A steam curing detection system applied to smart beam yards

Abstract

The application discloses a steam curing detection system applied to a smart beam field, relates to the technical field of steam curing detection, and discloses a steam curing detection setting point module, a steam curing detection execution module, a detection anomaly marking module, a detection execution adjustment module and a detection cycle modification module. The steam curing detection setting point module, the steam curing detection execution module and the detection anomaly marking module are arranged to divide a plurality of equidistant steam curing detection points on a concrete beam body, so that the comprehensiveness and balance of steam curing detection are ensured, the precision steam curing detection of a large-size concrete beam body is met, and each steam curing detection point of the concrete beam body is subjected to all-around detection analysis based on a certain period. In addition, the steam curing detection points with high detection requirements on the concrete beam body are preferentially subjected to detection analysis, so that the position with poor steam curing performance on the concrete beam body can be efficiently and accurately found.

Description

Technical Field

[0001] This invention relates to the field of steam curing testing technology, and more specifically, to a steam curing testing system applied to smart beam yards. Background Technology

[0002] In the modern construction industry, intelligent beam yards, as important prefabrication sites for components, have extremely high requirements for the production quality and efficiency of concrete beams. Steam curing, as a key step in the concrete beam production process, is crucial for improving the strength and durability of concrete. The steam curing process accelerates the hardening process of concrete by controlling parameters such as temperature and humidity, thereby enhancing its physical properties.

[0003] However, traditional steam curing testing methods often have many shortcomings. On the one hand, when dealing with large-sized concrete beams, they cannot provide comprehensive and balanced testing, easily leading to biased and inaccurate test results. On the other hand, traditional testing methods lack intelligent and automated means, resulting in low testing efficiency and difficulty in timely detection and handling of abnormalities during the steam curing process.

[0004] Therefore, this invention proposes a steam curing detection system for use in smart beam yards. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a steam curing detection system for use in smart beam yards.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A steam curing inspection system for smart beam yards includes a steam curing inspection point setting module, a steam curing inspection execution module, an anomaly marking module, an inspection execution adjustment module, and an inspection cycle modification module.

[0008] Before the steam curing of the concrete beam, the steam curing detection point setting module divides the concrete beam into multiple equally spaced steam curing detection points.

[0009] During the steam curing process of the concrete beam, the steam curing detection execution module acquires the steam curing detection requirement value of the steam curing detection point on the concrete beam whenever the cycle node of the steam curing detection cycle is reached, and then generates the steam curing detection sequence.

[0010] The abnormality detection marking module sequentially detects the steam curing detection points on the concrete beam according to the steam curing detection sequence. After each steam curing detection point is detected, the steam curing depth detection value of the steam curing detection point is obtained. Based on the comparison result between the steam curing depth detection value and the steam curing depth detection threshold, it is determined whether to mark the steam curing detection point as a steam curing detection abnormality point.

[0011] When an abnormal point is detected in the steam curing test, the detection execution adjustment module marks all other untested steam curing test points as abnormal points, generates an abnormal steam curing test sequence, and performs the steam curing abnormal test on the abnormal points in sequence according to the abnormal steam curing test sequence.

[0012] When all steam curing test points on the concrete beam have completed testing, the test cycle modification module obtains the steam curing test modification value of the concrete beam. Based on the comparison result between the steam curing test modification value and the steam curing test modification limit, it determines whether to modify the cycle length of the steam curing test.

[0013] Furthermore, the steam curing detection requirements at the steam curing detection points are obtained through the following steps: Obtain the s steam curing depth detection values ​​previously acquired at the steam curing detection points; sort all steam curing depth detection values ​​sequentially according to the order of acquisition; calculate the difference between two adjacent steam curing depth detection values ​​after sorting and take the absolute value to obtain the depth detection fluctuation value; sum all depth detection fluctuation values ​​and take the average value to obtain the depth detection fluctuation mean Ajp; sum all steam curing depth detection values ​​and take the average value to obtain the depth detection mean Ajy; and then use the formula... The steam curing detection requirement value Nmt for this steam curing detection point is obtained, where b1 is the detection fluctuation mean coefficient and b2 is the depth detection mean coefficient.

[0014] Furthermore, the steam curing test sequence is generated through the following steps: all steam curing test points on the concrete beam are sorted in descending order of the required steam curing test values, and the steam curing test sequence is generated according to the sorting order.

