Construction quality monitoring method and control system for high-altitude mass fan foundation concrete

Abstract

The present application relates to the construction quality monitoring method and control system of high altitude large volume fan foundation concrete, the method includes the following steps: A. collect the state video of concrete in concrete construction, carry out super definition visual effect processing to the state video;B. maintain the internal and external temperature difference of concrete in the preset range through the temperature measuring element;C. calculate the Hessian matrix and neighborhood value according to the concrete surface image, filter out the filtering image meeting the requirements, splice all filtering images to generate the total image of concrete surface, and extract the crack characteristics of concrete surface;according to the crack characteristics, start the grouting module to carry out intelligent grouting;D. calculate the spatial variability of impact response intensity and average impact response intensity, the strength quality characteristics of concrete layer, and then obtain the hardness detection result, and execute step B and / or step C according to the hardness detection result.The present application can accurately control the temperature and detect the crack with high sensitivity in the concrete construction, greatly improve the quality of concrete and the reliability of overall structure.

Description

Technical Field

[0001] This invention relates to the field of concrete construction, specifically to a method and control system for monitoring and controlling the construction quality of concrete foundations for large-volume wind turbines at high altitudes. Background Technology

[0002] Concrete is one of the most important civil engineering materials in modern times. It is an artificial stone material made by mixing cementitious materials, granular aggregates (also known as aggregates), water, and, when necessary, admixtures and additives in a certain proportion, followed by uniform mixing, compaction, and curing. Concrete is characterized by abundant and inexpensive raw materials and a simple production process, leading to its increasing use. Furthermore, concrete possesses high compressive strength, good durability, and a wide range of strength grades. These characteristics make it widely applicable, and concrete is an important material in many fields.

[0003] However, in the existing high-altitude, large-volume wind turbine foundation concrete construction, due to the high altitude and large concrete volume, it is difficult to accurately measure the concrete temperature, which affects the temperature control effect; at the same time, the accuracy of existing concrete crack detection is also low, which poses a hidden danger to the construction quality of concrete.

[0004] Existing technologies for temperature control and crack detection in concrete foundations for large-volume wind turbines at high altitudes have the following main problems:

[0005] 1. Inaccuracy in concrete temperature control:

[0006] In high-altitude areas, controlling the temperature of concrete to keep it within a suitable range to ensure full curing is a challenge due to the unique environmental conditions (such as temperature fluctuations and extreme climate).

[0007] If the temperature is too high or too low during the curing process of concrete, it will affect the rate of chemical reaction and the final structural strength. Too high a temperature will cause moisture to evaporate prematurely, affecting the curing effect of the concrete; while too low a temperature will cause the internal structure of the concrete to not form completely, affecting its final strength.

[0008] Existing temperature control systems cannot accurately measure or regulate the internal temperature of concrete, resulting in an inability to effectively control concrete quality.

[0009] 2. Insufficient accuracy in concrete crack detection:

[0010] Cracks can occur in concrete during the curing process due to improper temperature control, mismatch between cement and aggregate, or improper operation during construction.

[0011] The presence of cracks reduces the overall stability and durability of concrete structures, especially when subjected to heavy loads such as wind turbines, where the impact of cracks is more significant.

[0012] Current technology cannot accurately detect and locate tiny cracks in concrete, which limits the ability to repair and strengthen concrete structures in a timely manner.

[0013] The shortcomings of existing technologies in temperature control and crack detection of large-volume wind turbine foundation concrete at high altitudes have led to insufficient strength and poor durability of the concrete structure, which in turn affects the safety and stability of the entire wind power facility. Summary of the Invention

[0014] This invention provides a construction quality monitoring method and control system for high-altitude, large-volume wind turbine foundation concrete, to achieve precise temperature control and highly sensitive crack detection during concrete construction, thereby improving the quality of concrete and the reliability of the overall structure.

[0015] The present invention provides a method for monitoring the construction quality of concrete foundations for large-volume wind turbines at high altitudes, comprising the following steps:

[0016] A. Video surveillance: Collect video footage of the concrete condition during concrete construction, including the temperature control process and surface images of the concrete, and perform ultra-high-definition visual processing on the video footage using video enhancement algorithms;

[0017] B. Temperature control: Temperature sensing elements are pre-embedded inside the concrete to maintain the temperature difference between the inside and outside of the concrete within a preset range;

[0018] C. Crack Detection and Repair: The concrete surface image obtained in step A is filtered to obtain a filtered image. The Hessian matrix of the filtered image is calculated, and the neighborhood value corresponding to the filtered image is calculated based on the Hessian matrix. Pixels with neighborhood values ​​greater than or equal to a preset screening threshold are selected, and the Euclidean contrast between the pixel and the pixel in the preset reference image is calculated. Filtered images with Euclidean contrast greater than the preset contrast threshold are retained as screening images. All screening images are stitched together to generate a general concrete surface map. Crack features of the concrete surface are extracted based on the general concrete surface map. The grouting module is started for intelligent grouting based on the crack features. During intelligent grouting, the internal and external temperatures of the concrete are detected in real time through a temperature control step. During crack detection, the temperature data detected above is judged to meet the actual requirements based on the crack features, and the internal and external temperatures of the concrete are controlled accordingly based on the crack features.

