external flaw detection on a specimen and evaluating the antifreeze performance of the specimen;

[0066] Figure 7 It is a structural schematic diagram of the fully automatic concrete rapid freeze-thaw test working system. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0069] Embodiment 1

[0070] like Figure 1 As shown, the present invention provides a fully automatic concrete rapid freeze-thaw test working method, the method comprising:

[0071] S100: pre-treat the specimens and number the specimens that have completed the pre-treatment;

[0072] Pretreatment should make the specimen meet the test standards. At the same time, after pretreatment, ensure that the surface of the specimen is clean and free of obvious contamination to avoid affecting the test results.

[0073] S200: Obtaining the initial values ​​of relevant parameters of each specimen before the test;

[0074] S300: Determine and limit the process transition information of the test based on the MPC model;

[0075] In this solution, the MPC model is selected as the control model for several reasons:

[0076] Multivariable control: MPC can handle multiple input and output variables and is suitable for systems that require simultaneous control of multiple parameters, such as the need to control temperature and time in rapid freeze-thaw tests on concrete.

[0077] Unknown or changing models: MPC does not require precise mathematical models, so it performs well in dealing with systems that are difficult to model or when the model needs to change. In this solution, the control of temperature and time is limited by the range of fluctuations, and the MPC model can better cope with changes within this range.

[0078] Predictive capabilities: MPC uses predictions of future time steps to optimize control inputs, which allows the system to better respond to future changes and improves control performance.

[0079] Real-time: MPC aims at real-time control and can calculate the optimal control input in each control cycle. It is suitable for systems that need to respond quickly to changes.

[0080] S400: monitors the mechanical information during the test process and issues early warning for problem information;

[0081] The mechanical information of the test process refers to the mechanical parameters of the equipment that controls the temperature, such as whether the compressor is overpressure, whether the flow meter is overpressure, whether the electric heating is overtemperature, whether the compressor, water pump and fan are thermally overloaded, etc. These mechanical parameter information are the prerequisite for ensuring that the temperature and other parameters of the entire test process are adjusted under the specified requirements.

[0082] S500: Perform external flaw detection on the specimens after each completed cycle and evaluate the antifreeze performance of the specimens.

[0083] The number of cycles is divided into small cycles and large cycles. The large cycle is the main cycle of the test, usually representing the total number of cycles of the test. The large cycle number is the core parameter of the test, which determines how many complete freeze-thaw cycles the test will perform. The setting of the large cycle number is determined according to the research purpose and standard of the test; the small cycle is the sub-cycle that constitutes the large cycle. Each small cycle generally includes 25-30 complete freeze-thaw cycles. The freeze-thaw process is the process of the concrete specimen from freezing to melting and then freezing. The external flaw detection of the specimen and the measurement of related parameters should be carried out after each large and small cycle to determine how many cycles the specimen reaches the termination condition.

[0084] The technical solution of the present invention effectively solves the problems of the original method's insufficient flexibility and accuracy in parameter control and insufficient automated monitoring of the entire process, thereby improving the intelligence of parameter control and automated monitoring of the entire test process.

[0085] In this scheme, the establishment of the MPC model and the monitoring of mechanical information are particularly important, which involves the entire test hardware and software. Through effective model establishment and mechanical information monitoring, the comprehensive automated control and monitoring of the entire test process can be guaranteed, ensuring the safety and smoothness of the entire test and reducing manual intervention.

[0086] As a preferred embodiment of the above, Figure 2 As shown, step S100, obtaining the initial values ​​of the relevant parameters of each specimen before the test, includes:

[0087] S110: The specimen is made into a prism specimen of 100 mm*100 mm*400 mm;

[0088] S120: Specifies the total number of specimens for a single test;

[0089] S130: Number the specimens and group them into three groups.

[0090] Three of the specimens were from the same concrete sample.

[0091] Specifically, the total number of single tests is determined according to the actual equipment used, generally thirty, and it is ensured that every three specimens come from the same concrete sample. The performance of concrete materials can be affected by the heterogeneity of the material. By selecting multiple specimens from the same sample, the uniformity and representativeness of the sample can be better considered, while avoiding the interference of extreme values ​​on related parameters after the experiment.

[0092] As a preferred embodiment of the above embodiment, step S200, obtaining the initial values ​​of the relevant parameters of each test piece before the test, includes:

[0093] S210: measuring the initial mass of the pre-treated specimen;

[0094] S220: Obtain the initial value of the transverse fundamental frequency of the specimen.

