Continuous wall joint construction process optimization method based on big data analysis

Abstract

The invention discloses a continuous wall joint construction process optimization method based on big data analysis, and belongs to the technical field of continuous wall joint construction, and the method comprises the following steps: S1, collecting real-time data of a continuous wall joint construction process; s2, analyzing the characteristic data of the continuous wall joint construction process, discovering rules, trends and relevance in the characteristic data of the continuous wall joint construction process based on machine learning, identifying problems in the continuous wall joint construction process, and determining an analysis result of the continuous wall joint construction process; and S3, formulating optimization measures in the construction process of the continuous wall joint, and evaluating the optimization effect after the optimization measures are implemented. The problem that the construction quality and efficiency are low due to the fact that real-time monitoring and intelligent control and optimization cannot be conducted on the continuous wall joint construction process in the prior art is solved. According to the invention, real-time monitoring and data analysis can be carried out on the construction process of the continuous wall joint, intelligent control and optimization of the construction process can be realized, and the construction quality and efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of continuous wall joint construction, and in particular to a continuous wall joint construction process optimization method based on big data analysis. Background Art

[0002] Continuous wall, also known as underground continuous wall, is a foundation project that uses a trenching machine on the ground to dig a long and narrow deep trench along the peripheral axis of the deep excavation project under the condition of mud wall protection; after cleaning the trench, a steel cage is hung in the trench, and then underwater concrete is poured using the conduit method to build a unit trench section; this is done section by section to build a continuous reinforced concrete wall underground as a water interception, anti-seepage, load-bearing, and water-retaining structure. Among them, the continuous wall joint is a commonly used construction process in deep foundation pit engineering, and its construction quality directly affects the stability and safety of the entire project.

[0003] In the existing continuous wall joint construction process, the continuous wall joint construction process cannot be monitored in real time and data analysis cannot be performed, and the intelligent control and optimization of the construction process cannot be achieved, resulting in low construction quality and efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a continuous wall joint construction process optimization method based on big data analysis, which can perform real-time monitoring and data analysis on the continuous wall joint construction process, realize intelligent control and optimization of the construction process, improve construction quality and efficiency, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The optimization method of continuous wall joint construction process based on big data analysis includes the following steps: S1: Real-time data of the continuous wall joint construction process is collected through sensors and monitoring cameras, and the collected real-time data of the continuous wall joint construction process is transmitted to the cloud for analysis. The real-time data of the continuous wall joint construction process is processed to determine the characteristic data of the continuous wall joint construction process; S2: Build an optimal continuous wall joint construction process analysis model, use big data analysis technology to deeply mine and analyze the characteristic data of the continuous wall joint construction process, discover the laws, trends and correlations in the characteristic data of the continuous wall joint construction process based on machine learning, identify problems in the continuous wall joint construction process in a timely manner, and determine the analysis results of the continuous wall joint construction process; S3: Formulate optimization measures for the continuous wall joint construction process, evaluate the optimization effect after implementing the optimization measures for the continuous wall joint construction process, compare the real-time data of the continuous wall joint construction process after the implementation of the optimization measures with the real-time data of the continuous wall joint construction process before the implementation of the optimization measures, evaluate the optimization effect of the continuous wall joint construction process, and provide feedback and improvements on the optimization measures for the continuous wall joint construction process.

[0006] Preferably, in S1, real-time data of the continuous wall joint construction process is collected in real time, and the following operations are performed: The construction progress of the continuous wall joint construction process is monitored and continuously collected in real time through sensors and monitoring cameras to obtain the construction progress data of the continuous wall joint construction process; Through sensors and surveillance cameras, the personnel safety situation during the continuous wall joint construction process is monitored in real time and continuously collected to obtain personnel safety data during the continuous wall joint construction process; The mechanical operation status during the continuous wall joint construction process is monitored and continuously collected through sensors and monitoring cameras to obtain the mechanical operation data during the continuous wall joint construction process; Among them, the real-time data of the continuous wall joint construction process is determined based on the construction progress data of the continuous wall joint construction process, the personnel safety data of the continuous wall joint construction process and the machinery operation data of the continuous wall joint construction process.

[0007] Preferably, in S1, the real-time data of the continuous wall joint construction process is processed, and the following operations are performed: Obtain real-time data of continuous wall joint construction process; Cleaning and processing of real-time data of continuous wall joint construction process, including: Conduct consistency checks on real-time data during the construction of continuous wall joints; According to the data consistency requirement, check whether there is inconsistent data in the real-time data of the continuous wall joint construction process that is useless for optimizing the continuous wall joint construction process, and remove the inconsistent data in the real-time data of the continuous wall joint construction process; Check the invalid and missing values ​​of the real-time data of the continuous wall joint construction process; According to the requirements of data validity and integrity, check whether there are invalid values ​​and missing values ​​in the real-time data of the continuous wall joint construction process that are useless for optimizing the continuous wall joint construction process, and remove the invalid values ​​and missing values ​​in the real-time data of the continuous wall joint construction process; Determine real-time diaphragm wall joint construction process data useful for diaphragm wall joint construction process optimization.