[0019] D. Quality Assessment: Obtain impact response strength data through elastic wave data of concrete structure, calculate spatial variability and average impact response strength to obtain concrete surface strength quality characteristics, obtain concrete surface image based on the concrete surface strength quality characteristics to obtain concrete hardness test results, and execute steps B and / or C based on the hardness test results.

[0020] Furthermore, the video enhancement algorithm described in step A involves obtaining the resolution of the state video, selecting a corresponding enhancement algorithm based on the resolution, and using the enhancement algorithm to perform high-resolution enhancement on the state video, thereby improving the clarity, contrast, and detail recognition capabilities of the video image.

[0021] Furthermore, the filtering process in step C employs a Gaussian filter. The Hessian matrix of the filtered image is generated by calculating the single second derivative and the double second derivative of the target pixel. The calculated double second derivative is then filled into an empty matrix according to a predetermined arrangement to form the Hessian matrix. The neighborhood value is calculated using the single second derivative and the double second derivative.

[0022] Furthermore, in step C, the intelligent grouting uses a pressure sensor to monitor the grouting pressure in real time, grouting from the inside of the tower to the outside, and when the pressure is insufficient, the high-level funnel is activated to increase the pressure for continuous grouting.

[0023] Furthermore, the crack features mentioned in step C refer to the use of a convolutional neural network model to extract the edge texture features of concrete through a downsampling layer, which are then compressed by an encoder and reconstructed by a deconvolutional layer. The width, length, and orientation parameters of the crack are then output in combination with an activation function.

[0024] Furthermore, in step D, the spatial variability of the impact response intensity is used to analyze the variation of the impact response intensity at different detection points. The calculation formula is as follows:

[0025]

[0026] in, The value represents the spatial variability of the impact response intensity at a depth of h, where n represents the number of detection points and is a positive integer. This represents the average impact response intensity at a depth of h. P represents the average concrete strength at a depth of h. i (h) represents the impact response intensity at depth h corresponding to the i-th detection point, where i is a positive integer less than or equal to n; x, y, and z are the coordinates of the detection point in three-dimensional space during the impact detection process, and x, y, and z are all greater than 0;

[0027] The average impact response intensity at a depth of h The calculation formula is:

[0028]

[0029] Where P(h) represents the impact response intensity at a depth of h, v represents the volume of the casting chamber, and d represents the spacing between detection points or the characteristic dimensions of the material, or a parameter used to calculate the spatial distribution of the impact response intensity, depending on the application scenario of this formula.

[0030] Furthermore, in step D, the step of obtaining the strength quality characteristics of the concrete surface is as follows:

[0031] By analyzing the spatial variability of impact response intensity, the trend of response intensity variation at each test point at different depths of concrete was analyzed.

[0032] Filter out regions where the change in response intensity is greater than a set value;

[0033] Numerical fitting or hierarchical clustering methods are used to divide the layer strength regions at different depths, establish a layer strength quality distribution map, and extract the concrete layer strength quality characteristics from the layer strength quality distribution map.

[0034] Furthermore, in step D, the method for obtaining the hardness test results of the concrete is as follows: based on the obtained distribution of impact response intensity and combined with a known material strength model, the hardness values ​​of different layers of the concrete are derived; or,

[0035] By training a regression analysis model or machine learning model using the acquired impact response strength data, the material hardness of each layer of concrete can be calculated.

[0036] The present invention also provides a control system for the above-described construction quality monitoring method, comprising:

[0037] Main control module: connects and coordinates the operation of each module;

[0038] Video monitoring module: used to collect video of the concrete status during concrete construction, including the temperature control process of the concrete and images of the concrete surface, and to perform ultra-high-definition visual processing on the status video through video enhancement algorithms;

[0039] Temperature measurement module: used for multi-point monitoring of the internal and surface temperature of concrete;

[0040] Crack identification module: Based on the ultra-high-definition video output by the video monitoring module and the monitoring data from the temperature measurement module, concrete cracks are identified by calculating based on the Hessian matrix and neighborhood values.

[0041] Temperature control module: Dynamically adjusts the concrete temperature based on the output of the crack detection module;

[0042] Grouting module: Automatically performs grouting operations based on the crack detection results from the crack identification module;

[0043] Hardness testing module: Calculates the spatial variability of impact response intensity using elastic wave data;

[0044] Quality assessment module: Based on the output of the hardness testing module, comprehensively analyze temperature, crack, and hardness data;

[0045] Display module: Displays the detection and evaluation results of each module in real time.

