A monitoring method and system for concrete pouring process

Abstract

The present invention discloses a monitoring method and system for a concrete pouring process, which relates to the field of concrete pouring technology. The method obtains a plurality of pouring calibration data, performs data preprocessing on each pouring calibration data, obtains a pouring calibration set, uses the pouring calibration set to calibrate a preset initial concrete pouring simulation model, obtains a target concrete pouring simulation model, and when pouring data to be tested is received, performs pouring simulation processing on the pouring data to be tested through the target concrete pouring simulation model to obtain corresponding pouring defect data, performs pouring evaluation on the pouring defect data based on the pouring data to obtain corresponding pouring monitoring results. The method solves the technical problem that traditional concrete pouring process monitoring mainly relies on manual observation and sampling detection, cannot reflect the bubble distribution, crack formation and temperature field changes in the pouring process in real time, makes it difficult for construction personnel to discover potential defects in a timely manner, and reduces the reliability of the concrete pouring process.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete pouring, and in particular to a monitoring method and system for a concrete pouring process. Background Art

[0002] In the construction industry, concrete pouring is a critical process that impacts structural quality. This is especially true for complex projects like high-rise buildings and long-span bridges, which place extremely high demands on the concrete's density, crack resistance, and durability. Therefore, effectively monitoring the concrete pouring process is crucial.

[0003] At present, traditional monitoring methods for the concrete pouring process mainly rely on manual observation and sampling inspection, but they cannot reflect key parameters such as bubble distribution, crack formation, and temperature field changes during the pouring process in real time. This makes it difficult for construction workers to detect potential defects in a timely manner, reducing the reliability of the concrete pouring process. Summary of the Invention

[0004] The present invention provides a method and system for monitoring the concrete pouring process, which solves the technical problem that traditional concrete pouring process monitoring methods mainly rely on manual observation and sampling detection, but cannot reflect key parameters such as bubble distribution, crack formation and temperature field changes during the pouring process in real time, making it difficult for construction personnel to discover potential defects in a timely manner, thereby reducing the reliability of the concrete pouring process.

[0005] A first aspect of the present invention provides a method for monitoring a concrete pouring process, comprising:

[0006] Acquire multiple pouring calibration data, and perform data preprocessing on each of the pouring calibration data to obtain a pouring calibration set;

[0007] Calibrate the preset initial concrete pouring simulation model using the pouring calibration set to obtain a target concrete pouring simulation model;

[0008] When the pouring data to be tested is received, pouring simulation processing is performed on the pouring data to be tested by the target concrete pouring simulation model to obtain corresponding pouring defect data;

[0009] A pouring assessment is performed on the pouring defect data according to the pouring data to be tested to obtain corresponding pouring monitoring results.

[0010] Optionally, the step of calibrating a preset initial concrete pouring simulation model using the pouring calibration set to obtain a target concrete pouring simulation model includes:

[0011] Using the pouring calibration set to input a preset initial concrete pouring simulation model, and outputting calibration pouring defect data;

[0012] Calculating an error function value of the casting calibration set based on the calibration casting defect data;

[0013] When the error function value is greater than or equal to a preset error threshold, the model parameters of the initial concrete pouring simulation model are adjusted, and the step of using the pouring calibration set to input the preset initial concrete pouring simulation model and outputting calibration pouring defect data is executed until the error function value is less than the error threshold;

[0014] When the error function value is less than the error threshold, a target concrete pouring simulation model is generated.

[0015] Optionally, the pouring data to be tested includes multiple ambient temperatures, multiple ambient humidity levels, and multiple atmospheric pressures, and the pouring defect data includes bubble distribution data, crack count, crack length mean, total crack length, and field data. The step of performing pouring evaluation on the pouring defect data based on the pouring data to be tested to obtain corresponding pouring monitoring results includes:

[0016] Inputting the bubble distribution data into a preset bubble evaluation function to obtain a corresponding bubble evaluation index;

[0017] Performing a weighted calculation on the number of cracks, the mean crack length, and the total crack length according to a preset crack assessment weight to obtain a corresponding crack assessment index;

[0018] Performing mean processing on each of the ambient temperatures to obtain a corresponding ambient temperature mean;

[0019] Performing multi-field coupling analysis on the field data according to the ambient temperature mean to obtain a corresponding field data evaluation index;

[0020] Performing an environmental impact assessment using the average ambient temperature, each ambient humidity, and each atmospheric pressure to obtain a corresponding environmental assessment value;

[0021] Based on a preset pouring evaluation function, a pouring quality analysis is performed on the bubble evaluation index, the crack evaluation index, the environmental evaluation value and the field data evaluation index to obtain corresponding pouring monitoring results.

[0022] Optionally, the field data includes a maximum flow velocity, a minimum flow velocity, a pressure field variance, and a stress field variance, and the step of performing a multi-field coupling analysis on the field data according to the ambient temperature mean to obtain a corresponding field data evaluation index includes:

[0023] Performing difference processing on the maximum flow velocity and the minimum flow velocity to obtain a corresponding flow velocity field difference;

[0024] Performing difference processing on the ambient temperature mean value and a preset standard temperature value to obtain a corresponding first difference value;

[0025] A weighted operation is performed on the first difference, the flow velocity field difference, the pressure field variance, and the stress field variance according to a preset field evaluation weight to obtain a corresponding field data evaluation index.

[0026] Optionally, the step of performing environmental impact assessment using the mean ambient temperature, each ambient humidity, and each atmospheric pressure to obtain a corresponding environmental assessment value includes:

[0027] Performing mean processing on each of the environmental humidity values ​​to obtain a corresponding mean environmental humidity value;

[0028] Performing averaging on each of the atmospheric pressures to obtain a corresponding atmospheric pressure average;

[0029] Performing difference processing on the ambient temperature mean and a preset reference ambient temperature to obtain a second difference, and performing absolute value processing on the second difference to obtain a corresponding ambient temperature deviation value;

[0030] Performing difference processing on the ambient humidity mean value and a preset reference ambient humidity value to obtain a third difference value, and performing absolute value processing on the third difference value to obtain a corresponding ambient humidity deviation value;

[0031] performing difference processing on the atmospheric pressure mean value and a preset reference atmospheric pressure to obtain a fourth difference value, and performing absolute value processing on the fourth difference value to obtain a corresponding atmospheric pressure deviation value;

[0032] A weighted operation is performed on the ambient temperature deviation value, the ambient humidity deviation value, and the atmospheric pressure deviation value according to a preset environmental assessment weight to obtain a corresponding environmental assessment value.

[0033] Optionally, the step of performing a pouring quality analysis on the bubble assessment index, the crack assessment index, the environmental assessment value, and the field data assessment index based on a preset pouring assessment function to obtain corresponding pouring monitoring results includes:

[0034] Inputting the bubble evaluation index, the crack evaluation index, the environmental evaluation value, and the field data evaluation index into a preset pouring evaluation function to obtain a corresponding pouring evaluation value;

[0035] Determining whether the pouring evaluation value is less than a preset evaluation threshold;

[0036] When the pouring evaluation value is less than the evaluation threshold, a pouring monitoring result indicating that the concrete pouring is normal is generated;

[0037] When the pouring evaluation value is greater than or equal to the evaluation threshold, a pouring monitoring result indicating abnormal concrete pouring is generated.