[0008] Preferably, in S1, the real-time data of the continuous wall joint construction process is processed, and the following operations are further performed: Extracting video image data acquired by the surveillance camera; Performing frame processing on the video image data to obtain a plurality of frame image data corresponding to the video image data; Use sensors to monitor the light intensity values ​​in real time during the construction of continuous wall joints; Obtain the corresponding light intensity value according to the time corresponding to each frame image data; Obtaining a first contrast adjustment coefficient according to a light intensity value corresponding to each frame of image data; The first contrast adjustment coefficient is obtained by the following formula: Among them, S 01 represents the first contrast adjustment coefficient; n represents the number of frame image data; H zi Represents the central grayscale value corresponding to the i-th frame image data; H zc Indicates the preset center grayscale reference value; Q c Indicates the preset light intensity reference value; Q i Indicates the light intensity value corresponding to the i-th frame image data; Q b Indicates the standard deviation of the illumination intensity corresponding to n frames of image data; D min and D max Indicates the maximum and minimum contrast values ​​corresponding to n frames of image data; Q min The light intensity value corresponding to the frame image data representing the minimum contrast value; Q max The light intensity value corresponding to the frame image data representing the maximum contrast; The second contrast adjustment coefficient corresponding to each frame image data is obtained by using the grayscale value corresponding to each pixel point of each frame image data, and the contrast of each frame image data is adjusted by using the first contrast adjustment coefficient in combination with the second contrast adjustment coefficient.

[0009] Preferably, the second contrast adjustment coefficient corresponding to each frame image data is obtained by using the grayscale value corresponding to each pixel point of each frame image data, and the contrast of each frame image data is adjusted by using the first contrast adjustment coefficient in combination with the second contrast adjustment coefficient, including: Extracting the central grayscale value of each frame image data; Using the central grayscale value of each frame image data, obtaining the central grayscale value standard deviation corresponding to all frame image data; Extract the grayscale value corresponding to each pixel of each frame image data; Obtaining a second contrast adjustment coefficient using the grayscale value corresponding to each pixel point combined with the standard deviation of the central grayscale value corresponding to all frame image data; The second contrast adjustment coefficient is obtained by the following formula: Among them, S 02 represents the second contrast adjustment coefficient; Q c Indicates the preset light intensity reference value; Q i Indicates the light intensity value corresponding to the i-th frame image data; Q b Indicates the standard deviation of the illumination intensity corresponding to n frames of image data; H zc Indicates the preset center grayscale reference value; H b represents the standard deviation of the central grayscale value corresponding to n frames of image data; Q represents the light intensity value corresponding to each frame of image data; m represents the number of pixels contained in each frame of image data; H i Indicates the gray value corresponding to the i-th pixel; H z Indicates the central gray value corresponding to each frame image data; Retrieving the first contrast adjustment coefficient; Using the first contrast adjustment coefficient and the second contrast adjustment coefficient to obtain a target contrast value of each frame of image data; The target contrast value is obtained by the following formula: Where D represents the target contrast value; D0 represents the contrast before adjustment corresponding to the frame image data; S 01 represents the first contrast adjustment coefficient; S 02 represents the second contrast adjustment coefficient; The contrast of each frame image data is adjusted according to the target contrast value corresponding to each frame image data to obtain the frame image data after contrast adjustment.

[0010] Preferably, in S1, the real-time data of the continuous wall joint construction process is processed, and the following operations are further performed: Acquire real-time data of continuous wall joint construction process useful for optimizing continuous wall joint construction process; The real-time data of the continuous wall joint construction process useful for optimizing the continuous wall joint construction process is converted and processed, and the dimension difference between the real-time data of the continuous wall joint construction process useful for optimizing the continuous wall joint construction process is eliminated; Unify the formats of continuous wall joint construction process real-time data useful for continuous wall joint construction process optimization, and determine standardized continuous wall joint construction process real-time data; Feature extraction of real-time data of standardized continuous wall joint construction process; Extract features that reflect the optimization of the construction process of continuous wall joints; Determine the characteristic data of the continuous wall joint construction process.

[0011] Preferably, in S2, an optimal continuous wall joint construction process analysis model is constructed, and the following operations are performed: According to the optimization requirements of the continuous wall joint construction process based on big data analysis, the historical data of the continuous wall joint construction process are collected, the historical data of the continuous wall joint construction process are divided, and the training set and the test set are determined; Select a machine learning model framework suitable for optimizing the construction process of continuous wall joints, train the selected machine learning model framework suitable for optimizing the construction process of continuous wall joints based on the training set, and determine a continuous wall joint construction process analysis model based on big data analysis; Based on the test set, the performance test of the continuous wall joint construction process analysis model based on big data analysis is carried out to determine whether the performance of the continuous wall joint construction process analysis model based on big data analysis reaches the expected effect and determine the performance test results of the continuous wall joint construction process analysis model; Based on the performance test results of the continuous wall joint construction process analysis model, the parameters of the continuous wall joint construction process analysis model based on big data analysis are adjusted and the structure is improved. The continuous wall joint construction process analysis model based on big data analysis is repeatedly iteratively optimized to determine the optimal continuous wall joint construction process analysis model.