[0046] The beneficial effects of this invention include:

[0047] 1. Ultra-high-definition video is provided through video enhancement algorithms, which enhances monitoring quality and provides a basis for crack data analysis.

[0048] 2. It can monitor concrete surface cracks in real time and automatically trigger the grouting module to quickly and effectively repair cracks, thereby improving the stability and safety of the wind turbine foundation.

[0049] 3. It can comprehensively evaluate the hardness and quality of concrete based on various parameters, ensuring the long-term stability and durability of concrete.

[0050] 4. It is suitable for concrete construction under extreme climatic conditions such as high altitude, ensuring the quality and reliability of the project in complex environments. Attached Figure Description

[0051] Figure 1 This is a flowchart of the construction quality monitoring method for high-altitude, large-volume wind turbine foundation concrete according to the present invention.

[0052] Figure 2 This is a block diagram of the construction quality control system for high-altitude, large-volume wind turbine foundation concrete according to the present invention. Detailed Implementation

[0053] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings, so as to provide a better understanding of the concept of this application, the technical problem solved, the technical features constituting the technical solution and the technical effects brought about.

[0054] Example 1:

[0055] like Figure 1 The present invention provides a method for monitoring the construction quality of high-altitude, large-volume wind turbine foundation concrete, comprising the following steps:

[0056] A. Video Surveillance: High-definition cameras are installed at the wind farm construction site for large-volume wind turbine foundations at high altitudes to collect real-time video footage of the concrete during construction. This footage is uploaded to a cloud server, including the temperature control process and surface images of the concrete. The cloud server downloads and decodes the received video, then applies a video enhancement algorithm to perform ultra-high-definition visual processing. This video enhancement algorithm involves obtaining the resolution of the video, selecting a corresponding existing enhancement algorithm based on that resolution, and then using this algorithm to enhance the video at high resolution, improving the clarity, contrast, and detail recognition capabilities of the video image.

[0057] The high-resolution video provided by the video enhancement algorithm not only facilitates monitoring of the concrete temperature control process and status, but more importantly, it significantly improves image clarity, contrast, and detail recognition capabilities. This allows for more accurate extraction of temperature changes, crack development, and other critical status information during concrete construction. Optimizing this video data makes it easier to identify abnormal temperature distributions, crack propagation, or other quality defects during crack detection and quality assessment, providing construction personnel with more intuitive judgment criteria and data support for overall construction quality management.

[0058] The video enhancement algorithm can employ a deep learning-based enhancement model or a traditional image processing algorithm:

[0059] Deep learning-based enhancement models, such as Generative Adversarial Networks (GANs) or Convolutional Neural Networks (CNNs), can improve the contrast of concrete surface details and enhance defect detection capabilities through training.

[0060] Traditional image processing algorithms include histogram equalization, adaptive contrast enhancement (CLAHE), Laplacian enhancement, edge detection (Canny operator), wavelet transform, etc., to enhance the texture, cracks and other features of concrete surfaces.

[0061] The process of video enhancement algorithms is as follows:

[0062] (1) Image preprocessing: Perform grayscale transformation, noise removal (such as Gaussian filtering), contrast stretching and other operations on the acquired concrete surface image.

[0063] (2) Feature extraction and enhancement: Deep learning models (ResNet, YOLO, etc.) or traditional image processing are used to extract key defect areas such as cracks, holes, and bubbles, and high dynamic range (HDR) fusion technology is used to enhance texture details.

[0064] (3) Output results: The processed high-resolution concrete images are used for defect identification or subsequent structural quality assessment.

[0065] B. Temperature Control: Temperature has a very direct impact on the hardness of concrete, affecting its hardness and other physical properties. For example, during the concrete curing process, temperature fluctuations can affect the hardening rate and final hardness. During the pouring and curing stages, excessively high or low temperatures can lead to uneven or substandard concrete hardness. Therefore, controlling the surface and internal temperature of the concrete within a suitable range is crucial to ensuring it achieves the desired hardness.