[0038] A second aspect of the present invention provides a monitoring system for a concrete pouring process, comprising:

[0039] An acquisition module is used to obtain a plurality of pouring calibration data and perform data preprocessing on each of the pouring calibration data to obtain a pouring calibration set;

[0040] a calibration module, configured to calibrate a preset initial concrete pouring simulation model using the pouring calibration set to obtain a target concrete pouring simulation model;

[0041] a simulation module configured to, upon receiving pouring data to be tested, perform pouring simulation processing on the pouring data to be tested using the target concrete pouring simulation model to obtain corresponding pouring defect data;

[0042] The monitoring module is used to perform a casting evaluation on the casting defect data according to the casting data to be tested to obtain a corresponding casting monitoring result.

[0043] A third aspect of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for monitoring a concrete pouring process as described in any one of the above items.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the method for monitoring a concrete pouring process as described in any one of the above items.

[0045] A fifth aspect of the present invention provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the method for monitoring a concrete pouring process as described in any one of the above items.

[0046] It can be seen from the above technical solutions that the present invention has the following advantages:

[0047] The present invention calibrates a preset initial concrete pouring simulation model using a pouring calibration set to obtain a target concrete pouring simulation model. When receiving pouring data to be tested, the target concrete pouring simulation model performs pouring simulation processing on the pouring data to be tested to obtain corresponding pouring defect data. The pouring defect data is then evaluated based on the pouring data to obtain corresponding pouring monitoring results. This overcomes the technical problem that traditional concrete pouring process monitoring methods mainly rely on manual observation and sampling detection, but are unable to reflect key parameters such as bubble distribution, crack formation, and temperature field changes during the pouring process in real time, making it difficult for construction personnel to promptly detect potential defects and reducing the reliability of the concrete pouring process. Compared with the traditional concrete pouring process monitoring method, the present invention calibrates the preset initial concrete pouring simulation model by using a pouring calibration set to obtain a target concrete pouring simulation model, so that the target concrete pouring simulation model can accurately simulate the bubble distribution, crack formation and temperature field changes during the pouring process. At the same time, the pouring defect data is evaluated in combination with the pouring data to be tested to obtain the corresponding pouring monitoring results, so that construction personnel can adjust the parameters in the pouring process in time according to the pouring monitoring results, thereby improving the reliability of the concrete pouring process. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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.

[0049] Figure 1 A flowchart of a method for monitoring a concrete pouring process provided in Example 1 of the present invention;

[0050] Figure 2 A flowchart of a method for monitoring a concrete pouring process provided in a second embodiment of the present invention;

[0051] Figure 3 A relationship diagram between the bubble evaluation index and the pouring evaluation index provided in the second embodiment of the present invention;

[0052] Figure 4 A relationship diagram between the crack assessment index and the pouring evaluation index provided in the second embodiment of the present invention;

[0053] Figure 5 A relationship diagram between the field data evaluation index and the pouring evaluation index provided in the second embodiment of the present invention;

[0054] Figure 6This is a structural block diagram of a monitoring system for a concrete pouring process provided in a third embodiment of the present invention;

[0055] Figure 7 This is a structural block diagram of a computer device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present invention provide a method and system for monitoring the concrete pouring process, which is used to solve the technical problem that traditional concrete pouring process monitoring methods mainly rely on manual observation and sampling detection, but cannot reflect key parameters such as bubble distribution, crack formation and temperature field changes during the pouring process in real time, making it difficult for construction personnel to detect potential defects in a timely manner, thereby reducing the reliability of the concrete pouring process.

[0057] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 work are within the scope of protection of the present invention.

[0058] See also Figure 1 , Figure 1 This is a flowchart of the steps of a method for monitoring a concrete pouring process provided in Example 1 of the present invention.

[0059] The present invention provides a method for monitoring a concrete pouring process, comprising:

[0060] Step 101: Acquire multiple pouring calibration data, and perform data preprocessing on each pouring calibration data to obtain a pouring calibration set.

[0061] Pouring calibration data refers to the pouring condition data obtained through the pouring test. The pouring condition data includes but is not limited to the test concrete material properties (i.e., the compressive strength, fluidity, and crack resistance of concrete at different mix proportions during the pouring test), test vibration parameters (i.e., the start and end time points, vibration frequency, and vibration amplitude of each round of vibration during the pouring test), test pouring parameters (i.e., the pouring speed and concrete fluidity at the pouring entrance during the pouring test), test environmental parameters (i.e., the ambient humidity, ambient temperature, and atmospheric pressure during the pouring test), test bubble distribution data (i.e., the number of bubbles and the average bubble diameter in the pouring area during the pouring test), test crack distribution data (i.e., the number of cracks, the average crack length, and the total crack length in the pouring area during the pouring test), and test field data (i.e., the maximum flow velocity, the minimum flow velocity, the pressure field variance, and the stress field variance in the pouring area during the pouring test).

[0062] The pour calibration set refers to a set of standardized test data used to calibrate the initial concrete pour simulation model.

[0063] In an embodiment of the present invention, multiple pouring calibration data are obtained through multiple pouring tests, and each pouring calibration data is subjected to data preprocessing to obtain a pouring calibration set. The data preprocessing includes but is not limited to data cleaning, outlier processing, and data normalization.

[0064] Step 102: calibrate the preset initial concrete pouring simulation model using the pouring calibration set to obtain a target concrete pouring simulation model.

[0065] The target concrete pouring simulation model refers to the initial concrete pouring simulation model that has been calibrated.

[0066] It should be noted that the initial concrete pour simulation model can be constructed using finite element analysis software such as ANSYS, Abaqus, or COMSOL Multiphysics. The inputs to this model include: concrete compressive strength, fluidity, crack resistance, start and end times of each vibration cycle, vibration frequency, vibration amplitude, pouring speed, concrete fluidity at the pouring inlet, ambient humidity, ambient temperature, and atmospheric pressure. The outputs of this model include: the number of bubbles, average bubble diameter, number of cracks, average crack length, total crack length, maximum and minimum flow velocity, pressure field variance, and stress field variance within the pouring area. The boundary conditions for the pouring area in this model include: concrete inflow velocity (i.e., inlet condition), atmospheric pressure (i.e., outlet condition), and no-slip boundaries (i.e., lateral boundaries, simulating the interaction between concrete and the pouring vessel).

[0067] In an embodiment of the present invention, a pouring calibration set is input into a preset initial concrete pouring simulation model, and calibrated pouring defect data is output. The calibrated pouring defect data and the pouring calibration set are input into a preset error function to obtain a corresponding error function value. When the error function value is greater than or equal to a preset error threshold, the model parameters of the initial concrete pouring simulation model are adjusted, and the process jumps to the step of inputting the preset initial concrete pouring simulation model using the pouring calibration set and outputting the calibrated pouring defect data until the error function value is less than the error threshold. When the error function value is less than the error threshold, a target concrete pouring simulation model is generated.

[0068] It should be noted that the error function is specifically:

[0069]

[0070] in, is the error function value, To calibrate pouring defect data, Calibrate the trial pour defect data associated with the pour defect data for the pour calibration set.

[0071] Step 103: When the pouring data to be tested is received, pouring simulation processing is performed on the pouring data to be tested by the target concrete pouring simulation model to obtain corresponding pouring defect data.