[0015] Furthermore, for each steam curing testing point, testing is completed to obtain the steam curing depth detection value for that testing point. Specifically, this involves: collecting various steam curing index data from the testing point, obtaining the corresponding steam curing index models for each index, inputting the various steam curing index data into their respective models to obtain the compliance values ​​for each index, obtaining the discrete mean Ptdg of the steam curing index, summing all the compliance values ​​and taking the average to obtain the average compliance value Bmg, setting a high and low compliance value for the steam curing index, marking the data as compliant when the compliance value is greater than or equal to the high compliance value, and marking the total number of compliant data as Rz, and marking the data as non-compliant when the compliance value is less than or equal to the low compliance value, and marking the total number of non-compliant data as Ek, using the formula... The steam curing depth detection value Zsh of the steam curing detection point is obtained, where v1 is the dispersion coefficient of the steam curing index, v2 is the average compliance coefficient of steam curing, v3 is the compliance coefficient of steam curing, and v4 is the failure coefficient of steam curing.

[0016] Furthermore, the discrete mean Ptdg of the steam curing index is obtained through the following steps: all steam curing index data are paired to form a steam curing index group; the difference between the steam curing index compliance values ​​of the two steam curing index data in the steam curing index group is calculated and the absolute value is taken to obtain the discrete value of the steam curing index group; the discrete values ​​of the steam curing index of all steam curing index groups are summed and the average value is taken to obtain the discrete mean Ptdg of the steam curing index.

[0017] Furthermore, based on the comparison between the steam curing depth detection value and the steam curing depth detection threshold, it is determined whether to mark the steam curing detection point as an abnormal point. Specifically, a steam curing depth detection threshold is set. When the steam curing depth detection value of the steam curing detection point is greater than or equal to the steam curing depth detection threshold, the steam curing detection point is marked as an abnormal point. When the steam curing depth detection value of the steam curing detection point is less than the steam curing depth detection threshold, the next steam curing detection point is detected according to the steam curing detection sequence.

[0018] Furthermore, the steam curing anomaly detection sequence is generated through the following steps: obtain the anomaly correlation value of each steam curing anomaly detection point, sort all steam curing anomaly detection points in descending order of their anomaly correlation values, and generate the steam curing anomaly detection sequence according to the sorting order.

[0019] Furthermore, the correlation value of the abnormal inspection point in steam curing is obtained through the following steps: Identify an abnormal inspection point in steam curing; calculate the distance difference between the abnormal inspection point and the abnormal steam curing inspection point to obtain the inspection point spacing Lc; obtain the steam curing depth detection values ​​obtained at the previous cycle node for both the abnormal inspection point and the abnormal steam curing inspection point; sum the two steam curing depth detection values ​​to obtain the depth detection additional value Rw; calculate the difference between the two steam curing depth detection values ​​and take the absolute value to obtain the depth detection difference value Yd; and then use the formula... The correlation value Atp of the abnormal detection point of steam curing was obtained.

[0020] Furthermore, based on the comparison between the modified value and the modified limit of the steam curing test, it is determined whether to modify the cycle length of the steam curing test. Specifically, a modified limit for steam curing test is set. When the modified value is greater than the modified limit, the ratio of the modified limit to the modified value is calculated to obtain the modification ratio e, and the cycle length of the steam curing test is adjusted to T. SC e .

[0021] Furthermore, the modified value of the steam curing test of the concrete beam is obtained through the following steps: obtain the total number of abnormal points detected by steam curing and mark it as Ewcu; sum the steam curing depth detection values ​​of the abnormal points and take the average value to obtain the abnormal depth detection value Fbmk; use the formula Jxg=Ewcu*j1+Fbmk*j2 to obtain the modified value of the steam curing test of the concrete beam Jxg, where j1 is the coefficient of the number of abnormal points detected and j2 is the coefficient of the depth detection of abnormal points.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. Set up a steam curing test point module, a steam curing test execution module, and a test anomaly marking module. Divide the concrete beam into multiple equally spaced steam curing test points to ensure the comprehensiveness and uniformity of steam curing test, meet the precision steam curing test requirements of large-size concrete beams, and conduct comprehensive test analysis on each steam curing test point of the concrete beam based on a certain period. Prioritize the test analysis of steam curing test points with high test requirements on the concrete beam to ensure efficient and accurate detection of locations with poor steam curing performance on the concrete beam.