[0066] To achieve temperature control of both the concrete surface and interior, the concrete pouring temperature is controlled to not exceed 25℃, and cooling measures are implemented when the temperature in the construction area exceeds 25℃. Temperature sensing elements are pre-embedded at seven different temperature measurement points inside the concrete to collect temperature data at different locations (e.g., 50mm from the surface and 50mm from the bottom). Temperature measurement of the concrete's internal temperature begins immediately after pouring, maintaining a temperature difference of no more than 25℃ between the inside and outside of the concrete. The temperature measurement area is defined by half the axis of symmetry of the selected large-volume wind turbine foundation concrete pouring body. Within this area, the temperature measurement points are arranged in planar layers. There are at least four temperature measurement points; in this embodiment, seven are used. Within the temperature measurement zone, the location and number of temperature measurement points are determined based on the temperature field and temperature control requirements within the concrete casting. The surface temperature of the large-volume fan foundation concrete casting is based on the temperature 50mm inside the surface of the concrete. The bottom temperature of the large-volume fan foundation concrete casting is based on the temperature 50mm above the bottom surface. To reduce measurement errors, at least two temperature measurement points are set at each location along the thickness direction of the large-volume fan foundation concrete casting. Simultaneously, a thermometer is installed 1.5 meters above the surface of the large-volume fan foundation concrete to measure the atmospheric temperature. At each pump inlet of the large-volume fan foundation concrete, a temperature probe and temperature wire are fixed to a probe rod to measure the concrete's temperature upon entry into the formwork.

[0067] The method for pre-embedding temperature sensing elements inside concrete is as follows:

[0068] Use steel bars as vertical supports to fix the temperature sensing wire and probe to the steel bars. Wrap the temperature probe with plastic sheeting. Fix a small wooden block (30mm*30mm*30mm) to the steel bars to isolate the temperature probe from the steel bar. Label the other end of the temperature sensing wire according to its serial number. The temperature sensing plug should be wrapped with plastic film before pouring the concrete for the large-volume fan foundation. Before installation, the temperature sensing wire and probe must be immersed in water at 1m for 24 hours without damage.

[0069] Admixtures are added to concrete during construction to improve its workability and reduce heat of hydration. During hot seasons, construction is carried out during periods of lower temperatures, and insulation measures are implemented to maintain the concrete's temperature within a reasonable range upon placement. During cold seasons, when the temperature falls below the design control temperature, insulation measures are also taken for the concrete. For example, temperature data collected by temperature-sensing elements is obtained through a temperature control module to monitor the internal and surface temperatures of the concrete in real time. This, combined with an external intelligent water spray system or industrial automatic temperature-controlled insulation blankets, ensures that the concrete cures within the optimal temperature range. Industrial automatic temperature-controlled insulation blankets mainly consist of an outer layer, an insulation layer, a heating layer, and a temperature control device. They are widely used for heating tanks, pipes, and molds, as well as for frost protection and insulation of concrete pavements and bridges. If the temperature difference between the inside and outside of the concrete exceeds a set threshold, the temperature control module sends instructions to adjust cooling water circulation, surface insulation, or other temperature control measures to maintain a reasonable temperature distribution and prevent cracking.

[0070] Temperature changes can cause concrete to expand or contract, leading to cracks. Monitoring the hardness and temperature of concrete can help prevent temperature-related structural damage.

[0071] C. Crack Detection and Repair: The concrete surface image obtained in step A is filtered to obtain a filtered image. The Hessian matrix of the filtered image is calculated, and the neighborhood value corresponding to the filtered image is calculated based on the Hessian matrix. Pixels with neighborhood values ​​greater than or equal to a preset screening threshold are selected, and the Euclidean contrast between the pixel and the pixel in the preset reference image is calculated. Filtered images with Euclidean contrast values ​​greater than the preset contrast threshold are retained as screening images. All screening images are stitched together to generate a general concrete surface image. Crack features of the concrete surface are extracted based on the general concrete surface image. The grouting module is activated for intelligent grouting based on the crack features. During intelligent grouting, the internal and external temperatures of the concrete are monitored in real time through a temperature control step. During crack detection, the temperature data detected above is judged to meet the actual requirements based on the crack features, and the internal and external temperatures of the concrete are controlled accordingly based on the crack features.

[0072] Specifically, the foundation concrete needs to be kept moist before grouting to allow it to fully absorb water. The grouting formwork should be made of a non-absorbent or very low-absorption material, typically wood, but custom-made iron formwork can be made according to the fan size. The grouting formwork should also be thoroughly moistened before grouting. Because grouting relies primarily on the weight of the grout material, the height of the grouting formwork must be at least 3 cm higher than the bottom of the equipment foundation slab.

[0073] When performing intelligent grouting via the grouting module, the mixer is controlled to agitate the grouting material:

[0074] Step 1: Put 100% water into a blender;

[0075] Step 2: Stir continuously and slowly add MasterFlow 872 material (BASF grouting material);

[0076] Step 3: Continue stirring for at least 2 minutes until a lump-free mixture is formed;

[0077] Step 4: Let it sit for at least 2 minutes;

[0078] Step 5: Stir again for about 30 seconds.