[0095] In this scheme, the initial mass and initial value of the transverse fundamental frequency are measured in order to compare and calculate the relevant parameters of the specimen after the freeze-thaw cycle in preparation for the subsequent performance evaluation;

[0096] As a preferred embodiment of the above, Figure 3 As shown, step S300 determines and limits the process conversion information of the test based on the MPC model, including:

[0097] S310: Based on the MPC model, a control model for the entire test process is established;

[0098] S320: Determine the restriction requirements of the conversion information and control the parameter input within the specified range;

[0099] S330: In each control cycle, calculating the parameter control input within a specified range;

[0100] S340: Adjust parameter control input in real time according to the test process status and the value of the objective function.

[0101] Specifically, firstly, a control model of the entire test process is established. For the concrete rapid freeze-thaw test, this means establishing a mathematical model between temperature and time parameters to describe how they affect the concrete performance and determine the restriction requirements of the conversion information. According to the test requirements, the following restriction requirements need to be set in the control model:

[0102] The freezing and thawing temperature is based on the central specimen. Each freezing and thawing cycle should be completed within 2 to 4 hours, of which the time used for thawing should not be less than 1 / 4 of the entire freezing and thawing time. At the end of freezing and thawing, the lowest and highest temperatures in the center of the specimen should be controlled at (-18±2)℃ and (5±2)℃ respectively. The time taken for each specimen to drop from 3℃ to -16℃ should not be less than 1 / 2 of the freezing time. The time taken for each specimen to rise from -16℃ to 3℃ should not be less than 1 / 2 of the entire thawing time. The conversion time between freezing and thawing should not exceed 10 minutes.

[0103] In each control cycle, the control cycle should be set according to the above constraints, and the MPC model is used to calculate the control input of the parameters, which includes the temperature setting values ​​for freezing and melting to meet the objective function and constraint requirements of the test. The control input of the parameters can be adjusted in real time according to the test process status and the value of the objective function. For example, if an abnormality or deviation from the target occurs during the test, the MPC model will re-optimize the control input to bring the test back to the target trajectory.

[0104] As a preferred embodiment of the above, Figure 4 As shown, step S310, based on the MPC model, establishes a control model for the entire test process, including:

[0105] S311: Systematically model the entire test process and establish mathematical models of all parameters that need to be controlled;

[0106] S312: Setting the objective function of each parameter to be controlled;

[0107] S313: Use the MPC framework to transform the multi-parameter control problem into a multi-variable optimization problem.

[0108] Specifically, the entire process of rapid freeze-thaw test of concrete is systematically modeled, which includes establishing mathematical models of various control parameters related to the test process. In this test, the main mathematical models that need to be established are time and temperature. The mathematical model should describe the relationship between parameters, as well as their relationship with test results, physical processes such as material transfer, etc.

[0109] Set clear objective functions for each parameter that needs to be controlled. These objective functions are mathematical representations of the test objectives and performance requirements. Objective functions can include making the temperature fluctuate within a specified range, controlling the freeze-thaw cycle time, ensuring the heating and cooling rates, etc. The weight of each objective function can be set according to its importance. Use the MPC framework to handle multi-parameter control problems. The core idea of ​​MPC is to optimize multiple parameter control inputs in each control cycle to achieve the objective functions and restriction requirements of each parameter. In each control cycle, the MPC model calculates the optimal parameter control inputs so that the test process runs according to the predetermined goals. The system not only considers multiple input parameters, such as temperature and time, but also integrates them into a comprehensive optimization problem to find the best parameter combination in each control cycle to meet the test requirements.

[0110] As a preferred embodiment of the above, Figure 5 As shown, step S400 monitors the mechanical information of the test process and issues an early warning for problem information, including:

[0111] S410: Acquiring mechanical parameter information during the test process;

[0112] S420: Processing and analyzing mechanical parameter information;

[0113] S430: Using a machine learning algorithm, compare the mechanical parameter information with preset values ​​to monitor abnormal data;

[0114] S440: When abnormal data is detected, a warning signal is output and the test process is terminated.

[0115] Specifically, the mechanical parameter information is cleaned, filtered, and feature extracted to ensure data availability. A machine learning algorithm is used to build a model to monitor the reasonable range of these mechanical parameters and predict their changing trends. In each time step or cycle, the mechanical parameter data is monitored to see if they exceed the normal range or show abnormal trends. When abnormal data is detected, a warning signal is output and the test process is terminated in a timely manner.