[0072] The pouring data to be tested refers to a set of dynamic parameters collected in real time during the concrete pouring process and used to input into the calibrated simulation model, including but not limited to the compressive strength, fluidity, crack resistance of the concrete, the start and end time points of each round of vibration, the vibration frequency, the vibration amplitude, the pouring speed, the fluidity of the concrete at the pouring entrance, the ambient humidity, the ambient temperature and the atmospheric pressure.

[0073] Pouring defect data refers to the number of bubbles, average bubble diameter, number of cracks, average crack length, total crack length, maximum flow velocity, minimum flow velocity, pressure field variance, and stress field variance within the pouring area predicted by the target concrete pouring simulation model.

[0074] In an embodiment of the present invention, when the pouring data to be tested collected in real time is received, the pouring data to be tested is input into the target concrete pouring simulation model to obtain corresponding pouring defect data.

[0075] Step 104: Perform a pouring assessment on the pouring defect data according to the pouring data to be tested to obtain corresponding pouring monitoring results.

[0076] In an embodiment of the present invention, the bubble distribution data is input into a preset bubble evaluation function to obtain a corresponding bubble evaluation index. The number of cracks, the mean crack length, and the total crack length are input into a preset crack evaluation function to obtain a corresponding crack evaluation index. Each ambient temperature is averaged to obtain a corresponding ambient temperature mean. The ambient temperature mean and the field data are input into a preset field data evaluation function to obtain a corresponding field data evaluation index. Each ambient humidity is averaged to obtain a corresponding ambient humidity mean. Each atmospheric pressure is averaged to obtain a corresponding atmospheric pressure mean. The ambient temperature mean, ambient humidity mean, and atmospheric pressure mean are input into a preset environmental evaluation function to obtain a corresponding environmental evaluation value. Based on the preset casting evaluation function, a casting quality analysis is performed on the bubble evaluation index, the crack evaluation index, the environmental evaluation value, and the field data evaluation index to obtain a corresponding casting monitoring result.

[0077] It should be noted that the crack assessment function is specifically:

[0078] ;

[0079] in, is the crack assessment index, is the first crack assessment weight coefficient, Evaluate the weight coefficient for the second crack, The weight coefficient for the third crack evaluation, is the number of cracks, is the mean crack length, is the total length of the crack.

[0080] It should be noted that the field data evaluation function is specifically:

[0081] ;

[0082] in, is the flow velocity field difference, is the maximum flow velocity, is the minimum flow velocity, is the field data evaluation index, is the mean ambient temperature, is the standard temperature value, is the pressure field variance, is the stress field variance, is the first evaluation weight coefficient, is the weight coefficient for the second field evaluation, is the third evaluation weight coefficient, Evaluate the weight coefficients for the fourth field.

[0083] It should be noted that the environmental assessment function is specifically:

[0084] ;

[0085] in, is the environmental assessment value, is the first environmental assessment weight coefficient, is the second environment assessment weight coefficient, is the third environment assessment weight coefficient, is the mean ambient temperature, is the reference ambient temperature, is the mean ambient humidity, is the reference ambient humidity, is the mean atmospheric pressure, is the base atmospheric pressure.

[0086] In an embodiment of the present invention, a preset initial concrete pouring simulation model is calibrated using a pouring calibration set to obtain a target concrete pouring simulation model. When pouring data to be tested is received, pouring simulation processing is performed on the pouring data to be tested using the target concrete pouring simulation model to obtain corresponding pouring defect data. The pouring defect data is then evaluated based on the pouring data to obtain corresponding pouring monitoring results. This overcomes the technical problem that traditional concrete pouring process monitoring methods mainly rely on manual observation and sampling detection, but are unable to reflect key parameters such as bubble distribution, crack formation, and temperature field changes during the pouring process in real time, making it difficult for construction personnel to promptly detect potential defects and reducing the reliability of the concrete pouring process. Compared with the traditional concrete pouring process monitoring method, the present invention calibrates the preset initial concrete pouring simulation model by using a pouring calibration set to obtain a target concrete pouring simulation model, so that the target concrete pouring simulation model can accurately simulate the bubble distribution, crack formation and temperature field changes during the pouring process. At the same time, the pouring defect data is evaluated in combination with the pouring data to be tested to obtain the corresponding pouring monitoring results, so that construction personnel can adjust the parameters in the pouring process in time according to the pouring monitoring results, thereby improving the reliability of the concrete pouring process.

[0087] See also Figure 2 , Figure 2 This is a flowchart of the steps of a method for monitoring a concrete pouring process provided in the second embodiment of the present invention.

[0088] The present invention provides a method for monitoring a concrete pouring process, comprising:

[0089] Step 201: Acquire multiple pouring calibration data, and perform data preprocessing on each pouring calibration data to obtain a pouring calibration set.

[0090] In an embodiment of the present invention, a plurality of pouring calibration data are obtained through multiple pouring tests, and data preprocessing is performed on each pouring calibration data to obtain a pouring calibration set.

[0091] It is worth mentioning that for each pouring test, different pouring parameters and vibration parameters are set, and the test concrete material properties (i.e., the compressive strength, fluidity and crack resistance of concrete under different mix proportions during the pouring test), test vibration parameters (i.e., the start and end time points, vibration frequency and vibration amplitude of each round of vibration work during the pouring test), test pouring parameters (i.e., pouring speed during the pouring test, concrete fluidity at the pouring entrance), test environment parameters (i.e., ambient humidity, ambient temperature and atmospheric pressure during the pouring test), test bubble distribution data (i.e., the number of bubbles and average bubble diameter in the pouring area during the pouring test), test crack distribution data (i.e., the number of cracks in the pouring area during the pouring test, the average crack length and the total crack length) and test field data (i.e., the maximum flow velocity, minimum flow velocity, pressure field variance and stress field variance in the pouring area during the pouring test) are recorded during each pouring test.

[0092] Step 202: Use the pouring calibration set to input a preset initial concrete pouring simulation model and output calibrated pouring defect data.

[0093] The calibrated pouring defect data refers to the number of bubbles, average bubble diameter, number of cracks, average crack length, total crack length, maximum flow velocity, minimum flow velocity, pressure field variance, and stress field variance predicted by the initial concrete pouring simulation model.

[0094] In an embodiment of the present invention, the pouring calibration set is used as an input of a preset initial concrete pouring simulation model to obtain calibrated pouring defect data.

[0095] Step 203: Calculate the error function value of the casting calibration set based on the calibration casting defect data.

[0096] In an embodiment of the present invention, the calibration pouring defect data and the pouring calibration set are input into a preset error function to obtain a corresponding error function value.

[0097] Step 204: When the error function value is greater than or equal to the preset error threshold, the model parameters of the initial concrete pouring simulation model are adjusted, and the step of using the pouring calibration set to input the preset initial concrete pouring simulation model and outputting the calibration pouring defect data is jumped to execution until the error function value is less than the error threshold.

[0098] The error threshold refers to the maximum acceptable deviation limit between the model prediction data and the actual test data when calibrating the simulation model, and its value is 5%.

[0099] In the embodiment of the present invention, when the error function value is greater than or equal to 5%, the model parameters of the initial concrete pouring simulation model are adjusted, and the process jumps to steps 202 - 204 .

[0100] Step 205: When the error function value is less than the error threshold, a target concrete pouring simulation model is generated.

[0101] In an embodiment of the present invention, when the error function value is less than 5%, a target concrete pouring simulation model is generated.