[0012] Preferably, in S2, the characteristic data of the continuous wall joint construction process is deeply mined and analyzed, and the following operations are performed: Obtain the optimal analysis model for the construction process of continuous wall joints; The optimal continuous wall joint construction process analysis model is deployed in the actual continuous wall joint construction process, and the characteristic data of the continuous wall joint construction process is deeply mined and analyzed based on the optimal continuous wall joint construction process analysis model; Discover the patterns, trends and correlations in the characteristic data of the continuous wall joint construction process, identify problems in the continuous wall joint construction process in a timely manner, and determine the analysis results of the continuous wall joint construction process.

[0013] Preferably, in S3, after implementing the optimization measures for the continuous wall joint construction process, the optimization effect is evaluated, and the following operations are performed: Based on the analysis results of the continuous wall joint construction process, formulate optimization measures for the continuous wall joint construction process; Based on the optimization measures of the continuous wall joint construction process, the continuous wall joint construction process is optimized, and the optimization effect of the continuous wall joint construction process after the optimization measures are implemented is evaluated; Among them, after the optimization measures are implemented, the real-time data of the continuous wall joint construction process after the optimization measures are implemented is obtained through sensors and monitoring cameras; The real-time data of the continuous wall joint construction process after the implementation of the optimization measures are compared with the real-time data of the continuous wall joint construction process before the implementation of the optimization measures. The changes between the real-time data of the continuous wall joint construction process after the implementation of the optimization measures and the real-time data of the continuous wall joint construction process before the implementation of the optimization measures are compared to determine the optimization effect of the continuous wall joint construction process.

[0014] Preferably, in S3, feedback and improvement are provided on optimization measures for the construction process of the continuous wall joint, and the following operations are performed: When the real-time data of the continuous wall joint construction process after the optimization measures are implemented is better than the real-time data of the continuous wall joint construction process before the optimization measures are implemented, the optimization effect of the continuous wall joint construction process is good; When the real-time data of the continuous wall joint construction process after the optimization measures are implemented is inferior to the real-time data of the continuous wall joint construction process before the optimization measures are implemented, the optimization effect of the continuous wall joint construction process is poor; According to the optimization effect of the continuous wall joint construction process, the optimization measures of the continuous wall joint construction process are fed back and improved.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention collects real-time data of the continuous wall joint construction process through sensors and monitoring cameras, and transmits the collected real-time data of the continuous wall joint construction process to the cloud for analysis. By processing the real-time data of the continuous wall joint construction process, the characteristic data of the continuous wall joint construction process is determined, and the optimal continuous wall joint construction process analysis model is constructed. The characteristic data of the continuous wall joint construction process is deeply mined and analyzed by big data analysis technology. Based on machine learning, the laws, trends and correlations in the characteristic data of the continuous wall joint construction process are discovered, problems in the continuous wall joint construction process are timely identified, and the analysis results of the continuous wall joint construction process are determined.

[0016] 2. The present invention formulates optimization measures for the continuous wall joint construction process, evaluates the optimization effect after implementing the optimization measures for the continuous wall joint construction process, compares the real-time data of the continuous wall joint construction process after the implementation of the optimization measures with the real-time data of the continuous wall joint construction process before the implementation of the optimization measures, evaluates the optimization effect of the continuous wall joint construction process, and provides feedback and improvements on the optimization measures for the continuous wall joint construction process. The continuous wall joint construction process can be monitored in real time and data analyzed, intelligent control and optimization of the construction process can be achieved, and construction quality and efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1This is an operational flow chart of the method for optimizing the continuous wall joint construction process based on big data analysis of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In order to solve the problem that the existing continuous wall joint construction process cannot be monitored in real time and data analyzed, and the intelligent control and optimization of the construction process cannot be realized, resulting in low construction quality and efficiency, please refer to Figure 1 , this embodiment provides the following technical solutions: The optimization method of continuous wall joint construction process based on big data analysis includes the following steps: S1: Real-time data of the continuous wall joint construction process is collected through sensors and monitoring cameras, and the collected real-time data of the continuous wall joint construction process is transmitted to the cloud for analysis. The real-time data of the continuous wall joint construction process is processed to determine the characteristic data of the continuous wall joint construction process; In this embodiment, real-time data of the continuous wall joint construction process is collected in real time, and the following operations are performed: The construction progress of the continuous wall joint construction process is monitored and continuously collected in real time through sensors and monitoring cameras to obtain the construction progress data of the continuous wall joint construction process; Through sensors and surveillance cameras, the personnel safety situation during the continuous wall joint construction process is monitored in real time and continuously collected to obtain personnel safety data during the continuous wall joint construction process; The mechanical operation status during the continuous wall joint construction process is monitored and continuously collected through sensors and monitoring cameras to obtain the mechanical operation data during the continuous wall joint construction process; Among them, the real-time data of the continuous wall joint construction process is determined based on the construction progress data of the continuous wall joint construction process, the personnel safety data of the continuous wall joint construction process and the machinery operation data of the continuous wall joint construction process.