[0079] During grouting, the grouting direction should be selected based on elevation difference calculations, automatically detecting and selecting the optimal grouting point, and grouting from the inside of the tower outwards. The grouting module, combined with an existing intelligent flow control system, uses sensors to monitor grouting flow rate, pressure, and filling degree in real time, and adjusts the grouting volume based on real-time data to ensure that the grout injected along the inclined template can fully overflow to the outlet end, achieving continuous and uniform grouting. When insufficient grouting pressure is detected, an elevated funnel or booster pump is automatically activated to achieve continuous grouting and improve grouting efficiency.

[0080] The filtering process employs a Gaussian filter to filter multiple concrete surface images, resulting in multiple filtered images. The formula is as follows:

[0081] L(x,t)=G(t)·I(x,t);

[0082] Where L(x,t) is the filtered image, G(t) is the standard Gaussian function matrix used by the Gaussian filter, and I(x,t) is the concrete surface image. x represents the spatial coordinates used to locate the position of the pixel in the concrete surface image on the two-dimensional plane, which can accurately indicate the pixel information at different positions in the image. t represents the scale parameter, which is related to the width of the standard Gaussian function and determines the smoothness of the image by the Gaussian filter. Different t values ​​will cause the Gaussian filter to process the image at different scales, thus affecting the filtering effect.

[0083] The filtered image contains a Hessian matrix. A Hessian matrix is ​​a matrix used to describe the local curvature of a multidimensional function, including the second-order partial derivatives of that multidimensional function with respect to individual variables. In image processing, the Hessian matrix is ​​often used for feature detection, such as corner detection and edge detection, because it can effectively represent local shape changes in an image.

[0084] In this invention, a pixel in the filtered image is arbitrarily selected as the target pixel. Centered on this pixel, the single and double second derivatives of the target pixel in each direction (typically the x and y directions) are calculated. The double second derivative represents the rate of change of image brightness and is used to capture edge and texture information in the image. The single and double second derivatives are arranged in a preset order into an empty matrix to obtain the Hessian matrix. Then, the neighborhood value is calculated using the single and double second derivatives.

[0085] The process of solving for the single second derivative and the double second derivative of the target pixel includes:

[0086] Obtain the target pixel value corresponding to the target pixel point, calculate the derivative of the target pixel value with respect to the first reference axis in the target pixel point, and obtain the reference first derivative;

[0087] Taking the second derivative of the reference first derivative based on the first reference axis yields a single second derivative. Specifically:

[0088] Single second derivative: Calculate the second derivative of the target pixel in the x and y directions respectively, denoted as \(\frac{\partial^2f}{\partial x^2}\) and \(\frac{\partial^2f}{\partial y^2}\).

[0089] Double second derivative: Calculate the derivative of the target pixel value with respect to the second reference axis in the target pixel to obtain the local reference derivative. Then, perform a second derivative of the local reference derivative based on the first reference axis to obtain the double second derivative. Specifically:

[0090] Calculate the mixed second derivative of the target pixel in the x and y directions, denoted as \(\frac{\partial^2f}{\partial x\partial y}\) or \(\frac{\partial^2f}{\partial y\partial x}\).

[0091] In digital image processing, the second derivative is often approximated by convolution operations. Specific filters (such as the Laplacian operator, Sobel operator, etc.) can be used to estimate the second derivative of an image.

[0092] For a single second derivative, second derivative filters can be applied separately in the x and y directions.

[0093] For double second derivatives, a filter that can capture changes in both the x and y directions is required.

[0094] Construct the Hessian matrix:

[0095] The calculated double second derivatives are then filled into an empty matrix according to a predetermined order to form the Hessian matrix. For a two-dimensional image, the Hessian matrix \(H\) is defined as follows:

[0096] \[H=\begin{bmatrix}\frac{\partial^2f}{\partial x^2}&\frac{\partial^2f}{\partial x\partial y}\\\frac{\partial^2f}{\partial y\partial x}&\frac{\partial^2f}{\partial y^2}\end{bmatrix}\].

[0097] The Hessian matrix can be used for a variety of image processing tasks. For example, in feature point detection, corners or edges in an image can be identified by analyzing the eigenvalues ​​of the Hessian matrix. The magnitude and sign of the eigenvalues ​​can reflect the shape characteristics of local regions in the image, such as concavity, flatness, or edges.

[0098] This process generates a Hessian matrix for each target pixel, allowing for the analysis of the image's local structural features.

[0099] The formula for calculating the neighborhood value is:

[0100]

[0101] Where det(H) is the neighborhood value, It is a double second derivative. It is a single second derivative. This refers to the preset global second derivative. The global second derivative can be set in several ways: First, it can be based on statistical analysis of a large amount of experimental data, obtaining a representative value after multiple measurements of different types of samples. Second, it can be based on existing standard values ​​in specific fields such as concrete testing, according to relevant industry standards or specifications. Third, it can be determined by referring to values ​​used in previous similar studies and making appropriate adjustments based on the specific needs and conditions of the current research.