[0102] Step 206: When the pouring data to be tested is received, pouring simulation processing is performed on the pouring data to be tested by the target concrete pouring simulation model to obtain corresponding pouring defect data.

[0103] In an embodiment of the present invention, pouring data to be measured during the pouring process is acquired in real time, and the pouring data to be measured is input into a target concrete pouring simulation model to obtain corresponding pouring defect data.

[0104] Step 207: Perform a pouring assessment on the pouring defect data according to the pouring data to be tested to obtain a corresponding pouring monitoring result.

[0105] Furthermore, the pouring data to be tested includes multiple ambient temperatures, multiple ambient humidity levels, and multiple atmospheric pressures, and the pouring defect data includes bubble distribution data, crack count, crack length mean, total crack length, and field data. Step 207 includes the following sub-steps:

[0106] S11. Input the bubble distribution data into a preset bubble evaluation function to obtain a corresponding bubble evaluation index.

[0107] In an embodiment of the present invention, the bubble distribution data is input into a preset bubble evaluation function to obtain a corresponding bubble evaluation index, wherein the bubble distribution data includes a plurality of bubble diameters and the number of bubbles.

[0108] It should be noted that the bubble evaluation function is specifically:

[0109] ;

[0110] in, is the bubble evaluation index, is the number of bubbles, is the average bubble diameter, is the diameter of the i-th bubble, i is the bubble index, and k1 is the influence coefficient of the average bubble diameter.

[0111] It's worth noting that the presence of bubbles in concrete directly impacts its physical and chemical properties. Excessive or excessively large bubbles can reduce concrete strength. Therefore, the bubble evaluation index effectively reflects the impact of bubbles on concrete performance. Large bubble diameters can significantly reduce concrete's compressive strength, so the value of k1 should be between [1 and 1.5], depending on the specific situation.

[0112] S12. Perform weighted calculation on the number of cracks, the mean crack length, and the total crack length according to preset crack assessment weights to obtain a corresponding crack assessment index.

[0113] Crack assessment weights refer to the relative importance of different parameters in crack assessment. For example, the crack assessment weights include the first crack assessment weight (weighting the number of cracks), the second crack assessment weight (weighting the average crack length), and the third crack assessment weight (weighting the total crack length). The third crack assessment weight is less than the first crack assessment weight and less than the second crack assessment weight. The average crack length is given a higher weight, indicating that crack severity is more important than the number alone when assessing crack impact. This is because even if there are only a few cracks, long individual cracks can have a greater impact on the structure. The number of cracks is given a lower weight than the average crack length, indicating that in practical applications, crack length and severity are more important factors. However, the number of cracks remains important because the number of cracks can lead to the cumulative effect of multiple small cracks, which can affect structural safety. The total crack length is given the lowest weight, meaning that while total crack length provides a comprehensive perspective in crack assessment, its impact is less than the length or number of individual cracks. Total crack length should only serve as an auxiliary indicator to help understand the overall distribution of cracks in a structure.

[0114] In an embodiment of the present invention, a weighted operation is performed on the number of cracks, the mean crack length, and the total crack length based on the preset first crack assessment weight coefficient, second crack assessment weight coefficient, and third crack assessment weight coefficient to obtain a corresponding crack assessment index.

[0115] It should be noted that the mean crack length and the total crack length are obtained by counting the number of cracks and the length of each crack in the pouring area of ​​the target concrete pouring simulation model (or inputting the number of cracks and the length of each crack into a preset crack statistical function).

[0116] The specific crack statistical function is:

[0117] ;

[0118] in, is the total length of the crack, is the mean crack length, is the number of cracks, is the length of the j-th crack, and j is the crack length index.

[0119] It's worth noting that cracks in concrete structures can lead to decreased strength and durability, as well as durability issues such as water seepage and corrosion. By effectively quantifying the number of cracks, average crack length, and total crack length, potential issues can be identified promptly during the concrete pouring process, thereby improving the overall quality and reliability of the concrete. The number of cracks directly reflects their distribution; a greater number indicates a greater potential risk. The average crack length reflects the severity of the cracks; larger average crack lengths may indicate more serious structural issues. The total crack length takes into account the cumulative impact of cracks across the entire area; longer and more numerous cracks may have a greater impact on structural safety.

[0120] S13: average the ambient temperatures to obtain the corresponding ambient temperature average.

[0121] In the embodiment of the present invention, the average values ​​of the various ambient temperatures are calculated to obtain the corresponding ambient temperature average value.

[0122] It's important to note that the mean ambient temperature reflects the current temperature across the entire pouring area. Temperature is a crucial factor influencing the properties of concrete during pouring and hardening. By analyzing the mean temperature, we can promptly identify abnormal temperatures and guide temperature adjustments during the pouring process, such as adding insulation or cooling measures, to ensure concrete strength and durability.

[0123] S14. Perform multi-field coupling analysis on the field data according to the average value of the ambient temperature to obtain a corresponding field data evaluation index.

[0124] Furthermore, the field data includes the maximum flow velocity, the minimum flow velocity, the pressure field variance, and the stress field variance. S14 includes the following sub-steps:

[0125] S141. Perform difference processing on the maximum flow velocity and the minimum flow velocity to obtain a corresponding flow velocity field difference.

[0126] The maximum flow velocity refers to the maximum flow velocity in the pouring area.

[0127] The minimum flow velocity refers to the minimum flow velocity in the pouring area.

[0128] In the embodiment of the present invention, the difference between the maximum flow velocity and the minimum flow velocity is calculated to obtain the corresponding flow velocity field difference.

[0129] It should be noted that the velocity field difference reflects the fluidity and uniformity of concrete during the pouring process. A large velocity field difference may indicate uneven concrete flow, such as the formation of holes or voids. By monitoring this indicator, appropriate measures can be taken to optimize the concrete mix ratio and pouring method to ensure uniform pouring.

[0130] S142: Perform difference processing on the average ambient temperature and a preset standard temperature value to obtain a corresponding first difference.

[0131] The standard temperature value refers to the temperature reference value that is most conducive to concrete hydration reaction, strength development and crack control during the concrete pouring process, and is set at 25°C.

[0132] In an embodiment of the present invention, the difference between the average ambient temperature and a preset standard temperature value is calculated to obtain a corresponding first difference.

[0133] S143. Perform a weighted operation on the first difference, the flow velocity field difference, the pressure field variance, and the stress field variance according to a preset field evaluation weight to obtain a corresponding field data evaluation index.

[0134] Field assessment weights refer to the relative importance of different parameters in the physical field assessment. For example, the field assessment weights include the first field assessment weight coefficient (i.e., the weight coefficient for the first difference), the second field assessment weight coefficient (i.e., the weight coefficient for the flow velocity field difference), the third field assessment weight coefficient (i.e., the weight coefficient for the pressure field variance), and the fourth field assessment weight coefficient (i.e., the weight coefficient for the stress field variance), with the third field assessment weight coefficient = the fourth field assessment weight coefficient < the second field assessment weight coefficient < the first field assessment weight coefficient. The first field assessment weight coefficient has the most significant impact on the chemical reactions and physical properties of concrete. During the pouring process, temperature suitability is directly related to the strength and durability of concrete, so it is assigned the highest weight. The flow velocity field difference has a significant impact on pouring uniformity and is directly related to construction quality, so it should be given a higher weight. The pressure field variance and stress field variance reflect the uniformity of the material's internal state. Since they are of similar importance, their weights are the same and lower than the flow velocity weight.