[0020] In this embodiment, the real-time data of the continuous wall joint construction process is processed, and the following operations are performed: Obtain real-time data of continuous wall joint construction process; Cleaning and processing of real-time data of continuous wall joint construction process, including: Conduct consistency checks on real-time data during the construction of continuous wall joints; According to the data consistency requirement, check whether there is inconsistent data in the real-time data of the continuous wall joint construction process that is useless for optimizing the continuous wall joint construction process, and remove the inconsistent data in the real-time data of the continuous wall joint construction process; Check the invalid and missing values ​​of the real-time data of the continuous wall joint construction process; According to the requirements of data validity and integrity, check whether there are invalid values ​​and missing values ​​in the real-time data of the continuous wall joint construction process that are useless for optimizing the continuous wall joint construction process, and remove the invalid values ​​and missing values ​​in the real-time data of the continuous wall joint construction process; Determining real-time diaphragm wall joint construction process data useful for optimizing the diaphragm wall joint construction process; It should be noted that data cleaning refers to the process of re-examining and verifying data, with the aim of discovering and correcting errors in data files, processing invalid values ​​and missing values, etc., in order to improve the quality and availability of data.

[0021] Specifically, in S1, the real-time data of the continuous wall joint construction process is processed, and the following operations are also performed: Extracting video image data acquired by the surveillance camera; Performing frame processing on the video image data to obtain a plurality of frame image data corresponding to the video image data; Use sensors to monitor the light intensity values ​​in real time during the construction of continuous wall joints; Obtain the corresponding light intensity value according to the time corresponding to each frame image data; Obtaining a first contrast adjustment coefficient according to a light intensity value corresponding to each frame of image data; The first contrast adjustment coefficient is obtained by the following formula: Among them, S 01 represents the first contrast adjustment coefficient; n represents the number of frame image data; H zi Represents the central grayscale value corresponding to the i-th frame image data; H zc Indicates the preset center grayscale reference value; Q c Indicates the preset light intensity reference value; Q i Indicates the light intensity value corresponding to the i-th frame image data; Q b Indicates the standard deviation of the illumination intensity corresponding to n frames of image data; D min and D max Indicates the maximum and minimum contrast values ​​corresponding to n frames of image data; Q min The light intensity value corresponding to the frame image data representing the minimum contrast value; Q maxThe light intensity value corresponding to the frame image data representing the maximum contrast; The second contrast adjustment coefficient corresponding to each frame image data is obtained by using the grayscale value corresponding to each pixel point of each frame image data, and the contrast of each frame image data is adjusted by using the first contrast adjustment coefficient in combination with the second contrast adjustment coefficient.

[0022] The technical effect of the above technical solution is: by real-time monitoring of the light intensity value during the construction of the continuous wall joint and adjusting the contrast of the frame image data accordingly, the technical solution can significantly improve the visibility and clarity of the image under different lighting conditions. Especially in an environment where the lighting conditions change frequently, this dynamic adjustment mechanism can ensure the accuracy and integrity of the image information. The first contrast adjustment coefficient is introduced, which comprehensively considers factors such as the central grayscale value of the frame image data, the light intensity value and its standard deviation, the maximum and minimum values ​​of the contrast, and realizes the fine adjustment of the contrast. This multi-dimensional and multi-parameter adjustment method can more accurately reflect the lighting changes in the actual scene, thereby optimizing the contrast performance of the image. Combined with the grayscale value corresponding to each pixel point of each frame image data, the second contrast adjustment coefficient is obtained, and it works together with the first contrast adjustment coefficient to adjust the contrast of each frame image data. This pixel-level contrast adjustment can further improve the overall quality of the image and reduce the blur and distortion in the image. In the process of continuous wall joint construction, clear image information is crucial for timely discovery and handling of potential problems. This technical solution improves the efficiency and accuracy of construction monitoring by optimizing image contrast, helping to reduce misjudgments and missed judgments caused by image quality issues. This technical solution realizes the automatic processing and analysis of video image data, reducing the need for manual intervention. At the same time, by introducing sensors and algorithm models, the intelligent level of construction monitoring is improved, providing strong support for the intelligent management of the construction process.

[0023] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in dynamic lighting adaptability, contrast optimization, image quality improvement, construction monitoring efficiency improvement, and automation and intelligence level improvement. These technical effects work together in the monitoring and management of the continuous wall joint construction process, which helps to improve construction quality and safety.