[0102] The overall image of the concrete surface is input into a convolutional neural network (CNN) model, such as VGG or ResNet. The CNN model consists of an input layer, a downsampling layer, an encoder, and a decoder. The downsampling layer contains multiple convolutional layers, which perform successive convolutions on the overall concrete surface image to obtain a high-dimensional feature dataset, such as edge features (pixel gradient changes at the edges of cracks on the concrete surface), texture features (texture-related features indicating whether the crack perimeter is rough or smooth), and shape features (linear, tortuous, or other shape characteristics of the cracks).

[0103] The encoder employs a fully connected neural network structure. Taking a simple three-layer fully connected network as an example, the input is a high-dimensional feature dataset. The first layer maps the input features to a certain number of neurons, the second layer further combines and transforms the features, and the third layer outputs an encoded feature dataset. The high-dimensional feature dataset output by the convolutional neural network model is compressed by the encoder and then deconvolved by a deconvolutional layer to obtain a deconvolutional dataset. This deconvolutional dataset is then decoded by a decoder to obtain a decoded dataset. The crack morphology is reconstructed, and the decoded dataset is input into an activation function to obtain crack activation values. These crack activation values ​​are compared with a preset detection difference table to output crack features corresponding to the overall concrete surface map, containing the encoded feature dataset. This reflects the condition of the concrete cracks, including crack length (reflecting its extension), crack width (reflecting the crack size), and crack orientation (indicating the direction of crack extension). For example, the convolutional neural network model outputs a high-dimensional dataset containing 1000 features. After processing by the encoder, it outputs an encoded feature dataset with a dimension of 128. This dataset condenses the key information from the original high-dimensional data for subsequent crack detection and analysis.

[0104] Crack detection is then performed based on crack characteristics: image recognition algorithms, such as edge detection, image segmentation, or deep learning methods, are applied to identify cracks in the images. These algorithms determine which areas belong to cracks based on extracted crack features. The detected crack data is then analyzed to assess the severity of the cracks and their potential impact on structural integrity. This involves comparing the size, number, and distribution of cracks against predefined criteria or thresholds. The results of crack detection and analysis are compiled into reports that help the maintenance team understand what actions must be taken. Based on the crack identification results, the most appropriate repair strategy or preventative measures are determined to prevent further crack propagation.

[0105] D. Quality Assessment: Elastic wave data of the concrete structure of the high-altitude, large-volume wind turbine foundation is obtained. Based on the number of detection points and impact response intensity data in the elastic wave data, the spatial variability and average impact response intensity are calculated to obtain the strength quality characteristics of the concrete surface. The spatial variability of the impact response intensity is used to analyze the variation of the impact response intensity at different detection points, specifically:

[0106] The formula for calculating the spatial variability of impact response intensity is:

[0107]

[0108] in, The value represents the spatial variability of the impact response intensity at a depth of h, where n represents the number of detection points and is a positive integer. This represents the average impact response intensity at a depth of h. P represents the average concrete strength at a depth of h. i (h) represents the impact response intensity at depth h corresponding to the i-th detection point, where i is a positive integer less than or equal to n; x, y, and z are the coordinates of the detection point in three-dimensional space during the impact detection process, and x, y, and z are all greater than 0. Among them, x and y represent the coordinates in the horizontal direction, such as the east-west direction (x) and the north-south direction (y), respectively, and z represents the depth direction, that is, the measured value of the impact response intensity of the detection point at different depths h.

[0109] The average impact response intensity at a depth of h The calculation formula is:

[0110]

[0111] Where P(h) represents the impact response intensity at a depth of h, v represents the volume of the casting chamber, and d represents the distance between detection points, i.e., the horizontal or vertical interval between adjacent detection points, or the characteristic size of the material, such as the average diameter of concrete particles, or a parameter used to calculate the spatial distribution of impact response intensity, depending on the application scenario of this formula.

[0112] Obtain the strength and quality characteristics of the concrete surface:

[0113] By analyzing the spatial variability of impact response intensity, the trend of response intensity variation at each test point at different depths of concrete was analyzed.

[0114] The regions where the change in response intensity is greater than the set value are selected. These regions may correspond to different geological layers, changes in concrete density, or the location of structural defects.

[0115] Numerical fitting or hierarchical clustering methods are used to divide the layer strength regions at different depths, establish a layer strength quality distribution map, and extract the concrete layer strength quality characteristics from the layer strength quality distribution map.

[0116] Then, based on the obtained impact response intensity distribution and combined with known material strength models, the hardness values ​​of different concrete layers are derived; or, by training a regression analysis model or machine learning model using the obtained impact response intensity data, the material hardness of each concrete layer is calculated. According to concrete quality standards, the test area is divided into quality levels such as high density, uniform density, local defects, and severe defects. The obtained hardness data can be compared with standard material hardness to determine the concrete strength level and analyze possible damage or abnormal areas. The concrete layer strength quality characteristics are obtained through the hardness data. The concrete surface image obtained from the concrete layer strength quality characteristics yields the concrete hardness test results. Based on the hardness test results, steps B and / or C are executed, and an evaluation report is generated.