[0024] 2. The system includes a detection execution adjustment module and a detection cycle modification module. When a location on the concrete beam exhibits poor steam curing performance, the system quickly adjusts the detection sequence of the remaining steam curing detection points on the concrete beam. This allows for rapid screening of other locations with poor steam curing performance, significantly improving the response speed and targeting of the detection. Furthermore, the system dynamically adjusts the steam curing detection cycle based on the overall steam curing performance of the concrete beam, ensuring that the concrete beam is inspected within a reasonable steam curing detection cycle. Attached Figure Description

[0025] Figure 1 This is a system module diagram of a steam curing and testing system applied to a smart beam yard;

[0026] Figure 2 This is a system operation flowchart for a steam curing and testing system applied to a smart beam yard;

[0027] Figure 3 A flowchart for determining whether to mark steam curing test points as abnormal steam curing test points. Detailed Implementation

[0028] Reference Figures 1 to 3 A steam curing inspection system for use in smart beam yards includes a steam curing inspection point setting module, a steam curing inspection execution module, an anomaly marking module, an inspection execution adjustment module, and an inspection cycle modification module.

[0029] Steam curing testing point setting module: Set the cycle length of the steam curing testing period to T. SC(During the steam curing process, the steam curing inspection cycle of the concrete beam is infinitely repeated.) Before the steam curing of the concrete beam, multiple equally spaced steam curing inspection points are marked on the concrete beam.

[0030] Steam curing inspection execution module: During the steam curing process of the concrete beam, whenever the cycle node of the steam curing inspection period is reached, the steam curing inspection requirement value of the steam curing inspection point on the concrete beam is obtained, and then the steam curing inspection sequence is generated (the steam curing inspection sequence contains all the steam curing inspection points on the concrete beam, and the steam curing inspection points are sorted in a certain order).

[0031] The steam curing test sequence is generated through the following steps: All steam curing test points on the concrete beam are sorted in descending order of their steam curing test requirement values, and a steam curing test sequence is generated according to the sorting order (e.g., if there are four steam curing test points on the concrete beam, namely steam curing test point a, steam curing test point b, and steam curing test point d, and the steam curing test requirement value of steam curing test point a > the steam curing test requirement value of steam curing test point c > the steam curing test requirement value of steam curing test point b > the steam curing test requirement value of steam curing test point d, then the steam curing test sequence is steam curing test point a, steam curing test point c, steam curing test point b, and steam curing test point d).

[0032] The steam curing depth detection requirements at the steam curing detection points are obtained through the following steps: Obtain the s steam curing depth detection values ​​previously acquired at the steam curing detection points; sort all steam curing depth detection values ​​in the order they were acquired; calculate the difference between any two adjacent steam curing depth detection values ​​after sorting and take the absolute value to obtain the depth detection fluctuation value; sum all depth detection fluctuation values ​​and take the average value to obtain the depth detection fluctuation mean Ajp; sum all steam curing depth detection values ​​and take the average value to obtain the depth detection mean Ajy; and then use the formula... The steam curing test requirement value Nmt for this steam curing test point is obtained, where b1 is the test fluctuation mean coefficient and b2 is the depth test mean coefficient, with b1 taking the value of 0.42 and b2 taking the value of 0.44.

[0033] Anomaly Detection Marking Module: The module sequentially detects the steam curing points on the concrete beam according to the steam curing detection sequence. After each steam curing detection point is detected, the steam curing depth detection value for that point is obtained. A steam curing depth detection threshold (the threshold value is a set threshold of the steam curing detection system) is set. When the steam curing depth detection value of a steam curing detection point is greater than or equal to the threshold value, the steam curing detection point is marked as an anomaly. When the steam curing depth detection value of a steam curing detection point is less than the threshold value, the module continues to detect the next steam curing detection point according to the steam curing detection sequence.