[0116] As a preferred embodiment of the above, Figure 6 As shown, step S500, performing external flaw detection on each test piece that completes a set number of cycles, and evaluating the antifreeze performance of the test piece, includes:

[0117] S510: Use laser sensor to perform external flaw detection on the test piece;

[0118] S520: Calculate relevant evaluation parameters for the test pieces that have passed the external flaw detection;

[0119] S530: Evaluate the antifreeze performance of the specimen based on the measurement.

[0120] Laser sensors are used to inspect the external surface of the test piece, which can include scanning the surface of the test piece to look for possible cracks, damage or other abnormal features. Laser sensors can generate high-resolution surface images or point cloud data to help identify potential problems. Once the external flaw detection test is completed, it is necessary to measure the relevant evaluation parameters of the test piece that passed the test. Through these measurements, information about the state of the test piece can be obtained, which is beneficial for the subsequent antifreeze performance evaluation. In this step, the antifreeze performance of the test piece is evaluated based on the parameters calculated in S520 and the results of the external flaw detection test. This may involve comparing the actual state of the test piece with the requirements of the expected standard or specification. If the test piece exhibits abnormal characteristics or damage, a more in-depth analysis may be required to determine whether its antifreeze performance meets the expected requirements.

[0121] As a preferred embodiment of the above embodiment, step S520, measuring relevant parameters of the test piece that has passed the external flaw detection, includes:

[0122] S521: Measure the mass of the specimen and calculate the mass loss rate;

[0123] S522: Measure the transverse fundamental frequency value of the specimen and calculate the relative dynamic elastic modulus. The formula is:

[0124]

[0125] Among them, P i is the relative dynamic elastic modulus, f ni is the transverse fundamental frequency of the specimen after the nth freeze-thaw cycle, f 0i is the initial value of the transverse fundamental frequency of the specimen;

[0126] S523: Calculate the average value of the mass loss rate and relative dynamic elastic modulus of each group of specimens.

[0127] Generally speaking, the transverse fundamental frequency of the specimen is measured every 25 freeze-thaw cycles. Before measurement, the scum on the surface of the specimen is cleaned and the surface moisture is wiped off, and then the external damage is checked and the mass of the specimen is weighed. Then the transverse fundamental frequency is measured according to the prescribed method. The test is stopped when the concrete specimen reaches 300 freeze-thaw cycles, the mass loss exceeds 5%, or the relative dynamic elastic modulus is less than 60%.

[0128] The arithmetic mean needs to be calculated for both the mass loss rate and the relative dynamic elastic modulus. For the relative dynamic elastic modulus, when the difference between the maximum or minimum value and the middle value exceeds 15% of the middle value, this value should be eliminated, and the arithmetic mean of the remaining two values ​​should be taken as the measured value; when the difference between the maximum and minimum values ​​and the middle value exceeds 15% of the middle value, the middle value should be taken as the measured value. For the mass loss rate, when a test result shows a negative value, the value 0 should be taken, and then the average value of the three specimens should be taken. When the difference between the maximum or minimum value and the middle value among the three values ​​exceeds 1%, this value should be eliminated, and the arithmetic mean of the remaining two values ​​should be taken as the measured value; when the difference between the maximum and minimum values ​​and the middle value exceeds 1%, the middle value should be taken as the measured value.

[0129] Embodiment 2

[0130] Based on the same inventive concept as the fully automatic concrete rapid freeze-thaw test working method in the aforementioned embodiment, the present invention also provides a fully automatic concrete rapid freeze-thaw test working system, such as Figure 7 As shown, the system includes:

[0131] A pre-processing module is used to pre-process the specimens and number the specimens that have completed the pre-processing;

[0132] The initial parameter acquisition module obtains the initial values ​​of the relevant parameters of each specimen before the test;

[0133] The conversion information determination module determines and limits the process conversion information of the test based on the MPC model;

[0134] Mechanical information monitoring module monitors the mechanical information during the test process and issues early warning for problem information;

[0135] The antifreeze performance evaluation module performs external flaw detection on each specimen that completes a set number of freeze-thaw cycles and evaluates the antifreeze performance of the specimen.