[0135] In an embodiment of the present invention, a weighted sum is performed on the first difference, the flow velocity field difference, the pressure field variance, and the stress field variance according to preset field evaluation weights to obtain a corresponding field data evaluation index.

[0136] It should be noted that the pressure field variance and the stress field variance can be obtained by statistically analyzing the various pressure values ​​and stress values ​​in the pouring area of ​​the target concrete pouring simulation model (or inputting the various pressure values ​​into a preset pressure field variance function, and inputting the various stress values ​​into a preset stress field variance function).

[0137] The specific pressure field variance function is:

[0138] ;

[0139] in, is the pressure field variance, is the number of sampling points, is the pressure value of the oth sampling point, is the mean value between each pressure value, and o is the sampling point index.

[0140] The stress field variance function is specifically:

[0141]

[0142] in, is the stress field variance, is the stress value of the oth sampling point, is the mean value among the stress values.

[0143] It's worth noting that the pressure and stress field variances measure the uniformity of pressure and stress distribution. Smaller variances indicate more uniform pressure and stress distribution. Uniform pressure and stress distribution improves the overall stability and safety of concrete structures, reducing the risk of cracks and other structural damage. By measuring the pressure and stress field variances during the pouring process, potential non-uniformities can be identified promptly, allowing adjustments to be made to improve construction safety and effectiveness.

[0144] S15. Perform an environmental impact assessment using the average ambient temperature, various ambient humidity levels, and various atmospheric pressures to obtain corresponding environmental assessment values.

[0145] Furthermore, S15 includes the following sub-steps:

[0146] S151. Perform mean processing on the ambient humidity of each environment to obtain the corresponding mean value of the ambient humidity.

[0147] In the embodiment of the present invention, the average values ​​of the various ambient humidity values ​​are calculated to obtain the corresponding ambient humidity average value.

[0148] S152. Performing average processing on each atmospheric pressure to obtain the corresponding atmospheric pressure average.

[0149] In the embodiment of the present invention, the average values ​​of the various atmospheric pressures are calculated to obtain the corresponding atmospheric pressure average value.

[0150] S153 , performing difference processing on the ambient temperature mean and the preset reference ambient temperature to obtain a second difference, and performing absolute value processing on the second difference to obtain a corresponding ambient temperature deviation value.

[0151] The reference ambient temperature refers to the reference temperature value used to quantify the impact of ambient temperature during the concrete pouring process, and its value is 25°C.

[0152] In the embodiment of the present invention, the difference between the ambient temperature mean and the preset reference ambient temperature is calculated to obtain a second difference, and the absolute value of the second difference is taken as the ambient temperature deviation value.

[0153] S154 , performing difference processing on the ambient humidity mean value and the preset reference ambient humidity to obtain a third difference value, and performing absolute value processing on the third difference value to obtain a corresponding ambient humidity deviation value.

[0154] The benchmark ambient humidity refers to the reference humidity value used to quantify the impact of ambient humidity during the concrete pouring process, and its value is 50%.

[0155] In the embodiment of the present invention, the difference between the ambient humidity mean and the preset reference ambient humidity is calculated to obtain a third difference, and the absolute value of the third difference is taken as the ambient humidity deviation value.

[0156] S155 . Perform difference processing on the atmospheric pressure mean value and the preset reference atmospheric pressure to obtain a fourth difference value, and perform absolute value processing on the fourth difference value to obtain a corresponding atmospheric pressure deviation value.

[0157] The reference atmospheric pressure refers to the reference pressure value used to quantify the impact of environmental pressure during the concrete pouring process, and its value is 101.3kPa.

[0158] In an embodiment of the present invention, the difference between the mean atmospheric pressure and a preset reference atmospheric pressure is calculated to obtain a fourth difference, and the absolute value of the fourth difference is taken as the atmospheric pressure deviation value.

[0159] S156. Perform weighted calculation on the ambient temperature deviation value, the ambient humidity deviation value, and the atmospheric pressure deviation value according to preset environmental assessment weights to obtain a corresponding environmental assessment value.

[0160] Environmental assessment weights quantify the relative importance of different environmental parameters (temperature, humidity, and atmospheric pressure) on the concrete pouring process. For example, the environmental assessment weights include the first environmental assessment weight (i.e., the weight for ambient temperature deviation), the second environmental assessment weight (i.e., the weight for ambient humidity deviation), and the third environmental assessment weight (i.e., the weight for atmospheric pressure deviation). The third environmental assessment weight < the second environmental assessment weight < the first environmental assessment weight. Ambient temperature deviation is a key factor affecting concrete hydration and curing. Temperature fluctuations significantly impact strength and construction conditions, so it has the highest weight. Ambient humidity deviation, while less important than temperature, affects water evaporation and concrete hydration. However, it still has a significant impact on construction quality, so its weight is set to medium to high. (However, on rainy days, humidity can rise due to rainfall. In this case, it is acceptable to set the humidity weight to the highest. However, in general, it is reasonable to set the humidity weight slightly lower than temperature.) Atmospheric pressure deviation affects concrete setting. While important in specific circumstances, its overall impact is relatively small, so it is set to the lowest weight.

[0161] In an embodiment of the present invention, a weighted operation is performed on the ambient temperature deviation value, the ambient humidity deviation value and the atmospheric pressure deviation value according to the preset first environmental assessment weight coefficient, the second environmental assessment weight coefficient and the third environmental assessment weight coefficient to obtain a corresponding environmental assessment value.

[0162] It should be noted that the environmental assessment value reflects the comprehensive impact of external environmental factors, such as temperature, humidity, and atmospheric pressure, on the quality and effect of concrete pouring during the pouring process. This index involves the degree of change in environmental conditions during the pouring process, especially the deviation from the baseline ambient temperature, baseline ambient humidity, and baseline atmospheric pressure. A high environmental impact index indicates that the current pouring environment is far from the ideal baseline state, which may cause quality problems such as cracks, insufficient strength, and surface defects in the concrete. Therefore, the environmental assessment value can provide timely feedback to the construction team and can also be used for an objective and reasonable evaluation of the subsequent pouring process.

[0163] S16. Perform pouring quality analysis on the bubble assessment index, crack assessment index, environmental assessment value, and field data assessment index based on a preset pouring assessment function to obtain corresponding pouring monitoring results.

[0164] Furthermore, S16 includes the following sub-steps:

[0165] S161. Input the bubble evaluation index, crack evaluation index, environmental evaluation value and field data evaluation index into a preset pouring evaluation function to obtain a corresponding pouring evaluation value.

[0166] In an embodiment of the present invention, the bubble evaluation index, the crack evaluation index, the environmental evaluation value and the field data evaluation index are used as inputs of a preset pouring evaluation function to obtain a corresponding pouring evaluation value.

[0167] It should be noted that the pouring evaluation function is specifically:

[0168]

[0169] in, is the pouring evaluation index, is the bubble evaluation index weight coefficient, is the crack assessment index weight coefficient, Evaluate the index weight coefficient for field data, is the first pouring evaluation coefficient, is the second pouring evaluation coefficient, is the pouring assessment value, r is the nonlinear coefficient of the pouring evaluation index, z is the nonlinear coefficient of the environmental assessment value, and s is the nonlinear index.