[0034] For each steam curing test point, the steam curing depth test value for that point is obtained. Specifically, this involves collecting various steam curing index data (including beam internal temperature, beam internal humidity, beam slump, beam spread, and beam air content, etc.) from the test point, obtaining the corresponding steam curing index model for each index, inputting each index into its corresponding model to obtain the compliance value for each index, pairing all the index data into a steam curing index group, calculating the difference between the compliance values ​​of the two indexes in the group and taking the absolute value to obtain the discrete value of the index group, summing all the discrete values ​​of the index groups and taking the mean to obtain the discrete mean Ptdg of the steam curing index. The average compliance value of the steam curing index is obtained by summing and averaging the values. A high and low compliance value for the steam curing index are set (the high compliance value is greater than the low compliance value; both the high and low compliance values ​​are thresholds set by the steam curing testing system). When the compliance value of the steam curing index is greater than or equal to the high compliance value, the data is marked as compliant, and the total number of compliant data is marked as Rz. When the compliance value is less than or equal to the low compliance value, the data is marked as non-compliant, and the total number of non-compliant data is marked as Ek. When the compliance value of the steam curing index is between the high and low compliance values, no further processing is performed, and the result is determined using the formula... The steam curing depth detection value Zsh of the steam curing detection point is obtained, where v1 is the dispersion coefficient of the steam curing index, v2 is the average compliance coefficient of steam curing, v3 is the compliance coefficient of steam curing, and v4 is the failure coefficient of steam curing. The values ​​of v1 are 2.31, v2 are 1.52, v3 are 0.79, and v4 are 0.81.

[0035] Each type of steam curing index data corresponds to a steam curing index model. The difference between different steam curing index models lies only in the different training data. In a specific embodiment of the present invention, the construction method of the steam curing index model corresponding to the internal temperature of the beam will be disclosed: multiple internal temperatures of the beam are collected, a neural network model is constructed, the internal temperature of the beam is used as the training data of the neural network model, and a steam curing index compliance value is assigned to each training data. The range of the compliance value of the steam curing index is (1.1~4.9). The larger the compliance value of the steam curing index, the more the internal temperature of the beam conforms to the steam curing index. The smaller the compliance value of the steam curing index, the less the internal temperature of the beam conforms to the steam curing index. The training data is divided into a training set, a validation set, and a test set according to a set ratio of 4:1:1. The neural network is iteratively trained on the training set, the validation set, and the test set. After training is completed, the steam curing index model of the internal temperature of the beam is constructed.

[0036] If we are constructing a steam curing index model for beam slump, then for multiple beam slump values, the larger the value of the steam curing index compliance value, the more the beam slump meets the steam curing index; the smaller the value of the steam curing index compliance value, the less the beam slump meets the steam curing index.

[0037] The system includes a steam curing test point setting module, a steam curing test execution module, and a test anomaly marking module. Multiple equally spaced steam curing test points are divided on the concrete beam to ensure the comprehensiveness and uniformity of the steam curing test, meeting the precision steam curing test requirements of large-size concrete beams. Based on a certain period, each steam curing test point on the concrete beam is subjected to comprehensive test analysis, with priority given to the steam curing test points on the concrete beam with high test requirements, ensuring efficient and accurate identification of locations on the concrete beam with poor steam curing performance.

[0038] Detection execution adjustment module: When an abnormal point is found in the steam curing detection, all other steam curing detection points that have not been detected are marked as steam curing abnormal detection points. The abnormal detection correlation value of each steam curing abnormal detection point is obtained. All steam curing abnormal detection points are sorted in descending order of the abnormal detection correlation value. A steam curing abnormal detection sequence is generated according to the sorting order. The steam curing abnormal detection points are detected in sequence according to the steam curing abnormal detection sequence.

[0039] The correlation value of the abnormal inspection point in steam curing is obtained through the following steps: First, identify an abnormal inspection point in steam curing. Calculate the distance difference between this abnormal inspection point and the abnormal steam curing inspection point to obtain the inspection point spacing Lc. Second, obtain the steam curing depth detection values ​​obtained at the previous cycle node for both the abnormal inspection point and the abnormal steam curing inspection point. Third, sum the two steam curing depth detection values ​​to obtain the depth detection additional value Rw. Finally, calculate the difference between the two steam curing depth detection values ​​and take the absolute value to obtain the depth detection difference value Yd. Then, use the formula... The correlation value Atp of the abnormal detection point of steam curing was obtained.