[0136] The above-mentioned adjustment system in the present invention can effectively realize the working method of the fully automatic concrete rapid freeze-thaw test, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0137] As a preferred embodiment of the above, the mechanical information monitoring module includes:

[0138] A mechanical information acquisition unit, which acquires mechanical parameter information during the test process;

[0139] Information processing and analysis unit, which processes and analyzes mechanical parameter information;

[0140] The abnormal data monitoring unit uses a machine learning algorithm to compare the mechanical parameter information with the preset values ​​to monitor abnormal data;

[0141] The warning signal output unit outputs a warning signal and terminates the test process when abnormal data is detected.

[0142] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0143] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A fully automatic concrete rapid freeze-thaw test working method, characterized in that: The method comprises: Pre-treating the specimens and numbering the specimens that have completed the pre-treatment, including: The test piece is made into a prism test piece of 100mm*100mm*400mm; Specify the total number of specimens to be tested in a single test; The test pieces are numbered and grouped, with three test pieces in each group. Three of the said specimens were from the same concrete sample; Obtain the initial values ​​of relevant parameters of each specimen before the test; Based on the MPC model, the process transition information of the test is determined and limited, including: Based on the MPC model, establish the control model of the entire test process; Determine the restriction requirements of the conversion information and control the parameter input within the specified range; In each control cycle, calculating the parameter control input within the specified range; According to the test process status and the value of the objective function, the parameter control input is adjusted in real time; Based on the MPC model, a control model for the entire test process is established, including: Conduct system modeling for the entire test process and establish mathematical models for each parameter that needs to be controlled; Setting the objective function of each of the parameters that need to be controlled; Using the MPC framework, the multi-parameter control problem is transformed into a multi-variable optimization problem; Monitor the mechanical information of the test process and issue early warnings for problem information, including: Obtaining mechanical parameter information of the test process; Processing and analyzing the mechanical parameter information; Using a machine learning algorithm, the mechanical parameter information is compared with preset values ​​to monitor abnormal data; When the abnormal data is detected, a warning signal is output and the test process is terminated; Perform external flaw detection on the specimen after each set number of freeze-thaw cycles, and evaluate the antifreeze performance of the specimen, including: Performing external flaw detection on the test piece using a laser sensor; Calculating relevant evaluation parameters of the test piece that has passed the external flaw detection; Based on the calculation, the antifreeze performance of the test piece is evaluated.

2. The fully automatic concrete rapid freeze-thaw test working method according to claim 1 is characterized in that: Obtaining the initial values ​​of the relevant parameters of each specimen before the test, including: Measuring the initial mass of the test piece after the pretreatment; The initial value of the transverse fundamental frequency of the specimen is obtained.

3. The fully automatic concrete rapid freeze-thaw test working method according to claim 1 is characterized in that , measuring and calculating relevant parameters of the test piece that has passed the external flaw detection, including: measuring the mass of the test piece and calculating the mass loss rate; The transverse fundamental frequency value of the specimen is measured and the relative dynamic elastic modulus is calculated using the formula: in, is the relative dynamic elastic modulus, is the transverse fundamental frequency value of the specimen after the nth cycle, is the initial value of the transverse fundamental frequency of the specimen; The average values ​​of the mass loss rate and the relative dynamic elastic modulus of each group of test pieces are calculated.

4. A fully automatic concrete rapid freeze-thaw test system, characterized in that: The fully automatic concrete rapid freeze-thaw test working method according to any one of claims 1 to 3 comprises: A preprocessing module, which preprocesses the specimens and numbers the specimens that have completed the preprocessing; The initial parameter acquisition module obtains the initial values ​​of the relevant parameters of each specimen before the test; A conversion information determination module, based on the MPC model, determines and limits the process conversion information of the test; A mechanical information monitoring module monitors the mechanical information of the test process and issues early warnings for problem information; The antifreeze performance evaluation module performs external flaw detection on the test piece after each set number of freeze-thaw cycles, and evaluates the antifreeze performance of the test piece.

5. The fully automatic concrete rapid freeze-thaw test system according to claim 4 is characterized in that: Mechanical information monitoring module, including: A mechanical information acquisition unit, for acquiring mechanical parameter information of the test process; An information processing and analysis unit processes and analyzes the mechanical parameter information; An abnormal data monitoring unit uses a machine learning algorithm to compare the mechanical parameter information with a preset value to monitor abnormal data; The warning signal output unit outputs a warning signal and terminates the test process when the abnormal data is detected.

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