[0170] It should be noted that different engineering projects may attach different importance to pouring quality and environmental impact. By adjusting the first pouring assessment coefficient and the second pouring assessment coefficient, it is possible to adapt to the needs of different situations. The recommended value range of the nonlinear coefficient of the pouring evaluation index is generally between 1.0 and 2.0. r=1.2 is suitable for concrete quality assessment under general circumstances, and can moderately reflect the impact of quality improvement on the pouring assessment value. When it is necessary to emphasize the importance of quality improvement on the overall impact, r=1.5 can be set. The recommended value range of the nonlinear coefficient of the environmental assessment value is generally between 1.0 and 2.0. z=1.2 is suitable for common environmental impact assessments, and can reasonably reflect the impact of environmental factors on the pouring assessment value. z=1.5 is applicable to situations where environmental conditions have a significant impact on construction. The recommended value range of the nonlinear index is between 1.0 and 3.0. s=1.0 is suitable for linear evaluation (i.e., assuming that the total impact is proportional to the sum of the impacts of each part). s=1.5 is suitable for situations where the comprehensive effect needs to be emphasized, and can better reflect the inhibitory effect on the casting evaluation value when a certain indicator is too high. s=2.0 or higher is suitable for situations where the nonlinear effect needs to be emphasized, especially when a single indicator is too high and the casting evaluation value is significantly reduced.

[0171] It is worth mentioning that the pouring evaluation index can comprehensively reflect the quality and status of concrete pouring by comprehensively considering the evaluation of three aspects: bubbles, cracks, and field data. The smaller the bubble evaluation index, crack evaluation index, or field data evaluation index, the larger the pouring evaluation index, which indicates that there are fewer bubbles and cracks in the concrete and the field data is in good condition, so the quality of the concrete is higher. The bubble evaluation index focuses on the bubble content and structure of the concrete. An increase in the number of bubbles usually means a decrease in the density of the concrete, which will lead to a decrease in the strength and durability of the concrete. In addition, the size of the bubbles also affects the performance of the concrete. Larger bubbles may lead to more pores. The smaller the bubble evaluation index, the smaller the number of bubbles and the smaller the bubble size, thus the better the density and quality of the concrete. The larger the pouring evaluation index, the better the pouring effect. Conversely, a larger bubble evaluation index means poor density of the concrete and poor pouring effect. The crack assessment index focuses on the number and severity of cracks. An increase in cracks directly impacts structural safety. The mean and total crack lengths further reflect crack severity and potential impact. A smaller crack assessment index indicates fewer and shorter cracks, indicating greater stability under load and during use, higher placement quality, and a correspondingly higher placement evaluation index. Conversely, a smaller crack assessment index indicates more and larger cracks, less stable performance, and poor placement results. The field data assessment index provides crucial information such as temperature, fluidity, pressure, and stress. This information systematically reflects the environmental and physical conditions during concrete placement, directly impacting concrete properties such as hydration, fluidity, and mechanical properties. Good field data indicates optimal concrete placement conditions. A smaller field data assessment index indicates better environmental conditions during concrete placement, ensuring concrete quality and performance. The better the placement results and quality, the higher the corresponding placement evaluation index. The crack assessment index plays a key role in the long-term performance of concrete structures. The number and severity of cracks are directly related to the strength, durability, and service life of concrete. Therefore, in the overall evaluation, the crack assessment index should occupy a higher weight to highlight its importance in concrete quality assessment. Although the bubble assessment index is important for the workability and frost resistance of concrete, its influence is relatively weak compared to the severity of cracks. The field data assessment index combines dynamic factors such as temperature, fluidity, pressure and stress, and can reflect the instantaneous state during the construction process. Although field data is also important, its influence in pouring evaluation is slightly weaker than that of the bubble assessment index. Therefore, it can be obtained The pouring evaluation index obtained through such comprehensive analysis helps to fully understand the performance of concrete after pouring.

[0172] It's worth noting that the pouring evaluation function places the pouring evaluation index and environmental assessment value in separate terms within the numerator, yet they contribute to the same denominator. This emphasizes the combined influence of these two factors on the overall evaluation. This design clearly demonstrates that as either index increases, the pouring evaluation value also changes. By introducing nonlinear representations for the pouring evaluation index and environmental assessment value, the degree of influence of each index on the final result can be better reflected. This nonlinear relationship emphasizes the sensitivity of pouring quality and environmental impacts under specific conditions (e.g., extremely high fluidity or extreme environmental conditions), making the pouring evaluation value more realistic.

[0173] S162. Determine whether the pouring evaluation value is less than a preset evaluation threshold.

[0174] S163. When the pouring evaluation value is less than the evaluation threshold, a pouring monitoring result indicating that the concrete pouring is normal is generated.

[0175] Evaluation threshold refers to the critical value used to judge whether the pouring assessment value is qualified during the concrete pouring process.

[0176] In an embodiment of the present invention, it is determined whether the pouring evaluation value is less than a preset evaluation threshold. If the pouring evaluation value is less than the evaluation threshold, it indicates that the pouring process complies with engineering specifications and a normal concrete pouring monitoring result is generated.

[0177] S164. When the pouring evaluation value is greater than or equal to the evaluation threshold, a pouring monitoring result indicating abnormal concrete pouring is generated.

[0178] In an embodiment of the present invention, when the pouring evaluation value is greater than or equal to the evaluation threshold, it indicates that the pouring process does not comply with engineering specifications and a pouring monitoring result indicating abnormal concrete pouring is generated.

[0179] It is worth mentioning that, referring to Table 1, 15 sets of relevant data from different concrete pouring processes were obtained, and the corresponding bubble evaluation index, crack evaluation index and field data evaluation index were calculated. The calculation was performed based on the pouring evaluation function. =0.2, =0.5, = 0.3, e is the base of the natural logarithm, and e is set to 2.718. When the bubble evaluation index, crack evaluation index, and field data evaluation index are low, the pouring evaluation index is high. For example, when the bubble evaluation index is 0.25, the crack evaluation index is 0.20, and the field data evaluation index is 0.15, the pouring evaluation index is 1.911. This indicates that under these conditions, the quality of the concrete is good. Conversely, when the values ​​of these three evaluation indices increase, the pouring evaluation index decreases. For example, when the bubble evaluation index is 0.95, the crack evaluation index is 0.90, and the field data evaluation index is 0.85, the pouring evaluation index drops to 1.267. This trend indicates that an increase in bubbles and cracks has a negative impact on the quality of concrete.

[0180] Table 1

[0181]

[0182] It is worth mentioning that see Figure 3 As shown in the figure, according to the distribution of the pouring evaluation index values, it can be seen that as the bubble evaluation index increases, the value of the pouring evaluation index gradually decreases. Therefore, the fitting curve of the pouring evaluation index shows a downward trend. The bubble evaluation index reflects the influence of the number of bubbles and the average diameter of bubbles. As the number of bubbles increases and the average diameter of bubbles increases, the strength of concrete may also decrease, because the corresponding pouring evaluation index will also decrease.

[0183] It is worth mentioning that see Figure 4 As shown in the figure, according to the distribution of the casting evaluation index values, it can be seen that as the crack evaluation index increases, the value of the casting evaluation index decreases. Therefore, the fitting curve of the casting evaluation index also shows a downward trend, reflecting that the crack evaluation index is inversely correlated with the casting evaluation index. The crack evaluation index reflects the influence of the number of cracks, the mean crack length and the total crack length. In concrete structures, the more cracks or the larger the cracks, the greater the possibility that the structure may be dangerous, and the lower the overall quality and reliability of the concrete. Therefore, the corresponding casting evaluation index is lower.