[0040] Inspection cycle modification module: When all steam curing inspection points on the concrete beam have completed inspection, the module acquires the modified steam curing inspection value of the concrete beam, sets the steam curing inspection modification limit (the steam curing inspection modification limit is the set threshold of the steam curing inspection system), and when the modified steam curing inspection value exceeds the steam curing inspection modification limit, the module calculates the ratio between the steam curing inspection modification limit and the modified steam curing inspection value to obtain the modification ratio e, and adjusts the cycle length of the steam curing inspection to T. SC e When the modified value of the steam curing test is less than or equal to the modified limit value of the steam curing test, no further processing is performed.

[0041] The modified value of the steam curing test for the concrete beam is obtained through the following steps: The total number of abnormal steam curing test points is obtained and marked as Ewcu. The steam curing depth test values ​​of the abnormal steam curing test points are summed and averaged to obtain the abnormal depth test value Fbmk. The modified value of the steam curing test for the concrete beam, Jxg, is obtained using the formula Jxg = Ewcu*j1 + Fbmk*j2, where j1 is the abnormality quantity coefficient and j2 is the abnormality depth test coefficient, with j1 taking the value of 0.73 and j2 taking the value of 0.34.

[0042] The system includes a detection execution adjustment module and a detection cycle modification module. When a location on the concrete beam exhibits poor steam curing performance, the system quickly adjusts the detection sequence of the remaining steam curing detection points on the concrete beam. This allows for rapid screening of other locations with poor steam curing performance, significantly improving the response speed and targeting of the detection. Furthermore, the system dynamically adjusts the steam curing detection cycle based on the overall steam curing performance of the concrete beam, ensuring that the concrete beam is inspected within a reasonable steam curing detection cycle.

[0043] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0044] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0045] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0046] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0047] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0048] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0049] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0050] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A steam curing detection system applied to a smart beam field, characterized in that, The steam curing detection set point module, the steam curing detection execution module, the detection anomaly marking module, the detection execution adjustment module, and the detection cycle modification module are included. The steam curing detection set point module divides a plurality of equally spaced steam curing detection points on the concrete beam body before steam curing of the concrete beam body. The steam curing detection execution module obtains the steam curing detection requirement value of the steam curing detection point on the concrete beam body when the period node of the steam curing detection cycle is reached during the steam curing process of the concrete beam body, and then generates a steam curing detection sequence. The detection anomaly marking module detects the steam curing detection points on the concrete beam body in sequence according to the steam curing detection sequence, obtains the steam curing depth detection value of each steam curing detection point after detecting each steam curing detection point, and determines whether to mark the steam curing detection point as a steam curing detection anomaly point based on the comparison result of the steam curing depth detection value and a steam curing depth detection critical value. The detection execution adjustment module marks the remaining steam curing detection points that have not been detected as steam curing anomaly detection points when a steam curing detection anomaly point is present, generates a steam curing anomaly detection sequence, and detects the steam curing anomaly detection points in sequence according to the steam curing anomaly detection sequence. The detection cycle modification module obtains a steam curing detection modification value of the concrete beam body when all the steam curing detection points on the concrete beam body have been detected, and determines whether to modify the period length of the steam curing detection cycle based on the comparison result of the steam curing detection modification value and a steam curing detection modification limit value.

2. The steam curing detection system applied to the smart beam field according to claim 1, characterized in that, The steam curing detection requirement value of the steam curing detection point is obtained through the following steps: obtaining the s steam curing depth detection values of the steam curing detection point obtained previously, sequentially sorting all the steam curing depth detection values according to the order of acquisition, calculating the difference value of the two adjacent steam curing depth detection values after sorting and taking the absolute value to obtain the depth detection floating value, summing all the depth detection floating values and taking the average to obtain the depth detection floating average value Ajp, summing all the steam curing depth detection values and taking the average to obtain the depth detection average value Ajy, and using the formula to obtain the steam curing detection requirement value Nmt of the steam curing detection point, wherein b1 is the detection floating average coefficient, and b2 is the depth detection average coefficient.

3. The steam curing detection system applied to the smart beam field according to claim 1, characterized in that, The steam curing detection sequence is generated by the following steps: sorting all the steam curing detection points on the concrete beam body in descending order of the steam curing detection requirement value, and generating the steam curing detection sequence according to the sorting order.