[0024] Specifically, the second contrast adjustment coefficient corresponding to each frame image data is obtained by using the grayscale value corresponding to each pixel point of each frame image data, and the contrast of each frame image data is adjusted by using the first contrast adjustment coefficient in combination with the second contrast adjustment coefficient, including: Extracting the central grayscale value of each frame image data; Using the central grayscale value of each frame image data, obtaining the central grayscale value standard deviation corresponding to all frame image data; Extract the grayscale value corresponding to each pixel of each frame image data; Obtaining a second contrast adjustment coefficient using the grayscale value corresponding to each pixel point combined with the standard deviation of the central grayscale value corresponding to all frame image data; The second contrast adjustment coefficient is obtained by the following formula: Among them, S 02 represents the second contrast adjustment coefficient; Q c Indicates the preset light intensity reference value; Q i Indicates the light intensity value corresponding to the i-th frame image data; Q b Indicates the standard deviation of the illumination intensity corresponding to n frames of image data; H zc Indicates the preset center grayscale reference value; H b represents the standard deviation of the central grayscale value corresponding to n frames of image data; Q represents the light intensity value corresponding to each frame of image data; m represents the number of pixels contained in each frame of image data; H i Indicates the gray value corresponding to the i-th pixel; H z Indicates the central gray value corresponding to each frame image data; Retrieving the first contrast adjustment coefficient; Using the first contrast adjustment coefficient and the second contrast adjustment coefficient to obtain a target contrast value of each frame of image data; The target contrast value is obtained by the following formula: Where D represents the target contrast value; D0 represents the contrast before adjustment corresponding to the frame image data; S 01 represents the first contrast adjustment coefficient; S 02 represents the second contrast adjustment coefficient; The contrast of each frame image data is adjusted according to the target contrast value corresponding to each frame image data to obtain the frame image data after contrast adjustment.

[0025] The technical effect of the above technical solution is: the technical solution realizes contrast adjustment based on pixel level by calculating the second contrast adjustment coefficient of each frame image data. Combined with the first contrast adjustment coefficient (based on overall illumination and grayscale distribution), this dual adjustment mechanism can adjust the contrast of the image more finely, so that the image can maintain good visual effects under different illumination and grayscale changes. By accurately calculating the relationship between the grayscale value of each pixel and the target contrast, the technical solution can optimize the detail information in the image and improve the clarity of the image. In particular, this optimization effect is particularly obvious in scenes with complex illumination conditions or large grayscale changes. Using parameters such as the standard deviation of the central grayscale value and the standard deviation of the illumination intensity value, the technical solution can evaluate the overall stability of the image and the changes in illumination conditions. By dynamically adjusting the contrast, the technical solution can reduce the image distortion or blurring caused by illumination changes or grayscale fluctuations, thereby improving the stability of the image. The technical solution can be applied to scenes with different illumination conditions and grayscale distributions. Whether it is a strong light or weak light environment, whether it is a scene with uniform or drastic grayscale changes, the technical solution can maintain a good visual effect of the image by dynamically adjusting the contrast. In scenarios such as continuous wall joint construction, clear images are essential for monitoring construction quality. This technical solution optimizes the contrast of images to make detailed information during the construction process more visible, which helps to promptly discover and deal with potential problems, thereby improving the efficiency of construction monitoring. This technical solution realizes the automated processing and analysis of video image data, reducing the need for manual intervention. By introducing an algorithm model and parameter adjustment mechanism, this technical solution can intelligently adjust the contrast of images and improve the intelligence level of construction monitoring.

[0026] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in refined contrast adjustment, improved image clarity, enhanced image stability, strong adaptability, improved construction monitoring efficiency, and improved automation and intelligence levels. These technical effects work together in the monitoring and management of the continuous wall joint construction process, which helps to improve construction quality and safety, while reducing monitoring costs and improving work efficiency.

[0027] Among them, the consistency check is to check whether the data meets the requirements based on the reasonable value range and mutual relationship of each variable, and to find data that exceeds the normal range, is logically unreasonable or contradictory. For example, a variable measured by a 1-7 scale has a value of 0, and a negative weight should be considered to be beyond the normal value range. Computer software such as SPSS, SAS and Excel can automatically identify each variable value that exceeds the range based on the defined value range. Logically inconsistent answers may appear in many forms: for example, many respondents said that they drove to work, but reported that they did not have a car; or respondents reported that they were heavy buyers and users of a certain brand, but at the same time gave a very low score on the familiarity scale. When inconsistencies are found, the questionnaire number, record number, variable name, error category, etc. should be listed to facilitate further verification and correction.

[0028] Therefore, by cleaning the real-time data of the continuous wall joint construction process, inconsistent data, invalid values ​​and missing values ​​that are useless for optimizing the continuous wall joint construction process can be removed, and the subsequent processing accuracy and efficiency of the real-time data of the continuous wall joint construction process can be improved.

[0029] Acquire real-time data of continuous wall joint construction process useful for optimizing continuous wall joint construction process; The real-time data of the continuous wall joint construction process useful for optimizing the continuous wall joint construction process is converted and processed, and the dimension difference between the real-time data of the continuous wall joint construction process useful for optimizing the continuous wall joint construction process is eliminated; Unify the formats of continuous wall joint construction process real-time data useful for continuous wall joint construction process optimization, and determine standardized continuous wall joint construction process real-time data; Feature extraction of real-time data of standardized continuous wall joint construction process; Extract features that reflect the optimization of the construction process of continuous wall joints; Determine the characteristic data of the continuous wall joint construction process.