[0184] It is worth mentioning that see Figure 5As shown in the figure, according to the distribution of the pouring evaluation index value, it can be seen that as the field data evaluation index value increases, the pouring evaluation index value decreases. Therefore, the pouring evaluation index fitting curve shows a downward trend. The field data evaluation index reflects the difference in the flow velocity field, the mean temperature, and the variance of the pressure field and stress field. The larger the difference in the flow velocity field, the greater the fluidity of the pouring process may be, which may lead to poor uniformity. The larger the difference between the mean temperature and the suitable temperature value, the less time the pouring process is at the suitable temperature value, which will affect the curing process of the concrete, the strength and durability of the concrete, and also cause uneven concrete pouring. The variance of the pressure field and stress field is used to evaluate whether the force inside the concrete is uniform. The larger the variance, the greater the discreteness of the data, that is, the uneven distribution of pressure and stress, which may indicate that there are cracks or other defects in the poured concrete structure. Therefore, the larger the field data evaluation index, the smaller the pouring evaluation index, and the two are inversely correlated.

[0185] In an embodiment of the present invention, a preset initial concrete pouring simulation model is calibrated using a pouring calibration set to obtain a target concrete pouring simulation model. When pouring data to be tested is received, pouring simulation processing is performed on the pouring data to be tested using the target concrete pouring simulation model to obtain corresponding pouring defect data. The pouring defect data is then evaluated based on the pouring data to obtain corresponding pouring monitoring results. This overcomes the technical problem that traditional concrete pouring process monitoring methods mainly rely on manual observation and sampling detection, but are unable to reflect key parameters such as bubble distribution, crack formation, and temperature field changes during the pouring process in real time, making it difficult for construction personnel to promptly detect potential defects and reducing the reliability of the concrete pouring process. Compared with the traditional concrete pouring process monitoring method, the present invention calibrates the preset initial concrete pouring simulation model by using a pouring calibration set to obtain a target concrete pouring simulation model, so that the target concrete pouring simulation model can accurately simulate the bubble distribution, crack formation and temperature field changes during the pouring process. At the same time, the pouring defect data is evaluated in combination with the pouring data to be tested to obtain the corresponding pouring monitoring results, so that construction personnel can adjust the parameters in the pouring process in time according to the pouring monitoring results, thereby improving the reliability of the concrete pouring process.

[0186] See also Figure 6 , Figure 6 This is a structural block diagram of a monitoring system for a concrete pouring process provided in Example 3 of the present invention.

[0187] The present invention provides a monitoring system for a concrete pouring process, comprising:

[0188] The acquisition module 301 is used to obtain a plurality of pouring calibration data and perform data preprocessing on each pouring calibration data to obtain a pouring calibration set;

[0189] A calibration module 302 is configured to calibrate a preset initial concrete pouring simulation model using a pouring calibration set to obtain a target concrete pouring simulation model;

[0190] The simulation module 303 is used for, when receiving the pouring data to be tested, performing pouring simulation processing on the pouring data to be tested through the target concrete pouring simulation model to obtain corresponding pouring defect data;

[0191] The monitoring module 304 is used to perform a pouring assessment on the pouring defect data according to the pouring data to be tested, and obtain corresponding pouring monitoring results.

[0192] Furthermore, the calibration module 302 includes:

[0193] The first simulation submodule is used to input a preset initial concrete pouring simulation model using a pouring calibration set and output calibrated pouring defect data;

[0194] The error calculation submodule is used to calculate the error function value of the casting calibration set based on the calibration casting defect data;

[0195] The first analysis submodule is configured to adjust the model parameters of the initial concrete pouring simulation model when the error function value is greater than or equal to a preset error threshold, and jump to the step of using the pouring calibration set to input the preset initial concrete pouring simulation model and output calibration pouring defect data until the error function value is less than the error threshold;

[0196] The second analysis submodule is used to generate a target concrete pouring simulation model when the error function value is less than the error threshold.

[0197] Furthermore, the pouring data to be tested includes multiple ambient temperatures, multiple ambient humidity levels, and multiple atmospheric pressures; the pouring defect data includes bubble distribution data, crack count, crack length average, total crack length, and field data; the monitoring module 304 includes:

[0198] The bubble evaluation submodule is used to input the bubble distribution data into a preset bubble evaluation function to obtain the corresponding bubble evaluation index;

[0199] The crack assessment submodule is used to perform weighted calculations on the number of cracks, the mean crack length, and the total crack length according to preset crack assessment weights to obtain the corresponding crack assessment index;

[0200] The first mean submodule is used to perform mean processing on each ambient temperature to obtain the corresponding ambient temperature mean;

[0201] The physical field evaluation submodule is used to perform multi-field coupling analysis on the field data according to the mean value of the ambient temperature and obtain the corresponding field data evaluation index;

[0202] The environmental assessment submodule is used to perform environmental impact assessment using the average ambient temperature, various ambient humidity values, and various atmospheric pressure values ​​to obtain corresponding environmental assessment values;

[0203] The comprehensive evaluation submodule is used to analyze the pouring quality of the bubble evaluation index, crack evaluation index, environmental evaluation value and field data evaluation index based on the preset pouring evaluation function to obtain the corresponding pouring monitoring results.

[0204] Furthermore, the field data includes the maximum flow velocity, the minimum flow velocity, the pressure field variance, and the stress field variance. The physical field evaluation submodule includes:

[0205] The velocity difference unit is used to perform difference processing on the maximum flow velocity and the minimum flow velocity to obtain the corresponding flow velocity field difference;

[0206] A first difference unit is used to perform difference processing on the ambient temperature mean value and a preset standard temperature value to obtain a corresponding first difference value;

[0207] The first weighting unit is used to perform weighted calculation on the first difference, the flow velocity field difference, the pressure field variance and the stress field variance according to a preset field evaluation weight to obtain a corresponding field data evaluation index.

[0208] Furthermore, the environmental assessment submodule includes:

[0209] The first averaging unit is used to perform averaging processing on each ambient humidity to obtain a corresponding ambient humidity average;

[0210] The second averaging unit is used to average the atmospheric pressures to obtain the corresponding atmospheric pressure average;

[0211] a second difference unit, configured to perform difference processing on the ambient temperature mean value and a preset reference ambient temperature to obtain a second difference value, and perform absolute value processing on the second difference value to obtain a corresponding ambient temperature deviation value;

[0212] a third difference unit, configured to perform difference processing on the ambient humidity mean value and a preset reference ambient humidity value to obtain a third difference value, and perform absolute value processing on the third difference value to obtain a corresponding ambient humidity deviation value;

[0213] a fourth difference unit, configured to perform difference processing on the atmospheric pressure mean value and a preset reference atmospheric pressure to obtain a fourth difference value, and perform absolute value processing on the fourth difference value to obtain a corresponding atmospheric pressure deviation value;

[0214] The second weighting unit is used to perform weighted calculation on the ambient temperature deviation value, the ambient humidity deviation value and the atmospheric pressure deviation value according to a preset environmental assessment weight to obtain a corresponding environmental assessment value.