[0117] Example 2:

[0118] like Figure 2 As shown, the present invention also provides a construction quality control system for high-altitude large-volume wind turbine foundation concrete using the construction quality monitoring method described in Embodiment 1, comprising:

[0119] Main control module: Connects and coordinates the operation of each module, and integrates data from the hardness testing module and temperature measurement module, so that the crack identification module, temperature control module, quality assessment module, etc. can more comprehensively analyze the state of concrete;

[0120] Video monitoring module: This module captures video footage of the concrete during construction, including the temperature control process and surface images. The captured video images are then transmitted to a remote cloud server, where video enhancement algorithms are used to process the video footage in ultra-high definition.

[0121] Temperature measurement module: used for multi-point monitoring of the internal and surface temperature of concrete;

[0122] Crack identification module: Based on the ultra-high-definition video output by the video monitoring module and the monitoring data from the temperature measurement module, concrete cracks are identified by calculating based on the Hessian matrix and neighborhood values; through image processing technology and corresponding recognition models, the filtered concrete surface image is analyzed to determine the type of crack (such as surface cracks, through cracks, etc.), the degree of cracking (measure parameters such as crack width and length), and the location distribution.

[0123] Temperature control module: Dynamically adjusts the concrete temperature based on the output of the crack detection module;

[0124] Grouting Module: After treating the concrete substrate, grouting is performed on the concrete. It also automatically executes grouting based on crack detection results from the crack identification module. For narrow, short micro-cracks, low-pressure penetrating grouting is used, selecting grouting materials with good fluidity and small particle size, such as epoxy resin. For wider cracks, pressure grouting is used, with cement-based grouting materials to ensure the crack space is filled. During grouting, the predetermined procedure is followed. In low-pressure penetrating grouting, the material is injected slowly to allow natural penetration; in pressure grouting, the pressure is controlled within a suitable range, and the material is continuously injected until the crack is filled and a small amount overflows.

[0125] Hardness testing module: Calculates the spatial variability of impact response intensity using elastic wave data. The hardness testing module reflects the strength and quality characteristics of the entire high-altitude, large-volume wind turbine foundation concrete structure. If the concrete pouring temperature is too high or too low, it will affect the strength of the concrete structure; the hardness testing module can indirectly reflect the effectiveness of concrete temperature control.

[0126] Quality assessment module: Based on the output of the hardness testing module, comprehensively analyze temperature, crack, and hardness data;

[0127] Display module: Displays the detection and evaluation results of each module in real time.

[0128] The terms "connection" and "fixation" appearing in the description of this application can refer to fixed connection, processing and forming, welding, or mechanical connection. The specific meaning of the above terms in this application should be understood according to the specific circumstances.

[0129] In the description of this application, the terms "center", "upper", "lower", "horizontal", "inner", "outer", etc., are used only to indicate the orientation or positional relationship for the convenience of describing this application and simplifying the description, and do not indicate or imply a specific orientation that the device or element referred to must have, and therefore should not be construed as a limitation of this application.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. Construction quality monitoring methods for high-altitude, large-volume wind turbine foundation concrete. Its features include the following steps: A. Video surveillance: Collect video footage of the concrete condition during concrete construction, including the temperature control process and surface images of the concrete, and perform ultra-high-definition visual processing on the video footage using video enhancement algorithms; B. Temperature control: Temperature sensing elements are pre-embedded inside the concrete to maintain the temperature difference between the inside and outside of the concrete within a preset range; C. Crack Detection and Repair: The concrete surface image obtained in step A is filtered to obtain a filtered image. The Hessian matrix of the filtered image is calculated, and the neighborhood value corresponding to the filtered image is calculated based on the Hessian matrix. Pixels with neighborhood values ​​greater than or equal to a preset screening threshold are selected, and the Euclidean contrast between the pixel and the pixel in the preset reference image is calculated. Filtered images with Euclidean contrast greater than the preset contrast threshold are retained as screening images. All screening images are stitched together to generate a general concrete surface map. Crack features of the concrete surface are extracted based on the general concrete surface map. The grouting module is started for intelligent grouting based on the crack features. During intelligent grouting, the internal and external temperatures of the concrete are detected in real time through a temperature control step. During crack detection, the temperature data detected above is judged to meet the actual requirements based on the crack features, and the internal and external temperatures of the concrete are controlled accordingly based on the crack features. D. Quality Assessment: Obtain impact response strength data through elastic wave data of concrete structure, calculate spatial variability and average impact response strength to obtain concrete surface strength quality characteristics, obtain concrete surface image based on the concrete surface strength quality characteristics to obtain concrete hardness test results, and execute steps B and / or C based on the hardness test results.