4. The steam curing detection system applied to the smart beam field according to claim 1, characterized in that, Each pair of one steam curing detection point completes detection, obtains the steam curing depth detection value of the steam curing detection point, specifically: collecting various steam curing index data of the steam curing detection point, obtaining steam curing index models corresponding to various steam curing index data, inputting various steam curing index data into corresponding steam curing index models respectively, obtaining steam curing index standard values of various steam curing index data, obtaining steam curing index discrete mean Ptdg, summing and averaging steam curing index standard values of all steam curing index data to obtain steam curing average standard value Bmg, setting steam curing index standard high value and steam curing index standard low value, when the steam curing index standard value of the steam curing index data is greater than or equal to the steam curing index standard high value, the steam curing index data is marked as steam curing standard data, the total number of steam curing standard data is marked as Rz, when the steam curing index standard value of the steam curing index data is less than or equal to the steam curing index standard low value, the steam curing index data is marked as steam curing off-standard data, and the total number of steam curing off-standard data is marked as Ek, and the steam curing depth detection value Zsh of the steam curing detection point is obtained by using the formula wherein v1 is a steam curing index discrete coefficient, v2 is a steam curing average standard coefficient, v3 is a steam curing standard coefficient, and v4 is a steam curing off-standard coefficient.

5. The steam curing detection system applied to the smart beam field according to claim 4, characterized in that, The steam curing index dispersion mean Ptdg is obtained by the following steps: matching all the steam curing index data into a steam curing index group, calculating the difference between the steam curing index target values of the two steam curing index data in the steam curing index group and taking the absolute value to obtain the steam curing index dispersion value of the steam curing index group, and summing all the steam curing index dispersion values of the steam curing index groups and taking the mean value to obtain the steam curing index dispersion mean Ptdg.

6. The steam curing detection system applied to the smart beam field according to claim 1, characterized in that, Based on the comparison result of the steam curing depth detection value and the steam curing depth detection critical value, it is determined whether to mark the steam curing detection point as a steam curing detection anomaly point. Specifically, the steam curing depth detection critical value is set, and when the steam curing depth detection value of the steam curing detection point is greater than or equal to the steam curing depth detection critical value, the steam curing detection point is marked as a steam curing detection anomaly point, and when the steam curing depth detection value of the steam curing detection point is less than the steam curing depth detection critical value, the next steam curing detection point is detected according to the steam curing detection sequence.

7. The steam curing detection system applied to the smart beam field according to claim 1, characterized in that, The steam curing anomaly detection sequence is generated by the following steps: obtaining the anomaly detection related value of each steam curing anomaly detection point, sorting all the steam curing anomaly detection points in descending order of the anomaly detection related value, and generating the steam curing anomaly detection sequence according to the sorting order.

8. The steam curing detection system applied to the smart beam field according to claim 7, characterized in that, The abnormality detection correlation value of the steam curing abnormality detection point is obtained through the following steps: determining a steam curing abnormality detection point, calculating the distance difference value between the steam curing abnormality detection point and the steam curing detection abnormality point to obtain the detection point distance Lc, obtaining the steam curing depth detection value of the steam curing abnormality detection point and the steam curing detection abnormality point at the previous cycle node, summing the two steam curing depth detection values to obtain the depth detection additional value Rw, calculating the difference value of the two steam curing depth detection values and taking the absolute value to obtain the depth detection difference value Yd, and using the formula to obtain the abnormality detection correlation value Atp of the steam curing abnormality detection point. 9.The steam curing detection system applied to the smart beam field of claim 1, wherein, Based on the comparison result of the steam curing detection modification value and the steam curing detection modification limit value, whether to modify the period length of the steam curing detection period is determined, specifically: setting the steam curing detection modification limit value, when the steam curing detection modification value is greater than the steam curing detection modification limit value, the steam curing detection modification limit value and the steam curing detection modification value are calculated by ratio, to obtain a modification ratio e, and the period length of the steam curing detection period is adjusted to T SC e .

10. The steam curing detection system applied to the smart beam field according to claim 1, characterized in that, The modified value of the steam curing detection of the concrete beam body is obtained through the following steps: obtaining the total number of steam curing detection abnormal points and marking as Ewcu, summing up the steam curing depth detection values of the steam curing detection abnormal points and taking the average to obtain the abnormal depth detection value Fbmk, and using the formula Jxg=Ewcu*j1+Fbmk*j2 to obtain the modified value Jxg of the steam curing detection of the concrete beam body, wherein j1 is a detection abnormal quantity coefficient, and j2 is an abnormal depth detection coefficient.

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