[0030] S2: Build an optimal continuous wall joint construction process analysis model, use big data analysis technology to deeply mine and analyze the characteristic data of the continuous wall joint construction process, discover the laws, trends and correlations in the characteristic data of the continuous wall joint construction process based on machine learning, identify problems in the continuous wall joint construction process in a timely manner, and determine the analysis results of the continuous wall joint construction process; In this embodiment, an optimal continuous wall joint construction process analysis model is constructed, and the following operations are performed: According to the optimization requirements of the continuous wall joint construction process based on big data analysis, the historical data of the continuous wall joint construction process are collected, the historical data of the continuous wall joint construction process are divided, and the training set and the test set are determined; Select a machine learning model framework suitable for optimizing the construction process of continuous wall joints, train the selected machine learning model framework suitable for optimizing the construction process of continuous wall joints based on the training set, and determine a continuous wall joint construction process analysis model based on big data analysis; Based on the test set, the performance test of the continuous wall joint construction process analysis model based on big data analysis is carried out to determine whether the performance of the continuous wall joint construction process analysis model based on big data analysis reaches the expected effect and determine the performance test results of the continuous wall joint construction process analysis model; Based on the performance test results of the continuous wall joint construction process analysis model, the parameters of the continuous wall joint construction process analysis model based on big data analysis are adjusted and the structure is improved. The continuous wall joint construction process analysis model based on big data analysis is repeatedly iteratively optimized to determine the optimal continuous wall joint construction process analysis model.

[0031] In this embodiment, the characteristic data of the continuous wall joint construction process is deeply mined and analyzed, and the following operations are performed: Obtain the optimal analysis model for the construction process of continuous wall joints; The optimal continuous wall joint construction process analysis model is deployed in the actual continuous wall joint construction process, and the characteristic data of the continuous wall joint construction process is deeply mined and analyzed based on the optimal continuous wall joint construction process analysis model; Discover the patterns, trends and correlations in the characteristic data of the continuous wall joint construction process, identify problems in the continuous wall joint construction process in a timely manner, and determine the analysis results of the continuous wall joint construction process.

[0032] S3: Formulate optimization measures for the continuous wall joint construction process, evaluate the optimization effect after implementing the optimization measures for the continuous wall joint construction process, compare the real-time data of the continuous wall joint construction process after the implementation of the optimization measures with the real-time data of the continuous wall joint construction process before the implementation of the optimization measures, evaluate the optimization effect of the continuous wall joint construction process, and provide feedback and improvements on the optimization measures for the continuous wall joint construction process.

[0033] In this embodiment, after implementing the optimization measures for the continuous wall joint construction process, the optimization effect is evaluated by performing the following operations: Based on the analysis results of the continuous wall joint construction process, formulate optimization measures for the continuous wall joint construction process; Based on the optimization measures of the continuous wall joint construction process, the continuous wall joint construction process is optimized, and the optimization effect of the continuous wall joint construction process after the optimization measures are implemented is evaluated; Among them, after the optimization measures are implemented, the real-time data of the continuous wall joint construction process after the optimization measures are implemented is obtained through sensors and monitoring cameras; The real-time data of the continuous wall joint construction process after the implementation of the optimization measures are compared with the real-time data of the continuous wall joint construction process before the implementation of the optimization measures. The changes between the real-time data of the continuous wall joint construction process after the implementation of the optimization measures and the real-time data of the continuous wall joint construction process before the implementation of the optimization measures are compared to determine the optimization effect of the continuous wall joint construction process.

[0034] Among them, when the real-time data of the continuous wall joint construction process after the optimization measures are implemented is better than the real-time data of the continuous wall joint construction process before the optimization measures are implemented, the optimization effect of the continuous wall joint construction process is good; Among them, when the real-time data of the continuous wall joint construction process after the optimization measures are implemented is inferior to the real-time data of the continuous wall joint construction process before the optimization measures are implemented, the optimization effect of the continuous wall joint construction process is poor; Therefore, according to the optimization effect of the continuous wall joint construction process, the optimization measures of the continuous wall joint construction process are fed back and improved.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the construction process of continuous wall joints based on big data analysis, characterized in that: The steps include: S1: Real-time data of the continuous wall joint construction process is collected through sensors and monitoring cameras, and the collected real-time data of the continuous wall joint construction process is transmitted to the cloud for analysis. The real-time data of the continuous wall joint construction process is processed to determine the characteristic data of the continuous wall joint construction process; S2: Build an optimal continuous wall joint construction process analysis model, use big data analysis technology to deeply mine and analyze the characteristic data of the continuous wall joint construction process, discover the laws, trends and correlations in the characteristic data of the continuous wall joint construction process based on machine learning, identify problems in the continuous wall joint construction process in a timely manner, and determine the analysis results of the continuous wall joint construction process; S3: Formulate optimization measures for the continuous wall joint construction process, evaluate the optimization effect after implementing the optimization measures for the continuous wall joint construction process, compare the real-time data of the continuous wall joint construction process after the implementation of the optimization measures with the real-time data of the continuous wall joint construction process before the implementation of the optimization measures, evaluate the optimization effect of the continuous wall joint construction process, and provide feedback and improvements on the optimization measures for the continuous wall joint construction process.

2. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 1, characterized in that: In S1, real-time data of the continuous wall joint construction process is collected in real time, and the following operations are performed: The construction progress of the continuous wall joint construction process is monitored and continuously collected in real time through sensors and monitoring cameras to obtain the construction progress data of the continuous wall joint construction process; Through sensors and surveillance cameras, the personnel safety situation during the continuous wall joint construction process is monitored in real time and continuously collected to obtain personnel safety data during the continuous wall joint construction process; The mechanical operation status during the continuous wall joint construction process is monitored and continuously collected in real time through sensors and monitoring cameras to obtain the mechanical operation data during the continuous wall joint construction process; Among them, the real-time data of the continuous wall joint construction process is determined based on the construction progress data of the continuous wall joint construction process, the personnel safety data of the continuous wall joint construction process and the machinery operation data of the continuous wall joint construction process.

3. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 2 is characterized in that: In S1, the real-time data of the continuous wall joint construction process is processed, and the following operations are performed: Obtain real-time data of continuous wall joint construction process; Cleaning and processing of real-time data of continuous wall joint construction process, including: Conduct consistency checks on real-time data during the construction of continuous wall joints; According to the data consistency requirement, check whether there is inconsistent data in the real-time data of the continuous wall joint construction process that is useless for optimizing the continuous wall joint construction process, and remove the inconsistent data in the real-time data of the continuous wall joint construction process; Check the invalid and missing values ​​of the real-time data of the continuous wall joint construction process; According to the requirements of data validity and integrity, check whether there are invalid values ​​and missing values ​​in the real-time data of the continuous wall joint construction process that are useless for optimizing the continuous wall joint construction process, and remove the invalid values ​​and missing values ​​in the real-time data of the continuous wall joint construction process; Determine real-time diaphragm wall joint construction process data useful for diaphragm wall joint construction process optimization.

4. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 2, characterized in that: In S1, the real-time data of the continuous wall joint construction process is processed, and the following operations are performed: Extracting video image data acquired by the surveillance camera; Performing frame processing on the video image data to obtain a plurality of frame image data corresponding to the video image data; Use sensors to monitor the light intensity value in real time during the construction of continuous wall joints; Obtain the corresponding light intensity value according to the time corresponding to each frame image data; Obtaining a first contrast adjustment coefficient according to a light intensity value corresponding to each frame of image data; The first contrast adjustment coefficient is obtained by the following formula: Among them, S 01 represents the first contrast adjustment coefficient; n represents the number of frame image data; H zi Represents the central grayscale value corresponding to the i-th frame image data; H zc Indicates the preset center grayscale reference value; Q c Indicates the preset light intensity reference value; Q i Indicates the light intensity value corresponding to the i-th frame image data; Q b Indicates the standard deviation of the illumination intensity corresponding to n frames of image data; D min and D max Indicates the maximum and minimum contrast values ​​corresponding to n frames of image data; Q min The light intensity value corresponding to the frame image data representing the minimum contrast value; Q max The light intensity value corresponding to the frame image data representing the maximum contrast; The second contrast adjustment coefficient corresponding to each frame image data is obtained by using the grayscale value corresponding to each pixel point of each frame image data, and the contrast of each frame image data is adjusted by using the first contrast adjustment coefficient in combination with the second contrast adjustment coefficient.

5. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 4 is characterized in that: The method comprises: obtaining a second contrast adjustment coefficient corresponding to each frame of image data by using the gray value corresponding to each pixel point of each frame of image data, and adjusting the contrast of each frame of image data by using the first contrast adjustment coefficient in combination with the second contrast adjustment coefficient, including: Extracting the central grayscale value of each frame image data; Using the central grayscale value of each frame image data, obtaining the central grayscale value standard deviation corresponding to all frame image data; Extract the grayscale value corresponding to each pixel of each frame image data; Obtaining a second contrast adjustment coefficient using the grayscale value corresponding to each pixel point combined with the standard deviation of the central grayscale value corresponding to all frame image data; The second contrast adjustment coefficient is obtained by the following formula: Among them, S 02 represents the second contrast adjustment coefficient; Q c Indicates the preset light intensity reference value; Q i Indicates the light intensity value corresponding to the i-th frame image data; Q b Indicates the standard deviation of the illumination intensity corresponding to n frames of image data; H zc Indicates the preset center grayscale reference value; H b represents the standard deviation of the central grayscale value corresponding to n frames of image data; Q represents the light intensity value corresponding to each frame of image data; m represents the number of pixels contained in each frame of image data; H i Indicates the gray value corresponding to the i-th pixel; H z Indicates the central gray value corresponding to each frame image data; Retrieving the first contrast adjustment coefficient; Obtaining a target contrast value of each frame of image data using the first contrast adjustment coefficient and the second contrast adjustment coefficient; The target contrast value is obtained by the following formula: Where D represents the target contrast value; D0 represents the contrast before adjustment corresponding to the frame image data; S 01 represents the first contrast adjustment coefficient; S 02 represents the second contrast adjustment coefficient; The contrast of each frame image data is adjusted according to the target contrast value corresponding to each frame image data to obtain the frame image data after contrast adjustment.

6. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 3 is characterized in that: In S1, the real-time data of the continuous wall joint construction process is processed, and the following operations are performed: Acquire real-time data of continuous wall joint construction process useful for optimizing continuous wall joint construction process; The real-time data of the continuous wall joint construction process useful for optimizing the continuous wall joint construction process is converted and processed, and the dimension difference between the real-time data of the continuous wall joint construction process useful for optimizing the continuous wall joint construction process is eliminated; Unify the formats of continuous wall joint construction process real-time data useful for continuous wall joint construction process optimization, and determine standardized continuous wall joint construction process real-time data; Feature extraction of real-time data of standardized continuous wall joint construction process; Extract features that reflect the optimization of the construction process of continuous wall joints; Determine the characteristic data of the continuous wall joint construction process.

7. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 6, characterized in that: In S2, an optimal continuous wall joint construction process analysis model is constructed, and the following operations are performed: According to the optimization requirements of the continuous wall joint construction process based on big data analysis, the historical data of the continuous wall joint construction process are collected, the historical data of the continuous wall joint construction process are divided, and the training set and the test set are determined; Select a machine learning model framework suitable for optimizing the construction process of continuous wall joints, train the selected machine learning model framework suitable for optimizing the construction process of continuous wall joints based on the training set, and determine a continuous wall joint construction process analysis model based on big data analysis; Based on the test set, the performance test of the continuous wall joint construction process analysis model based on big data analysis is carried out to determine whether the performance of the continuous wall joint construction process analysis model based on big data analysis reaches the expected effect and determine the performance test results of the continuous wall joint construction process analysis model; Based on the performance test results of the continuous wall joint construction process analysis model, the parameters of the continuous wall joint construction process analysis model based on big data analysis are adjusted and the structure is improved. The continuous wall joint construction process analysis model based on big data analysis is repeatedly iteratively optimized to determine the optimal continuous wall joint construction process analysis model.

8. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 7, characterized in that: In S2, the characteristic data of the continuous wall joint construction process is deeply mined and analyzed, and the following operations are performed: Obtain the optimal analysis model for the construction process of continuous wall joints; The optimal continuous wall joint construction process analysis model is deployed in the actual continuous wall joint construction process, and the characteristic data of the continuous wall joint construction process is deeply mined and analyzed based on the optimal continuous wall joint construction process analysis model; Discover the patterns, trends and correlations in the characteristic data of the continuous wall joint construction process, identify problems in the continuous wall joint construction process in a timely manner, and determine the analysis results of the continuous wall joint construction process.

9. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 8, characterized in that: In S3, after implementing the optimization measures for the continuous wall joint construction process, the optimization effect is evaluated, and the following operations are performed: Based on the analysis results of the continuous wall joint construction process, formulate optimization measures for the continuous wall joint construction process; Based on the optimization measures of the continuous wall joint construction process, the continuous wall joint construction process is optimized, and the optimization effect of the continuous wall joint construction process after the optimization measures are implemented is evaluated; Among them, after the optimization measures are implemented, the real-time data of the continuous wall joint construction process after the optimization measures are implemented is obtained through sensors and monitoring cameras; The real-time data of the continuous wall joint construction process after the implementation of the optimization measures are compared with the real-time data of the continuous wall joint construction process before the implementation of the optimization measures. The changes between the real-time data of the continuous wall joint construction process after the implementation of the optimization measures and the real-time data of the continuous wall joint construction process before the implementation of the optimization measures are compared to determine the optimization effect of the continuous wall joint construction process.

10. The method for optimizing the construction process of continuous wall joints based on big data analysis according to claim 9, characterized in that: In S3, feedback and improvement are provided on the optimization measures for the continuous wall joint construction process, and the following operations are performed: When the real-time data of the continuous wall joint construction process after the optimization measures are implemented is better than the real-time data of the continuous wall joint construction process before the optimization measures are implemented, the optimization effect of the continuous wall joint construction process is good; When the real-time data of the continuous wall joint construction process after the optimization measures are implemented is inferior to the real-time data of the continuous wall joint construction process before the optimization measures are implemented, the optimization effect of the continuous wall joint construction process is poor; According to the optimization effect of the continuous wall joint construction process, the optimization measures of the continuous wall joint construction process are fed back and improved.

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