2. The construction quality monitoring method for high-altitude large-volume wind turbine foundation concrete as described in claim 1, characterized in that: The video enhancement algorithm described in step A involves obtaining the resolution of the state video, selecting a corresponding enhancement algorithm based on that resolution, and then using that enhancement algorithm to perform high-resolution enhancement on the state video, thereby improving the clarity, contrast, and detail recognition capabilities of the video image.

3. The method for monitoring the construction quality of high-altitude, large-volume wind turbine foundation concrete as described in claim 1, characterized by the following steps: The filtering process described in C uses a Gaussian filter. The Hessian matrix of the filtered image is generated by calculating the single second derivative and the double second derivative of the target pixel. The calculated double second derivatives are filled into an empty matrix according to a predetermined arrangement order to form the Hessian matrix. The neighborhood value is calculated using the single second derivative and the double second derivative.

4. The construction quality monitoring method for high-altitude large-volume wind turbine foundation concrete as described in claim 1, characterized in that: In step C, the intelligent grouting uses a pressure sensor to monitor the grouting pressure in real time, and grouts are injected from the inside of the tower to the outside. When the pressure is insufficient, the high-level funnel is activated to increase the pressure for continuous grouting.

5. The construction quality monitoring method for high-altitude large-volume wind turbine foundation concrete as described in claim 1, characterized in that: The crack features mentioned in step C refer to the use of a convolutional neural network model to extract the edge texture features of concrete through a downsampling layer, compress them through an encoder, reconstruct the crack morphology through a deconvolution layer, and output the width, length, and direction parameters of the crack by combining the activation function.

6. The construction quality monitoring method for high-altitude large-volume wind turbine foundation concrete as described in claim 1, characterized in that: In step D, the spatial variability of the impact response intensity is used to analyze the variation of the impact response intensity at different detection points. The calculation formula is as follows: ； in, The value represents the spatial variability of the impact response intensity at a depth of h, where n represents the number of detection points and is a positive integer. This represents the average impact response intensity at a depth of h. P represents the average concrete strength at a depth of h. i (h) represents the impact response intensity at depth h corresponding to the i-th detection point, where i is a positive integer less than or equal to n; x, y, and z are the coordinates of the detection point in three-dimensional space during the impact detection process, and x, y, and z are all greater than 0; The average impact response intensity at a depth of h The calculation formula is: ； Where P(h) represents the impact response intensity at a depth of h, v represents the volume of the casting chamber, and d represents the spacing between detection points or the characteristic dimensions of the material, or a parameter used to calculate the spatial distribution of the impact response intensity, depending on the application scenario of this formula.

7. The construction quality monitoring method for high-altitude large-volume wind turbine foundation concrete as described in claim 1, characterized in that: In step D, the step of obtaining the strength quality characteristics of the concrete surface is as follows: By analyzing the spatial variability of impact response intensity, the trend of response intensity variation at each test point at different depths of concrete was analyzed. Filter out regions where the change in response intensity is greater than a set value; Numerical fitting or hierarchical clustering methods are used to divide the layer strength regions at different depths, establish a layer strength quality distribution map, and extract the concrete layer strength quality characteristics from the layer strength quality distribution map.

8. The construction quality monitoring method for high-altitude large-volume wind turbine foundation concrete as described in claim 1, characterized in that: In step D, the method for obtaining the hardness test results of the concrete is as follows: based on the obtained distribution of impact response intensity and combined with the known material strength model, the hardness values ​​of different layers of the concrete are derived; or, By training a regression analysis model or machine learning model using the acquired impact response strength data, the material hardness of each layer of concrete can be calculated.

9. A control system for the construction quality monitoring method according to any one of claims 1 to 8, characterized in that: Main control module: connects and coordinates the operation of each module; Video monitoring module: used to collect video of the concrete status during concrete construction, including the temperature control process of the concrete and images of the concrete surface, and to perform ultra-high-definition visual processing on the status video through video enhancement algorithms; Temperature measurement module: used for multi-point monitoring of the internal and surface temperature of concrete; Crack identification module: Based on the ultra-high-definition video output by the video monitoring module and the monitoring data from the temperature measurement module, concrete cracks are identified by calculating based on the Hessian matrix and neighborhood values. Temperature control module: Dynamically adjusts the concrete temperature based on the output of the crack detection module; Grouting module: Automatically performs grouting operations based on the crack detection results from the crack identification module; Hardness testing module: Calculates the spatial variability of impact response intensity using elastic wave data; Quality assessment module: Based on the output of the hardness testing module, comprehensively analyze temperature, crack, and hardness data; Display module: Displays the detection and evaluation results of each module in real time.

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