[0215] Furthermore, the comprehensive assessment submodule includes:

[0216] A first analysis unit is configured to input the bubble evaluation index, the crack evaluation index, the environmental evaluation value, and the field data evaluation index into a preset pouring evaluation function to obtain a corresponding pouring evaluation value;

[0217] The second analysis unit is used to determine whether the pouring evaluation value is less than a preset evaluation threshold;

[0218] When the pouring assessment value is less than the evaluation threshold, a pouring monitoring result indicating that the concrete pouring is normal is generated;

[0219] When the pouring evaluation value is greater than or equal to the evaluation threshold, a pouring monitoring result indicating abnormal concrete pouring is generated.

[0220] See also Figure 7 , Figure 7 This is a structural block diagram of a computer device provided in Example 4 of the present invention.

[0221] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402, wherein the memory 401 stores a computer program; when the computer program is executed by the processor 402, the processor 402 executes the monitoring method of the concrete pouring process as described in any of the above embodiments.

[0222] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for executing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program codes may be compressed, for example, in a suitable format. When executed by a processing device, these codes cause the processing device to execute the various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program codes may be compressed, for example, in a suitable format. When these codes are executed by a computing and processing device, they cause the computing and processing device to execute the various steps of the above-described method for monitoring a concrete pouring process.

[0223] The fifth embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for monitoring the concrete pouring process as described in any of the above embodiments is implemented.

[0224] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes a method for monitoring a concrete pouring process as described in any of the above embodiments.

[0225] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0226] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0227] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0228] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0229] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0230] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring a concrete pouring process, characterized in that: include: Acquire multiple pouring calibration data, and perform data preprocessing on each of the pouring calibration data to obtain a pouring calibration set; Calibrate the preset initial concrete pouring simulation model using the pouring calibration set to obtain a target concrete pouring simulation model; When the pouring data to be tested is received, pouring simulation processing is performed on the pouring data to be tested by the target concrete pouring simulation model to obtain corresponding pouring defect data; Performing a pouring assessment on the pouring defect data according to the pouring data to be tested to obtain a corresponding pouring monitoring result; The step of calibrating the preset initial concrete pouring simulation model using the pouring calibration set to obtain a target concrete pouring simulation model includes: Using the pouring calibration set to input a preset initial concrete pouring simulation model, and outputting calibration pouring defect data; Calculating an error function value of the casting calibration set based on the calibration casting defect data; When the error function value is greater than or equal to a preset error threshold, the model parameters of the initial concrete pouring simulation model are adjusted, and the step of using the pouring calibration set to input the preset initial concrete pouring simulation model and outputting calibration pouring defect data is executed until the error function value is less than the error threshold; When the error function value is less than the error threshold, a target concrete pouring simulation model is generated; The pouring data to be tested includes multiple ambient temperatures, multiple ambient humidity levels, and multiple atmospheric pressures; the pouring defect data includes bubble distribution data, crack count, crack length mean, total crack length, and field data; and the step of performing pouring evaluation on the pouring defect data based on the pouring data to be tested to obtain corresponding pouring monitoring results includes: Inputting the bubble distribution data into a preset bubble evaluation function to obtain a corresponding bubble evaluation index; Performing a weighted calculation on the number of cracks, the mean crack length, and the total crack length according to a preset crack assessment weight to obtain a corresponding crack assessment index; Performing mean processing on each of the ambient temperatures to obtain a corresponding ambient temperature mean; Performing multi-field coupling analysis on the field data according to the ambient temperature mean to obtain a corresponding field data evaluation index; Performing an environmental impact assessment using the average ambient temperature, each ambient humidity, and each atmospheric pressure to obtain a corresponding environmental assessment value; Based on a preset pouring evaluation function, a pouring quality analysis is performed on the bubble evaluation index, the crack evaluation index, the environmental evaluation value and the field data evaluation index to obtain corresponding pouring monitoring results.

2. The method for monitoring the concrete pouring process according to claim 1, characterized in that: The field data includes a maximum flow velocity, a minimum flow velocity, a pressure field variance, and a stress field variance. The step of performing a multi-field coupling analysis on the field data according to the ambient temperature mean to obtain a corresponding field data evaluation index includes: Performing difference processing on the maximum flow velocity and the minimum flow velocity to obtain a corresponding flow velocity field difference; Performing difference processing on the ambient temperature mean value and a preset standard temperature value to obtain a corresponding first difference value; A weighted operation is performed on the first difference, the flow velocity field difference, the pressure field variance, and the stress field variance according to a preset field evaluation weight to obtain a corresponding field data evaluation index.

3. The method for monitoring the concrete pouring process according to claim 1, wherein: The step of performing environmental impact assessment using the average ambient temperature, each ambient humidity, and each atmospheric pressure to obtain a corresponding environmental assessment value includes: Performing mean processing on each of the environmental humidity values ​​to obtain a corresponding mean environmental humidity value; Performing averaging on each of the atmospheric pressures to obtain a corresponding atmospheric pressure average; Performing difference processing on the ambient temperature mean and a preset reference ambient temperature to obtain a second difference, and performing absolute value processing on the second difference to obtain a corresponding ambient temperature deviation value; Performing difference processing on the ambient humidity mean value and a preset reference ambient humidity value to obtain a third difference value, and performing absolute value processing on the third difference value to obtain a corresponding ambient humidity deviation value; performing difference processing on the atmospheric pressure mean value and a preset reference atmospheric pressure to obtain a fourth difference value, and performing absolute value processing on the fourth difference value to obtain a corresponding atmospheric pressure deviation value; A weighted operation is performed on the ambient temperature deviation value, the ambient humidity deviation value, and the atmospheric pressure deviation value according to a preset environmental assessment weight to obtain a corresponding environmental assessment value.

4. The method for monitoring the concrete pouring process according to claim 1, wherein: The step of performing pouring quality analysis on the bubble assessment index, the crack assessment index, the environmental assessment value, and the field data assessment index based on a preset pouring assessment function to obtain corresponding pouring monitoring results includes: Inputting the bubble evaluation index, the crack evaluation index, the environmental evaluation value, and the field data evaluation index into a preset pouring evaluation function to obtain a corresponding pouring evaluation value; Determining whether the pouring evaluation value is less than a preset evaluation threshold; When the pouring evaluation value is less than the evaluation threshold, a pouring monitoring result indicating that the concrete pouring is normal is generated; When the pouring evaluation value is greater than or equal to the evaluation threshold, a pouring monitoring result indicating abnormal concrete pouring is generated.

5. A monitoring system for a concrete pouring process, based on the monitoring method for a concrete pouring process according to any one of claims 1 to 4, characterized in that: include: An acquisition module is used to obtain a plurality of pouring calibration data and perform data preprocessing on each of the pouring calibration data to obtain a pouring calibration set; a calibration module, configured to calibrate a preset initial concrete pouring simulation model using the pouring calibration set to obtain a target concrete pouring simulation model; a simulation module configured to, upon receiving pouring data to be tested, perform pouring simulation processing on the pouring data to be tested using the target concrete pouring simulation model to obtain corresponding pouring defect data; The monitoring module is used to perform a casting evaluation on the casting defect data according to the casting data to be tested to obtain a corresponding casting monitoring result.

6. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for monitoring a concrete pouring process according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for monitoring the concrete pouring process according to any one of claims 1 to 4 is implemented.

8. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the method for monitoring a concrete pouring process according to any one of claims 1 to 4.

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